Merge branch 'freqtrade:develop' into develop

.github/workflows/ci.yml

@@ -3,7 +3,6 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
       - stable
       - develop
     tags:
@@ -20,7 +19,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04 ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -39,7 +38,7 @@ 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 +49,9 @@ 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 +69,7 @@ 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 +101,20 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
+    - name: Discord notification
-      uses: lazy-actions/slatify@v3.0.0
+      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 }}
+          severity: error
-        job_name: '*Freqtrade CI ${{ matrix.os }}*'
+          details: Freqtrade CI failed on ${{ matrix.os }}
-        mention: 'here'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
 
   build_macos:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -136,7 +133,7 @@ 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 +144,11 @@ 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 +160,7 @@ 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 +192,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
+    - name: Discord notification
-      uses: lazy-actions/slatify@v3.0.0
+      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 }}
+          severity: info
-        job_name: '*Freqtrade CI ${{ matrix.os }}*'
+          details: Test Succeeded!
-        mention: 'here'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
-
 
   build_windows:
 
@@ -212,7 +206,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -224,7 +218,6 @@ 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 +250,13 @@ jobs:
       run: |
         mypy freqtrade scripts
 
-    - name: Slack Notification
+    - name: Discord notification
-      uses: lazy-actions/slatify@v3.0.0
+      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 }}
+          severity: error
-        job_name: '*Freqtrade CI windows*'
+          details: Test Failed
-        mention: 'here'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
 
   docs_check:
     runs-on: ubuntu-20.04
@@ -288,14 +278,13 @@ jobs:
         pip install mkdocs
         mkdocs build
 
-    - name: Slack Notification
+    - name: Discord notification
-      uses: lazy-actions/slatify@v3.0.0
+      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 }}
+          severity: error
-        job_name: '*Freqtrade Docs*'
+          details: Freqtrade doc test failed!
-        channel: '#notifications'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        url: ${{ secrets.SLACK_WEBHOOK }}
 
   cleanup-prior-runs:
     runs-on: ubuntu-20.04
@@ -306,7 +295,7 @@ 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 +309,13 @@ jobs:
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
-    - name: Slack Notification
+    - name: Discord notification
-      uses: lazy-actions/slatify@v3.0.0
+      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 }}
+          severity: info
-        job_name: '*Freqtrade CI*'
+          details: Test Completed!
-        channel: '#notifications'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        url: ${{ secrets.SLACK_WEBHOOK }}
 
   deploy:
     needs: [ build_linux, build_macos, build_windows, docs_check ]
@@ -400,17 +388,13 @@ jobs:
       run: |
         build_helpers/publish_docker_multi.sh
 
-
+    - name: Discord notification
-    - name: Slack Notification
+      uses: rjstone/discord-webhook-notify@v1
-      uses: lazy-actions/slatify@v3.0.0
       if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-        type: ${{ job.status }}
+          severity: info
-        job_name: '*Freqtrade CI Deploy*'
+          details: Deploy Succeeded!
-        mention: 'here'
+          webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
-        mention_if: 'failure'
-        channel: '#notifications'
-        url: ${{ secrets.SLACK_WEBHOOK }}
 
 
   deploy_arm:

.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

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.9-slim-bullseye as base
+FROM python:3.10.0-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

README.md

@@ -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 >= 3.7](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 +1,21 @@
 # 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 ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.22-cp37-cp37m-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp37-cp37m-win_amd64.whl
 }
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.22-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.22-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.23-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

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,

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
 

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

@@ -312,7 +312,7 @@ 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 +399,7 @@ 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 +426,8 @@ 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.
@@ -484,8 +485,8 @@ 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/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)
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
-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/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/installation.md

@@ -56,10 +56,6 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
 !!! Note
     Python3.7 or higher and the corresponding pip are assumed to be available.
 
-!!! Warning "Python 3.10 support"
-    Due to issues with dependencies, freqtrade is currently unable to support python 3.10.
-    We're working on supporting python 3.10, are however dependant on support from dependencies.
-
 === "Debian/Ubuntu"
     #### Install necessary dependencies
 
@@ -424,16 +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

@@ -283,6 +283,8 @@ 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.
 
@@ -292,6 +294,8 @@ 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==8.1.0
+mkdocs-material==8.1.4
 mdx_truly_sane_lists==1.2
 pymdown-extensions==9.1

docs/strategy-advanced.md

@@ -222,9 +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/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‑0.4.22‑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-0.4.23-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
 
-Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8 and 3.9) and for 64bit Windows.
+Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8, 3.9 and 3.10) and for 64bit Windows.
 Other versions must be downloaded from the above link.
 
 ``` powershell

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/data/btanalysis.py

@@ -9,21 +9,13 @@ import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.exceptions import OperationalException
 from freqtrade.misc import 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',
@@ -167,23 +159,9 @@ 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(
-        if not df.empty:
+            "Backtest-results with only trades data are no longer supported.")
-            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 +303,7 @@ 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 +339,19 @@ 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
-                           value_col: str = 'profit_ratio'
+                          ) -> pd.DataFrame:
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
+    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'
+                         ):
     """
     Calculate max drawdown and the corresponding close dates
     :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
@@ -375,10 +364,29 @@ 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 = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
-    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+
-    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    return max_drawdown_df
-    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+
+
+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 +396,18 @@ 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/idatahandler.py

@@ -201,7 +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
@@ -228,7 +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
@@ -238,12 +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/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):
+        if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
-            asyncio.get_event_loop().run_until_complete(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:
@@ -652,7 +658,8 @@ 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]
@@ -1091,7 +1098,8 @@ 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,
@@ -1115,8 +1123,10 @@ 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)
@@ -1224,7 +1234,7 @@ 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)}.")
@@ -1326,8 +1336,10 @@ class Exchange:
         results_df = {}
         # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
         for input_coro in chunks(input_coroutines, 100):
-            results = asyncio.get_event_loop().run_until_complete(
+            async def gather_stuff():
-                asyncio.gather(*input_coro, return_exceptions=True))
+                return await asyncio.gather(*input_coro, return_exceptions=True)
+
+            results = self.loop.run_until_complete(gather_stuff())
 
             # handle caching
             for res in results:
@@ -1563,7 +1575,7 @@ 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/okex.py

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

freqtrade/freqtradebot.py

@@ -126,6 +126,7 @@ class FreqtradeBot(LoggingMixin):
 
         self.rpc.cleanup()
         cleanup_db()
+        self.exchange.close()
 
     def startup(self) -> None:
         """

freqtrade/optimize/backtesting.py

@@ -246,6 +246,9 @@ 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 +257,8 @@ 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 +270,8 @@ 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,
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
-                                         startup_candles=self.required_startup)
+                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)
@@ -416,7 +420,9 @@ 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:
@@ -519,7 +525,8 @@ 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

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/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(
@@ -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_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

@@ -299,8 +299,7 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
+    def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
-                               has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -309,33 +308,26 @@ 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_account'] = None
-            trials['results_metrics.max_drawdown'] = None
 
-        if not legacy_mode:
+        # New mode, using backtest result for metrics
-            # New mode, using backtest result for metrics
+        trials['results_metrics.winsdrawslosses'] = trials.apply(
-            trials['results_metrics.winsdrawslosses'] = trials.apply(
+            lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
-                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                      f"{x['results_metrics.losses']:>4}", axis=1)
-                          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',
-                             'loss', 'is_initial_point', 'is_best']]
 
-        else:
+        trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
-            # Legacy mode
+                         'results_metrics.winsdrawslosses',
-            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+                         'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
-                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
+                         'results_metrics.profit_total', 'results_metrics.holding_avg',
-                             'results_metrics.total_profit', 'results_metrics.profit',
+                         'results_metrics.max_drawdown',
-                             'results_metrics.duration', 'results_metrics.max_drawdown',
+                         'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
+                         'loss', 'is_initial_point', 'is_best']]
-                             'is_best']]
 
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+        trials.columns = [
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
+            'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
+            'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            ]
 
         return trials
 
@@ -351,10 +343,9 @@ class HyperoptTools():
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        legacy_mode = 'results_metrics.total_trades' not in trials
+        has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
-        has_drawdown = 'results_metrics.max_drawdown_abs' 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 +353,12 @@ 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 +370,25 @@ class HyperoptTools():
 
         stake_currency = config['stake_currency']
 
-        if has_drawdown:
+        trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
-            trials['Max Drawdown'] = trials.apply(
+            lambda x: "{} {}".format(
-                lambda x: '{} {}'.format(
+                round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    round_coin_value(x['max_drawdown_abs'], stake_currency),
+                (f"({x['max_drawdown_account']:,.2%})"
-                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
+                    if has_account_drawdown
-                ).rjust(25 + len(stake_currency))
+                    else f"({x['max_drawdown']:,.2%})"
-                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
+                 ).rjust(10, ' ')
-                axis=1
+            ).rjust(25 + len(stake_currency))
-            )
+            if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
-        else:
+            else '--'.rjust(25 + len(stake_currency)),
-            trials = trials.drop(columns=['Max Drawdown'])
+            axis=1
+        )
 
-        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
@@ -98,11 +99,11 @@ 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 +113,7 @@ 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 +195,21 @@ 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():
+    for strategy, result in bt_stats.items():
-        tabular_data.append(_generate_result_line(
+        tabular_data.append(deepcopy(result['results_per_pair'][-1]))
-            results['results'], results['config']['dry_run_wallet'], strategy)
+        # Update "key" to strategy (results_per_pair has it as "Total").
-        )
+        tabular_data[-1]['key'] = strategy
-        try:
+        tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
-            max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
+        tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
-                                                                  value_col='profit_ratio')
+            result['max_drawdown_abs'], result['stake_currency'], False)
-            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)
     return tabular_data
 
 
@@ -352,14 +345,14 @@ 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 +365,11 @@ 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 +378,7 @@ 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 +422,7 @@ 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 +455,14 @@ 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(
+        (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
-            results, value_col='profit_abs')
+         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 +482,7 @@ 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,
@@ -515,13 +511,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
     """
     result: Dict[str, Any] = {'strategy': {}}
     market_change = calculate_market_change(btdata, 'close')
-
+    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)
         result['strategy'][strategy] = strat_stats
 
-    strategy_results = generate_strategy_comparison(all_results=all_results)
+    strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
 
     result['strategy_comparison'] = strategy_results
 
@@ -646,7 +642,12 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
     headers.append('Drawdown')
 
     # Align drawdown string on the center two space separator.
-    drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
+    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 +717,10 @@ 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/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"
@@ -460,7 +503,12 @@ 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"
-    df_comb = combine_dataframes_with_mean(data, "close")
+    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 +525,30 @@ 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,
+    fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
-                        row_width=[1, 1, 1],
+                        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/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:
-                    pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
+                    if self._exchange._ft_has["ohlcv_volume_currency"] == "base":
-                                                     + pair_candles['close']) / 3
+                        pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
-                    pair_candles['quoteVolume'] = (
+                                                         + pair_candles['close']) / 3
-                        pair_candles['volume'] * pair_candles['typical_price']
-                    )
 
+                        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/rpc/api_server/api_backtest.py

@@ -33,6 +33,9 @@ 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():

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/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
 

freqtrade/rpc/telegram.py

@@ -199,8 +199,8 @@ class Telegram(RPCHandler):
 
         self._updater.start_polling(
             bootstrap_retries=-1,
-            timeout=30,
+            timeout=20,
-            read_latency=60,
+            read_latency=60,  # Assumed transmission latency
             drop_pending_updates=True,
         )
         logger.info(
@@ -213,6 +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:

freqtrade/strategy/interface.py

@@ -703,23 +703,21 @@ class IStrategy(ABC, HyperStrategyMixin):
                             custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
                     else:
                         custom_reason = None
-            # TODO: return here if sell-signal should be favored over ROI
+            if sell_signal in (SellType.CUSTOM_SELL, SellType.SELL_SIGNAL):
+                logger.debug(f"{trade.pair} - Sell signal received. "
+                             f"sell_type=SellType.{sell_signal.name}" +
+                             (f", custom_reason={custom_reason}" if custom_reason else ""))
+                return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
 
         # Start evaluations
         # Sequence:
-        # ROI (if not stoploss)
         # Sell-signal
+        # ROI (if not stoploss)
         # Stoploss
         if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
             logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
             return SellCheckTuple(sell_type=SellType.ROI)
 
-        if sell_signal != SellType.NONE:
-            logger.debug(f"{trade.pair} - Sell signal received. "
-                         f"sell_type=SellType.{sell_signal.name}" +
-                         (f", custom_reason={custom_reason}" if custom_reason else ""))
-            return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
-
         if stoplossflag.sell_flag:
 
             logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")

freqtrade/worker.py

@@ -85,9 +85,12 @@ class Worker:
 
         # Log state transition
         if state != old_state:
-            self.freqtrade.notify_status(f'{state.name.lower()}')
 
-            logger.info(f"Changing state to: {state.name}")
+            if old_state != State.RELOAD_CONFIG:
+                self.freqtrade.notify_status(f'{state.name.lower()}')
+
+            logger.info(
+                f"Changing state{f' from {old_state.name}' if old_state else ''} to: {state.name}")
             if state == State.RUNNING:
                 self.freqtrade.startup()
 

pyproject.toml

@@ -23,6 +23,7 @@ exclude = '''
 line_length = 100
 multi_line_output=0
 lines_after_imports=2
+skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*"]
 
 [build-system]
 requires = ["setuptools >= 46.4.0", "wheel"]

requirements-dev.txt

@@ -6,7 +6,7 @@
 coveralls==3.3.1
 flake8==4.0.1
 flake8-tidy-imports==4.5.0
-mypy==0.910
+mypy==0.930
 pytest==6.2.5
 pytest-asyncio==0.16.0
 pytest-cov==3.0.0
@@ -14,16 +14,16 @@ pytest-mock==3.6.1
 pytest-random-order==1.0.4
 isort==5.10.1
 # For datetime mocking
-time-machine==2.4.1
+time-machine==2.5.0
 
 # Convert jupyter notebooks to markdown documents
 nbconvert==6.3.0
 
 # mypy types
-types-cachetools==4.2.6
+types-cachetools==4.2.7
 types-filelock==3.2.1
-types-requests==2.26.1
+types-requests==2.26.3
-types-tabulate==0.8.3
+types-tabulate==0.8.4
 
 # Extensions to datetime library
-types-python-dateutil==2.8.3
+types-python-dateutil==2.8.4

requirements-hyperopt.txt

@@ -3,9 +3,8 @@
 
 # Required for hyperopt
 scipy==1.7.3
-scikit-learn==1.0.1
+scikit-learn==1.0.2
 scikit-optimize==0.9.0
-filelock==3.4.0
+filelock==3.4.2
 joblib==1.1.0
-psutil==5.8.0
 progressbar2==3.55.0

requirements-plot.txt

@@ -1,5 +1,5 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.4.0
+plotly==5.5.0
 

requirements.txt

@@ -1,24 +1,25 @@
-numpy==1.21.4
+numpy==1.21.5; python_version <= '3.7'
+numpy==1.22.0; python_version > '3.7'
 pandas==1.3.5
 pandas-ta==0.3.14b
 
-ccxt==1.63.65
+ccxt==1.66.32
 # Pin cryptography for now due to rust build errors with piwheels
-cryptography==36.0.0
+cryptography==36.0.1
 aiohttp==3.8.1
-SQLAlchemy==1.4.28
+SQLAlchemy==1.4.29
 python-telegram-bot==13.9
 arrow==1.2.1
 cachetools==4.2.2
 requests==2.26.0
 urllib3==1.26.7
-jsonschema==4.2.1
+jsonschema==4.3.3
-TA-Lib==0.4.22
+TA-Lib==0.4.23
 technical==1.3.0
 tabulate==0.8.9
 pycoingecko==2.2.0
 jinja2==3.0.3
-tables==3.6.1
+tables==3.7.0
 blosc==1.10.6
 
 # find first, C search in arrays
@@ -35,7 +36,7 @@ fastapi==0.70.1
 uvicorn==0.16.0
 pyjwt==2.3.0
 aiofiles==0.8.0
-psutil==5.8.0
+psutil==5.9.0
 
 # Support for colorized terminal output
 colorama==0.4.4

setup.cfg

@@ -17,6 +17,7 @@ classifiers =
     Programming Language :: Python :: 3.7
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
     Operating System :: MacOS
     Operating System :: Unix
     Topic :: Office/Business :: Financial :: Investment

setup.py

@@ -43,7 +43,7 @@ setup(
         ],
     install_requires=[
         # from requirements.txt
-        'ccxt>=1.60.11',
+        'ccxt>=1.66.32',
         'SQLAlchemy',
         'python-telegram-bot>=13.4',
         'arrow>=0.17.0',

setup.sh

@@ -25,7 +25,7 @@ function check_installed_python() {
         exit 2
     fi
 
-    for v in 9 8 7
+    for v in 9 10 8 7
     do
         PYTHON="python3.${v}"
         which $PYTHON
@@ -37,7 +37,6 @@ function check_installed_python() {
     done
 
     echo "No usable python found. Please make sure to have python3.7 or newer installed."
-    echo "python3.10 is currently not supported."
     exit 1
 }
 
@@ -220,7 +219,7 @@ function install() {
         install_redhat
     else
         echo "This script does not support your OS."
-        echo "If you have Python version 3.7 - 3.9, pip, virtualenv, ta-lib you can continue."
+        echo "If you have Python version 3.7 - 3.10, pip, virtualenv, ta-lib you can continue."
         echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
         sleep 10
     fi

tests/conftest.py

@@ -4,7 +4,6 @@ import logging
 import re
 from copy import deepcopy
 from datetime import datetime, timedelta
-from functools import reduce
 from pathlib import Path
 from unittest.mock import MagicMock, Mock, PropertyMock
 
@@ -50,17 +49,23 @@ def pytest_configure(config):
 
 
 def log_has(line, logs):
-    # caplog mocker returns log as a tuple: ('freqtrade.something', logging.WARNING, 'foobar')
+    """Check if line is found on some caplog's message."""
-    # and we want to match line against foobar in the tuple
+    return any(line == message for message in logs.messages)
-    return reduce(lambda a, b: a or b,
-                  filter(lambda x: x[2] == line, logs.record_tuples),
-                  False)
 
 
 def log_has_re(line, logs):
-    return reduce(lambda a, b: a or b,
+    """Check if line matches some caplog's message."""
-                  filter(lambda x: re.match(line, x[2]), logs.record_tuples),
+    return any(re.match(line, message) for message in logs.messages)
-                  False)
+
+
+def num_log_has(line, logs):
+    """Check how many times line is found in caplog's messages."""
+    return sum(line == message for message in logs.messages)
+
+
+def num_log_has_re(line, logs):
+    """Check how many times line matches caplog's messages."""
+    return sum(bool(re.match(line, message)) for message in logs.messages)
 
 
 def get_args(args):
@@ -2015,7 +2020,7 @@ def saved_hyperopt_results():
             'params_dict': {
                 'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}},  # noqa: E501
-            'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'},  # noqa: E501
+            'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625,  'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625,  'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'},  # noqa: E501
             'results_explanation': '     2 trades. Avg profit  -1.25%. Total profit -0.00125625 BTC (  -2.51Σ%). Avg duration 3930.0 min.',  # noqa: E501
             'total_profit': -0.00125625,
             'current_epoch': 1,
@@ -2031,7 +2036,7 @@ def saved_hyperopt_results():
                 'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'},  # noqa: E501
                 'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0},  # noqa: E501
                 'stoploss': {'stoploss': -0.338070047333259}},
-            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)},  # noqa: E501
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)},  # noqa: E501
             'results_explanation': '     1 trades. Avg profit   0.12%. Total profit  0.00006185 BTC (   0.12Σ%). Avg duration 1200.0 min.',  # noqa: E501
             'total_profit': 6.185e-05,
             'current_epoch': 2,
@@ -2041,7 +2046,7 @@ def saved_hyperopt_results():
             'loss': 14.241196856510731,
             'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}},  # noqa: E501
-            'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)},  # noqa: E501
+            'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)},  # noqa: E501
             'results_explanation': '   621 trades. Avg profit  -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.',  # noqa: E501
             'total_profit': -0.13639474,
             'current_epoch': 3,
@@ -2058,7 +2063,7 @@ def saved_hyperopt_results():
             'loss': 0.22195522184191518,
             'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014},   # noqa: E501
             'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}},  # noqa: E501
-            'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)},  # noqa: E501
+            'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)},  # noqa: E501
             'results_explanation': '    14 trades. Avg profit  -0.35%. Total profit -0.00248014 BTC (  -4.96Σ%). Avg duration 3402.9 min.',  # noqa: E501
             'total_profit': -0.002480140000000001,
             'current_epoch': 5,
@@ -2068,7 +2073,7 @@ def saved_hyperopt_results():
             'loss': 0.545315889154162,
             'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}},  # noqa: E501
-            'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)},  # noqa: E501
+            'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)},  # noqa: E501
             'results_explanation': '    39 trades. Avg profit  -0.21%. Total profit -0.00417730 BTC (  -8.35Σ%). Avg duration 636.9 min.',  # noqa: E501
             'total_profit': -0.0041773,
             'current_epoch': 6,
@@ -2080,7 +2085,7 @@ def saved_hyperopt_results():
             'params_details': {
                 'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0},  # noqa: E501
                 'stoploss': {'stoploss': -0.14613268022709905}},  # noqa: E501
-            'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)},  # noqa: E501
+            'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)},  # noqa: E501
             'results_explanation': '   318 trades. Avg profit  -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.',  # noqa: E501
             'total_profit': -0.06339929,
             'current_epoch': 7,
@@ -2090,7 +2095,7 @@ def saved_hyperopt_results():
             'loss': 20.0,  # noqa: E501
             'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}},  # noqa: E501
-            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)},  # noqa: E501
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)},  # noqa: E501
             'results_explanation': '     1 trades. Avg profit   0.00%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration 5340.0 min.',  # noqa: E501
             'total_profit': 0.0,
             'current_epoch': 8,
@@ -2100,7 +2105,7 @@ def saved_hyperopt_results():
             'loss': 2.4731817780991223,
             'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}},  # noqa: E501
-            'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)},  # noqa: E501
+            'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)},  # noqa: E501
             'results_explanation': '   229 trades. Avg profit  -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.',  # noqa: E501
             'total_profit': -0.044050070000000004,  # noqa: E501
             'current_epoch': 9,
@@ -2110,7 +2115,7 @@ def saved_hyperopt_results():
             'loss': -0.2604606005845212,  # noqa: E501
             'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}},  # noqa: E501
-            'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)},  # noqa: E501
+            'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)},  # noqa: E501
             'results_explanation': '     4 trades. Avg profit   0.11%. Total profit  0.00021629 BTC (   0.43Σ%). Avg duration 2850.0 min.',  # noqa: E501
             'total_profit': 0.00021629,
             'current_epoch': 10,
@@ -2121,7 +2126,7 @@ def saved_hyperopt_results():
             'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}},  # noqa: E501
             # New Hyperopt mode!
-            'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)},  # noqa: E501
+            'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)},  # noqa: E501
             'results_explanation': '   117 trades. Avg profit  -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.',  # noqa: E501
             'total_profit': -0.07436117,
             'current_epoch': 11,
@@ -2131,7 +2136,7 @@ def saved_hyperopt_results():
             'loss': 100000,
             'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806},  # noqa: E501
             'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}},  # noqa: E501
-            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()},  # noqa: E501
+            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()},  # noqa: E501
             'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
             'total_profit': 0,
             'current_epoch': 12,

tests/data/test_btanalysis.py

@@ -8,14 +8,14 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import LAST_BT_RESULT_FN
-from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
+from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism, calculate_csum,
-                                       analyze_trade_parallelism, calculate_csum,
                                        calculate_market_change, calculate_max_drawdown,
-                                       combine_dataframes_with_mean, create_cum_profit,
+                                       calculate_underwater, combine_dataframes_with_mean,
-                                       extract_trades_of_period, get_latest_backtest_filename,
+                                       create_cum_profit, extract_trades_of_period,
-                                       get_latest_hyperopt_file, load_backtest_data, load_trades,
+                                       get_latest_backtest_filename, get_latest_hyperopt_file,
-                                       load_trades_from_db)
+                                       load_backtest_data, load_trades, load_trades_from_db)
 from freqtrade.data.history import load_data, load_pair_history
+from freqtrade.exceptions import OperationalException
 from tests.conftest import create_mock_trades
 from tests.conftest_trades import MOCK_TRADE_COUNT
 
@@ -51,20 +51,14 @@ def test_get_latest_hyperopt_file(testdatadir, mocker):
     assert res == testdatadir.parent / "hyperopt_results.pickle"
 
 
-def test_load_backtest_data_old_format(testdatadir):
+def test_load_backtest_data_old_format(testdatadir, mocker):
 
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_test222.json"
-    bt_data = load_backtest_data(filename)
+    mocker.patch('freqtrade.data.btanalysis.load_backtest_stats', return_value=[])
-    assert isinstance(bt_data, DataFrame)
-    assert list(bt_data.columns) == BT_DATA_COLUMNS_OLD + ['profit_abs', 'profit_ratio']
-    assert len(bt_data) == 179
 
-    # Test loading from string (must yield same result)
+    with pytest.raises(OperationalException,
-    bt_data2 = load_backtest_data(str(filename))
+                       match=r"Backtest-results with only trades data are no longer supported."):
-    assert bt_data.equals(bt_data2)
+        load_backtest_data(filename)
-
-    with pytest.raises(ValueError, match=r"File .* does not exist\."):
-        load_backtest_data(str("filename") + "nofile")
 
 
 def test_load_backtest_data_new_format(testdatadir):
@@ -72,7 +66,7 @@ def test_load_backtest_data_new_format(testdatadir):
     filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
     assert isinstance(bt_data, DataFrame)
-    assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
+    assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
     assert len(bt_data) == 179
 
     # Test loading from string (must yield same result)
@@ -96,7 +90,7 @@ def test_load_backtest_data_multi(testdatadir):
     for strategy in ('StrategyTestV2', 'TestStrategy'):
         bt_data = load_backtest_data(filename, strategy=strategy)
         assert isinstance(bt_data, DataFrame)
-        assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
+        assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
         assert len(bt_data) == 179
 
         # Test loading from string (must yield same result)
@@ -167,8 +161,8 @@ def test_extract_trades_of_period(testdatadir):
     assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
 
 
-def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
+def test_analyze_trade_parallelism(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
 
     res = analyze_trade_parallelism(bt_data, "5m")
@@ -234,8 +228,15 @@ def test_combine_dataframes_with_mean(testdatadir):
     assert "mean" in df.columns
 
 
+def test_combine_dataframes_with_mean_no_data(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe='6m')
+    with pytest.raises(ValueError, match=r"No objects to concatenate"):
+        combine_dataframes_with_mean(data)
+
+
 def test_create_cum_profit(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
     timerange = TimeRange.parse_timerange("20180110-20180112")
 
@@ -251,7 +252,7 @@ def test_create_cum_profit(testdatadir):
 
 
 def test_create_cum_profit1(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
     # Move close-time to "off" the candle, to make sure the logic still works
     bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
@@ -273,23 +274,31 @@ def test_create_cum_profit1(testdatadir):
 
 
 def test_calculate_max_drawdown(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
-    drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(bt_data)
+    _, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
+        bt_data, value_col="profit_abs")
     assert isinstance(drawdown, float)
-    assert pytest.approx(drawdown) == 0.21142322
+    assert pytest.approx(drawdown) == 0.12071099
     assert isinstance(hdate, Timestamp)
     assert isinstance(lowdate, Timestamp)
     assert isinstance(hval, float)
     assert isinstance(lval, float)
-    assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
+    assert hdate == Timestamp('2018-01-25 01:30:00', tz='UTC')
-    assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
+    assert lowdate == Timestamp('2018-01-25 03:50:00', tz='UTC')
+
+    underwater = calculate_underwater(bt_data)
+    assert isinstance(underwater, DataFrame)
+
     with pytest.raises(ValueError, match='Trade dataframe empty.'):
-        drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
+        calculate_max_drawdown(DataFrame())
+
+    with pytest.raises(ValueError, match='Trade dataframe empty.'):
+        calculate_underwater(DataFrame())
 
 
 def test_calculate_csum(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
     csum_min, csum_max = calculate_csum(bt_data)
 
@@ -317,12 +326,13 @@ def test_calculate_max_drawdown2():
     # sort by profit and reset index
     df = df.sort_values('profit').reset_index(drop=True)
     df1 = df.copy()
-    drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
+    drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
         df, date_col='open_date', value_col='profit')
     # Ensure df has not been altered.
     assert df.equals(df1)
 
     assert isinstance(drawdown, float)
+    assert isinstance(drawdown_rel, float)
     # High must be before low
     assert hdate < ldate
     # High value must be higher than low value

tests/data/test_history.py

@@ -311,7 +311,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
     assert td != len(data['UNITTEST/BTC'])
     start_real = data['UNITTEST/BTC'].iloc[0, 0]
     assert log_has(f'Missing data at start for pair '
-                   f'UNITTEST/BTC, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
+                   f'UNITTEST/BTC at 5m, data starts at {start_real.strftime("%Y-%m-%d %H:%M:%S")}',
                    caplog)
     # Make sure we start fresh - test missing data at end
     caplog.clear()
@@ -326,7 +326,7 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
     # Shift endtime with +5 - as last candle is dropped (partial candle)
     end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
     assert log_has(f'Missing data at end for pair '
-                   f'UNITTEST/BTC, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
+                   f'UNITTEST/BTC at 5m, data ends at {end_real.strftime("%Y-%m-%d %H:%M:%S")}',
                    caplog)
 
 

tests/exchange/test_bitpanda.py

@@ -0,0 +1,47 @@
+from datetime import datetime
+from unittest.mock import MagicMock
+
+from tests.conftest import get_patched_exchange
+
+
+def test_get_trades_for_order(default_conf, mocker):
+    exchange_name = 'bitpanda'
+    order_id = 'ABCD-ABCD'
+    since = datetime(2018, 5, 5, 0, 0, 0)
+    default_conf["dry_run"] = False
+    mocker.patch('freqtrade.exchange.Exchange.exchange_has', return_value=True)
+    api_mock = MagicMock()
+
+    api_mock.fetch_my_trades = MagicMock(return_value=[{'id': 'TTR67E-3PFBD-76IISV',
+                                                        'order': 'ABCD-ABCD',
+                                                        'info': {'pair': 'XLTCZBTC',
+                                                                 'time': 1519860024.4388,
+                                                                 'type': 'buy',
+                                                                 'ordertype': 'limit',
+                                                                 'price': '20.00000',
+                                                                 'cost': '38.62000',
+                                                                 'fee': '0.06179',
+                                                                 'vol': '5',
+                                                                 'id': 'ABCD-ABCD'},
+                                                        'timestamp': 1519860024438,
+                                                        'datetime': '2018-02-28T23:20:24.438Z',
+                                                        'symbol': 'LTC/BTC',
+                                                        'type': 'limit',
+                                                        'side': 'buy',
+                                                        'price': 165.0,
+                                                        'amount': 0.2340606,
+                                                        'fee': {'cost': 0.06179, 'currency': 'BTC'}
+                                                        }])
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
+
+    orders = exchange.get_trades_for_order(order_id, 'LTC/BTC', since)
+    assert len(orders) == 1
+    assert orders[0]['price'] == 165
+    assert api_mock.fetch_my_trades.call_count == 1
+    # since argument should be
+    assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
+    assert api_mock.fetch_my_trades.call_args[0][0] == 'LTC/BTC'
+    # Same test twice, hardcoded number and doing the same calculation
+    assert api_mock.fetch_my_trades.call_args[0][1] == 1525478395000
+    # bitpanda requires "to" argument.
+    assert 'to' in api_mock.fetch_my_trades.call_args[1]['params']

tests/exchange/test_ccxt_compat.py

@@ -19,36 +19,49 @@ from tests.conftest import get_default_conf
 EXCHANGES = {
     'bittrex': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': False,
         'timeframe': '1h',
     },
     'binance': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
     'kraken': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
     'ftx': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
     'kucoin': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
     'gateio': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
     'okex': {
         'pair': 'BTC/USDT',
+        'stake_currency': 'USDT',
+        'hasQuoteVolume': True,
+        'timeframe': '5m',
+    },
+    'bitvavo': {
+        'pair': 'BTC/EUR',
+        'stake_currency': 'EUR',
         'hasQuoteVolume': True,
         'timeframe': '5m',
     },
@@ -68,6 +81,7 @@ def exchange_conf():
 @pytest.fixture(params=EXCHANGES, scope="class")
 def exchange(request, exchange_conf):
     exchange_conf['exchange']['name'] = request.param
+    exchange_conf['stake_currency'] = EXCHANGES[request.param]['stake_currency']
     exchange = ExchangeResolver.load_exchange(request.param, exchange_conf, validate=True)
 
     yield exchange, request.param

tests/exchange/test_exchange.py

@@ -20,7 +20,7 @@ from freqtrade.exchange.exchange import (market_is_active, timeframe_to_minutes,
                                          timeframe_to_next_date, timeframe_to_prev_date,
                                          timeframe_to_seconds)
 from freqtrade.resolvers.exchange_resolver import ExchangeResolver
-from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re
+from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re, num_log_has_re
 
 
 # Make sure to always keep one exchange here which is NOT subclassed!!
@@ -1740,6 +1740,44 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
                                                  (arrow.utcnow().int_timestamp - 2000) * 1000)
 
 
+@pytest.mark.asyncio
+async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
+    caplog.set_level(logging.INFO)
+    api_mock = MagicMock()
+    api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
+        "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
+        "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
+        "429 Too Many Requests" '{"code":"429000","msg":"Too Many Requests"}'))
+    exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kucoin")
+
+    msg = "Kucoin 429 error, avoid triggering DDosProtection backoff delay"
+    assert not num_log_has_re(msg, caplog)
+
+    for _ in range(3):
+        with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
+            await exchange._async_get_candle_history(
+                "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
+    assert num_log_has_re(msg, caplog) == 3
+
+    caplog.clear()
+    # Test regular non-kucoin message
+    api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.DDoSProtection(
+        "kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?"
+        "symbol=ETH-BTC&type=5min&startAt=1640268735&endAt=1640418735"
+        "429 Too Many Requests" '{"code":"2222222","msg":"Too Many Requests"}'))
+
+    msg = r'_async_get_candle_history\(\) returned exception: .*'
+    msg2 = r'Applying DDosProtection backoff delay: .*'
+    with patch('freqtrade.exchange.common.asyncio.sleep', get_mock_coro(None)):
+        for _ in range(3):
+            with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
+                await exchange._async_get_candle_history(
+                    "ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
+        # Expect the "returned exception" message 12 times (4 retries * 3 (loop))
+        assert num_log_has_re(msg, caplog) == 12
+        assert num_log_has_re(msg2, caplog) == 9
+
+
 @pytest.mark.asyncio
 async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
     """ Test empty exchange result """

tests/optimize/test_backtest_detail.py

@@ -426,8 +426,6 @@ tc26 = BTContainer(data=[
 
 # Test 27: Sell with signal sell in candle 3 (ROI at signal candle)
 # Stoploss at 10% (irrelevant), ROI at 5% (will trigger) - Wins over Sell-signal
-# TODO: figure out if sell-signal should win over ROI
-# Sell-signal wins over stoploss
 tc27 = BTContainer(data=[
     # D  O     H     L     C     V    B  S
     [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
@@ -436,8 +434,8 @@ tc27 = BTContainer(data=[
     [3, 5010, 5012, 4986, 5010, 6172, 0, 1],  # sell-signal
     [4, 5010, 5251, 4855, 4995, 6172, 0, 0],  # Triggers ROI, sell-signal acted on
     [5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
-    stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.05, use_sell_signal=True,
+    stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.002, use_sell_signal=True,
-    trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
+    trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
 )
 
 # Test 28: trailing_stop should raise so candle 3 causes a stoploss

tests/optimize/test_backtesting.py

@@ -1,6 +1,7 @@
 # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
 
 import random
+from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from unittest.mock import MagicMock, PropertyMock
@@ -648,7 +649,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
     processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     result = backtesting.backtest(
-        processed=processed,
+        processed=deepcopy(processed),
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
@@ -887,7 +888,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     processed = backtesting.strategy.advise_all_indicators(data)
     min_date, max_date = get_timerange(processed)
     backtest_conf = {
-        'processed': processed,
+        'processed': deepcopy(processed),
         'start_date': min_date,
         'end_date': max_date,
         'max_open_trades': 3,
@@ -909,7 +910,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
         'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
 
     backtest_conf = {
-        'processed': processed,
+        'processed': deepcopy(processed),
         'start_date': min_date,
         'end_date': max_date,
         'max_open_trades': 1,

tests/optimize/test_hyperopt.py

@@ -1,5 +1,5 @@
 # pragma pylint: disable=missing-docstring,W0212,C0103
-from datetime import datetime
+from datetime import datetime, timedelta
 from pathlib import Path
 from unittest.mock import ANY, MagicMock
 
@@ -22,6 +22,29 @@ from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
                             patched_configuration_load_config_file)
 
 
+def generate_result_metrics():
+    return {
+            'trade_count': 1,
+            'total_trades': 1,
+            'avg_profit': 0.1,
+            'total_profit': 0.001,
+            'profit': 0.01,
+            'duration': 20.0,
+            'wins': 1,
+            'draws': 0,
+            'losses': 0,
+            'profit_mean': 0.01,
+            'profit_total_abs': 0.001,
+            'profit_total': 0.01,
+            'holding_avg': timedelta(minutes=20),
+            'max_drawdown': 0.001,
+            'max_drawdown_abs': 0.001,
+            'loss': 0.001,
+            'is_initial_point': 0.001,
+            'is_best': 1,
+        }
+
+
 def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
     patched_configuration_load_config_file(mocker, default_conf)
 
@@ -168,7 +191,8 @@ def test_start_no_hyperopt_allowed(mocker, hyperopt_conf, caplog) -> None:
         start_hyperopt(pargs)
 
 
-def test_start_no_data(mocker, hyperopt_conf) -> None:
+def test_start_no_data(mocker, hyperopt_conf, tmpdir) -> None:
+    hyperopt_conf['user_data_dir'] = Path(tmpdir)
     patched_configuration_load_config_file(mocker, hyperopt_conf)
     mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=pd.DataFrame))
     mocker.patch(
@@ -177,7 +201,6 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
     )
 
     patch_exchange(mocker)
-    # TODO: migrate to strategy-based hyperopt
     args = [
         'hyperopt',
         '--config', 'config.json',
@@ -189,6 +212,12 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
     with pytest.raises(OperationalException, match='No data found. Terminating.'):
         start_hyperopt(pargs)
 
+    # Cleanup since that failed hyperopt start leaves a lockfile.
+    try:
+        Path(Hyperopt.get_lock_filename(hyperopt_conf)).unlink()
+    except Exception:
+        pass
+
 
 def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
     hyperopt_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(hyperopt_conf)))
@@ -215,14 +244,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     hyperopt.print_results(
         {
             'loss': 1,
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-                {
-                    'trade_count': 1,
-                    'avg_profit': 0.1,
-                    'total_profit': 0.001,
-                    'profit': 1.0,
-                    'duration': 20.0
-                },
             'total_profit': 0,
             'current_epoch': 2,  # This starts from 1 (in a human-friendly manner)
             'is_initial_point': False,
@@ -231,7 +253,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     )
     out, err = capsys.readouterr()
     assert all(x in out
-               for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC    (1.00%)", "20.0 m"])
+               for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC    (1.00%)", "00:20:00"])
 
 
 def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
@@ -288,14 +310,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result',
             'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            },
         }])
     )
     patch_exchange(mocker)
@@ -352,7 +367,7 @@ def test_hyperopt_format_results(hyperopt):
         'backtest_start_time': 1619718665,
         'backtest_end_time': 1619718665,
     }
-    results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
+    results_metrics = generate_strategy_stats(['XRP/BTC'], '', bt_result,
                                               Arrow(2017, 11, 14, 19, 32, 00),
                                               Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
 
@@ -521,14 +536,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
                 'roi': {}, 'stoploss': {'stoploss': None},
                 'trailing': {'trailing_stop': None}
             },
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)
@@ -577,14 +585,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
                 'sell': {'sell-mfi-value': None},
                 'roi': {}, 'stoploss': {'stoploss': None}
             },
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)
@@ -622,14 +623,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
             'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)
@@ -669,14 +663,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)
@@ -749,14 +736,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)
@@ -798,14 +778,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
-            'results_metrics':
+            'results_metrics': generate_result_metrics(),
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
         }])
     )
     patch_exchange(mocker)

tests/optimize/test_optimize_reports.py

@@ -1,4 +1,3 @@
-import datetime
 import re
 from datetime import timedelta
 from pathlib import Path
@@ -49,7 +48,7 @@ def test_text_table_bt_results():
         '        0:20:00 |     2     0     1  66.7 |'
     )
 
-    pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
+    pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
                                          starting_balance=4, results=results)
     assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
 
@@ -103,7 +102,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
     assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
     assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
     # Above sample had no loosing trade
-    assert strat_stats['max_drawdown'] == 0.0
+    assert strat_stats['max_drawdown_account'] == 0.0
 
     # Retry with losing trade
     results = {'DefStrat': {
@@ -143,7 +142,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
     assert 'strategy_comparison' in stats
     strat_stats = stats['strategy']['DefStrat']
 
-    assert strat_stats['max_drawdown'] == 0.013803
+    assert pytest.approx(strat_stats['max_drawdown_account']) == 1.399999e-08
     assert strat_stats['drawdown_start'] == '2017-11-14 22:10:00'
     assert strat_stats['drawdown_end'] == '2017-11-14 22:43:00'
     assert strat_stats['drawdown_end_ts'] == 1510699380000
@@ -165,7 +164,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
     filename1 = Path(tmpdir / last_fn)
     assert filename1.is_file()
     content = filename1.read_text()
-    assert 'max_drawdown' in content
+    assert 'max_drawdown_account' in content
     assert 'strategy' in content
     assert 'pairlist' in content
 
@@ -208,7 +207,7 @@ def test_generate_pair_metrics():
         }
     )
 
-    pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
+    pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
                                          starting_balance=2, results=results)
     assert isinstance(pair_results, list)
     assert len(pair_results) == 2
@@ -227,9 +226,9 @@ def test_generate_daily_stats(testdatadir):
     assert isinstance(res, dict)
     assert round(res['backtest_best_day'], 4) == 0.1796
     assert round(res['backtest_worst_day'], 4) == -0.1468
-    assert res['winning_days'] == 14
+    assert res['winning_days'] == 19
-    assert res['draw_days'] == 4
+    assert res['draw_days'] == 0
-    assert res['losing_days'] == 3
+    assert res['losing_days'] == 2
 
     # Select empty dataframe!
     res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
@@ -324,51 +323,25 @@ def test_generate_sell_reason_stats():
     assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
 
 
-def test_text_table_strategy(default_conf):
+def test_text_table_strategy(testdatadir):
-    default_conf['max_open_trades'] = 2
+    filename = testdatadir / "backtest-result_multistrat.json"
-    default_conf['dry_run_wallet'] = 3
+    bt_res_data = load_backtest_stats(filename)
-    results = {}
+
-    date = datetime.datetime(year=2020, month=1, day=1, hour=12, minute=30)
+    bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
-    delta = datetime.timedelta(days=1)
-    results['TestStrategy1'] = {'results': pd.DataFrame(
-        {
-            'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
-            'close_date': [date, date + delta, date + delta * 2],
-            'profit_ratio': [0.1, 0.2, 0.3],
-            'profit_abs': [0.2, 0.4, 0.5],
-            'trade_duration': [10, 30, 10],
-            'wins': [2, 0, 0],
-            'draws': [0, 0, 0],
-            'losses': [0, 0, 1],
-            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
-        }
-    ), 'config': default_conf}
-    results['TestStrategy2'] = {'results': pd.DataFrame(
-        {
-            'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
-            'close_date': [date, date + delta, date + delta * 2],
-            'profit_ratio': [0.4, 0.2, 0.3],
-            'profit_abs': [0.4, 0.4, 0.5],
-            'trade_duration': [15, 30, 15],
-            'wins': [4, 1, 0],
-            'draws': [0, 0, 0],
-            'losses': [0, 0, 1],
-            'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
-        }
-    ), 'config': default_conf}
 
     result_str = (
-        '|      Strategy |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |'
+        '|       Strategy |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |'
         '   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |              Drawdown |\n'
-        '|---------------+--------+----------------+----------------+------------------+'
+        '|----------------+--------+----------------+----------------+------------------+'
         '----------------+----------------+-------------------------+-----------------------|\n'
-        '| TestStrategy1 |      3 |          20.00 |          60.00 |       1.10000000 |'
+        '| StrategyTestV2 |    179 |           0.08 |          14.39 |       0.02608550 |'
-        '          36.67 |        0:17:00 |     3     0     0   100 | 0.00000000 BTC  0.00% |\n'
+        '         260.85 |        3:40:00 |   170     0     9  95.0 | 0.00308222 BTC  8.67% |\n'
-        '| TestStrategy2 |      3 |          30.00 |          90.00 |       1.30000000 |'
+        '|   TestStrategy |    179 |           0.08 |          14.39 |       0.02608550 |'
-        '          43.33 |        0:20:00 |     3     0     0   100 | 0.00000000 BTC  0.00% |'
+        '         260.85 |        3:40:00 |   170     0     9  95.0 | 0.00308222 BTC  8.67% |'
     )
 
-    strategy_results = generate_strategy_comparison(all_results=results)
+    strategy_results = generate_strategy_comparison(bt_stats=bt_res_data['strategy'])
+    assert strategy_results == bt_res_data_comparison
     assert text_table_strategy(strategy_results, 'BTC') == result_str
 
 

tests/plugins/test_pairlist.py

@@ -1,5 +1,6 @@
 # pragma pylint: disable=missing-docstring,C0103,protected-access
 
+import logging
 import time
 from unittest.mock import MagicMock, PropertyMock
 
@@ -14,7 +15,7 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.resolvers import PairListResolver
 from tests.conftest import (create_mock_trades, get_patched_exchange, get_patched_freqtradebot,
-                            log_has, log_has_re)
+                            log_has, log_has_re, num_log_has)
 
 
 @pytest.fixture(scope="function")
@@ -217,6 +218,34 @@ def test_invalid_blacklist(mocker, markets, static_pl_conf, caplog):
     log_has_re(r"Pair blacklist contains an invalid Wildcard.*", caplog)
 
 
+def test_remove_logs_for_pairs_already_in_blacklist(mocker, markets, static_pl_conf, caplog):
+    logger = logging.getLogger(__name__)
+    freqtrade = get_patched_freqtradebot(mocker, static_pl_conf)
+    mocker.patch.multiple(
+        'freqtrade.exchange.Exchange',
+        exchange_has=MagicMock(return_value=True),
+        markets=PropertyMock(return_value=markets),
+    )
+    freqtrade.pairlists.refresh_pairlist()
+    whitelist = ['ETH/BTC', 'TKN/BTC']
+    caplog.clear()
+    caplog.set_level(logging.INFO)
+
+    # Ensure all except those in whitelist are removed.
+    assert set(whitelist) == set(freqtrade.pairlists.whitelist)
+    assert static_pl_conf['exchange']['pair_blacklist'] == freqtrade.pairlists.blacklist
+    # Ensure that log message wasn't generated.
+    assert not log_has('Pair BLK/BTC in your blacklist. Removing it from whitelist...', caplog)
+
+    for _ in range(3):
+        new_whitelist = freqtrade.pairlists.verify_blacklist(
+            whitelist + ['BLK/BTC'], logger.warning)
+        # Ensure that the pair is removed from the white list, and properly logged.
+        assert set(whitelist) == set(new_whitelist)
+    assert num_log_has('Pair BLK/BTC in your blacklist. Removing it from whitelist...',
+                       caplog) == 1
+
+
 def test_refresh_pairlist_dynamic(mocker, shitcoinmarkets, tickers, whitelist_conf):
 
     mocker.patch.multiple(
@@ -536,36 +565,41 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
                 assert log_has_re(r'^Removed .* from whitelist, because volatility.*$', caplog)
 
 
-@pytest.mark.parametrize("pairlists,base_currency,volumefilter_result", [
+@pytest.mark.parametrize("pairlists,base_currency,exchange,volumefilter_result", [
     # default refresh of 1800 to small for daily candle lookback
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_days": 1}],
-     "BTC", "default_refresh_too_short"),  # OperationalException expected
+     "BTC", "binance", "default_refresh_too_short"),  # OperationalException expected
     # ambigous configuration with lookback days and period
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_days": 1, "lookback_period": 1}],
-     "BTC", "lookback_days_and_period"),  # OperationalException expected
+     "BTC", "binance", "lookback_days_and_period"),  # OperationalException expected
     # negative lookback period
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_timeframe": "1d", "lookback_period": -1}],
-     "BTC", "lookback_period_negative"),  # OperationalException expected
+     "BTC", "binance", "lookback_period_negative"),  # OperationalException expected
     # lookback range exceedes exchange limit
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_timeframe": "1m", "lookback_period": 2000, "refresh_period": 3600}],
-     "BTC", 'lookback_exceeds_exchange_request_size'),  # OperationalException expected
+     "BTC", "binance", "lookback_exceeds_exchange_request_size"),  # OperationalException expected
     # expecing pairs as given
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
-     "BTC", ['HOT/BTC', 'LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC']),
+     "BTC", "binance", ['LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC', 'HOT/BTC']),
     # expecting pairs from default tickers, because 1h candles are not available
     ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
        "lookback_timeframe": "1h", "lookback_period": 2, "refresh_period": 3600}],
-     "BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'HOT/BTC', 'FUEL/BTC']),
+     "BTC", "binance", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'HOT/BTC', 'FUEL/BTC']),
+    # ftx data is already in Quote currency, therefore won't require conversion
+    ([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
+       "lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
+     "BTC", "ftx", ['HOT/BTC', 'LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC']),
 ])
 def test_VolumePairList_range(mocker, whitelist_conf, shitcoinmarkets, tickers, ohlcv_history,
-                              pairlists, base_currency, volumefilter_result, caplog) -> None:
+                              pairlists, base_currency, exchange, volumefilter_result) -> None:
     whitelist_conf['pairlists'] = pairlists
     whitelist_conf['stake_currency'] = base_currency
+    whitelist_conf['exchange']['name'] = exchange
 
     ohlcv_history_high_vola = ohlcv_history.copy()
     ohlcv_history_high_vola.loc[ohlcv_history_high_vola.index == 1, 'close'] = 0.00090
@@ -574,9 +608,14 @@ def test_VolumePairList_range(mocker, whitelist_conf, shitcoinmarkets, tickers,
     ohlcv_history_medium_volume = ohlcv_history.copy()
     ohlcv_history_medium_volume.loc[ohlcv_history_medium_volume.index == 2, 'volume'] = 5
 
-    # create candles for high volume with all candles high volume
+    # create candles for high volume with all candles high volume, but very low price.
     ohlcv_history_high_volume = ohlcv_history.copy()
     ohlcv_history_high_volume.loc[:, 'volume'] = 10
+    ohlcv_history_high_volume.loc[:, 'low'] = ohlcv_history_high_volume.loc[:, 'low'] * 0.01
+    ohlcv_history_high_volume.loc[:, 'high'] = ohlcv_history_high_volume.loc[:, 'high'] * 0.01
+    ohlcv_history_high_volume.loc[:, 'close'] = ohlcv_history_high_volume.loc[:, 'close'] * 0.01
+
+    mocker.patch('freqtrade.exchange.ftx.Ftx.market_is_tradable', return_value=True)
 
     ohlcv_data = {
         ('ETH/BTC', '1d'): ohlcv_history,

tests/rpc/test_rpc.py

@@ -424,7 +424,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
     assert stats['trade_count'] == 2
     assert stats['first_trade_date'] == 'just now'
     assert stats['latest_trade_date'] == 'just now'
-    assert stats['avg_duration'] in ('0:00:00', '0:00:01')
+    assert stats['avg_duration'] in ('0:00:00', '0:00:01', '0:00:02')
     assert stats['best_pair'] == 'ETH/BTC'
     assert prec_satoshi(stats['best_rate'], 6.2)
 
@@ -435,7 +435,7 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
     assert stats['trade_count'] == 2
     assert stats['first_trade_date'] == 'just now'
     assert stats['latest_trade_date'] == 'just now'
-    assert stats['avg_duration'] in ('0:00:00', '0:00:01')
+    assert stats['avg_duration'] in ('0:00:00', '0:00:01', '0:00:02')
     assert stats['best_pair'] == 'ETH/BTC'
     assert prec_satoshi(stats['best_rate'], 6.2)
     assert isnan(stats['profit_all_coin'])

tests/rpc/test_rpc_telegram.py

@@ -584,7 +584,7 @@ def test_monthly_handle(default_conf, update, ticker, limit_buy_order, fee,
     assert 'Monthly Profit over the last 2 months</b>:' in msg_mock.call_args_list[0][0][0]
     assert 'Month ' in msg_mock.call_args_list[0][0][0]
     today = datetime.utcnow().date()
-    current_month = f"{today.year}-{today.month} "
+    current_month = f"{today.year}-{today.month:02} "
     assert current_month in msg_mock.call_args_list[0][0][0]
     assert str('  0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
     assert str('  0.933 USD') in msg_mock.call_args_list[0][0][0]

tests/test_freqtradebot.py

@@ -1904,7 +1904,7 @@ def test_handle_trade_roi(default_conf_usdt, ticker_usdt, limit_buy_order_usdt_o
     #      we might just want to check if we are in a sell condition without
     #      executing
     # if ROI is reached we must sell
-    patch_get_signal(freqtrade, value=(False, True, None, None))
+    patch_get_signal(freqtrade, value=(False, False, None, None))
     assert freqtrade.handle_trade(trade)
     assert log_has("ETH/USDT - Required profit reached. sell_type=SellType.ROI",
                    caplog)
@@ -3241,7 +3241,7 @@ def test_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usdt,
     assert freqtrade.handle_trade(trade) is False
 
     # Test if buy-signal is absent (should sell due to roi = true)
-    patch_get_signal(freqtrade, value=(False, True, None, None))
+    patch_get_signal(freqtrade, value=(False, False, None, None))
     assert freqtrade.handle_trade(trade) is True
     assert trade.sell_reason == SellType.ROI.value
 
@@ -3427,11 +3427,11 @@ def test_disable_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usd
     trade = Trade.query.first()
     trade.update(limit_buy_order_usdt)
     # Sell due to min_roi_reached
-    patch_get_signal(freqtrade, value=(True, True, None, None))
+    patch_get_signal(freqtrade, value=(True, False, None, None))
     assert freqtrade.handle_trade(trade) is True
 
     # Test if buy-signal is absent
-    patch_get_signal(freqtrade, value=(False, True, None, None))
+    patch_get_signal(freqtrade, value=(False, False, None, None))
     assert freqtrade.handle_trade(trade) is True
     assert trade.sell_reason == SellType.ROI.value
 

tests/test_plotting.py

@@ -45,7 +45,7 @@ def test_init_plotscript(default_conf, mocker, testdatadir):
     default_conf['trade_source'] = "file"
     default_conf['timeframe'] = "5m"
     default_conf["datadir"] = testdatadir
-    default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
+    default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
     supported_markets = ["TRX/BTC", "ADA/BTC"]
     ret = init_plotscript(default_conf, supported_markets)
     assert "ohlcv" in ret
@@ -157,7 +157,7 @@ def test_plot_trades(testdatadir, caplog):
     assert fig == fig1
     assert log_has("No trades found.", caplog)
     pair = "ADA/BTC"
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     trades = load_backtest_data(filename)
     trades = trades.loc[trades['pair'] == pair]
 
@@ -294,7 +294,7 @@ def test_generate_plot_file(mocker, caplog):
 
 
 def test_add_profit(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
     timerange = TimeRange.parse_timerange("20180110-20180112")
 
@@ -314,7 +314,7 @@ def test_add_profit(testdatadir):
 
 
 def test_generate_profit_graph(testdatadir):
-    filename = testdatadir / "backtest-result_test.json"
+    filename = testdatadir / "backtest-result_new.json"
     trades = load_backtest_data(filename)
     timerange = TimeRange.parse_timerange("20180110-20180112")
     pairs = ["TRX/BTC", "XLM/BTC"]
@@ -336,15 +336,20 @@ def test_generate_profit_graph(testdatadir):
     assert fig.layout.yaxis3.title.text == "Profit BTC"
 
     figure = fig.layout.figure
-    assert len(figure.data) == 5
+    assert len(figure.data) == 7
 
     avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
     assert isinstance(avgclose, go.Scatter)
 
     profit = find_trace_in_fig_data(figure.data, "Profit")
     assert isinstance(profit, go.Scatter)
-    profit = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
+    drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 35.69%")
-    assert isinstance(profit, go.Scatter)
+    assert isinstance(drawdown, go.Scatter)
+    parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
+    assert isinstance(parallel, go.Scatter)
+
+    underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
+    assert isinstance(underwater, go.Scatter)
 
     for pair in pairs:
         profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
@@ -376,7 +381,7 @@ def test_load_and_plot_trades(default_conf, mocker, caplog, testdatadir):
 
     default_conf['trade_source'] = 'file'
     default_conf["datadir"] = testdatadir
-    default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
+    default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
     default_conf['indicators1'] = ["sma5", "ema10"]
     default_conf['indicators2'] = ["macd"]
     default_conf['pairs'] = ["ETH/BTC", "LTC/BTC"]
@@ -447,7 +452,7 @@ def test_plot_profit(default_conf, mocker, testdatadir):
                        match=r"No trades found, cannot generate Profit-plot.*"):
         plot_profit(default_conf)
 
-    default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
+    default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
 
     plot_profit(default_conf)
 

tests/test_worker.py

@@ -43,7 +43,7 @@ def test_worker_stopped(mocker, default_conf, caplog) -> None:
     worker.freqtrade.state = State.STOPPED
     state = worker._worker(old_state=State.RUNNING)
     assert state is State.STOPPED
-    assert log_has('Changing state to: STOPPED', caplog)
+    assert log_has('Changing state from RUNNING to: STOPPED', caplog)
     assert mock_throttle.call_count == 1