Merge pull request #6348 from freqtrade/new_release

New release 2022.1

.github/dependabot.yml

@@ -5,9 +5,17 @@ updates:
   schedule:
     interval: daily
   open-pull-requests-limit: 10
+
 - package-ecosystem: pip
   directory: "/"
   schedule:
     interval: weekly
   open-pull-requests-limit: 10
   target-branch: develop
+
+- package-ecosystem: "github-actions"
+  directory: "/"
+  schedule:
+    interval: "weekly"
+  open-pull-requests-limit: 10
+  target-branch: develop

.github/workflows/ci.yml

@@ -3,9 +3,9 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
       - stable
       - develop
+      - ci/*
     tags:
   release:
     types: [published]
@@ -20,7 +20,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04 ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -39,7 +39,7 @@ jobs:
 
     - name: pip cache (linux)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'ubuntu')
+      if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -50,8 +50,9 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
+      if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -69,7 +70,7 @@ jobs:
       if: matrix.python-version == '3.9'
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -101,23 +102,20 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( 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 }}
+          severity: error
+          details: Freqtrade CI failed on ${{ matrix.os }}
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   build_macos:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -136,7 +134,7 @@ jobs:
 
     - name: pip cache (macOS)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'macOS')
+      if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -147,10 +145,11 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - macOS
+      if: runner.os == 'macOS'
       run: |
         brew update
         brew install hdf5 c-blosc
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -162,7 +161,7 @@ jobs:
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -194,17 +193,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( 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 }}
-
+          severity: info
+          details: Test Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   build_windows:
 
@@ -212,7 +207,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -224,7 +219,6 @@ jobs:
 
     - name: Pip cache (Windows)
       uses: actions/cache@preview
-      if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -257,16 +251,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( 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 }}
+          severity: error
+          details: Test Failed
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   docs_check:
     runs-on: ubuntu-20.04
@@ -288,14 +279,13 @@ jobs:
         pip install mkdocs
         mkdocs build
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       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 }}
+          severity: error
+          details: Freqtrade doc test failed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   cleanup-prior-runs:
     runs-on: ubuntu-20.04
@@ -306,7 +296,7 @@ jobs:
       env:
         GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
 
-  # Notify on slack only once - when CI completes (and after deploy) in case it's successfull
+  # Notify only once - when CI completes (and after deploy) in case it's successfull
   notify-complete:
     needs: [ build_linux, build_macos, build_windows, docs_check ]
     runs-on: ubuntu-20.04
@@ -320,14 +310,13 @@ jobs:
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       if: always() && steps.check.outputs.has-permission && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
-        job_name: '*Freqtrade CI*'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
+          severity: info
+          details: Test Completed!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
     needs: [ build_linux, build_macos, build_windows, docs_check ]
@@ -385,7 +374,7 @@ jobs:
 
     - name: Set up Docker Buildx
       id: buildx
-      uses: crazy-max/ghaction-docker-buildx@v1
+      uses: crazy-max/ghaction-docker-buildx@v3.3.1
       with:
         buildx-version: latest
         qemu-version: latest
@@ -400,17 +389,13 @@ jobs:
       run: |
         build_helpers/publish_docker_multi.sh
 
-
-    - name: Slack Notification
-      uses: lazy-actions/slatify@v3.0.0
+    - name: Discord notification
+      uses: rjstone/discord-webhook-notify@v1
       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 }}
+          severity: info
+          details: Deploy Succeeded!
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
 
   deploy_arm:

.github/workflows/docker_update_readme.yml

@@ -10,7 +10,7 @@ jobs:
     steps:
     - uses: actions/checkout@v1
     - name: Docker Hub Description
-      uses: peter-evans/dockerhub-description@v2.1.0
+      uses: peter-evans/dockerhub-description@v2.4.3
       env:
         DOCKERHUB_USERNAME: ${{ secrets.DOCKER_USERNAME }}
         DOCKERHUB_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}

.travis.yml

@@ -1,55 +0,0 @@
-os:
-- linux
-dist: bionic
-language: python
-python:
-- 3.8
-services:
-  - docker
-env:
-  global:
-    - IMAGE_NAME=freqtradeorg/freqtrade
-install:
-- cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies; cd ..
-- export LD_LIBRARY_PATH=${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 .
-jobs:
-
-  include:
-    - stage: tests
-      script:
-      - pytest --random-order --cov=freqtrade --cov-config=.coveragerc
-      # Allow failure for coveralls
-      # - coveralls || true
-      name: pytest
-    - script:
-      - cp config_examples/config_bittrex.example.json config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
-      name: backtest
-    - script:
-      - cp config_examples/config_bittrex.example.json config.json
-      - freqtrade create-userdir --userdir user_data
-      - freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily
-      name: hyperopt
-    - script: flake8
-      name: flake8
-    - script:
-      # Test Documentation boxes -
-      # !!! <TYPE>: is not allowed!
-      # !!! <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
-
-notifications:
-  slack:
-    secure: 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
-cache:
-  pip: True
-  directories:
-    - $HOME/dependencies

README.md

@@ -49,7 +49,7 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
 
 ## Features
 
-- [x] **Based on Python 3.7+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
@@ -197,7 +197,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python >= 3.8](http://docs.python-guide.org/en/latest/starting/installation/)
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 - [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)

build_helpers/install_windows.ps1

@@ -1,19 +1,18 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 
-python -m pip install --upgrade pip
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
-if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.21-cp37-cp37m-win_amd64.whl
-}
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.21-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.21-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.24-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

config_examples/config_binance.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "ask_last_balance": 0.0,

config_examples/config_bittrex.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "use_order_book": true,

config_examples/config_ftx.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "ask_last_balance": 0.0,

config_examples/config_full.example.json

@@ -18,6 +18,7 @@
     "sell_profit_only": false,
     "sell_profit_offset": 0.0,
     "ignore_roi_if_buy_signal": false,
+    "ignore_buying_expired_candle_after": 300,
     "minimal_roi": {
         "40":  0.0,
         "30":  0.01,
@@ -27,7 +28,7 @@
     "stoploss": -0.10,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30,
+        "sell": 10,
         "exit_timeout_count": 0,
         "unit": "minutes"
     },

config_examples/config_kraken.example.json

@@ -9,7 +9,9 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 10,
+        "exit_timeout_count": 0,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "use_order_book": true,

docs/advanced-hyperopt.md

@@ -13,7 +13,7 @@ A sample of this can be found below, which is identical to the Default Hyperopt
 
 ``` python
 from datetime import datetime
-from typing import Dict
+from typing import Any, Dict
 
 from pandas import DataFrame
 
@@ -105,7 +105,7 @@ You can define your own estimator for Hyperopt by implementing `generate_estimat
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             return "RF"
 
 ```
@@ -119,13 +119,34 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
 ```python
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
-        def generate_estimator():
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
             from skopt.learning import ExtraTreesRegressor
             # Corresponds to "ET" - but allows additional parameters.
             return ExtraTreesRegressor(n_estimators=100)
 
 ```
 
+The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
+
+```python
+class MyAwesomeStrategy(IStrategy):
+    class HyperOpt:
+        def generate_estimator(dimensions: List['Dimension'], **kwargs):
+            from skopt.utils import cook_estimator
+            from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
+            kernel_bounds = (0.0001, 10000)
+            kernel = (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
+            )
+            kernel += (
+                ConstantKernel(1.0, kernel_bounds) * 
+                Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
+            )
+
+            return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
+```
+
 !!! Note
     While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
     If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.

docs/advanced-setup.md

@@ -176,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
 On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile 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

docs/backtesting.md

@@ -22,6 +22,7 @@ usage: freqtrade backtesting [-h] [-v] [--logfile FILE] [-V] [-c PATH]
                              [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
                              [--export {none,trades}] [--export-filename PATH]
                              [--breakdown {day,week,month} [{day,week,month} ...]]
+                             [--cache {none,day,week,month}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -76,6 +77,9 @@ optional arguments:
                         _today.json`
   --breakdown {day,week,month} [{day,week,month} ...]
                         Show backtesting breakdown per [day, week, month].
+  --cache {none,day,week,month}
+                        Load a cached backtest result no older than specified
+                        age (default: day).
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
@@ -115,7 +119,7 @@ The result of backtesting will confirm if your bot has better odds of making a p
 All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
 
 !!! Warning "Using dynamic pairlists for backtesting"
-    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Using dynamic pairlists is possible (not all of the handlers are allowed to be used in backtest mode), however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
     Also, when using pairlists other than StaticPairlist, reproducibility of backtesting-results cannot be guaranteed.
     Please read the [pairlists documentation](plugins.md#pairlists) for more information.
 
@@ -312,7 +316,7 @@ A backtesting result will look like that:
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
+| Drawdown (Account)    | 13.33%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
@@ -399,7 +403,7 @@ It contains some useful key metrics about performance of your strategy on backte
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
+| Drawdown (Account)    | 13.33%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
@@ -426,7 +430,8 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
 - `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
 - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
+- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
 - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
 - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
 - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
@@ -456,6 +461,14 @@ freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
 
 The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day.
 
+### Backtest result caching
+
+To save time, by default backtest will reuse a cached result from within the last day when the backtested strategy and config match that of a previous backtest. To force a new backtest despite existing result for an identical run specify `--cache none` parameter.
+
+!!! Warning
+    Caching is automatically disabled for open-ended timeranges (`--timerange 20210101-`), as freqtrade cannot ensure reliably that the underlying data didn't change. It can also use cached results where it shouldn't if the original backtest had missing data at the end, which was fixed by downloading more data.
+    In this instance, please use `--cache none` once to force a fresh backtest.
+
 ### Further backtest-result analysis
 
 To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
@@ -484,8 +497,8 @@ Since backtesting lacks some detailed information about what happens within a ca
   - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
 - Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
 - Evaluation sequence (if multiple signals happen on the same candle)
-  - ROI (if not stoploss)
   - Sell-signal
+  - ROI (if not stoploss)
   - Stoploss
 
 Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.

docs/bot-basics.md

@@ -38,6 +38,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
   * Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
   * Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
   * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
 * Check if trade-slots are still available (if `max_open_trades` is reached).
 * Verifies buy signal trying to enter new positions.
   * Determine buy-price based on `bid_strategy` configuration setting, or by using the `custom_entry_price()` callback.
@@ -56,7 +57,11 @@ This loop will be repeated again and again until the bot is stopped.
 * Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair).
 * Loops per candle simulating entry and exit points.
   * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
+  * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
+  * Determine stake size by calling the `custom_stake_amount()` callback.
+  * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
   * Call `custom_stoploss()` and `custom_sell()` to find custom exit points.
+  * For sells based on sell-signal and custom-sell: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
 * Generate backtest report output
 
 !!! Note

docs/configuration.md

@@ -126,14 +126,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**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.<br>**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.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
+| `exchange.uid` | API uid to use for the exchange. Only required when you are in production mode and for exchanges that use uid for API requests.<br>**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. Supports regex pairs as `.*/BTC`. Not used by VolumePairList. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
 | `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting. [More information](plugins.md#pairlists-and-pairlist-handlers). <br> **Datatype:** List
-| `exchange.ccxt_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for ccxt configurations. 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_config` | Additional CCXT parameters passed to both ccxt instances (sync and async). This is usually the correct place for additional ccxt configurations. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation). Please avoid adding exchange secrets here (use the dedicated fields instead), as they may be contained in logs. <br> **Datatype:** Dict
 | `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) 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` | 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` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`<br> **Datatype:** Boolean
 | `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **Datatype:** Boolean
+| `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
 | `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`<br> **Datatype:** Boolean
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
 | `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
@@ -170,6 +172,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
 | `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
 | `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
+| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
+| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
 
 ### Parameters in the strategy
 
@@ -194,6 +198,8 @@ Values set in the configuration file always overwrite values set in the strategy
 * `sell_profit_offset`
 * `ignore_roi_if_buy_signal`
 * `ignore_buying_expired_candle_after`
+* `position_adjustment_enable`
+* `max_entry_position_adjustment`
 
 ### Configuring amount per trade
 
@@ -300,6 +306,15 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
     When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve.
     It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise, it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
 
+#### Dynamic stake amount with position adjustment
+
+When you want to use position adjustment with unlimited stakes, you must also implement `custom_stake_amount` to a return a value depending on your strategy.
+Typical value would be in the range of 25% - 50% of the proposed stakes, but depends highly on your strategy and how much you wish to leave into the wallet as position adjustment buffer.
+
+For example if your position adjustment assumes it can do 2 additional buys with the same stake amounts then your buffer should be 66.6667% of the initially proposed unlimited stake amount.
+
+Or another example if your position adjustment assumes it can do 1 additional buy with 3x the original stake amount then `custom_stake_amount` should return 25% of proposed stake amount and leave 75% for possible later position adjustments.
+
 --8<-- "includes/pricing.md"
 
 ### Understand minimal_roi

docs/data-analysis.md

@@ -50,19 +50,22 @@ Repetitive tasks | Shell scripts
 Data analysis & visualization | Notebook
 
 1. Use the CLI to
+
     * download historical data
     * run a backtest
     * run with real-time data
     * export results
 
 1. Collect these actions in shell scripts
+
     * save complicated commands with arguments
     * execute multi-step operations
     * automate testing strategies and preparing data for analysis
 
 1. Use a notebook to
+
     * visualize data
-    * munge and plot to generate insights
+    * mangle and plot to generate insights
 
 ## Example utility snippets
 

docs/deprecated.md

@@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
 
 ### The **--dynamic-whitelist** command line option
 
-This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
-and in freqtrade 2019.7.
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
+Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
 
 ### the `--live` command line option
 

docs/developer.md

@@ -324,9 +324,8 @@ jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade
 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 `stable` and `develop`.
+* Docker images are build for the branches `stable` and `develop`, and are built as multiarch builds, supporting multiple platforms via the same tag.
 * Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
-* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:stable_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.

docs/docker_quickstart.md

@@ -126,6 +126,12 @@ All freqtrade arguments will be available by running `docker-compose run --rm fr
 !!! Note "`docker-compose run --rm`"
     Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
 
+??? Note "Using docker without docker-compose"
+    "`docker-compose run --rm`" will require a compose file to be provided.
+    Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.  
+    For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.  
+    This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
+
 #### Example: Download data with docker-compose
 
 Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.

docs/exchanges.md

@@ -199,6 +199,11 @@ OKEX requires a passphrase for each api key, you will therefore need to add this
 !!! Warning
     OKEX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
 
+## Gate.io
+
+Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
+The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
+
 ## All exchanges
 
 Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.

docs/faq.md

@@ -188,12 +188,12 @@ There is however nothing preventing you from using GPU-enabled indicators within
 Per default Hyperopt called without the `-e`/`--epochs` command line option will only
 run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
 to find a great result (unless if you are very lucky), so you probably
-have to run it for 10.000 or more. But it will take an eternity to
+have to run it for 10000 or more. But it will take an eternity to
 compute.
 
 Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
 
-It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
+It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
 
 ```bash
 freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
@@ -217,9 +217,9 @@ already 8\*10^9\*10 evaluations. A roughly total of 80 billion evaluations.
 Did you run 100 000 evaluations? Congrats, you've done roughly 1 / 100 000 th
 of the search space, assuming that the bot never tests the same parameters more than once.
 
-* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
+* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 100000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades. 
 
-Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days. 
+Example: 4% profit 650 times vs 0,3% profit a trade 10000 times in a year. If we assume you set the --timerange to 365 days. 
 
 Example:
 `freqtrade --config config.json --strategy SampleStrategy --hyperopt SampleHyperopt -e 1000 --timerange 20190601-20200601`

docs/includes/pairlists.md

@@ -196,7 +196,7 @@ Trade count is used as a tie breaker.
 You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
 Not defining this parameter (or setting it to 0) will use all-time performance.
 
-The optional `min_profit` parameter defines the minimum profit a pair must have to be considered.
+The optional `min_profit` (as ratio -> a setting of `0.01` corresponds to 1%) parameter defines the minimum profit a pair must have to be considered.
 Pairs below this level will be filtered out.
 Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
 
@@ -206,7 +206,7 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
     {
         "method": "PerformanceFilter",
         "minutes": 1440,  // rolling 24h
-        "min_profit": 0.01
+        "min_profit": 0.01  // minimal profit 1%
     }
 ],
 ```
@@ -220,6 +220,9 @@ As this Filter uses past performance of the bot, it'll have some startup-period
 
 Filters low-value coins which would not allow setting stoplosses.
 
+!!! Warning "Backtesting"
+    `PrecisionFilter` does not support backtesting mode using multiple strategies.
+
 #### PriceFilter
 
 The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
@@ -257,7 +260,7 @@ Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 -
 Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
 
 !!! Tip
-    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.
 
 #### SpreadFilter
 

docs/index.md

@@ -11,7 +11,7 @@
 
 ## Introduction
 
-Freqtrade is a crypto-currency algorithmic trading software developed in python (3.7+) and supported on Windows, macOS and Linux.
+Freqtrade is a crypto-currency algorithmic trading software developed in python (3.8+) and supported on Windows, macOS and Linux.
 
 !!! Danger "DISCLAIMER"
     This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -67,7 +67,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.7+
+- Python 3.8+
 - pip (pip3)
 - git
 - TA-Lib

docs/installation.md

@@ -36,9 +36,13 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 
 These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
 
+!!! Note "ARM64 systems"
+    If you are running an ARM64 system (like a MacOS M1 or an Oracle VM), please use [docker](docker_quickstart.md) to run freqtrade.
+    While native installation is possible with some manual effort, this is not supported at the moment.
+
 ### Install guide
 
-* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [Python >= 3.8.x](http://docs.python-guide.org/en/latest/starting/installation/)
 * [pip](https://pip.pypa.io/en/stable/installing/)
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
@@ -416,16 +420,3 @@ open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10
 ```
 
 If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-
-### MacOS installation error with python 3.9
-
-When using python 3.9 on macOS, it's currently necessary to install some os-level modules to allow dependencies to compile.
-The errors you'll see happen during installation and are related to the installation of `tables` or `blosc`.
-
-You can install the necessary libraries with the following command:
-
-```bash
-brew install hdf5 c-blosc
-```
-
-After this, please run the installation (script) again.

docs/plotting.md

@@ -164,7 +164,7 @@ The resulting plot will have the following elements:
 
 An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
 
-Additional features when using plot_config include:
+Additional features when using `plot_config` include:
 
 * Specify colors per indicator
 * Specify additional subplots
@@ -174,6 +174,7 @@ The sample plot configuration below specifies fixed colors for the indicators. O
 It also allows multiple subplots to display both MACD and RSI at the same time.
 
 Plot type can be configured using `type` key. Possible types are:
+
 * `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
 * `bar` corresponding to `plotly.graph_objects.Bar` class.
 
@@ -181,6 +182,54 @@ Extra parameters to `plotly.graph_objects.*` constructor can be specified in `pl
 
 Sample configuration with inline comments explaining the process:
 
+``` python
+@property
+def plot_config(self):
+    """
+        There are a lot of solutions how to build the return dictionary.
+        The only important point is the return value.
+        Example:
+            plot_config = {'main_plot': {}, 'subplots': {}}
+
+    """
+    plot_config = {}
+    plot_config['main_plot'] = {
+        # Configuration for main plot indicators.
+        # Assumes 2 parameters, emashort and emalong to be specified.
+        f'ema_{self.emashort.value}': {'color': 'red'},
+        f'ema_{self.emalong.value}': {'color': '#CCCCCC'},
+        # By omitting color, a random color is selected.
+        'sar': {},
+        # fill area between senkou_a and senkou_b
+        'senkou_a': {
+            'color': 'green', #optional
+            'fill_to': 'senkou_b',
+            'fill_label': 'Ichimoku Cloud', #optional
+            'fill_color': 'rgba(255,76,46,0.2)', #optional
+        },
+        # plot senkou_b, too. Not only the area to it.
+        'senkou_b': {}
+    }
+    plot_config['subplots'] = {
+         # Create subplot MACD
+        "MACD": {
+            'macd': {'color': 'blue', 'fill_to': 'macdhist'},
+            'macdsignal': {'color': 'orange'},
+            'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}}
+        },
+        # Additional subplot RSI
+        "RSI": {
+            'rsi': {'color': 'red'}
+        }
+    }
+
+    return plot_config
+```
+
+??? Note "As attribute (former method)"
+    Assigning plot_config is also possible as Attribute (this used to be the default way).
+    This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
+
     ``` python
         plot_config = {
             'main_plot': {
@@ -216,6 +265,7 @@ Sample configuration with inline comments explaining the process:
 
     ```
 
+
 !!! Note
     The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
     `macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
@@ -223,6 +273,9 @@ Sample configuration with inline comments explaining the process:
 !!! Warning
     `plotly` arguments are only supported with plotly library and will not work with freq-ui.
 
+!!! Note "Trade position adjustments"
+    If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
+
 ## Plot profit
 
 ![plot-profit](assets/plot-profit.png)
@@ -233,6 +286,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
 * The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
 * Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
 The first graph is good to get a grip of how the overall market progresses.
 
@@ -242,6 +297,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
 mkdocs==1.2.3
-mkdocs-material==7.3.6
+mkdocs-material==8.1.9
 mdx_truly_sane_lists==1.2
 pymdown-extensions==9.1

docs/strategy-advanced.md

@@ -127,6 +127,21 @@ The provided exit-tag is then used as sell-reason - and shown as such in backtes
 !!! Note
     `sell_reason` is limited to 100 characters, remaining data will be truncated.
 
+## Strategy version
+
+You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
+
+``` python
+def version(self) -> str:
+    """
+    Returns version of the strategy.
+    """
+    return "1.1"
+```
+
+!!! Note
+    You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
+
 ## Derived strategies
 
 The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
@@ -207,9 +222,9 @@ should be rewritten to
 ```python
 frames = [dataframe]
 for val in self.buy_ema_short.range:
-    frames.append({
+    frames.append(DataFrame({
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
-    })
+    }))
 
 # Append columns to existing dataframe
 merged_frame = pd.concat(frames, axis=1)

docs/strategy-callbacks.md

@@ -15,6 +15,7 @@ Currently available callbacks:
 * [`check_buy_timeout()` and `check_sell_timeout()](#custom-order-timeout-rules)
 * [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
 * [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+* [`adjust_trade_position()`](#adjust-trade-position)
 
 !!! Tip "Callback calling sequence"
     You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
@@ -53,7 +54,7 @@ Called before entering a trade, makes it possible to manage your position size w
 class AwesomeStrategy(IStrategy):
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: float, max_stake: float,
-                            **kwargs) -> float:
+                            entry_tag: Optional[str], **kwargs) -> float:
 
         dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
         current_candle = dataframe.iloc[-1].squeeze()
@@ -73,7 +74,7 @@ class AwesomeStrategy(IStrategy):
 Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
 
 !!! Tip
-    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
 
 !!! Tip
     Returning `0` or `None` will prevent trades from being placed.
@@ -361,8 +362,8 @@ class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
-    def custom_entry_price(self, pair: str, current_time: datetime,
-                           proposed_rate, **kwargs) -> float:
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float, 
+                           entry_tag: Optional[str], **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
@@ -387,8 +388,10 @@ class AwesomeStrategy(IStrategy):
     **Example**:  
     If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
 
-!!! Warning "No backtesting support"
-    Custom entry-prices are currently not supported during backtesting.
+!!! Warning "Backtesting"
+    While Custom prices are supported in backtesting (starting with 2021.12), prices will be moved to within the candle's high/low prices.
+    This behavior is currently being tested, and might be changed at a later point.
+    `custom_exit_price()` is only called for sells of type Sell_signal and Custom sell. All other sell-types will use regular backtesting prices.
 
 ## Custom order timeout rules
 
@@ -410,7 +413,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
 The function must return either `True` (cancel order) or `False` (keep order alive).
 
 ``` python
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 from freqtrade.persistence import Trade
 
 class AwesomeStrategy(IStrategy):
@@ -423,22 +426,24 @@ class AwesomeStrategy(IStrategy):
         'sell': 60 * 25
     }
 
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
+                          current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
             return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
            return True
         return False
 
 
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
+                           current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
             return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
            return True
         return False
 ```
@@ -497,7 +502,8 @@ class AwesomeStrategy(IStrategy):
     # ... populate_* methods
 
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            **kwargs) -> bool:
         """
         Called right before placing a buy order.
         Timing for this function is critical, so avoid doing heavy computations or
@@ -566,3 +572,110 @@ class AwesomeStrategy(IStrategy):
         return True
 
 ```
+
+## Adjust trade position
+
+The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
+For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
+`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
+
+`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
+
+The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional buy order should be made (position is increased).
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
+
+This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
+`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
+
+!!! Note "About stake size"
+    Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
+    If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
+    Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+
+!!! Warning
+    Stoploss is still calculated from the initial opening price, not averaged price.
+
+!!! Warning "/stopbuy"
+    While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+
+!!! Warning "Backtesting"
+    During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class DigDeeperStrategy(IStrategy):
+    
+    position_adjustment_enable = True
+    
+    # Attempts to handle large drops with DCA. High stoploss is required.
+    stoploss = -0.30
+    
+    # ... populate_* methods
+    
+    # Example specific variables
+    max_entry_position_adjustment = 3
+    # This number is explained a bit further down
+    max_dca_multiplier = 5.5
+    
+    # This is called when placing the initial order (opening trade)
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+        
+        # We need to leave most of the funds for possible further DCA orders
+        # This also applies to fixed stakes
+        return proposed_stake / self.max_dca_multiplier
+        
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be increased.
+        This means extra buy orders with additional fees.
+ 
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current buy rate.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param min_stake: Minimal stake size allowed by exchange.
+        :param max_stake: Balance available for trading.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade
+        """
+        
+        if current_profit > -0.05:
+            return None
+
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        # Only buy when not actively falling price.
+        last_candle = dataframe.iloc[-1].squeeze()
+        previous_candle = dataframe.iloc[-2].squeeze()
+        if last_candle['close'] < previous_candle['close']:
+            return None
+
+        filled_buys = trade.select_filled_orders('buy')
+        count_of_buys = trade.nr_of_successful_buys
+        # Allow up to 3 additional increasingly larger buys (4 in total)
+        # Initial buy is 1x
+        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+        # If that falls down to -5% again, we buy 1.5x more
+        # If that falls once again down to -5%, we buy 1.75x more
+        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+        # That is why max_dca_multiplier is 5.5
+        # Hope you have a deep wallet!
+        try:
+            # This returns first order stake size
+            stake_amount = filled_buys[0].cost
+            # This then calculates current safety order size
+            stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
+            return stake_amount
+        except Exception as exception:
+            return None
+
+        return None
+
+```

docs/strategy-customization.md

@@ -838,7 +838,7 @@ In some situations it may be confusing to deal with stops relative to current ra
 
     from datetime import datetime
     from freqtrade.persistence import Trade
-    from freqtrade.strategy import IStrategy, stoploss_from_open
+    from freqtrade.strategy import IStrategy, stoploss_from_absolute
 
     class AwesomeStrategy(IStrategy):
 

docs/strategy_analysis_example.md

@@ -50,7 +50,9 @@ candles.head()
 ```python
 # Load strategy using values set above
 from freqtrade.resolvers import StrategyResolver
+from freqtrade.data.dataprovider import DataProvider
 strategy = StrategyResolver.load_strategy(config)
+strategy.dp = DataProvider(config, None, None)
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})
@@ -228,7 +230,7 @@ graph = generate_candlestick_graph(pair=pair,
 # Show graph inline
 # graph.show()
 
-# Render graph in a separate window
+# Render graph in a seperate window
 graph.show(renderer="browser")
 
 ```

docs/utils.md

@@ -59,7 +59,7 @@ $ freqtrade new-config --config config_binance.json
 ? Do you want to enable Dry-run (simulated trades)?  Yes
 ? Please insert your stake currency: BTC
 ? Please insert your stake amount: 0.05
-? Please insert max_open_trades (Integer or 'unlimited'): 3
+? Please insert max_open_trades (Integer or -1 for unlimited open trades): 3
 ? Please insert your desired timeframe (e.g. 5m): 5m
 ? Please insert your display Currency (for reporting): USD
 ? Select exchange  binance

docs/webhook-config.md

@@ -50,7 +50,7 @@ Sample configuration (tested using IFTTT).
 
 The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
 
-You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use `"format": "form"` or `"format": "json"` respectively. Example configuration for Mattermost Cloud integration:
+You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw data. Use `"format": "form"`, `"format": "json"`, or `"format": "raw"` respectively. Example configuration for Mattermost Cloud integration:
 
 ```json
   "webhook": {
@@ -63,7 +63,36 @@ You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use
     },
 ```
 
-The result would be POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
+The result would be a POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
+
+When using the Form-Encoded or JSON-Encoded configuration you can configure any number of payload values, and both the key and value will be ouput in the POST request. However, when using the raw data format you can only configure one value and it **must** be named `"data"`. In this instance the data key will not be output in the POST request, only the value. For example:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "format": "raw",
+        "webhookstatus": {
+            "data": "Status: {status}"
+        }
+    },
+```
+
+The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.
+
+Optional parameters are available to enable automatic retries for webhook messages. The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook. Example configuration for retries:
+
+```json
+  "webhook": {
+        "enabled": true,
+        "url": "https://<YOURHOOKURL>",
+        "retries": 3,
+        "retry_delay": 0.2,
+        "webhookstatus": {
+            "status": "Status: {status}"
+        }
+    },
+```
 
 Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
 
@@ -75,11 +104,13 @@ Possible parameters are:
 * `trade_id`
 * `exchange`
 * `pair`
-* `limit`
+* ~~`limit` # Deprecated - should no longer be used.~~
+* `open_rate`
 * `amount`
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `order_type`
 * `current_rate`
@@ -98,6 +129,7 @@ Possible parameters are:
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `order_type`
 * `current_rate`
@@ -116,7 +148,10 @@ Possible parameters are:
 * `open_date`
 * `stake_amount`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
+* `order_type`
+* `current_rate`
 * `buy_tag`
 
 ### Webhooksell
@@ -134,6 +169,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
@@ -156,6 +192,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`
@@ -178,6 +215,7 @@ Possible parameters are:
 * `profit_amount`
 * `profit_ratio`
 * `stake_currency`
+* `base_currency`
 * `fiat_currency`
 * `sell_reason`
 * `order_type`

docs/windows_installation.md

@@ -23,9 +23,9 @@ git clone https://github.com/freqtrade/freqtrade.git
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.21-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
 
-Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
+Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
 Other versions must be downloaded from the above link.
 
 ``` powershell

environment.yml

@@ -4,7 +4,7 @@ channels:
 # - defaults
 dependencies:
 # 1/4 req main
-    - python>=3.7,<3.9
+    - python>=3.8,<=3.10
     - numpy
     - pandas
     - pip
@@ -25,9 +25,12 @@ dependencies:
     - fastapi
     - uvicorn
     - pyjwt
+    - aiofiles
+    - psutil
     - colorama
     - questionary
     - prompt-toolkit
+    - python-dateutil
 
 
     # ============================

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.11'
+__version__ = '2022.1'
 
 if __version__ == 'develop':
 

freqtrade/__main__.py

@@ -3,7 +3,7 @@
 __main__.py for Freqtrade
 To launch Freqtrade as a module
 
-> python -m freqtrade (with Python >= 3.7)
+> python -m freqtrade (with Python >= 3.8)
 """
 
 from freqtrade import main

freqtrade/commands/arguments.py

@@ -24,7 +24,7 @@ ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
 ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
                                         "enable_protections", "dry_run_wallet", "timeframe_detail",
                                         "strategy_list", "export", "exportfilename",
-                                        "backtest_breakdown"]
+                                        "backtest_breakdown", "backtest_cache"]
 
 ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
                                         "position_stacking", "use_max_market_positions",

freqtrade/commands/build_config_commands.py

@@ -76,17 +76,14 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "max_open_trades",
-            "message": f"Please insert max_open_trades (Integer or '{UNLIMITED_STAKE_AMOUNT}'):",
+            "message": "Please insert max_open_trades (Integer or -1 for unlimited open trades):",
             "default": "3",
-            "validate": lambda val: val == UNLIMITED_STAKE_AMOUNT or validate_is_int(val),
-            "filter": lambda val: '"' + UNLIMITED_STAKE_AMOUNT + '"'
-            if val == UNLIMITED_STAKE_AMOUNT
-            else val
+            "validate": lambda val: validate_is_int(val)
         },
         {
             "type": "select",
             "name": "timeframe_in_config",
-            "message": "Tim",
+            "message": "Time",
             "choices": ["Have the strategy define timeframe.", "Override in configuration."]
         },
         {

freqtrade/commands/cli_options.py

@@ -205,6 +205,12 @@ AVAILABLE_CLI_OPTIONS = {
         nargs='+',
         choices=constants.BACKTEST_BREAKDOWNS
     ),
+    "backtest_cache": Arg(
+        '--cache',
+        help='Load a cached backtest result no older than specified age (default: %(default)s).',
+        default=constants.BACKTEST_CACHE_DEFAULT,
+        choices=constants.BACKTEST_CACHE_AGE,
+    ),
     # Edge
     "stoploss_range": Arg(
         '--stoplosses',

freqtrade/configuration/PeriodicCache.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timezone
 
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 
 
 class PeriodicCache(TTLCache):

freqtrade/configuration/configuration.py

@@ -276,6 +276,9 @@ class Configuration:
         self._args_to_config(config, argname='backtest_breakdown',
                              logstring='Parameter --breakdown detected ...')
 
+        self._args_to_config(config, argname='backtest_cache',
+                             logstring='Parameter --cache={} detected ...')
+
         self._args_to_config(config, argname='disableparamexport',
                              logstring='Parameter --disableparamexport detected: {} ...')
 

freqtrade/constants.py

@@ -34,6 +34,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
+BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
+BACKTEST_CACHE_DEFAULT = 'day'
 DRY_RUN_WALLET = 1000
 DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
@@ -50,6 +52,8 @@ USERPATH_STRATEGIES = 'strategies'
 USERPATH_NOTEBOOKS = 'notebooks'
 
 TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
+WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
+
 ENV_VAR_PREFIX = 'FREQTRADE__'
 
 NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
@@ -312,10 +316,16 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'enabled': {'type': 'boolean'},
+                'url': {'type': 'string'},
+                'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
+                'retries': {'type': 'integer', 'minimum': 0},
+                'retry_delay': {'type': 'number', 'minimum': 0},
                 'webhookbuy': {'type': 'object'},
                 'webhookbuycancel': {'type': 'object'},
+                'webhookbuyfill': {'type': 'object'},
                 'webhooksell': {'type': 'object'},
                 'webhooksellcancel': {'type': 'object'},
+                'webhooksellfill': {'type': 'object'},
                 'webhookstatus': {'type': 'object'},
             },
         },
@@ -361,7 +371,9 @@ CONF_SCHEMA = {
             'type': 'string',
             'enum': AVAILABLE_DATAHANDLERS,
             'default': 'jsongz'
-        }
+        },
+        'position_adjustment_enable': {'type': 'boolean'},
+        'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1},
     },
     'definitions': {
         'exchange': {
@@ -387,6 +399,7 @@ CONF_SCHEMA = {
                     },
                     'uniqueItems': True
                 },
+                'unknown_fee_rate': {'type': 'number'},
                 'outdated_offset': {'type': 'integer', 'minimum': 1},
                 'markets_refresh_interval': {'type': 'integer'},
                 'ccxt_config': {'type': 'object'},

freqtrade/data/btanalysis.py

@@ -2,6 +2,8 @@
 Helpers when analyzing backtest data
 """
 import logging
+from copy import copy
+from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -9,21 +11,13 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
-from freqtrade.misc import json_load
+from freqtrade.exceptions import OperationalException
+from freqtrade.misc import get_backtest_metadata_filename, json_load
 from freqtrade.persistence import LocalTrade, Trade, init_db
 
 
 logger = logging.getLogger(__name__)
 
-# Old format - maybe remove?
-BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
-                       "trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
-
-# Mid-term format, created by BacktestResult Named Tuple
-BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
-                       'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
-                       'fee_close', 'amount', 'profit_abs', 'profit_ratio']
-
 # Newest format
 BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
                    'open_rate', 'close_rate',
@@ -106,10 +100,30 @@ def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str =
     if isinstance(directory, str):
         directory = Path(directory)
     if predef_filename:
+        if Path(predef_filename).is_absolute():
+            raise OperationalException(
+                "--hyperopt-filename expects only the filename, not an absolute path.")
         return directory / predef_filename
     return directory / get_latest_hyperopt_filename(directory)
 
 
+def load_backtest_metadata(filename: Union[Path, str]) -> Dict[str, Any]:
+    """
+    Read metadata dictionary from backtest results file without reading and deserializing entire
+    file.
+    :param filename: path to backtest results file.
+    :return: metadata dict or None if metadata is not present.
+    """
+    filename = get_backtest_metadata_filename(filename)
+    try:
+        with filename.open() as fp:
+            return json_load(fp)
+    except FileNotFoundError:
+        return {}
+    except Exception as e:
+        raise OperationalException('Unexpected error while loading backtest metadata.') from e
+
+
 def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
     """
     Load backtest statistics file.
@@ -126,9 +140,80 @@ def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
     with filename.open() as file:
         data = json_load(file)
 
+    # Legacy list format does not contain metadata.
+    if isinstance(data, dict):
+        data['metadata'] = load_backtest_metadata(filename)
+
     return data
 
 
+def _load_and_merge_backtest_result(strategy_name: str, filename: Path, results: Dict[str, Any]):
+    bt_data = load_backtest_stats(filename)
+    for k in ('metadata', 'strategy'):
+        results[k][strategy_name] = bt_data[k][strategy_name]
+    comparison = bt_data['strategy_comparison']
+    for i in range(len(comparison)):
+        if comparison[i]['key'] == strategy_name:
+            results['strategy_comparison'].append(comparison[i])
+            break
+
+
+def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
+                                 min_backtest_date: datetime = None) -> Dict[str, Any]:
+    """
+    Find existing backtest stats that match specified run IDs and load them.
+    :param dirname: pathlib.Path object, or string pointing to the file.
+    :param run_ids: {strategy_name: id_string} dictionary.
+    :param min_backtest_date: do not load a backtest older than specified date.
+    :return: results dict.
+    """
+    # Copy so we can modify this dict without affecting parent scope.
+    run_ids = copy(run_ids)
+    dirname = Path(dirname)
+    results: Dict[str, Any] = {
+        'metadata': {},
+        'strategy': {},
+        'strategy_comparison': [],
+    }
+
+    # Weird glob expression here avoids including .meta.json files.
+    for filename in reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))):
+        metadata = load_backtest_metadata(filename)
+        if not metadata:
+            # Files are sorted from newest to oldest. When file without metadata is encountered it
+            # is safe to assume older files will also not have any metadata.
+            break
+
+        for strategy_name, run_id in list(run_ids.items()):
+            strategy_metadata = metadata.get(strategy_name, None)
+            if not strategy_metadata:
+                # This strategy is not present in analyzed backtest.
+                continue
+
+            if min_backtest_date is not None:
+                try:
+                    backtest_date = strategy_metadata['backtest_start_time']
+                except KeyError:
+                    # TODO: this can be removed starting from feb 2022
+                    # The metadata-file without start_time was only available in develop
+                    # and was never included in an official release.
+                    # Older metadata format without backtest time, too old to consider.
+                    return results
+                backtest_date = datetime.fromtimestamp(backtest_date, tz=timezone.utc)
+                if backtest_date < min_backtest_date:
+                    # Do not use a cached result for this strategy as first result is too old.
+                    del run_ids[strategy_name]
+                    continue
+
+            if strategy_metadata['run_id'] == run_id:
+                del run_ids[strategy_name]
+                _load_and_merge_backtest_result(strategy_name, filename, results)
+
+        if len(run_ids) == 0:
+            break
+    return results
+
+
 def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load backtest data file.
@@ -167,23 +252,9 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                               )
     else:
         # old format - only with lists.
-        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
+        raise OperationalException(
+            "Backtest-results with only trades data are no longer supported.")
     if not df.empty:
-            df['open_date'] = pd.to_datetime(df['open_date'],
-                                             unit='s',
-                                             utc=True,
-                                             infer_datetime_format=True
-                                             )
-            df['close_date'] = pd.to_datetime(df['close_date'],
-                                              unit='s',
-                                              utc=True,
-                                              infer_datetime_format=True
-                                              )
-            # Create compatibility with new format
-            df['profit_abs'] = df['close_rate'] - df['open_rate']
-    if not df.empty:
-        if 'profit_ratio' not in df.columns:
-            df['profit_ratio'] = df['profit_percent']
         df = df.sort_values("open_date").reset_index(drop=True)
     return df
 
@@ -325,6 +396,7 @@ def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
     :param column: Column in the original dataframes to use
     :return: DataFrame with the column renamed to the dict key, and a column
         named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
     """
     df_comb = pd.concat([data[pair].set_index('date').rename(
         {column: pair}, axis=1)[pair] for pair in data], axis=1)
@@ -360,9 +432,19 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     return df
 
 
-def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
+                          ) -> pd.DataFrame:
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+    max_drawdown_df['date'] = profit_results.loc[:, date_col]
+    return max_drawdown_df
+
+
+def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
                          value_col: str = 'profit_ratio'
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
+                         ):
     """
     Calculate max drawdown and the corresponding close dates
     :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
@@ -375,10 +457,29 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     if len(trades) == 0:
         raise ValueError("Trade dataframe empty.")
     profit_results = trades.sort_values(date_col).reset_index(drop=True)
-    max_drawdown_df = pd.DataFrame()
-    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
-    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
-    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
+
+    return max_drawdown_df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                           value_col: str = 'profit_abs', starting_balance: float = 0
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
+    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
+             with absolute max drawdown, high and low time and high and low value,
+             and the relative account drawdown
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
 
     idxmin = max_drawdown_df['drawdown'].idxmin()
     if idxmin == 0:
@@ -388,7 +489,18 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
                                    ['high_value'].idxmax(), 'cumulative']
     low_val = max_drawdown_df.loc[idxmin, 'cumulative']
-    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
+    max_drawdown_rel = 0.0
+    if high_val + starting_balance != 0:
+        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
+
+    return (
+        abs(min(max_drawdown_df['drawdown'])),
+        high_date,
+        low_date,
+        high_val,
+        low_val,
+        max_drawdown_rel
+    )
 
 
 def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:

freqtrade/data/history/hdf5datahandler.py

@@ -6,7 +6,6 @@ from typing import List, Optional
 import numpy as np
 import pandas as pd
 
-from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
                                  ListPairsWithTimeframes, TradeList)
@@ -61,10 +60,10 @@ class HDF5DataHandler(IDataHandler):
 
         filename = self._pair_data_filename(self._datadir, pair, timeframe)
 
-        ds = pd.HDFStore(filename, mode='a', complevel=9, complib='blosc')
-        ds.put(key, _data.loc[:, self._columns], format='table', data_columns=['date'])
-
-        ds.close()
+        _data.loc[:, self._columns].to_hdf(
+            filename, key, mode='a', complevel=9, complib='blosc',
+            format='table', data_columns=['date']
+        )
 
     def _ohlcv_load(self, pair: str, timeframe: str,
                     timerange: Optional[TimeRange] = None) -> pd.DataFrame:
@@ -99,19 +98,6 @@ class HDF5DataHandler(IDataHandler):
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
         return pairdata
 
-    def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :param timeframe: Timeframe (e.g. "5m")
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_data_filename(self._datadir, pair, timeframe)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
-
     def ohlcv_append(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
         """
         Append data to existing data structures
@@ -142,11 +128,11 @@ class HDF5DataHandler(IDataHandler):
         """
         key = self._pair_trades_key(pair)
 
-        ds = pd.HDFStore(self._pair_trades_filename(self._datadir, pair),
-                         mode='a', complevel=9, complib='blosc')
-        ds.put(key, pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS),
-               format='table', data_columns=['timestamp'])
-        ds.close()
+        pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
+            self._pair_trades_filename(self._datadir, pair), key,
+            mode='a', complevel=9, complib='blosc',
+            format='table', data_columns=['timestamp']
+        )
 
     def trades_append(self, pair: str, data: TradeList):
         """
@@ -180,17 +166,9 @@ class HDF5DataHandler(IDataHandler):
         trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
         return trades.values.tolist()
 
-    def trades_purge(self, pair: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_trades_filename(self._datadir, pair)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
+    @classmethod
+    def _get_file_extension(cls):
+        return "h5"
 
     @classmethod
     def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
@@ -199,15 +177,3 @@ class HDF5DataHandler(IDataHandler):
     @classmethod
     def _pair_trades_key(cls, pair: str) -> str:
         return f"{pair}/trades"
-
-    @classmethod
-    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-{timeframe}.h5')
-        return filename
-
-    @classmethod
-    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-trades.h5')
-        return filename

freqtrade/data/history/history_utils.py

@@ -5,7 +5,7 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import arrow
-from pandas import DataFrame
+from pandas import DataFrame, concat
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
@@ -208,7 +208,7 @@ def _download_pair_history(pair: str, *,
         else:
             # Run cleaning again to ensure there were no duplicate candles
             # Especially between existing and new data.
-            data = clean_ohlcv_dataframe(data.append(new_dataframe), timeframe, pair,
+            data = clean_ohlcv_dataframe(concat([data, new_dataframe], axis=0), timeframe, pair,
                                          fill_missing=False, drop_incomplete=False)
 
         logger.debug("New  Start: %s",

freqtrade/data/history/idatahandler.py

@@ -12,6 +12,7 @@ from typing import List, Optional, Type
 
 from pandas import DataFrame
 
+from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import ListPairsWithTimeframes, TradeList
 from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
@@ -26,6 +27,13 @@ class IDataHandler(ABC):
     def __init__(self, datadir: Path) -> None:
         self._datadir = datadir
 
+    @classmethod
+    def _get_file_extension(cls) -> str:
+        """
+        Get file extension for this particular datahandler
+        """
+        raise NotImplementedError()
+
     @abstractclassmethod
     def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
         """
@@ -70,7 +78,6 @@ class IDataHandler(ABC):
         :return: DataFrame with ohlcv data, or empty DataFrame
         """
 
-    @abstractmethod
     def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
         """
         Remove data for this pair
@@ -78,6 +85,11 @@ class IDataHandler(ABC):
         :param timeframe: Timeframe (e.g. "5m")
         :return: True when deleted, false if file did not exist.
         """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
 
     @abstractmethod
     def ohlcv_append(self, pair: str, timeframe: str, data: DataFrame) -> None:
@@ -123,13 +135,17 @@ class IDataHandler(ABC):
         :return: List of trades
         """
 
-    @abstractmethod
     def trades_purge(self, pair: str) -> bool:
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
         :return: True when deleted, false if file did not exist.
         """
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if filename.exists():
+            filename.unlink()
+            return True
+        return False
 
     def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
         """
@@ -141,6 +157,18 @@ class IDataHandler(ABC):
         """
         return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
 
+    @classmethod
+    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
+        return filename
+
+    @classmethod
+    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
+        pair_s = misc.pair_to_filename(pair)
+        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
+        return filename
+
     def ohlcv_load(self, pair, timeframe: str,
                    timerange: Optional[TimeRange] = None,
                    fill_missing: bool = True,
@@ -173,7 +201,7 @@ class IDataHandler(ABC):
             enddate = pairdf.iloc[-1]['date']
 
             if timerange_startup:
-                self._validate_pairdata(pair, pairdf, timerange_startup)
+                self._validate_pairdata(pair, pairdf, timeframe, timerange_startup)
                 pairdf = trim_dataframe(pairdf, timerange_startup)
                 if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
                     return pairdf
@@ -200,7 +228,7 @@ class IDataHandler(ABC):
             return True
         return False
 
-    def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
+    def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str, timerange: TimeRange):
         """
         Validates pairdata for missing data at start end end and logs warnings.
         :param pairdata: Dataframe to validate
@@ -210,12 +238,12 @@ class IDataHandler(ABC):
         if timerange.starttype == 'date':
             start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
             if pairdata.iloc[0]['date'] > start:
-                logger.warning(f"Missing data at start for pair {pair}, "
+                logger.warning(f"Missing data at start for pair {pair} at {timeframe}, "
                                f"data starts at {pairdata.iloc[0]['date']:%Y-%m-%d %H:%M:%S}")
         if timerange.stoptype == 'date':
             stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
             if pairdata.iloc[-1]['date'] < stop:
-                logger.warning(f"Missing data at end for pair {pair}, "
+                logger.warning(f"Missing data at end for pair {pair} at {timeframe}, "
                                f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
 
 

freqtrade/data/history/jsondatahandler.py

@@ -174,34 +174,10 @@ class JsonDataHandler(IDataHandler):
             pass
         return tradesdata
 
-    def trades_purge(self, pair: str) -> bool:
-        """
-        Remove data for this pair
-        :param pair: Delete data for this pair.
-        :return: True when deleted, false if file did not exist.
-        """
-        filename = self._pair_trades_filename(self._datadir, pair)
-        if filename.exists():
-            filename.unlink()
-            return True
-        return False
-
-    @classmethod
-    def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-{timeframe}.{cls._get_file_extension()}')
-        return filename
-
     @classmethod
     def _get_file_extension(cls):
         return "json.gz" if cls._use_zip else "json"
 
-    @classmethod
-    def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
-        pair_s = misc.pair_to_filename(pair)
-        filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
-        return filename
-
 
 class JsonGzDataHandler(JsonDataHandler):
 

freqtrade/enums/__init__.py

@@ -1,5 +1,6 @@
 # flake8: noqa: F401
 from freqtrade.enums.backteststate import BacktestState
+from freqtrade.enums.ordertypevalue import OrderTypeValues
 from freqtrade.enums.rpcmessagetype import RPCMessageType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.selltype import SellType

freqtrade/enums/ordertypevalue.py

@@ -0,0 +1,6 @@
+from enum import Enum
+
+
+class OrderTypeValues(str, Enum):
+    limit = 'limit'
+    market = 'market'

freqtrade/exchange/__init__.py

@@ -5,6 +5,7 @@ from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.bibox import Bibox
 from freqtrade.exchange.binance import Binance
+from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bittrex import Bittrex
 from freqtrade.exchange.bybit import Bybit
 from freqtrade.exchange.coinbasepro import Coinbasepro

freqtrade/exchange/bitpanda.py

@@ -0,0 +1,37 @@
+""" Bitpanda exchange subclass """
+import logging
+from datetime import datetime, timezone
+from typing import Dict, List, Optional
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitpanda(Exchange):
+    """
+    Bitpanda exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
+                             params: Optional[Dict] = None) -> 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.
+        """
+        params = {'to': int(datetime.now(timezone.utc).timestamp() * 1000)}
+        return super().get_trades_for_order(order_id, pair, since, params)

freqtrade/exchange/common.py

@@ -4,9 +4,20 @@ import time
 from functools import wraps
 
 from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
+from freqtrade.mixins import LoggingMixin
 
 
 logger = logging.getLogger(__name__)
+__logging_mixin = None
+
+
+def _get_logging_mixin():
+    # Logging-mixin to cache kucoin responses
+    # Only to be used in retrier
+    global __logging_mixin
+    if not __logging_mixin:
+        __logging_mixin = LoggingMixin(logger)
+    return __logging_mixin
 
 
 # Maximum default retry count.
@@ -72,28 +83,33 @@ def calculate_backoff(retrycount, max_retries):
 def retrier_async(f):
     async def wrapper(*args, **kwargs):
         count = kwargs.pop('count', API_RETRY_COUNT)
+        kucoin = args[0].name == "Kucoin"  # Check if the exchange is KuCoin.
         try:
             return await f(*args, **kwargs)
         except TemporaryError as ex:
-            logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+            msg = f'{f.__name__}() returned exception: "{ex}". '
             if count > 0:
-                logger.warning('retrying %s() still for %s times', f.__name__, count)
+                msg += f'Retrying still for {count} times.'
                 count -= 1
-                kwargs.update({'count': count})
+                kwargs['count'] = count
                 if isinstance(ex, DDosProtection):
-                    if "kucoin" in str(ex) and "429000" in str(ex):
+                    if kucoin and "429000" in str(ex):
                         # Temporary fix for 429000 error on kucoin
                         # see https://github.com/freqtrade/freqtrade/issues/5700 for details.
-                        logger.warning(
+                        _get_logging_mixin().log_once(
                             f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
-                            f"{count} tries left before giving up")
+                            f"{count} tries left before giving up", logmethod=logger.warning)
+                        # Reset msg to avoid logging too many times.
+                        msg = ''
                     else:
                         backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
                         logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
                         await asyncio.sleep(backoff_delay)
+                if msg:
+                    logger.warning(msg)
                 return await wrapper(*args, **kwargs)
             else:
-                logger.warning('Giving up retrying: %s()', f.__name__)
+                logger.warning(msg + 'Giving up.')
                 raise ex
     return wrapper
 
@@ -106,9 +122,9 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
             try:
                 return f(*args, **kwargs)
             except (TemporaryError, RetryableOrderError) as ex:
-                logger.warning('%s() returned exception: "%s"', f.__name__, ex)
+                msg = f'{f.__name__}() returned exception: "{ex}". '
                 if count > 0:
-                    logger.warning('retrying %s() still for %s times', f.__name__, count)
+                    logger.warning(msg + f'Retrying still for {count} times.')
                     count -= 1
                     kwargs.update({'count': count})
                     if isinstance(ex, (DDosProtection, RetryableOrderError)):
@@ -118,7 +134,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
                         time.sleep(backoff_delay)
                     return wrapper(*args, **kwargs)
                 else:
-                    logger.warning('Giving up retrying: %s()', f.__name__)
+                    logger.warning(msg + 'Giving up.')
                     raise ex
         return wrapper
     # Support both @retrier and @retrier(retries=2) syntax

freqtrade/exchange/exchange.py

@@ -67,6 +67,8 @@ class Exchange:
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
+        # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
+        "ohlcv_volume_currency": "base",  # "base" or "quote"
         "trades_pagination": "time",  # Possible are "time" or "id"
         "trades_pagination_arg": "since",
         "l2_limit_range": None,
@@ -83,6 +85,8 @@ class Exchange:
         self._api: ccxt.Exchange = None
         self._api_async: ccxt_async.Exchange = None
         self._markets: Dict = {}
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
 
         self._config.update(config)
 
@@ -170,8 +174,10 @@ class Exchange:
 
     def close(self):
         logger.debug("Exchange object destroyed, closing async loop")
-        if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
-            asyncio.get_event_loop().run_until_complete(self._api_async.close())
+        if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
+                and self._api_async.session):
+            logger.info("Closing async ccxt session.")
+            self.loop.run_until_complete(self._api_async.close())
 
     def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
                    ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
@@ -326,7 +332,7 @@ class Exchange:
     def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
-                asyncio.get_event_loop().run_until_complete(
+                self.loop.run_until_complete(
                     self._api_async.load_markets(reload=reload))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
@@ -606,8 +612,9 @@ class Exchange:
             'cost': _amount * rate,
             'type': ordertype,
             'side': side,
+            'filled': 0,
             'remaining': _amount,
-            'datetime': arrow.utcnow().isoformat(),
+            'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
             'timestamp': arrow.utcnow().int_timestamp * 1000,
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
@@ -621,6 +628,7 @@ class Exchange:
             average = self.get_dry_market_fill_price(pair, side, amount, rate)
             dry_order.update({
                 'average': average,
+                'filled': _amount,
                 'cost': dry_order['amount'] * average,
             })
             dry_order = self.add_dry_order_fee(pair, dry_order)
@@ -652,7 +660,8 @@ class Exchange:
             max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
-            filled_amount = 0
+            filled_amount = 0.0
+            book_entry_price = 0.0
             for book_entry in ob[ob_type]:
                 book_entry_price = book_entry[0]
                 book_entry_coin_volume = book_entry[1]
@@ -685,6 +694,7 @@ class Exchange:
         if not self.exchange_has('fetchL2OrderBook'):
             return True
         ob = self.fetch_l2_order_book(pair, 1)
+        try:
             if side == 'buy':
                 price = ob['asks'][0][0]
                 logger.debug(f"{pair} checking dry buy-order: price={price}, limit={limit}")
@@ -695,6 +705,9 @@ class Exchange:
                 logger.debug(f"{pair} checking dry sell-order: price={price}, limit={limit}")
                 if limit <= price:
                     return True
+        except IndexError:
+            # Ignore empty orderbooks when filling - can be filled with the next iteration.
+            pass
         return False
 
     def check_dry_limit_order_filled(self, order: Dict[str, Any]) -> Dict[str, Any]:
@@ -940,7 +953,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_tickers(self, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
         """
         :param cached: Allow cached result
         :return: fetch_tickers result
@@ -950,7 +963,7 @@ class Exchange:
             if tickers:
                 return tickers
         try:
-            tickers = self._api.fetch_tickers()
+            tickers = self._api.fetch_tickers(symbols)
             self._fetch_tickers_cache['fetch_tickers'] = tickers
             return tickers
         except ccxt.NotSupported as e:
@@ -1087,7 +1100,8 @@ class Exchange:
     # Fee handling
 
     @retrier
-    def get_trades_for_order(self, order_id: str, pair: str, since: datetime) -> List:
+    def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
+                             params: Optional[Dict] = None) -> 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,
@@ -1111,8 +1125,10 @@ class Exchange:
         try:
             # Allow 5s offset to catch slight time offsets (discovered in #1185)
             # since needs to be int in milliseconds
+            _params = params if params else {}
             my_trades = self._api.fetch_my_trades(
-                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000))
+                pair, int((since.replace(tzinfo=timezone.utc).timestamp() - 5) * 1000),
+                params=_params)
             matched_trades = [trade for trade in my_trades if trade['order'] == order_id]
 
             self._log_exchange_response('get_trades_for_order', matched_trades)
@@ -1190,9 +1206,11 @@ class Exchange:
                 tick = self.fetch_ticker(comb)
 
                 fee_to_quote_rate = safe_value_fallback2(tick, tick, 'last', 'ask')
-                return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
             except ExchangeError:
+                fee_to_quote_rate = self._config['exchange'].get('unknown_fee_rate', None)
+                if not fee_to_quote_rate:
                     return None
+            return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
 
     def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
         """
@@ -1218,7 +1236,7 @@ class Exchange:
         :param since_ms: Timestamp in milliseconds to get history from
         :return: List with candle (OHLCV) data
         """
-        pair, timeframe, data = asyncio.get_event_loop().run_until_complete(
+        pair, timeframe, data = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, is_new_pair=is_new_pair))
         logger.info(f"Downloaded data for {pair} with length {len(data)}.")
@@ -1263,7 +1281,7 @@ class Exchange:
             results = await asyncio.gather(*input_coro, return_exceptions=True)
             for res in results:
                 if isinstance(res, Exception):
-                    logger.warning("Async code raised an exception: %s", res.__class__.__name__)
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
                     if raise_:
                         raise
                     continue
@@ -1294,7 +1312,7 @@ class Exchange:
         cached_pairs = []
         # Gather coroutines to run
         for pair, timeframe in set(pair_list):
-            if ((pair, timeframe) not in self._klines
+            if ((pair, timeframe) not in self._klines or not cache
                     or self._now_is_time_to_refresh(pair, timeframe)):
                 if not since_ms and self.required_candle_call_count > 1:
                     # Multiple calls for one pair - to get more history
@@ -1317,14 +1335,18 @@ class Exchange:
                 )
                 cached_pairs.append((pair, timeframe))
 
-        results = asyncio.get_event_loop().run_until_complete(
-            asyncio.gather(*input_coroutines, return_exceptions=True))
-
         results_df = {}
+        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        for input_coro in chunks(input_coroutines, 100):
+            async def gather_stuff():
+                return await asyncio.gather(*input_coro, return_exceptions=True)
+
+            results = self.loop.run_until_complete(gather_stuff())
+
             # handle caching
             for res in results:
                 if isinstance(res, Exception):
-                logger.warning("Async code raised an exception: %s", res.__class__.__name__)
+                    logger.warning(f"Async code raised an exception: {repr(res)}")
                     continue
                 # Deconstruct tuple (has 3 elements)
                 pair, timeframe, ticks = res
@@ -1338,6 +1360,7 @@ class Exchange:
                 results_df[(pair, timeframe)] = ohlcv_df
                 if cache:
                     self._klines[(pair, timeframe)] = ohlcv_df
+
         # Return cached klines
         for pair, timeframe in cached_pairs:
             results_df[(pair, timeframe)] = self.klines((pair, timeframe), copy=False)
@@ -1554,7 +1577,7 @@ class Exchange:
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not support downloading Trades.")
 
-        return asyncio.get_event_loop().run_until_complete(
+        return self.loop.run_until_complete(
             self._async_get_trade_history(pair=pair, since=since,
                                           until=until, from_id=from_id))
 

freqtrade/exchange/ftx.py

@@ -19,6 +19,7 @@ class Ftx(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "ohlcv_candle_limit": 1500,
+        "ohlcv_volume_currency": "quote",
     }
 
     def market_is_tradable(self, market: Dict[str, Any]) -> bool:

freqtrade/exchange/gateio.py

@@ -21,6 +21,7 @@ class Gateio(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
+        "ohlcv_volume_currency": "quote",
     }
 
     _headers = {'X-Gate-Channel-Id': 'freqtrade'}

freqtrade/exchange/kraken.py

@@ -1,6 +1,6 @@
 """ Kraken exchange subclass """
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 import ccxt
 
@@ -33,6 +33,12 @@ class Kraken(Exchange):
         return (parent_check and
                 market.get('darkpool', False) is False)
 
+    def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
+        # Only fetch tickers for current stake currency
+        # Otherwise the request for kraken becomes too large.
+        symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))
+        return super().get_tickers(symbols=symbols, cached=cached)
+
     @retrier
     def get_balances(self) -> dict:
         if self._config['dry_run']:

freqtrade/exchange/okex.py

@@ -14,5 +14,5 @@ class Okex(Exchange):
     """
 
     _ft_has: Dict = {
-        "ohlcv_candle_limit": 100,
+        "ohlcv_candle_limit": 300,
     }

freqtrade/freqtradebot.py

@@ -7,16 +7,14 @@ import traceback
 from datetime import datetime, timezone
 from math import isclose
 from threading import Lock
-from typing import Any, Dict, List, Optional
-
-import arrow
+from typing import Any, Dict, List, Optional, Tuple
 
 from freqtrade import __version__, constants
 from freqtrade.configuration import validate_config_consistency
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
-from freqtrade.enums import RPCMessageType, SellType, State
+from freqtrade.enums import RPCMessageType, RunMode, SellType, State
 from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, PricingError)
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -126,6 +124,7 @@ class FreqtradeBot(LoggingMixin):
 
         self.rpc.cleanup()
         cleanup_db()
+        self.exchange.close()
 
     def startup(self) -> None:
         """
@@ -178,6 +177,11 @@ class FreqtradeBot(LoggingMixin):
             # First process current opened trades (positions)
             self.exit_positions(trades)
 
+        # Check if we need to adjust our current positions before attempting to buy new trades.
+        if self.strategy.position_adjustment_enable:
+            with self._exit_lock:
+                self.process_open_trade_positions()
+
         # Then looking for buy opportunities
         if self.get_free_open_trades():
             self.enter_positions()
@@ -278,15 +282,17 @@ class FreqtradeBot(LoggingMixin):
                 if order:
                     logger.info(f"Updating sell-fee on trade {trade} for order {order.order_id}.")
                     self.update_trade_state(trade, order.order_id,
-                                            stoploss_order=order.ft_order_side == 'stoploss')
+                                            stoploss_order=order.ft_order_side == 'stoploss',
+                                            send_msg=False)
 
         trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
         for trade in trades:
             if trade.is_open and not trade.fee_updated('buy'):
                 order = trade.select_order('buy', False)
-                if order:
+                open_order = trade.select_order('buy', True)
+                if order and open_order is None:
                     logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
-                    self.update_trade_state(trade, order.order_id)
+                    self.update_trade_state(trade, order.order_id, send_msg=False)
 
     def handle_insufficient_funds(self, trade: Trade):
         """
@@ -308,7 +314,7 @@ class FreqtradeBot(LoggingMixin):
         order = trade.select_order('buy', False)
         if order:
             logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
-            self.update_trade_state(trade, order.order_id)
+            self.update_trade_state(trade, order.order_id, send_msg=False)
 
     def refind_lost_order(self, trade):
         """
@@ -442,6 +448,59 @@ class FreqtradeBot(LoggingMixin):
         else:
             return False
 
+#
+# BUY / increase positions / DCA logic and methods
+#
+    def process_open_trade_positions(self):
+        """
+        Tries to execute additional buy or sell orders for open trades (positions)
+        """
+        # Walk through each pair and check if it needs changes
+        for trade in Trade.get_open_trades():
+            # If there is any open orders, wait for them to finish.
+            if trade.open_order_id is None:
+                try:
+                    self.check_and_call_adjust_trade_position(trade)
+                except DependencyException as exception:
+                    logger.warning(
+                        f"Unable to adjust position of trade for {trade.pair}: {exception}")
+
+    def check_and_call_adjust_trade_position(self, trade: Trade):
+        """
+        Check the implemented trading strategy for adjustment command.
+        If the strategy triggers the adjustment, a new order gets issued.
+        Once that completes, the existing trade is modified to match new data.
+        """
+        if self.strategy.max_entry_position_adjustment > -1:
+            count_of_buys = trade.nr_of_successful_buys
+            if count_of_buys > self.strategy.max_entry_position_adjustment:
+                logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
+                return
+        else:
+            logger.debug("Max adjustment entries is set to unlimited.")
+        current_rate = self.exchange.get_rate(trade.pair, refresh=True, side="buy")
+        current_profit = trade.calc_profit_ratio(current_rate)
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(trade.pair,
+                                                                   current_rate,
+                                                                   self.strategy.stoploss)
+        max_stake_amount = self.wallets.get_available_stake_amount()
+        logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_rate,
+            current_profit=current_profit, min_stake=min_stake_amount, max_stake=max_stake_amount)
+
+        if stake_amount is not None and stake_amount > 0.0:
+            # We should increase our position
+            self.execute_entry(trade.pair, stake_amount, trade=trade)
+
+        if stake_amount is not None and stake_amount < 0.0:
+            # We should decrease our position
+            # TODO: Selling part of the trade not implemented yet.
+            logger.error(f"Unable to decrease trade position / sell partially"
+                         f" for pair {trade.pair}, feature not implemented.")
+
     def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
         """
         Checks depth of market before executing a buy
@@ -466,58 +525,40 @@ class FreqtradeBot(LoggingMixin):
             logger.info(f"Bids to asks delta for {pair} does not satisfy condition.")
             return False
 
-    def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None,
-                      forcebuy: bool = False, buy_tag: Optional[str] = None) -> bool:
+    def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None, *,
+                      ordertype: Optional[str] = None, buy_tag: Optional[str] = None,
+                      trade: Optional[Trade] = None) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
         :param stake_amount: amount of stake-currency for the pair
         :return: True if a buy order is created, false if it fails.
         """
-        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if price:
-            enter_limit_requested = price
-        else:
-            # Calculate price
-            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
-            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
-                                                       default_retval=proposed_enter_rate)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                proposed_rate=proposed_enter_rate)
+        pos_adjust = trade is not None
 
-            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
-
-        if not enter_limit_requested:
-            raise PricingError('Could not determine buy price.')
-
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
-                                                                   self.strategy.stoploss)
-
-        if not self.edge:
-            max_stake_amount = self.wallets.get_available_stake_amount()
-            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
-                                                 default_retval=stake_amount)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                current_rate=enter_limit_requested, proposed_stake=stake_amount,
-                min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
+        enter_limit_requested, stake_amount = self.get_valid_enter_price_and_stake(
+            pair, price, stake_amount, buy_tag, trade)
 
         if not stake_amount:
             return False
 
+        if pos_adjust:
+            logger.info(f"Position adjust: about to create a new order for {pair} with stake: "
+                        f"{stake_amount} for {trade}")
+        else:
             logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
                         f"{stake_amount} ...")
 
         amount = stake_amount / enter_limit_requested
-        order_type = self.strategy.order_types['buy']
-        if forcebuy:
-            # Forcebuy can define a different ordertype
-            order_type = self.strategy.order_types.get('forcebuy', order_type)
+        order_type = ordertype or self.strategy.order_types['buy']
+        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
+        if not pos_adjust and not strategy_safe_wrapper(
+                self.strategy.confirm_trade_entry, default_retval=True)(
                 pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
-                time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
+                time_in_force=time_in_force, current_time=datetime.now(timezone.utc),
+                entry_tag=buy_tag):
             logger.info(f"User requested abortion of buying {pair}")
             return False
         amount = self.exchange.amount_to_precision(pair, amount)
@@ -527,6 +568,7 @@ class FreqtradeBot(LoggingMixin):
         order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
         order_id = order['id']
         order_status = order.get('status', None)
+        logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
 
         # we assume the order is executed at the price requested
         enter_limit_filled_price = enter_limit_requested
@@ -562,6 +604,8 @@ class FreqtradeBot(LoggingMixin):
 
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
+        # This is a new trade
+        if trade is None:
             trade = Trade(
                 pair=pair,
                 stake_amount=stake_amount,
@@ -575,44 +619,110 @@ class FreqtradeBot(LoggingMixin):
                 open_date=datetime.utcnow(),
                 exchange=self.exchange.id,
                 open_order_id=order_id,
+                fee_open_currency=None,
                 strategy=self.strategy.get_strategy_name(),
                 buy_tag=buy_tag,
                 timeframe=timeframe_to_minutes(self.config['timeframe'])
             )
+        else:
+            # This is additional buy, we reset fee_open_currency so timeout checking can work
+            trade.is_open = True
+            trade.fee_open_currency = None
+            trade.open_rate_requested = enter_limit_requested
+            trade.open_order_id = order_id
+
         trade.orders.append(order_obj)
-
-        # Update fees if order is closed
-        if order_status == 'closed':
-            self.update_trade_state(trade, order_id, order)
-
+        trade.recalc_trade_from_orders()
         Trade.query.session.add(trade)
         Trade.commit()
 
         # Updating wallets
         self.wallets.update()
 
-        self._notify_enter(trade, order_type)
+        self._notify_enter(trade, order, order_type)
+
+        if pos_adjust:
+            if order_status == 'closed':
+                logger.info(f"DCA order closed, trade should be up to date: {trade}")
+                trade = self.cancel_stoploss_on_exchange(trade)
+            else:
+                logger.info(f"DCA order {order_status}, will wait for resolution: {trade}")
+
+        # Update fees if order is closed
+        if order_status == 'closed':
+            self.update_trade_state(trade, order_id, order)
 
         return True
 
-    def _notify_enter(self, trade: Trade, order_type: str) -> None:
+    def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
+        # First cancelling stoploss on exchange ...
+        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
+            try:
+                logger.info(f"Canceling stoploss on exchange for {trade}")
+                co = self.exchange.cancel_stoploss_order_with_result(
+                    trade.stoploss_order_id, trade.pair, trade.amount)
+                trade.update_order(co)
+            except InvalidOrderException:
+                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        return trade
+
+    def get_valid_enter_price_and_stake(
+            self, pair: str, price: Optional[float], stake_amount: float,
+            entry_tag: Optional[str],
+            trade: Optional[Trade]) -> Tuple[float, float]:
+        if price:
+            enter_limit_requested = price
+        else:
+            # Calculate price
+            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
+            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                                       default_retval=proposed_enter_rate)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                proposed_rate=proposed_enter_rate, entry_tag=entry_tag)
+
+            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
+        if not enter_limit_requested:
+            raise PricingError('Could not determine buy price.')
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
+                                                                   self.strategy.stoploss)
+        if not self.edge and trade is None:
+            max_stake_amount = self.wallets.get_available_stake_amount()
+            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
+                                                 default_retval=stake_amount)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                current_rate=enter_limit_requested, proposed_stake=stake_amount,
+                min_stake=min_stake_amount, max_stake=max_stake_amount, entry_tag=entry_tag)
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
+        return enter_limit_requested, stake_amount
+
+    def _notify_enter(self, trade: Trade, order: Dict, order_type: Optional[str] = None,
+                      fill: bool = False) -> None:
         """
         Sends rpc notification when a buy occurred.
         """
+        open_rate = safe_value_fallback(order, 'average', 'price')
+        if open_rate is None:
+            open_rate = trade.open_rate
+
+        current_rate = trade.open_rate_requested
+        if self.dataprovider.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            current_rate = self.exchange.get_rate(trade.pair, refresh=False, side="buy")
+
         msg = {
             'trade_id': trade.id,
-            'type': RPCMessageType.BUY,
+            'type': RPCMessageType.BUY_FILL if fill else RPCMessageType.BUY,
             'buy_tag': trade.buy_tag,
             'exchange': self.exchange.name.capitalize(),
             'pair': trade.pair,
-            'limit': trade.open_rate,
+            'limit': open_rate,  # Deprecated (?)
+            'open_rate': open_rate,
             'order_type': order_type,
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': trade.amount,
+            'amount': safe_value_fallback(order, 'filled', 'amount') or trade.amount,
             'open_date': trade.open_date or datetime.utcnow(),
-            'current_rate': trade.open_rate_requested,
+            'current_rate': current_rate,
         }
 
         # Send the message
@@ -644,22 +754,6 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_enter_fill(self, trade: Trade) -> None:
-        msg = {
-            'trade_id': trade.id,
-            'type': RPCMessageType.BUY_FILL,
-            'buy_tag': trade.buy_tag,
-            'exchange': self.exchange.name.capitalize(),
-            'pair': trade.pair,
-            'open_rate': trade.open_rate,
-            'stake_amount': trade.stake_amount,
-            'stake_currency': self.config['stake_currency'],
-            'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': trade.amount,
-            'open_date': trade.open_date,
-        }
-        self.rpc.send_msg(msg)
-
 #
 # SELL / exit positions / close trades logic and methods
 #
@@ -682,7 +776,7 @@ class FreqtradeBot(LoggingMixin):
                     trades_closed += 1
 
             except DependencyException as exception:
-                logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
+                logger.warning(f'Unable to sell trade {trade.pair}: {exception}')
 
         # Updating wallets if any trade occurred
         if trades_closed:
@@ -868,24 +962,10 @@ class FreqtradeBot(LoggingMixin):
             logger.info(
                 f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}. '
                 f'Tag: {exit_tag if exit_tag is not None else "None"}')
-            self.execute_trade_exit(trade, exit_rate, should_sell, exit_tag)
+            self.execute_trade_exit(trade, exit_rate, should_sell, exit_tag=exit_tag)
             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_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
-            timeout_kwargs = {timeout_unit: -timeout}
-            timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).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 necessary
@@ -906,28 +986,28 @@ class FreqtradeBot(LoggingMixin):
 
             if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
                     fully_cancelled
-                    or self._check_timed_out('buy', order)
-                    or strategy_safe_wrapper(self.strategy.check_buy_timeout,
-                                             default_retval=False)(pair=trade.pair,
-                                                                   trade=trade,
-                                                                   order=order))):
+                    or self.strategy.ft_check_timed_out(
+                        'buy', trade, order, datetime.now(timezone.utc))
+                        )):
                 self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
             elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                   fully_cancelled
-                  or self._check_timed_out('sell', order)
-                  or strategy_safe_wrapper(self.strategy.check_sell_timeout,
-                                           default_retval=False)(pair=trade.pair,
-                                                                 trade=trade,
-                                                                 order=order))):
+                  or self.strategy.ft_check_timed_out(
+                      'sell', trade, order, datetime.now(timezone.utc)))
+                  ):
                 self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
                 canceled_count = trade.get_exit_order_count()
                 max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
                 if max_timeouts > 0 and canceled_count >= max_timeouts:
                     logger.warning(f'Emergencyselling trade {trade}, as the sell order '
                                    f'timed out {max_timeouts} times.')
-                    self.execute_trade_exit(trade, order.get('price'), sell_reason=SellCheckTuple(
-                        sell_type=SellType.EMERGENCY_SELL))
+                    try:
+                        self.execute_trade_exit(
+                            trade, order.get('price'),
+                            sell_reason=SellCheckTuple(sell_type=SellType.EMERGENCY_SELL))
+                    except DependencyException as exception:
+                        logger.warning(f'Unable to emergency sell trade {trade.pair}: {exception}')
 
     def cancel_all_open_orders(self) -> None:
         """
@@ -987,10 +1067,16 @@ class FreqtradeBot(LoggingMixin):
         filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
         if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
             logger.info('Buy order fully cancelled. Removing %s from database.', trade)
-            # if trade is not partially completed, just delete the trade
+            # if trade is not partially completed and it's the only order, just delete the trade
+            if len(trade.orders) <= 1:
                 trade.delete()
                 was_trade_fully_canceled = True
                 reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            else:
+                # FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
+                self.update_trade_state(trade, trade.open_order_id, corder)
+                trade.open_order_id = None
+                logger.info('Partial buy order timeout for %s.', trade)
         else:
             # if trade is partially complete, edit the stake details for the trade
             # and close the order
@@ -1081,7 +1167,10 @@ class FreqtradeBot(LoggingMixin):
             trade: Trade,
             limit: float,
             sell_reason: SellCheckTuple,
-            exit_tag: Optional[str] = None) -> bool:
+            *,
+            exit_tag: Optional[str] = None,
+            ordertype: Optional[str] = None,
+            ) -> bool:
         """
         Executes a trade exit for the given trade and limit
         :param trade: Trade instance
@@ -1111,22 +1200,12 @@ class FreqtradeBot(LoggingMixin):
         limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
 
         # First cancelling stoploss on exchange ...
-        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
-            try:
-                co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
-                                                                     trade.pair, trade.amount)
-                trade.update_order(co)
-            except InvalidOrderException:
-                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        trade = self.cancel_stoploss_on_exchange(trade)
 
-        order_type = self.strategy.order_types[sell_type]
+        order_type = ordertype or self.strategy.order_types[sell_type]
         if sell_reason.sell_type == SellType.EMERGENCY_SELL:
             # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergencysell", "market")
-        if sell_reason.sell_type == SellType.FORCE_SELL:
-            # Force sells (default to the sell_type defined in the strategy,
-            # but we allow this value to be changed)
-            order_type = self.strategy.order_types.get("forcesell", order_type)
 
         amount = self._safe_exit_amount(trade.pair, trade.amount)
         time_in_force = self.strategy.order_time_in_force['sell']
@@ -1158,16 +1237,16 @@ class FreqtradeBot(LoggingMixin):
         trade.sell_order_status = ''
         trade.close_rate_requested = limit
         trade.sell_reason = exit_tag or sell_reason.sell_reason
-        # In case of market sell orders the order can be closed immediately
-        if order.get('status', 'unknown') in ('closed', 'expired'):
-            self.update_trade_state(trade, trade.open_order_id, order)
-        Trade.commit()
 
         # Lock pair for one candle to prevent immediate re-buys
         self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
                                 reason='Auto lock')
 
         self._notify_exit(trade, order_type)
+        # In case of market sell orders the order can be closed immediately
+        if order.get('status', 'unknown') in ('closed', 'expired'):
+            self.update_trade_state(trade, trade.open_order_id, order)
+        Trade.commit()
 
         return True
 
@@ -1264,13 +1343,14 @@ class FreqtradeBot(LoggingMixin):
 #
 
     def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
-                           stoploss_order: bool = False) -> bool:
+                           stoploss_order: bool = False, send_msg: bool = True) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
         Handles closing both buy and sell orders.
         :param trade: Trade object of the trade we're analyzing
         :param order_id: Order-id of the order we're analyzing
         :param action_order: Already acquired order object
+        :param send_msg: Send notification - should always be True except in "recovery" methods
         :return: True if order has been cancelled without being filled partially, False otherwise
         """
         if not order_id:
@@ -1278,7 +1358,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # Update trade with order values
-        logger.info('Found open order for %s', trade)
+        logger.info(f'Found open order for {trade}')
         try:
             order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id,
                                                                                 trade.pair,
@@ -1294,29 +1374,26 @@ class FreqtradeBot(LoggingMixin):
             # Handling of this will happen in check_handle_timedout.
             return True
 
-        # Try update amount (binance-fix)
-        try:
-            new_amount = self.get_real_amount(trade, order)
-            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
-                           abs_tol=constants.MATH_CLOSE_PREC):
-                order['amount'] = new_amount
-                order.pop('filled', None)
-                trade.recalc_open_trade_value()
-        except DependencyException as exception:
-            logger.warning("Could not update trade amount: %s", exception)
+        order = self.handle_order_fee(trade, order)
 
         trade.update(order)
+        trade.recalc_trade_from_orders()
         Trade.commit()
 
+        if order['status'] in constants.NON_OPEN_EXCHANGE_STATES:
+            # If a buy order was closed, force update on stoploss on exchange
+            if order.get('side', None) == 'buy':
+                trade = self.cancel_stoploss_on_exchange(trade)
             # Updating wallets when order is closed
+            self.wallets.update()
+
         if not trade.is_open:
-            if not stoploss_order and not trade.open_order_id:
+            if send_msg and not stoploss_order and not trade.open_order_id:
                 self._notify_exit(trade, '', True)
             self.handle_protections(trade.pair)
-            self.wallets.update()
-        elif not trade.open_order_id:
+        elif send_msg and not trade.open_order_id:
             # Buy fill
-            self._notify_enter_fill(trade)
+            self._notify_enter(trade, order, fill=True)
 
         return False
 
@@ -1351,6 +1428,18 @@ class FreqtradeBot(LoggingMixin):
             return real_amount
         return amount
 
+    def handle_order_fee(self, trade: Trade, order: Dict[str, Any]) -> Dict[str, Any]:
+        # Try update amount (binance-fix)
+        try:
+            new_amount = self.get_real_amount(trade, order)
+            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
+                           abs_tol=constants.MATH_CLOSE_PREC):
+                order['amount'] = new_amount
+                order.pop('filled', None)
+        except DependencyException as exception:
+            logger.warning("Could not update trade amount: %s", exception)
+        return order
+
     def get_real_amount(self, trade: Trade, order: Dict) -> float:
         """
         Detect and update trade fee.

freqtrade/loggers.py

@@ -7,11 +7,25 @@ from typing import Any, Dict
 from freqtrade.exceptions import OperationalException
 
 
+class FTBufferingHandler(BufferingHandler):
+    def flush(self):
+        """
+        Override Flush behaviour - we keep half of the configured capacity
+        otherwise, we have moments with "empty" logs.
+        """
+        self.acquire()
+        try:
+            # Keep half of the records in buffer.
+            self.buffer = self.buffer[-int(self.capacity / 2):]
+        finally:
+            self.release()
+
+
 logger = logging.getLogger(__name__)
 LOGFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
 
 # Initialize bufferhandler - will be used for /log endpoints
-bufferHandler = BufferingHandler(1000)
+bufferHandler = FTBufferingHandler(1000)
 bufferHandler.setFormatter(Formatter(LOGFORMAT))
 
 

freqtrade/main.py

@@ -9,8 +9,8 @@ from typing import Any, List
 
 
 # check min. python version
-if sys.version_info < (3, 7):  # pragma: no cover
-    sys.exit("Freqtrade requires Python version >= 3.7")
+if sys.version_info < (3, 8):  # pragma: no cover
+    sys.exit("Freqtrade requires Python version >= 3.8")
 
 from freqtrade.commands import Arguments
 from freqtrade.exceptions import FreqtradeException, OperationalException

freqtrade/misc.py

@@ -2,11 +2,13 @@
 Various tool function for Freqtrade and scripts
 """
 import gzip
+import hashlib
 import logging
 import re
+from copy import deepcopy
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Iterator, List
+from typing import Any, Iterator, List, Union
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -228,3 +230,34 @@ def parse_db_uri_for_logging(uri: str):
         return uri
     pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
     return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
+
+
+def get_strategy_run_id(strategy) -> str:
+    """
+    Generate unique identification hash for a backtest run. Identical config and strategy file will
+    always return an identical hash.
+    :param strategy: strategy object.
+    :return: hex string id.
+    """
+    digest = hashlib.sha1()
+    config = deepcopy(strategy.config)
+
+    # Options that have no impact on results of individual backtest.
+    not_important_keys = ('strategy_list', 'original_config', 'telegram', 'api_server')
+    for k in not_important_keys:
+        if k in config:
+            del config[k]
+
+    # Explicitly allow NaN values (e.g. max_open_trades).
+    # as it does not matter for getting the hash.
+    digest.update(rapidjson.dumps(config, default=str,
+                                  number_mode=rapidjson.NM_NAN).encode('utf-8'))
+    with open(strategy.__file__, 'rb') as fp:
+        digest.update(fp.read())
+    return digest.hexdigest().lower()
+
+
+def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path:
+    """Return metadata filename for specified backtest results file."""
+    filename = Path(filename)
+    return filename.parent / Path(f'{filename.stem}.meta{filename.suffix}')

freqtrade/optimize/backtesting.py

@@ -11,20 +11,22 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
+from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
-from freqtrade.data.btanalysis import trade_list_to_dataframe
+from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import BacktestState, SellType
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.misc import get_strategy_run_id
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.bt_progress import BTProgress
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                  store_backtest_stats)
-from freqtrade.persistence import LocalTrade, PairLocks, Trade
+from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -60,9 +62,10 @@ class Backtesting:
 
         LoggingMixin.show_output = False
         self.config = config
-        self.results: Optional[Dict[str, Any]] = None
+        self.results: Dict[str, Any] = {}
 
         config['dry_run'] = True
+        self.run_ids: Dict[str, str] = {}
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
@@ -246,6 +249,9 @@ class Backtesting:
         Helper function to convert a processed dataframes into lists for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
+
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
@@ -254,7 +260,8 @@ class Backtesting:
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
         # Create dict with data
-        for pair, pair_data in processed.items():
+        for pair in processed.keys():
+            pair_data = processed[pair]
             self.check_abort()
             self.progress.increment()
             if not pair_data.empty:
@@ -266,8 +273,8 @@ class Backtesting:
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
             # Trim startup period from analyzed dataframe
-            df_analyzed = trim_dataframe(df_analyzed, self.timerange,
-                                         startup_candles=self.required_startup)
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup)
             # To avoid using data from future, we use buy/sell signals shifted
             # from the previous candle
             df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
@@ -342,10 +349,7 @@ class Backtesting:
                     # use Open rate if open_rate > calculated sell rate
                     return sell_row[OPEN_IDX]
 
-                # Use the maximum between close_rate and low as we
-                # cannot sell outside of a candle.
-                # Applies when a new ROI setting comes in place and the whole candle is above that.
-                return min(max(close_rate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
+                return close_rate
 
             else:
                 # This should not be reached...
@@ -353,8 +357,37 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
+    def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
+                                           ) -> LocalTrade:
+
+        current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
+        min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
+        max_stake = self.wallets.get_available_stake_amount()
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
+            current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
+
+        # Check if we should increase our position
+        if stake_amount is not None and stake_amount > 0.0:
+            pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
+            if pos_trade is not None:
+                return pos_trade
+
+        return trade
+
     def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
                                          sell_row: Tuple) -> Optional[LocalTrade]:
+
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            check_adjust_buy = True
+            if self.strategy.max_entry_position_adjustment > -1:
+                count_of_buys = trade.nr_of_successful_buys
+                check_adjust_buy = (count_of_buys <= self.strategy.max_entry_position_adjustment)
+            if check_adjust_buy:
+                trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
+
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
                                          sell_candle_time, sell_row[BUY_IDX],
@@ -366,6 +399,17 @@ class Backtesting:
 
             trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
             closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
+            # call the custom exit price,with default value as previous closerate
+            current_profit = trade.calc_profit_ratio(closerate)
+            if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
+                # Custom exit pricing only for sell-signals
+                closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
+                                                  default_retval=closerate)(
+                    pair=trade.pair, trade=trade,
+                    current_time=sell_row[DATE_IDX],
+                    proposed_rate=closerate, current_profit=current_profit)
+            # Use the maximum between close_rate and low as we cannot sell outside of a candle.
+            closerate = min(max(closerate, sell_row[LOW_IDX]), sell_row[HIGH_IDX])
 
             # Confirm trade exit:
             time_in_force = self.strategy.order_time_in_force['sell']
@@ -408,7 +452,9 @@ class Backtesting:
                 return self._get_sell_trade_entry_for_candle(trade, sell_row)
             detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
             detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
-            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
+            detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
+            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
             for det_row in detail_data[headers].values.tolist():
                 res = self._get_sell_trade_entry_for_candle(trade, det_row)
                 if res:
@@ -419,49 +465,94 @@ class Backtesting:
         else:
             return self._get_sell_trade_entry_for_candle(trade, sell_row)
 
-    def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
+    def _enter_trade(self, pair: str, row: Tuple, stake_amount: Optional[float] = None,
+                     trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
+
+        current_time = row[DATE_IDX].to_pydatetime()
+        entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
+        # let's call the custom entry price, using the open price as default price
+        propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                             default_retval=row[OPEN_IDX])(
+            pair=pair, current_time=current_time,
+            proposed_rate=row[OPEN_IDX], entry_tag=entry_tag)  # default value is the open rate
+
+        # Move rate to within the candle's low/high rate
+        propose_rate = min(max(propose_rate, row[LOW_IDX]), row[HIGH_IDX])
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, propose_rate, -0.05) or 0
+        max_stake_amount = self.wallets.get_available_stake_amount()
+
+        pos_adjust = trade is not None
+        if not pos_adjust:
             try:
                 stake_amount = self.wallets.get_trade_stake_amount(pair, None)
             except DependencyException:
-            return None
-
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, row[OPEN_IDX], -0.05) or 0
-        max_stake_amount = self.wallets.get_available_stake_amount()
+                return trade
 
             stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
                                                  default_retval=stake_amount)(
-            pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
-            proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
+                pair=pair, current_time=current_time, current_rate=propose_rate,
+                proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount,
+                entry_tag=entry_tag)
+
         stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
 
         if not stake_amount:
-            return None
+            # In case of pos adjust, still return the original trade
+            # If not pos adjust, trade is None
+            return trade
 
         order_type = self.strategy.order_types['buy']
         time_in_force = self.strategy.order_time_in_force['sell']
         # Confirm trade entry:
+        if not pos_adjust:
             if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
-                pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
-                time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
+                    pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
+                    time_in_force=time_in_force, current_time=current_time,
+                    entry_tag=entry_tag):
                 return None
 
         if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
+            amount = round(stake_amount / propose_rate, 8)
+            if trade is None:
                 # Enter trade
-            has_buy_tag = len(row) >= BUY_TAG_IDX + 1
                 trade = LocalTrade(
                     pair=pair,
-                open_rate=row[OPEN_IDX],
-                open_date=row[DATE_IDX].to_pydatetime(),
+                    open_rate=propose_rate,
+                    open_date=current_time,
                     stake_amount=stake_amount,
-                amount=round(stake_amount / row[OPEN_IDX], 8),
+                    amount=amount,
                     fee_open=self.fee,
                     fee_close=self.fee,
                     is_open=True,
-                buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
+                    buy_tag=entry_tag,
                     exchange='backtesting',
+                    orders=[]
                 )
+            trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
+
+            order = Order(
+                ft_is_open=False,
+                ft_pair=trade.pair,
+                symbol=trade.pair,
+                ft_order_side="buy",
+                side="buy",
+                order_type="market",
+                status="closed",
+                order_date=current_time,
+                order_filled_date=current_time,
+                order_update_date=current_time,
+                price=propose_rate,
+                average=propose_rate,
+                amount=amount,
+                filled=amount,
+                cost=stake_amount + trade.fee_open
+            )
+            trade.orders.append(order)
+            if pos_adjust:
+                trade.recalc_trade_from_orders()
+
         return trade
-        return None
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
                          data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
@@ -503,7 +594,8 @@ class Backtesting:
         Of course try to not have ugly code. By some accessor are sometime slower than functions.
         Avoid extensive logging in this method and functions it calls.
 
-        :param processed: a processed dictionary with format {pair, data}
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         :param start_date: backtesting timerange start datetime
         :param end_date: backtesting timerange end datetime
         :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@@ -650,6 +742,7 @@ class Backtesting:
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({
+            'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
         })
@@ -657,6 +750,33 @@ class Backtesting:
 
         return min_date, max_date
 
+    def _get_min_cached_backtest_date(self):
+        min_backtest_date = None
+        backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
+        if self.timerange.stopts == 0 or datetime.fromtimestamp(
+           self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
+            logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
+        elif backtest_cache_age == 'day':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
+        elif backtest_cache_age == 'week':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
+        elif backtest_cache_age == 'month':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
+        return min_backtest_date
+
+    def load_prior_backtest(self):
+        self.run_ids = {
+            strategy.get_strategy_name(): get_strategy_run_id(strategy)
+            for strategy in self.strategylist
+        }
+
+        # Load previous result that will be updated incrementally.
+        # This can be circumvented in certain instances in combination with downloading more data
+        min_backtest_date = self._get_min_cached_backtest_date()
+        if min_backtest_date is not None:
+            self.results = find_existing_backtest_stats(
+                self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
+
     def start(self) -> None:
         """
         Run backtesting end-to-end
@@ -668,15 +788,38 @@ class Backtesting:
         self.load_bt_data_detail()
         logger.info("Dataload complete. Calculating indicators")
 
-        for strat in self.strategylist:
-            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
-        if len(self.strategylist) > 0:
+        self.load_prior_backtest()
 
-            self.results = generate_backtest_stats(data, self.all_results,
-                                                   min_date=min_date, max_date=max_date)
+        for strat in self.strategylist:
+            if self.results and strat.get_strategy_name() in self.results['strategy']:
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+                continue
+            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
+
+        # Update old results with new ones.
+        if len(self.all_results) > 0:
+            results = generate_backtest_stats(
+                data, self.all_results, min_date=min_date, max_date=max_date)
+            if self.results:
+                self.results['metadata'].update(results['metadata'])
+                self.results['strategy'].update(results['strategy'])
+                self.results['strategy_comparison'].extend(results['strategy_comparison'])
+            else:
+                self.results = results
 
             if self.config.get('export', 'none') == 'trades':
                 store_backtest_stats(self.config['exportfilename'], self.results)
 
+        # Results may be mixed up now. Sort them so they follow --strategy-list order.
+        if 'strategy_list' in self.config and len(self.results) > 0:
+            self.results['strategy_comparison'] = sorted(
+                self.results['strategy_comparison'],
+                key=lambda c: self.config['strategy_list'].index(c['key']))
+            self.results['strategy'] = dict(
+                sorted(self.results['strategy'].items(),
+                       key=lambda kv: self.config['strategy_list'].index(kv[0])))
+
+        if len(self.strategylist) > 0:
             # Show backtest results
             show_backtest_results(self.config, self.results)

freqtrade/optimize/bt_progress.py

@@ -12,7 +12,7 @@ class BTProgress:
     def init_step(self, action: BacktestState, max_steps: float):
         self._action = action
         self._max_steps = max_steps
-        self._proress = 0
+        self._progress = 0
 
     def set_new_value(self, new_value: float):
         self._progress = new_value

freqtrade/optimize/edge_cli.py

@@ -34,7 +34,7 @@ class EdgeCli:
         self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
         self.strategy = StrategyResolver.load_strategy(self.config)
-        self.strategy.dp = DataProvider(config, None)
+        self.strategy.dp = DataProvider(config, self.exchange)
 
         validate_config_consistency(self.config)
 

freqtrade/optimize/hyperopt.py

@@ -76,6 +76,7 @@ class Hyperopt:
         self.config = config
 
         self.backtesting = Backtesting(self.config)
+        self.pairlist = self.backtesting.pairlists.whitelist
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
@@ -332,7 +333,7 @@ class Hyperopt:
         params_details = self._get_params_details(params_dict)
 
         strat_stats = generate_strategy_stats(
-            processed, self.backtesting.strategy.get_strategy_name(),
+            self.pairlist, self.backtesting.strategy.get_strategy_name(),
             backtesting_results, min_date, max_date, market_change=0
         )
         results_explanation = HyperoptTools.format_results_explanation_string(
@@ -366,7 +367,7 @@ class Hyperopt:
         }
 
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
-        estimator = self.custom_hyperopt.generate_estimator()
+        estimator = self.custom_hyperopt.generate_estimator(dimensions=dimensions)
 
         acq_optimizer = "sampling"
         if isinstance(estimator, str):
@@ -422,6 +423,7 @@ class Hyperopt:
         self.backtesting.exchange.close()
         self.backtesting.exchange._api = None  # type: ignore
         self.backtesting.exchange._api_async = None  # type: ignore
+        self.backtesting.exchange.loop = None  # type: ignore
         # self.backtesting.exchange = None  # type: ignore
         self.backtesting.pairlists = None  # type: ignore
 

freqtrade/optimize/hyperopt_auto.py

@@ -91,5 +91,5 @@ class HyperOptAuto(IHyperOpt):
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
 
-    def generate_estimator(self) -> EstimatorType:
-        return self._get_func('generate_estimator')()
+    def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
+        return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

freqtrade/optimize/hyperopt_interface.py

@@ -40,7 +40,7 @@ class IHyperOpt(ABC):
         IHyperOpt.ticker_interval = str(config['timeframe'])  # DEPRECATED
         IHyperOpt.timeframe = str(config['timeframe'])
 
-    def generate_estimator(self) -> EstimatorType:
+    def generate_estimator(self, dimensions: List[Dimension], **kwargs) -> EstimatorType:
         """
         Return base_estimator.
         Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class

freqtrade/optimize/hyperopt_loss_calmar.py

@@ -47,10 +47,9 @@ class CalmarHyperOptLoss(IHyperOptLoss):
 
         # calculate max drawdown
         try:
-            _, _, _, high_val, low_val = calculate_max_drawdown(
+            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
                 results, value_col="profit_abs"
             )
-            max_drawdown = (high_val - low_val) / high_val
         except ValueError:
             max_drawdown = 0
 

freqtrade/optimize/hyperopt_tools.py

@@ -137,6 +137,7 @@ class HyperoptTools():
         }
         if not HyperoptTools._test_hyperopt_results_exist(results_file):
             # No file found.
+            logger.warning(f"Hyperopt file {results_file} not found.")
             return [], 0
 
         epochs = []
@@ -299,8 +300,7 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
-                               has_drawdown: bool) -> pd.DataFrame:
+    def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -309,33 +309,26 @@ class HyperoptTools():
 
         if not has_drawdown:
             # Ensure compatibility with older versions of hyperopt results
-            trials['results_metrics.max_drawdown_abs'] = None
-            trials['results_metrics.max_drawdown'] = None
+            trials['results_metrics.max_drawdown_account'] = None
 
-        if not legacy_mode:
         # New mode, using backtest result for metrics
         trials['results_metrics.winsdrawslosses'] = trials.apply(
             lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
                       f"{x['results_metrics.losses']:>4}", axis=1)
+
         trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
                          'results_metrics.winsdrawslosses',
                          'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
                          'results_metrics.profit_total', 'results_metrics.holding_avg',
-                             'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
+                         'results_metrics.max_drawdown',
+                         'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
                          'loss', 'is_initial_point', 'is_best']]
 
-        else:
-            # Legacy mode
-            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
-                             'results_metrics.total_profit', 'results_metrics.profit',
-                             'results_metrics.duration', 'results_metrics.max_drawdown',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
-                             'is_best']]
-
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
-                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
+        trials.columns = [
+            'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+            'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            ]
 
         return trials
 
@@ -351,10 +344,9 @@ class HyperoptTools():
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        legacy_mode = 'results_metrics.total_trades' not in trials
-        has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
+        has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
 
-        trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
+        trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
 
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
@@ -362,12 +354,12 @@ class HyperoptTools():
         trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
-        perc_multi = 1 if legacy_mode else 100
+        # perc_multi = 1 if legacy_mode else 100
         trials['Epoch'] = trials['Epoch'].apply(
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
             lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
@@ -379,24 +371,25 @@ class HyperoptTools():
 
         stake_currency = config['stake_currency']
 
-        if has_drawdown:
-            trials['Max Drawdown'] = trials.apply(
-                lambda x: '{} {}'.format(
+        trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
+            lambda x: "{} {}".format(
                 round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
+                (f"({x['max_drawdown_account']:,.2%})"
+                    if has_account_drawdown
+                    else f"({x['max_drawdown']:,.2%})"
+                 ).rjust(10, ' ')
             ).rjust(25 + len(stake_currency))
-                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
+            if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
+            else '--'.rjust(25 + len(stake_currency)),
             axis=1
         )
-        else:
-            trials = trials.drop(columns=['Max Drawdown'])
 
-        trials = trials.drop(columns=['max_drawdown_abs'])
+        trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
 
         trials['Profit'] = trials.apply(
             lambda x: '{} {}'.format(
                 round_coin_value(x['Total profit'], stake_currency),
-                '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
+                f"({x['Profit']:,.2%})".rjust(10, ' ')
             ).rjust(25+len(stake_currency))
             if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
             axis=1

freqtrade/optimize/optimize_reports.py

@@ -1,4 +1,5 @@
 import logging
+from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Union
@@ -10,7 +11,8 @@ from tabulate import tabulate
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
                                        calculate_max_drawdown)
-from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
+from freqtrade.misc import (decimals_per_coin, file_dump_json, get_backtest_metadata_filename,
+                            round_coin_value)
 
 
 logger = logging.getLogger(__name__)
@@ -32,6 +34,11 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
             recordfilename.parent,
             f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
         ).with_suffix(recordfilename.suffix)
+
+    # Store metadata separately.
+    file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
+    del stats['metadata']
+
     file_dump_json(filename, stats)
 
     latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
@@ -98,11 +105,11 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
     }
 
 
-def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
+def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int,
                           results: DataFrame, skip_nan: bool = False) -> List[Dict]:
     """
     Generates and returns a list  for the given backtest data and the results dataframe
-    :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
+    :param pairlist: Pairlist used
     :param stake_currency: stake-currency - used to correctly name headers
     :param starting_balance: Starting balance
     :param results: Dataframe containing the backtest results
@@ -112,7 +119,7 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
 
     tabular_data = []
 
-    for pair in data:
+    for pair in pairlist:
         result = results[results['pair'] == pair]
         if skip_nan and result['profit_abs'].isnull().all():
             continue
@@ -194,29 +201,21 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
     return tabular_data
 
 
-def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
+def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
     """
     Generate summary per strategy
-    :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
+    :param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
     :return: List of Dicts containing the metrics per Strategy
     """
 
     tabular_data = []
-    for strategy, results in all_results.items():
-        tabular_data.append(_generate_result_line(
-            results['results'], results['config']['dry_run_wallet'], strategy)
-        )
-        try:
-            max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
-                                                                  value_col='profit_ratio')
-            max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
-                                                                  value_col='profit_abs')
-        except ValueError:
-            max_drawdown_per = 0
-            max_drawdown_abs = 0
-        tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
-        tabular_data[-1]['max_drawdown_abs'] = \
-            round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
+    for strategy, result in bt_stats.items():
+        tabular_data.append(deepcopy(result['results_per_pair'][-1]))
+        # Update "key" to strategy (results_per_pair has it as "Total").
+        tabular_data[-1]['key'] = strategy
+        tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
+        tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
+            result['max_drawdown_abs'], result['stake_currency'], False)
     return tabular_data
 
 
@@ -352,14 +351,14 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
     }
 
 
-def generate_strategy_stats(btdata: Dict[str, DataFrame],
+def generate_strategy_stats(pairlist: List[str],
                             strategy: str,
                             content: Dict[str, Any],
                             min_date: datetime, max_date: datetime,
                             market_change: float
                             ) -> Dict[str, Any]:
     """
-    :param btdata: Backtest data
+    :param pairlist: List of pairs to backtest
     :param strategy: Strategy name
     :param content: Backtest result data in the format:
                     {'results: results, 'config: config}}.
@@ -372,11 +371,11 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     if not isinstance(results, DataFrame):
         return {}
     config = content['config']
-    max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
+    max_open_trades = min(config['max_open_trades'], len(pairlist))
     starting_balance = config['dry_run_wallet']
     stake_currency = config['stake_currency']
 
-    pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+    pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                          starting_balance=starting_balance,
                                          results=results, skip_nan=False)
 
@@ -385,7 +384,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
 
     sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
-    left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                               starting_balance=starting_balance,
                                               results=results.loc[results['is_open']],
                                               skip_nan=True)
@@ -429,7 +428,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
 
         'trades_per_day': round(len(results) / backtest_days, 2),
         'market_change': market_change,
-        'pairlist': list(btdata.keys()),
+        'pairlist': pairlist,
         'stake_amount': config['stake_amount'],
         'stake_currency': config['stake_currency'],
         'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
@@ -462,12 +461,14 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     }
 
     try:
-        max_drawdown, _, _, _, _ = calculate_max_drawdown(
+        max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
             results, value_col='profit_ratio')
-        drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
-            results, value_col='profit_abs')
+        (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
+         max_drawdown) = calculate_max_drawdown(
+             results, value_col='profit_abs', starting_balance=starting_balance)
         strat_stats.update({
-            'max_drawdown': max_drawdown,
+            'max_drawdown': max_drawdown_legacy,  # Deprecated - do not use
+            'max_drawdown_account': max_drawdown,
             'max_drawdown_abs': drawdown_abs,
             'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
             'drawdown_start_ts': drawdown_start.timestamp() * 1000,
@@ -487,6 +488,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     except ValueError:
         strat_stats.update({
             'max_drawdown': 0.0,
+            'max_drawdown_account': 0.0,
             'max_drawdown_abs': 0.0,
             'max_drawdown_low': 0.0,
             'max_drawdown_high': 0.0,
@@ -513,16 +515,26 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
     :param max_date: Backtest end date
     :return: Dictionary containing results per strategy and a strategy summary.
     """
-    result: Dict[str, Any] = {'strategy': {}}
+    result: Dict[str, Any] = {
+        'metadata': {},
+        'strategy': {},
+        'strategy_comparison': [],
+    }
     market_change = calculate_market_change(btdata, 'close')
-
+    metadata = {}
+    pairlist = list(btdata.keys())
     for strategy, content in all_results.items():
-        strat_stats = generate_strategy_stats(btdata, strategy, content,
+        strat_stats = generate_strategy_stats(pairlist, strategy, content,
                                               min_date, max_date, market_change=market_change)
+        metadata[strategy] = {
+            'run_id': content['run_id'],
+            'backtest_start_time': content['backtest_start_time'],
+        }
         result['strategy'][strategy] = strat_stats
 
-    strategy_results = generate_strategy_comparison(all_results=all_results)
+    strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
 
+    result['metadata'] = metadata
     result['strategy_comparison'] = strategy_results
 
     return result
@@ -646,7 +658,12 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
     headers.append('Drawdown')
 
     # Align drawdown string on the center two space separator.
+    if 'max_drawdown_account' in strategy_results[0]:
+        drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
+    else:
+        # Support for prior backtest results
         drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
+
     dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
     dd_pad_per = max([len(dd) for dd in drawdown])
     drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency}  {dd:>{dd_pad_per}}%'
@@ -716,7 +733,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Max balance', round_coin_value(strat_results['csum_max'],
                                              strat_results['stake_currency'])),
 
-            ('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
+            # Compatibility to show old hyperopt results
+            ('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
+            if 'max_drawdown_account' in strat_results else (
+                'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
             ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
                                           strat_results['stake_currency'])),
             ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],

freqtrade/persistence/models.py

@@ -424,10 +424,10 @@ class LocalTrade():
             # Update open rate and actual amount
             self.open_rate = float(safe_value_fallback(order, 'average', 'price'))
             self.amount = float(safe_value_fallback(order, 'filled', 'amount'))
-            self.recalc_open_trade_value()
             if self.is_open:
                 logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
             self.open_order_id = None
+            self.recalc_trade_from_orders()
         elif order_type in ('market', 'limit') and order['side'] == 'sell':
             if self.is_open:
                 logger.info(f'{order_type.upper()}_SELL has been fulfilled for {self}.')
@@ -568,6 +568,38 @@ class LocalTrade():
         profit_ratio = (close_trade_value / self.open_trade_value) - 1
         return float(f"{profit_ratio:.8f}")
 
+    def recalc_trade_from_orders(self):
+        # We need at least 2 entry orders for averaging amounts and rates.
+        if len(self.select_filled_orders('buy')) < 2:
+            # Just in case, still recalc open trade value
+            self.recalc_open_trade_value()
+            return
+
+        total_amount = 0.0
+        total_stake = 0.0
+        for o in self.orders:
+            if (o.ft_is_open or
+                    (o.ft_order_side != 'buy') or
+                    (o.status not in NON_OPEN_EXCHANGE_STATES)):
+                continue
+
+            tmp_amount = o.amount
+            tmp_price = o.average or o.price
+            if o.filled is not None:
+                tmp_amount = o.filled
+            if tmp_amount > 0.0 and tmp_price is not None:
+                total_amount += tmp_amount
+                total_stake += tmp_price * tmp_amount
+
+        if total_amount > 0:
+            self.open_rate = total_stake / total_amount
+            self.stake_amount = total_stake
+            self.amount = total_amount
+            self.fee_open_cost = self.fee_open * self.stake_amount
+            self.recalc_open_trade_value()
+            if self.stop_loss_pct is not None and self.open_rate is not None:
+                self.adjust_stop_loss(self.open_rate, self.stop_loss_pct)
+
     def select_order(self, order_side: str, is_open: Optional[bool]) -> Optional[Order]:
         """
         Finds latest order for this orderside and status
@@ -583,6 +615,34 @@ class LocalTrade():
         else:
             return None
 
+    def select_filled_orders(self, order_side: str) -> List['Order']:
+        """
+        Finds filled orders for this orderside.
+        :param order_side: Side of the order (either 'buy' or 'sell')
+        :return: array of Order objects
+        """
+        return [o for o in self.orders if o.ft_order_side == order_side and
+                o.ft_is_open is False and
+                (o.filled or 0) > 0 and
+                o.status in NON_OPEN_EXCHANGE_STATES]
+
+    @property
+    def nr_of_successful_buys(self) -> int:
+        """
+        Helper function to count the number of buy orders that have been filled.
+        :return: int count of buy orders that have been filled for this trade.
+        """
+
+        return len(self.select_filled_orders('buy'))
+
+    @property
+    def nr_of_successful_sells(self) -> int:
+        """
+        Helper function to count the number of sell orders that have been filled.
+        :return: int count of sell orders that have been filled for this trade.
+        """
+        return len(self.select_filled_orders('sell'))
+
     @staticmethod
     def get_trades_proxy(*, pair: str = None, is_open: bool = None,
                          open_date: datetime = None, close_date: datetime = None,
@@ -670,7 +730,7 @@ class Trade(_DECL_BASE, LocalTrade):
 
     id = Column(Integer, primary_key=True)
 
-    orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
+    orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan", lazy="joined")
 
     exchange = Column(String(25), nullable=False)
     pair = Column(String(25), nullable=False, index=True)

freqtrade/plot/plotting.py

@@ -5,7 +5,8 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframes_with_mean,
+from freqtrade.data.btanalysis import (analyze_trade_parallelism, calculate_max_drawdown,
+                                       calculate_underwater, combine_dataframes_with_mean,
                                        create_cum_profit, extract_trades_of_period, load_trades)
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
@@ -160,7 +161,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
     Add scatter points indicating max drawdown
     """
     try:
-        max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
+        _, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
 
         drawdown = go.Scatter(
             x=[highdate, lowdate],
@@ -185,6 +186,48 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
     return fig
 
 
+def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots:
+    """
+    Add underwater plot
+    """
+    try:
+        underwater = calculate_underwater(trades, value_col="profit_abs")
+
+        underwater = go.Scatter(
+            x=underwater['date'],
+            y=underwater['drawdown'],
+            name="Underwater Plot",
+            fill='tozeroy',
+            fillcolor='#cc362b',
+            line={'color': '#cc362b'},
+        )
+        fig.add_trace(underwater, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting underwater plot")
+    return fig
+
+
+def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
+    """
+    Add Chart showing trade parallelism
+    """
+    try:
+        result = analyze_trade_parallelism(trades, timeframe)
+
+        drawdown = go.Scatter(
+            x=result.index,
+            y=result['open_trades'],
+            name="Parallel trades",
+            fill='tozeroy',
+            fillcolor='#242222',
+            line={'color': '#242222'},
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting Parallelism.")
+    return fig
+
+
 def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     """
     Add trades to "fig"
@@ -192,8 +235,10 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     # Trades can be empty
     if trades is not None and len(trades) > 0:
         # Create description for sell summarizing the trade
-        trades['desc'] = trades.apply(lambda row: f"{row['profit_ratio']:.2%}, "
-                                                  f"{row['sell_reason']}, "
+        trades['desc'] = trades.apply(
+            lambda row: f"{row['profit_ratio']:.2%}, " +
+            (f"{row['buy_tag']}, " if row['buy_tag'] is not None else "") +
+            f"{row['sell_reason']}, " +
             f"{row['trade_duration']} min",
             axis=1)
         trade_buys = go.Scatter(
@@ -460,7 +505,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
 def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
                           trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
     # Combine close-values for all pairs, rename columns to "pair"
+    try:
         df_comb = combine_dataframes_with_mean(data, "close")
+    except ValueError:
+        raise OperationalException(
+            "No data found. Please make sure that data is available for "
+            "the timerange and pairs selected.")
 
     # Trim trades to available OHLCV data
     trades = extract_trades_of_period(df_comb, trades, date_index=True)
@@ -477,20 +527,30 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
         name='Avg close price',
     )
 
-    fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
-                        row_width=[1, 1, 1],
+    fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
+                        row_heights=[1, 1, 1, 0.5, 1],
                         vertical_spacing=0.05,
-                        subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
+                        subplot_titles=[
+                            "AVG Close Price",
+                            "Combined Profit",
+                            "Profit per pair",
+                            "Parallelism",
+                            "Underwater",
+                            ])
     fig['layout'].update(title="Freqtrade Profit plot")
     fig['layout']['yaxis1'].update(title='Price')
     fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
     fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
+    fig['layout']['yaxis4'].update(title='Trade count')
+    fig['layout']['yaxis5'].update(title='Underwater Plot')
     fig['layout']['xaxis']['rangeslider'].update(visible=False)
     fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
 
     fig.add_trace(avgclose, 1, 1)
     fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
     fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
+    fig = add_parallelism(fig, 4, trades, timeframe)
+    fig = add_underwater(fig, 5, trades)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -68,14 +68,14 @@ class PerformanceFilter(IPairList):
         #  - then pair name alphametically
         sorted_df = list_df.merge(performance, on='pair', how='left')\
             .fillna(0).sort_values(by=['count', 'pair'], ascending=True)\
-            .sort_values(by=['profit'], ascending=False)
+            .sort_values(by=['profit_ratio'], ascending=False)
         if self._min_profit is not None:
-            removed = sorted_df[sorted_df['profit'] < self._min_profit]
+            removed = sorted_df[sorted_df['profit_ratio'] < self._min_profit]
             for _, row in removed.iterrows():
                 self.log_once(
-                    f"Removing pair {row['pair']} since {row['profit']} is "
+                    f"Removing pair {row['pair']} since {row['profit_ratio']} is "
                     f"below {self._min_profit}", logger.info)
-            sorted_df = sorted_df[sorted_df['profit'] >= self._min_profit]
+            sorted_df = sorted_df[sorted_df['profit_ratio'] >= self._min_profit]
 
         pairlist = sorted_df['pair'].tolist()
 

freqtrade/plugins/pairlist/ShuffleFilter.py

@@ -5,6 +5,7 @@ import logging
 import random
 from typing import Any, Dict, List
 
+from freqtrade.enums import RunMode
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -18,7 +19,15 @@ class ShuffleFilter(IPairList):
                  pairlist_pos: int) -> None:
         super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
 
+        # Apply seed in backtesting mode to get comparable results,
+        # but not in live modes to get a non-repeating order of pairs during live modes.
+        if config.get('runmode') in (RunMode.LIVE, RunMode.DRY_RUN):
+            self._seed = None
+            logger.info("Live mode detected, not applying seed.")
+        else:
             self._seed = pairlistconfig.get('seed')
+            logger.info(f"Backtesting mode detected, applying seed value: {self._seed}")
+
         self._random = random.Random(self._seed)
 
     @property

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -47,7 +47,7 @@ class SpreadFilter(IPairList):
             spread = 1 - ticker['bid'] / ticker['ask']
             if spread > self._max_spread_ratio:
                 self.log_once(f"Removed {pair} from whitelist, because spread "
-                              f"{spread * 100:.3%} > {self._max_spread_ratio:.3%}",
+                              f"{spread:.3%} > {self._max_spread_ratio:.3%}",
                               logger.info)
                 return False
             else:

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional
 
 import arrow
 import numpy as np
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 from pandas import DataFrame
 
 from freqtrade.exceptions import OperationalException

freqtrade/plugins/pairlist/VolumePairList.py

@@ -4,11 +4,10 @@ Volume PairList provider
 Provides dynamic pair list based on trade volumes
 """
 import logging
-from functools import partial
 from typing import Any, Dict, List
 
 import arrow
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
@@ -120,10 +119,17 @@ class VolumePairList(IPairList):
         else:
             # Use fresh pairlist
             # Check if pair quote currency equals to the stake currency.
+            _pairlist = [k for k in self._exchange.get_markets(
+                quote_currencies=[self._stake_currency],
+                pairs_only=True, active_only=True).keys()]
+            # No point in testing for blacklisted pairs...
+            _pairlist = self.verify_blacklist(_pairlist, logger.info)
+
             filtered_tickers = [
                 v for k, v in tickers.items()
                 if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
-                    and (self._use_range or v[self._sort_key] is not None))]
+                    and (self._use_range or v[self._sort_key] is not None)
+                    and v['symbol'] in _pairlist)]
             pairlist = [s['symbol'] for s in filtered_tickers]
 
             pairlist = self.filter_pairlist(pairlist, tickers)
@@ -178,12 +184,16 @@ class VolumePairList(IPairList):
                 ] if (p['symbol'], self._lookback_timeframe) in candles else None
                 # in case of candle data calculate typical price and quoteVolume for candle
                 if pair_candles is not None and not pair_candles.empty:
+                    if self._exchange._ft_has["ohlcv_volume_currency"] == "base":
                         pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
                                                          + pair_candles['close']) / 3
+
                         pair_candles['quoteVolume'] = (
                             pair_candles['volume'] * pair_candles['typical_price']
                         )
-
+                    else:
+                        # Exchange ohlcv data is in quote volume already.
+                        pair_candles['quoteVolume'] = pair_candles['volume']
                     # ensure that a rolling sum over the lookback_period is built
                     # if pair_candles contains more candles than lookback_period
                     quoteVolume = (pair_candles['quoteVolume']
@@ -204,7 +214,7 @@ class VolumePairList(IPairList):
 
         # Validate whitelist to only have active market pairs
         pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
-        pairs = self.verify_blacklist(pairs, partial(self.log_once, logmethod=logger.info))
+        pairs = self.verify_blacklist(pairs, logmethod=logger.info)
         # Limit pairlist to the requested number of pairs
         pairs = pairs[:self._number_pairs]
 

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -6,7 +6,7 @@ from copy import deepcopy
 from typing import Any, Dict, List, Optional
 
 import arrow
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 from pandas import DataFrame
 
 from freqtrade.exceptions import OperationalException

freqtrade/plugins/pairlistmanager.py

@@ -2,13 +2,14 @@
 PairList manager class
 """
 import logging
-from copy import deepcopy
+from functools import partial
 from typing import Dict, List
 
 from cachetools import TTLCache, cached
 
 from freqtrade.constants import ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
+from freqtrade.mixins import LoggingMixin
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.resolvers import PairListResolver
@@ -17,7 +18,7 @@ from freqtrade.resolvers import PairListResolver
 logger = logging.getLogger(__name__)
 
 
-class PairListManager():
+class PairListManager(LoggingMixin):
 
     def __init__(self, exchange, config: dict) -> None:
         self._exchange = exchange
@@ -41,6 +42,9 @@ class PairListManager():
         if not self._pairlist_handlers:
             raise OperationalException("No Pairlist Handlers defined")
 
+        refresh_period = config.get('pairlist_refresh_period', 3600)
+        LoggingMixin.__init__(self, logger, refresh_period)
+
     @property
     def whitelist(self) -> List[str]:
         """The current whitelist"""
@@ -108,9 +112,10 @@ class PairListManager():
         except ValueError as err:
             logger.error(f"Pair blacklist contains an invalid Wildcard: {err}")
             return []
-        for pair in deepcopy(pairlist):
+        log_once = partial(self.log_once, logmethod=logmethod)
+        for pair in pairlist.copy():
             if pair in blacklist:
-                logmethod(f"Pair {pair} in your blacklist. Removing it from whitelist...")
+                log_once(f"Pair {pair} in your blacklist. Removing it from whitelist...")
                 pairlist.remove(pair)
         return pairlist
 

freqtrade/plugins/protections/max_drawdown_protection.py

@@ -55,7 +55,8 @@ class MaxDrawdown(IProtection):
 
         # Drawdown is always positive
         try:
-            drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
+            # TODO: This should use absolute profit calculation, considering account balance.
+            drawdown, _, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
         except ValueError:
             return False, None, None
 

freqtrade/resolvers/strategy_resolver.py

@@ -96,7 +96,9 @@ class StrategyResolver(IResolver):
                       ("ignore_roi_if_buy_signal",        False),
                       ("sell_profit_offset",              0.0),
                       ("disable_dataframe_checks",        False),
-                      ("ignore_buying_expired_candle_after",  0)
+                      ("ignore_buying_expired_candle_after",  0),
+                      ("position_adjustment_enable",      False),
+                      ("max_entry_position_adjustment",      -1),
                       ]
         for attribute, default in attributes:
             StrategyResolver._override_attribute_helper(strategy, config,

freqtrade/rpc/api_server/api_backtest.py

@@ -33,10 +33,14 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
         if settings[setting] is not None:
             btconfig[setting] = settings[setting]
 
+    # Force dry-run for backtesting
+    btconfig['dry_run'] = True
+
     # Start backtesting
     # Initialize backtesting object
     def run_backtest():
-        from freqtrade.optimize.optimize_reports import generate_backtest_stats
+        from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
+                                                         store_backtest_stats)
         from freqtrade.resolvers import StrategyResolver
         asyncio.set_event_loop(asyncio.new_event_loop())
         try:
@@ -73,13 +77,25 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             lastconfig['enable_protections'] = btconfig.get('enable_protections')
             lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
 
+            ApiServer._bt.results = {}
+            ApiServer._bt.load_prior_backtest()
+
             ApiServer._bt.abort = False
+            if (ApiServer._bt.results and
+                    strat.get_strategy_name() in ApiServer._bt.results['strategy']):
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+            else:
                 min_date, max_date = ApiServer._bt.backtest_one_strategy(
                     strat, ApiServer._bt_data, ApiServer._bt_timerange)
 
                 ApiServer._bt.results = generate_backtest_stats(
                     ApiServer._bt_data, ApiServer._bt.all_results,
                     min_date=min_date, max_date=max_date)
+
+            if btconfig.get('export', 'none') == 'trades':
+                store_backtest_stats(btconfig['exportfilename'], ApiServer._bt.results)
+
             logger.info("Backtest finished.")
 
         except DependencyException as e:

freqtrade/rpc/api_server/api_schemas.py

@@ -4,6 +4,7 @@ from typing import Any, Dict, List, Optional, Union
 from pydantic import BaseModel
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.enums import OrderTypeValues
 
 
 class Ping(BaseModel):
@@ -125,25 +126,26 @@ class Daily(BaseModel):
 
 
 class UnfilledTimeout(BaseModel):
-    buy: int
-    sell: int
-    unit: str
+    buy: Optional[int]
+    sell: Optional[int]
+    unit: Optional[str]
     exit_timeout_count: Optional[int]
 
 
 class OrderTypes(BaseModel):
-    buy: str
-    sell: str
-    emergencysell: Optional[str]
-    forcesell: Optional[str]
-    forcebuy: Optional[str]
-    stoploss: str
+    buy: OrderTypeValues
+    sell: OrderTypeValues
+    emergencysell: Optional[OrderTypeValues]
+    forcesell: Optional[OrderTypeValues]
+    forcebuy: Optional[OrderTypeValues]
+    stoploss: OrderTypeValues
     stoploss_on_exchange: bool
     stoploss_on_exchange_interval: Optional[int]
 
 
 class ShowConfig(BaseModel):
     version: str
+    strategy_version: Optional[str]
     api_version: float
     dry_run: bool
     stake_currency: str
@@ -158,7 +160,7 @@ class ShowConfig(BaseModel):
     trailing_stop_positive_offset: Optional[float]
     trailing_only_offset_is_reached: Optional[bool]
     unfilledtimeout: UnfilledTimeout
-    order_types: OrderTypes
+    order_types: Optional[OrderTypes]
     use_custom_stoploss: Optional[bool]
     timeframe: Optional[str]
     timeframe_ms: int
@@ -171,6 +173,8 @@ class ShowConfig(BaseModel):
     bot_name: str
     state: str
     runmode: str
+    position_adjustment_enable: bool
+    max_entry_position_adjustment: int
 
 
 class TradeSchema(BaseModel):
@@ -274,10 +278,13 @@ class Logs(BaseModel):
 class ForceBuyPayload(BaseModel):
     pair: str
     price: Optional[float]
+    ordertype: Optional[OrderTypeValues]
+    stakeamount: Optional[float]
 
 
 class ForceSellPayload(BaseModel):
     tradeid: str
+    ordertype: Optional[OrderTypeValues]
 
 
 class BlacklistPayload(BaseModel):

freqtrade/rpc/api_server/api_v1.py

@@ -3,7 +3,7 @@ from copy import deepcopy
 from pathlib import Path
 from typing import List, Optional
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Query
 from fastapi.exceptions import HTTPException
 
 from freqtrade import __version__
@@ -20,7 +20,7 @@ from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, Blac
                                                   Stats, StatusMsg, StrategyListResponse,
                                                   StrategyResponse, SysInfo, Version,
                                                   WhitelistResponse)
-from freqtrade.rpc.api_server.deps import get_config, get_rpc, get_rpc_optional
+from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
 from freqtrade.rpc.rpc import RPCException
 
 
@@ -29,7 +29,10 @@ logger = logging.getLogger(__name__)
 # API version
 # Pre-1.1, no version was provided
 # Version increments should happen in "small" steps (1.1, 1.12, ...) unless big changes happen.
-API_VERSION = 1.1
+# 1.11: forcebuy and forcesell accept ordertype
+# 1.12: add blacklist delete endpoint
+# 1.13: forcebuy supports stake_amount
+API_VERSION = 1.13
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -120,16 +123,21 @@ def edge(rpc: RPC = Depends(get_rpc)):
 @router.get('/show_config', response_model=ShowConfig, tags=['info'])
 def show_config(rpc: Optional[RPC] = Depends(get_rpc_optional), config=Depends(get_config)):
     state = ''
+    strategy_version = None
     if rpc:
         state = rpc._freqtrade.state
-    resp = RPC._rpc_show_config(config, state)
+        strategy_version = rpc._freqtrade.strategy.version()
+    resp = RPC._rpc_show_config(config, state, strategy_version)
     resp['api_version'] = API_VERSION
     return resp
 
 
 @router.post('/forcebuy', response_model=ForceBuyResponse, tags=['trading'])
 def forcebuy(payload: ForceBuyPayload, rpc: RPC = Depends(get_rpc)):
-    trade = rpc._rpc_forcebuy(payload.pair, payload.price)
+    ordertype = payload.ordertype.value if payload.ordertype else None
+    stake_amount = payload.stakeamount if payload.stakeamount else None
+
+    trade = rpc._rpc_forcebuy(payload.pair, payload.price, ordertype, stake_amount)
 
     if trade:
         return ForceBuyResponse.parse_obj(trade.to_json())
@@ -139,7 +147,8 @@ def forcebuy(payload: ForceBuyPayload, rpc: RPC = Depends(get_rpc)):
 
 @router.post('/forcesell', response_model=ResultMsg, tags=['trading'])
 def forcesell(payload: ForceSellPayload, rpc: RPC = Depends(get_rpc)):
-    return rpc._rpc_forcesell(payload.tradeid)
+    ordertype = payload.ordertype.value if payload.ordertype else None
+    return rpc._rpc_forcesell(payload.tradeid, ordertype)
 
 
 @router.get('/blacklist', response_model=BlacklistResponse, tags=['info', 'pairlist'])
@@ -152,6 +161,13 @@ def blacklist_post(payload: BlacklistPayload, rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_blacklist(payload.blacklist)
 
 
+@router.delete('/blacklist', response_model=BlacklistResponse, tags=['info', 'pairlist'])
+def blacklist_delete(pairs_to_delete: List[str] = Query([]), rpc: RPC = Depends(get_rpc)):
+    """Provide a list of pairs to delete from the blacklist"""
+
+    return rpc._rpc_blacklist_delete(pairs_to_delete)
+
+
 @router.get('/whitelist', response_model=WhitelistResponse, tags=['info', 'pairlist'])
 def whitelist(rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_whitelist()
@@ -198,18 +214,21 @@ def reload_config(rpc: RPC = Depends(get_rpc)):
 
 
 @router.get('/pair_candles', response_model=PairHistory, tags=['candle data'])
-def pair_candles(pair: str, timeframe: str, limit: Optional[int], rpc: RPC = Depends(get_rpc)):
+def pair_candles(
+        pair: str, timeframe: str, limit: Optional[int] = None, rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_analysed_dataframe(pair, timeframe, limit)
 
 
 @router.get('/pair_history', response_model=PairHistory, tags=['candle data'])
 def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
-                 config=Depends(get_config)):
+                 config=Depends(get_config), exchange=Depends(get_exchange)):
+    # The initial call to this endpoint can be slow, as it may need to initialize
+    # the exchange class.
     config = deepcopy(config)
     config.update({
         'strategy': strategy,
     })
-    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange)
+    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange, exchange)
 
 
 @router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])

freqtrade/rpc/api_server/deps.py

@@ -1,5 +1,7 @@
 from typing import Any, Dict, Iterator, Optional
 
+from fastapi import Depends
+
 from freqtrade.persistence import Trade
 from freqtrade.rpc.rpc import RPC, RPCException
 
@@ -28,3 +30,11 @@ def get_config() -> Dict[str, Any]:
 
 def get_api_config() -> Dict[str, Any]:
     return ApiServer._config['api_server']
+
+
+def get_exchange(config=Depends(get_config)):
+    if not ApiServer._exchange:
+        from freqtrade.resolvers import ExchangeResolver
+        ApiServer._exchange = ExchangeResolver.load_exchange(
+            config['exchange']['name'], config)
+    return ApiServer._exchange

freqtrade/rpc/api_server/uvicorn_threaded.py

@@ -47,7 +47,7 @@ class UvicornServer(uvicorn.Server):
         else:
             asyncio.set_event_loop(uvloop.new_event_loop())
         try:
-            loop = asyncio.get_event_loop()
+            loop = asyncio.get_running_loop()
         except RuntimeError:
             # When running in a thread, we'll not have an eventloop yet.
             loop = asyncio.new_event_loop()

freqtrade/rpc/api_server/webserver.py

@@ -41,6 +41,8 @@ class ApiServer(RPCHandler):
     _has_rpc: bool = False
     _bgtask_running: bool = False
     _config: Dict[str, Any] = {}
+    # Exchange - only available in webserver mode.
+    _exchange = None
 
     def __new__(cls, *args, **kwargs):
         """

freqtrade/rpc/fiat_convert.py

@@ -7,7 +7,7 @@ import datetime
 import logging
 from typing import Dict, List
 
-from cachetools.ttl import TTLCache
+from cachetools import TTLCache
 from pycoingecko import CoinGeckoAPI
 from requests.exceptions import RequestException
 
@@ -77,6 +77,9 @@ class CryptoToFiatConverter:
             else:
                 return None
         found = [x for x in self._coinlistings if x['symbol'] == crypto_symbol]
+        if crypto_symbol == 'eth':
+            found = [x for x in self._coinlistings if x['id'] == 'ethereum']
+
         if len(found) == 1:
             return found[0]['id']
 

freqtrade/rpc/rpc.py

@@ -98,7 +98,8 @@ class RPC:
             self._fiat_converter = CryptoToFiatConverter()
 
     @staticmethod
-    def _rpc_show_config(config, botstate: Union[State, str]) -> Dict[str, Any]:
+    def _rpc_show_config(config, botstate: Union[State, str],
+                         strategy_version: Optional[str] = None) -> Dict[str, Any]:
         """
         Return a dict of config options.
         Explicitly does NOT return the full config to avoid leakage of sensitive
@@ -106,6 +107,7 @@ class RPC:
         """
         val = {
             'version': __version__,
+            'strategy_version': strategy_version,
             'dry_run': config['dry_run'],
             'stake_currency': config['stake_currency'],
             'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
@@ -134,7 +136,12 @@ class RPC:
             'ask_strategy': config.get('ask_strategy', {}),
             'bid_strategy': config.get('bid_strategy', {}),
             'state': str(botstate),
-            'runmode': config['runmode'].value
+            'runmode': config['runmode'].value,
+            'position_adjustment_enable': config.get('position_adjustment_enable', False),
+            'max_entry_position_adjustment': (
+                config.get('max_entry_position_adjustment', -1)
+                if config.get('max_entry_position_adjustment') != float('inf')
+                else -1)
         }
         return val
 
@@ -236,18 +243,28 @@ class RPC:
                         profit_str += f" ({fiat_profit:.2f})"
                         fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
                             else fiat_profit_sum + fiat_profit
-                trades_list.append([
+                detail_trade = [
                     trade.id,
                     trade.pair + ('*' if (trade.open_order_id is not None
                                           and trade.close_rate_requested is None) else '')
                     + ('**' if (trade.close_rate_requested is not None) else ''),
                     shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
                     profit_str
-                ])
+                ]
+                if self._config.get('position_adjustment_enable', False):
+                    max_buy_str = ''
+                    if self._config.get('max_entry_position_adjustment', -1) > 0:
+                        max_buy_str = f"/{self._config['max_entry_position_adjustment'] + 1}"
+                    filled_buys = trade.nr_of_successful_buys
+                    detail_trade.append(f"{filled_buys}{max_buy_str}")
+                trades_list.append(detail_trade)
             profitcol = "Profit"
             if self._fiat_converter:
                 profitcol += " (" + fiat_display_currency + ")"
 
+            if self._config.get('position_adjustment_enable', False):
+                columns = ['ID', 'Pair', 'Since', profitcol, '# Buys']
+            else:
                 columns = ['ID', 'Pair', 'Since', profitcol]
             return trades_list, columns, fiat_profit_sum
 
@@ -590,6 +607,7 @@ class RPC:
         value = self._fiat_converter.convert_amount(
             total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
 
+        trade_count = len(Trade.get_trades_proxy())
         starting_capital_ratio = 0.0
         starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
         starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
@@ -606,6 +624,7 @@ class RPC:
             'starting_capital_fiat': starting_cap_fiat,
             'starting_capital_fiat_ratio': starting_cap_fiat_ratio,
             'starting_capital_fiat_pct': round(starting_cap_fiat_ratio * 100, 2),
+            'trade_count': trade_count,
             'note': 'Simulated balances' if self._freqtrade.config['dry_run'] else ''
         }
 
@@ -640,7 +659,7 @@ class RPC:
 
         return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
 
-    def _rpc_forcesell(self, trade_id: str) -> Dict[str, str]:
+    def _rpc_forcesell(self, trade_id: str, ordertype: Optional[str] = None) -> Dict[str, str]:
         """
         Handler for forcesell <id>.
         Sells the given trade at current price
@@ -664,7 +683,11 @@ class RPC:
                 current_rate = self._freqtrade.exchange.get_rate(
                     trade.pair, refresh=False, side="sell")
                 sell_reason = SellCheckTuple(sell_type=SellType.FORCE_SELL)
-                self._freqtrade.execute_trade_exit(trade, current_rate, sell_reason)
+                order_type = ordertype or self._freqtrade.strategy.order_types.get(
+                    "forcesell", self._freqtrade.strategy.order_types["sell"])
+
+                self._freqtrade.execute_trade_exit(
+                    trade, current_rate, sell_reason, ordertype=order_type)
         # ---- EOF def _exec_forcesell ----
 
         if self._freqtrade.state != State.RUNNING:
@@ -692,7 +715,8 @@ class RPC:
             self._freqtrade.wallets.update()
             return {'result': f'Created sell order for trade {trade_id}.'}
 
-    def _rpc_forcebuy(self, pair: str, price: Optional[float]) -> Optional[Trade]:
+    def _rpc_forcebuy(self, pair: str, price: Optional[float], order_type: Optional[str] = None,
+                      stake_amount: Optional[float] = None) -> Optional[Trade]:
         """
         Handler for forcebuy <asset> <price>
         Buys a pair trade at the given or current price
@@ -714,13 +738,19 @@ class RPC:
         # check if pair already has an open pair
         trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
         if trade:
+            if not self._freqtrade.strategy.position_adjustment_enable:
                 raise RPCException(f'position for {pair} already open - id: {trade.id}')
 
+        if not stake_amount:
             # gen stake amount
-        stakeamount = self._freqtrade.wallets.get_trade_stake_amount(pair)
+            stake_amount = self._freqtrade.wallets.get_trade_stake_amount(pair)
 
         # execute buy
-        if self._freqtrade.execute_entry(pair, stakeamount, price, forcebuy=True):
+        if not order_type:
+            order_type = self._freqtrade.strategy.order_types.get(
+                'forcebuy', self._freqtrade.strategy.order_types['buy'])
+        if self._freqtrade.execute_entry(pair, stake_amount, price,
+                                         ordertype=order_type, trade=trade):
             Trade.commit()
             trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
             return trade
@@ -850,6 +880,20 @@ class RPC:
                }
         return res
 
+    def _rpc_blacklist_delete(self, delete: List[str]) -> Dict:
+        """ Removes pairs from currently active blacklist """
+        errors = {}
+        for pair in delete:
+            if pair in self._freqtrade.pairlists.blacklist:
+                self._freqtrade.pairlists.blacklist.remove(pair)
+            else:
+                errors[pair] = {
+                    'error_msg': f"Pair {pair} is not in the current blacklist."
+                    }
+        resp = self._rpc_blacklist()
+        resp['errors'] = errors
+        return resp
+
     def _rpc_blacklist(self, add: List[str] = None) -> Dict:
         """ Returns the currently active blacklist"""
         errors = {}
@@ -960,7 +1004,7 @@ class RPC:
 
     @staticmethod
     def _rpc_analysed_history_full(config, pair: str, timeframe: str,
-                                   timerange: str) -> Dict[str, Any]:
+                                   timerange: str, exchange) -> Dict[str, Any]:
         timerange_parsed = TimeRange.parse_timerange(timerange)
 
         _data = load_data(
@@ -975,7 +1019,7 @@ class RPC:
         from freqtrade.data.dataprovider import DataProvider
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
         strategy = StrategyResolver.load_strategy(config)
-        strategy.dp = DataProvider(config, exchange=None, pairlists=None)
+        strategy.dp = DataProvider(config, exchange=exchange, pairlists=None)
 
         df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
 

freqtrade/rpc/rpc_manager.py

@@ -60,6 +60,10 @@ class RPCManager:
         }
         """
         logger.info('Sending rpc message: %s', msg)
+        if 'pair' in msg:
+            msg.update({
+                'base_currency': self._rpc._freqtrade.exchange.get_pair_base_currency(msg['pair'])
+                })
         for mod in self.registered_modules:
             logger.debug('Forwarding message to rpc.%s', mod.name)
             try:
@@ -81,12 +85,14 @@ class RPCManager:
         timeframe = config['timeframe']
         exchange_name = config['exchange']['name']
         strategy_name = config.get('strategy', '')
+        pos_adjust_enabled = 'On' if config['position_adjustment_enable'] else 'Off'
         self.send_msg({
             'type': RPCMessageType.STARTUP,
             'status': f'*Exchange:* `{exchange_name}`\n'
                       f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
                       f'*Minimum ROI:* `{minimal_roi}`\n'
                       f'*{"Trailing " if trailing_stop else ""}Stoploss:* `{stoploss}`\n'
+                      f'*Position adjustment:* `{pos_adjust_enabled}`\n'
                       f'*Timeframe:* `{timeframe}`\n'
                       f'*Strategy:* `{strategy_name}`'
         })

freqtrade/rpc/telegram.py

@@ -111,8 +111,9 @@ class Telegram(RPCHandler):
                                  r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
                                  r'/stats$', r'/count$', r'/locks$', r'/balance$',
                                  r'/stopbuy$', r'/reload_config$', r'/show_config$',
-                                 r'/logs$', r'/whitelist$', r'/blacklist$', r'/edge$',
-                                 r'/forcebuy$', r'/help$', r'/version$']
+                                 r'/logs$', r'/whitelist$', r'/blacklist$', r'/bl_delete$',
+                                 r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
+                                 r'/forcebuy$', r'/edge$', r'/help$', r'/version$']
         # Create keys for generation
         valid_keys_print = [k.replace('$', '') for k in valid_keys]
 
@@ -169,6 +170,7 @@ class Telegram(RPCHandler):
             CommandHandler('stopbuy', self._stopbuy),
             CommandHandler('whitelist', self._whitelist),
             CommandHandler('blacklist', self._blacklist),
+            CommandHandler(['blacklist_delete', 'bl_delete'], self._blacklist_delete),
             CommandHandler('logs', self._logs),
             CommandHandler('edge', self._edge),
             CommandHandler('help', self._help),
@@ -197,8 +199,8 @@ class Telegram(RPCHandler):
 
         self._updater.start_polling(
             bootstrap_retries=-1,
-            timeout=30,
-            read_latency=60,
+            timeout=20,
+            read_latency=60,  # Assumed transmission latency
             drop_pending_updates=True,
         )
         logger.info(
@@ -211,6 +213,7 @@ class Telegram(RPCHandler):
         Stops all running telegram threads.
         :return: None
         """
+        # This can take up to `timeout` from the call to `start_polling`.
         self._updater.stop()
 
     def _format_buy_msg(self, msg: Dict[str, Any]) -> str:
@@ -762,14 +765,17 @@ class Telegram(RPCHandler):
                     f"(< {balance_dust_level} {result['stake']}):*\n"
                     f"\t`Est. {result['stake']}: "
                     f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
+            tc = result['trade_count'] > 0
+            stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else ''
+            fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else ''
 
             output += ("\n*Estimated Value*:\n"
                        f"\t`{result['stake']}: "
                        f"{round_coin_value(result['total'], result['stake'], False)}`"
-                       f" `({result['starting_capital_ratio']:.2%})`\n"
+                       f"{stake_improve}\n"
                        f"\t`{result['symbol']}: "
                        f"{round_coin_value(result['value'], result['symbol'], False)}`"
-                       f" `({result['starting_capital_fiat_ratio']:.2%})`\n")
+                       f"{fiat_val}\n")
             self._send_msg(output, reload_able=True, callback_path="update_balance",
                            query=update.callback_query)
         except RPCException as e:
@@ -1161,9 +1167,9 @@ class Telegram(RPCHandler):
         Handler for /blacklist
         Shows the currently active blacklist
         """
-        try:
+        self.send_blacklist_msg(self._rpc._rpc_blacklist(context.args))
 
-            blacklist = self._rpc._rpc_blacklist(context.args)
+    def send_blacklist_msg(self, blacklist: Dict):
         errmsgs = []
         for pair, error in blacklist['errors'].items():
             errmsgs.append(f"Error adding `{pair}` to blacklist: `{error['error_msg']}`")
@@ -1175,8 +1181,14 @@ class Telegram(RPCHandler):
 
         logger.debug(message)
         self._send_msg(message)
-        except RPCException as e:
-            self._send_msg(str(e))
+
+    @authorized_only
+    def _blacklist_delete(self, update: Update, context: CallbackContext) -> None:
+        """
+        Handler for /bl_delete
+        Deletes pair(s) from current blacklist
+        """
+        self.send_blacklist_msg(self._rpc._rpc_blacklist_delete(context.args or []))
 
     @authorized_only
     def _logs(self, update: Update, context: CallbackContext) -> None:
@@ -1257,6 +1269,8 @@ class Telegram(RPCHandler):
             "*/whitelist:* `Show current whitelist` \n"
             "*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
             "to the blacklist.` \n"
+            "*/blacklist_delete [pairs]| /bl_delete [pairs]:* "
+            "`Delete pair / pattern from blacklist. Will reset on reload_conf.` \n"
             "*/reload_config:* `Reload configuration file` \n"
             "*/unlock <pair|id>:* `Unlock this Pair (or this lock id if it's numeric)`\n"
 
@@ -1304,7 +1318,12 @@ class Telegram(RPCHandler):
         :param update: message update
         :return: None
         """
-        self._send_msg('*Version:* `{}`'.format(__version__))
+        strategy_version = self._rpc._freqtrade.strategy.version()
+        version_string = f'*Version:* `{__version__}`'
+        if strategy_version is not None:
+            version_string += f', *Strategy version: * `{strategy_version}`'
+
+        self._send_msg(version_string)
 
     @authorized_only
     def _show_config(self, update: Update, context: CallbackContext) -> None:
@@ -1328,6 +1347,14 @@ class Telegram(RPCHandler):
         else:
             sl_info = f"*Stoploss:* `{val['stoploss']}`\n"
 
+        if val['position_adjustment_enable']:
+            pa_info = (
+                f"*Position adjustment:* On\n"
+                f"*Max enter position adjustment:* `{val['max_entry_position_adjustment']}`\n"
+            )
+        else:
+            pa_info = "*Position adjustment:* Off\n"
+
         self._send_msg(
             f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
             f"*Exchange:* `{val['exchange']}`\n"
@@ -1337,6 +1364,7 @@ class Telegram(RPCHandler):
             f"*Ask strategy:* ```\n{json.dumps(val['ask_strategy'])}```\n"
             f"*Bid strategy:* ```\n{json.dumps(val['bid_strategy'])}```\n"
             f"{sl_info}"
+            f"{pa_info}"
             f"*Timeframe:* `{val['timeframe']}`\n"
             f"*Strategy:* `{val['strategy']}`\n"
             f"*Current state:* `{val['state']}`"

freqtrade/rpc/webhook.py

@@ -2,6 +2,7 @@
 This module manages webhook communication
 """
 import logging
+import time
 from typing import Any, Dict
 
 from requests import RequestException, post
@@ -28,12 +29,9 @@ class Webhook(RPCHandler):
         super().__init__(rpc, config)
 
         self._url = self._config['webhook']['url']
-
         self._format = self._config['webhook'].get('format', 'form')
-
-        if self._format != 'form' and self._format != 'json':
-            raise NotImplementedError('Unknown webhook format `{}`, possible values are '
-                                      '`form` (default) and `json`'.format(self._format))
+        self._retries = self._config['webhook'].get('retries', 0)
+        self._retry_delay = self._config['webhook'].get('retry_delay', 0.1)
 
     def cleanup(self) -> None:
         """
@@ -77,13 +75,30 @@ class Webhook(RPCHandler):
     def _send_msg(self, payload: dict) -> None:
         """do the actual call to the webhook"""
 
+        success = False
+        attempts = 0
+        while not success and attempts <= self._retries:
+            if attempts:
+                if self._retry_delay:
+                    time.sleep(self._retry_delay)
+                logger.info("Retrying webhook...")
+
+            attempts += 1
+
             try:
                 if self._format == 'form':
-                post(self._url, data=payload)
+                    response = post(self._url, data=payload)
                 elif self._format == 'json':
-                post(self._url, json=payload)
+                    response = post(self._url, json=payload)
+                elif self._format == 'raw':
+                    response = post(self._url, data=payload['data'],
+                                    headers={'Content-Type': 'text/plain'})
                 else:
                     raise NotImplementedError('Unknown format: {}'.format(self._format))
 
+                # Throw a RequestException if the post was not successful
+                response.raise_for_status()
+                success = True
+
             except RequestException as exc:
                 logger.warning("Could not call webhook url. Exception: %s", exc)