Merge pull request #2656 from freqtrade/new_release

New release - 2019.11

.coveragerc

@@ -1,6 +1,7 @@
 [run]
 omit =
     scripts/*
+    freqtrade/templates/*
     freqtrade/vendor/*
     freqtrade/__main__.py
     tests/*

.github/workflows/ci.yml

@@ -0,0 +1,235 @@
+name: Freqtrade CI
+
+on:
+  push:
+    branches:
+      - master
+      - develop
+      - github_actions_tests
+    tags:
+  pull_request:
+  schedule:
+    - cron:  '0 5 * * 4'
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ ubuntu-18.04, macos-latest ]
+        python-version: [3.7]
+
+    steps:
+    - uses: actions/checkout@v1
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Cache_dependencies
+      uses: actions/cache@v1
+      id: cache
+      with:
+        path: ~/dependencies/
+        key: ${{ runner.os }}-dependencies
+
+    - name: pip cache (linux)
+      uses: actions/cache@preview
+      if: startsWith(matrix.os, 'ubuntu')
+      with:
+        path: ~/.cache/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: pip cache (macOS)
+      uses: actions/cache@preview
+      if: startsWith(matrix.os, 'macOS')
+      with:
+        path: ~/Library/Caches/pip
+        key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
+
+    - name: TA binary *nix
+      if: steps.cache.outputs.cache-hit != 'true'
+      run: |
+        cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
+
+    - name: Installation - *nix
+      run: |
+        python -m pip install --upgrade pip
+        export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
+        export TA_LIBRARY_PATH=${HOME}/dependencies/lib
+        export TA_INCLUDE_PATH=${HOME}/dependencies/include
+        pip install -r requirements-dev.txt
+        pip install -e .
+
+    - name: Tests
+      env:
+        COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
+        COVERALLS_SERVICE_NAME: travis-ci
+        TRAVIS: "true"
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+        # Allow failure for coveralls
+        # Fake travis environment to get coveralls working correctly
+        export TRAVIS_PULL_REQUEST="https://github.com/${GITHUB_REPOSITORY}/pull/$(cat $GITHUB_EVENT_PATH | jq -r .number)"
+        export TRAVIS_BRANCH=${GITHUB_REF#"ref/heads"}
+        export CI_BRANCH=${GITHUB_REF#"ref/heads"}
+        echo "${TRAVIS_BRANCH}"
+        coveralls || true
+
+    - name: Backtesting
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI ${{ matrix.os }}*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  build_windows:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ windows-latest ]
+        python-version: [3.7]
+
+    steps:
+    - uses: actions/checkout@v1
+
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Pip cache (Windows)
+      uses: actions/cache@preview
+      if: startsWith(runner.os, 'Windows')
+      with:
+        path: ~\AppData\Local\pip\Cache
+        key: ${{ runner.os }}-pip
+        restore-keys: ${{ runner.os }}-pip
+
+    - name: Installation
+      run: |
+        ./build_helpers/install_windows.ps1
+
+    - name: Tests
+      run: |
+        pytest --random-order --cov=freqtrade --cov-config=.coveragerc
+
+    - name: Backtesting
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
+
+    - name: Hyperopt
+      run: |
+        cp config.json.example config.json
+        freqtrade create-userdir --userdir user_data
+        freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
+
+    - name: Flake8
+      run: |
+        flake8
+
+    - name: Mypy
+      run: |
+        mypy freqtrade scripts
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI windows*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  docs_check:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+
+    - name: Documentation syntax
+      run: |
+        ./tests/test_docs.sh
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade Docs*'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+
+  deploy:
+    needs: [ build, build_windows, docs_check ]
+    runs-on: ubuntu-18.04
+    if: (github.event_name == 'push' || github.event_name == 'schedule') && github.repository == 'freqtrade/freqtrade'
+    steps:
+    - uses: actions/checkout@v1
+
+    - name: Extract branch name
+      shell: bash
+      run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
+      id: extract_branch
+
+    - name: Build and test and push docker image
+      env:
+        IMAGE_NAME: freqtradeorg/freqtrade
+        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
+      run: |
+        build_helpers/publish_docker.sh
+
+    - name: Build raspberry image for ${{ steps.extract_branch.outputs.branch }}_pi
+      uses: elgohr/Publish-Docker-Github-Action@2.7
+      with:
+        name: freqtradeorg/freqtrade:${{ steps.extract_branch.outputs.branch }}_pi
+        username: ${{ secrets.DOCKER_USERNAME }}
+        password: ${{ secrets.DOCKER_PASSWORD }}
+        dockerfile: Dockerfile.pi
+        # cache: true
+        cache: ${{ github.event_name != 'schedule' }}
+        tag_names: true
+
+    - name: Slack Notification
+      uses: homoluctus/slatify@v1.8.0
+      if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
+      with:
+        type: ${{ job.status }}
+        job_name: '*Freqtrade CI Deploy*'
+        mention: 'here'
+        mention_if: 'failure'
+        channel: '#notifications'
+        url: ${{ secrets.SLACK_WEBHOOK }}
+

.github/workflows/docker_update_readme.yml

@@ -0,0 +1,18 @@
+name: Update Docker Hub Description
+on:
+  push:
+    branches:
+    - master
+
+jobs:
+  dockerHubDescription:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+    - name: Docker Hub Description
+      uses: peter-evans/dockerhub-description@v2.1.0
+      env:
+        DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
+        DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
+        DOCKERHUB_REPOSITORY: freqtradeorg/freqtrade
+

.travis.yml

@@ -24,31 +24,34 @@ jobs:
       script:
       - pytest --random-order --cov=freqtrade --cov-config=.coveragerc
       # Allow failure for coveralls
-      - coveralls || true
+      # - coveralls || true
       name: pytest
     - script:
       - cp config.json.example config.json
-      - freqtrade --datadir tests/testdata backtesting
+      - freqtrade create-userdir --userdir user_data
+      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
       name: backtest
     - script:
       - cp config.json.example config.json
-      - freqtrade --datadir tests/testdata hyperopt -e 5
+      - freqtrade create-userdir --userdir user_data
+      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
       name: hyperopt
     - script: flake8
       name: flake8
     - script:
       # Test Documentation boxes -
       # !!! <TYPE>: is not allowed!
-      - grep -Er '^!{3}\s\S+:' docs/*; test $? -ne 0
+      # !!! <TYPE> "title" - Title needs to be quoted!
+      - grep -Er '^!{3}\s\S+:|^!{3}\s\S+\s[^"]' docs/*; test $? -ne 0
       name: doc syntax
     - script: mypy freqtrade scripts
       name: mypy
 
-    - stage: docker
+    # - stage: docker
-      if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
+    #   if: branch in (master, develop, feat/improve_travis) AND (type in (push, cron))
-      script:
+    #   script:
-        - build_helpers/publish_docker.sh
+    #     - build_helpers/publish_docker.sh
-      name: "Build and test and push docker image"
+    #   name: "Build and test and push docker image"
 
 notifications:
   slack:

Dockerfile

@@ -24,3 +24,5 @@ RUN pip install numpy --no-cache-dir \
 COPY . /freqtrade/
 RUN pip install -e . --no-cache-dir
 ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

Dockerfile.pi

@@ -38,3 +38,4 @@ RUN ~/berryconda3/bin/pip install -e . --no-cache-dir
 RUN [ "cross-build-end" ]
 
 ENTRYPOINT ["/root/berryconda3/bin/python","./freqtrade/main.py"]
+CMD [ "trade" ]

README.md

@@ -62,7 +62,6 @@ git checkout develop
 
 For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
 
-
 ## Basic Usage
 
 ### Bot commands
@@ -106,7 +105,7 @@ optional arguments:
 
 ### Telegram RPC commands
 
-Telegram is not mandatory. However, this is a great way to control your bot. More details on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
+Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
 
 - `/start`: Starts the trader
 - `/stop`: Stops the trader
@@ -129,11 +128,6 @@ The project is currently setup in two main branches:
 - `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
 - `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
 
-## A note on Binance
-
-For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
-Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
-
 ## Support
 
 ### Help / Slack

build_helpers/install_windows.ps1

@@ -0,0 +1,8 @@
+# Downloads don't work automatically, since the URL is regenerated via javascript.
+# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
+# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
+
+pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+
+pip install -r requirements-dev.txt
+pip install -e .

build_helpers/publish_docker.sh

@@ -1,17 +1,17 @@
 #!/bin/sh
-# - export TAG=`if [ "$TRAVIS_BRANCH" == "develop" ]; then echo "latest"; else echo $TRAVIS_BRANCH ; fi`
-# Replace / with _ to create a valid tag
-TAG=$(echo "${TRAVIS_BRANCH}" | sed -e "s/\//_/")
 
+# Replace / with _ to create a valid tag
+TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
+echo "Running for ${TAG}"
 
 # Add commit and commit_message to docker container
-echo "${TRAVIS_COMMIT} ${TRAVIS_COMMIT_MESSAGE}" > freqtrade_commit
+echo "${GITHUB_SHA}" > freqtrade_commit
 
-if [ "${TRAVIS_EVENT_TYPE}" = "cron" ]; then
+if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
-    echo "event ${TRAVIS_EVENT_TYPE}: full rebuild - skipping cache"
+    echo "event ${GITHUB_EVENT_NAME}: full rebuild - skipping cache"
     docker build -t freqtrade:${TAG} .
 else
-    echo "event ${TRAVIS_EVENT_TYPE}: building with cache"
+    echo "event ${GITHUB_EVENT_NAME}: building with cache"
     # Pull last build to avoid rebuilding the whole image
     docker pull ${IMAGE_NAME}:${TAG}
     docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
@@ -23,7 +23,7 @@ if [ $? -ne 0 ]; then
 fi
 
 # Run backtest
-docker run --rm -it -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} --datadir /tests/testdata backtesting
+docker run --rm -v $(pwd)/config.json.example:/freqtrade/config.json:ro -v $(pwd)/tests:/tests freqtrade:${TAG} backtesting --datadir /tests/testdata --strategy DefaultStrategy
 
 if [ $? -ne 0 ]; then
     echo "failed running backtest"
@@ -38,12 +38,12 @@ if [ $? -ne 0 ]; then
 fi
 
 # Tag as latest for develop builds
-if [ "${TRAVIS_BRANCH}" = "develop" ]; then
+if [ "${TAG}" = "develop" ]; then
     docker tag freqtrade:$TAG ${IMAGE_NAME}:latest
 fi
 
 # Login
-echo "$DOCKER_PASS" | docker login -u $DOCKER_USER --password-stdin
+docker login -u $DOCKER_USERNAME -p $DOCKER_PASSWORD
 
 if [ $? -ne 0 ]; then
     echo "failed login"

config.json.example

@@ -52,6 +52,9 @@
             "DOGE/BTC"
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,
@@ -68,7 +71,7 @@
         "remove_pumps": false
     },
     "telegram": {
-        "enabled": true,
+        "enabled": false,
         "token": "your_telegram_token",
         "chat_id": "your_telegram_chat_id"
     },

config_binance.json.example

@@ -37,23 +37,29 @@
             "rateLimit": 200
         },
         "pair_whitelist": [
-            "AST/BTC",
+            "ALGO/BTC",
-            "ETC/BTC",
+            "ATOM/BTC",
-            "ETH/BTC",
+            "BAT/BTC",
+            "BCH/BTC",
+            "BRD/BTC",
             "EOS/BTC",
+            "ETH/BTC",
             "IOTA/BTC",
+            "LINK/BTC",
             "LTC/BTC",
-            "MTH/BTC",
+            "NEO/BTC",
-            "NCASH/BTC",
+            "NXS/BTC",
-            "TNT/BTC",
             "XMR/BTC",
-            "XLM/BTC",
+            "XRP/BTC",
-            "XRP/BTC"
+            "XTZ/BTC"
         ],
         "pair_blacklist": [
             "BNB/BTC"
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,

config_full.json.example

@@ -50,14 +50,18 @@
         "buy": "gtc",
         "sell": "gtc"
     },
-    "pairlist": {
+    "pairlists": [
+        {"method": "StaticPairList"},
+        {
             "method": "VolumePairList",
-        "config": {
             "number_assets": 20,
             "sort_key": "quoteVolume",
-            "precision_filter": false
+            "refresh_period": 1800
-        }
         },
+        {"method": "PrecisionFilter"},
+        {"method": "PriceFilter", "low_price_ratio": 0.01
+        }
+    ],
     "exchange": {
         "name": "bittrex",
         "sandbox": false,

config_kraken.json.example

@@ -38,14 +38,34 @@
             "rateLimit": 1000
         },
         "pair_whitelist": [
-            "ETH/EUR",
+            "ADA/EUR",
+            "ATOM/EUR",
+            "BAT/EUR",
+            "BCH/EUR",
             "BTC/EUR",
-            "BCH/EUR"
+            "DAI/EUR",
+            "DASH/EUR",
+            "EOS/EUR",
+            "ETC/EUR",
+            "ETH/EUR",
+            "LINK/EUR",
+            "LTC/EUR",
+            "QTUM/EUR",
+            "REP/EUR",
+            "WAVES/EUR",
+            "XLM/EUR",
+            "XMR/EUR",
+            "XRP/EUR",
+            "XTZ/EUR",
+            "ZEC/EUR"
         ],
         "pair_blacklist": [
 
         ]
     },
+    "pairlists": [
+        {"method": "StaticPairList"}
+    ],
     "edge": {
         "enabled": false,
         "process_throttle_secs": 3600,

docs/advanced-hyperopt.md

@@ -0,0 +1,63 @@
+# Advanced Hyperopt
+
+This page explains some advanced Hyperopt topics that may require higher
+coding skills and Python knowledge than creation of an ordinal hyperoptimization
+class.
+
+## Creating and using a custom loss function
+
+To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
+For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this function is being used.
+
+A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
+
+``` python
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
+TARGET_TRADES = 600
+EXPECTED_MAX_PROFIT = 3.0
+MAX_ACCEPTED_TRADE_DURATION = 300
+
+class SuperDuperHyperOptLoss(IHyperOptLoss):
+    """
+    Defines the default loss function for hyperopt
+    """
+
+    @staticmethod
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               *args, **kwargs) -> float:
+        """
+        Objective function, returns smaller number for better results
+        This is the legacy algorithm (used until now in freqtrade).
+        Weights are distributed as follows:
+        * 0.4 to trade duration
+        * 0.25: Avoiding trade loss
+        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
+        """
+        total_profit = results.profit_percent.sum()
+        trade_duration = results.trade_duration.mean()
+
+        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
+        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
+        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+        result = trade_loss + profit_loss + duration_loss
+        return result
+```
+
+Currently, the arguments are:
+
+* `results`: DataFrame containing the result  
+    The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
+    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
+* `trade_count`: Amount of trades (identical to `len(results)`)
+* `min_date`: Start date of the hyperopting TimeFrame
+* `min_date`: End date of the hyperopting TimeFrame
+
+This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
+
+!!! Note
+    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
+
+!!! Note
+    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.

docs/advanced-setup.md

@@ -0,0 +1,92 @@
+# Advanced Post-installation Tasks
+
+This page explains some advanced tasks and configuration options that can be performed after the bot installation and may be uselful in some environments.
+
+If you do not know what things mentioned here mean, you probably do not need it.
+
+## Configure the bot running as a systemd service
+
+Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+
+!!! Note
+    Certain systems (like Raspbian) don't load service unit files from the user directory. In this case, copy `freqtrade.service` into `/etc/systemd/user/` (requires superuser permissions).
+
+After that you can start the daemon with:
+
+```bash
+systemctl --user start freqtrade
+```
+
+For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
+
+```bash
+sudo loginctl enable-linger "$USER"
+```
+
+If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot 
+state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the 
+configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd 
+using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped) 
+when it changes. 
+
+The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd 
+as the watchdog.
+
+!!! Note
+    The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
+
+## Advanced Logging
+
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
+
+### Logging to syslog
+
+To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
+
+* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+
+The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
+
+So, the following are the examples of possible usages:
+
+* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
+* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+
+Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
+
+* `tail -f /var/log/user`, or 
+* install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+
+For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
+```
+if $programname startswith "freqtrade" then -/var/log/freqtrade.log
+```
+to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
+
+For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+```
+# Filter duplicated messages
+$RepeatedMsgReduction on
+```
+
+### Logging to journald
+
+This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
+
+To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
+
+* `--logfilename journald` -- send log messages to `journald`.
+
+Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
+
+* `journalctl -f` -- shows Freqtrade log messages sent to `journald` along with other log messages fetched by `journald`.
+* `journalctl -f -u freqtrade.service` -- this command can be used when the bot is run as a `systemd` service.
+
+There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
+
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

docs/backtesting.md

@@ -11,14 +11,15 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
 real data. This is what we call
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file
+Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
-and load ticker data from `user_data/data/<exchange>` by default.
+If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
-If no data is available for the exchange / pair / ticker interval combination, backtesting will
-ask you to download them first using `freqtrade download-data`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 
+!!! Tip "Using dynamic pairlists for backtesting"
+    While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the [`test-pairlist`](utils.md#test-pairlist) command, and needs to be specified as `"pair_whitelist"` attribute in the configuration.
+
 ### Run a backtesting against the currencies listed in your config file
 
 #### With 5 min tickers (Per default)
@@ -45,7 +46,7 @@ freqtrade --datadir user_data/data/bittrex-20180101 backtesting
 #### With a (custom) strategy file
 
 ```bash
-freqtrade -s SampleStrategy backtesting
+freqtrade backtesting -s SampleStrategy
 ```
 
 Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
@@ -72,6 +73,8 @@ The exported trades can be used for [further analysis](#further-backtest-result-
 freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 
+Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
+
 #### Supplying custom fee value
 
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.

docs/bot-usage.md

@@ -5,20 +5,18 @@ This page explains the different parameters of the bot and how to run it.
 !!! Note
     If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
 
-
 ## Bot commands
 
 ```
-usage: freqtrade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+usage: freqtrade [-h] [-V]
-                 [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                 {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
-                 [--db-url PATH] [--sd-notify]
-                 {backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
                  ...
 
 Free, open source crypto trading bot
 
 positional arguments:
-  {backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+  {trade,backtesting,edge,hyperopt,create-userdir,list-exchanges,list-timeframes,download-data,plot-dataframe,plot-profit}
+    trade               Trade module.
     backtesting         Backtesting module.
     edge                Edge module.
     hyperopt            Hyperopt module.
@@ -32,6 +30,27 @@ positional arguments:
 
 optional arguments:
   -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+
+```
+
+### Bot trading commands
+
+```
+usage: freqtrade trade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                       [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                       [--db-url PATH] [--sd-notify] [--dry-run]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite://` for Dry Run).
+  --sd-notify           Notify systemd service manager.
+  --dry-run             Enforce dry-run for trading (removes Exchange secrets
+                        and simulates trades).
+
+Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
   --logfile FILE        Log to the file specified.
   -V, --version         show program's version number and exit
@@ -43,15 +62,12 @@ optional arguments:
                         Path to directory with historical backtesting data.
   --userdir PATH, --user-data-dir PATH
                         Path to userdata directory.
-  -s NAME, --strategy NAME
-                        Specify strategy class name (default:
-                        `DefaultStrategy`).
-  --strategy-path PATH  Specify additional strategy lookup path.
-  --db-url PATH         Override trades database URL, this is useful in custom
-                        deployments (default: `sqlite:///tradesv3.sqlite` for
-                        Live Run mode, `sqlite://` for Dry Run).
-  --sd-notify           Notify systemd service manager.
 
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 ```
 
 ### How to specify which configuration file be used?
@@ -60,7 +76,7 @@ The bot allows you to select which configuration file you want to use by means o
 the `-c/--config` command line option:
 
 ```bash
-freqtrade -c path/far/far/away/config.json
+freqtrade trade -c path/far/far/away/config.json
 ```
 
 Per default, the bot loads the `config.json` configuration file from the current
@@ -73,22 +89,22 @@ The bot allows you to use multiple configuration files by specifying multiple
 defined in the latter configuration files override parameters with the same name
 defined in the previous configuration files specified in the command line earlier.
 
-For example, you can make a separate configuration file with your key and secrete
+For example, you can make a separate configuration file with your key and secret
 for the Exchange you use for trading, specify default configuration file with
-empty key and secrete values while running in the Dry Mode (which does not actually
+empty key and secret values while running in the Dry Mode (which does not actually
 require them):
 
 ```bash
-freqtrade -c ./config.json
+freqtrade trade -c ./config.json
 ```
 
 and specify both configuration files when running in the normal Live Trade Mode:
 
 ```bash
-freqtrade -c ./config.json -c path/to/secrets/keys.config.json
+freqtrade trade -c ./config.json -c path/to/secrets/keys.config.json
 ```
 
-This could help you hide your private Exchange key and Exchange secrete on you local machine
+This could help you hide your private Exchange key and Exchange secret on you local machine
 by setting appropriate file permissions for the file which contains actual secrets and, additionally,
 prevent unintended disclosure of sensitive private data when you publish examples
 of your configuration in the project issues or in the Internet.
@@ -134,7 +150,7 @@ In `user_data/strategies` you have a file `my_awesome_strategy.py` which has
 a strategy class called `AwesomeStrategy` to load it:
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
 If the bot does not find your strategy file, it will display in an error
@@ -149,7 +165,7 @@ This parameter allows you to add an additional strategy lookup path, which gets
 checked before the default locations (The passed path must be a directory!):
 
 ```bash
-freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
+freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
 ```
 
 #### How to install a strategy?
@@ -165,7 +181,7 @@ using `--db-url`. This can also be used to specify a custom database
 in production mode. Example command:
 
 ```bash
-freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
+freqtrade trade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 ```
 
 ## Backtesting commands
@@ -173,8 +189,10 @@ freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
 Backtesting also uses the config specified via `-c/--config`.
 
 ```
-usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                             [--max_open_trades INT]
+                             [-d PATH] [--userdir PATH] [-s NAME]
+                             [--strategy-path PATH] [-i TICKER_INTERVAL]
+                             [--timerange TIMERANGE] [--max_open_trades INT]
                              [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
                              [--eps] [--dmmp]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
@@ -211,11 +229,29 @@ optional arguments:
   --export EXPORT       Export backtest results, argument are: trades.
                         Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename
+                        Save backtest results to the file with this filename.
-                        (default: `user_data/backtest_results/backtest-
+                        Requires `--export` to be set as well. Example:
-                        result.json`). Requires `--export` to be set as well.
+                        `--export-filename=user_data/backtest_results/backtest
-                        Example: `--export-filename=user_data/backtest_results
+                        _today.json`
-                        /backtest_today.json`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 
 ```
 
@@ -223,7 +259,7 @@ optional arguments:
 
 The first time your run Backtesting, you will need to download some historic data first.
 This can be accomplished by using `freqtrade download-data`.  
-Check the corresponding [help page section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) for more details
+Check the corresponding [Data Downloading](data-download.md) section for more details
 
 ## Hyperopt commands
 
@@ -231,12 +267,14 @@ To optimize your strategy, you can use hyperopt parameter hyperoptimization
 to find optimal parameter values for your stategy.
 
 ```
-usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                          [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                          [-i TICKER_INTERVAL] [--timerange TIMERANGE]
                           [--max_open_trades INT]
                           [--stake_amount STAKE_AMOUNT] [--fee FLOAT]
-                          [--customhyperopt NAME] [--hyperopt-path PATH]
+                          [--hyperopt NAME] [--hyperopt-path PATH] [--eps]
-                          [--eps] [-e INT]
+                          [-e INT]
-                          [-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
+                          [--spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
                           [--dmmp] [--print-all] [--no-color] [--print-json]
                           [-j JOBS] [--random-state INT] [--min-trades INT]
                           [--continue] [--hyperopt-loss NAME]
@@ -254,16 +292,15 @@ optional arguments:
                         Specify stake_amount.
   --fee FLOAT           Specify fee ratio. Will be applied twice (on trade
                         entry and exit).
-  --customhyperopt NAME
+  --hyperopt NAME       Specify hyperopt class name which will be used by the
-                        Specify hyperopt class name (default:
+                        bot.
-                        `DefaultHyperOpt`).
+  --hyperopt-path PATH  Specify additional lookup path for Hyperopt and
-  --hyperopt-path PATH  Specify additional lookup path for Hyperopts and
                         Hyperopt Loss functions.
   --eps, --enable-position-stacking
                         Allow buying the same pair multiple times (position
                         stacking).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  -s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
+  --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
                         Specify which parameters to hyperopt. Space-separated
                         list. Default: `all`.
   --dmmp, --disable-max-market-positions
@@ -292,8 +329,27 @@ optional arguments:
                         generate completely different results, since the
                         target for optimization is different. Built-in
                         Hyperopt-loss-functions are: DefaultHyperOptLoss,
-                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss.(default:
+                        OnlyProfitHyperOptLoss, SharpeHyperOptLoss (default:
                         `DefaultHyperOptLoss`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 ```
 
 ## Edge commands
@@ -301,7 +357,9 @@ optional arguments:
 To know your trade expectancy and winrate against historical data, you can use Edge.
 
 ```
-usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
+usage: freqtrade edge [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
+                      [--userdir PATH] [-s NAME] [--strategy-path PATH]
+                      [-i TICKER_INTERVAL] [--timerange TIMERANGE]
                       [--max_open_trades INT] [--stake_amount STAKE_AMOUNT]
                       [--fee FLOAT] [--stoplosses STOPLOSS_RANGE]
 
@@ -324,6 +382,24 @@ optional arguments:
                         (without any space). Example:
                         `--stoplosses=-0.01,-0.1,-0.001`
 
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --strategy-path PATH  Specify additional strategy lookup path.
 ```
 
 To understand edge and how to read the results, please read the [edge documentation](edge.md).

docs/configuration.md

@@ -38,85 +38,92 @@ The prevelance for all Options is as follows:
 
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
-|  Command | Default | Description |
+|  Command | Description |
-|----------|---------|-------------|
+|----------|-------------|
-| `max_open_trades` | 3 | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades)
+| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades).<br> ***Datatype:*** *Positive integer or -1.*
-| `stake_currency` | BTC | **Required.** Crypto-currency used for trading.
+| `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `stake_amount` | 0.05 | **Required.** Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to `"unlimited"` to allow the bot to use all available balance.
+| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#understand-stake_amount). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Positive float or `"unlimited"`.*
-| `amount_reserve_percent` | 0.05 | Reserve some amount in min pair stake amount. Default is 5%. The bot will reserve `amount_reserve_percent` + stop-loss value when calculating min pair stake amount in order to avoid possible trade refusals.
+| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> ***Datatype:*** *Positive Float as ratio.*
-| `ticker_interval` | [1m, 5m, 15m, 30m, 1h, 1d, ...] | The ticker interval to use (1min, 5 min, 15 min, 30 min, 1 hour or 1 day). Default is 5 minutes. [Strategy Override](#parameters-in-the-strategy).
+| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *String*
-| `fiat_display_currency` | USD | **Required.** Fiat currency used to show your profits. More information below.
+| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> ***Datatype:*** *String*
-| `dry_run` | true | **Required.** Define if the bot must be in Dry-run or production mode.
+| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `dry_run_wallet` | 999.9 | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason.
+| `dry_run_wallet` | Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason. <br> ***Datatype:*** *Float*
-| `process_only_new_candles` | false | If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy).
+| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `minimal_roi` | See below | Set the threshold in percent the bot will use to sell a trade. More information below. [Strategy Override](#parameters-in-the-strategy).
+| `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `stoploss` | -0.10 | Value of the stoploss in percent used by the bot. More information below. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> ***Datatype:*** *Float (as ratio)*
-| `trailing_stop` | false | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Boolean*
-| `trailing_stop_positive` | 0 | Changes stop-loss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Float*
-| `trailing_stop_positive_offset` | 0 | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> ***Datatype:*** *Float*
-| `trailing_only_offset_is_reached` | false | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
+| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> ***Datatype:*** *Boolean*
-| `unfilledtimeout.buy` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
+| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.  <br> ***Datatype:*** *Integer*
-| `unfilledtimeout.sell` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
+| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. <br> ***Datatype:*** *Integer*
-| `bid_strategy.ask_last_balance` | 0.0 | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance).
+| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance). 
-| `bid_strategy.use_order_book` | false | Allows buying of pair using the rates in Order Book Bids.
+| `bid_strategy.use_order_book` | Enable buying using the rates in Order Book Bids. <br> ***Datatype:*** *Boolean*
-| `bid_strategy.order_book_top` | 0 | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
+| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.  *Defaults to `1`.*  <br> ***Datatype:*** *Positive Integer*
-| `bid_strategy. check_depth_of_market.enabled` | false | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
+| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | 0 | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
+| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.  *Defaults to `0`.*  <br> ***Datatype:*** *Float (as ratio)*
-| `ask_strategy.use_order_book` | false | Allows selling of open traded pair using the rates in Order Book Asks.
+| `ask_strategy.use_order_book` | Enable selling of open trades using Order Book Asks. <br> ***Datatype:*** *Boolean*
-| `ask_strategy.order_book_min` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.order_book_max` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
+| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> ***Datatype:*** *Positive Integer*
-| `ask_strategy.use_sell_signal` | true | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.sell_profit_only` | false | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.sell_profit_only` | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `ask_strategy.ignore_roi_if_buy_signal` | false | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
+| `ask_strategy.ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> ***Datatype:*** *Boolean*
-| `order_types` | None | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).
+| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> ***Datatype:*** *Dict*
-| `order_time_in_force` | None | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy).
+| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> ***Datatype:*** *Dict*
-| `exchange.name` |  | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
+| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> ***Datatype:*** *String*
-| `exchange.sandbox` | false | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.
+| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> ***Datatype:*** *Boolean*
-| `exchange.key` | '' | API key to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
+| `exchange.key` | API key to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.secret` | '' | API secret to use for the exchange. Only required when you are in production mode. ***Keep it in secrete, do not disclose publicly.***
+| `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.password` | '' | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. ***Keep it in secrete, do not disclose publicly.***
+| `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `exchange.pair_whitelist` | [] | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.pair_blacklist` | [] | List of pairs the bot must absolutely avoid for trading and backtesting. Can be overriden by dynamic pairlists (see [below](#dynamic-pairlists)).
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> ***Datatype:*** *List*
-| `exchange.ccxt_config` | None | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
+| `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.ccxt_async_config` | None | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
+| `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> ***Datatype:*** *Dict*
-| `exchange.markets_refresh_interval` | 60 | The interval in minutes in which markets are reloaded.
+| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> ***Datatype:*** *Positive Integer*
-| `edge` | false | Please refer to [edge configuration document](edge.md) for detailed explanation.
+| `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
-| `experimental.block_bad_exchanges` | true | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now.
+| `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> ***Datatype:*** *Boolean*
-| `pairlist.method` | StaticPairList | Use static or dynamic volume-based pairlist. [More information below](#dynamic-pairlists).
+| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> ***Datatype:*** *List of Dicts*
-| `pairlist.config` | None | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
+| `telegram.enabled` | Enable the usage of Telegram. <br> ***Datatype:*** *Boolean*
-| `telegram.enabled` | true | **Required.** Enable or not the usage of Telegram.
+| `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `telegram.token` | token | Your Telegram bot token. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
+| `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. **Keep it in secret, do not disclose publicly.** <br> ***Datatype:*** *String*
-| `telegram.chat_id` | chat_id | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. ***Keep it in secrete, do not disclose publicly.***
+| `webhook.enabled` | Enable usage of Webhook notifications <br> ***Datatype:*** *Boolean*
-| `webhook.enabled` | false | Enable usage of Webhook notifications
+| `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.url` | false | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
+| `webhook.webhookbuy` | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhookbuy` | false | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `webhook.webhooksell` | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhooksell` | false | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details. <br> ***Datatype:*** *String*
-| `webhook.webhookstatus` | false | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
+| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Boolean*
-| `db_url` | `sqlite:///tradesv3.sqlite`| Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
+| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *IPv4*
-| `initial_state` | running | Defines the initial application state. More information below.
+| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br> ***Datatype:*** *Integer between 1024 and 65535*
-| `forcebuy_enable` | false | Enables the RPC Commands to force a buy. More information below.
+| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. **Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
-| `strategy` | DefaultStrategy | Defines Strategy class to use.
+| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. **Keep it in secret, do not disclose publicly.**<br> ***Datatype:*** *String*
-| `strategy_path` | null | Adds an additional strategy lookup path (must be a directory).
+| `db_url` | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `true`, and to `sqlite:///tradesv3.sqlite` for production instances. <br> ***Datatype:*** *String, SQLAlchemy connect string*
-| `internals.process_throttle_secs` | 5 | **Required.** Set the process throttle. Value in second.
+| `initial_state` | Defines the initial application state. More information below. <br>*Defaults to `stopped`.* <br> ***Datatype:*** *Enum, either `stopped` or `running`*
-| `internals.heartbeat_interval` | 60 | Print heartbeat message every X seconds. Set to 0 to disable heartbeat messages.
+| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> ***Datatype:*** *Boolean*
-| `internals.sd_notify` | false | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details.
+| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> ***Datatype:*** *ClassName*
-| `logfile` | | Specify Logfile. Uses a rolling strategy of 10 files, with 1Mb per file.
+| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> ***Datatype:*** *String*
-| `user_data_dir` | cwd()/user_data | Directory containing user data. Defaults to `./user_data/`.
+| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> ***Datatype:*** *Positive Integer*
+| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> ***Datatype:*** *Positive Integer or 0*
+| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> ***Datatype:*** *Boolean*
+| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> ***Datatype:*** *String*
+| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> ***Datatype:*** *String*
 
 ### Parameters in the strategy
 
 The following parameters can be set in either configuration file or strategy.
 Values set in the configuration file always overwrite values set in the strategy.
 
-* `ticker_interval`
 * `minimal_roi`
+* `ticker_interval`
 * `stoploss`
 * `trailing_stop`
 * `trailing_stop_positive`
 * `trailing_stop_positive_offset`
+* `trailing_only_offset_is_reached`
 * `process_only_new_candles`
 * `order_types`
 * `order_time_in_force`
+* `stake_currency`
+* `stake_amount`
 * `use_sell_signal` (ask_strategy)
 * `sell_profit_only` (ask_strategy)
 * `ignore_roi_if_buy_signal` (ask_strategy)
@@ -124,18 +131,22 @@ Values set in the configuration file always overwrite values set in the strategy
 ### Understand stake_amount
 
 The `stake_amount` configuration parameter is an amount of crypto-currency your bot will use for each trade.
-The minimal value is 0.0005. If there is not enough crypto-currency in
+
-the account an exception is generated.
+The minimal configuration value is 0.0001. Please check your exchange's trading minimums to avoid problems.
+
+This setting works in combination with `max_open_trades`. The maximum capital engaged in trades is `stake_amount * max_open_trades`.
+For example, the bot will at most use (0.05 BTC x 3) = 0.15 BTC, assuming a configuration of `max_open_trades=3` and `stake_amount=0.05`.
+
 To allow the bot to trade all the available `stake_currency` in your account set
 
 ```json
 "stake_amount" : "unlimited",
 ```
 
-In this case a trade amount is calclulated as:
+In this case a trade amount is calculated as:
 
 ```python
-currency_balanse / (max_open_trades - current_open_trades)
+currency_balance / (max_open_trades - current_open_trades)
 ```
 
 ### Understand minimal_roi
@@ -215,6 +226,11 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
 `emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
 The below is the default which is used if this is not configured in either strategy or configuration file.
 
+Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
+`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1%.
+Calculation example: we bought the asset at 100$.
+Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
+
 Syntax for Strategy:
 
 ```python
@@ -224,7 +240,8 @@ order_types = {
     "emergencysell": "market",
     "stoploss": "market",
     "stoploss_on_exchange": False,
-    "stoploss_on_exchange_interval": 60
+    "stoploss_on_exchange_interval": 60,
+    "stoploss_on_exchange_limit_ratio": 0.99,
 }
 ```
 
@@ -254,7 +271,7 @@ Configuration:
 !!! Note
     If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
 
-!!! Warning stoploss_on_exchange failures
+!!! Warning "Warning: stoploss_on_exchange failures"
     If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however this is not advised.
 
 ### Understand order_time_in_force
@@ -331,7 +348,7 @@ This configuration enables binance, as well as rate limiting to avoid bans from
     Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
     We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
 
-#### Advanced FreqTrade Exchange configuration
+#### Advanced Freqtrade Exchange configuration
 
 Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behaviours.
 
@@ -370,6 +387,91 @@ The valid values are:
 "BTC", "ETH", "XRP", "LTC", "BCH", "USDT"
 ```
 
+## Pairlists
+
+Pairlists define the list of pairs that the bot should trade.
+There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
+
+[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
+
+All pairlists can be chained, and a combination of all pairlists will become your new whitelist. Pairlists are executed in the sequence they are configured. You should always configure either `StaticPairList` or `DynamicPairList` as starting pairlists.
+
+Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
+
+### Available Pairlists
+
+* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
+* [`VolumePairList`](#volume-pair-list)
+* [`PrecisionFilter`](#precision-filter)
+* [`PriceFilter`](#price-pair-filter)
+
+!!! Tip "Testing pairlists"
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
+
+#### Static Pair List
+
+By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. 
+
+It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
+
+```json
+"pairlists": [
+    {"method": "StaticPairList"}
+    ],
+```
+
+#### Volume Pair List
+
+`VolumePairList` selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume` and defaults to `quoteVolume`.
+
+`VolumePairList` considers outputs of previous pairlists unless  it's the first configured pairlist, it does not consider `pair_whitelist`, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+
+`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+
+```json
+"pairlists": [{
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+        "refresh_period": 1800,
+],
+```
+
+#### Precision Filter
+
+Filters low-value coins which would not allow setting a stoploss.
+
+#### Price Pair Filter
+
+The `PriceFilter` allows filtering of pairs by price.
+Currently, only `low_price_ratio` is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
+This option is disabled by default, and will only apply if set to <> 0.
+
+Calculation example:  
+Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value. 
+
+These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
+
+### Full Pairlist example
+
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
+
+```json
+"exchange": {
+    "pair_whitelist": [],
+    "pair_blacklist": ["BNB/BTC"]
+},
+"pairlists": [
+    {
+        "method": "VolumePairList",
+        "number_assets": 20,
+        "sort_key": "quoteVolume",
+    },
+    {"method": "PrecisionFilter"},
+    {"method": "PriceFilter", "low_price_ratio": 0.01}
+    ],
+```
+
 ## Switch to Dry-run mode
 
 We recommend starting the bot in the Dry-run mode to see how your bot will
@@ -385,7 +487,7 @@ creating trades on the exchange.
 "db_url": "sqlite:///tradesv3.dryrun.sqlite",
 ```
 
-3. Remove your Exchange API key and secrete (change them by empty values or fake credentials):
+3. Remove your Exchange API key and secret (change them by empty values or fake credentials):
 
 ```json
 "exchange": {
@@ -399,39 +501,6 @@ creating trades on the exchange.
 Once you will be happy with your bot performance running in the Dry-run mode,
 you can switch it to production mode.
 
-### Dynamic Pairlists
-
-Dynamic pairlists select pairs for you based on the logic configured.
-The bot runs against all pairs (with that stake) on the exchange, and a number of assets
-(`number_assets`) is selected based on the selected criteria.
-
-By default, the `StaticPairList` method is used.
-The Pairlist method is configured as `pair_whitelist` parameter under the `exchange`
-section of the configuration.
-
-**Available Pairlist methods:**
-
-* `StaticPairList`
-  * It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
-* `VolumePairList`
-  * It selects `number_assets` top pairs based on `sort_key`, which can be one of
-`askVolume`, `bidVolume` and `quoteVolume`, defaults to `quoteVolume`.
-  * There is a possibility to filter low-value coins that would not allow setting a stop loss
-(set `precision_filter` parameter to `true` for this).
-
-Example:
-
-```json
-"pairlist": {
-        "method": "VolumePairList",
-        "config": {
-            "number_assets": 20,
-            "sort_key": "quoteVolume",
-            "precision_filter": false
-        }
-    },
-```
-
 ## Switch to production mode
 
 In production mode, the bot will engage your money. Be careful, since a wrong
@@ -457,12 +526,14 @@ you run it in production mode.
         "secret": "08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5",
         ...
 }
-
 ```
+
 !!! Note
     If you have an exchange API key yet, [see our tutorial](/pre-requisite).
 
-### Using proxy with FreqTrade
+You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
+
+### Using proxy with Freqtrade
 
 To use a proxy with freqtrade, add the kwarg `"aiohttp_trust_env"=true` to the `"ccxt_async_kwargs"` dict in the exchange section of the configuration.
 
@@ -482,14 +553,13 @@ export HTTPS_PROXY="http://addr:port"
 freqtrade
 ```
 
-
+## Embedding Strategies
-### Embedding Strategies
 
 FreqTrade provides you with with an easy way to embed the strategy into your configuration file.
 This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
 in your chosen config file.
 
-#### Encoding a string as BASE64
+### Encoding a string as BASE64
 
 This is a quick example, how to generate the BASE64 string in python
 

docs/data-download.md

@@ -8,7 +8,7 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
 Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
 Otherwise `--exchange` becomes mandatory.
 
-!!! Tip Updating existing data
+!!! Tip "Tip: Updating existing data"
     If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
     Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
 
@@ -78,10 +78,8 @@ freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --d
 !!! Warning
     The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
 
-### Historic Kraken data
+!!! Note "Kraken user"
-
+    Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
-The Kraken API does only provide 720 historic candles, which is sufficient for FreqTrade dry-run and live trade modes, but is a problem for backtesting.
-To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
 
 ## Next step
 

docs/developer.md

@@ -46,15 +46,18 @@ def test_method_to_test(caplog):
 The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
 
 #### Install
+
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [docker](https://docs.docker.com/install/)
 * [docker-compose](https://docs.docker.com/compose/install/)
 
 #### Starting the bot
 ##### Use the develop dockerfile
+
 ``` bash
 rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
 ```
+
 #### Docker Compose
 
 ##### Starting
@@ -62,9 +65,11 @@ rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
 ``` bash
 docker-compose up
 ```
+
 ![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
 
 ##### Rebuilding
+
 ``` bash
 docker-compose build
 ```
@@ -77,8 +82,8 @@ that can be effected by `docker-compose up` or `docker-compose run freqtrade_dev
 ``` bash
 docker-compose exec freqtrade_develop /bin/bash
 ```
-![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
 
+![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
 
 ## Modules
 
@@ -95,22 +100,22 @@ This is a simple provider, which however serves as a good example on how to star
 
 Next, modify the classname of the provider (ideally align this with the Filename).
 
-The base-class provides the an instance of the bot (`self._freqtrade`), as well as the configuration (`self._config`), and initiates both `_blacklist` and `_whitelist`.
+The base-class provides an instance of the exchange (`self._exchange`) the pairlist manager (`self._pairlistmanager`), as well as the main configuration (`self._config`), the pairlist dedicated configuration (`self._pairlistconfig`) and the absolute position within the list of pairlists.
 
 ```python
-        self._freqtrade = freqtrade
+        self._exchange = exchange
+        self._pairlistmanager = pairlistmanager
         self._config = config
-        self._whitelist = self._config['exchange']['pair_whitelist']
+        self._pairlistconfig = pairlistconfig
-        self._blacklist = self._config['exchange'].get('pair_blacklist', [])
+        self._pairlist_pos = pairlist_pos
 ```
 
-
 Now, let's step through the methods which require actions:
 
-#### configuration
+#### Pairlist configuration
 
 Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
-This Pairlist-object may contain a `"config"` dict with additional configurations for the configured pairlist.
+This Pairlist-object may contain configurations with additional configurations for the configured pairlist.
 By convention, `"number_assets"` is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
 
 Additional elements can be configured as needed. `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
@@ -120,29 +125,30 @@ Additional elements can be configured as needed. `VolumePairList` uses `"sort_ke
 Returns a description used for Telegram messages.
 This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format `"PairlistName - top/bottom X pairs"`.
 
-#### refresh_pairlist
+#### filter_pairlist
 
 Override this method and run all calculations needed in this method.
 This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
 
-Assign the resulting whiteslist to `self._whitelist` and `self._blacklist` respectively. These will then be used to run the bot in this iteration. Pairs with open trades will be added to the whitelist to have the sell-methods run correctly.
+It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
 
-Please also run `self._validate_whitelist(pairs)` and to check and remove pairs with inactive markets. This function is available in the Parent class (`StaticPairList`) and should ideally not be overwritten.
+It must return the resulting pairlist (which may then be passed into the next pairlist filter).
+
+Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
 
 ##### sample
 
 ``` python
-    def refresh_pairlist(self) -> None:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         # Generate dynamic whitelist
-        pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)
+        pairs = self._calculate_pairlist(pairlist, tickers)
-        # Validate whitelist to only have active market pairs
+        return pairs
-        self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
 ```
 
 #### _gen_pair_whitelist
 
 This is a simple method used by `VolumePairList` - however serves as a good example.
-It implements caching (`@cached(TTLCache(maxsize=1, ttl=1800))`) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
+In VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
 
 ## Implement a new Exchange (WIP)
 
@@ -194,24 +200,38 @@ If the day shows the same day, then the last candle can be assumed as incomplete
 To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
 
 ``` bash
-jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace user_data/notebooks/strategy_analysis_example.ipynb
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb
-jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown user_data/notebooks/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
+jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
 ```
 
+## Continuous integration
+
+This documents some decisions taken for the CI Pipeline.
+
+* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
+* Docker images are build for the branches `master` and `develop`.
+* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
+* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
+* Full docker image rebuilds are run once a week via schedule.
+* Deployments run on ubuntu.
+* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
+* All tests must pass for a PR to be merged to `master` or `develop`.
+
 ## Creating a release
 
 This part of the documentation is aimed at maintainers, and shows how to create a release.
 
 ### Create release branch
 
-``` bash
+First, pick a commit that's about one week old (to not include latest additions to releases).
-# make sure you're in develop branch
-git checkout develop
 
+``` bash
 # create new branch
-git checkout -b new_release
+git checkout -b new_release <commitid>
 ```
 
+Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
+
 * Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
 * Commit this part
 * push that branch to the remote and create a PR against the master branch
@@ -219,23 +239,18 @@ git checkout -b new_release
 ### Create changelog from git commits
 
 !!! Note
-    Make sure that both master and develop are up-todate!.
+    Make sure that the master branch is uptodate!
 
 ``` bash
 # Needs to be done before merging / pulling that branch.
-git log --oneline --no-decorate --no-merges master..develop
+git log --oneline --no-decorate --no-merges master..new_release
 ```
 
 ### Create github release / tag
 
 Once the PR against master is merged (best right after merging):
 
-* Use the button "Draft a new release" in the Github UI (subsection releases)
+* Use the button "Draft a new release" in the Github UI (subsection releases).
 * Use the version-number specified as tag.
 * Use "master" as reference (this step comes after the above PR is merged).
-* Use the above changelog as release comment (as codeblock)
+* Use the above changelog as release comment (as codeblock).
-
-### After-release
-
-* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
-* Create a PR against develop to update that branch.

docs/docker.md

@@ -26,7 +26,7 @@ To update the image, simply run the above commands again and restart your runnin
 
 Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
 
-!!! Note Docker image update frequency
+!!! Note "Docker image update frequency"
     The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
     In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
 
@@ -160,7 +160,7 @@ docker run -d \
   -v ~/.freqtrade/config.json:/freqtrade/config.json \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
-  freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
 ```
 
 !!! Note
@@ -170,6 +170,9 @@ docker run -d \
 !!! Note
     All available bot command line parameters can be added to the end of the `docker run` command.
 
+!!! Note
+    You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
+
 ### Monitor your Docker instance
 
 You can use the following commands to monitor and manage your container:
@@ -199,7 +202,7 @@ docker run -d \
   -v ~/.freqtrade/config.json:/freqtrade/config.json \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
-  freqtrade --strategy AwsomelyProfitableStrategy backtesting
+  freqtrade backtesting --strategy AwsomelyProfitableStrategy
 ```
 
 Head over to the [Backtesting Documentation](backtesting.md) for more details.

docs/edge.md

@@ -235,7 +235,7 @@ An example of its output:
 ### Update cached pairs with the latest data
 
 Edge requires historic data the same way as backtesting does.
-Please refer to the [download section](backtesting.md#Getting-data-for-backtesting-and-hyperopt) of the documentation for details.
+Please refer to the [Data Downloading](data-download.md) section of the documentation for details.
 
 ### Precising stoploss range
 

docs/exchanges.md

@@ -0,0 +1,63 @@
+# Exchange-specific Notes
+
+This page combines common gotchas and informations which are exchange-specific and most likely don't apply to other exchanges.
+
+## Binance
+
+!!! Tip "Stoploss on Exchange"
+    Binance is currently the only exchange supporting `stoploss_on_exchange`. It provides great advantages, so we recommend to benefit from it.
+
+### Blacklists
+
+For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
+Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
+
+### Binance sites
+
+Binance has been split into 3, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
+
+* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
+* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
+* [binance.je](https://www.binance.je/) - Binance Jersey, trading fiat currencies. Use exchange id: `binanceje`.
+
+## Kraken
+
+### Historic Kraken data
+
+The Kraken API does only provide 720 historic candles, which is sufficient for Freqtrade dry-run and live trade modes, but is a problem for backtesting.
+To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
+
+## Bittrex
+
+### Restricted markets
+
+Bittrex split its exchange into US and International versions.
+The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
+
+If you have restricted pairs in your whitelist, you'll get a warning message in the log on Freqtrade startup for each restricted pair.
+
+The warning message will look similar to the following:
+
+``` output
+[...] Message: bittrex {"success":false,"message":"RESTRICTED_MARKET","result":null,"explanation":null}"
+```
+
+If you're an "International" customer on the Bittrex exchange, then this warning will probably not impact you.
+If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your whitelist.
+
+You can get a list of restricted markets by using the following snippet:
+
+``` python
+import ccxt
+ct = ccxt.bittrex()
+_ = ct.load_markets()
+res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarkets()['result'] if x['IsRestricted']]
+print(res)
+```
+
+## Random notes for other exchanges
+
+* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
+```shell
+$ pip3 install web3
+```

docs/faq.md

@@ -4,7 +4,7 @@
 
 ### The bot does not start
 
-Running the bot with `freqtrade --config config.json` does show the output `freqtrade: command not found`.
+Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
 
 This could have the following reasons:
 
@@ -48,12 +48,46 @@ You can use the `/forcesell all` command from Telegram.
 ### I get the message "RESTRICTED_MARKET"
 
 Currently known to happen for US Bittrex users.  
-Bittrex split its exchange into US and International versions.
-The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
 
-If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair.
+Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
-If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
+
-If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
+### How do I search the bot logs for something?
+
+By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
+
+* In unix shells, this normally can be done as simple as:
+```shell
+$ freqtrade --some-options 2>&1 >/dev/null | grep 'something'
+```
+(note, `2>&1` and `>/dev/null` should be written in this order)
+
+* Bash interpreter also supports so called process substitution syntax, you can grep the log for a string with it as:
+```shell
+$ freqtrade --some-options 2> >(grep 'something') >/dev/null
+```
+or
+```shell
+$ freqtrade --some-options 2> >(grep -v 'something' 1>&2)
+```
+
+* You can also write the copy of Freqtrade log messages to a file with the `--logfile` option:
+```shell
+$ freqtrade --logfile /path/to/mylogfile.log --some-options
+```
+and then grep it as:
+```shell
+$ cat /path/to/mylogfile.log | grep 'something'
+```
+or even on the fly, as the bot works and the logfile grows:
+```shell
+$ tail -f /path/to/mylogfile.log | grep 'something'
+```
+from a separate terminal window.
+
+On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+```
+> type \path\to\mylogfile.log | findstr "something"
+```
 
 ## Hyperopt module
 

docs/hyperopt.md

@@ -15,30 +15,50 @@ To learn how to get data for the pairs and exchange you're interrested in, head
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
-the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py).
+the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
 
 Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
 
+The simplest way to get started is to use `freqtrade new-hyperopt --hyperopt AwesomeHyperopt`.
+This will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
+
 ### Checklist on all tasks / possibilities in hyperopt
 
 Depending on the space you want to optimize, only some of the below are required:
 
-* fill `populate_indicators` - probably a copy from your strategy
 * fill `buy_strategy_generator` - for buy signal optimization
 * fill `indicator_space` - for buy signal optimzation
 * fill `sell_strategy_generator` - for sell signal optimization
 * fill `sell_indicator_space` - for sell signal optimzation
 
-Optional, but recommended:
+!!! Note
+    `populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
 
+Optional - can also be loaded from a strategy:
+
+* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
 * copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
 * copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
 
+!!! Note
+    Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
+
 Rarely you may also need to override:
 
 * `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
-* `generate_roi_table` - for custom ROI optimization (if you need more than 4 entries in the ROI table)
+* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
 * `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
+* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
+
+!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
+    You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything (i.e. without creation of a "complete" Hyperopt class with dimensions, parameters, triggers and guards, as described in this document) from the default hyperopt template by relying on your strategy to do most of the calculations.
+
+    ``` python
+    # Have a working strategy at hand.
+    freqtrade new-hyperopt --hyperopt EmptyHyperopt
+
+    freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
+    ``` 
 
 ### 1. Install a Custom Hyperopt File
 
@@ -156,7 +176,7 @@ that minimizes the value of the [loss function](#loss-functions).
 
 The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
 When you want to test an indicator that isn't used by the bot currently, remember to
-add it to the `populate_indicators()` method in `hyperopt.py`.
+add it to the `populate_indicators()` method in your custom hyperopt file.
 
 ## Loss-functions
 
@@ -173,63 +193,7 @@ Currently, the following loss functions are builtin:
 * `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
 * `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
 
-### Creating and using a custom loss function
+Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
-
-To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
-For the sample below, you then need to add the command line parameter `--hyperopt-loss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
-
-A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
-
-``` python
-from freqtrade.optimize.hyperopt import IHyperOptLoss
-
-TARGET_TRADES = 600
-EXPECTED_MAX_PROFIT = 3.0
-MAX_ACCEPTED_TRADE_DURATION = 300
-
-class SuperDuperHyperOptLoss(IHyperOptLoss):
-    """
-    Defines the default loss function for hyperopt
-    """
-
-    @staticmethod
-    def hyperopt_loss_function(results: DataFrame, trade_count: int,
-                               min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
-        """
-        Objective function, returns smaller number for better results
-        This is the legacy algorithm (used until now in freqtrade).
-        Weights are distributed as follows:
-        * 0.4 to trade duration
-        * 0.25: Avoiding trade loss
-        * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
-        """
-        total_profit = results.profit_percent.sum()
-        trade_duration = results.trade_duration.mean()
-
-        trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
-        profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-        duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
-        result = trade_loss + profit_loss + duration_loss
-        return result
-```
-
-Currently, the arguments are:
-
-* `results`: DataFrame containing the result  
-    The following columns are available in results (corresponds to the output-file of backtesting when used with `--export trades`):  
-    `pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reason`
-* `trade_count`: Amount of trades (identical to `len(results)`)
-* `min_date`: Start date of the hyperopting TimeFrame
-* `min_date`: End date of the hyperopting TimeFrame
-
-This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
-
-!!! Note
-    This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
-
-!!! Note
-    Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
 
 ## Execute Hyperopt
 
@@ -239,15 +203,15 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
 We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
 
 ```bash
-freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all
+freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
 ```
 
 Use  `<hyperoptname>` as the name of the custom hyperopt used.
 
-The `-e` flag will set how many evaluations hyperopt will do. We recommend
+The `-e` option will set how many evaluations hyperopt will do. We recommend
 running at least several thousand evaluations.
 
-The `--spaces all` flag determines that all possible parameters should be optimized. Possibilities are listed below.
+The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
 
 !!! Note
     By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
@@ -270,9 +234,17 @@ For example, to use one month of data, pass the following parameter to the hyper
 freqtrade hyperopt --timerange 20180401-20180501
 ```
 
+### Running Hyperopt using methods from a strategy
+
+Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
+
+```bash
+freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
+```
+
 ### Running Hyperopt with Smaller Search Space
 
-Use the `--spaces` argument to limit the search space used by hyperopt.
+Use the `--spaces` option to limit the search space used by hyperopt.
 Letting Hyperopt optimize everything is a huuuuge search space. Often it
 might make more sense to start by just searching for initial buy algorithm.
 Or maybe you just want to optimize your stoploss or roi table for that awesome
@@ -285,8 +257,12 @@ Legal values are:
 * `sell`: just search for a new sell strategy
 * `roi`: just optimize the minimal profit table for your strategy
 * `stoploss`: search for the best stoploss value
+* `trailing`: search for the best trailing stop values
+* `default`: `all` except `trailing`
 * space-separated list of any of the above values for example `--spaces roi stoploss`
 
+The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
+
 ### Position stacking and disabling max market positions
 
 In some situations, you may need to run Hyperopt (and Backtesting) with the 
@@ -341,8 +317,7 @@ So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that t
 (dataframe['rsi'] < 29.0)
 ```
 
-Translating your whole hyperopt result as the new buy-signal
+Translating your whole hyperopt result as the new buy-signal would then look like:
-would then look like:
 
 ```python
 def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@@ -361,19 +336,13 @@ You can use the `--print-all` command line option if you would like to see all r
 
 ### Understand Hyperopt ROI results
 
-If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
+If you are optimizing ROI (i.e. if optimization search-space contains 'all', 'default' or 'roi'), your result will look as follows and include a ROI table:
 
 ```
 Best result:
 
     44/100:    135 trades. Avg profit  0.57%. Total profit  0.03871918 BTC (0.7722Σ%). Avg duration 180.4 mins. Objective: 1.94367
 
-Buy hyperspace params:
-{   'adx-value': 44,
-    'rsi-value': 29,
-    'adx-enabled': False,
-    'rsi-enabled': True,
-    'trigger': 'bb_lower'}
 ROI table:
 {   0: 0.10674,
     21: 0.09158,
@@ -397,7 +366,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
 
 #### Default ROI Search Space
 
-If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values can vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
+If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
 
 | # step | 1m |  | 5m |  | 1h |  | 1d |  |
 |---|---|---|---|---|---|---|---|---|
@@ -410,11 +379,11 @@ These ranges should be sufficient in most cases. The minutes in the steps (ROI d
 
 If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
 
-Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
 
 ### Understand Hyperopt Stoploss results
 
-If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
+If you are optimizing stoploss values (i.e. if optimization search-space contains 'all', 'default' or 'stoploss'), your result will look as follows and include stoploss:
 
 ```
 Best result:
@@ -441,13 +410,51 @@ As stated in the comment, you can also use it as the value of the `stoploss` set
 
 #### Default Stoploss Search Space
 
-If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.35...-0.02, which is sufficient in most cases.
+If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace vary in the range -0.35...-0.02, which is sufficient in most cases.
 
 If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
 
-Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
 
-### Validate backtesting results
+### Understand Hyperopt Trailing Stop results
+
+If you are optimizing trailing stop values (i.e. if optimization search-space contains 'all' or 'trailing'), your result will look as follows and include trailing stop parameters:
+
+```
+Best result:
+
+    45/100:    606 trades. Avg profit  1.04%. Total profit  0.31555614 BTC ( 630.48Σ%). Avg duration 150.3 mins. Objective: -1.10161
+
+Trailing stop:
+{   'trailing_only_offset_is_reached': True,
+    'trailing_stop': True,
+    'trailing_stop_positive': 0.02001,
+    'trailing_stop_positive_offset': 0.06038}
+```
+
+In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
+
+```
+    # Trailing stop
+    # These attributes will be overridden if the config file contains corresponding values.
+    trailing_stop = True
+    trailing_stop_positive = 0.02001
+    trailing_stop_positive_offset = 0.06038
+    trailing_only_offset_is_reached = True
+```
+As stated in the comment, you can also use it as the values of the corresponding settings in the configuration file.
+
+#### Default Trailing Stop Search Space
+
+If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
+
+Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py).
+
+## Show details of Hyperopt results
+
+After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` subcommands. The usage of these subcommands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
+
+## Validate backtesting results
 
 Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
 

docs/installation.md

@@ -26,24 +26,32 @@ You will need to create API Keys (Usually you get `key` and `secret`) from the E
 
 ## Quick start
 
-Freqtrade provides a Linux/MacOS script to install all dependencies and help you to configure the bot.
+Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
-
-!!! Note
-    Python3.6 or higher and the corresponding pip are assumed to be available. The install-script will warn and stop if that's not the case.
-
-```bash
-git clone git@github.com:freqtrade/freqtrade.git
-cd freqtrade
-git checkout develop
-./setup.sh --install
-```
 
 !!! Note
     Windows installation is explained [here](#windows).
 
-## Easy Installation - Linux Script
+The easiest way to install and run Freqtrade is to clone the bot GitHub repository and then run the Easy Installation script, if it's available for your platform.
 
-If you are on Debian, Ubuntu or MacOS freqtrade provides a script to Install, Update, Configure, and Reset your bot.
+!!! Note "Version considerations"
+    When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
+
+!!! Note
+    Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
+
+This can be achieved with the following commands:
+
+```bash
+git clone git@github.com:freqtrade/freqtrade.git
+cd freqtrade
+git checkout master  # Optional, see (1)
+./setup.sh --install
+```
+(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
+
+## Easy Installation Script (Linux/MacOS)
+
+If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
 
 ```bash
 $ ./setup.sh
@@ -56,25 +64,25 @@ usage:
 
 ** --install **
 
-This script will install everything you need to run the bot:
+With this option, the script will install everything you need to run the bot:
 
 * Mandatory software as: `ta-lib`
 * Setup your virtualenv
 * Configure your `config.json` file
 
-This script is a combination of `install script` `--reset`, `--config`
+This option is a combination of installation tasks, `--reset` and `--config`.
 
 ** --update **
 
-Update parameter will pull the last version of your current branch and update your virtualenv.
+This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
 
 ** --reset **
 
-Reset parameter will hard reset your branch (only if you are on `master` or `develop`) and recreate your virtualenv.
+This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
 
 ** --config **
 
-Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
+Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
 
 ------
 
@@ -95,29 +103,26 @@ sudo apt-get update
 sudo apt-get install build-essential git
 ```
 
-#### Raspberry Pi / Raspbian
+### Raspberry Pi / Raspbian
 
-Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
+The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
+This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
 
-The following assumes that miniconda3 is installed and available in your environment. Since the last miniconda3 installation file uses python 3.4, we will update to python 3.6 on this installation.
+Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
-It's recommended to use (mini)conda for this as installation/compilation of `numpy` and `pandas` takes a long time.
-
-Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
 
 ``` bash
-conda config --add channels rpi
+sudo apt-get install python3-venv libatlas-base-dev
-conda install python=3.6
+git clone https://github.com/freqtrade/freqtrade.git
-conda create -n freqtrade python=3.6
+cd freqtrade
-conda activate freqtrade
-conda install pandas numpy
 
-sudo apt install libffi-dev
+bash setup.sh -i
-python3 -m pip install -r requirements-common.txt
-python3 -m pip install -e .
 ```
 
+!!! Note "Installation duration"
+    Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
+
 !!! Note
-    This does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
+    The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
     We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
 
 ### Common
@@ -151,13 +156,13 @@ python3 -m venv .env
 source .env/bin/activate
 ```
 
-#### 3. Install FreqTrade
+#### 3. Install Freqtrade
 
 Clone the git repository:
 
 ```bash
 git clone https://github.com/freqtrade/freqtrade.git
-
+cd freqtrade
 ```
 
 Optionally checkout the master branch to get the latest stable release:
@@ -166,59 +171,37 @@ Optionally checkout the master branch to get the latest stable release:
 git checkout master
 ```
 
-#### 4. Initialize the configuration
+#### 4. Install python dependencies
-
-```bash
-cd freqtrade
-cp config.json.example config.json
-```
-
-> *To edit the config please refer to [Bot Configuration](configuration.md).*
-
-#### 5. Install python dependencies
 
 ``` bash
 python3 -m pip install --upgrade pip
 python3 -m pip install -e .
 ```
 
+#### 5. Initialize the configuration
+
+```bash
+# Initialize the user_directory
+freqtrade create-userdir --userdir user_data/
+
+cp config.json.example config.json
+```
+
+> *To edit the config please refer to [Bot Configuration](configuration.md).*
+
 #### 6. Run the Bot
 
 If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
 
 ```bash
-freqtrade -c config.json
+freqtrade trade -c config.json
 ```
 
 *Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
 
-#### 7. [Optional] Configure `freqtrade` as a `systemd` service
+#### 7. (Optional) Post-installation Tasks
 
-From the freqtrade repo... copy `freqtrade.service` to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
+On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
-
-After that you can start the daemon with:
-
-```bash
-systemctl --user start freqtrade
-```
-
-For this to be persistent (run when user is logged out) you'll need to enable `linger` for your freqtrade user.
-
-```bash
-sudo loginctl enable-linger "$USER"
-```
-
-If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot 
-state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the 
-configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd 
-using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped) 
-when it changes. 
-
-The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd 
-as the watchdog.
-
-!!! Note
-    The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
 
 ------
 
@@ -242,6 +225,12 @@ If that is not available on your system, feel free to try the instructions below
 
 ### Install freqtrade manually
 
+!!! Note
+    Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
+
+!!! Hint
+    Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
+
 #### Clone the git repository
 
 ```bash

docs/plotting.md

@@ -23,13 +23,15 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
 Possible arguments:
 
 ```
-usage: freqtrade plot-dataframe [-h] [-p PAIRS [PAIRS ...]]
+usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                [-d PATH] [--userdir PATH] [-s NAME]
+                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
                                 [--indicators1 INDICATORS1 [INDICATORS1 ...]]
                                 [--indicators2 INDICATORS2 [INDICATORS2 ...]]
                                 [--plot-limit INT] [--db-url PATH]
                                 [--trade-source {DB,file}] [--export EXPORT]
                                 [--export-filename PATH]
-                                [--timerange TIMERANGE]
+                                [--timerange TIMERANGE] [-i TICKER_INTERVAL]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -62,6 +64,28 @@ optional arguments:
                         /backtest_today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+Strategy arguments:
+  -s NAME, --strategy NAME
+                        Specify strategy class name (default:
+                        `DefaultStrategy`).
+  --strategy-path PATH  Specify additional strategy lookup path.
 
 ```
 
@@ -79,11 +103,11 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
 Specify custom indicators.
 Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
 
-!!! tip
+!!! Tip
     You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma ema --indicators2 macd
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
 ```
 
 ### Further usage examples
@@ -91,25 +115,25 @@ freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma
 To plot multiple pairs, separate them with a space:
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH XRP/ETH
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH XRP/ETH
 ```
 
 To plot a timerange (to zoom in)
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --timerange=20180801-20180805
+freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
 ```
 
 To plot trades stored in a database use `--db-url` in combination with `--trade-source DB`:
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
+freqtrade plot-dataframe --strategy AwesomeStrategy --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB
 ```
 
 To plot trades from a backtesting result, use `--export-filename <filename>`
 
 ``` bash
-freqtrade --strategy AwesomeStrategy plot-dataframe --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
+freqtrade plot-dataframe --strategy AwesomeStrategy --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH
 ```
 
 ## Plot profit
@@ -133,10 +157,11 @@ The third graph can be useful to spot outliers, events in pairs that cause profi
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```
-usage: freqtrade plot-profit [-h] [-p PAIRS [PAIRS ...]]
+usage: freqtrade plot-profit [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
                              [--timerange TIMERANGE] [--export EXPORT]
                              [--export-filename PATH] [--db-url PATH]
-                             [--trade-source {DB,file}]
+                             [--trade-source {DB,file}] [-i TICKER_INTERVAL]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -159,6 +184,22 @@ optional arguments:
   --trade-source {DB,file}
                         Specify the source for trades (Can be DB or file
                         (backtest file)) Default: file
+  -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  -d PATH, --datadir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
 
 ```
 

docs/requirements-docs.txt

@@ -1,2 +1,2 @@
-mkdocs-material==4.4.3
+mkdocs-material==4.5.1
 mdx_truly_sane_lists==1.2

docs/rest-api.md

@@ -16,13 +16,20 @@ Sample configuration:
     },
 ```
 
-!!! Danger Security warning
+!!! Danger "Security warning"
     By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
 
-!!! Danger Password selection
+!!! Danger "Password selection"
     Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
 
-You can then access the API by going to `http://127.0.0.1:8080/api/v1/version` to check if the API is running correctly.
+You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
+This should return the response:
+
+``` output
+{"status":"pong"}
+```
+
+All other endpoints return sensitive info and require authentication, so are not available through a web browser.
 
 To generate a secure password, either use a password manager, or use the below code snipped.
 
@@ -58,7 +65,7 @@ docker run -d \
   -v ~/.freqtrade/user_data/:/freqtrade/user_data \
   -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
   -p 127.0.0.1:8080:8080 \
-  freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
+  freqtrade trade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy
 ```
 
 !!! Danger "Security warning"
@@ -99,6 +106,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `stop` | | Stops the trader
 | `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `reload_conf` | | Reloads the configuration file
+| `show_config` | | Shows part of the current configuration with relevant settings to operation
 | `status` | | Lists all open trades
 | `count` | | Displays number of trades used and available
 | `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
@@ -165,6 +173,10 @@ reload_conf
         Reload configuration
         :returns: json object
 
+show_config
+        Returns part of the configuration, relevant for trading operations.
+        :return: json object containing the version
+
 start
         Start the bot if it's in stopped state.
         :returns: json object

docs/stoploss.md

@@ -3,74 +3,101 @@
 The `stoploss` configuration parameter is loss in percentage that should trigger a sale.
 For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
 
-Most of the strategy files already include the optimal `stoploss`
+Most of the strategy files already include the optimal `stoploss` value.
-value. This parameter is optional. If you use it in the configuration file, it will take over the
-`stoploss` value from the strategy file.
 
-## Stop Loss support
+!!! Info
+    All stoploss properties mentioned in this file can be set in the Strategy, or in the configuration. Configuration values will override the strategy values.
+
+## Stop Loss Types
 
 At this stage the bot contains the following stoploss support modes:
 
-1. static stop loss, defined in either the strategy or configuration.
+1. Static stop loss.
-2. trailing stop loss, defined in the configuration.
+2. Trailing stop loss.
-3. trailing stop loss, custom positive loss, defined in configuration.
+3. Trailing stop loss, custom positive loss.
+4. Trailing stop loss only once the trade has reached a certain offset.
 
-!!! Note
+Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
-    All stoploss properties can be configured in either Strategy or configuration. Configuration values override strategy values.
 
-Those stoploss modes can be *on exchange* or *off exchange*. If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfuly. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
+In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
 
-In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary. As an example in case of trailing stoploss if the order is on the exchange and the market is going up then the bot automatically cancels the previous stoploss order and put a new one with a stop value higher than previous one. It is clear that the bot cannot do it every 5 seconds otherwise it gets banned. So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
+For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
+The bot cannot do this every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
+So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
+This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
 !!! Note
     Stoploss on exchange is only supported for Binance as of now.
 
 ## Static Stop Loss
 
-This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which
+This is very simple, you define a stop loss of x (as a ratio of price, i.e. x * 100% of price). This will try to sell the asset once the loss exceeds the defined loss.
-will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
 
 ## Trailing Stop Loss
 
-The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally.
+The initial value for this is `stoploss`, just as you would define your static Stop loss.
-To enable this Feauture all you have to do is to define the configuration element:
+To enable trailing stoploss:
 
-``` json
+``` python
-"trailing_stop" : True
+trailing_stop = True
 ```
 
-This will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
+This will now activate an algorithm, which automatically moves the stop loss up every time the price of your asset increases.
 
-For example, simplified math,
+For example, simplified math:
 
-* you buy an asset at a price of 100$
+* the bot buys an asset at a price of 100$
-* your stop loss is defined at 2%
+* the stop loss is defined at 2%
-* which means your stop loss, gets triggered once your asset dropped below 98$
+* the stop loss would get triggered once the asset dropps below 98$
-* assuming your asset now increases to 102$
+* assuming the asset now increases to 102$
-* your stop loss, will now be 2% of 102$ or 99.96$
+* the stop loss will now be 2% of 102$ or 99.96$
-* now your asset drops in value to 101$, your stop loss, will still be 99.96$
+* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$.
 
-basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
+In summary: The stoploss will be adjusted to be always be 2% of the highest observed price.
 
-### Custom positive loss
+### Custom positive stoploss
 
-Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage,
+It is also possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can have a different value.
-the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit,
+For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
-it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
 
-Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
+Both values require `trailing_stop` to be set to true.
 
-``` json
+``` python
-    "trailing_stop_positive":  0.01,
+    trailing_stop_positive = 0.01
-    "trailing_stop_positive_offset":  0.011,
+    trailing_stop_positive_offset = 0.011
-    "trailing_only_offset_is_reached": false
 ```
 
 The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
+Before this, `stoploss` is used for the trailing stoploss.
 
-You should also make sure to have this value (`trailing_stop_positive_offset`) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
+Read the [next section](#trailing-only-once-offset-is-reached) to keep stoploss at 5% of the entry point.
+
+!!! Tip
+    Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
+
+### Trailing only once offset is reached
+
+It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
 
 If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
+This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
+
+``` python
+    trailing_stop_positive_offset = 0.011
+    trailing_only_offset_is_reached = true
+```
+
+Simplified example:
+
+``` python
+    stoploss = 0.05
+    trailing_stop_positive_offset = 0.03
+    trailing_only_offset_is_reached = True
+```
+
+* the bot buys an asset at a price of 100$
+* the stop loss is defined at 5%
+* the stop loss will remain at 95% until profit reaches +3%
 
 ## Changing stoploss on open trades
 

docs/strategy-customization.md

@@ -1,4 +1,4 @@
-# Optimization
+# Strategy Customization
 
 This page explains where to customize your strategies, and add new
 indicators.
@@ -7,24 +7,28 @@ indicators.
 
 This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
 
-Let assume you have a class called `AwesomeStrategy` in the file `awesome-strategy.py`:
+Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
 
-1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
+1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
 2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
-## Change your strategy
+## Develop your own strategy
 
-The bot includes a default strategy file. However, we recommend you to
+The bot includes a default strategy file.
-use your own file to not have to lose your parameters every time the default
+Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
-strategy file will be updated on Github. Put your custom strategy file
-into the directory `user_data/strategies`.
 
-Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes.
+You will however most likely have your own idea for a strategy.
-`cp  user_data/strategies/sample_strategy.py user_data/strategies/awesome-strategy.py`
+This document intends to help you develop one for yourself.
+
+To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
+This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
+
+!!! Note
+    This is just a template file, which will most likely not be profitable out of the box.
 
 ### Anatomy of a strategy
 
@@ -45,19 +49,19 @@ The current version is 2 - which is also the default when it's not set explicitl
 Future versions will require this to be set.
 
 ```bash
-freqtrade --strategy AwesomeStrategy
+freqtrade trade --strategy AwesomeStrategy
 ```
 
-**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py)
+**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py)
 file as reference.**
 
-!!! Note Strategies and Backtesting
+!!! Note "Strategies and Backtesting"
     To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
     that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
     It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
     avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
 
-!!! Warning Using future data
+!!! Warning "Warning: Using future data"
     Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
     needs to take care to avoid having the strategy utilize data from the future.
     Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
@@ -114,9 +118,40 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
 ```
 
 !!! Note "Want more indicator examples?"
-    Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
+    Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
     Then uncomment indicators you need.
 
+### Strategy startup period
+
+Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+To account for this, the strategy can be assigned the `startup_candle_count` attribute.
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
+
+In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
+
+``` python
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+```
+
+By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
+
+!!! Warning
+    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+
+#### Example
+
+Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
+
+``` bash
+freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
+```
+
+Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
+If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
+
+!!! Note
+    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
+
 ### Buy signal rules
 
 Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
@@ -283,9 +318,9 @@ Please always check the mode of operation to select the correct method to get da
 #### Possible options for DataProvider
 
 - `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
-- `ohlcv(pair, ticker_interval)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
+- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
-- `historic_ohlcv(pair, ticker_interval)` - Returns historical data stored on disk.
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
-- `get_pair_dataframe(pair, ticker_interval)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
 - `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
 - `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
 - `runmode` - Property containing the current runmode.
@@ -296,15 +331,15 @@ Please always check the mode of operation to select the correct method to get da
 if self.dp:
     inf_pair, inf_timeframe = self.informative_pairs()[0]
     informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                             ticker_interval=inf_timeframe)
+                                             timeframe=inf_timeframe)
 ```
 
-!!! Warning Warning about backtesting
+!!! Warning "Warning about backtesting"
     Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
     for the backtesting runmode) provides the full time-range in one go,
     so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
 
-!!! Warning Warning in hyperopt
+!!! Warning "Warning in hyperopt"
     This option cannot currently be used during hyperopt.
 
 #### Orderbook
@@ -374,6 +409,52 @@ if self.wallets:
 - `get_used(asset)` - currently tied up balance (open orders)
 - `get_total(asset)` - total available balance - sum of the 2 above
 
+### Additional data (Trades)
+
+A history of Trades can be retrieved in the strategy by querying the database.
+
+At the top of the file, import Trade.
+
+```python
+from freqtrade.persistence import Trade
+```
+
+The following example queries for the current pair and trades from today, however other filters can easily be added.
+
+``` python
+if self.config['runmode'] in ('live', 'dry_run'):
+    trades = Trade.get_trades([Trade.pair == metadata['pair'],
+                               Trade.open_date > datetime.utcnow() - timedelta(days=1),
+                               Trade.is_open == False,
+                ]).order_by(Trade.close_date).all()
+    # Summarize profit for this pair.
+    curdayprofit = sum(trade.close_profit for trade in trades)
+```
+
+Get amount of stake_currency currently invested in Trades:
+
+``` python
+if self.config['runmode'] in ('live', 'dry_run'):
+    total_stakes = Trade.total_open_trades_stakes()
+```
+
+Retrieve performance per pair.
+Returns a List of dicts per pair.
+
+``` python
+if self.config['runmode'] in ('live', 'dry_run'):
+    performance = Trade.get_overall_performance()
+```
+
+Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
+
+``` json
+{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
+```
+
+!!! Warning
+    Trade history is not available during backtesting or hyperopt.
+
 ### Print created dataframe
 
 To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.
@@ -401,14 +482,14 @@ Printing more than a few rows is also possible (simply use  `print(dataframe)` i
 ### Where can i find a strategy template?
 
 The strategy template is located in the file
-[user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
+[user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
 
 ### Specify custom strategy location
 
 If you want to use a strategy from a different directory you can pass `--strategy-path`
 
 ```bash
-freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
+freqtrade trade --strategy AwesomeStrategy --strategy-path /some/directory
 ```
 
 ### Common mistakes when developing strategies

docs/strategy_analysis_example.md

@@ -10,7 +10,7 @@ from pathlib import Path
 # Customize these according to your needs.
 
 # Define some constants
-ticker_interval = "5m"
+timeframe = "5m"
 # Name of the strategy class
 strategy_name = 'SampleStrategy'
 # Path to user data
@@ -29,7 +29,7 @@ pair = "BTC_USDT"
 from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
-                            ticker_interval=ticker_interval,
+                            timeframe=timeframe,
                             pair=pair)
 
 # Confirm success
@@ -107,6 +107,22 @@ trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
 trades.groupby("pair")["sell_reason"].value_counts()
 ```
 
+## Analyze the loaded trades for trade parallelism
+This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
+
+`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
+
+
+```python
+from freqtrade.data.btanalysis import analyze_trade_parallelism
+
+# Analyze the above
+parallel_trades = analyze_trade_parallelism(trades, '5m')
+
+
+parallel_trades.plot()
+```
+
 ## Plot results
 
 Freqtrade offers interactive plotting capabilities based on plotly.

docs/telegram-usage.md

@@ -53,6 +53,7 @@ official commands. You can ask at any moment for help with `/help`.
 | `/stop` | | Stops the trader
 | `/stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `/reload_conf` | | Reloads the configuration file
+| `/show_config` | | Shows part of the current configuration with relevant settings to operation
 | `/status` | | Lists all open trades
 | `/status table` | | List all open trades in a table format
 | `/count` | | Displays number of trades used and available
@@ -93,7 +94,7 @@ Once all positions are sold, run `/stop` to completely stop the bot.
 
 `/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command. 
 
-!!! warning
+!!! Warning
    The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
 
 ### /status

docs/utils.md

@@ -2,6 +2,112 @@
 
 Besides the Live-Trade and Dry-Run run modes, the `backtesting`, `edge` and `hyperopt` optimization subcommands, and the `download-data` subcommand which prepares historical data, the bot contains a number of utility subcommands. They are described in this section.
 
+## Create userdir
+
+Creates the directory structure to hold your files for freqtrade.
+Will also create strategy and hyperopt examples for you to get started.
+Can be used multiple times - using `--reset` will reset the sample strategy and hyperopt files to their default state. 
+
+```
+usage: freqtrade create-userdir [-h] [--userdir PATH] [--reset]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  --reset               Reset sample files to their original state.
+```
+
+!!! Warning
+    Using `--reset` may result in loss of data, since this will overwrite all sample files without asking again.
+
+```
+├── backtest_results
+├── data
+├── hyperopt_results
+├── hyperopts
+│   ├── sample_hyperopt_advanced.py
+│   ├── sample_hyperopt_loss.py
+│   └── sample_hyperopt.py
+├── notebooks
+│   └── strategy_analysis_example.ipynb
+├── plot
+└── strategies
+    └── sample_strategy.py
+```
+
+## Create new strategy
+
+Creates a new strategy from a template similar to SampleStrategy.
+The file will be named inline with your class name, and will not overwrite existing files.
+
+Results will be located in `user_data/strategies/<strategyclassname>.py`.
+
+``` output
+usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
+                              [--template {full,minimal}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  -s NAME, --strategy NAME
+                        Specify strategy class name which will be used by the
+                        bot.
+  --template {full,minimal}
+                        Use a template which is either `minimal` or `full`
+                        (containing multiple sample indicators). Default:
+                        `full`.
+
+```
+
+### Sample usage of new-strategy
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy
+```
+
+With custom user directory
+
+```bash
+freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
+```
+
+## Create new hyperopt
+
+Creates a new hyperopt from a template similar to SampleHyperopt.
+The file will be named inline with your class name, and will not overwrite existing files.
+
+Results will be located in `user_data/hyperopts/<classname>.py`.
+
+``` output
+usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
+                              [--template {full,minimal}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+  --hyperopt NAME       Specify hyperopt class name which will be used by the
+                        bot.
+  --template {full,minimal}
+                        Use a template which is either `minimal` or `full`
+                        (containing multiple sample indicators). Default:
+                        `full`.
+```
+
+### Sample usage of new-hyperopt
+
+```bash
+freqtrade new-hyperopt --hyperopt AwesomeHyperopt
+```
+
+With custom user directory
+
+```bash
+freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
+```
+
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.
@@ -124,3 +230,100 @@ $ freqtrade -c config_binance.json list-pairs --all --base BTC ETH --quote USDT
 ```
 $ freqtrade list-markets --exchange kraken --all
 ```
+
+## Test pairlist
+
+Use the `test-pairlist` subcommand to test the configuration of [dynamic pairlists](configuration.md#pairlists).
+
+Requires a configuration with specified `pairlists` attribute.
+Can be used to generate static pairlists to be used during backtesting / hyperopt.
+
+```
+usage: freqtrade test-pairlist [-h] [-c PATH]
+                               [--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
+                               [-1] [--print-json]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c PATH, --config PATH
+                        Specify configuration file (default: `config.json`).
+                        Multiple --config options may be used. Can be set to
+                        `-` to read config from stdin.
+  --quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]
+                        Specify quote currency(-ies). Space-separated list.
+  -1, --one-column      Print output in one column.
+  --print-json          Print list of pairs or market symbols in JSON format.
+```
+
+### Examples
+
+Show whitelist when using a [dynamic pairlist](configuration.md#pairlists).
+
+```
+freqtrade test-pairlist --config config.json --quote USDT BTC
+```
+
+## List Hyperopt results
+
+You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` subcommand.
+
+```
+usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [--no-color] [--print-json]
+                               [--no-details]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  --no-color            Disable colorization of hyperopt results. May be
+                        useful if you are redirecting output to a file.
+  --print-json          Print best result detailization in JSON format.
+  --no-details          Do not print best epoch details.
+```
+ 
+### Examples
+
+List all results, print details of the best result at the end:
+```
+freqtrade hyperopt-list
+```
+
+List only epochs with positive profit. Do not print the details of the best epoch, so that the list can be iterated in a script:
+```
+freqtrade hyperopt-list --profitable --no-details
+```
+
+## Show details of Hyperopt results
+
+You can show the details of any hyperoptimization epoch previously evaluated by the Hyperopt module with the `hyperopt-show` subcommand.
+
+```
+usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                               [-d PATH] [--userdir PATH] [--best]
+                               [--profitable] [-n INT] [--print-json]
+                               [--no-header]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --best                Select only best epochs.
+  --profitable          Select only profitable epochs.
+  -n INT, --index INT   Specify the index of the epoch to print details for.
+  --print-json          Print best result detailization in JSON format.
+  --no-header           Do not print epoch details header.
+```
+
+### Examples
+
+Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
+
+```
+freqtrade hyperopt-show -n 168
+```
+
+Prints JSON data with details for the last best epoch (i.e., the best of all epochs):
+
+```
+freqtrade hyperopt-show --best -n -1 --print-json --no-header
+```

freqtrade.service

@@ -6,7 +6,7 @@ After=network.target
 # Set WorkingDirectory and ExecStart to your file paths accordingly
 # NOTE: %h will be resolved to /home/<username>
 WorkingDirectory=%h/freqtrade
-ExecStart=/usr/bin/freqtrade
+ExecStart=/usr/bin/freqtrade trade
 Restart=on-failure
 
 [Install]

freqtrade.service.watchdog

@@ -6,7 +6,7 @@ After=network.target
 # Set WorkingDirectory and ExecStart to your file paths accordingly
 # NOTE: %h will be resolved to /home/<username>
 WorkingDirectory=%h/freqtrade
-ExecStart=/usr/bin/freqtrade --sd-notify
+ExecStart=/usr/bin/freqtrade trade --sd-notify
 
 Restart=always
 #Restart=on-failure

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ FreqTrade bot """
-__version__ = '2019.10'
+__version__ = '2019.11'
 
 if __version__ == 'develop':
 

freqtrade/configuration/__init__.py

@@ -1,4 +1,5 @@
 from freqtrade.configuration.arguments import Arguments  # noqa: F401
+from freqtrade.configuration.check_exchange import check_exchange, remove_credentials  # noqa: F401
 from freqtrade.configuration.timerange import TimeRange  # noqa: F401
 from freqtrade.configuration.configuration import Configuration  # noqa: F401
 from freqtrade.configuration.config_validation import validate_config_consistency  # noqa: F401

freqtrade/configuration/arguments.py

@@ -13,7 +13,7 @@ ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_dat
 
 ARGS_STRATEGY = ["strategy", "strategy_path"]
 
-ARGS_MAIN = ARGS_COMMON + ARGS_STRATEGY + ["db_url", "sd_notify"]
+ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
 
 ARGS_COMMON_OPTIMIZE = ["ticker_interval", "timerange",
                         "max_open_trades", "stake_amount", "fee"]
@@ -37,35 +37,44 @@ ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
 ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one_column",
                    "print_csv", "base_currencies", "quote_currencies", "list_pairs_all"]
 
-ARGS_CREATE_USERDIR = ["user_data_dir"]
+ARGS_TEST_PAIRLIST = ["config", "quote_currencies", "print_one_column", "list_pairs_print_json"]
+
+ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]
+
+ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
+
+ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
 
 ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
                       "timeframes", "erase"]
 
-ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit", "db_url",
+ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
-                       "trade_source", "export", "exportfilename", "timerange", "ticker_interval"]
+                       "db_url", "trade_source", "export", "exportfilename",
+                       "timerange", "ticker_interval"]
 
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "ticker_interval"]
 
-NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
+ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable", "print_colorized",
-                    "plot-dataframe", "plot-profit"]
+                      "print_json", "hyperopt_list_no_details"]
 
-NO_CONF_ALLOWED = ["create-userdir", "list-exchanges"]
+ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
+                      "print_json", "hyperopt_show_no_header"]
+
+NO_CONF_REQURIED = ["download-data", "list-timeframes", "list-markets", "list-pairs",
+                    "hyperopt-list", "hyperopt-show", "plot-dataframe", "plot-profit"]
+
+NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
 
 class Arguments:
     """
     Arguments Class. Manage the arguments received by the cli
     """
+
     def __init__(self, args: Optional[List[str]]) -> None:
         self.args = args
         self._parsed_arg: Optional[argparse.Namespace] = None
-        self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
-
-    def _load_args(self) -> None:
-        self._build_args(optionlist=ARGS_MAIN)
-        self._build_subcommands()
 
     def get_parsed_arg(self) -> Dict[str, Any]:
         """
@@ -73,7 +82,7 @@ class Arguments:
         :return: List[str] List of arguments
         """
         if self._parsed_arg is None:
-            self._load_args()
+            self._build_subcommands()
             self._parsed_arg = self._parse_args()
 
         return vars(self._parsed_arg)
@@ -84,22 +93,17 @@ class Arguments:
         """
         parsed_arg = self.parser.parse_args(self.args)
 
-        # When no config is provided, but a config exists, use that configuration!
-        subparser = parsed_arg.subparser if 'subparser' in parsed_arg else None
-
         # Workaround issue in argparse with action='append' and default value
         # (see https://bugs.python.org/issue16399)
         # Allow no-config for certain commands (like downloading / plotting)
-        if (parsed_arg.config is None
+        if ('config' in parsed_arg and parsed_arg.config is None and
-                and subparser not in NO_CONF_ALLOWED
+            ((Path.cwd() / constants.DEFAULT_CONFIG).is_file() or
-                and ((Path.cwd() / constants.DEFAULT_CONFIG).is_file()
+             not ('command' in parsed_arg and parsed_arg.command in NO_CONF_REQURIED))):
-                     or (subparser not in NO_CONF_REQURIED))):
             parsed_arg.config = [constants.DEFAULT_CONFIG]
 
         return parsed_arg
 
-    def _build_args(self, optionlist, parser=None):
+    def _build_args(self, optionlist, parser):
-        parser = parser or self.parser
 
         for val in optionlist:
             opt = AVAILABLE_CLI_OPTIONS[val]
@@ -110,38 +114,82 @@ class Arguments:
         Builds and attaches all subcommands.
         :return: None
         """
+        # Build shared arguments (as group Common Options)
+        _common_parser = argparse.ArgumentParser(add_help=False)
+        group = _common_parser.add_argument_group("Common arguments")
+        self._build_args(optionlist=ARGS_COMMON, parser=group)
+
+        _strategy_parser = argparse.ArgumentParser(add_help=False)
+        strategy_group = _strategy_parser.add_argument_group("Strategy arguments")
+        self._build_args(optionlist=ARGS_STRATEGY, parser=strategy_group)
+
+        # Build main command
+        self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
+        self._build_args(optionlist=['version'], parser=self.parser)
+
         from freqtrade.optimize import start_backtesting, start_hyperopt, start_edge
         from freqtrade.utils import (start_create_userdir, start_download_data,
-                                     start_list_exchanges, start_list_timeframes,
+                                     start_hyperopt_list, start_hyperopt_show,
-                                     start_list_markets)
+                                     start_list_exchanges, start_list_markets,
+                                     start_new_hyperopt, start_new_strategy,
+                                     start_list_timeframes, start_test_pairlist, start_trading)
+        from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
 
-        subparsers = self.parser.add_subparsers(dest='subparser')
+        subparsers = self.parser.add_subparsers(dest='command',
+                                                # Use custom message when no subhandler is added
+                                                # shown from `main.py`
+                                                # required=True
+                                                )
+
+        # Add trade subcommand
+        trade_cmd = subparsers.add_parser('trade', help='Trade module.',
+                                          parents=[_common_parser, _strategy_parser])
+        trade_cmd.set_defaults(func=start_trading)
+        self._build_args(optionlist=ARGS_TRADE, parser=trade_cmd)
 
         # Add backtesting subcommand
-        backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.')
+        backtesting_cmd = subparsers.add_parser('backtesting', help='Backtesting module.',
+                                                parents=[_common_parser, _strategy_parser])
         backtesting_cmd.set_defaults(func=start_backtesting)
         self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
 
         # Add edge subcommand
-        edge_cmd = subparsers.add_parser('edge', help='Edge module.')
+        edge_cmd = subparsers.add_parser('edge', help='Edge module.',
+                                         parents=[_common_parser, _strategy_parser])
         edge_cmd.set_defaults(func=start_edge)
         self._build_args(optionlist=ARGS_EDGE, parser=edge_cmd)
 
         # Add hyperopt subcommand
-        hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.')
+        hyperopt_cmd = subparsers.add_parser('hyperopt', help='Hyperopt module.',
+                                             parents=[_common_parser, _strategy_parser],
+                                             )
         hyperopt_cmd.set_defaults(func=start_hyperopt)
         self._build_args(optionlist=ARGS_HYPEROPT, parser=hyperopt_cmd)
 
         # add create-userdir subcommand
         create_userdir_cmd = subparsers.add_parser('create-userdir',
-                                                   help="Create user-data directory.")
+                                                   help="Create user-data directory.",
+                                                   )
         create_userdir_cmd.set_defaults(func=start_create_userdir)
         self._build_args(optionlist=ARGS_CREATE_USERDIR, parser=create_userdir_cmd)
 
+        # add new-strategy subcommand
+        build_strategy_cmd = subparsers.add_parser('new-strategy',
+                                                   help="Create new strategy")
+        build_strategy_cmd.set_defaults(func=start_new_strategy)
+        self._build_args(optionlist=ARGS_BUILD_STRATEGY, parser=build_strategy_cmd)
+
+        # add new-hyperopt subcommand
+        build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
+                                                   help="Create new hyperopt")
+        build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
+        self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
+
         # Add list-exchanges subcommand
         list_exchanges_cmd = subparsers.add_parser(
             'list-exchanges',
-            help='Print available exchanges.'
+            help='Print available exchanges.',
+            parents=[_common_parser],
         )
         list_exchanges_cmd.set_defaults(func=start_list_exchanges)
         self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
@@ -149,7 +197,8 @@ class Arguments:
         # Add list-timeframes subcommand
         list_timeframes_cmd = subparsers.add_parser(
             'list-timeframes',
-            help='Print available ticker intervals (timeframes) for the exchange.'
+            help='Print available ticker intervals (timeframes) for the exchange.',
+            parents=[_common_parser],
         )
         list_timeframes_cmd.set_defaults(func=start_list_timeframes)
         self._build_args(optionlist=ARGS_LIST_TIMEFRAMES, parser=list_timeframes_cmd)
@@ -157,7 +206,8 @@ class Arguments:
         # Add list-markets subcommand
         list_markets_cmd = subparsers.add_parser(
             'list-markets',
-            help='Print markets on exchange.'
+            help='Print markets on exchange.',
+            parents=[_common_parser],
         )
         list_markets_cmd.set_defaults(func=partial(start_list_markets, pairs_only=False))
         self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_markets_cmd)
@@ -165,24 +215,34 @@ class Arguments:
         # Add list-pairs subcommand
         list_pairs_cmd = subparsers.add_parser(
             'list-pairs',
-            help='Print pairs on exchange.'
+            help='Print pairs on exchange.',
+            parents=[_common_parser],
         )
         list_pairs_cmd.set_defaults(func=partial(start_list_markets, pairs_only=True))
         self._build_args(optionlist=ARGS_LIST_PAIRS, parser=list_pairs_cmd)
 
+        # Add test-pairlist subcommand
+        test_pairlist_cmd = subparsers.add_parser(
+            'test-pairlist',
+            help='Test your pairlist configuration.',
+        )
+        test_pairlist_cmd.set_defaults(func=start_test_pairlist)
+        self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
+
         # Add download-data subcommand
         download_data_cmd = subparsers.add_parser(
             'download-data',
-            help='Download backtesting data.'
+            help='Download backtesting data.',
+            parents=[_common_parser],
         )
         download_data_cmd.set_defaults(func=start_download_data)
         self._build_args(optionlist=ARGS_DOWNLOAD_DATA, parser=download_data_cmd)
 
         # Add Plotting subcommand
-        from freqtrade.plot.plot_utils import start_plot_dataframe, start_plot_profit
         plot_dataframe_cmd = subparsers.add_parser(
             'plot-dataframe',
-            help='Plot candles with indicators.'
+            help='Plot candles with indicators.',
+            parents=[_common_parser, _strategy_parser],
         )
         plot_dataframe_cmd.set_defaults(func=start_plot_dataframe)
         self._build_args(optionlist=ARGS_PLOT_DATAFRAME, parser=plot_dataframe_cmd)
@@ -190,7 +250,26 @@ class Arguments:
         # Plot profit
         plot_profit_cmd = subparsers.add_parser(
             'plot-profit',
-            help='Generate plot showing profits.'
+            help='Generate plot showing profits.',
+            parents=[_common_parser],
         )
         plot_profit_cmd.set_defaults(func=start_plot_profit)
         self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
+
+        # Add hyperopt-list subcommand
+        hyperopt_list_cmd = subparsers.add_parser(
+            'hyperopt-list',
+            help='List Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_list_cmd.set_defaults(func=start_hyperopt_list)
+        self._build_args(optionlist=ARGS_HYPEROPT_LIST, parser=hyperopt_list_cmd)
+
+        # Add hyperopt-show subcommand
+        hyperopt_show_cmd = subparsers.add_parser(
+            'hyperopt-show',
+            help='Show details of Hyperopt results',
+            parents=[_common_parser],
+        )
+        hyperopt_show_cmd.set_defaults(func=start_hyperopt_show)
+        self._build_args(optionlist=ARGS_HYPEROPT_SHOW, parser=hyperopt_show_cmd)

freqtrade/configuration/check_exchange.py

@@ -10,6 +10,19 @@ from freqtrade.state import RunMode
 logger = logging.getLogger(__name__)
 
 
+def remove_credentials(config: Dict[str, Any]):
+    """
+    Removes exchange keys from the configuration and specifies dry-run
+    Used for backtesting / hyperopt / edge and utils.
+    Modifies the input dict!
+    """
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+    config['exchange']['password'] = ''
+    config['exchange']['uid'] = ''
+    config['dry_run'] = True
+
+
 def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
     """
     Check if the exchange name in the config file is supported by Freqtrade
@@ -21,7 +34,8 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
              and thus is not known for the Freqtrade at all.
     """
 
-    if config['runmode'] in [RunMode.PLOT] and not config.get('exchange', {}).get('name'):
+    if (config['runmode'] in [RunMode.PLOT, RunMode.UTIL_NO_EXCHANGE, RunMode.OTHER]
+       and not config.get('exchange', {}).get('name')):
         # Skip checking exchange in plot mode, since it requires no exchange
         return True
     logger.info("Checking exchange...")

freqtrade/configuration/cli_options.py

@@ -18,6 +18,18 @@ def check_int_positive(value: str) -> int:
     return uint
 
 
+def check_int_nonzero(value: str) -> int:
+    try:
+        uint = int(value)
+        if uint == 0:
+            raise ValueError
+    except ValueError:
+        raise argparse.ArgumentTypeError(
+            f"{value} is invalid for this parameter, should be a non-zero integer value"
+        )
+    return uint
+
+
 class Arg:
     # Optional CLI arguments
     def __init__(self, *args, **kwargs):
@@ -36,7 +48,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "logfile": Arg(
         '--logfile',
-        help='Log to the file specified.',
+        help="Log to the file specified. Special values are: 'syslog', 'journald'. "
+             "See the documentation for more details.",
         metavar='FILE',
     ),
     "version": Arg(
@@ -62,12 +75,16 @@ AVAILABLE_CLI_OPTIONS = {
         help='Path to userdata directory.',
         metavar='PATH',
     ),
+    "reset": Arg(
+        '--reset',
+        help='Reset sample files to their original state.',
+        action='store_true',
+    ),
     # Main options
     "strategy": Arg(
         '-s', '--strategy',
-        help='Specify strategy class name (default: `%(default)s`).',
+        help='Specify strategy class name which will be used by the bot.',
         metavar='NAME',
-        default='DefaultStrategy',
     ),
     "strategy_path": Arg(
         '--strategy-path',
@@ -86,6 +103,11 @@ AVAILABLE_CLI_OPTIONS = {
         help='Notify systemd service manager.',
         action='store_true',
     ),
+    "dry_run": Arg(
+        '--dry-run',
+        help='Enforce dry-run for trading (removes Exchange secrets and simulates trades).',
+        action='store_true',
+    ),
     # Optimize common
     "ticker_interval": Arg(
         '-i', '--ticker-interval',
@@ -136,7 +158,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "exportfilename": Arg(
         '--export-filename',
-        help='Save backtest results to the file with this filename (default: `%(default)s`). '
+        help='Save backtest results to the file with this filename. '
         'Requires `--export` to be set as well. '
         'Example: `--export-filename=user_data/backtest_results/backtest_today.json`',
         metavar='PATH',
@@ -156,14 +178,13 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     # Hyperopt
     "hyperopt": Arg(
-        '--customhyperopt',
+        '--hyperopt',
-        help='Specify hyperopt class name (default: `%(default)s`).',
+        help='Specify hyperopt class name which will be used by the bot.',
         metavar='NAME',
-        default=constants.DEFAULT_HYPEROPT,
     ),
     "hyperopt_path": Arg(
         '--hyperopt-path',
-        help='Specify additional lookup path for Hyperopts and Hyperopt Loss functions.',
+        help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
         metavar='PATH',
     ),
     "epochs": Arg(
@@ -174,12 +195,11 @@ AVAILABLE_CLI_OPTIONS = {
         default=constants.HYPEROPT_EPOCH,
     ),
     "spaces": Arg(
-        '-s', '--spaces',
+        '--spaces',
-        help='Specify which parameters to hyperopt. Space-separated list. '
+        help='Specify which parameters to hyperopt. Space-separated list.',
-        'Default: `%(default)s`.',
+        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'default'],
-        choices=['all', 'buy', 'sell', 'roi', 'stoploss'],
         nargs='+',
-        default='all',
+        default='default',
     ),
     "print_all": Arg(
         '--print-all',
@@ -331,6 +351,14 @@ AVAILABLE_CLI_OPTIONS = {
         help='Clean all existing data for the selected exchange/pairs/timeframes.',
         action='store_true',
     ),
+    # Templating options
+    "template": Arg(
+        '--template',
+        help='Use a template which is either `minimal` or '
+        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
+        choices=['full', 'minimal'],
+        default='full',
+    ),
     # Plot dataframe
     "indicators1": Arg(
         '--indicators1',
@@ -361,4 +389,31 @@ AVAILABLE_CLI_OPTIONS = {
         choices=["DB", "file"],
         default="file",
     ),
+    # hyperopt-list, hyperopt-show
+    "hyperopt_list_profitable": Arg(
+        '--profitable',
+        help='Select only profitable epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_best": Arg(
+        '--best',
+        help='Select only best epochs.',
+        action='store_true',
+    ),
+    "hyperopt_list_no_details": Arg(
+        '--no-details',
+        help='Do not print best epoch details.',
+        action='store_true',
+    ),
+    "hyperopt_show_index": Arg(
+        '-n', '--index',
+        help='Specify the index of the epoch to print details for.',
+        type=check_int_nonzero,
+        metavar='INT',
+    ),
+    "hyperopt_show_no_header": Arg(
+        '--no-header',
+        help='Do not print epoch details header.',
+        action='store_true',
+    ),
 }

freqtrade/configuration/config_validation.py

@@ -5,7 +5,7 @@ from jsonschema import Draft4Validator, validators
 from jsonschema.exceptions import ValidationError, best_match
 
 from freqtrade import constants, OperationalException
-
+from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
@@ -61,9 +61,15 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
     :param conf: Config in JSON format
     :return: Returns None if everything is ok, otherwise throw an OperationalException
     """
+
     # validating trailing stoploss
     _validate_trailing_stoploss(conf)
     _validate_edge(conf)
+    _validate_whitelist(conf)
+
+    # validate configuration before returning
+    logger.info('Validating configuration ...')
+    validate_config_schema(conf)
 
 
 def _validate_trailing_stoploss(conf: Dict[str, Any]) -> None:
@@ -111,3 +117,29 @@ def _validate_edge(conf: Dict[str, Any]) -> None:
             "Edge and VolumePairList are incompatible, "
             "Edge will override whatever pairs VolumePairlist selects."
         )
+
+
+def _validate_whitelist(conf: Dict[str, Any]) -> None:
+    """
+    Dynamic whitelist does not require pair_whitelist to be set - however StaticWhitelist does.
+    """
+    if conf.get('runmode', RunMode.OTHER) in [RunMode.OTHER, RunMode.PLOT,
+                                              RunMode.UTIL_NO_EXCHANGE, RunMode.UTIL_EXCHANGE]:
+        return
+
+    for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
+        if (pl.get('method') == 'StaticPairList'
+                and not conf.get('exchange', {}).get('pair_whitelist')):
+            raise OperationalException("StaticPairList requires pair_whitelist to be set.")
+
+        if pl.get('method') == 'StaticPairList':
+            stake = conf['stake_currency']
+            invalid_pairs = []
+            for pair in conf['exchange'].get('pair_whitelist'):
+                if not pair.endswith(f'/{stake}'):
+                    invalid_pairs.append(pair)
+
+            if invalid_pairs:
+                raise OperationalException(
+                    f"Stake-currency '{stake}' not compatible with pair-whitelist. "
+                    f"Please remove the following pairs: {invalid_pairs}")

freqtrade/configuration/configuration.py

@@ -9,15 +9,13 @@ from typing import Any, Callable, Dict, List, Optional
 
 from freqtrade import OperationalException, constants
 from freqtrade.configuration.check_exchange import check_exchange
-from freqtrade.configuration.config_validation import (validate_config_consistency,
-                                                       validate_config_schema)
 from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
 from freqtrade.configuration.directory_operations import (create_datadir,
                                                           create_userdata_dir)
 from freqtrade.configuration.load_config import load_config_file
 from freqtrade.loggers import setup_logging
 from freqtrade.misc import deep_merge_dicts, json_load
-from freqtrade.state import RunMode
+from freqtrade.state import RunMode, TRADING_MODES, NON_UTIL_MODES
 
 logger = logging.getLogger(__name__)
 
@@ -81,9 +79,8 @@ class Configuration:
         if 'ask_strategy' not in config:
             config['ask_strategy'] = {}
 
-        # validate configuration before returning
+        if 'pairlists' not in config:
-        logger.info('Validating configuration ...')
+            config['pairlists'] = []
-        validate_config_schema(config)
 
         return config
 
@@ -93,19 +90,21 @@ class Configuration:
         :return: Configuration dictionary
         """
         # Load all configs
-        config: Dict[str, Any] = self.load_from_files(self.args["config"])
+        config: Dict[str, Any] = self.load_from_files(self.args.get("config", []))
 
         # Keep a copy of the original configuration file
         config['original_config'] = deepcopy(config)
 
+        self._process_runmode(config)
+
         self._process_common_options(config)
 
+        self._process_trading_options(config)
+
         self._process_optimize_options(config)
 
         self._process_plot_options(config)
 
-        self._process_runmode(config)
-
         # Check if the exchange set by the user is supported
         check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
 
@@ -113,8 +112,6 @@ class Configuration:
 
         process_temporary_deprecated_settings(config)
 
-        validate_config_consistency(config)
-
         return config
 
     def _process_logging_options(self, config: Dict[str, Any]) -> None:
@@ -130,21 +127,9 @@ class Configuration:
 
         setup_logging(config)
 
-    def _process_common_options(self, config: Dict[str, Any]) -> None:
+    def _process_trading_options(self, config: Dict[str, Any]) -> None:
-
+        if config['runmode'] not in TRADING_MODES:
-        self._process_logging_options(config)
+            return
-
-        # Set strategy if not specified in config and or if it's non default
-        if self.args.get("strategy") != constants.DEFAULT_STRATEGY or not config.get('strategy'):
-            config.update({'strategy': self.args.get("strategy")})
-
-        self._args_to_config(config, argname='strategy_path',
-                             logstring='Using additional Strategy lookup path: {}')
-
-        if ('db_url' in self.args and self.args["db_url"] and
-                self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
-            config.update({'db_url': self.args["db_url"]})
-            logger.info('Parameter --db-url detected ...')
 
         if config.get('dry_run', False):
             logger.info('Dry run is enabled')
@@ -158,17 +143,33 @@ class Configuration:
 
         logger.info(f'Using DB: "{config["db_url"]}"')
 
+    def _process_common_options(self, config: Dict[str, Any]) -> None:
+
+        self._process_logging_options(config)
+
+        # Set strategy if not specified in config and or if it's non default
+        if self.args.get("strategy") or not config.get('strategy'):
+            config.update({'strategy': self.args.get("strategy")})
+
+        self._args_to_config(config, argname='strategy_path',
+                             logstring='Using additional Strategy lookup path: {}')
+
+        if ('db_url' in self.args and self.args["db_url"] and
+                self.args["db_url"] != constants.DEFAULT_DB_PROD_URL):
+            config.update({'db_url': self.args["db_url"]})
+            logger.info('Parameter --db-url detected ...')
+
         if config.get('forcebuy_enable', False):
             logger.warning('`forcebuy` RPC message enabled.')
 
-        # Setting max_open_trades to infinite if -1
-        if config.get('max_open_trades') == -1:
-            config['max_open_trades'] = float('inf')
-
         # Support for sd_notify
         if 'sd_notify' in self.args and self.args["sd_notify"]:
             config['internals'].update({'sd_notify': True})
 
+        self._args_to_config(config, argname='dry_run',
+                             logstring='Parameter --dry-run detected, '
+                             'overriding dry_run to: {} ...')
+
     def _process_datadir_options(self, config: Dict[str, Any]) -> None:
         """
         Extract information for sys.argv and load directory configurations
@@ -179,6 +180,9 @@ class Configuration:
             config['exchange']['name'] = self.args["exchange"]
             logger.info(f"Using exchange {config['exchange']['name']}")
 
+        if 'pair_whitelist' not in config['exchange']:
+            config['exchange']['pair_whitelist'] = []
+
         if 'user_data_dir' in self.args and self.args["user_data_dir"]:
             config.update({'user_data_dir': self.args["user_data_dir"]})
         elif 'user_data_dir' not in config:
@@ -209,6 +213,10 @@ class Configuration:
         self._args_to_config(config, argname='position_stacking',
                              logstring='Parameter --enable-position-stacking detected ...')
 
+        # Setting max_open_trades to infinite if -1
+        if config.get('max_open_trades') == -1:
+            config['max_open_trades'] = float('inf')
+
         if 'use_max_market_positions' in self.args and not self.args["use_max_market_positions"]:
             config.update({'use_max_market_positions': False})
             logger.info('Parameter --disable-max-market-positions detected ...')
@@ -217,7 +225,7 @@ class Configuration:
             config.update({'max_open_trades': self.args["max_open_trades"]})
             logger.info('Parameter --max_open_trades detected, '
                         'overriding max_open_trades to: %s ...', config.get('max_open_trades'))
-        else:
+        elif config['runmode'] in NON_UTIL_MODES:
             logger.info('Using max_open_trades: %s ...', config.get('max_open_trades'))
 
         self._args_to_config(config, argname='stake_amount',
@@ -292,6 +300,21 @@ class Configuration:
         self._args_to_config(config, argname='hyperopt_loss',
                              logstring='Using Hyperopt loss class name: {}')
 
+        self._args_to_config(config, argname='hyperopt_show_index',
+                             logstring='Parameter -n/--index detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_best',
+                             logstring='Parameter --best detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_profitable',
+                             logstring='Parameter --profitable detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_list_no_details',
+                             logstring='Parameter --no-details detected: {}')
+
+        self._args_to_config(config, argname='hyperopt_show_no_header',
+                             logstring='Parameter --no-header detected: {}')
+
     def _process_plot_options(self, config: Dict[str, Any]) -> None:
 
         self._args_to_config(config, argname='pairs',

freqtrade/configuration/deprecated_settings.py

@@ -57,3 +57,26 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
                                'experimental', 'sell_profit_only')
     process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
                                'experimental', 'ignore_roi_if_buy_signal')
+
+    if not config.get('pairlists') and not config.get('pairlists'):
+        config['pairlists'] = [{'method': 'StaticPairList'}]
+        logger.warning(
+            "DEPRECATED: "
+            "Pairlists must be defined explicitly in the future."
+            "Defaulting to StaticPairList for now.")
+
+    if config.get('pairlist', {}).get("method") == 'VolumePairList':
+        logger.warning(
+            "DEPRECATED: "
+            f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
+            "Please refer to the docs on configuration details")
+        pl = {'method': 'VolumePairList'}
+        pl.update(config.get('pairlist', {}).get('config'))
+        config['pairlists'].append(pl)
+
+    if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
+        logger.warning(
+            "DEPRECATED: "
+            f"Using precision_filter setting is deprecated and has been replaced by"
+            "PrecisionFilter. Please refer to the docs on configuration details")
+        config['pairlists'].append({'method': 'PrecisionFilter'})

freqtrade/configuration/directory_operations.py

@@ -1,8 +1,10 @@
 import logging
-from typing import Any, Dict, Optional
+import shutil
 from pathlib import Path
+from typing import Any, Dict, Optional
 
 from freqtrade import OperationalException
+from freqtrade.constants import USER_DATA_FILES
 
 logger = logging.getLogger(__name__)
 
@@ -31,7 +33,8 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "plot", "strategies", ]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
+                "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:
@@ -48,3 +51,26 @@ def create_userdata_dir(directory: str, create_dir=False) -> Path:
         if not subfolder.is_dir():
             subfolder.mkdir(parents=False)
     return folder
+
+
+def copy_sample_files(directory: Path, overwrite: bool = False) -> None:
+    """
+    Copy files from templates to User data directory.
+    :param directory: Directory to copy data to
+    :param overwrite: Overwrite existing sample files
+    """
+    if not directory.is_dir():
+        raise OperationalException(f"Directory `{directory}` does not exist.")
+    sourcedir = Path(__file__).parents[1] / "templates"
+    for source, target in USER_DATA_FILES.items():
+        targetdir = directory / target
+        if not targetdir.is_dir():
+            raise OperationalException(f"Directory `{targetdir}` does not exist.")
+        targetfile = targetdir / source
+        if targetfile.exists():
+            if not overwrite:
+                logger.warning(f"File `{targetfile}` exists already, not deploying sample file.")
+                continue
+            else:
+                logger.warning(f"File `{targetfile}` exists already, overwriting.")
+        shutil.copy(str(sourcedir / source), str(targetfile))

freqtrade/configuration/timerange.py

@@ -1,11 +1,14 @@
 """
 This module contains the argument manager class
 """
+import logging
 import re
 from typing import Optional
 
 import arrow
 
+logger = logging.getLogger(__name__)
+
 
 class TimeRange:
     """
@@ -27,6 +30,34 @@ class TimeRange:
         return (self.starttype == other.starttype and self.stoptype == other.stoptype
                 and self.startts == other.startts and self.stopts == other.stopts)
 
+    def subtract_start(self, seconds) -> None:
+        """
+        Subtracts <seconds> from startts if startts is set.
+        :param seconds: Seconds to subtract from starttime
+        :return: None (Modifies the object in place)
+        """
+        if self.startts:
+            self.startts = self.startts - seconds
+
+    def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
+                                  min_date: arrow.Arrow) -> None:
+        """
+        Adjust startts by <startup_candles> candles.
+        Applies only if no startup-candles have been available.
+        :param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
+        :param startup_candles: Number of candles to move start-date forward
+        :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
+                         has to be moved
+        :return: None (Modifies the object in place)
+        """
+        if (not self.starttype or (startup_candles
+                                   and min_date.timestamp >= self.startts)):
+            # If no startts was defined, or backtest-data starts at the defined backtest-date
+            logger.warning("Moving start-date by %s candles to account for startup time.",
+                           startup_candles)
+            self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
+            self.starttype = 'date'
+
     @staticmethod
     def parse_timerange(text: Optional[str]):
         """

freqtrade/constants.py

@@ -6,11 +6,8 @@ bot constants
 DEFAULT_CONFIG = 'config.json'
 DEFAULT_EXCHANGE = 'bittrex'
 PROCESS_THROTTLE_SECS = 5  # sec
-DEFAULT_TICKER_INTERVAL = 5  # min
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
-DEFAULT_STRATEGY = 'DefaultStrategy'
-DEFAULT_HYPEROPT = 'DefaultHyperOpt'
 DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
 DEFAULT_DB_DRYRUN_URL = 'sqlite://'
@@ -20,11 +17,23 @@ REQUIRED_ORDERTIF = ['buy', 'sell']
 REQUIRED_ORDERTYPES = ['buy', 'sell', 'stoploss', 'stoploss_on_exchange']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'PrecisionFilter', 'PriceFilter']
 DRY_RUN_WALLET = 999.9
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
 
-TICKER_INTERVALS = [
+USERPATH_HYPEROPTS = 'hyperopts'
+USERPATH_STRATEGY = 'strategies'
+
+# Soure files with destination directories within user-directory
+USER_DATA_FILES = {
+    'sample_strategy.py': USERPATH_STRATEGY,
+    'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
+    'sample_hyperopt.py': USERPATH_HYPEROPTS,
+    'strategy_analysis_example.ipynb': 'notebooks',
+}
+
+TIMEFRAMES = [
     '1m', '3m', '5m', '15m', '30m',
     '1h', '2h', '4h', '6h', '8h', '12h',
     '1d', '3d', '1w',
@@ -56,13 +65,13 @@ MINIMAL_CONFIG = {
 CONF_SCHEMA = {
     'type': 'object',
     'properties': {
-        'max_open_trades': {'type': 'integer', 'minimum': -1},
+        'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
-        'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
+        'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
         'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
         'stake_amount': {
-            "type": ["number", "string"],
+            'type': ['number', 'string'],
-            "minimum": 0.0005,
+            'minimum': 0.0001,
-            "pattern": UNLIMITED_STAKE_AMOUNT
+            'pattern': UNLIMITED_STAKE_AMOUNT
         },
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
@@ -84,8 +93,8 @@ CONF_SCHEMA = {
         'unfilledtimeout': {
             'type': 'object',
             'properties': {
-                'buy': {'type': 'number', 'minimum': 3},
+                'buy': {'type': 'number', 'minimum': 1},
-                'sell': {'type': 'number', 'minimum': 10}
+                'sell': {'type': 'number', 'minimum': 1}
             }
         },
         'bid_strategy': {
@@ -97,7 +106,7 @@ CONF_SCHEMA = {
                     'maximum': 1,
                     'exclusiveMaximum': False,
                     'use_order_book': {'type': 'boolean'},
-                    'order_book_top': {'type': 'number', 'maximum': 20, 'minimum': 1},
+                    'order_book_top': {'type': 'integer', 'maximum': 20, 'minimum': 1},
                     'check_depth_of_market': {
                         'type': 'object',
                         'properties': {
@@ -113,8 +122,8 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'use_order_book': {'type': 'boolean'},
-                'order_book_min': {'type': 'number', 'minimum': 1},
+                'order_book_min': {'type': 'integer', 'minimum': 1},
-                'order_book_max': {'type': 'number', 'minimum': 1, 'maximum': 50},
+                'order_book_max': {'type': 'integer', 'minimum': 1, 'maximum': 50},
                 'use_sell_signal': {'type': 'boolean'},
                 'sell_profit_only': {'type': 'boolean'},
                 'ignore_roi_if_buy_signal': {'type': 'boolean'}
@@ -151,13 +160,16 @@ CONF_SCHEMA = {
                 'block_bad_exchanges': {'type': 'boolean'}
             }
         },
-        'pairlist': {
+        'pairlists': {
+            'type': 'array',
+            'items': {
                 'type': 'object',
                 'properties': {
                     'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
                     'config': {'type': 'object'}
                 },
-            'required': ['method']
+                'required': ['method'],
+            }
         },
         'telegram': {
             'type': 'object',
@@ -184,8 +196,8 @@ CONF_SCHEMA = {
                 'listen_ip_address': {'format': 'ipv4'},
                 'listen_port': {
                     'type': 'integer',
-                    "minimum": 1024,
+                    'minimum': 1024,
-                    "maximum": 65535
+                    'maximum': 65535
                 },
                 'username': {'type': 'string'},
                 'password': {'type': 'string'},
@@ -198,7 +210,7 @@ CONF_SCHEMA = {
         'internals': {
             'type': 'object',
             'properties': {
-                'process_throttle_secs': {'type': 'number'},
+                'process_throttle_secs': {'type': 'integer'},
                 'interval': {'type': 'integer'},
                 'sd_notify': {'type': 'boolean'},
             }
@@ -235,37 +247,37 @@ CONF_SCHEMA = {
                 'ccxt_config': {'type': 'object'},
                 'ccxt_async_config': {'type': 'object'}
             },
-            'required': ['name', 'pair_whitelist']
+            'required': ['name']
         },
         'edge': {
             'type': 'object',
             'properties': {
-                "enabled": {'type': 'boolean'},
+                'enabled': {'type': 'boolean'},
-                "process_throttle_secs": {'type': 'integer', 'minimum': 600},
+                'process_throttle_secs': {'type': 'integer', 'minimum': 600},
-                "calculate_since_number_of_days": {'type': 'integer'},
+                'calculate_since_number_of_days': {'type': 'integer'},
-                "allowed_risk": {'type': 'number'},
+                'allowed_risk': {'type': 'number'},
-                "capital_available_percentage": {'type': 'number'},
+                'capital_available_percentage': {'type': 'number'},
-                "stoploss_range_min": {'type': 'number'},
+                'stoploss_range_min': {'type': 'number'},
-                "stoploss_range_max": {'type': 'number'},
+                'stoploss_range_max': {'type': 'number'},
-                "stoploss_range_step": {'type': 'number'},
+                'stoploss_range_step': {'type': 'number'},
-                "minimum_winrate": {'type': 'number'},
+                'minimum_winrate': {'type': 'number'},
-                "minimum_expectancy": {'type': 'number'},
+                'minimum_expectancy': {'type': 'number'},
-                "min_trade_number": {'type': 'number'},
+                'min_trade_number': {'type': 'number'},
-                "max_trade_duration_minute": {'type': 'integer'},
+                'max_trade_duration_minute': {'type': 'integer'},
-                "remove_pumps": {'type': 'boolean'}
+                'remove_pumps': {'type': 'boolean'}
             },
             'required': ['process_throttle_secs', 'allowed_risk', 'capital_available_percentage']
         }
     },
-    'anyOf': [
-        {'required': ['exchange']}
-    ],
     'required': [
+        'exchange',
         'max_open_trades',
         'stake_currency',
         'stake_amount',
         'dry_run',
         'bid_strategy',
         'unfilledtimeout',
+        'stoploss',
+        'minimal_roi',
     ]
 }

freqtrade/data/btanalysis.py

@@ -7,7 +7,7 @@ from typing import Dict
 
 import numpy as np
 import pandas as pd
-import pytz
+from datetime import timezone
 
 from freqtrade import persistence
 from freqtrade.misc import json_load
@@ -52,16 +52,18 @@ def load_backtest_data(filename) -> pd.DataFrame:
     return df
 
 
-def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
+def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataFrame:
     """
     Find overlapping trades by expanding each trade once per period it was open
-    and then counting overlaps
+    and then counting overlaps.
     :param results: Results Dataframe - can be loaded
-    :param freq: Frequency used for the backtest
+    :param timeframe: Timeframe used for backtest
-    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in timeframe
-    :return: dataframe with open-counts per time-period in freq
     """
-    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
+    from freqtrade.exchange import timeframe_to_minutes
+    timeframe_min = timeframe_to_minutes(timeframe)
+    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time,
+                                     freq=f"{timeframe_min}min"))
              for row in results[['open_time', 'close_time']].iterrows()]
     deltas = [len(x) for x in dates]
     dates = pd.Series(pd.concat(dates).values, name='date')
@@ -69,8 +71,23 @@ def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int
 
     df2 = pd.concat([dates, df2], axis=1)
     df2 = df2.set_index('date')
-    df_final = df2.resample(freq)[['pair']].count()
+    df_final = df2.resample(f"{timeframe_min}min")[['pair']].count()
-    return df_final[df_final['pair'] > max_open_trades]
+    df_final = df_final.rename({'pair': 'open_trades'}, axis=1)
+    return df_final
+
+
+def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
+                          max_open_trades: int) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps
+    :param results: Results Dataframe - can be loaded
+    :param timeframe: Frequency used for the backtest
+    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in freq
+    """
+    df_final = analyze_trade_parallelism(results, timeframe)
+    return df_final[df_final['open_trades'] > max_open_trades]
 
 
 def load_trades_from_db(db_url: str) -> pd.DataFrame:
@@ -89,8 +106,8 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                "stop_loss", "initial_stop_loss", "strategy", "ticker_interval"]
 
     trades = pd.DataFrame([(t.pair,
-                            t.open_date.replace(tzinfo=pytz.UTC),
+                            t.open_date.replace(tzinfo=timezone.utc),
-                            t.close_date.replace(tzinfo=pytz.UTC) if t.close_date else None,
+                            t.close_date.replace(tzinfo=timezone.utc) if t.close_date else None,
                             t.calc_profit(), t.calc_profit_percent(),
                             t.open_rate, t.close_rate, t.amount,
                             (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
@@ -106,7 +123,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                             t.stop_loss, t.initial_stop_loss,
                             t.strategy, t.ticker_interval
                             )
-                           for t in Trade.query.all()],
+                           for t in Trade.get_trades().all()],
                           columns=columns)
 
     return trades
@@ -161,9 +178,9 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     :return: Returns df with one additional column, col_name, containing the cumulative profit.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
-    # Resample to ticker_interval to make sure trades match candles
+    # Resample to timeframe to make sure trades match candles
-    _trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
     df.loc[:, col_name] = _trades_sum.cumsum()
     # Set first value to 0
     df.loc[df.iloc[0].name, col_name] = 0

freqtrade/data/converter.py

@@ -10,13 +10,13 @@ from pandas import DataFrame, to_datetime
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
+def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
                            fill_missing: bool = True,
                            drop_incomplete: bool = True) -> DataFrame:
     """
     Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
     :param ticker: ticker list, as returned by exchange.async_get_candle_history
-    :param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
+    :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
                          (see ohlcv_fill_up_missing_data for details)
@@ -52,12 +52,12 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
         logger.debug('Dropping last candle')
 
     if fill_missing:
-        return ohlcv_fill_up_missing_data(frame, ticker_interval, pair)
+        return ohlcv_fill_up_missing_data(frame, timeframe, pair)
     else:
         return frame
 
 
-def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
+def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
     """
     Fills up missing data with 0 volume rows,
     using the previous close as price for "open", "high" "low" and "close", volume is set to 0
@@ -72,7 +72,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
         'close': 'last',
         'volume': 'sum'
     }
-    ticker_minutes = timeframe_to_minutes(ticker_interval)
+    ticker_minutes = timeframe_to_minutes(timeframe)
     # Resample to create "NAN" values
     df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
 

freqtrade/data/dataprovider.py

@@ -37,52 +37,53 @@ class DataProvider:
     @property
     def available_pairs(self) -> List[Tuple[str, str]]:
         """
-        Return a list of tuples containing pair, ticker_interval for which data is currently cached.
+        Return a list of tuples containing (pair, timeframe) for which data is currently cached.
         Should be whitelist + open trades.
         """
         return list(self._exchange._klines.keys())
 
-    def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
+    def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
         Get ohlcv data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: Ticker timeframe to get data for
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            return self._exchange.klines((pair, ticker_interval or self._config['ticker_interval']),
+            return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
                                          copy=copy)
         else:
             return DataFrame()
 
-    def historic_ohlcv(self, pair: str, ticker_interval: str = None) -> DataFrame:
+    def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
         Get stored historic ohlcv data
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: timeframe to get data for
         """
         return load_pair_history(pair=pair,
-                                 ticker_interval=ticker_interval or self._config['ticker_interval'],
+                                 timeframe=timeframe or self._config['ticker_interval'],
                                  datadir=Path(self._config['datadir'])
                                  )
 
-    def get_pair_dataframe(self, pair: str, ticker_interval: str = None) -> DataFrame:
+    def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
         Return pair ohlcv data, either live or cached historical -- depending
         on the runmode.
         :param pair: pair to get the data for
-        :param ticker_interval: ticker interval to get data for
+        :param timeframe: timeframe to get data for
+        :return: Dataframe for this pair
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
             # Get live ohlcv data.
-            data = self.ohlcv(pair=pair, ticker_interval=ticker_interval)
+            data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
             # Get historic ohlcv data (cached on disk).
-            data = self.historic_ohlcv(pair=pair, ticker_interval=ticker_interval)
+            data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
-            logger.warning(f"No data found for ({pair}, {ticker_interval}).")
+            logger.warning(f"No data found for ({pair}, {timeframe}).")
         return data
 
     def market(self, pair: str) -> Optional[Dict[str, Any]]:

freqtrade/data/history.py

@@ -8,7 +8,8 @@ Includes:
 
 import logging
 import operator
-from datetime import datetime
+from copy import deepcopy
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -18,7 +19,7 @@ from pandas import DataFrame
 from freqtrade import OperationalException, misc
 from freqtrade.configuration import TimeRange
 from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
-from freqtrade.exchange import Exchange, timeframe_to_minutes
+from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
 
 logger = logging.getLogger(__name__)
 
@@ -49,13 +50,30 @@ def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
     return tickerlist[start_index:stop_index]
 
 
-def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
+def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date') -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    :param df: Dataframe to trim
+    :param timerange: timerange (use start and end date if available)
+    :param: df_date_col: Column in the dataframe to use as Date column
+    :return: trimmed dataframe
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
+        df = df.loc[df[df_date_col] <= stop, :]
+    return df
+
+
+def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
                          timerange: Optional[TimeRange] = None) -> Optional[list]:
     """
     Load a pair from file, either .json.gz or .json
     :return: tickerlist or None if unsuccessful
     """
-    filename = pair_data_filename(datadir, pair, ticker_interval)
+    filename = pair_data_filename(datadir, pair, timeframe)
     pairdata = misc.file_load_json(filename)
     if not pairdata:
         return []
@@ -66,11 +84,11 @@ def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
 
 
 def store_tickerdata_file(datadir: Path, pair: str,
-                          ticker_interval: str, data: list, is_zip: bool = False):
+                          timeframe: str, data: list, is_zip: bool = False):
     """
     Stores tickerdata to file
     """
-    filename = pair_data_filename(datadir, pair, ticker_interval)
+    filename = pair_data_filename(datadir, pair, timeframe)
     misc.file_dump_json(filename, data, is_zip=is_zip)
 
 
@@ -107,18 +125,19 @@ def _validate_pairdata(pair, pairdata, timerange: TimeRange):
 
 
 def load_pair_history(pair: str,
-                      ticker_interval: str,
+                      timeframe: str,
                       datadir: Path,
                       timerange: Optional[TimeRange] = None,
                       refresh_pairs: bool = False,
                       exchange: Optional[Exchange] = None,
                       fill_up_missing: bool = True,
-                      drop_incomplete: bool = True
+                      drop_incomplete: bool = True,
+                      startup_candles: int = 0,
                       ) -> DataFrame:
     """
     Loads cached ticker history for the given pair.
     :param pair: Pair to load data for
-    :param ticker_interval: Ticker-interval (e.g. "5m")
+    :param timeframe: Ticker timeframe (e.g. "5m")
     :param datadir: Path to the data storage location.
     :param timerange: Limit data to be loaded to this timerange
     :param refresh_pairs: Refresh pairs from exchange.
@@ -126,65 +145,88 @@ def load_pair_history(pair: str,
     :param exchange: Exchange object (needed when using "refresh_pairs")
     :param fill_up_missing: Fill missing values with "No action"-candles
     :param drop_incomplete: Drop last candle assuming it may be incomplete.
-    :return: DataFrame with ohlcv data
+    :param startup_candles: Additional candles to load at the start of the period
+    :return: DataFrame with ohlcv data, or empty DataFrame
     """
 
+    timerange_startup = deepcopy(timerange)
+    if startup_candles > 0 and timerange_startup:
+        timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
+
     # The user forced the refresh of pairs
     if refresh_pairs:
         download_pair_history(datadir=datadir,
                               exchange=exchange,
                               pair=pair,
-                              ticker_interval=ticker_interval,
+                              timeframe=timeframe,
                               timerange=timerange)
 
-    pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
+    pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
 
     if pairdata:
-        if timerange:
+        if timerange_startup:
-            _validate_pairdata(pair, pairdata, timerange)
+            _validate_pairdata(pair, pairdata, timerange_startup)
-        return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
+        return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
                                       fill_missing=fill_up_missing,
                                       drop_incomplete=drop_incomplete)
     else:
         logger.warning(
-            f'No history data for pair: "{pair}", interval: {ticker_interval}. '
+            f'No history data for pair: "{pair}", timeframe: {timeframe}. '
             'Use `freqtrade download-data` to download the data'
         )
-        return None
+        return DataFrame()
 
 
 def load_data(datadir: Path,
-              ticker_interval: str,
+              timeframe: str,
               pairs: List[str],
               refresh_pairs: bool = False,
               exchange: Optional[Exchange] = None,
               timerange: Optional[TimeRange] = None,
               fill_up_missing: bool = True,
+              startup_candles: int = 0,
+              fail_without_data: bool = False
               ) -> Dict[str, DataFrame]:
     """
     Loads ticker history data for a list of pairs
-    :return: dict(<pair>:<tickerlist>)
+    :param datadir: Path to the data storage location.
+    :param timeframe: Ticker Timeframe (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param refresh_pairs: Refresh pairs from exchange.
+        (Note: Requires exchange to be passed as well.)
+    :param exchange: Exchange object (needed when using "refresh_pairs")
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param startup_candles: Additional candles to load at the start of the period
+    :param fail_without_data: Raise OperationalException if no data is found.
+    :return: dict(<pair>:<Dataframe>)
     TODO: refresh_pairs is still used by edge to keep the data uptodate.
         This should be replaced in the future. Instead, writing the current candles to disk
         from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
         exchange and refresh_pairs are then not needed here nor in load_pair_history.
     """
     result: Dict[str, DataFrame] = {}
+    if startup_candles > 0 and timerange:
+        logger.info(f'Using indicator startup period: {startup_candles} ...')
 
     for pair in pairs:
-        hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
+        hist = load_pair_history(pair=pair, timeframe=timeframe,
                                  datadir=datadir, timerange=timerange,
                                  refresh_pairs=refresh_pairs,
                                  exchange=exchange,
-                                 fill_up_missing=fill_up_missing)
+                                 fill_up_missing=fill_up_missing,
-        if hist is not None:
+                                 startup_candles=startup_candles)
+        if not hist.empty:
             result[pair] = hist
+
+    if fail_without_data and not result:
+        raise OperationalException("No data found. Terminating.")
     return result
 
 
-def pair_data_filename(datadir: Path, pair: str, ticker_interval: str) -> Path:
+def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
     pair_s = pair.replace("/", "_")
-    filename = datadir.joinpath(f'{pair_s}-{ticker_interval}.json')
+    filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
     return filename
 
 
@@ -194,7 +236,7 @@ def pair_trades_filename(datadir: Path, pair: str) -> Path:
     return filename
 
 
-def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: str,
+def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
                                    timerange: Optional[TimeRange]) -> Tuple[List[Any],
                                                                             Optional[int]]:
     """
@@ -212,12 +254,12 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
         if timerange.starttype == 'date':
             since_ms = timerange.startts * 1000
         elif timerange.stoptype == 'line':
-            num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
+            num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
             since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
 
     # read the cached file
     # Intentionally don't pass timerange in - since we need to load the full dataset.
-    data = load_tickerdata_file(datadir, pair, ticker_interval)
+    data = load_tickerdata_file(datadir, pair, timeframe)
     # remove the last item, could be incomplete candle
     if data:
         data.pop()
@@ -238,18 +280,18 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
 def download_pair_history(datadir: Path,
                           exchange: Optional[Exchange],
                           pair: str,
-                          ticker_interval: str = '5m',
+                          timeframe: str = '5m',
                           timerange: Optional[TimeRange] = None) -> bool:
     """
-    Download the latest ticker intervals from the exchange for the pair passed in parameters
+    Download latest candles from the exchange for the pair and timeframe passed in parameters
-    The data is downloaded starting from the last correct ticker interval data that
+    The data is downloaded starting from the last correct data that
     exists in a cache. If timerange starts earlier than the data in the cache,
     the full data will be redownloaded
 
     Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
 
     :param pair: pair to download
-    :param ticker_interval: ticker interval
+    :param timeframe: Ticker Timeframe (e.g 5m)
     :param timerange: range of time to download
     :return: bool with success state
     """
@@ -260,17 +302,17 @@ def download_pair_history(datadir: Path,
 
     try:
         logger.info(
-            f'Download history data for pair: "{pair}", interval: {ticker_interval} '
+            f'Download history data for pair: "{pair}", timeframe: {timeframe} '
             f'and store in {datadir}.'
         )
 
-        data, since_ms = _load_cached_data_for_updating(datadir, pair, ticker_interval, timerange)
+        data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
 
         logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
         logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
 
         # Default since_ms to 30 days if nothing is given
-        new_data = exchange.get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
+        new_data = exchange.get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                                since_ms=since_ms if since_ms
                                                else
                                                int(arrow.utcnow().shift(
@@ -280,12 +322,12 @@ def download_pair_history(datadir: Path,
         logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
         logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
 
-        store_tickerdata_file(datadir, pair, ticker_interval, data=data)
+        store_tickerdata_file(datadir, pair, timeframe, data=data)
         return True
 
     except Exception as e:
         logger.error(
-            f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
+            f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
             f'Error: {e}'
         )
         return False
@@ -305,17 +347,17 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
             pairs_not_available.append(pair)
             logger.info(f"Skipping pair {pair}...")
             continue
-        for ticker_interval in timeframes:
+        for timeframe in timeframes:
 
-            dl_file = pair_data_filename(dl_path, pair, ticker_interval)
+            dl_file = pair_data_filename(dl_path, pair, timeframe)
             if erase and dl_file.exists():
                 logger.info(
-                    f'Deleting existing data for pair {pair}, interval {ticker_interval}.')
+                    f'Deleting existing data for pair {pair}, interval {timeframe}.')
                 dl_file.unlink()
 
-            logger.info(f'Downloading pair {pair}, interval {ticker_interval}.')
+            logger.info(f'Downloading pair {pair}, interval {timeframe}.')
             download_pair_history(datadir=dl_path, exchange=exchange,
-                                  pair=pair, ticker_interval=str(ticker_interval),
+                                  pair=pair, timeframe=str(timeframe),
                                   timerange=timerange)
     return pairs_not_available
 
@@ -421,7 +463,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
 
 
 def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
-                           max_date: datetime, ticker_interval_mins: int) -> bool:
+                           max_date: datetime, timeframe_mins: int) -> bool:
     """
     Validates preprocessed backtesting data for missing values and shows warnings about it that.
 
@@ -429,10 +471,10 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
     :param pair: pair used for log output.
     :param min_date: start-date of the data
     :param max_date: end-date of the data
-    :param ticker_interval_mins: ticker interval in minutes
+    :param timeframe_mins: ticker Timeframe in minutes
     """
-    # total difference in minutes / interval-minutes
+    # total difference in minutes / timeframe-minutes
-    expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
+    expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
     found_missing = False
     dflen = len(data)
     if dflen < expected_frames:

freqtrade/edge/__init__.py

@@ -97,10 +97,11 @@ class Edge:
         data = history.load_data(
             datadir=Path(self.config['datadir']),
             pairs=pairs,
-            ticker_interval=self.strategy.ticker_interval,
+            timeframe=self.strategy.ticker_interval,
             refresh_pairs=self._refresh_pairs,
             exchange=self.exchange,
-            timerange=self._timerange
+            timerange=self._timerange,
+            startup_candles=self.strategy.startup_candle_count,
         )
 
         if not data:

freqtrade/exchange/__init__.py

@@ -1,4 +1,5 @@
-from freqtrade.exchange.exchange import Exchange, MAP_EXCHANGE_CHILDCLASS  # noqa: F401
+from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS  # noqa: F401
+from freqtrade.exchange.exchange import Exchange  # noqa: F401
 from freqtrade.exchange.exchange import (get_exchange_bad_reason,  # noqa: F401
                                          is_exchange_bad,
                                          is_exchange_known_ccxt,
@@ -14,3 +15,4 @@ from freqtrade.exchange.exchange import (market_is_active,  # noqa: F401
                                          symbol_is_pair)
 from freqtrade.exchange.kraken import Kraken  # noqa: F401
 from freqtrade.exchange.binance import Binance  # noqa: F401
+from freqtrade.exchange.bibox import Bibox  # noqa: F401

freqtrade/exchange/bibox.py

@@ -0,0 +1,22 @@
+""" Bibox exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+logger = logging.getLogger(__name__)
+
+
+class Bibox(Exchange):
+    """
+    Bibox exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    # fetchCurrencies API point requires authentication for Bibox,
+    # so switch it off for Freqtrade load_markets()
+    _ccxt_config: Dict = {"has": {"fetchCurrencies": False}}

freqtrade/exchange/common.py

@@ -0,0 +1,124 @@
+import logging
+
+from freqtrade import DependencyException, TemporaryError
+
+logger = logging.getLogger(__name__)
+
+
+API_RETRY_COUNT = 4
+BAD_EXCHANGES = {
+    "bitmex": "Various reasons.",
+    "bitstamp": "Does not provide history. "
+                "Details in https://github.com/freqtrade/freqtrade/issues/1983",
+    "hitbtc": "This API cannot be used with Freqtrade. "
+              "Use `hitbtc2` exchange id to access this exchange.",
+    **dict.fromkeys([
+        'adara',
+        'anxpro',
+        'bigone',
+        'coinbase',
+        'coinexchange',
+        'coinmarketcap',
+        'lykke',
+        'xbtce',
+    ], "Does not provide timeframes. ccxt fetchOHLCV: False"),
+    **dict.fromkeys([
+        'bcex',
+        'bit2c',
+        'bitbay',
+        'bitflyer',
+        'bitforex',
+        'bithumb',
+        'bitso',
+        'bitstamp1',
+        'bl3p',
+        'braziliex',
+        'btcbox',
+        'btcchina',
+        'btctradeim',
+        'btctradeua',
+        'bxinth',
+        'chilebit',
+        'coincheck',
+        'coinegg',
+        'coinfalcon',
+        'coinfloor',
+        'coingi',
+        'coinmate',
+        'coinone',
+        'coinspot',
+        'coolcoin',
+        'crypton',
+        'deribit',
+        'exmo',
+        'exx',
+        'flowbtc',
+        'foxbit',
+        'fybse',
+        # 'hitbtc',
+        'ice3x',
+        'independentreserve',
+        'indodax',
+        'itbit',
+        'lakebtc',
+        'latoken',
+        'liquid',
+        'livecoin',
+        'luno',
+        'mixcoins',
+        'negociecoins',
+        'nova',
+        'paymium',
+        'southxchange',
+        'stronghold',
+        'surbitcoin',
+        'therock',
+        'tidex',
+        'vaultoro',
+        'vbtc',
+        'virwox',
+        'yobit',
+        'zaif',
+    ], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
+}
+
+MAP_EXCHANGE_CHILDCLASS = {
+    'binanceus': 'binance',
+    'binanceje': 'binance',
+}
+
+
+def retrier_async(f):
+    async def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return await f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return await wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper
+
+
+def retrier(f):
+    def wrapper(*args, **kwargs):
+        count = kwargs.pop('count', API_RETRY_COUNT)
+        try:
+            return f(*args, **kwargs)
+        except (TemporaryError, DependencyException) as ex:
+            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            if count > 0:
+                count -= 1
+                kwargs.update({'count': count})
+                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                return wrapper(*args, **kwargs)
+            else:
+                logger.warning('Giving up retrying: %s()', f.__name__)
+                raise ex
+    return wrapper

freqtrade/exchange/exchange.py

@@ -14,141 +14,25 @@ from typing import Any, Dict, List, Optional, Tuple
 import arrow
 import ccxt
 import ccxt.async_support as ccxt_async
-from ccxt.base.decimal_to_precision import ROUND_UP, ROUND_DOWN
+from ccxt.base.decimal_to_precision import ROUND_DOWN, ROUND_UP
 from pandas import DataFrame
 
 from freqtrade import (DependencyException, InvalidOrderException,
                        OperationalException, TemporaryError, constants)
 from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
 from freqtrade.misc import deep_merge_dicts
 
-
 logger = logging.getLogger(__name__)
 
 
-API_RETRY_COUNT = 4
-BAD_EXCHANGES = {
-    "bitmex": "Various reasons.",
-    "bitstamp": "Does not provide history. "
-                "Details in https://github.com/freqtrade/freqtrade/issues/1983",
-    "hitbtc": "This API cannot be used with Freqtrade. "
-              "Use `hitbtc2` exchange id to access this exchange.",
-    **dict.fromkeys([
-        'adara',
-        'anxpro',
-        'bigone',
-        'coinbase',
-        'coinexchange',
-        'coinmarketcap',
-        'lykke',
-        'xbtce',
-        ], "Does not provide timeframes. ccxt fetchOHLCV: False"),
-    **dict.fromkeys([
-        'bcex',
-        'bit2c',
-        'bitbay',
-        'bitflyer',
-        'bitforex',
-        'bithumb',
-        'bitso',
-        'bitstamp1',
-        'bl3p',
-        'braziliex',
-        'btcbox',
-        'btcchina',
-        'btctradeim',
-        'btctradeua',
-        'bxinth',
-        'chilebit',
-        'coincheck',
-        'coinegg',
-        'coinfalcon',
-        'coinfloor',
-        'coingi',
-        'coinmate',
-        'coinone',
-        'coinspot',
-        'coolcoin',
-        'crypton',
-        'deribit',
-        'exmo',
-        'exx',
-        'flowbtc',
-        'foxbit',
-        'fybse',
-        # 'hitbtc',
-        'ice3x',
-        'independentreserve',
-        'indodax',
-        'itbit',
-        'lakebtc',
-        'latoken',
-        'liquid',
-        'livecoin',
-        'luno',
-        'mixcoins',
-        'negociecoins',
-        'nova',
-        'paymium',
-        'southxchange',
-        'stronghold',
-        'surbitcoin',
-        'therock',
-        'tidex',
-        'vaultoro',
-        'vbtc',
-        'virwox',
-        'yobit',
-        'zaif',
-        ], "Does not provide timeframes. ccxt fetchOHLCV: emulated"),
-    }
-
-MAP_EXCHANGE_CHILDCLASS = {
-    'binanceus': 'binance',
-    'binanceje': 'binance',
-}
-
-
-def retrier_async(f):
-    async def wrapper(*args, **kwargs):
-        count = kwargs.pop('count', API_RETRY_COUNT)
-        try:
-            return await f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
-            if count > 0:
-                count -= 1
-                kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
-                return await wrapper(*args, **kwargs)
-            else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
-                raise ex
-    return wrapper
-
-
-def retrier(f):
-    def wrapper(*args, **kwargs):
-        count = kwargs.pop('count', API_RETRY_COUNT)
-        try:
-            return f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
-            if count > 0:
-                count -= 1
-                kwargs.update({'count': count})
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
-                return wrapper(*args, **kwargs)
-            else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
-                raise ex
-    return wrapper
-
-
 class Exchange:
 
     _config: Dict = {}
 
+    # Parameters to add directly to ccxt sync/async initialization.
+    _ccxt_config: Dict = {}
+
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
     _params: Dict = {}
 
@@ -210,10 +94,17 @@ class Exchange:
         self._trades_pagination_arg = self._ft_has['trades_pagination_arg']
 
         # Initialize ccxt objects
+        ccxt_config = self._ccxt_config.copy()
+        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
+                                       ccxt_config)
         self._api = self._init_ccxt(
-            exchange_config, ccxt_kwargs=exchange_config.get('ccxt_config'))
+            exchange_config, ccxt_kwargs=ccxt_config)
+
+        ccxt_async_config = self._ccxt_config.copy()
+        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
+                                             ccxt_async_config)
         self._api_async = self._init_ccxt(
-            exchange_config, ccxt_async, ccxt_kwargs=exchange_config.get('ccxt_async_config'))
+            exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info('Using Exchange "%s"', self.name)
 
@@ -228,6 +119,7 @@ class Exchange:
             self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
+            self.validate_required_startup_candles(config.get('startup_candle_count', 0))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -443,6 +335,16 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
+    def validate_required_startup_candles(self, startup_candles) -> None:
+        """
+        Checks if required startup_candles is more than ohlcv_candle_limit.
+        Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
+        """
+        if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
+            raise OperationalException(
+                f"This strategy requires {startup_candles} candles to start. "
+                f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
+
     def exchange_has(self, endpoint: str) -> bool:
         """
         Checks if exchange implements a specific API endpoint.
@@ -644,40 +546,40 @@ class Exchange:
             logger.info("returning cached ticker-data for %s", pair)
             return self._cached_ticker[pair]
 
-    def get_historic_ohlcv(self, pair: str, ticker_interval: str,
+    def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int) -> List:
         """
         Gets candle history using asyncio and returns the list of candles.
         Handles all async doing.
         Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param ticker_interval: Interval to get
+        :param timeframe: Ticker Timeframe to get
         :param since_ms: Timestamp in milliseconds to get history from
         :returns List of tickers
         """
         return asyncio.get_event_loop().run_until_complete(
-            self._async_get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
+            self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms))
 
     async def _async_get_historic_ohlcv(self, pair: str,
-                                        ticker_interval: str,
+                                        timeframe: str,
                                         since_ms: int) -> List:
 
-        one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
+        one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
             arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
         )
         input_coroutines = [self._async_get_candle_history(
-            pair, ticker_interval, since) for since in
+            pair, timeframe, since) for since in
             range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
 
         tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
         # Combine tickers
         data: List = []
-        for p, ticker_interval, ticker in tickers:
+        for p, timeframe, ticker in tickers:
             if p == pair:
                 data.extend(ticker)
         # Sort data again after extending the result - above calls return in "async order"
@@ -697,14 +599,14 @@ class Exchange:
         input_coroutines = []
 
         # Gather coroutines to run
-        for pair, ticker_interval in set(pair_list):
+        for pair, timeframe in set(pair_list):
-            if (not ((pair, ticker_interval) in self._klines)
+            if (not ((pair, timeframe) in self._klines)
-                    or self._now_is_time_to_refresh(pair, ticker_interval)):
+                    or self._now_is_time_to_refresh(pair, timeframe)):
-                input_coroutines.append(self._async_get_candle_history(pair, ticker_interval))
+                input_coroutines.append(self._async_get_candle_history(pair, timeframe))
             else:
                 logger.debug(
-                    "Using cached ohlcv data for pair %s, interval %s ...",
+                    "Using cached ohlcv data for pair %s, timeframe %s ...",
-                    pair, ticker_interval
+                    pair, timeframe
                 )
 
         tickers = asyncio.get_event_loop().run_until_complete(
@@ -716,40 +618,40 @@ class Exchange:
                 logger.warning("Async code raised an exception: %s", res.__class__.__name__)
                 continue
             pair = res[0]
-            ticker_interval = res[1]
+            timeframe = res[1]
             ticks = res[2]
             # keeping last candle time as last refreshed time of the pair
             if ticks:
-                self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
+                self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
             # keeping parsed dataframe in cache
-            self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
+            self._klines[(pair, timeframe)] = parse_ticker_dataframe(
-                ticks, ticker_interval, pair=pair, fill_missing=True,
+                ticks, timeframe, pair=pair, fill_missing=True,
                 drop_incomplete=self._ohlcv_partial_candle)
         return tickers
 
-    def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:
+    def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
         # Calculating ticker interval in seconds
-        interval_in_sec = timeframe_to_seconds(ticker_interval)
+        interval_in_sec = timeframe_to_seconds(timeframe)
 
-        return not ((self._pairs_last_refresh_time.get((pair, ticker_interval), 0)
+        return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
                      + interval_in_sec) >= arrow.utcnow().timestamp)
 
     @retrier_async
-    async def _async_get_candle_history(self, pair: str, ticker_interval: str,
+    async def _async_get_candle_history(self, pair: str, timeframe: str,
                                         since_ms: Optional[int] = None) -> Tuple[str, str, List]:
         """
         Asynchronously gets candle histories using fetch_ohlcv
-        returns tuple: (pair, ticker_interval, ohlcv_list)
+        returns tuple: (pair, timeframe, ohlcv_list)
         """
         try:
             # fetch ohlcv asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, interval %s, since %s %s...",
-                pair, ticker_interval, since_ms, s
+                pair, timeframe, since_ms, s
             )
 
-            data = await self._api_async.fetch_ohlcv(pair, timeframe=ticker_interval,
+            data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
                                                      since=since_ms)
 
             # Because some exchange sort Tickers ASC and other DESC.
@@ -761,9 +663,9 @@ class Exchange:
                     data = sorted(data, key=lambda x: x[0])
             except IndexError:
                 logger.exception("Error loading %s. Result was %s.", pair, data)
-                return pair, ticker_interval, []
+                return pair, timeframe, []
-            logger.debug("Done fetching pair %s, interval %s ...", pair, ticker_interval)
+            logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
-            return pair, ticker_interval, data
+            return pair, timeframe, data
 
         except ccxt.NotSupported as e:
             raise OperationalException(
@@ -910,7 +812,6 @@ class Exchange:
         Handles all async doing.
         Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param ticker_interval: Interval to get
         :param since: Timestamp in milliseconds to get history from
         :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
         :param from_id: Download data starting with ID (if id is known)
@@ -983,6 +884,22 @@ class Exchange:
 
     @retrier
     def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
+        """
+        Fetch Orders using the "fetch_my_trades" endpoint and filter them by order-id.
+        The "since" argument passed in is coming from the database and is in UTC,
+        as timezone-native datetime object.
+        From the python documentation:
+            > Naive datetime instances are assumed to represent local time
+        Therefore, calling "since.timestamp()" will get the UTC timestamp, after applying the
+        transformation from local timezone to UTC.
+        This works for timezones UTC+ since then the result will contain trades from a few hours
+        instead of from the last 5 seconds, however fails for UTC- timezones,
+        since we're then asking for trades with a "since" argument in the future.
+
+        :param order_id order_id: Order-id as given when creating the order
+        :param pair: Pair the order is for
+        :param since: datetime object of the order creation time. Assumes object is in UTC.
+        """
         if self._config['dry_run']:
             return []
         if not self.exchange_has('fetchMyTrades'):
@@ -990,7 +907,8 @@ class Exchange:
         try:
             # Allow 5s offset to catch slight time offsets (discovered in #1185)
             # since needs to be int in milliseconds
-            my_trades = self._api.fetch_my_trades(pair, int((since.timestamp() - 5) * 1000))
+            my_trades = self._api.fetch_my_trades(
+                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000))
             matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
 
             return matched_trades
@@ -1049,27 +967,27 @@ def available_exchanges(ccxt_module=None) -> List[str]:
     return [x for x in exchanges if not is_exchange_bad(x)]
 
 
-def timeframe_to_seconds(ticker_interval: str) -> int:
+def timeframe_to_seconds(timeframe: str) -> int:
     """
     Translates the timeframe interval value written in the human readable
     form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
     of seconds for one timeframe interval.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval)
+    return ccxt.Exchange.parse_timeframe(timeframe)
 
 
-def timeframe_to_minutes(ticker_interval: str) -> int:
+def timeframe_to_minutes(timeframe: str) -> int:
     """
     Same as timeframe_to_seconds, but returns minutes.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval) // 60
+    return ccxt.Exchange.parse_timeframe(timeframe) // 60
 
 
-def timeframe_to_msecs(ticker_interval: str) -> int:
+def timeframe_to_msecs(timeframe: str) -> int:
     """
     Same as timeframe_to_seconds, but returns milliseconds.
     """
-    return ccxt.Exchange.parse_timeframe(ticker_interval) * 1000
+    return ccxt.Exchange.parse_timeframe(timeframe) * 1000
 
 
 def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:

freqtrade/freqtradebot.py

@@ -20,9 +20,9 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
 from freqtrade.persistence import Trade
-from freqtrade.resolvers import (ExchangeResolver, PairListResolver,
+from freqtrade.resolvers import ExchangeResolver, StrategyResolver
-                                 StrategyResolver)
 from freqtrade.rpc import RPCManager, RPCMessageType
+from freqtrade.pairlist.pairlistmanager import PairListManager
 from freqtrade.state import State
 from freqtrade.strategy.interface import IStrategy, SellType
 from freqtrade.wallets import Wallets
@@ -70,14 +70,13 @@ class FreqtradeBot:
         # Attach Wallets to Strategy baseclass
         IStrategy.wallets = self.wallets
 
-        pairlistname = self.config.get('pairlist', {}).get('method', 'StaticPairList')
+        self.pairlists = PairListManager(self.exchange, self.config)
-        self.pairlists = PairListResolver(pairlistname, self, self.config).pairlist
 
         # Initializing Edge only if enabled
         self.edge = Edge(self.config, self.exchange, self.strategy) if \
             self.config.get('edge', {}).get('enabled', False) else None
 
-        self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist']
+        self.active_pair_whitelist = self._refresh_whitelist()
 
         persistence.init(self.config.get('db_url', None),
                          clean_open_orders=self.config.get('dry_run', False))
@@ -123,21 +122,10 @@ class FreqtradeBot:
         # Check whether markets have to be reloaded
         self.exchange._reload_markets()
 
-        # Refresh whitelist
-        self.pairlists.refresh_pairlist()
-        self.active_pair_whitelist = self.pairlists.whitelist
-
-        # Calculating Edge positioning
-        if self.edge:
-            self.edge.calculate()
-            self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
-
         # Query trades from persistence layer
         trades = Trade.get_open_trades()
 
-        # Extend active-pair whitelist with pairs from open trades
+        self.active_pair_whitelist = self._refresh_whitelist(trades)
-        # It ensures that tickers are downloaded for open trades
-        self._extend_whitelist_with_trades(self.active_pair_whitelist, trades)
 
         # Refreshing candles
         self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
@@ -150,7 +138,6 @@ class FreqtradeBot:
         if len(trades) < self.config['max_open_trades']:
             self.process_maybe_execute_buys()
 
-        if 'unfilledtimeout' in self.config:
         # Check and handle any timed out open orders
         self.check_handle_timedout()
         Trade.session.flush()
@@ -160,11 +147,24 @@ class FreqtradeBot:
             logger.info(f"Bot heartbeat. PID={getpid()}")
             self._heartbeat_msg = arrow.utcnow().timestamp
 
-    def _extend_whitelist_with_trades(self, whitelist: List[str], trades: List[Any]):
+    def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
         """
-        Extend whitelist with pairs from open trades
+        Refresh whitelist from pairlist or edge and extend it with trades.
         """
-        whitelist.extend([trade.pair for trade in trades if trade.pair not in whitelist])
+        # Refresh whitelist
+        self.pairlists.refresh_pairlist()
+        _whitelist = self.pairlists.whitelist
+
+        # Calculating Edge positioning
+        if self.edge:
+            self.edge.calculate()
+            _whitelist = self.edge.adjust(_whitelist)
+
+        if trades:
+            # Extend active-pair whitelist with pairs from open trades
+            # It ensures that tickers are downloaded for open trades
+            _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
+        return _whitelist
 
     def _create_pair_whitelist(self, pairs: List[str]) -> List[Tuple[str, str]]:
         """
@@ -266,7 +266,11 @@ class FreqtradeBot:
             amount_reserve_percent += self.strategy.stoploss
         # it should not be more than 50%
         amount_reserve_percent = max(amount_reserve_percent, 0.5)
-        return min(min_stake_amounts) / amount_reserve_percent
+
+        # The value returned should satisfy both limits: for amount (base currency) and
+        # for cost (quote, stake currency), so max() is used here.
+        # See also #2575 at github.
+        return max(min_stake_amounts) / amount_reserve_percent
 
     def create_trades(self) -> bool:
         """
@@ -317,7 +321,6 @@ class FreqtradeBot:
                         (bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0):
                     if self._check_depth_of_market_buy(_pair, bidstrat_check_depth_of_market):
                         buycount += self.execute_buy(_pair, stake_amount)
-                    else:
                     continue
 
                 buycount += self.execute_buy(_pair, stake_amount)
@@ -632,8 +635,8 @@ class FreqtradeBot:
         Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
         :return: True if the order succeeded, and False in case of problems.
         """
-        # Limit price threshold: As limit price should always be below price
+        # Limit price threshold: As limit price should always be below stop-price
-        LIMIT_PRICE_PCT = 0.99
+        LIMIT_PRICE_PCT = self.strategy.order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
 
         try:
             stoploss_order = self.exchange.stoploss_limit(pair=trade.pair, amount=trade.amount,
@@ -755,23 +758,28 @@ class FreqtradeBot:
             return True
         return False
 
+    def _check_timed_out(self, side: str, order: dict) -> bool:
+        """
+        Check if timeout is active, and if the order is still open and timed out
+        """
+        timeout = self.config.get('unfilledtimeout', {}).get(side)
+        ordertime = arrow.get(order['datetime']).datetime
+        if timeout is not None:
+            timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
+
+            return (order['status'] == 'open' and order['side'] == side
+                    and ordertime < timeout_threshold)
+        return False
+
     def check_handle_timedout(self) -> None:
         """
         Check if any orders are timed out and cancel if neccessary
         :param timeoutvalue: Number of minutes until order is considered timed out
         :return: None
         """
-        buy_timeout = self.config['unfilledtimeout']['buy']
-        sell_timeout = self.config['unfilledtimeout']['sell']
-        buy_timeout_threshold = arrow.utcnow().shift(minutes=-buy_timeout).datetime
-        sell_timeout_threshold = arrow.utcnow().shift(minutes=-sell_timeout).datetime
 
-        for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
+        for trade in Trade.get_open_order_trades():
             try:
-                # FIXME: Somehow the query above returns results
-                # where the open_order_id is in fact None.
-                # This is probably because the record got
-                # updated via /forcesell in a different thread.
                 if not trade.open_order_id:
                     continue
                 order = self.exchange.get_order(trade.open_order_id, trade.pair)
@@ -781,23 +789,20 @@ class FreqtradeBot:
                     trade,
                     traceback.format_exc())
                 continue
-            ordertime = arrow.get(order['datetime']).datetime
 
             # Check if trade is still actually open
-            if float(order['remaining']) == 0.0:
+            if float(order.get('remaining', 0.0)) == 0.0:
                 self.wallets.update()
                 continue
 
             if ((order['side'] == 'buy' and order['status'] == 'canceled')
-                or (order['status'] == 'open'
+                    or (self._check_timed_out('buy', order))):
-                    and order['side'] == 'buy' and ordertime < buy_timeout_threshold)):
 
                 self.handle_timedout_limit_buy(trade, order)
                 self.wallets.update()
 
             elif ((order['side'] == 'sell' and order['status'] == 'canceled')
-                  or (order['status'] == 'open'
+                  or (self._check_timed_out('sell', order))):
-                      and order['side'] == 'sell' and ordertime < sell_timeout_threshold)):
                 self.handle_timedout_limit_sell(trade, order)
                 self.wallets.update()
 
@@ -812,7 +817,8 @@ class FreqtradeBot:
         })
 
     def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
-        """Buy timeout - cancel order
+        """
+        Buy timeout - cancel order
         :return: True if order was fully cancelled
         """
         reason = "cancelled due to timeout"
@@ -823,18 +829,22 @@ class FreqtradeBot:
             corder = order
             reason = "canceled on Exchange"
 
-        if corder['remaining'] == corder['amount']:
+        if corder.get('remaining', order['remaining']) == order['amount']:
             # if trade is not partially completed, just delete the trade
             self.handle_buy_order_full_cancel(trade, reason)
             return True
 
         # if trade is partially complete, edit the stake details for the trade
         # and close the order
-        trade.amount = corder['amount'] - corder['remaining']
+        # cancel_order may not contain the full order dict, so we need to fallback
+        # to the order dict aquired before cancelling.
+        # we need to fall back to the values from order if corder does not contain these keys.
+        trade.amount = order['amount'] - corder.get('remaining', order['remaining'])
         trade.stake_amount = trade.amount * trade.open_rate
         # verify if fees were taken from amount to avoid problems during selling
         try:
-            new_amount = self.get_real_amount(trade, corder, trade.amount)
+            new_amount = self.get_real_amount(trade, corder if 'fee' in corder else order,
+                                              trade.amount)
             if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
                 trade.amount = new_amount
                 # Fee was applied, so set to 0

freqtrade/loggers.py

@@ -1,9 +1,12 @@
 import logging
 import sys
 
-from logging.handlers import RotatingFileHandler
+from logging import Formatter
+from logging.handlers import RotatingFileHandler, SysLogHandler
 from typing import Any, Dict, List
 
+from freqtrade import OperationalException
+
 
 logger = logging.getLogger(__name__)
 
@@ -33,11 +36,39 @@ def setup_logging(config: Dict[str, Any]) -> None:
     # Log level
     verbosity = config['verbosity']
 
-    # Log to stdout, not stderr
+    # Log to stderr
-    log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stdout)]
+    log_handlers: List[logging.Handler] = [logging.StreamHandler(sys.stderr)]
 
-    if config.get('logfile'):
+    logfile = config.get('logfile')
-        log_handlers.append(RotatingFileHandler(config['logfile'],
+    if logfile:
+        s = logfile.split(':')
+        if s[0] == 'syslog':
+            # Address can be either a string (socket filename) for Unix domain socket or
+            # a tuple (hostname, port) for UDP socket.
+            # Address can be omitted (i.e. simple 'syslog' used as the value of
+            # config['logfilename']), which defaults to '/dev/log', applicable for most
+            # of the systems.
+            address = (s[1], int(s[2])) if len(s) > 2 else s[1] if len(s) > 1 else '/dev/log'
+            handler = SysLogHandler(address=address)
+            # No datetime field for logging into syslog, to allow syslog
+            # to perform reduction of repeating messages if this is set in the
+            # syslog config. The messages should be equal for this.
+            handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
+            log_handlers.append(handler)
+        elif s[0] == 'journald':
+            try:
+                from systemd.journal import JournaldLogHandler
+            except ImportError:
+                raise OperationalException("You need the systemd python package be installed in "
+                                           "order to use logging to journald.")
+            handler = JournaldLogHandler()
+            # No datetime field for logging into journald, to allow syslog
+            # to perform reduction of repeating messages if this is set in the
+            # syslog config. The messages should be equal for this.
+            handler.setFormatter(Formatter('%(name)s - %(levelname)s - %(message)s'))
+            log_handlers.append(handler)
+        else:
+            log_handlers.append(RotatingFileHandler(logfile,
                                                     maxBytes=1024 * 1024,  # 1Mb
                                                     backupCount=10))
 

freqtrade/main.py

@@ -15,7 +15,6 @@ from typing import Any, List
 
 from freqtrade import OperationalException
 from freqtrade.configuration import Arguments
-from freqtrade.worker import Worker
 
 
 logger = logging.getLogger('freqtrade')
@@ -28,21 +27,23 @@ def main(sysargv: List[str] = None) -> None:
     """
 
     return_code: Any = 1
-    worker = None
     try:
         arguments = Arguments(sysargv)
         args = arguments.get_parsed_arg()
 
-        # A subcommand has been issued.
+        # Call subcommand.
-        # Means if Backtesting or Hyperopt have been called we exit the bot
         if 'func' in args:
-            args['func'](args)
+            return_code = args['func'](args)
-            # TODO: fetch return_code as returned by the command function here
-            return_code = 0
         else:
-            # Load and run worker
+            # No subcommand was issued.
-            worker = Worker(args)
+            raise OperationalException(
-            worker.run()
+                "Usage of Freqtrade requires a subcommand to be specified.\n"
+                "To have the previous behavior (bot executing trades in live/dry-run modes, "
+                "depending on the value of the `dry_run` setting in the config), run freqtrade "
+                "as `freqtrade trade [options...]`.\n"
+                "To see the full list of options available, please use "
+                "`freqtrade --help` or `freqtrade <command> --help`."
+                )
 
     except SystemExit as e:
         return_code = e
@@ -55,8 +56,6 @@ def main(sysargv: List[str] = None) -> None:
     except Exception:
         logger.exception('Fatal exception!')
     finally:
-        if worker:
-            worker.exit()
         sys.exit(return_code)
 
 

freqtrade/misc.py

@@ -127,3 +127,16 @@ def round_dict(d, n):
 
 def plural(num, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
+
+
+def render_template(templatefile: str, arguments: dict = {}):
+
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader('freqtrade', 'templates'),
+        autoescape=select_autoescape(['html', 'xml'])
+    )
+    template = env.get_template(templatefile)
+
+    return template.render(**arguments)

freqtrade/optimize/__init__.py

@@ -78,7 +78,7 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
     except Timeout:
         logger.info("Another running instance of freqtrade Hyperopt detected.")
         logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
-                    "Hyperopt module is resource hungry. Please run your Hyperopts sequentially "
+                    "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
                     "or on separate machines.")
         logger.info("Quitting now.")
         # TODO: return False here in order to help freqtrade to exit

freqtrade/optimize/backtesting.py

@@ -10,18 +10,19 @@ from pathlib import Path
 from typing import Any, Dict, List, NamedTuple, Optional
 
 from pandas import DataFrame
+from tabulate import tabulate
 
 from freqtrade import OperationalException
-from freqtrade.configuration import TimeRange
+from freqtrade.configuration import (TimeRange, remove_credentials,
+                                     validate_config_consistency)
 from freqtrade.data import history
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.misc import file_dump_json
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
 from freqtrade.strategy.interface import IStrategy, SellType
-from tabulate import tabulate
 
 logger = logging.getLogger(__name__)
 
@@ -57,11 +58,7 @@ class Backtesting:
         self.config = config
 
         # Reset keys for backtesting
-        self.config['exchange']['key'] = ''
+        remove_credentials(self.config)
-        self.config['exchange']['secret'] = ''
-        self.config['exchange']['password'] = ''
-        self.config['exchange']['uid'] = ''
-        self.config['dry_run'] = True
         self.strategylist: List[IStrategy] = []
         self.exchange = ExchangeResolver(self.config['exchange']['name'], self.config).exchange
 
@@ -79,17 +76,21 @@ class Backtesting:
                 stratconf = deepcopy(self.config)
                 stratconf['strategy'] = strat
                 self.strategylist.append(StrategyResolver(stratconf).strategy)
+                validate_config_consistency(stratconf)
 
         else:
             # No strategy list specified, only one strategy
             self.strategylist.append(StrategyResolver(self.config).strategy)
+            validate_config_consistency(self.config)
 
         if "ticker_interval" not in self.config:
             raise OperationalException("Ticker-interval needs to be set in either configuration "
                                        "or as cli argument `--ticker-interval 5m`")
-        self.ticker_interval = str(self.config.get('ticker_interval'))
+        self.timeframe = str(self.config.get('ticker_interval'))
-        self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
+        self.timeframe_mins = timeframe_to_minutes(self.timeframe)
 
+        # Get maximum required startup period
+        self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
         # Load one (first) strategy
         self._set_strategy(self.strategylist[0])
 
@@ -103,6 +104,31 @@ class Backtesting:
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
 
+    def load_bt_data(self):
+        timerange = TimeRange.parse_timerange(None if self.config.get(
+            'timerange') is None else str(self.config.get('timerange')))
+
+        data = history.load_data(
+            datadir=Path(self.config['datadir']),
+            pairs=self.config['exchange']['pair_whitelist'],
+            timeframe=self.timeframe,
+            timerange=timerange,
+            startup_candles=self.required_startup,
+            fail_without_data=True,
+        )
+
+        min_date, max_date = history.get_timeframe(data)
+
+        logger.info(
+            'Loading data from %s up to %s (%s days)..',
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
+        )
+        # Adjust startts forward if not enough data is available
+        timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
+                                            self.required_startup, min_date)
+
+        return data, timerange
+
     def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
                              skip_nan: bool = False) -> str:
         """
@@ -218,7 +244,8 @@ class Backtesting:
         ticker: Dict = {}
         # Create ticker dict
         for pair, pair_data in processed.items():
-            pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+            pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
+            pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
 
             ticker_data = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -351,7 +378,7 @@ class Backtesting:
         lock_pair_until: Dict = {}
         # Indexes per pair, so some pairs are allowed to have a missing start.
         indexes: Dict = {}
-        tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
+        tmp = start_date + timedelta(minutes=self.timeframe_mins)
 
         # Loop timerange and get candle for each pair at that point in time
         while tmp < end_date:
@@ -403,7 +430,7 @@ class Backtesting:
                     lock_pair_until[pair] = end_date.datetime
 
             # Move time one configured time_interval ahead.
-            tmp += timedelta(minutes=self.ticker_interval_mins)
+            tmp += timedelta(minutes=self.timeframe_mins)
         return DataFrame.from_records(trades, columns=BacktestResult._fields)
 
     def start(self) -> None:
@@ -412,39 +439,18 @@ class Backtesting:
         :return: None
         """
         data: Dict[str, Any] = {}
-        pairs = self.config['exchange']['pair_whitelist']
         logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
         logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
-
-        timerange = TimeRange.parse_timerange(None if self.config.get(
-            'timerange') is None else str(self.config.get('timerange')))
-        data = history.load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=pairs,
-            ticker_interval=self.ticker_interval,
-            timerange=timerange,
-        )
-
-        if not data:
-            logger.critical("No data found. Terminating.")
-            return
         # Use max_open_trades in backtesting, except --disable-max-market-positions is set
         if self.config.get('use_max_market_positions', True):
             max_open_trades = self.config['max_open_trades']
         else:
             logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
             max_open_trades = 0
+
+        data, timerange = self.load_bt_data()
+
         all_results = {}
-
-        min_date, max_date = history.get_timeframe(data)
-
-        logger.info(
-            'Backtesting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
-            max_date.isoformat(),
-            (max_date - min_date).days
-        )
-
         for strat in self.strategylist:
             logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
             self._set_strategy(strat)
@@ -452,6 +458,15 @@ class Backtesting:
             # need to reprocess data every time to populate signals
             preprocessed = self.strategy.tickerdata_to_dataframe(data)
 
+            # Trim startup period from analyzed dataframe
+            for pair, df in preprocessed.items():
+                preprocessed[pair] = history.trim_dataframe(df, timerange)
+            min_date, max_date = history.get_timeframe(preprocessed)
+
+            logger.info(
+                'Backtesting with data from %s up to %s (%s days)..',
+                min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
+            )
             # Execute backtest and print results
             all_results[self.strategy.get_strategy_name()] = self.backtest(
                 {

freqtrade/optimize/edge_cli.py

@@ -4,12 +4,14 @@
 This module contains the edge backtesting interface
 """
 import logging
-from typing import Dict, Any
+from typing import Any, Dict
-from tabulate import tabulate
-from freqtrade import constants
-from freqtrade.edge import Edge
 
-from freqtrade.configuration import TimeRange
+from tabulate import tabulate
+
+from freqtrade import constants
+from freqtrade.configuration import (TimeRange, remove_credentials,
+                                     validate_config_consistency)
+from freqtrade.edge import Edge
 from freqtrade.exchange import Exchange
 from freqtrade.resolvers import StrategyResolver
 
@@ -29,15 +31,13 @@ class EdgeCli:
         self.config = config
 
         # Reset keys for edge
-        self.config['exchange']['key'] = ''
+        remove_credentials(self.config)
-        self.config['exchange']['secret'] = ''
-        self.config['exchange']['password'] = ''
-        self.config['exchange']['uid'] = ''
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
-        self.config['dry_run'] = True
         self.exchange = Exchange(self.config)
         self.strategy = StrategyResolver(self.config).strategy
 
+        validate_config_consistency(self.config)
+
         self.edge = Edge(config, self.exchange, self.strategy)
         # Set refresh_pairs to false for edge-cli (it must be true for edge)
         self.edge._refresh_pairs = False

freqtrade/optimize/hyperopt.py

@@ -4,9 +4,9 @@
 This module contains the hyperopt logic
 """
 
+import locale
 import logging
 import sys
-
 from collections import OrderedDict
 from operator import itemgetter
 from pathlib import Path
@@ -14,23 +14,23 @@ from pprint import pprint
 from typing import Any, Dict, List, Optional
 
 import rapidjson
-
-from colorama import init as colorama_init
 from colorama import Fore, Style
-from joblib import Parallel, delayed, dump, load, wrap_non_picklable_objects, cpu_count
+from colorama import init as colorama_init
+from joblib import (Parallel, cpu_count, delayed, dump, load,
+                    wrap_non_picklable_objects)
 from pandas import DataFrame
 from skopt import Optimizer
 from skopt.space import Dimension
 
-from freqtrade.configuration import TimeRange
+from freqtrade import OperationalException
-from freqtrade.data.history import load_data, get_timeframe
+from freqtrade.data.history import get_timeframe, trim_dataframe
-from freqtrade.misc import round_dict
+from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
 from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F4
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F4
-from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
+from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
-
+                                                   HyperOptResolver)
 
 logger = logging.getLogger(__name__)
 
@@ -55,10 +55,10 @@ class Hyperopt:
     def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
-        self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
-
         self.backtesting = Backtesting(self.config)
 
+        self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
+
         self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
 
@@ -75,6 +75,8 @@ class Hyperopt:
         else:
             logger.info("Continuing on previous hyperopt results.")
 
+        self.num_trials_saved = 0
+
         # Previous evaluations
         self.trials: List = []
 
@@ -103,6 +105,10 @@ class Hyperopt:
                 self.config['ask_strategy'] = {}
             self.config['ask_strategy']['use_sell_signal'] = True
 
+        self.print_all = self.config.get('print_all', False)
+        self.print_colorized = self.config.get('print_colorized', False)
+        self.print_json = self.config.get('print_json', False)
+
     @staticmethod
     def get_lock_filename(config) -> str:
 
@@ -118,125 +124,179 @@ class Hyperopt:
                 logger.info(f"Removing `{p}`.")
                 p.unlink()
 
-    def get_args(self, params):
+    def _get_params_dict(self, raw_params: List[Any]) -> Dict:
 
-        dimensions = self.dimensions
+        dimensions: List[Dimension] = self.dimensions
 
         # Ensure the number of dimensions match
-        # the number of parameters in the list x.
+        # the number of parameters in the list.
-        if len(params) != len(dimensions):
+        if len(raw_params) != len(dimensions):
-            raise ValueError('Mismatch in number of search-space dimensions. '
+            raise ValueError('Mismatch in number of search-space dimensions.')
-                             f'len(dimensions)=={len(dimensions)} and len(x)=={len(params)}')
 
-        # Create a dict where the keys are the names of the dimensions
+        # Return a dict where the keys are the names of the dimensions
-        # and the values are taken from the list of parameters x.
+        # and the values are taken from the list of parameters.
-        arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
+        return {d.name: v for d, v in zip(dimensions, raw_params)}
-        return arg_dict
 
-    def save_trials(self) -> None:
+    def save_trials(self, final: bool = False) -> None:
         """
         Save hyperopt trials to file
         """
-        if self.trials:
+        num_trials = len(self.trials)
-            logger.info("Saving %d evaluations to '%s'", len(self.trials), self.trials_file)
+        if num_trials > self.num_trials_saved:
+            logger.info(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
             dump(self.trials, self.trials_file)
+            self.num_trials_saved = num_trials
+        if final:
+            logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
+                        f"saved to '{self.trials_file}'.")
 
-    def read_trials(self) -> List:
+    @staticmethod
+    def _read_trials(trials_file) -> List:
         """
         Read hyperopt trials file
         """
-        logger.info("Reading Trials from '%s'", self.trials_file)
+        logger.info("Reading Trials from '%s'", trials_file)
-        trials = load(self.trials_file)
+        trials = load(trials_file)
-        self.trials_file.unlink()
         return trials
 
-    def log_trials_result(self) -> None:
+    def _get_params_details(self, params: Dict) -> Dict:
         """
-        Display Best hyperopt result
+        Return the params for each space
         """
-        results = sorted(self.trials, key=itemgetter('loss'))
+        result: Dict = {}
-        best_result = results[0]
-        params = best_result['params']
-        log_str = self.format_results_logstring(best_result)
-        print(f"\nBest result:\n\n{log_str}\n")
 
-        if self.config.get('print_json'):
-            result_dict: Dict = {}
-            if self.has_space('buy') or self.has_space('sell'):
-                result_dict['params'] = {}
         if self.has_space('buy'):
-                result_dict['params'].update({p.name: params.get(p.name)
+            result['buy'] = {p.name: params.get(p.name)
-                                             for p in self.hyperopt_space('buy')})
+                             for p in self.hyperopt_space('buy')}
         if self.has_space('sell'):
-                result_dict['params'].update({p.name: params.get(p.name)
+            result['sell'] = {p.name: params.get(p.name)
-                                             for p in self.hyperopt_space('sell')})
+                              for p in self.hyperopt_space('sell')}
         if self.has_space('roi'):
+            result['roi'] = self.custom_hyperopt.generate_roi_table(params)
+        if self.has_space('stoploss'):
+            result['stoploss'] = {p.name: params.get(p.name)
+                                  for p in self.hyperopt_space('stoploss')}
+        if self.has_space('trailing'):
+            result['trailing'] = {p.name: params.get(p.name)
+                                  for p in self.hyperopt_space('trailing')}
+
+        return result
+
+    @staticmethod
+    def print_epoch_details(results, total_epochs, print_json: bool,
+                            no_header: bool = False, header_str: str = None) -> None:
+        """
+        Display details of the hyperopt result
+        """
+        params = results.get('params_details', {})
+
+        # Default header string
+        if header_str is None:
+            header_str = "Best result"
+
+        if not no_header:
+            explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
+            print(f"\n{header_str}:\n\n{explanation_str}\n")
+
+        if print_json:
+            result_dict: Dict = {}
+            for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
+                Hyperopt._params_update_for_json(result_dict, params, s)
+            print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
+
+        else:
+            Hyperopt._params_pretty_print(params, 'buy', "Buy hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'sell', "Sell hyperspace params:")
+            Hyperopt._params_pretty_print(params, 'roi', "ROI table:")
+            Hyperopt._params_pretty_print(params, 'stoploss', "Stoploss:")
+            Hyperopt._params_pretty_print(params, 'trailing', "Trailing stop:")
+
+    @staticmethod
+    def _params_update_for_json(result_dict, params, space: str):
+        if space in params:
+            space_params = Hyperopt._space_params(params, space)
+            if space in ['buy', 'sell']:
+                result_dict.setdefault('params', {}).update(space_params)
+            elif space == 'roi':
                 # Convert keys in min_roi dict to strings because
                 # rapidjson cannot dump dicts with integer keys...
                 # OrderedDict is used to keep the numeric order of the items
                 # in the dict.
                 result_dict['minimal_roi'] = OrderedDict(
-                    (str(k), v) for k, v in self.custom_hyperopt.generate_roi_table(params).items()
+                    (str(k), v) for k, v in space_params.items()
                 )
-            if self.has_space('stoploss'):
+            else:  # 'stoploss', 'trailing'
-                result_dict['stoploss'] = params.get('stoploss')
+                result_dict.update(space_params)
-            print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
-        else:
-            if self.has_space('buy'):
-                print('Buy hyperspace params:')
-                pprint({p.name: params.get(p.name) for p in self.hyperopt_space('buy')},
-                       indent=4)
-            if self.has_space('sell'):
-                print('Sell hyperspace params:')
-                pprint({p.name: params.get(p.name) for p in self.hyperopt_space('sell')},
-                       indent=4)
-            if self.has_space('roi'):
-                print("ROI table:")
-                # Round printed values to 5 digits after the decimal point
-                pprint(round_dict(self.custom_hyperopt.generate_roi_table(params), 5), indent=4)
-            if self.has_space('stoploss'):
-                # Also round to 5 digits after the decimal point
-                print(f"Stoploss: {round(params.get('stoploss'), 5)}")
 
-    def log_results(self, results) -> None:
+    @staticmethod
+    def _params_pretty_print(params, space: str, header: str):
+        if space in params:
+            space_params = Hyperopt._space_params(params, space, 5)
+            if space == 'stoploss':
+                print(header, space_params.get('stoploss'))
+            else:
+                print(header)
+                pprint(space_params, indent=4)
+
+    @staticmethod
+    def _space_params(params, space: str, r: int = None) -> Dict:
+        d = params[space]
+        # Round floats to `r` digits after the decimal point if requested
+        return round_dict(d, r) if r else d
+
+    @staticmethod
+    def is_best_loss(results, current_best_loss) -> bool:
+        return results['loss'] < current_best_loss
+
+    def print_results(self, results) -> None:
         """
         Log results if it is better than any previous evaluation
         """
-        print_all = self.config.get('print_all', False)
+        is_best = results['is_best']
-        is_best_loss = results['loss'] < self.current_best_loss
+        if not self.print_all:
-        if print_all or is_best_loss:
+            # Print '\n' after each 100th epoch to separate dots from the log messages.
-            if is_best_loss:
+            # Otherwise output is messy on a terminal.
-                self.current_best_loss = results['loss']
+            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
-            log_str = self.format_results_logstring(results)
-            # Colorize output
-            if self.config.get('print_colorized', False):
-                if results['total_profit'] > 0:
-                    log_str = Fore.GREEN + log_str
-                if print_all and is_best_loss:
-                    log_str = Style.BRIGHT + log_str
-            if print_all:
-                print(log_str)
-            else:
-                print('\n' + log_str)
-        else:
-            print('.', end='')
             sys.stdout.flush()
 
-    def format_results_logstring(self, results) -> str:
+        if self.print_all or is_best:
-        # Output human-friendly index here (starting from 1)
+            if not self.print_all:
-        current = results['current_epoch'] + 1
+                # Separate the results explanation string from dots
-        total = self.total_epochs
+                print("\n")
-        res = results['results_explanation']
+            self.print_results_explanation(results, self.total_epochs, self.print_all,
-        loss = results['loss']
+                                           self.print_colorized)
-        log_str = f'{current:5d}/{total}: {res} Objective: {loss:.5f}'
+
-        log_str = f'*{log_str}' if results['is_initial_point'] else f' {log_str}'
+    @staticmethod
-        return log_str
+    def print_results_explanation(results, total_epochs, highlight_best: bool,
+                                  print_colorized: bool) -> None:
+        """
+        Log results explanation string
+        """
+        explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
+        # Colorize output
+        if print_colorized:
+            if results['total_profit'] > 0:
+                explanation_str = Fore.GREEN + explanation_str
+            if highlight_best and results['is_best']:
+                explanation_str = Style.BRIGHT + explanation_str
+        print(explanation_str)
+
+    @staticmethod
+    def _format_explanation_string(results, total_epochs) -> str:
+        return (("*" if results['is_initial_point'] else " ") +
+                f"{results['current_epoch']:5d}/{total_epochs}: " +
+                f"{results['results_explanation']} " +
+                f"Objective: {results['loss']:.5f}")
 
     def has_space(self, space: str) -> bool:
         """
-        Tell if a space value is contained in the configuration
+        Tell if the space value is contained in the configuration
         """
+        # The 'trailing' space is not included in the 'default' set of spaces
+        if space == 'trailing':
             return any(s in self.config['spaces'] for s in [space, 'all'])
+        else:
+            return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
 
     def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
         """
@@ -246,47 +306,66 @@ class Hyperopt:
         for all hyperspaces used.
         """
         spaces: List[Dimension] = []
+
         if space == 'buy' or (space is None and self.has_space('buy')):
             logger.debug("Hyperopt has 'buy' space")
             spaces += self.custom_hyperopt.indicator_space()
+
         if space == 'sell' or (space is None and self.has_space('sell')):
             logger.debug("Hyperopt has 'sell' space")
             spaces += self.custom_hyperopt.sell_indicator_space()
+
         if space == 'roi' or (space is None and self.has_space('roi')):
             logger.debug("Hyperopt has 'roi' space")
             spaces += self.custom_hyperopt.roi_space()
+
         if space == 'stoploss' or (space is None and self.has_space('stoploss')):
             logger.debug("Hyperopt has 'stoploss' space")
             spaces += self.custom_hyperopt.stoploss_space()
+
+        if space == 'trailing' or (space is None and self.has_space('trailing')):
+            logger.debug("Hyperopt has 'trailing' space")
+            spaces += self.custom_hyperopt.trailing_space()
+
         return spaces
 
-    def generate_optimizer(self, _params: Dict, iteration=None) -> Dict:
+    def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function. Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
-        params = self.get_args(_params)
+        params_dict = self._get_params_dict(raw_params)
+        params_details = self._get_params_details(params_dict)
 
         if self.has_space('roi'):
             self.backtesting.strategy.minimal_roi = \
-                    self.custom_hyperopt.generate_roi_table(params)
+                    self.custom_hyperopt.generate_roi_table(params_dict)
 
         if self.has_space('buy'):
             self.backtesting.strategy.advise_buy = \
-                    self.custom_hyperopt.buy_strategy_generator(params)
+                    self.custom_hyperopt.buy_strategy_generator(params_dict)
 
         if self.has_space('sell'):
             self.backtesting.strategy.advise_sell = \
-                    self.custom_hyperopt.sell_strategy_generator(params)
+                    self.custom_hyperopt.sell_strategy_generator(params_dict)
 
         if self.has_space('stoploss'):
-            self.backtesting.strategy.stoploss = params['stoploss']
+            self.backtesting.strategy.stoploss = params_dict['stoploss']
+
+        if self.has_space('trailing'):
+            self.backtesting.strategy.trailing_stop = params_dict['trailing_stop']
+            self.backtesting.strategy.trailing_stop_positive = \
+                params_dict['trailing_stop_positive']
+            self.backtesting.strategy.trailing_stop_positive_offset = \
+                params_dict['trailing_stop_positive_offset']
+            self.backtesting.strategy.trailing_only_offset_is_reached = \
+                params_dict['trailing_only_offset_is_reached']
 
         processed = load(self.tickerdata_pickle)
 
         min_date, max_date = get_timeframe(processed)
 
-        results = self.backtesting.backtest(
+        backtesting_results = self.backtesting.backtest(
             {
                 'stake_amount': self.config['stake_amount'],
                 'processed': processed,
@@ -296,56 +375,63 @@ class Hyperopt:
                 'end_date': max_date,
             }
         )
-        results_explanation = self.format_results(results)
+        return self._get_results_dict(backtesting_results, min_date, max_date,
+                                      params_dict, params_details)
 
-        trade_count = len(results.index)
+    def _get_results_dict(self, backtesting_results, min_date, max_date,
-        total_profit = results.profit_abs.sum()
+                          params_dict, params_details):
+        results_metrics = self._calculate_results_metrics(backtesting_results)
+        results_explanation = self._format_results_explanation_string(results_metrics)
+
+        trade_count = results_metrics['trade_count']
+        total_profit = results_metrics['total_profit']
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
         # in order to cast this hyperspace point away from optimization
         # path. We do not want to optimize 'hodl' strategies.
-        if trade_count < self.config['hyperopt_min_trades']:
+        loss: float = MAX_LOSS
-            return {
+        if trade_count >= self.config['hyperopt_min_trades']:
-                'loss': MAX_LOSS,
+            loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
-                'params': params,
-                'results_explanation': results_explanation,
-                'total_profit': total_profit,
-            }
-
-        loss = self.calculate_loss(results=results, trade_count=trade_count,
                                        min_date=min_date.datetime, max_date=max_date.datetime)
-
         return {
             'loss': loss,
-            'params': params,
+            'params_dict': params_dict,
+            'params_details': params_details,
+            'results_metrics': results_metrics,
             'results_explanation': results_explanation,
             'total_profit': total_profit,
         }
 
-    def format_results(self, results: DataFrame) -> str:
+    def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
+        return {
+            'trade_count': len(backtesting_results.index),
+            'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
+            'total_profit': backtesting_results.profit_abs.sum(),
+            'profit': backtesting_results.profit_percent.sum() * 100.0,
+            'duration': backtesting_results.trade_duration.mean(),
+        }
+
+    def _format_results_explanation_string(self, results_metrics: Dict) -> str:
         """
         Return the formatted results explanation in a string
         """
-        trades = len(results.index)
-        avg_profit = results.profit_percent.mean() * 100.0
-        total_profit = results.profit_abs.sum()
         stake_cur = self.config['stake_currency']
-        profit = results.profit_percent.sum() * 100.0
+        return (f"{results_metrics['trade_count']:6d} trades. "
-        duration = results.trade_duration.mean()
+                f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
+                f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
+                f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
+                f"Avg duration {results_metrics['duration']:5.1f} mins."
+                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
 
-        return (f'{trades:6d} trades. Avg profit {avg_profit: 5.2f}%. '
+    def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
-                f'Total profit {total_profit: 11.8f} {stake_cur} '
-                f'({profit: 7.2f}Σ%). Avg duration {duration:5.1f} mins.')
-
-    def get_optimizer(self, dimensions, cpu_count) -> Optimizer:
         return Optimizer(
             dimensions,
             base_estimator="ET",
             acq_optimizer="auto",
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
-            random_state=self.config.get('hyperopt_random_state', None)
+            random_state=self.config.get('hyperopt_random_state', None),
         )
 
     def fix_optimizer_models_list(self):
@@ -369,56 +455,51 @@ class Hyperopt:
         return parallel(delayed(
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
-    def load_previous_results(self):
+    @staticmethod
-        """ read trials file if we have one """
+    def load_previous_results(trials_file) -> List:
-        if self.trials_file.is_file() and self.trials_file.stat().st_size > 0:
+        """
-            self.trials = self.read_trials()
+        Load data for epochs from the file if we have one
-            logger.info(
+        """
-                'Loaded %d previous evaluations from disk.',
+        trials: List = []
-                len(self.trials)
+        if trials_file.is_file() and trials_file.stat().st_size > 0:
-            )
+            trials = Hyperopt._read_trials(trials_file)
+            if trials[0].get('is_best') is None:
+                raise OperationalException(
+                        "The file with Hyperopt results is incompatible with this version "
+                        "of Freqtrade and cannot be loaded.")
+            logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
+        return trials
 
     def start(self) -> None:
-        timerange = TimeRange.parse_timerange(None if self.config.get(
+        data, timerange = self.backtesting.load_bt_data()
-            'timerange') is None else str(self.config.get('timerange')))
-        data = load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=self.config['exchange']['pair_whitelist'],
-            ticker_interval=self.backtesting.ticker_interval,
-            timerange=timerange
-        )
 
-        if not data:
+        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
-            logger.critical("No data found. Terminating.")
-            return
 
+        # Trim startup period from analyzed dataframe
+        for pair, df in preprocessed.items():
+            preprocessed[pair] = trim_dataframe(df, timerange)
         min_date, max_date = get_timeframe(data)
 
         logger.info(
             'Hyperopting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
-            max_date.isoformat(),
-            (max_date - min_date).days
         )
-
-        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
-
         dump(preprocessed, self.tickerdata_pickle)
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
 
-        self.load_previous_results()
+        self.trials = self.load_previous_results(self.trials_file)
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
         config_jobs = self.config.get('hyperopt_jobs', -1)
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
-        self.dimensions = self.hyperopt_space()
+        self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
 
-        if self.config.get('print_colorized', False):
+        if self.print_colorized:
             colorama_init(autoreset=True)
 
         try:
@@ -432,15 +513,38 @@ class Hyperopt:
                     self.opt.tell(asked, [v['loss'] for v in f_val])
                     self.fix_optimizer_models_list()
                     for j in range(jobs):
-                        current = i * jobs + j
+                        # Use human-friendly indexes here (starting from 1)
+                        current = i * jobs + j + 1
                         val = f_val[j]
                         val['current_epoch'] = current
-                        val['is_initial_point'] = current < INITIAL_POINTS
+                        val['is_initial_point'] = current <= INITIAL_POINTS
-                        self.log_results(val)
-                        self.trials.append(val)
                         logger.debug(f"Optimizer epoch evaluated: {val}")
+
+                        is_best = self.is_best_loss(val, self.current_best_loss)
+                        # This value is assigned here and not in the optimization method
+                        # to keep proper order in the list of results. That's because
+                        # evaluations can take different time. Here they are aligned in the
+                        # order they will be shown to the user.
+                        val['is_best'] = is_best
+
+                        self.print_results(val)
+
+                        if is_best:
+                            self.current_best_loss = val['loss']
+                        self.trials.append(val)
+                        # Save results after each best epoch and every 100 epochs
+                        if is_best or current % 100 == 0:
+                            self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self.save_trials()
+        self.save_trials(final=True)
-        self.log_trials_result()
+
+        if self.trials:
+            sorted_trials = sorted(self.trials, key=itemgetter('loss'))
+            results = sorted_trials[0]
+            self.print_epoch_details(results, self.total_epochs, self.print_json)
+        else:
+            # This is printed when Ctrl+C is pressed quickly, before first epochs have
+            # a chance to be evaluated.
+            print("No epochs evaluated yet, no best result.")

freqtrade/optimize/hyperopt_interface.py

@@ -1,15 +1,14 @@
 """
 IHyperOpt interface
-This module defines the interface to apply for hyperopts
+This module defines the interface to apply for hyperopt
 """
 import logging
 import math
 
-from abc import ABC, abstractmethod
+from abc import ABC
 from typing import Dict, Any, Callable, List
 
-from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real
-from skopt.space import Dimension, Integer, Real
 
 from freqtrade import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
@@ -28,8 +27,8 @@ def _format_exception_message(method: str, space: str) -> str:
 
 class IHyperOpt(ABC):
     """
-    Interface for freqtrade hyperopts
+    Interface for freqtrade hyperopt
-    Defines the mandatory structure must follow any custom hyperopts
+    Defines the mandatory structure must follow any custom hyperopt
 
     Class attributes you can use:
         ticker_interval -> int: value of the ticker interval to use for the strategy
@@ -42,15 +41,6 @@ class IHyperOpt(ABC):
         # Assign ticker_interval to be used in hyperopt
         IHyperOpt.ticker_interval = str(config['ticker_interval'])
 
-    @staticmethod
-    @abstractmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Populate indicators that will be used in the Buy and Sell strategy.
-        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe().
-        :return: A Dataframe with all mandatory indicators for the strategies.
-        """
-
     @staticmethod
     def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
         """
@@ -116,10 +106,10 @@ class IHyperOpt(ABC):
         roi_t_alpha = 1.0
         roi_p_alpha = 1.0
 
-        ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
+        timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
 
         # We define here limits for the ROI space parameters automagically adapted to the
-        # ticker_interval used by the bot:
+        # timeframe used by the bot:
         #
         # * 'roi_t' (limits for the time intervals in the ROI tables) components
         #   are scaled linearly.
@@ -127,8 +117,8 @@ class IHyperOpt(ABC):
         #
         # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
         # method for the 5m ticker interval.
-        roi_t_scale = ticker_interval_mins / 5
+        roi_t_scale = timeframe_mins / 5
-        roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
+        roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
         roi_limits = {
             'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
             'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),
@@ -184,6 +174,27 @@ class IHyperOpt(ABC):
             Real(-0.35, -0.02, name='stoploss'),
         ]
 
+    @staticmethod
+    def trailing_space() -> List[Dimension]:
+        """
+        Create a trailing stoploss space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
+            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
+            # trailing_stop is set False.
+            # This parameter is included into the hyperspace dimensions rather than assigning
+            # it explicitly in the code in order to have it printed in the results along with
+            # other 'trailing' hyperspace parameters.
+            Categorical([True], name='trailing_stop'),
+
+            Real(0.02, 0.35, name='trailing_stop_positive'),
+            Real(0.01, 0.1, name='trailing_stop_positive_offset'),
+            Categorical([True, False], name='trailing_only_offset_is_reached'),
+        ]
+
     # This is needed for proper unpickling the class attribute ticker_interval
     # which is set to the actual value by the resolver.
     # Why do I still need such shamanic mantras in modern python?

freqtrade/optimize/hyperopt_loss_interface.py

@@ -1,6 +1,6 @@
 """
 IHyperOptLoss interface
-This module defines the interface for the loss-function for hyperopts
+This module defines the interface for the loss-function for hyperopt
 """
 
 from abc import ABC, abstractmethod
@@ -11,7 +11,7 @@ from pandas import DataFrame
 
 class IHyperOptLoss(ABC):
     """
-    Interface for freqtrade hyperopts Loss functions.
+    Interface for freqtrade hyperopt Loss functions.
     Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
     """
     ticker_interval: str

freqtrade/pairlist/IPairList.py

@@ -5,22 +5,31 @@ Provides lists as configured in config.json
 
  """
 import logging
-from abc import ABC, abstractmethod
+from abc import ABC, abstractmethod, abstractproperty
-from typing import List
+from copy import deepcopy
+from typing import Dict, List
 
 from freqtrade.exchange import market_is_active
 
-
 logger = logging.getLogger(__name__)
 
 
 class IPairList(ABC):
 
-    def __init__(self, freqtrade, config: dict) -> None:
+    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
-        self._freqtrade = freqtrade
+                 pairlist_pos: int) -> None:
+        """
+        :param exchange: Exchange instance
+        :param pairlistmanager: Instanciating Pairlist manager
+        :param config: Global bot configuration
+        :param pairlistconfig: Configuration for this pairlist - can be empty.
+        :param pairlist_pos: Position of the filter in the pairlist-filter-list
+        """
+        self._exchange = exchange
+        self._pairlistmanager = pairlistmanager
         self._config = config
-        self._whitelist = self._config['exchange']['pair_whitelist']
+        self._pairlistconfig = pairlistconfig
-        self._blacklist = self._config['exchange'].get('pair_blacklist', [])
+        self._pairlist_pos = pairlist_pos
 
     @property
     def name(self) -> str:
@@ -30,21 +39,13 @@ class IPairList(ABC):
         """
         return self.__class__.__name__
 
-    @property
+    @abstractproperty
-    def whitelist(self) -> List[str]:
+    def needstickers(self) -> bool:
         """
-        Has the current whitelist
+        Boolean property defining if tickers are necessary.
-        -> no need to overwrite in subclasses
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
         """
-        return self._whitelist
-
-    @property
-    def blacklist(self) -> List[str]:
-        """
-        Has the current blacklist
-        -> no need to overwrite in subclasses
-        """
-        return self._blacklist
 
     @abstractmethod
     def short_desc(self) -> str:
@@ -54,36 +55,62 @@ class IPairList(ABC):
         """
 
     @abstractmethod
-    def refresh_pairlist(self) -> None:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
-        Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
         -> Please overwrite in subclasses
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
         """
 
-    def _validate_whitelist(self, whitelist: List[str]) -> List[str]:
+    @staticmethod
+    def verify_blacklist(pairlist: List[str], blacklist: List[str]) -> List[str]:
+        """
+        Verify and remove items from pairlist - returning a filtered pairlist.
+        """
+        for pair in deepcopy(pairlist):
+            if pair in blacklist:
+                logger.warning(f"Pair {pair} in your blacklist. Removing it from whitelist...")
+                pairlist.remove(pair)
+        return pairlist
+
+    def _verify_blacklist(self, pairlist: List[str]) -> List[str]:
+        """
+        Proxy method to verify_blacklist for easy access for child classes.
+        """
+        return IPairList.verify_blacklist(pairlist, self._pairlistmanager.blacklist)
+
+    def _whitelist_for_active_markets(self, pairlist: List[str]) -> List[str]:
         """
         Check available markets and remove pair from whitelist if necessary
         :param whitelist: the sorted list of pairs the user might want to trade
         :return: the list of pairs the user wants to trade without those unavailable or
         black_listed
         """
-        markets = self._freqtrade.exchange.markets
+        markets = self._exchange.markets
 
-        sanitized_whitelist = set()
+        sanitized_whitelist: List[str] = []
-        for pair in whitelist:
+        for pair in pairlist:
-            # pair is not in the generated dynamic market, or in the blacklist ... ignore it
+            # pair is not in the generated dynamic market or has the wrong stake currency
-            if (pair in self.blacklist or pair not in markets
+            if pair not in markets:
-                    or not pair.endswith(self._config['stake_currency'])):
                 logger.warning(f"Pair {pair} is not compatible with exchange "
-                               f"{self._freqtrade.exchange.name} or contained in "
+                               f"{self._exchange.name}. Removing it from whitelist..")
-                               f"your blacklist. Removing it from whitelist..")
                 continue
+            if not pair.endswith(self._config['stake_currency']):
+                logger.warning(f"Pair {pair} is not compatible with your stake currency "
+                               f"{self._config['stake_currency']}. Removing it from whitelist..")
+                continue
+
             # Check if market is active
             market = markets[pair]
             if not market_is_active(market):
                 logger.info(f"Ignoring {pair} from whitelist. Market is not active.")
                 continue
-            sanitized_whitelist.add(pair)
+            if pair not in sanitized_whitelist:
+                sanitized_whitelist.append(pair)
 
+        sanitized_whitelist = self._verify_blacklist(sanitized_whitelist)
         # We need to remove pairs that are unknown
-        return list(sanitized_whitelist)
+        return sanitized_whitelist

freqtrade/pairlist/PrecisionFilter.py

@@ -0,0 +1,63 @@
+import logging
+from copy import deepcopy
+from typing import Dict, List
+
+from freqtrade.pairlist.IPairList import IPairList
+
+logger = logging.getLogger(__name__)
+
+
+class PrecisionFilter(IPairList):
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - Filtering untradable pairs."
+
+    def _validate_precision_filter(self, ticker: dict, stoploss: float) -> bool:
+        """
+        Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
+        low value pairs.
+        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param stoploss: stoploss value as set in the configuration
+                        (already cleaned to be 1 - stoploss)
+        :return: True if the pair can stay, false if it should be removed
+        """
+        stop_price = ticker['ask'] * stoploss
+        # Adjust stop-prices to precision
+        sp = self._exchange.symbol_price_prec(ticker["symbol"], stop_price)
+        stop_gap_price = self._exchange.symbol_price_prec(ticker["symbol"], stop_price * 0.99)
+        logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
+        if sp <= stop_gap_price:
+            logger.info(f"Removed {ticker['symbol']} from whitelist, "
+                        f"because stop price {sp} would be <= stop limit {stop_gap_price}")
+            return False
+        return True
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+        """
+        Filters and sorts pairlists and assigns and returns them again.
+        """
+        stoploss = None
+        if self._config.get('stoploss') is not None:
+            # Precalculate sanitized stoploss value to avoid recalculation for every pair
+            stoploss = 1 - abs(self._config.get('stoploss'))
+        # Copy list since we're modifying this list
+        for p in deepcopy(pairlist):
+            ticker = tickers.get(p)
+            # Filter out assets which would not allow setting a stoploss
+            if not ticker or (stoploss and not self._validate_precision_filter(ticker, stoploss)):
+                pairlist.remove(p)
+                continue
+
+        return pairlist

freqtrade/pairlist/PriceFilter.py

@@ -0,0 +1,69 @@
+import logging
+from copy import deepcopy
+from typing import Dict, List
+
+from freqtrade.pairlist.IPairList import IPairList
+
+logger = logging.getLogger(__name__)
+
+
+class PriceFilter(IPairList):
+
+    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        self._low_price_ratio = pairlistconfig.get('low_price_ratio', 0)
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - Filtering pairs priced below {self._low_price_ratio * 100}%."
+
+    def _validate_ticker_lowprice(self, ticker) -> bool:
+        """
+        Check if if one price-step (pip) is > than a certain barrier.
+        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param precision: Precision
+        :return: True if the pair can stay, false if it should be removed
+        """
+        precision = self._exchange.markets[ticker['symbol']]['precision']['price']
+
+        compare = ticker['last'] + 1 / pow(10, precision)
+        changeperc = (compare - ticker['last']) / ticker['last']
+        if changeperc > self._low_price_ratio:
+            logger.info(f"Removed {ticker['symbol']} from whitelist, "
+                        f"because 1 unit is {changeperc * 100:.3f}%")
+            return False
+        return True
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
+        """
+        # Copy list since we're modifying this list
+        for p in deepcopy(pairlist):
+            ticker = tickers.get(p)
+            if not ticker:
+                pairlist.remove(p)
+
+            # Filter out assets which would not allow setting a stoploss
+            if self._low_price_ratio and not self._validate_ticker_lowprice(ticker):
+                pairlist.remove(p)
+
+        return pairlist

freqtrade/pairlist/StaticPairList.py

@@ -5,6 +5,7 @@ Provides lists as configured in config.json
 
  """
 import logging
+from typing import Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
@@ -13,18 +14,28 @@ logger = logging.getLogger(__name__)
 
 class StaticPairList(IPairList):
 
-    def __init__(self, freqtrade, config: dict) -> None:
+    @property
-        super().__init__(freqtrade, config)
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
 
     def short_desc(self) -> str:
         """
         Short whitelist method description - used for startup-messages
         -> Please overwrite in subclasses
         """
-        return f"{self.name}: {self.whitelist}"
+        return f"{self.name}"
 
-    def refresh_pairlist(self) -> None:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
-        Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
         """
-        self._whitelist = self._validate_whitelist(self._config['exchange']['pair_whitelist'])
+        return self._whitelist_for_active_markets(self._config['exchange']['pair_whitelist'])

freqtrade/pairlist/VolumePairList.py

@@ -5,11 +5,12 @@ Provides lists as configured in config.json
 
  """
 import logging
-from typing import List
+from datetime import datetime
-from cachetools import TTLCache, cached
+from typing import Dict, List
 
-from freqtrade.pairlist.IPairList import IPairList
 from freqtrade import OperationalException
+from freqtrade.pairlist.IPairList import IPairList
+
 logger = logging.getLogger(__name__)
 
 SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
@@ -17,18 +18,19 @@ SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
 
 class VolumePairList(IPairList):
 
-    def __init__(self, freqtrade, config: dict) -> None:
+    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
-        super().__init__(freqtrade, config)
+                 pairlist_pos: int) -> None:
-        self._whitelistconf = self._config.get('pairlist', {}).get('config')
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
-        if 'number_assets' not in self._whitelistconf:
+
+        if 'number_assets' not in self._pairlistconfig:
             raise OperationalException(
                 f'`number_assets` not specified. Please check your configuration '
                 'for "pairlist.config.number_assets"')
-        self._number_pairs = self._whitelistconf['number_assets']
+        self._number_pairs = self._pairlistconfig['number_assets']
-        self._sort_key = self._whitelistconf.get('sort_key', 'quoteVolume')
+        self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
-        self._precision_filter = self._whitelistconf.get('precision_filter', False)
+        self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
 
-        if not self._freqtrade.exchange.exchange_has('fetchTickers'):
+        if not self._exchange.exchange_has('fetchTickers'):
             raise OperationalException(
                 'Exchange does not support dynamic whitelist.'
                 'Please edit your config and restart the bot'
@@ -36,6 +38,16 @@ class VolumePairList(IPairList):
         if not self._validate_keys(self._sort_key):
             raise OperationalException(
                 f'key {self._sort_key} not in {SORT_VALUES}')
+        self._last_refresh = 0
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return True
 
     def _validate_keys(self, key):
         return key in SORT_VALUES
@@ -43,54 +55,54 @@ class VolumePairList(IPairList):
     def short_desc(self) -> str:
         """
         Short whitelist method description - used for startup-messages
-        -> Please overwrite in subclasses
         """
-        return f"{self.name} - top {self._whitelistconf['number_assets']} volume pairs."
+        return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
 
-    def refresh_pairlist(self) -> None:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
-        Refreshes pairlists and assigns them to self._whitelist and self._blacklist respectively
+        Filters and sorts pairlist and returns the whitelist again.
-        -> Please overwrite in subclasses
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
         """
         # Generate dynamic whitelist
-        self._whitelist = self._gen_pair_whitelist(
+        if self._last_refresh + self.refresh_period < datetime.now().timestamp():
-            self._config['stake_currency'], self._sort_key)[:self._number_pairs]
+            self._last_refresh = int(datetime.now().timestamp())
+            return self._gen_pair_whitelist(pairlist,
+                                            tickers,
+                                            self._config['stake_currency'],
+                                            self._sort_key,
+                                            )
+        else:
+            return pairlist
 
-    @cached(TTLCache(maxsize=1, ttl=1800))
+    def _gen_pair_whitelist(self, pairlist, tickers, base_currency: str, key: str) -> List[str]:
-    def _gen_pair_whitelist(self, base_currency: str, key: str) -> List[str]:
         """
         Updates the whitelist with with a dynamically generated list
         :param base_currency: base currency as str
         :param key: sort key (defaults to 'quoteVolume')
+        :param tickers: Tickers (from exchange.get_tickers()).
         :return: List of pairs
         """
 
-        tickers = self._freqtrade.exchange.get_tickers()
+        if self._pairlist_pos == 0:
+            # If VolumePairList is the first in the list, use fresh pairlist
             # check length so that we make sure that '/' is actually in the string
-        tickers = [v for k, v in tickers.items()
+            filtered_tickers = [v for k, v in tickers.items()
                                 if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
                                     and v[key] is not None)]
-        sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
+        else:
+            # If other pairlist is in front, use the incomming pairlist.
+            filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
+
+        sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[key])
+
         # Validate whitelist to only have active market pairs
-        valid_pairs = self._validate_whitelist([s['symbol'] for s in sorted_tickers])
+        pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
-        valid_tickers = [t for t in sorted_tickers if t["symbol"] in valid_pairs]
+        pairs = self._verify_blacklist(pairs)
-
+        # Limit to X number of pairs
-        if self._freqtrade.strategy.stoploss is not None and self._precision_filter:
+        pairs = pairs[:self._number_pairs]
-
+        logger.info(f"Searching {self._number_pairs} pairs: {pairs}")
-            stop_prices = [self._freqtrade.get_target_bid(t["symbol"], t)
-                           * (1 - abs(self._freqtrade.strategy.stoploss)) for t in valid_tickers]
-            rates = [sp * 0.99 for sp in stop_prices]
-            logger.debug("\n".join([f"{sp} : {r}" for sp, r in zip(stop_prices[:10], rates[:10])]))
-            for i, t in enumerate(valid_tickers):
-                sp = self._freqtrade.exchange.symbol_price_prec(t["symbol"], stop_prices[i])
-                r = self._freqtrade.exchange.symbol_price_prec(t["symbol"], rates[i])
-                logger.debug(f"{t['symbol']} - {sp} : {r}")
-                if sp <= r:
-                    logger.info(f"Removed {t['symbol']} from whitelist, "
-                                f"because stop price {sp} would be <= stop limit {r}")
-                    valid_tickers.remove(t)
-
-        pairs = [s['symbol'] for s in valid_tickers]
-        logger.info(f"Searching pairs: {self._whitelist}")
 
         return pairs

freqtrade/pairlist/pairlistmanager.py

@@ -0,0 +1,95 @@
+"""
+Static List provider
+
+Provides lists as configured in config.json
+
+ """
+from cachetools import TTLCache, cached
+import logging
+from typing import Dict, List
+
+from freqtrade import OperationalException
+from freqtrade.pairlist.IPairList import IPairList
+from freqtrade.resolvers import PairListResolver
+
+logger = logging.getLogger(__name__)
+
+
+class PairListManager():
+
+    def __init__(self, exchange, config: dict) -> None:
+        self._exchange = exchange
+        self._config = config
+        self._whitelist = self._config['exchange'].get('pair_whitelist')
+        self._blacklist = self._config['exchange'].get('pair_blacklist', [])
+        self._pairlists: List[IPairList] = []
+        self._tickers_needed = False
+        for pl in self._config.get('pairlists', None):
+            if 'method' not in pl:
+                logger.warning(f"No method in {pl}")
+                continue
+            pairl = PairListResolver(pl.get('method'),
+                                     exchange=exchange,
+                                     pairlistmanager=self,
+                                     config=config,
+                                     pairlistconfig=pl,
+                                     pairlist_pos=len(self._pairlists)
+                                     ).pairlist
+            self._tickers_needed = pairl.needstickers or self._tickers_needed
+            self._pairlists.append(pairl)
+
+        if not self._pairlists:
+            raise OperationalException("No Pairlist defined!")
+
+    @property
+    def whitelist(self) -> List[str]:
+        """
+        Has the current whitelist
+        """
+        return self._whitelist
+
+    @property
+    def blacklist(self) -> List[str]:
+        """
+        Has the current blacklist
+        -> no need to overwrite in subclasses
+        """
+        return self._blacklist
+
+    @property
+    def name_list(self) -> List[str]:
+        """
+        Get list of loaded pairlists names
+        """
+        return [p.name for p in self._pairlists]
+
+    def short_desc(self) -> List[Dict]:
+        """
+        List of short_desc for each pairlist
+        """
+        return [{p.name: p.short_desc()} for p in self._pairlists]
+
+    @cached(TTLCache(maxsize=1, ttl=1800))
+    def _get_cached_tickers(self):
+        return self._exchange.get_tickers()
+
+    def refresh_pairlist(self) -> None:
+        """
+        Run pairlist through all configured pairlists.
+        """
+
+        pairlist = self._whitelist.copy()
+
+        # tickers should be cached to avoid calling the exchange on each call.
+        tickers: Dict = {}
+        if self._tickers_needed:
+            tickers = self._get_cached_tickers()
+
+        # Process all pairlists in chain
+        for pl in self._pairlists:
+            pairlist = pl.filter_pairlist(pairlist, tickers)
+
+        # Validation against blacklist happens after the pairlists to ensure blacklist is respected.
+        pairlist = IPairList.verify_blacklist(pairlist, self.blacklist)
+
+        self._whitelist = pairlist

freqtrade/persistence.py

@@ -8,17 +8,16 @@ from typing import Any, Dict, List, Optional
 
 import arrow
 from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
-                        create_engine, inspect)
+                        create_engine, desc, func, inspect)
 from sqlalchemy.exc import NoSuchModuleError
 from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import Query
 from sqlalchemy.orm.scoping import scoped_session
 from sqlalchemy.orm.session import sessionmaker
-from sqlalchemy import func
 from sqlalchemy.pool import StaticPool
 
 from freqtrade import OperationalException
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -52,9 +51,11 @@ def init(db_url: str, clean_open_orders: bool = False) -> None:
         raise OperationalException(f"Given value for db_url: '{db_url}' "
                                    f"is no valid database URL! (See {_SQL_DOCS_URL})")
 
-    session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
+    # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope
-    Trade.session = session()
+    # Scoped sessions proxy requests to the appropriate thread-local session.
-    Trade.query = session.query_property()
+    # We should use the scoped_session object - not a seperately initialized version
+    Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
+    Trade.query = Trade.session.query_property()
     _DECL_BASE.metadata.create_all(engine)
     check_migrate(engine)
 
@@ -393,6 +394,37 @@ class Trade(_DECL_BASE):
         profit_percent = (close_trade_price / open_trade_price) - 1
         return float(f"{profit_percent:.8f}")
 
+    @staticmethod
+    def get_trades(trade_filter=None) -> Query:
+        """
+        Helper function to query Trades using filters.
+        :param trade_filter: Optional filter to apply to trades
+                             Can be either a Filter object, or a List of filters
+                             e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
+                             e.g. `(trade_filter=Trade.id == trade_id)`
+        :return: unsorted query object
+        """
+        if trade_filter is not None:
+            if not isinstance(trade_filter, list):
+                trade_filter = [trade_filter]
+            return Trade.query.filter(*trade_filter)
+        else:
+            return Trade.query
+
+    @staticmethod
+    def get_open_trades() -> List[Any]:
+        """
+        Query trades from persistence layer
+        """
+        return Trade.get_trades(Trade.is_open.is_(True)).all()
+
+    @staticmethod
+    def get_open_order_trades():
+        """
+        Returns all open trades
+        """
+        return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
+
     @staticmethod
     def total_open_trades_stakes() -> float:
         """
@@ -405,11 +437,38 @@ class Trade(_DECL_BASE):
         return total_open_stake_amount or 0
 
     @staticmethod
-    def get_open_trades() -> List[Any]:
+    def get_overall_performance() -> List[Dict[str, Any]]:
         """
-        Query trades from persistence layer
+        Returns List of dicts containing all Trades, including profit and trade count
         """
-        return Trade.query.filter(Trade.is_open.is_(True)).all()
+        pair_rates = Trade.session.query(
+            Trade.pair,
+            func.sum(Trade.close_profit).label('profit_sum'),
+            func.count(Trade.pair).label('count')
+        ).filter(Trade.is_open.is_(False))\
+            .group_by(Trade.pair) \
+            .order_by(desc('profit_sum')) \
+            .all()
+        return [
+            {
+                'pair': pair,
+                'profit': rate,
+                'count': count
+            }
+            for pair, rate, count in pair_rates
+        ]
+
+    @staticmethod
+    def get_best_pair():
+        """
+        Get best pair with closed trade.
+        """
+        best_pair = Trade.session.query(
+            Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
+        ).filter(Trade.is_open.is_(False)) \
+            .group_by(Trade.pair) \
+            .order_by(desc('profit_sum')).first()
+        return best_pair
 
     @staticmethod
     def stoploss_reinitialization(desired_stoploss):

freqtrade/plot/plotting.py

@@ -39,7 +39,7 @@ def init_plotscript(config):
     tickers = history.load_data(
         datadir=Path(str(config.get("datadir"))),
         pairs=pairs,
-        ticker_interval=config.get('ticker_interval', '5m'),
+        timeframe=config.get('ticker_interval', '5m'),
         timerange=timerange,
     )
 
@@ -47,7 +47,7 @@ def init_plotscript(config):
                          db_url=config.get('db_url'),
                          exportfilename=config.get('exportfilename'),
                          )
-
+    trades = history.trim_dataframe(trades, timerange, 'open_time')
     return {"tickers": tickers,
             "trades": trades,
             "pairs": pairs,
@@ -300,12 +300,12 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
     return fig
 
 
-def generate_plot_filename(pair, ticker_interval) -> str:
+def generate_plot_filename(pair, timeframe) -> str:
     """
-    Generate filenames per pair/ticker_interval to be used for storing plots
+    Generate filenames per pair/timeframe to be used for storing plots
     """
     pair_name = pair.replace("/", "_")
-    file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
+    file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
 
     logger.info('Generate plot file for %s', pair)
 
@@ -316,8 +316,9 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
     """
     Generate a plot html file from pre populated fig plotly object
     :param fig: Plotly Figure to plot
-    :param pair: Pair to plot (used as filename and Plot title)
+    :param filename: Name to store the file as
-    :param ticker_interval: Used as part of the filename
+    :param directory: Directory to store the file in
+    :param auto_open: Automatically open files saved
     :return: None
     """
     directory.mkdir(parents=True, exist_ok=True)
@@ -376,12 +377,14 @@ def plot_profit(config: Dict[str, Any]) -> None:
     in helping out to find a good algorithm.
     """
     plot_elements = init_plotscript(config)
-    trades = load_trades(config['trade_source'],
+    trades = plot_elements['trades']
-                         db_url=str(config.get('db_url')),
-                         exportfilename=str(config.get('exportfilename')),
-                         )
     # Filter trades to relevant pairs
-    trades = trades[trades['pair'].isin(plot_elements["pairs"])]
+    # Remove open pairs - we don't know the profit yet so can't calculate profit for these.
+    # Also, If only one open pair is left, then the profit-generation would fail.
+    trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
+                    & (~trades['close_time'].isnull())
+                    ]
+
     # Create an average close price of all the pairs that were involved.
     # this could be useful to gauge the overall market trend
     fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],

freqtrade/resolvers/hyperopt_resolver.py

@@ -1,14 +1,14 @@
 # pragma pylint: disable=attribute-defined-outside-init
 
 """
-This module load custom hyperopts
+This module load custom hyperopt
 """
 import logging
 from pathlib import Path
 from typing import Optional, Dict
 
 from freqtrade import OperationalException
-from freqtrade.constants import DEFAULT_HYPEROPT, DEFAULT_HYPEROPT_LOSS
+from freqtrade.constants import DEFAULT_HYPEROPT_LOSS, USERPATH_HYPEROPTS
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
 from freqtrade.resolvers import IResolver
@@ -20,7 +20,6 @@ class HyperOptResolver(IResolver):
     """
     This class contains all the logic to load custom hyperopt class
     """
-
     __slots__ = ['hyperopt']
 
     def __init__(self, config: Dict) -> None:
@@ -28,12 +27,18 @@ class HyperOptResolver(IResolver):
         Load the custom class from config parameter
         :param config: configuration dictionary
         """
+        if not config.get('hyperopt'):
+            raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify "
+                                       "the Hyperopt class to use.")
+
+        hyperopt_name = config['hyperopt']
 
-        # Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
-        hyperopt_name = config.get('hyperopt') or DEFAULT_HYPEROPT
         self.hyperopt = self._load_hyperopt(hyperopt_name, config,
                                             extra_dir=config.get('hyperopt_path'))
 
+        if not hasattr(self.hyperopt, 'populate_indicators'):
+            logger.warning("Hyperopt class does not provide populate_indicators() method. "
+                           "Using populate_indicators from the strategy.")
         if not hasattr(self.hyperopt, 'populate_buy_trend'):
             logger.warning("Hyperopt class does not provide populate_buy_trend() method. "
                            "Using populate_buy_trend from the strategy.")
@@ -53,7 +58,7 @@ class HyperOptResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='hyperopts', extra_dir=extra_dir)
+                                            user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
 
         hyperopt = self._load_object(paths=abs_paths, object_type=IHyperOpt,
                                      object_name=hyperopt_name, kwargs={'config': config})
@@ -69,27 +74,28 @@ class HyperOptLossResolver(IResolver):
     """
     This class contains all the logic to load custom hyperopt loss class
     """
-
     __slots__ = ['hyperoptloss']
 
-    def __init__(self, config: Dict = None) -> None:
+    def __init__(self, config: Dict) -> None:
         """
         Load the custom class from config parameter
-        :param config: configuration dictionary or None
+        :param config: configuration dictionary
         """
-        config = config or {}
 
-        # Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
+        # Verify the hyperopt_loss is in the configuration, otherwise fallback to the
-        hyperopt_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
+        # default hyperopt loss
+        hyperoptloss_name = config.get('hyperopt_loss') or DEFAULT_HYPEROPT_LOSS
+
         self.hyperoptloss = self._load_hyperoptloss(
-            hyperopt_name, config, extra_dir=config.get('hyperopt_path'))
+            hyperoptloss_name, config, extra_dir=config.get('hyperopt_path'))
 
         # Assign ticker_interval to be used in hyperopt
         self.hyperoptloss.__class__.ticker_interval = str(config['ticker_interval'])
 
         if not hasattr(self.hyperoptloss, 'hyperopt_loss_function'):
             raise OperationalException(
-                f"Found hyperopt {hyperopt_name} does not implement `hyperopt_loss_function`.")
+                f"Found HyperoptLoss class {hyperoptloss_name} does not "
+                "implement `hyperopt_loss_function`.")
 
     def _load_hyperoptloss(
             self, hyper_loss_name: str, config: Dict,
@@ -104,7 +110,7 @@ class HyperOptLossResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='hyperopts', extra_dir=extra_dir)
+                                            user_subdir=USERPATH_HYPEROPTS, extra_dir=extra_dir)
 
         hyperoptloss = self._load_object(paths=abs_paths, object_type=IHyperOptLoss,
                                          object_name=hyper_loss_name)

freqtrade/resolvers/iresolver.py

@@ -17,13 +17,13 @@ class IResolver:
     This class contains all the logic to load custom classes
     """
 
-    def build_search_paths(self, config, current_path: Path, user_subdir: str,
+    def build_search_paths(self, config, current_path: Path, user_subdir: Optional[str] = None,
                            extra_dir: Optional[str] = None) -> List[Path]:
 
-        abs_paths = [
+        abs_paths: List[Path] = [current_path]
-            config['user_data_dir'].joinpath(user_subdir),
+
-            current_path,
+        if user_subdir:
-        ]
+            abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
 
         if extra_dir:
             # Add extra directory to the top of the search paths

freqtrade/resolvers/pairlist_resolver.py

@@ -20,13 +20,18 @@ class PairListResolver(IResolver):
 
     __slots__ = ['pairlist']
 
-    def __init__(self, pairlist_name: str, freqtrade, config: dict) -> None:
+    def __init__(self, pairlist_name: str, exchange, pairlistmanager,
+                 config: dict, pairlistconfig: dict, pairlist_pos: int) -> None:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary or None
         """
-        self.pairlist = self._load_pairlist(pairlist_name, config, kwargs={'freqtrade': freqtrade,
+        self.pairlist = self._load_pairlist(pairlist_name, config,
-                                                                           'config': config})
+                                            kwargs={'exchange': exchange,
+                                                    'pairlistmanager': pairlistmanager,
+                                                    'config': config,
+                                                    'pairlistconfig': pairlistconfig,
+                                                    'pairlist_pos': pairlist_pos})
 
     def _load_pairlist(
             self, pairlist_name: str, config: dict, kwargs: dict) -> IPairList:
@@ -40,7 +45,7 @@ class PairListResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('pairlist').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='pairlist', extra_dir=None)
+                                            user_subdir=None, extra_dir=None)
 
         pairlist = self._load_object(paths=abs_paths, object_type=IPairList,
                                      object_name=pairlist_name, kwargs=kwargs)

freqtrade/resolvers/strategy_resolver.py

@@ -32,8 +32,11 @@ class StrategyResolver(IResolver):
         """
         config = config or {}
 
-        # Verify the strategy is in the configuration, otherwise fallback to the default strategy
+        if not config.get('strategy'):
-        strategy_name = config.get('strategy') or constants.DEFAULT_STRATEGY
+            raise OperationalException("No strategy set. Please use `--strategy` to specify "
+                                       "the strategy class to use.")
+
+        strategy_name = config['strategy']
         self.strategy: IStrategy = self._load_strategy(strategy_name,
                                                        config=config,
                                                        extra_dir=config.get('strategy_path'))
@@ -57,6 +60,7 @@ class StrategyResolver(IResolver):
                       ("order_time_in_force",             None,        False),
                       ("stake_currency",                  None,        False),
                       ("stake_amount",                    None,        False),
+                      ("startup_candle_count",            None,        False),
                       ("use_sell_signal",                 True,        True),
                       ("sell_profit_only",                False,       True),
                       ("ignore_roi_if_buy_signal",        False,       True),
@@ -125,7 +129,8 @@ class StrategyResolver(IResolver):
         current_path = Path(__file__).parent.parent.joinpath('strategy').resolve()
 
         abs_paths = self.build_search_paths(config, current_path=current_path,
-                                            user_subdir='strategies', extra_dir=extra_dir)
+                                            user_subdir=constants.USERPATH_STRATEGY,
+                                            extra_dir=extra_dir)
 
         if ":" in strategy_name:
             logger.info("loading base64 encoded strategy")

freqtrade/rpc/api_server.py

@@ -169,6 +169,10 @@ class ApiServer(RPC):
                               view_func=self._status, methods=['GET'])
         self.app.add_url_rule(f'{BASE_URI}/version', 'version',
                               view_func=self._version, methods=['GET'])
+        self.app.add_url_rule(f'{BASE_URI}/show_config', 'show_config',
+                              view_func=self._show_config, methods=['GET'])
+        self.app.add_url_rule(f'{BASE_URI}/ping', 'ping',
+                              view_func=self._ping, methods=['GET'])
 
         # Combined actions and infos
         self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
@@ -224,6 +228,13 @@ class ApiServer(RPC):
         msg = self._rpc_stopbuy()
         return self.rest_dump(msg)
 
+    @rpc_catch_errors
+    def _ping(self):
+        """
+        simple poing version
+        """
+        return self.rest_dump({"status": "pong"})
+
     @require_login
     @rpc_catch_errors
     def _version(self):
@@ -232,6 +243,14 @@ class ApiServer(RPC):
         """
         return self.rest_dump({"version": __version__})
 
+    @require_login
+    @rpc_catch_errors
+    def _show_config(self):
+        """
+        Prints the bot's version
+        """
+        return self.rest_dump(self._rpc_show_config())
+
     @require_login
     @rpc_catch_errors
     def _reload_conf(self):
@@ -265,7 +284,7 @@ class ApiServer(RPC):
 
         stats = self._rpc_daily_profit(timescale,
                                        self._config['stake_currency'],
-                                       self._config['fiat_display_currency']
+                                       self._config.get('fiat_display_currency', '')
                                        )
 
         return self.rest_dump(stats)
@@ -293,7 +312,7 @@ class ApiServer(RPC):
         logger.info("LocalRPC - Profit Command Called")
 
         stats = self._rpc_trade_statistics(self._config['stake_currency'],
-                                           self._config['fiat_display_currency']
+                                           self._config.get('fiat_display_currency')
                                            )
 
         return self.rest_dump(stats)
@@ -321,8 +340,11 @@ class ApiServer(RPC):
 
         Returns the current status of the trades in json format
         """
+        try:
             results = self._rpc_trade_status()
             return self.rest_dump(results)
+        except RPCException:
+            return self.rest_dump([])
 
     @require_login
     @rpc_catch_errors
@@ -332,7 +354,8 @@ class ApiServer(RPC):
 
         Returns the current status of the trades in json format
         """
-        results = self._rpc_balance(self._config.get('fiat_display_currency', ''))
+        results = self._rpc_balance(self._config['stake_currency'],
+                                    self._config.get('fiat_display_currency', ''))
         return self.rest_dump(results)
 
     @require_login

freqtrade/rpc/rpc.py

@@ -3,17 +3,15 @@ This module contains class to define a RPC communications
 """
 import logging
 from abc import abstractmethod
-from datetime import timedelta, datetime, date
+from datetime import date, datetime, timedelta
-from decimal import Decimal
 from enum import Enum
-from typing import Dict, Any, List, Optional
+from math import isnan
+from typing import Any, Dict, List, Optional, Tuple
 
 import arrow
-import sqlalchemy as sql
+from numpy import NAN, mean
-from numpy import mean, NAN
-from pandas import DataFrame
 
-from freqtrade import TemporaryError, DependencyException
+from freqtrade import DependencyException, TemporaryError
 from freqtrade.misc import shorten_date
 from freqtrade.persistence import Trade
 from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@@ -82,6 +80,29 @@ class RPC:
     def send_msg(self, msg: Dict[str, str]) -> None:
         """ Sends a message to all registered rpc modules """
 
+    def _rpc_show_config(self) -> Dict[str, Any]:
+        """
+        Return a dict of config options.
+        Explicitly does NOT return the full config to avoid leakage of sensitive
+        information via rpc.
+        """
+        config = self._freqtrade.config
+        val = {
+            'dry_run': config.get('dry_run', False),
+            'stake_currency': config['stake_currency'],
+            'stake_amount': config['stake_amount'],
+            'minimal_roi': config['minimal_roi'].copy(),
+            'stoploss': config['stoploss'],
+            'trailing_stop': config['trailing_stop'],
+            'trailing_stop_positive': config.get('trailing_stop_positive'),
+            'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
+            'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached'),
+            'ticker_interval': config['ticker_interval'],
+            'exchange': config['exchange']['name'],
+            'strategy': config['strategy'],
+        }
+        return val
+
     def _rpc_trade_status(self) -> List[Dict[str, Any]]:
         """
         Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
@@ -118,7 +139,7 @@ class RPC:
                 results.append(trade_dict)
             return results
 
-    def _rpc_status_table(self) -> DataFrame:
+    def _rpc_status_table(self, stake_currency, fiat_display_currency: str) -> Tuple[List, List]:
         trades = Trade.get_open_trades()
         if not trades:
             raise RPCException('no active order')
@@ -131,17 +152,28 @@ class RPC:
                 except DependencyException:
                     current_rate = NAN
                 trade_perc = (100 * trade.calc_profit_percent(current_rate))
+                trade_profit = trade.calc_profit(current_rate)
+                profit_str = f'{trade_perc:.2f}%'
+                if self._fiat_converter:
+                    fiat_profit = self._fiat_converter.convert_amount(
+                            trade_profit,
+                            stake_currency,
+                            fiat_display_currency
+                        )
+                    if fiat_profit and not isnan(fiat_profit):
+                        profit_str += f" ({fiat_profit:.2f})"
                 trades_list.append([
                     trade.id,
                     trade.pair,
                     shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
-                    f'{trade_perc:.2f}%'
+                    profit_str
                 ])
+            profitcol = "Profit"
+            if self._fiat_converter:
+                profitcol += " (" + fiat_display_currency + ")"
 
-            columns = ['ID', 'Pair', 'Since', 'Profit']
+            columns = ['ID', 'Pair', 'Since', profitcol]
-            df_statuses = DataFrame.from_records(trades_list, columns=columns)
+            return trades_list, columns
-            df_statuses = df_statuses.set_index(columns[0])
-            return df_statuses
 
     def _rpc_daily_profit(
             self, timescale: int,
@@ -154,12 +186,11 @@ class RPC:
 
         for day in range(0, timescale):
             profitday = today - timedelta(days=day)
-            trades = Trade.query \
+            trades = Trade.get_trades(trade_filter=[
-                .filter(Trade.is_open.is_(False)) \
+                Trade.is_open.is_(False),
-                .filter(Trade.close_date >= profitday)\
+                Trade.close_date >= profitday,
-                .filter(Trade.close_date < (profitday + timedelta(days=1)))\
+                Trade.close_date < (profitday + timedelta(days=1))
-                .order_by(Trade.close_date)\
+            ]).order_by(Trade.close_date).all()
-                .all()
             curdayprofit = sum(trade.calc_profit() for trade in trades)
             profit_days[profitday] = {
                 'amount': f'{curdayprofit:.8f}',
@@ -192,7 +223,7 @@ class RPC:
     def _rpc_trade_statistics(
             self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
         """ Returns cumulative profit statistics """
-        trades = Trade.query.order_by(Trade.id).all()
+        trades = Trade.get_trades().order_by(Trade.id).all()
 
         profit_all_coin = []
         profit_all_perc = []
@@ -221,15 +252,11 @@ class RPC:
                 profit_percent = trade.calc_profit_percent(rate=current_rate)
 
             profit_all_coin.append(
-                trade.calc_profit(rate=Decimal(trade.close_rate or current_rate))
+                trade.calc_profit(rate=trade.close_rate or current_rate)
             )
             profit_all_perc.append(profit_percent)
 
-        best_pair = Trade.session.query(
+        best_pair = Trade.get_best_pair()
-            Trade.pair, sql.func.sum(Trade.close_profit).label('profit_sum')
-        ).filter(Trade.is_open.is_(False)) \
-            .group_by(Trade.pair) \
-            .order_by(sql.text('profit_sum DESC')).first()
 
         if not best_pair:
             raise RPCException('no closed trade')
@@ -270,34 +297,42 @@ class RPC:
             'best_rate': round(bp_rate * 100, 2),
         }
 
-    def _rpc_balance(self, fiat_display_currency: str) -> Dict:
+    def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
         """ Returns current account balance per crypto """
         output = []
         total = 0.0
-        for coin, balance in self._freqtrade.exchange.get_balances().items():
+        try:
-            if not balance['total']:
+            tickers = self._freqtrade.exchange.get_tickers()
+        except (TemporaryError, DependencyException):
+            raise RPCException('Error getting current tickers.')
+
+        for coin, balance in self._freqtrade.wallets.get_all_balances().items():
+            if not balance.total:
                 continue
 
-            if coin == 'BTC':
+            est_stake: float = 0
+            if coin == stake_currency:
                 rate = 1.0
+                est_stake = balance.total
             else:
                 try:
-                    pair = self._freqtrade.exchange.get_valid_pair_combination(coin, "BTC")
+                    pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
-                    if pair.startswith("BTC"):
+                    rate = tickers.get(pair, {}).get('bid', None)
-                        rate = 1.0 / self._freqtrade.get_sell_rate(pair, False)
+                    if rate:
-                    else:
+                        if pair.startswith(stake_currency):
-                        rate = self._freqtrade.get_sell_rate(pair, False)
+                            rate = 1.0 / rate
+                        est_stake = rate * balance.total
                 except (TemporaryError, DependencyException):
                     logger.warning(f" Could not get rate for pair {coin}.")
                     continue
-            est_btc: float = rate * balance['total']
+            total = total + (est_stake or 0)
-            total = total + est_btc
             output.append({
                 'currency': coin,
-                'free': balance['free'] if balance['free'] is not None else 0,
+                'free': balance.free if balance.free is not None else 0,
-                'balance': balance['total'] if balance['total'] is not None else 0,
+                'balance': balance.total if balance.total is not None else 0,
-                'used': balance['used'] if balance['used'] is not None else 0,
+                'used': balance.used if balance.used is not None else 0,
-                'est_btc': est_btc,
+                'est_stake': est_stake or 0,
+                'stake': stake_currency,
             })
         if total == 0.0:
             if self._freqtrade.config.get('dry_run', False):
@@ -389,11 +424,8 @@ class RPC:
             return {'result': 'Created sell orders for all open trades.'}
 
         # Query for trade
-        trade = Trade.query.filter(
+        trade = Trade.get_trades(
-            sql.and_(
+            trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True), ]
-                Trade.id == trade_id,
-                Trade.is_open.is_(True)
-            )
         ).first()
         if not trade:
             logger.warning('forcesell: Invalid argument received')
@@ -423,7 +455,7 @@ class RPC:
         # check if valid pair
 
         # check if pair already has an open pair
-        trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
+        trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
         if trade:
             raise RPCException(f'position for {pair} already open - id: {trade.id}')
 
@@ -432,28 +464,20 @@ class RPC:
 
         # execute buy
         if self._freqtrade.execute_buy(pair, stakeamount, price):
-            trade = Trade.query.filter(Trade.is_open.is_(True)).filter(Trade.pair.is_(pair)).first()
+            trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair.is_(pair)]).first()
             return trade
         else:
             return None
 
-    def _rpc_performance(self) -> List[Dict]:
+    def _rpc_performance(self) -> List[Dict[str, Any]]:
         """
         Handler for performance.
         Shows a performance statistic from finished trades
         """
-
+        pair_rates = Trade.get_overall_performance()
-        pair_rates = Trade.session.query(Trade.pair,
+        # Round and convert to %
-                                         sql.func.sum(Trade.close_profit).label('profit_sum'),
+        [x.update({'profit': round(x['profit'] * 100, 2)}) for x in pair_rates]
-                                         sql.func.count(Trade.pair).label('count')) \
+        return pair_rates
-            .filter(Trade.is_open.is_(False)) \
-            .group_by(Trade.pair) \
-            .order_by(sql.text('profit_sum DESC')) \
-            .all()
-        return [
-            {'pair': pair, 'profit': round(rate * 100, 2), 'count': count}
-            for pair, rate, count in pair_rates
-        ]
 
     def _rpc_count(self) -> Dict[str, float]:
         """ Returns the number of trades running """
@@ -469,7 +493,7 @@ class RPC:
 
     def _rpc_whitelist(self) -> Dict:
         """ Returns the currently active whitelist"""
-        res = {'method': self._freqtrade.pairlists.name,
+        res = {'method': self._freqtrade.pairlists.name_list,
                'length': len(self._freqtrade.active_pair_whitelist),
                'whitelist': self._freqtrade.active_pair_whitelist
                }
@@ -484,7 +508,7 @@ class RPC:
                         and pair not in self._freqtrade.pairlists.blacklist):
                     self._freqtrade.pairlists.blacklist.append(pair)
 
-        res = {'method': self._freqtrade.pairlists.name,
+        res = {'method': self._freqtrade.pairlists.name_list,
                'length': len(self._freqtrade.pairlists.blacklist),
                'blacklist': self._freqtrade.pairlists.blacklist,
                }

freqtrade/rpc/telegram.py

@@ -95,6 +95,7 @@ class Telegram(RPC):
             CommandHandler('daily', self._daily),
             CommandHandler('count', self._count),
             CommandHandler('reload_conf', self._reload_conf),
+            CommandHandler('show_config', self._show_config),
             CommandHandler('stopbuy', self._stopbuy),
             CommandHandler('whitelist', self._whitelist),
             CommandHandler('blacklist', self._blacklist),
@@ -234,8 +235,9 @@ class Telegram(RPC):
         :return: None
         """
         try:
-            df_statuses = self._rpc_status_table()
+            statlist, head = self._rpc_status_table(self._config['stake_currency'],
-            message = tabulate(df_statuses, headers='keys', tablefmt='simple')
+                                                    self._config.get('fiat_display_currency', ''))
+            message = tabulate(statlist, headers=head, tablefmt='simple')
             self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
         except RPCException as e:
             self._send_msg(str(e))
@@ -323,15 +325,16 @@ class Telegram(RPC):
     def _balance(self, update: Update, context: CallbackContext) -> None:
         """ Handler for /balance """
         try:
-            result = self._rpc_balance(self._config.get('fiat_display_currency', ''))
+            result = self._rpc_balance(self._config['stake_currency'],
+                                       self._config.get('fiat_display_currency', ''))
             output = ''
             for currency in result['currencies']:
-                if currency['est_btc'] > 0.0001:
+                if currency['est_stake'] > 0.0001:
                     curr_output = "*{currency}:*\n" \
                             "\t`Available: {free: .8f}`\n" \
                             "\t`Balance: {balance: .8f}`\n" \
                             "\t`Pending: {used: .8f}`\n" \
-                            "\t`Est. BTC: {est_btc: .8f}`\n".format(**currency)
+                            "\t`Est. {stake}: {est_stake: .8f}`\n".format(**currency)
                 else:
                     curr_output = "*{currency}:* not showing <1$ amount \n".format(**currency)
 
@@ -549,6 +552,7 @@ class Telegram(RPC):
                   "*/balance:* `Show account balance per currency`\n" \
                   "*/stopbuy:* `Stops buying, but handles open trades gracefully` \n" \
                   "*/reload_conf:* `Reload configuration file` \n" \
+                  "*/show_config:* `Show running configuration` \n" \
                   "*/whitelist:* `Show current whitelist` \n" \
                   "*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
                   "to the blacklist.` \n" \
@@ -569,6 +573,26 @@ class Telegram(RPC):
         """
         self._send_msg('*Version:* `{}`'.format(__version__))
 
+    @authorized_only
+    def _show_config(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /show_config.
+        Show config information information
+        :param bot: telegram bot
+        :param update: message update
+        :return: None
+        """
+        val = self._rpc_show_config()
+        self._send_msg(
+            f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
+            f"*Exchange:* `{val['exchange']}`\n"
+            f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
+            f"*Minimum ROI:* `{val['minimal_roi']}`\n"
+            f"*{'Trailing ' if val['trailing_stop'] else ''}Stoploss:* `{val['stoploss']}`\n"
+            f"*Ticker Interval:* `{val['ticker_interval']}`\n"
+            f"*Strategy:* `{val['strategy']}`'"
+        )
+
     def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:
         """
         Send given markdown message

freqtrade/state.py

@@ -25,5 +25,12 @@ class RunMode(Enum):
     BACKTEST = "backtest"
     EDGE = "edge"
     HYPEROPT = "hyperopt"
+    UTIL_EXCHANGE = "util_exchange"
+    UTIL_NO_EXCHANGE = "util_no_exchange"
     PLOT = "plot"
-    OTHER = "other"  # Used for plotting scripts and test
+    OTHER = "other"
+
+
+TRADING_MODES = [RunMode.LIVE, RunMode.DRY_RUN]
+OPTIMIZE_MODES = [RunMode.BACKTEST, RunMode.EDGE, RunMode.HYPEROPT]
+NON_UTIL_MODES = TRADING_MODES + OPTIMIZE_MODES

freqtrade/strategy/default_strategy.py

@@ -39,6 +39,9 @@ class DefaultStrategy(IStrategy):
         'stoploss_on_exchange': False
     }
 
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
     # Optional time in force for orders
     order_time_in_force = {
         'buy': 'gtc',
@@ -105,9 +108,6 @@ class DefaultStrategy(IStrategy):
         # EMA - Exponential Moving Average
         dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
 
-        # SMA - Simple Moving Average
-        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

freqtrade/strategy/interface.py

@@ -103,11 +103,14 @@ class IStrategy(ABC):
     # run "populate_indicators" only for new candle
     process_only_new_candles: bool = False
 
+    # Count of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 0
+
     # Class level variables (intentional) containing
     # the dataprovider (dp) (access to other candles, historic data, ...)
     # and wallets - access to the current balance.
-    dp: DataProvider
+    dp: Optional[DataProvider] = None
-    wallets: Wallets
+    wallets: Optional[Wallets] = None
 
     def __init__(self, config: dict) -> None:
         self.config = config
@@ -421,6 +424,7 @@ class IStrategy(ABC):
     def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
         """
         Creates a dataframe and populates indicators for given ticker data
+        Used by optimize operations only, not during dry / live runs.
         """
         return {pair: self.advise_indicators(pair_data, {'pair': pair})
                 for pair, pair_data in tickerdata.items()}

freqtrade/templates/base_hyperopt.py.j2

@@ -0,0 +1,127 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
+
+from freqtrade.optimize.hyperopt_interface import IHyperOpt
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class {{ hyperopt }}(IHyperOpt):
+    """
+    This is a Hyperopt template to get you started.
+
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md
+
+    You should:
+    - Add any lib you need to build your hyperopt.
+
+    You must keep:
+    - The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
+
+    The roi_space, generate_roi_table, stoploss_space methods are no longer required to be
+    copied in every custom hyperopt. However, you may override them if you need the
+    'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
+    Sample implementation of these methods can be found in
+    https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
+    """
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            {{ buy_guards | indent(12) }}
+
+            # TRIGGERS
+            if 'trigger' in params:
+                if params['trigger'] == 'bb_lower':
+                    conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+                if params['trigger'] == 'macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macd'], dataframe['macdsignal']
+                    ))
+                if params['trigger'] == 'sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['close'], dataframe['sar']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            {{ buy_space | indent(12) }}
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+            {{ sell_guards | indent(12) }}
+
+            # TRIGGERS
+            if 'sell-trigger' in params:
+                if params['sell-trigger'] == 'sell-bb_upper':
+                    conditions.append(dataframe['close'] > dataframe['bb_upperband'])
+                if params['sell-trigger'] == 'sell-macd_cross_signal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['macdsignal'], dataframe['macd']
+                    ))
+                if params['sell-trigger'] == 'sell-sar_reversal':
+                    conditions.append(qtpylib.crossed_above(
+                        dataframe['sar'], dataframe['close']
+                    ))
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            {{ sell_space | indent(12) }}
+        ]

freqtrade/templates/base_strategy.py.j2

@@ -0,0 +1,138 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class {{ strategy }}(IStrategy):
+    """
+    This is a strategy template to get you started.
+    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
+    populate_sell_trend, hyperopt_space, buy_strategy_generator
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal ticker interval for the strategy.
+    ticker_interval = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        {{ indicators | indent(8) }}
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                {{ buy_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                {{ sell_trend | indent(16) }}
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'sell'] = 1
+        return dataframe

freqtrade/templates/sample_hyperopt.py

@@ -1,19 +1,23 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
+# --- Do not remove these libs ---
 from functools import reduce
 from typing import Any, Callable, Dict, List
-from datetime import datetime
 
-import numpy as np
+import numpy as np  # noqa
-import talib.abstract as ta
+import pandas as pd  # noqa
 from pandas import DataFrame
-from skopt.space import Categorical, Dimension, Integer, Real
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
 
-class SampleHyperOpts(IHyperOpt):
+
+class SampleHyperOpt(IHyperOpt):
     """
     This is a sample Hyperopt to inspire you.
     Feel free to customize it.
@@ -34,34 +38,6 @@ class SampleHyperOpts(IHyperOpt):
     Sample implementation of these methods can be found in
     https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_advanced.py
     """
-    @staticmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        """
-        Add several indicators needed for buy and sell strategies defined below.
-        """
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-        # MACD
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        # MFI
-        dataframe['mfi'] = ta.MFI(dataframe)
-        # RSI
-        dataframe['rsi'] = ta.RSI(dataframe)
-        # Stochastic Fast
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        # Minus-DI
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['bb_upperband'] = bollinger['upper']
-        # SAR
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        return dataframe
 
     @staticmethod
     def buy_strategy_generator(params: Dict[str, Any]) -> Callable:

freqtrade/templates/sample_hyperopt_advanced.py

@@ -1,20 +1,23 @@
 # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
+# --- Do not remove these libs ---
 from functools import reduce
-from math import exp
 from typing import Any, Callable, Dict, List
-from datetime import datetime
 
-import numpy as np# noqa F401
+import numpy as np  # noqa
-import talib.abstract as ta
+import pandas as pd  # noqa
 from pandas import DataFrame
-from skopt.space import Categorical, Dimension, Integer, Real
+from skopt.space import Categorical, Dimension, Integer, Real  # noqa
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.optimize.hyperopt_interface import IHyperOpt
 
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
 
-class AdvancedSampleHyperOpts(IHyperOpt):
+
+class AdvancedSampleHyperOpt(IHyperOpt):
     """
     This is a sample hyperopt to inspire you.
     Feel free to customize it.
@@ -37,6 +40,9 @@ class AdvancedSampleHyperOpts(IHyperOpt):
     """
     @staticmethod
     def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
+        """
         dataframe['adx'] = ta.ADX(dataframe)
         macd = ta.MACD(dataframe)
         dataframe['macd'] = macd['macd']
@@ -227,10 +233,33 @@ class AdvancedSampleHyperOpts(IHyperOpt):
             Real(-0.5, -0.02, name='stoploss'),
         ]
 
+    @staticmethod
+    def trailing_space() -> List[Dimension]:
+        """
+        Create a trailing stoploss space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            # It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
+            # is used. Otherwise hyperopt will vary other parameters that won't have effect if
+            # trailing_stop is set False.
+            # This parameter is included into the hyperspace dimensions rather than assigning
+            # it explicitly in the code in order to have it printed in the results along with
+            # other 'trailing' hyperspace parameters.
+            Categorical([True], name='trailing_stop'),
+
+            Real(0.02, 0.35, name='trailing_stop_positive'),
+            Real(0.01, 0.1, name='trailing_stop_positive_offset'),
+            Categorical([True, False], name='trailing_only_offset_is_reached'),
+        ]
+
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Based on TA indicators. Should be a copy of from strategy
+        Based on TA indicators.
-        must align to populate_indicators in this file
+        Can be a copy of the corresponding method from the strategy,
+        or will be loaded from the strategy.
+        Must align to populate_indicators used (either from this File, or from the strategy)
         Only used when --spaces does not include buy
         """
         dataframe.loc[
@@ -246,8 +275,10 @@ class AdvancedSampleHyperOpts(IHyperOpt):
 
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Based on TA indicators. Should be a copy of from strategy
+        Based on TA indicators.
-        must align to populate_indicators in this file
+        Can be a copy of the corresponding method from the strategy,
+        or will be loaded from the strategy.
+        Must align to populate_indicators used (either from this File, or from the strategy)
         Only used when --spaces does not include sell
         """
         dataframe.loc[

freqtrade/templates/sample_strategy.py

@@ -1,13 +1,16 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
 
 # --- Do not remove these libs ---
-from freqtrade.strategy.interface import IStrategy
+import numpy as np  # noqa
+import pandas as pd  # noqa
 from pandas import DataFrame
-# --------------------------------
 
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
 # Add your lib to import here
 import talib.abstract as ta
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-import numpy  # noqa
 
 
 # This class is a sample. Feel free to customize it.
@@ -59,6 +62,9 @@ class SampleStrategy(IStrategy):
     sell_profit_only = False
     ignore_roi_if_buy_signal = False
 
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
     # Optional order type mapping.
     order_types = {
         'buy': 'limit',
@@ -104,15 +110,20 @@ class SampleStrategy(IStrategy):
         # RSI
         dataframe['rsi'] = ta.RSI(dataframe)
 
-        """
         # ADX
         dataframe['adx'] = ta.ADX(dataframe)
 
-        # Awesome oscillator
+        # # Aroon, Aroon Oscillator
-        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
 
-        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        # # Awesome oscillator
-        dataframe['cci'] = ta.CCI(dataframe)
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        # dataframe['cci'] = ta.CCI(dataframe)
 
         # MACD
         macd = ta.MACD(dataframe)
@@ -123,40 +134,39 @@ class SampleStrategy(IStrategy):
         # MFI
         dataframe['mfi'] = ta.MFI(dataframe)
 
-        # Minus Directional Indicator / Movement
+        # # Minus Directional Indicator / Movement
-        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
 
-        # Plus Directional Indicator / Movement
+        # # Plus Directional Indicator / Movement
-        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
 
-        # ROC
+        # # ROC
-        dataframe['roc'] = ta.ROC(dataframe)
+        # dataframe['roc'] = ta.ROC(dataframe)
 
-        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
-        rsi = 0.1 * (dataframe['rsi'] - 50)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
-        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
 
-        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
-        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
 
-        # Stoch
+        # # Stoch
-        stoch = ta.STOCH(dataframe)
+        # stoch = ta.STOCH(dataframe)
-        dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowd'] = stoch['slowd']
-        dataframe['slowk'] = stoch['slowk']
+        # dataframe['slowk'] = stoch['slowk']
 
         # Stoch fast
         stoch_fast = ta.STOCHF(dataframe)
         dataframe['fastd'] = stoch_fast['fastd']
         dataframe['fastk'] = stoch_fast['fastk']
 
-        # Stoch RSI
+        # # Stoch RSI
-        stoch_rsi = ta.STOCHRSI(dataframe)
+        # stoch_rsi = ta.STOCHRSI(dataframe)
-        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
-        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
-        """
 
         # Overlap Studies
         # ------------------------------------
@@ -167,21 +177,19 @@ class SampleStrategy(IStrategy):
         dataframe['bb_middleband'] = bollinger['mid']
         dataframe['bb_upperband'] = bollinger['upper']
 
-        """
+        # # EMA - Exponential Moving Average
-        # EMA - Exponential Moving Average
+        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
-        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
-        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
-        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
-        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        # dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
 
         # SAR Parabol
         dataframe['sar'] = ta.SAR(dataframe)
 
-        # SMA - Simple Moving Average
-        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-        """
-
         # TEMA - Triple Exponential Moving Average
         dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
 
@@ -194,65 +202,57 @@ class SampleStrategy(IStrategy):
 
         # Pattern Recognition - Bullish candlestick patterns
         # ------------------------------------
-        """
+        # # Hammer: values [0, 100]
-        # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
-        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
-        # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
-        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
-        # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
-        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
-        # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
-        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
-        # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
-        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
-        # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        """
 
         # Pattern Recognition - Bearish candlestick patterns
         # ------------------------------------
-        """
+        # # Hanging Man: values [0, 100]
-        # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
-        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
-        # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
-        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
-        # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
-        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
-        # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
-        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
-        # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
-        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
-        # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        """
 
         # Pattern Recognition - Bullish/Bearish candlestick patterns
         # ------------------------------------
-        """
+        # # Three Line Strike: values [0, -100, 100]
-        # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
-        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
-        # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
-        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
-        # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
-        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
-        # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
-        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
-        # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
-        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
-        # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        """
 
-        # Chart type
+        # # Chart type
-        # ------------------------------------
+        # # ------------------------------------
-        """
+        # # Heikinashi stategy
-        # Heikinashi stategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
-        heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
-        dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
-        dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
-        dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
-        dataframe['ha_low'] = heikinashi['low']
-        """
 
         # Retrieve best bid and best ask from the orderbook
         # ------------------------------------

freqtrade/templates/strategy_analysis_example.ipynb

@@ -26,7 +26,7 @@
     "# Customize these according to your needs.\n",
     "\n",
     "# Define some constants\n",
-    "ticker_interval = \"5m\"\n",
+    "timeframe = \"5m\"\n",
     "# Name of the strategy class\n",
     "strategy_name = 'SampleStrategy'\n",
     "# Path to user data\n",
@@ -49,7 +49,7 @@
     "from freqtrade.data.history import load_pair_history\n",
     "\n",
     "candles = load_pair_history(datadir=data_location,\n",
-    "                            ticker_interval=ticker_interval,\n",
+    "                            timeframe=timeframe,\n",
     "                            pair=pair)\n",
     "\n",
     "# Confirm success\n",
@@ -68,9 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "scrolled": true
-   },
    "outputs": [],
    "source": [
     "# Load strategy using values set above\n",
@@ -169,6 +167,31 @@
     "trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyze the loaded trades for trade parallelism\n",
+    "This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.\n",
+    "\n",
+    "`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
+    "\n",
+    "# Analyze the above\n",
+    "parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
+    "\n",
+    "\n",
+    "parallel_trades.plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

freqtrade/templates/subtemplates/buy_trend_full.j2

@@ -0,0 +1,3 @@
+(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+(dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
+(dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising

freqtrade/templates/subtemplates/buy_trend_minimal.j2

@@ -0,0 +1 @@
+(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30

freqtrade/templates/subtemplates/hyperopt_buy_guards_full.j2

@@ -0,0 +1,8 @@
+if params.get('mfi-enabled'):
+    conditions.append(dataframe['mfi'] < params['mfi-value'])
+if params.get('fastd-enabled'):
+    conditions.append(dataframe['fastd'] < params['fastd-value'])
+if params.get('adx-enabled'):
+    conditions.append(dataframe['adx'] > params['adx-value'])
+if params.get('rsi-enabled'):
+    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_guards_minimal.j2

@@ -0,0 +1,2 @@
+if params.get('rsi-enabled'):
+    conditions.append(dataframe['rsi'] < params['rsi-value'])

freqtrade/templates/subtemplates/hyperopt_buy_space_full.j2

@@ -0,0 +1,9 @@
+Integer(10, 25, name='mfi-value'),
+Integer(15, 45, name='fastd-value'),
+Integer(20, 50, name='adx-value'),
+Integer(20, 40, name='rsi-value'),
+Categorical([True, False], name='mfi-enabled'),
+Categorical([True, False], name='fastd-enabled'),
+Categorical([True, False], name='adx-enabled'),
+Categorical([True, False], name='rsi-enabled'),
+Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_buy_space_minimal.j2

@@ -0,0 +1,3 @@
+Integer(20, 40, name='rsi-value'),
+Categorical([True, False], name='rsi-enabled'),
+Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

freqtrade/templates/subtemplates/hyperopt_sell_guards_full.j2

@@ -0,0 +1,8 @@
+if params.get('sell-mfi-enabled'):
+    conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
+if params.get('sell-fastd-enabled'):
+    conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
+if params.get('sell-adx-enabled'):
+    conditions.append(dataframe['adx'] < params['sell-adx-value'])
+if params.get('sell-rsi-enabled'):
+    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

freqtrade/templates/subtemplates/hyperopt_sell_guards_minimal.j2

@@ -0,0 +1,2 @@
+if params.get('sell-rsi-enabled'):
+    conditions.append(dataframe['rsi'] > params['sell-rsi-value'])