Merge pull request #6348 from freqtrade/new_release

New release 2022.1

.github/workflows/ci.yml

@@ -3,9 +3,9 @@ name: Freqtrade CI
 on:
   push:
     branches:
-      - master
       - stable
       - develop
+      - ci/*
     tags:
   release:
     types: [published]
@@ -20,7 +20,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04 ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -39,7 +39,7 @@ jobs:
 
     - name: pip cache (linux)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'ubuntu')
+      if: runner.os == 'Linux'
       with:
         path: ~/.cache/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -50,8 +50,9 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - *nix
+      if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -69,7 +70,7 @@ jobs:
       if: matrix.python-version == '3.9'
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -114,7 +115,7 @@ jobs:
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -133,7 +134,7 @@ jobs:
 
     - name: pip cache (macOS)
       uses: actions/cache@v2
-      if: startsWith(matrix.os, 'macOS')
+      if: runner.os == 'macOS'
       with:
         path: ~/Library/Caches/pip
         key: test-${{ matrix.os }}-${{ matrix.python-version }}-pip
@@ -144,10 +145,11 @@ jobs:
         cd build_helpers && ./install_ta-lib.sh ${HOME}/dependencies/; cd ..
 
     - name: Installation - macOS
+      if: runner.os == 'macOS'
       run: |
         brew update
         brew install hdf5 c-blosc
-        python -m pip install --upgrade pip
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -159,7 +161,7 @@ jobs:
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc
 
     - name: Coveralls
-      if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.8')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -195,7 +197,7 @@ jobs:
       uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-          severity: error
+          severity: info
           details: Test Succeeded!
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
@@ -205,7 +207,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: [3.7, 3.8]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
@@ -217,7 +219,6 @@ jobs:
 
     - name: Pip cache (Windows)
       uses: actions/cache@preview
-      if: startsWith(runner.os, 'Windows')
       with:
         path: ~\AppData\Local\pip\Cache
         key: ${{ matrix.os }}-${{ matrix.python-version }}-pip

README.md

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

build_helpers/install_windows.ps1

@@ -1,19 +1,18 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 
-python -m pip install --upgrade pip
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
-if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.22-cp37-cp37m-win_amd64.whl
-}
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.22-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.22-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
+}
+if ($pyv -eq '3.10') {
+    pip install build_helpers\TA_Lib-0.4.24-cp310-cp310-win_amd64.whl
 }
-
 pip install -r requirements-dev.txt
 pip install -e .

config_examples/config_binance.example.json

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

config_examples/config_bittrex.example.json

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

config_examples/config_ftx.example.json

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

config_examples/config_full.example.json

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

config_examples/config_kraken.example.json

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

docs/advanced-hyperopt.md

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

docs/advanced-setup.md

@@ -176,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
 On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
 
 For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
+
 ```
 if $programname startswith "freqtrade" then -/var/log/freqtrade.log
 ```
+
 to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
 
 For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
+
 ```
 # Filter duplicated messages
 $RepeatedMsgReduction on

docs/backtesting.md

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

docs/bot-basics.md

@@ -38,6 +38,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
   * Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
   * Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
   * Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
+* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
 * Check if trade-slots are still available (if `max_open_trades` is reached).
 * Verifies buy signal trying to enter new positions.
   * Determine buy-price based on `bid_strategy` configuration setting, or by using the `custom_entry_price()` callback.
@@ -58,9 +59,9 @@ This loop will be repeated again and again until the bot is stopped.
   * Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
   * Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
   * Determine stake size by calling the `custom_stake_amount()` callback.
+  * Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
   * Call `custom_stoploss()` and `custom_sell()` to find custom exit points.
   * For sells based on sell-signal and custom-sell: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
-
 * Generate backtest report output
 
 !!! Note

docs/configuration.md

@@ -172,6 +172,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
 | `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
 | `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
+| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
+| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
 
 ### Parameters in the strategy
 
@@ -196,6 +198,8 @@ Values set in the configuration file always overwrite values set in the strategy
 * `sell_profit_offset`
 * `ignore_roi_if_buy_signal`
 * `ignore_buying_expired_candle_after`
+* `position_adjustment_enable`
+* `max_entry_position_adjustment`
 
 ### Configuring amount per trade
 
@@ -302,6 +306,15 @@ To allow the bot to trade all the available `stake_currency` in your account (mi
     When using `"stake_amount" : "unlimited",` in combination with Dry-Run, Backtesting or Hyperopt, the balance will be simulated starting with a stake of `dry_run_wallet` which will evolve.
     It is therefore important to set `dry_run_wallet` to a sensible value (like 0.05 or 0.01 for BTC and 1000 or 100 for USDT, for example), otherwise, it may simulate trades with 100 BTC (or more) or 0.05 USDT (or less) at once - which may not correspond to your real available balance or is less than the exchange minimal limit for the order amount for the stake currency.
 
+#### Dynamic stake amount with position adjustment
+
+When you want to use position adjustment with unlimited stakes, you must also implement `custom_stake_amount` to a return a value depending on your strategy.
+Typical value would be in the range of 25% - 50% of the proposed stakes, but depends highly on your strategy and how much you wish to leave into the wallet as position adjustment buffer.
+
+For example if your position adjustment assumes it can do 2 additional buys with the same stake amounts then your buffer should be 66.6667% of the initially proposed unlimited stake amount.
+
+Or another example if your position adjustment assumes it can do 1 additional buy with 3x the original stake amount then `custom_stake_amount` should return 25% of proposed stake amount and leave 75% for possible later position adjustments.
+
 --8<-- "includes/pricing.md"
 
 ### Understand minimal_roi

docs/data-analysis.md

@@ -1,6 +1,6 @@
-# Analyzing bot data with Jupyter notebooks  
+# Analyzing bot data with Jupyter notebooks
 
-You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.  
+You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
 
 ## Quick start with docker
 
@@ -41,32 +41,35 @@ ipython kernel install --user --name=freqtrade
 !!! Warning
     Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
 
-## Recommended workflow  
+## Recommended workflow
 
-| Task | Tool |  
-  --- | ---  
-Bot operations | CLI  
+| Task | Tool |
+  --- | ---
+Bot operations | CLI
 Repetitive tasks | Shell scripts
-Data analysis & visualization | Notebook  
+Data analysis & visualization | Notebook
 
 1. Use the CLI to
+
     * download historical data
     * run a backtest
     * run with real-time data
-    * export results  
+    * export results
 
 1. Collect these actions in shell scripts
+
     * save complicated commands with arguments
-    * execute multi-step operations  
+    * execute multi-step operations
     * automate testing strategies and preparing data for analysis
 
 1. Use a notebook to
+
     * visualize data
-    * munge and plot to generate insights
+    * mangle and plot to generate insights
 
-## Example utility snippets  
+## Example utility snippets
 
-### Change directory to root  
+### Change directory to root
 
 Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
 

docs/deprecated.md

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

docs/docker_quickstart.md

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

docs/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/index.md

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

docs/installation.md

@@ -42,7 +42,7 @@ These requirements apply to both [Script Installation](#script-installation) and
 
 ### Install guide
 
-* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
+* [Python >= 3.8.x](http://docs.python-guide.org/en/latest/starting/installation/)
 * [pip](https://pip.pypa.io/en/stable/installing/)
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
@@ -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

@@ -273,6 +273,9 @@ def plot_config(self):
 !!! Warning
     `plotly` arguments are only supported with plotly library and will not work with freq-ui.
 
+!!! Note "Trade position adjustments"
+    If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
+
 ## Plot profit
 
 ![plot-profit](assets/plot-profit.png)
@@ -283,6 +286,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
 * The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
 * Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
 The first graph is good to get a grip of how the overall market progresses.
 
@@ -292,6 +297,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
 mkdocs==1.2.3
-mkdocs-material==8.1.3
+mkdocs-material==8.1.9
 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/strategy-callbacks.md

@@ -15,6 +15,7 @@ Currently available callbacks:
 * [`check_buy_timeout()` and `check_sell_timeout()](#custom-order-timeout-rules)
 * [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
 * [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
+* [`adjust_trade_position()`](#adjust-trade-position)
 
 !!! Tip "Callback calling sequence"
     You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
@@ -53,7 +54,7 @@ Called before entering a trade, makes it possible to manage your position size w
 class AwesomeStrategy(IStrategy):
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: float, max_stake: float,
-                            **kwargs) -> float:
+                            entry_tag: Optional[str], **kwargs) -> float:
 
         dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
         current_candle = dataframe.iloc[-1].squeeze()
@@ -73,7 +74,7 @@ class AwesomeStrategy(IStrategy):
 Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
 
 !!! Tip
-    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
+    You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
 
 !!! Tip
     Returning `0` or `None` will prevent trades from being placed.
@@ -361,8 +362,8 @@ class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
-    def custom_entry_price(self, pair: str, current_time: datetime,
-                           proposed_rate, **kwargs) -> float:
+    def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float, 
+                           entry_tag: Optional[str], **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)
@@ -412,7 +413,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
 The function must return either `True` (cancel order) or `False` (keep order alive).
 
 ``` python
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 from freqtrade.persistence import Trade
 
 class AwesomeStrategy(IStrategy):
@@ -425,22 +426,24 @@ class AwesomeStrategy(IStrategy):
         'sell': 60 * 25
     }
 
-    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, 
+                          current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
             return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
            return True
         return False
 
 
-    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
-        if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
+                           current_time: datetime, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
             return True
-        elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
+        elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
             return True
-        elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
+        elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
            return True
         return False
 ```
@@ -499,7 +502,8 @@ class AwesomeStrategy(IStrategy):
     # ... populate_* methods
 
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str], 
+                            **kwargs) -> bool:
         """
         Called right before placing a buy order.
         Timing for this function is critical, so avoid doing heavy computations or
@@ -568,3 +572,110 @@ class AwesomeStrategy(IStrategy):
         return True
 
 ```
+
+## Adjust trade position
+
+The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
+For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
+`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
+
+`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
+
+The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional buy order should be made (position is increased).
+If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
+Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
+
+This callback is **not** called when there is an open order (either buy or sell) waiting for execution, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
+`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
+
+!!! Note "About stake size"
+    Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
+    If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
+    Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
+
+!!! Warning
+    Stoploss is still calculated from the initial opening price, not averaged price.
+
+!!! Warning "/stopbuy"
+    While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
+
+!!! Warning "Backtesting"
+    During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
+
+``` python
+from freqtrade.persistence import Trade
+
+
+class DigDeeperStrategy(IStrategy):
+    
+    position_adjustment_enable = True
+    
+    # Attempts to handle large drops with DCA. High stoploss is required.
+    stoploss = -0.30
+    
+    # ... populate_* methods
+    
+    # Example specific variables
+    max_entry_position_adjustment = 3
+    # This number is explained a bit further down
+    max_dca_multiplier = 5.5
+    
+    # This is called when placing the initial order (opening trade)
+    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
+                            proposed_stake: float, min_stake: float, max_stake: float,
+                            entry_tag: Optional[str], **kwargs) -> float:
+        
+        # We need to leave most of the funds for possible further DCA orders
+        # This also applies to fixed stakes
+        return proposed_stake / self.max_dca_multiplier
+        
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be increased.
+        This means extra buy orders with additional fees.
+ 
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current buy rate.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param min_stake: Minimal stake size allowed by exchange.
+        :param max_stake: Balance available for trading.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade
+        """
+        
+        if current_profit > -0.05:
+            return None
+
+        # Obtain pair dataframe (just to show how to access it)
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        # Only buy when not actively falling price.
+        last_candle = dataframe.iloc[-1].squeeze()
+        previous_candle = dataframe.iloc[-2].squeeze()
+        if last_candle['close'] < previous_candle['close']:
+            return None
+
+        filled_buys = trade.select_filled_orders('buy')
+        count_of_buys = trade.nr_of_successful_buys
+        # Allow up to 3 additional increasingly larger buys (4 in total)
+        # Initial buy is 1x
+        # If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
+        # If that falls down to -5% again, we buy 1.5x more
+        # If that falls once again down to -5%, we buy 1.75x more
+        # Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
+        # That is why max_dca_multiplier is 5.5
+        # Hope you have a deep wallet!
+        try:
+            # This returns first order stake size
+            stake_amount = filled_buys[0].cost
+            # This then calculates current safety order size
+            stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
+            return stake_amount
+        except Exception as exception:
+            return None
+
+        return None
+
+```

docs/strategy-customization.md

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

docs/utils.md

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

docs/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-0.4.24-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 (3.8, 3.9 and 3.10) and for 64bit Windows.
 Other versions must be downloaded from the above link.
 
 ``` powershell

environment.yml

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

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.12'
+__version__ = '2022.1'
 
 if __version__ == 'develop':
 

freqtrade/__main__.py

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

freqtrade/commands/arguments.py

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

freqtrade/commands/build_config_commands.py

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

freqtrade/commands/cli_options.py

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

freqtrade/configuration/configuration.py

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

freqtrade/constants.py

@@ -34,6 +34,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
+BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
+BACKTEST_CACHE_DEFAULT = 'day'
 DRY_RUN_WALLET = 1000
 DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
@@ -369,7 +371,9 @@ CONF_SCHEMA = {
             'type': 'string',
             'enum': AVAILABLE_DATAHANDLERS,
             'default': 'jsongz'
-        }
+        },
+        'position_adjustment_enable': {'type': 'boolean'},
+        'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1},
     },
     'definitions': {
         'exchange': {

freqtrade/data/btanalysis.py

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

freqtrade/data/history/history_utils.py

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

freqtrade/data/history/idatahandler.py

@@ -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/common.py

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

freqtrade/exchange/exchange.py

@@ -67,6 +67,8 @@ class Exchange:
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
+        # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
+        "ohlcv_volume_currency": "base",  # "base" or "quote"
         "trades_pagination": "time",  # Possible are "time" or "id"
         "trades_pagination_arg": "since",
         "l2_limit_range": None,
@@ -83,6 +85,8 @@ class Exchange:
         self._api: ccxt.Exchange = None
         self._api_async: ccxt_async.Exchange = None
         self._markets: Dict = {}
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
 
         self._config.update(config)
 
@@ -170,8 +174,10 @@ class Exchange:
 
     def close(self):
         logger.debug("Exchange object destroyed, closing async loop")
-        if self._api_async and inspect.iscoroutinefunction(self._api_async.close):
-            asyncio.get_event_loop().run_until_complete(self._api_async.close())
+        if (self._api_async and inspect.iscoroutinefunction(self._api_async.close)
+                and self._api_async.session):
+            logger.info("Closing async ccxt session.")
+            self.loop.run_until_complete(self._api_async.close())
 
     def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
                    ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
@@ -326,7 +332,7 @@ class Exchange:
     def _load_async_markets(self, reload: bool = False) -> None:
         try:
             if self._api_async:
-                asyncio.get_event_loop().run_until_complete(
+                self.loop.run_until_complete(
                     self._api_async.load_markets(reload=reload))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
@@ -606,8 +612,9 @@ class Exchange:
             'cost': _amount * rate,
             'type': ordertype,
             'side': side,
+            'filled': 0,
             'remaining': _amount,
-            'datetime': arrow.utcnow().isoformat(),
+            'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
             'timestamp': arrow.utcnow().int_timestamp * 1000,
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
@@ -621,6 +628,7 @@ class Exchange:
             average = self.get_dry_market_fill_price(pair, side, amount, rate)
             dry_order.update({
                 'average': average,
+                'filled': _amount,
                 'cost': dry_order['amount'] * average,
             })
             dry_order = self.add_dry_order_fee(pair, dry_order)
@@ -652,7 +660,8 @@ class Exchange:
             max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
-            filled_amount = 0
+            filled_amount = 0.0
+            book_entry_price = 0.0
             for book_entry in ob[ob_type]:
                 book_entry_price = book_entry[0]
                 book_entry_coin_volume = book_entry[1]
@@ -944,7 +953,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_tickers(self, cached: bool = False) -> Dict:
+    def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
         """
         :param cached: Allow cached result
         :return: fetch_tickers result
@@ -954,7 +963,7 @@ class Exchange:
             if tickers:
                 return tickers
         try:
-            tickers = self._api.fetch_tickers()
+            tickers = self._api.fetch_tickers(symbols)
             self._fetch_tickers_cache['fetch_tickers'] = tickers
             return tickers
         except ccxt.NotSupported as e:
@@ -1227,7 +1236,7 @@ class Exchange:
         :param since_ms: Timestamp in milliseconds to get history from
         :return: List with candle (OHLCV) data
         """
-        pair, timeframe, data = asyncio.get_event_loop().run_until_complete(
+        pair, timeframe, data = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, is_new_pair=is_new_pair))
         logger.info(f"Downloaded data for {pair} with length {len(data)}.")
@@ -1329,8 +1338,10 @@ 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(
-                asyncio.gather(*input_coro, return_exceptions=True))
+            async def gather_stuff():
+                return await asyncio.gather(*input_coro, return_exceptions=True)
+
+            results = self.loop.run_until_complete(gather_stuff())
 
             # handle caching
             for res in results:
@@ -1566,7 +1577,7 @@ class Exchange:
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not support downloading Trades.")
 
-        return asyncio.get_event_loop().run_until_complete(
+        return self.loop.run_until_complete(
             self._async_get_trade_history(pair=pair, since=since,
                                           until=until, from_id=from_id))
 

freqtrade/exchange/ftx.py

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

freqtrade/exchange/gateio.py

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

freqtrade/exchange/kraken.py

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

freqtrade/exchange/okex.py

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

freqtrade/freqtradebot.py

@@ -7,16 +7,14 @@ import traceback
 from datetime import datetime, timezone
 from math import isclose
 from threading import Lock
-from typing import Any, Dict, List, Optional
-
-import arrow
+from typing import Any, Dict, List, Optional, Tuple
 
 from freqtrade import __version__, constants
 from freqtrade.configuration import validate_config_consistency
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
-from freqtrade.enums import RPCMessageType, SellType, State
+from freqtrade.enums import RPCMessageType, RunMode, SellType, State
 from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, PricingError)
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -126,6 +124,7 @@ class FreqtradeBot(LoggingMixin):
 
         self.rpc.cleanup()
         cleanup_db()
+        self.exchange.close()
 
     def startup(self) -> None:
         """
@@ -178,6 +177,11 @@ class FreqtradeBot(LoggingMixin):
             # First process current opened trades (positions)
             self.exit_positions(trades)
 
+        # Check if we need to adjust our current positions before attempting to buy new trades.
+        if self.strategy.position_adjustment_enable:
+            with self._exit_lock:
+                self.process_open_trade_positions()
+
         # Then looking for buy opportunities
         if self.get_free_open_trades():
             self.enter_positions()
@@ -285,7 +289,8 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             if trade.is_open and not trade.fee_updated('buy'):
                 order = trade.select_order('buy', False)
-                if order:
+                open_order = trade.select_order('buy', True)
+                if order and open_order is None:
                     logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
                     self.update_trade_state(trade, order.order_id, send_msg=False)
 
@@ -443,6 +448,59 @@ class FreqtradeBot(LoggingMixin):
         else:
             return False
 
+#
+# BUY / increase positions / DCA logic and methods
+#
+    def process_open_trade_positions(self):
+        """
+        Tries to execute additional buy or sell orders for open trades (positions)
+        """
+        # Walk through each pair and check if it needs changes
+        for trade in Trade.get_open_trades():
+            # If there is any open orders, wait for them to finish.
+            if trade.open_order_id is None:
+                try:
+                    self.check_and_call_adjust_trade_position(trade)
+                except DependencyException as exception:
+                    logger.warning(
+                        f"Unable to adjust position of trade for {trade.pair}: {exception}")
+
+    def check_and_call_adjust_trade_position(self, trade: Trade):
+        """
+        Check the implemented trading strategy for adjustment command.
+        If the strategy triggers the adjustment, a new order gets issued.
+        Once that completes, the existing trade is modified to match new data.
+        """
+        if self.strategy.max_entry_position_adjustment > -1:
+            count_of_buys = trade.nr_of_successful_buys
+            if count_of_buys > self.strategy.max_entry_position_adjustment:
+                logger.debug(f"Max adjustment entries for {trade.pair} has been reached.")
+                return
+        else:
+            logger.debug("Max adjustment entries is set to unlimited.")
+        current_rate = self.exchange.get_rate(trade.pair, refresh=True, side="buy")
+        current_profit = trade.calc_profit_ratio(current_rate)
+
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(trade.pair,
+                                                                   current_rate,
+                                                                   self.strategy.stoploss)
+        max_stake_amount = self.wallets.get_available_stake_amount()
+        logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_rate,
+            current_profit=current_profit, min_stake=min_stake_amount, max_stake=max_stake_amount)
+
+        if stake_amount is not None and stake_amount > 0.0:
+            # We should increase our position
+            self.execute_entry(trade.pair, stake_amount, trade=trade)
+
+        if stake_amount is not None and stake_amount < 0.0:
+            # We should decrease our position
+            # TODO: Selling part of the trade not implemented yet.
+            logger.error(f"Unable to decrease trade position / sell partially"
+                         f" for pair {trade.pair}, feature not implemented.")
+
     def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
         """
         Checks depth of market before executing a buy
@@ -468,54 +526,39 @@ class FreqtradeBot(LoggingMixin):
             return False
 
     def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None, *,
-                      ordertype: Optional[str] = None, buy_tag: Optional[str] = None) -> bool:
+                      ordertype: Optional[str] = None, buy_tag: Optional[str] = None,
+                      trade: Optional[Trade] = None) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
         :param stake_amount: amount of stake-currency for the pair
         :return: True if a buy order is created, false if it fails.
         """
-        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if price:
-            enter_limit_requested = price
-        else:
-            # Calculate price
-            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
-            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
-                                                       default_retval=proposed_enter_rate)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                proposed_rate=proposed_enter_rate)
+        pos_adjust = trade is not None
 
-            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
-
-        if not enter_limit_requested:
-            raise PricingError('Could not determine buy price.')
-
-        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
-                                                                   self.strategy.stoploss)
-
-        if not self.edge:
-            max_stake_amount = self.wallets.get_available_stake_amount()
-            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
-                                                 default_retval=stake_amount)(
-                pair=pair, current_time=datetime.now(timezone.utc),
-                current_rate=enter_limit_requested, proposed_stake=stake_amount,
-                min_stake=min_stake_amount, max_stake=max_stake_amount)
-        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
+        enter_limit_requested, stake_amount = self.get_valid_enter_price_and_stake(
+            pair, price, stake_amount, buy_tag, trade)
 
         if not stake_amount:
             return False
 
-        logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
-                    f"{stake_amount} ...")
+        if pos_adjust:
+            logger.info(f"Position adjust: about to create a new order for {pair} with stake: "
+                        f"{stake_amount} for {trade}")
+        else:
+            logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
+                        f"{stake_amount} ...")
 
         amount = stake_amount / enter_limit_requested
         order_type = ordertype or self.strategy.order_types['buy']
+        time_in_force = self.strategy.order_time_in_force['buy']
 
-        if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
+        if not pos_adjust and not strategy_safe_wrapper(
+                self.strategy.confirm_trade_entry, default_retval=True)(
                 pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
-                time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
+                time_in_force=time_in_force, current_time=datetime.now(timezone.utc),
+                entry_tag=buy_tag):
             logger.info(f"User requested abortion of buying {pair}")
             return False
         amount = self.exchange.amount_to_precision(pair, amount)
@@ -525,6 +568,7 @@ class FreqtradeBot(LoggingMixin):
         order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
         order_id = order['id']
         order_status = order.get('status', None)
+        logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
 
         # we assume the order is executed at the price requested
         enter_limit_filled_price = enter_limit_requested
@@ -560,32 +604,49 @@ class FreqtradeBot(LoggingMixin):
 
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
-        trade = Trade(
-            pair=pair,
-            stake_amount=stake_amount,
-            amount=amount,
-            is_open=True,
-            amount_requested=amount_requested,
-            fee_open=fee,
-            fee_close=fee,
-            open_rate=enter_limit_filled_price,
-            open_rate_requested=enter_limit_requested,
-            open_date=datetime.utcnow(),
-            exchange=self.exchange.id,
-            open_order_id=order_id,
-            strategy=self.strategy.get_strategy_name(),
-            buy_tag=buy_tag,
-            timeframe=timeframe_to_minutes(self.config['timeframe'])
-        )
-        trade.orders.append(order_obj)
+        # This is a new trade
+        if trade is None:
+            trade = Trade(
+                pair=pair,
+                stake_amount=stake_amount,
+                amount=amount,
+                is_open=True,
+                amount_requested=amount_requested,
+                fee_open=fee,
+                fee_close=fee,
+                open_rate=enter_limit_filled_price,
+                open_rate_requested=enter_limit_requested,
+                open_date=datetime.utcnow(),
+                exchange=self.exchange.id,
+                open_order_id=order_id,
+                fee_open_currency=None,
+                strategy=self.strategy.get_strategy_name(),
+                buy_tag=buy_tag,
+                timeframe=timeframe_to_minutes(self.config['timeframe'])
+            )
+        else:
+            # This is additional buy, we reset fee_open_currency so timeout checking can work
+            trade.is_open = True
+            trade.fee_open_currency = None
+            trade.open_rate_requested = enter_limit_requested
+            trade.open_order_id = order_id
 
+        trade.orders.append(order_obj)
+        trade.recalc_trade_from_orders()
         Trade.query.session.add(trade)
         Trade.commit()
 
         # Updating wallets
         self.wallets.update()
 
-        self._notify_enter(trade, order_type)
+        self._notify_enter(trade, order, order_type)
+
+        if pos_adjust:
+            if order_status == 'closed':
+                logger.info(f"DCA order closed, trade should be up to date: {trade}")
+                trade = self.cancel_stoploss_on_exchange(trade)
+            else:
+                logger.info(f"DCA order {order_status}, will wait for resolution: {trade}")
 
         # Update fees if order is closed
         if order_status == 'closed':
@@ -593,26 +654,75 @@ class FreqtradeBot(LoggingMixin):
 
         return True
 
-    def _notify_enter(self, trade: Trade, order_type: Optional[str] = None,
+    def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
+        # First cancelling stoploss on exchange ...
+        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
+            try:
+                logger.info(f"Canceling stoploss on exchange for {trade}")
+                co = self.exchange.cancel_stoploss_order_with_result(
+                    trade.stoploss_order_id, trade.pair, trade.amount)
+                trade.update_order(co)
+            except InvalidOrderException:
+                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        return trade
+
+    def get_valid_enter_price_and_stake(
+            self, pair: str, price: Optional[float], stake_amount: float,
+            entry_tag: Optional[str],
+            trade: Optional[Trade]) -> Tuple[float, float]:
+        if price:
+            enter_limit_requested = price
+        else:
+            # Calculate price
+            proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
+            custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
+                                                       default_retval=proposed_enter_rate)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                proposed_rate=proposed_enter_rate, entry_tag=entry_tag)
+
+            enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
+        if not enter_limit_requested:
+            raise PricingError('Could not determine buy price.')
+        min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
+                                                                   self.strategy.stoploss)
+        if not self.edge and trade is None:
+            max_stake_amount = self.wallets.get_available_stake_amount()
+            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
+                                                 default_retval=stake_amount)(
+                pair=pair, current_time=datetime.now(timezone.utc),
+                current_rate=enter_limit_requested, proposed_stake=stake_amount,
+                min_stake=min_stake_amount, max_stake=max_stake_amount, entry_tag=entry_tag)
+        stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
+        return enter_limit_requested, stake_amount
+
+    def _notify_enter(self, trade: Trade, order: Dict, order_type: Optional[str] = None,
                       fill: bool = False) -> None:
         """
         Sends rpc notification when a buy occurred.
         """
+        open_rate = safe_value_fallback(order, 'average', 'price')
+        if open_rate is None:
+            open_rate = trade.open_rate
+
+        current_rate = trade.open_rate_requested
+        if self.dataprovider.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            current_rate = self.exchange.get_rate(trade.pair, refresh=False, side="buy")
+
         msg = {
             'trade_id': trade.id,
             'type': RPCMessageType.BUY_FILL if fill else RPCMessageType.BUY,
             'buy_tag': trade.buy_tag,
             'exchange': self.exchange.name.capitalize(),
             'pair': trade.pair,
-            'limit': trade.open_rate,  # Deprecated (?)
-            'open_rate': trade.open_rate,
+            'limit': open_rate,  # Deprecated (?)
+            'open_rate': open_rate,
             'order_type': order_type,
             'stake_amount': trade.stake_amount,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
-            'amount': trade.amount,
+            'amount': safe_value_fallback(order, 'filled', 'amount') or trade.amount,
             'open_date': trade.open_date or datetime.utcnow(),
-            'current_rate': trade.open_rate_requested,
+            'current_rate': current_rate,
         }
 
         # Send the message
@@ -856,20 +966,6 @@ class FreqtradeBot(LoggingMixin):
             return True
         return False
 
-    def _check_timed_out(self, side: str, order: dict) -> bool:
-        """
-        Check if timeout is active, and if the order is still open and timed out
-        """
-        timeout = self.config.get('unfilledtimeout', {}).get(side)
-        ordertime = arrow.get(order['datetime']).datetime
-        if timeout is not None:
-            timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
-            timeout_kwargs = {timeout_unit: -timeout}
-            timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
-            return (order['status'] == 'open' and order['side'] == side
-                    and ordertime < timeout_threshold)
-        return False
-
     def check_handle_timedout(self) -> None:
         """
         Check if any orders are timed out and cancel if necessary
@@ -890,20 +986,16 @@ class FreqtradeBot(LoggingMixin):
 
             if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
                     fully_cancelled
-                    or self._check_timed_out('buy', order)
-                    or strategy_safe_wrapper(self.strategy.check_buy_timeout,
-                                             default_retval=False)(pair=trade.pair,
-                                                                   trade=trade,
-                                                                   order=order))):
+                    or self.strategy.ft_check_timed_out(
+                        'buy', trade, order, datetime.now(timezone.utc))
+                        )):
                 self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
             elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
                   fully_cancelled
-                  or self._check_timed_out('sell', order)
-                  or strategy_safe_wrapper(self.strategy.check_sell_timeout,
-                                           default_retval=False)(pair=trade.pair,
-                                                                 trade=trade,
-                                                                 order=order))):
+                  or self.strategy.ft_check_timed_out(
+                      'sell', trade, order, datetime.now(timezone.utc)))
+                  ):
                 self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
                 canceled_count = trade.get_exit_order_count()
                 max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
@@ -975,10 +1067,16 @@ class FreqtradeBot(LoggingMixin):
         filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
         if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
             logger.info('Buy order fully cancelled. Removing %s from database.', trade)
-            # if trade is not partially completed, just delete the trade
-            trade.delete()
-            was_trade_fully_canceled = True
-            reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            # if trade is not partially completed and it's the only order, just delete the trade
+            if len(trade.orders) <= 1:
+                trade.delete()
+                was_trade_fully_canceled = True
+                reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
+            else:
+                # FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
+                self.update_trade_state(trade, trade.open_order_id, corder)
+                trade.open_order_id = None
+                logger.info('Partial buy order timeout for %s.', trade)
         else:
             # if trade is partially complete, edit the stake details for the trade
             # and close the order
@@ -1102,13 +1200,7 @@ class FreqtradeBot(LoggingMixin):
         limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
 
         # First cancelling stoploss on exchange ...
-        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
-            try:
-                co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
-                                                                     trade.pair, trade.amount)
-                trade.update_order(co)
-            except InvalidOrderException:
-                logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
+        trade = self.cancel_stoploss_on_exchange(trade)
 
         order_type = ordertype or self.strategy.order_types[sell_type]
         if sell_reason.sell_type == SellType.EMERGENCY_SELL:
@@ -1266,7 +1358,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # Update trade with order values
-        logger.info('Found open order for %s', trade)
+        logger.info(f'Found open order for {trade}')
         try:
             order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id,
                                                                                 trade.pair,
@@ -1282,29 +1374,26 @@ class FreqtradeBot(LoggingMixin):
             # Handling of this will happen in check_handle_timedout.
             return True
 
-        # Try update amount (binance-fix)
-        try:
-            new_amount = self.get_real_amount(trade, order)
-            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
-                           abs_tol=constants.MATH_CLOSE_PREC):
-                order['amount'] = new_amount
-                order.pop('filled', None)
-                trade.recalc_open_trade_value()
-        except DependencyException as exception:
-            logger.warning("Could not update trade amount: %s", exception)
+        order = self.handle_order_fee(trade, order)
 
         trade.update(order)
+        trade.recalc_trade_from_orders()
         Trade.commit()
 
-        # Updating wallets when order is closed
+        if order['status'] in constants.NON_OPEN_EXCHANGE_STATES:
+            # If a buy order was closed, force update on stoploss on exchange
+            if order.get('side', None) == 'buy':
+                trade = self.cancel_stoploss_on_exchange(trade)
+            # Updating wallets when order is closed
+            self.wallets.update()
+
         if not trade.is_open:
             if send_msg and not stoploss_order and not trade.open_order_id:
                 self._notify_exit(trade, '', True)
             self.handle_protections(trade.pair)
-            self.wallets.update()
         elif send_msg and not trade.open_order_id:
             # Buy fill
-            self._notify_enter(trade, fill=True)
+            self._notify_enter(trade, order, fill=True)
 
         return False
 
@@ -1339,6 +1428,18 @@ class FreqtradeBot(LoggingMixin):
             return real_amount
         return amount
 
+    def handle_order_fee(self, trade: Trade, order: Dict[str, Any]) -> Dict[str, Any]:
+        # Try update amount (binance-fix)
+        try:
+            new_amount = self.get_real_amount(trade, order)
+            if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
+                           abs_tol=constants.MATH_CLOSE_PREC):
+                order['amount'] = new_amount
+                order.pop('filled', None)
+        except DependencyException as exception:
+            logger.warning("Could not update trade amount: %s", exception)
+        return order
+
     def get_real_amount(self, trade: Trade, order: Dict) -> float:
         """
         Detect and update trade fee.

freqtrade/loggers.py

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

freqtrade/main.py

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

freqtrade/misc.py

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

freqtrade/optimize/backtesting.py

@@ -11,20 +11,22 @@ from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame
 
+from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
-from freqtrade.data.btanalysis import trade_list_to_dataframe
+from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import BacktestState, SellType
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
+from freqtrade.misc import get_strategy_run_id
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.bt_progress import BTProgress
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                  store_backtest_stats)
-from freqtrade.persistence import LocalTrade, PairLocks, Trade
+from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -60,9 +62,10 @@ class Backtesting:
 
         LoggingMixin.show_output = False
         self.config = config
-        self.results: Optional[Dict[str, Any]] = None
+        self.results: Dict[str, Any] = {}
 
         config['dry_run'] = True
+        self.run_ids: Dict[str, str] = {}
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
 
@@ -246,6 +249,9 @@ class Backtesting:
         Helper function to convert a processed dataframes into lists for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
+
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
@@ -254,7 +260,8 @@ class Backtesting:
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
         # Create dict with data
-        for pair, pair_data in processed.items():
+        for pair in processed.keys():
+            pair_data = processed[pair]
             self.check_abort()
             self.progress.increment()
             if not pair_data.empty:
@@ -266,8 +273,8 @@ class Backtesting:
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
             # Trim startup period from analyzed dataframe
-            df_analyzed = trim_dataframe(df_analyzed, self.timerange,
-                                         startup_candles=self.required_startup)
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup)
             # To avoid using data from future, we use buy/sell signals shifted
             # from the previous candle
             df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
@@ -350,8 +357,37 @@ class Backtesting:
         else:
             return sell_row[OPEN_IDX]
 
+    def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade, row: Tuple
+                                           ) -> LocalTrade:
+
+        current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
+        min_stake = self.exchange.get_min_pair_stake_amount(trade.pair, row[OPEN_IDX], -0.1)
+        max_stake = self.wallets.get_available_stake_amount()
+        stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
+                                             default_retval=None)(
+            trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
+            current_profit=current_profit, min_stake=min_stake, max_stake=max_stake)
+
+        # Check if we should increase our position
+        if stake_amount is not None and stake_amount > 0.0:
+            pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
+            if pos_trade is not None:
+                return pos_trade
+
+        return trade
+
     def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
                                          sell_row: Tuple) -> Optional[LocalTrade]:
+
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            check_adjust_buy = True
+            if self.strategy.max_entry_position_adjustment > -1:
+                count_of_buys = trade.nr_of_successful_buys
+                check_adjust_buy = (count_of_buys <= self.strategy.max_entry_position_adjustment)
+            if check_adjust_buy:
+                trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
+
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
                                          sell_candle_time, sell_row[BUY_IDX],
@@ -416,7 +452,9 @@ class Backtesting:
                 return self._get_sell_trade_entry_for_candle(trade, sell_row)
             detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
             detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
-            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
+            detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
+            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
             for det_row in detail_data[headers].values.tolist():
                 res = self._get_sell_trade_entry_for_candle(trade, det_row)
                 if res:
@@ -427,16 +465,16 @@ class Backtesting:
         else:
             return self._get_sell_trade_entry_for_candle(trade, sell_row)
 
-    def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
-        try:
-            stake_amount = self.wallets.get_trade_stake_amount(pair, None)
-        except DependencyException:
-            return None
+    def _enter_trade(self, pair: str, row: Tuple, stake_amount: Optional[float] = None,
+                     trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
+
+        current_time = row[DATE_IDX].to_pydatetime()
+        entry_tag = row[BUY_TAG_IDX] if len(row) >= BUY_TAG_IDX + 1 else None
         # let's call the custom entry price, using the open price as default price
         propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
                                              default_retval=row[OPEN_IDX])(
-            pair=pair, current_time=row[DATE_IDX].to_pydatetime(),
-            proposed_rate=row[OPEN_IDX])  # default value is the open rate
+            pair=pair, current_time=current_time,
+            proposed_rate=row[OPEN_IDX], entry_tag=entry_tag)  # default value is the open rate
 
         # Move rate to within the candle's low/high rate
         propose_rate = min(max(propose_rate, row[LOW_IDX]), row[HIGH_IDX])
@@ -444,40 +482,77 @@ class Backtesting:
         min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, propose_rate, -0.05) or 0
         max_stake_amount = self.wallets.get_available_stake_amount()
 
-        stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
-                                             default_retval=stake_amount)(
-            pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=propose_rate,
-            proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
+        pos_adjust = trade is not None
+        if not pos_adjust:
+            try:
+                stake_amount = self.wallets.get_trade_stake_amount(pair, None)
+            except DependencyException:
+                return trade
+
+            stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
+                                                 default_retval=stake_amount)(
+                pair=pair, current_time=current_time, current_rate=propose_rate,
+                proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount,
+                entry_tag=entry_tag)
+
         stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
 
         if not stake_amount:
-            return None
+            # In case of pos adjust, still return the original trade
+            # If not pos adjust, trade is None
+            return trade
 
         order_type = self.strategy.order_types['buy']
         time_in_force = self.strategy.order_time_in_force['sell']
         # Confirm trade entry:
-        if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
-                pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
-                time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
-            return None
+        if not pos_adjust:
+            if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
+                    pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
+                    time_in_force=time_in_force, current_time=current_time,
+                    entry_tag=entry_tag):
+                return None
 
         if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
-            # Enter trade
-            has_buy_tag = len(row) >= BUY_TAG_IDX + 1
-            trade = LocalTrade(
-                pair=pair,
-                open_rate=propose_rate,
-                open_date=row[DATE_IDX].to_pydatetime(),
-                stake_amount=stake_amount,
-                amount=round(stake_amount / propose_rate, 8),
-                fee_open=self.fee,
-                fee_close=self.fee,
-                is_open=True,
-                buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
-                exchange='backtesting',
+            amount = round(stake_amount / propose_rate, 8)
+            if trade is None:
+                # Enter trade
+                trade = LocalTrade(
+                    pair=pair,
+                    open_rate=propose_rate,
+                    open_date=current_time,
+                    stake_amount=stake_amount,
+                    amount=amount,
+                    fee_open=self.fee,
+                    fee_close=self.fee,
+                    is_open=True,
+                    buy_tag=entry_tag,
+                    exchange='backtesting',
+                    orders=[]
+                )
+            trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
+
+            order = Order(
+                ft_is_open=False,
+                ft_pair=trade.pair,
+                symbol=trade.pair,
+                ft_order_side="buy",
+                side="buy",
+                order_type="market",
+                status="closed",
+                order_date=current_time,
+                order_filled_date=current_time,
+                order_update_date=current_time,
+                price=propose_rate,
+                average=propose_rate,
+                amount=amount,
+                filled=amount,
+                cost=stake_amount + trade.fee_open
             )
-            return trade
-        return None
+            trade.orders.append(order)
+            if pos_adjust:
+                trade.recalc_trade_from_orders()
+
+        return trade
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
                          data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
@@ -519,7 +594,8 @@ class Backtesting:
         Of course try to not have ugly code. By some accessor are sometime slower than functions.
         Avoid extensive logging in this method and functions it calls.
 
-        :param processed: a processed dictionary with format {pair, data}
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        optimize memory usage!
         :param start_date: backtesting timerange start datetime
         :param end_date: backtesting timerange end datetime
         :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
@@ -666,6 +742,7 @@ class Backtesting:
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({
+            'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
         })
@@ -673,6 +750,33 @@ class Backtesting:
 
         return min_date, max_date
 
+    def _get_min_cached_backtest_date(self):
+        min_backtest_date = None
+        backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
+        if self.timerange.stopts == 0 or datetime.fromtimestamp(
+           self.timerange.stopts, tz=timezone.utc) > datetime.now(tz=timezone.utc):
+            logger.warning('Backtest result caching disabled due to use of open-ended timerange.')
+        elif backtest_cache_age == 'day':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(days=1)
+        elif backtest_cache_age == 'week':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=1)
+        elif backtest_cache_age == 'month':
+            min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(weeks=4)
+        return min_backtest_date
+
+    def load_prior_backtest(self):
+        self.run_ids = {
+            strategy.get_strategy_name(): get_strategy_run_id(strategy)
+            for strategy in self.strategylist
+        }
+
+        # Load previous result that will be updated incrementally.
+        # This can be circumvented in certain instances in combination with downloading more data
+        min_backtest_date = self._get_min_cached_backtest_date()
+        if min_backtest_date is not None:
+            self.results = find_existing_backtest_stats(
+                self.config['user_data_dir'] / 'backtest_results', self.run_ids, min_backtest_date)
+
     def start(self) -> None:
         """
         Run backtesting end-to-end
@@ -684,15 +788,38 @@ class Backtesting:
         self.load_bt_data_detail()
         logger.info("Dataload complete. Calculating indicators")
 
-        for strat in self.strategylist:
-            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
-        if len(self.strategylist) > 0:
+        self.load_prior_backtest()
 
-            self.results = generate_backtest_stats(data, self.all_results,
-                                                   min_date=min_date, max_date=max_date)
+        for strat in self.strategylist:
+            if self.results and strat.get_strategy_name() in self.results['strategy']:
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+                continue
+            min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
+
+        # Update old results with new ones.
+        if len(self.all_results) > 0:
+            results = generate_backtest_stats(
+                data, self.all_results, min_date=min_date, max_date=max_date)
+            if self.results:
+                self.results['metadata'].update(results['metadata'])
+                self.results['strategy'].update(results['strategy'])
+                self.results['strategy_comparison'].extend(results['strategy_comparison'])
+            else:
+                self.results = results
 
             if self.config.get('export', 'none') == 'trades':
                 store_backtest_stats(self.config['exportfilename'], self.results)
 
+        # Results may be mixed up now. Sort them so they follow --strategy-list order.
+        if 'strategy_list' in self.config and len(self.results) > 0:
+            self.results['strategy_comparison'] = sorted(
+                self.results['strategy_comparison'],
+                key=lambda c: self.config['strategy_list'].index(c['key']))
+            self.results['strategy'] = dict(
+                sorted(self.results['strategy'].items(),
+                       key=lambda kv: self.config['strategy_list'].index(kv[0])))
+
+        if len(self.strategylist) > 0:
             # Show backtest results
             show_backtest_results(self.config, self.results)

freqtrade/optimize/bt_progress.py

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

freqtrade/optimize/edge_cli.py

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

freqtrade/optimize/hyperopt.py

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

freqtrade/optimize/hyperopt_auto.py

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

freqtrade/optimize/hyperopt_interface.py

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

freqtrade/optimize/hyperopt_loss_calmar.py

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

freqtrade/optimize/hyperopt_tools.py

@@ -137,6 +137,7 @@ class HyperoptTools():
         }
         if not HyperoptTools._test_hyperopt_results_exist(results_file):
             # No file found.
+            logger.warning(f"Hyperopt file {results_file} not found.")
             return [], 0
 
         epochs = []
@@ -299,8 +300,7 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
-                               has_drawdown: bool) -> pd.DataFrame:
+    def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -309,33 +309,26 @@ class HyperoptTools():
 
         if not has_drawdown:
             # Ensure compatibility with older versions of hyperopt results
-            trials['results_metrics.max_drawdown_abs'] = None
-            trials['results_metrics.max_drawdown'] = None
+            trials['results_metrics.max_drawdown_account'] = None
 
-        if not legacy_mode:
-            # New mode, using backtest result for metrics
-            trials['results_metrics.winsdrawslosses'] = trials.apply(
-                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
-                          f"{x['results_metrics.losses']:>4}", axis=1)
-            trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
-                             'results_metrics.winsdrawslosses',
-                             'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
-                             'results_metrics.profit_total', 'results_metrics.holding_avg',
-                             'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
-                             'loss', 'is_initial_point', 'is_best']]
+        # New mode, using backtest result for metrics
+        trials['results_metrics.winsdrawslosses'] = trials.apply(
+            lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                      f"{x['results_metrics.losses']:>4}", axis=1)
 
-        else:
-            # Legacy mode
-            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
-                             'results_metrics.total_profit', 'results_metrics.profit',
-                             'results_metrics.duration', 'results_metrics.max_drawdown',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
-                             'is_best']]
+        trials = 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_account', 'results_metrics.max_drawdown_abs',
+                         'loss', 'is_initial_point', 'is_best']]
 
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
-                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
+        trials.columns = [
+            'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+            'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            ]
 
         return trials
 
@@ -351,10 +344,9 @@ class HyperoptTools():
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        legacy_mode = 'results_metrics.total_trades' not in trials
-        has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
+        has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
 
-        trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
+        trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
 
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
@@ -362,12 +354,12 @@ class HyperoptTools():
         trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
-        perc_multi = 1 if legacy_mode else 100
+        # perc_multi = 1 if legacy_mode else 100
         trials['Epoch'] = trials['Epoch'].apply(
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
             lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
@@ -379,24 +371,25 @@ class HyperoptTools():
 
         stake_currency = config['stake_currency']
 
-        if has_drawdown:
-            trials['Max Drawdown'] = trials.apply(
-                lambda x: '{} {}'.format(
-                    round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
-                ).rjust(25 + len(stake_currency))
-                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
-                axis=1
-            )
-        else:
-            trials = trials.drop(columns=['Max Drawdown'])
+        trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
+            lambda x: "{} {}".format(
+                round_coin_value(x['max_drawdown_abs'], stake_currency),
+                (f"({x['max_drawdown_account']:,.2%})"
+                    if has_account_drawdown
+                    else f"({x['max_drawdown']:,.2%})"
+                 ).rjust(10, ' ')
+            ).rjust(25 + len(stake_currency))
+            if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
+            else '--'.rjust(25 + len(stake_currency)),
+            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
@@ -10,7 +11,8 @@ from tabulate import tabulate
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
                                        calculate_max_drawdown)
-from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
+from freqtrade.misc import (decimals_per_coin, file_dump_json, get_backtest_metadata_filename,
+                            round_coin_value)
 
 
 logger = logging.getLogger(__name__)
@@ -32,6 +34,11 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
             recordfilename.parent,
             f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
         ).with_suffix(recordfilename.suffix)
+
+    # Store metadata separately.
+    file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
+    del stats['metadata']
+
     file_dump_json(filename, stats)
 
     latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
@@ -98,11 +105,11 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
     }
 
 
-def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
+def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int,
                           results: DataFrame, skip_nan: bool = False) -> List[Dict]:
     """
     Generates and returns a list  for the given backtest data and the results dataframe
-    :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
+    :param pairlist: Pairlist used
     :param stake_currency: stake-currency - used to correctly name headers
     :param starting_balance: Starting balance
     :param results: Dataframe containing the backtest results
@@ -112,7 +119,7 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
 
     tabular_data = []
 
-    for pair in data:
+    for pair in pairlist:
         result = results[results['pair'] == pair]
         if skip_nan and result['profit_abs'].isnull().all():
             continue
@@ -194,29 +201,21 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
     return tabular_data
 
 
-def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
+def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
     """
     Generate summary per strategy
-    :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
+    :param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
     :return: List of Dicts containing the metrics per Strategy
     """
 
     tabular_data = []
-    for strategy, results in all_results.items():
-        tabular_data.append(_generate_result_line(
-            results['results'], results['config']['dry_run_wallet'], strategy)
-        )
-        try:
-            max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
-                                                                  value_col='profit_ratio')
-            max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
-                                                                  value_col='profit_abs')
-        except ValueError:
-            max_drawdown_per = 0
-            max_drawdown_abs = 0
-        tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
-        tabular_data[-1]['max_drawdown_abs'] = \
-            round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
+    for strategy, result in bt_stats.items():
+        tabular_data.append(deepcopy(result['results_per_pair'][-1]))
+        # Update "key" to strategy (results_per_pair has it as "Total").
+        tabular_data[-1]['key'] = strategy
+        tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
+        tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
+            result['max_drawdown_abs'], result['stake_currency'], False)
     return tabular_data
 
 
@@ -352,14 +351,14 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
     }
 
 
-def generate_strategy_stats(btdata: Dict[str, DataFrame],
+def generate_strategy_stats(pairlist: List[str],
                             strategy: str,
                             content: Dict[str, Any],
                             min_date: datetime, max_date: datetime,
                             market_change: float
                             ) -> Dict[str, Any]:
     """
-    :param btdata: Backtest data
+    :param pairlist: List of pairs to backtest
     :param strategy: Strategy name
     :param content: Backtest result data in the format:
                     {'results: results, 'config: config}}.
@@ -372,11 +371,11 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     if not isinstance(results, DataFrame):
         return {}
     config = content['config']
-    max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
+    max_open_trades = min(config['max_open_trades'], len(pairlist))
     starting_balance = config['dry_run_wallet']
     stake_currency = config['stake_currency']
 
-    pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+    pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                          starting_balance=starting_balance,
                                          results=results, skip_nan=False)
 
@@ -385,7 +384,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
 
     sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
-    left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
+    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
                                               starting_balance=starting_balance,
                                               results=results.loc[results['is_open']],
                                               skip_nan=True)
@@ -429,7 +428,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
 
         'trades_per_day': round(len(results) / backtest_days, 2),
         'market_change': market_change,
-        'pairlist': list(btdata.keys()),
+        'pairlist': pairlist,
         'stake_amount': config['stake_amount'],
         'stake_currency': config['stake_currency'],
         'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
@@ -462,12 +461,14 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     }
 
     try:
-        max_drawdown, _, _, _, _ = calculate_max_drawdown(
+        max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
             results, value_col='profit_ratio')
-        drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
-            results, value_col='profit_abs')
+        (drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
+         max_drawdown) = calculate_max_drawdown(
+             results, value_col='profit_abs', starting_balance=starting_balance)
         strat_stats.update({
-            'max_drawdown': max_drawdown,
+            'max_drawdown': max_drawdown_legacy,  # Deprecated - do not use
+            'max_drawdown_account': max_drawdown,
             'max_drawdown_abs': drawdown_abs,
             'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
             'drawdown_start_ts': drawdown_start.timestamp() * 1000,
@@ -487,6 +488,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
     except ValueError:
         strat_stats.update({
             'max_drawdown': 0.0,
+            'max_drawdown_account': 0.0,
             'max_drawdown_abs': 0.0,
             'max_drawdown_low': 0.0,
             'max_drawdown_high': 0.0,
@@ -513,16 +515,26 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
     :param max_date: Backtest end date
     :return: Dictionary containing results per strategy and a strategy summary.
     """
-    result: Dict[str, Any] = {'strategy': {}}
+    result: Dict[str, Any] = {
+        'metadata': {},
+        'strategy': {},
+        'strategy_comparison': [],
+    }
     market_change = calculate_market_change(btdata, 'close')
-
+    metadata = {}
+    pairlist = list(btdata.keys())
     for strategy, content in all_results.items():
-        strat_stats = generate_strategy_stats(btdata, strategy, content,
+        strat_stats = generate_strategy_stats(pairlist, strategy, content,
                                               min_date, max_date, market_change=market_change)
+        metadata[strategy] = {
+            'run_id': content['run_id'],
+            'backtest_start_time': content['backtest_start_time'],
+        }
         result['strategy'][strategy] = strat_stats
 
-    strategy_results = generate_strategy_comparison(all_results=all_results)
+    strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
 
+    result['metadata'] = metadata
     result['strategy_comparison'] = strategy_results
 
     return result
@@ -646,7 +658,12 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
     headers.append('Drawdown')
 
     # Align drawdown string on the center two space separator.
-    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 +733,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Max balance', round_coin_value(strat_results['csum_max'],
                                              strat_results['stake_currency'])),
 
-            ('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
+            # Compatibility to show old hyperopt results
+            ('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
+            if 'max_drawdown_account' in strat_results else (
+                'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
             ('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
                                           strat_results['stake_currency'])),
             ('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],

freqtrade/persistence/models.py

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

freqtrade/plot/plotting.py

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

freqtrade/plugins/pairlist/SpreadFilter.py

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

freqtrade/plugins/pairlist/VolumePairList.py

@@ -4,7 +4,6 @@ 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
@@ -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']
-                                                     + pair_candles['close']) / 3
-                    pair_candles['quoteVolume'] = (
-                        pair_candles['volume'] * pair_candles['typical_price']
-                    )
+                    if self._exchange._ft_has["ohlcv_volume_currency"] == "base":
+                        pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
+                                                         + pair_candles['close']) / 3
 
+                        pair_candles['quoteVolume'] = (
+                            pair_candles['volume'] * pair_candles['typical_price']
+                        )
+                    else:
+                        # Exchange ohlcv data is in quote volume already.
+                        pair_candles['quoteVolume'] = pair_candles['volume']
                     # ensure that a rolling sum over the lookback_period is built
                     # if pair_candles contains more candles than lookback_period
                     quoteVolume = (pair_candles['quoteVolume']
@@ -204,7 +214,7 @@ class VolumePairList(IPairList):
 
         # Validate whitelist to only have active market pairs
         pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
-        pairs = self.verify_blacklist(pairs, partial(self.log_once, logmethod=logger.info))
+        pairs = self.verify_blacklist(pairs, logmethod=logger.info)
         # Limit pairlist to the requested number of pairs
         pairs = pairs[:self._number_pairs]
 

freqtrade/plugins/protections/max_drawdown_protection.py

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

freqtrade/resolvers/strategy_resolver.py

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

freqtrade/rpc/api_server/api_backtest.py

@@ -39,7 +39,8 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
     # Start backtesting
     # Initialize backtesting object
     def run_backtest():
-        from freqtrade.optimize.optimize_reports import generate_backtest_stats
+        from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
+                                                         store_backtest_stats)
         from freqtrade.resolvers import StrategyResolver
         asyncio.set_event_loop(asyncio.new_event_loop())
         try:
@@ -76,13 +77,25 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             lastconfig['enable_protections'] = btconfig.get('enable_protections')
             lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
 
-            ApiServer._bt.abort = False
-            min_date, max_date = ApiServer._bt.backtest_one_strategy(
-                strat, ApiServer._bt_data, ApiServer._bt_timerange)
+            ApiServer._bt.results = {}
+            ApiServer._bt.load_prior_backtest()
+
+            ApiServer._bt.abort = False
+            if (ApiServer._bt.results and
+                    strat.get_strategy_name() in ApiServer._bt.results['strategy']):
+                # When previous result hash matches - reuse that result and skip backtesting.
+                logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
+            else:
+                min_date, max_date = ApiServer._bt.backtest_one_strategy(
+                    strat, ApiServer._bt_data, ApiServer._bt_timerange)
+
+                ApiServer._bt.results = generate_backtest_stats(
+                    ApiServer._bt_data, ApiServer._bt.all_results,
+                    min_date=min_date, max_date=max_date)
+
+            if btconfig.get('export', 'none') == 'trades':
+                store_backtest_stats(btconfig['exportfilename'], ApiServer._bt.results)
 
-            ApiServer._bt.results = generate_backtest_stats(
-                ApiServer._bt_data, ApiServer._bt.all_results,
-                min_date=min_date, max_date=max_date)
             logger.info("Backtest finished.")
 
         except DependencyException as e:

freqtrade/rpc/api_server/api_schemas.py

@@ -173,6 +173,8 @@ class ShowConfig(BaseModel):
     bot_name: str
     state: str
     runmode: str
+    position_adjustment_enable: bool
+    max_entry_position_adjustment: int
 
 
 class TradeSchema(BaseModel):
@@ -277,6 +279,7 @@ class ForceBuyPayload(BaseModel):
     pair: str
     price: Optional[float]
     ordertype: Optional[OrderTypeValues]
+    stakeamount: Optional[float]
 
 
 class ForceSellPayload(BaseModel):

freqtrade/rpc/api_server/api_v1.py

@@ -20,7 +20,7 @@ from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, Blac
                                                   Stats, StatusMsg, StrategyListResponse,
                                                   StrategyResponse, SysInfo, Version,
                                                   WhitelistResponse)
-from freqtrade.rpc.api_server.deps import get_config, get_rpc, get_rpc_optional
+from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
 from freqtrade.rpc.rpc import RPCException
 
 
@@ -31,7 +31,8 @@ logger = logging.getLogger(__name__)
 # Version increments should happen in "small" steps (1.1, 1.12, ...) unless big changes happen.
 # 1.11: forcebuy and forcesell accept ordertype
 # 1.12: add blacklist delete endpoint
-API_VERSION = 1.12
+# 1.13: forcebuy supports stake_amount
+API_VERSION = 1.13
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -134,7 +135,9 @@ def show_config(rpc: Optional[RPC] = Depends(get_rpc_optional), config=Depends(g
 @router.post('/forcebuy', response_model=ForceBuyResponse, tags=['trading'])
 def forcebuy(payload: ForceBuyPayload, rpc: RPC = Depends(get_rpc)):
     ordertype = payload.ordertype.value if payload.ordertype else None
-    trade = rpc._rpc_forcebuy(payload.pair, payload.price, ordertype)
+    stake_amount = payload.stakeamount if payload.stakeamount else None
+
+    trade = rpc._rpc_forcebuy(payload.pair, payload.price, ordertype, stake_amount)
 
     if trade:
         return ForceBuyResponse.parse_obj(trade.to_json())
@@ -211,18 +214,21 @@ def reload_config(rpc: RPC = Depends(get_rpc)):
 
 
 @router.get('/pair_candles', response_model=PairHistory, tags=['candle data'])
-def pair_candles(pair: str, timeframe: str, limit: Optional[int], rpc: RPC = Depends(get_rpc)):
+def pair_candles(
+        pair: str, timeframe: str, limit: Optional[int] = None, rpc: RPC = Depends(get_rpc)):
     return rpc._rpc_analysed_dataframe(pair, timeframe, limit)
 
 
 @router.get('/pair_history', response_model=PairHistory, tags=['candle data'])
 def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
-                 config=Depends(get_config)):
+                 config=Depends(get_config), exchange=Depends(get_exchange)):
+    # The initial call to this endpoint can be slow, as it may need to initialize
+    # the exchange class.
     config = deepcopy(config)
     config.update({
         'strategy': strategy,
     })
-    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange)
+    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange, exchange)
 
 
 @router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])

freqtrade/rpc/api_server/deps.py

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

freqtrade/rpc/api_server/uvicorn_threaded.py

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

freqtrade/rpc/api_server/webserver.py

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

freqtrade/rpc/fiat_convert.py

@@ -77,6 +77,9 @@ class CryptoToFiatConverter:
             else:
                 return None
         found = [x for x in self._coinlistings if x['symbol'] == crypto_symbol]
+        if crypto_symbol == 'eth':
+            found = [x for x in self._coinlistings if x['id'] == 'ethereum']
+
         if len(found) == 1:
             return found[0]['id']
 

freqtrade/rpc/rpc.py

@@ -136,7 +136,12 @@ class RPC:
             'ask_strategy': config.get('ask_strategy', {}),
             'bid_strategy': config.get('bid_strategy', {}),
             'state': str(botstate),
-            'runmode': config['runmode'].value
+            'runmode': config['runmode'].value,
+            'position_adjustment_enable': config.get('position_adjustment_enable', False),
+            'max_entry_position_adjustment': (
+                config.get('max_entry_position_adjustment', -1)
+                if config.get('max_entry_position_adjustment') != float('inf')
+                else -1)
         }
         return val
 
@@ -238,19 +243,29 @@ class RPC:
                         profit_str += f" ({fiat_profit:.2f})"
                         fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
                             else fiat_profit_sum + fiat_profit
-                trades_list.append([
+                detail_trade = [
                     trade.id,
                     trade.pair + ('*' if (trade.open_order_id is not None
                                           and trade.close_rate_requested is None) else '')
-                               + ('**' if (trade.close_rate_requested is not None) else ''),
+                    + ('**' if (trade.close_rate_requested is not None) else ''),
                     shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
                     profit_str
-                ])
+                ]
+                if self._config.get('position_adjustment_enable', False):
+                    max_buy_str = ''
+                    if self._config.get('max_entry_position_adjustment', -1) > 0:
+                        max_buy_str = f"/{self._config['max_entry_position_adjustment'] + 1}"
+                    filled_buys = trade.nr_of_successful_buys
+                    detail_trade.append(f"{filled_buys}{max_buy_str}")
+                trades_list.append(detail_trade)
             profitcol = "Profit"
             if self._fiat_converter:
                 profitcol += " (" + fiat_display_currency + ")"
 
-            columns = ['ID', 'Pair', 'Since', profitcol]
+            if self._config.get('position_adjustment_enable', False):
+                columns = ['ID', 'Pair', 'Since', profitcol, '# Buys']
+            else:
+                columns = ['ID', 'Pair', 'Since', profitcol]
             return trades_list, columns, fiat_profit_sum
 
     def _rpc_daily_profit(
@@ -592,6 +607,7 @@ class RPC:
         value = self._fiat_converter.convert_amount(
             total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
 
+        trade_count = len(Trade.get_trades_proxy())
         starting_capital_ratio = 0.0
         starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
         starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
@@ -608,6 +624,7 @@ class RPC:
             'starting_capital_fiat': starting_cap_fiat,
             'starting_capital_fiat_ratio': starting_cap_fiat_ratio,
             'starting_capital_fiat_pct': round(starting_cap_fiat_ratio * 100, 2),
+            'trade_count': trade_count,
             'note': 'Simulated balances' if self._freqtrade.config['dry_run'] else ''
         }
 
@@ -698,8 +715,8 @@ class RPC:
             self._freqtrade.wallets.update()
             return {'result': f'Created sell order for trade {trade_id}.'}
 
-    def _rpc_forcebuy(self, pair: str, price: Optional[float],
-                      order_type: Optional[str] = None) -> Optional[Trade]:
+    def _rpc_forcebuy(self, pair: str, price: Optional[float], order_type: Optional[str] = None,
+                      stake_amount: Optional[float] = None) -> Optional[Trade]:
         """
         Handler for forcebuy <asset> <price>
         Buys a pair trade at the given or current price
@@ -721,16 +738,19 @@ class RPC:
         # check if pair already has an open pair
         trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
         if trade:
-            raise RPCException(f'position for {pair} already open - id: {trade.id}')
+            if not self._freqtrade.strategy.position_adjustment_enable:
+                raise RPCException(f'position for {pair} already open - id: {trade.id}')
 
-        # gen stake amount
-        stakeamount = self._freqtrade.wallets.get_trade_stake_amount(pair)
+        if not stake_amount:
+            # gen stake amount
+            stake_amount = self._freqtrade.wallets.get_trade_stake_amount(pair)
 
         # execute buy
         if not order_type:
             order_type = self._freqtrade.strategy.order_types.get(
                 'forcebuy', self._freqtrade.strategy.order_types['buy'])
-        if self._freqtrade.execute_entry(pair, stakeamount, price, ordertype=order_type):
+        if self._freqtrade.execute_entry(pair, stake_amount, price,
+                                         ordertype=order_type, trade=trade):
             Trade.commit()
             trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
             return trade
@@ -984,7 +1004,7 @@ class RPC:
 
     @staticmethod
     def _rpc_analysed_history_full(config, pair: str, timeframe: str,
-                                   timerange: str) -> Dict[str, Any]:
+                                   timerange: str, exchange) -> Dict[str, Any]:
         timerange_parsed = TimeRange.parse_timerange(timerange)
 
         _data = load_data(
@@ -999,7 +1019,7 @@ class RPC:
         from freqtrade.data.dataprovider import DataProvider
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
         strategy = StrategyResolver.load_strategy(config)
-        strategy.dp = DataProvider(config, exchange=None, pairlists=None)
+        strategy.dp = DataProvider(config, exchange=exchange, pairlists=None)
 
         df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
 

freqtrade/rpc/rpc_manager.py

@@ -85,12 +85,14 @@ class RPCManager:
         timeframe = config['timeframe']
         exchange_name = config['exchange']['name']
         strategy_name = config.get('strategy', '')
+        pos_adjust_enabled = 'On' if config['position_adjustment_enable'] else 'Off'
         self.send_msg({
             'type': RPCMessageType.STARTUP,
             'status': f'*Exchange:* `{exchange_name}`\n'
                       f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
                       f'*Minimum ROI:* `{minimal_roi}`\n'
                       f'*{"Trailing " if trailing_stop else ""}Stoploss:* `{stoploss}`\n'
+                      f'*Position adjustment:* `{pos_adjust_enabled}`\n'
                       f'*Timeframe:* `{timeframe}`\n'
                       f'*Strategy:* `{strategy_name}`'
         })

freqtrade/rpc/telegram.py

@@ -199,8 +199,8 @@ class Telegram(RPCHandler):
 
         self._updater.start_polling(
             bootstrap_retries=-1,
-            timeout=30,
-            read_latency=60,
+            timeout=20,
+            read_latency=60,  # Assumed transmission latency
             drop_pending_updates=True,
         )
         logger.info(
@@ -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:
@@ -764,14 +765,17 @@ class Telegram(RPCHandler):
                     f"(< {balance_dust_level} {result['stake']}):*\n"
                     f"\t`Est. {result['stake']}: "
                     f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
+            tc = result['trade_count'] > 0
+            stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else ''
+            fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else ''
 
             output += ("\n*Estimated Value*:\n"
                        f"\t`{result['stake']}: "
                        f"{round_coin_value(result['total'], result['stake'], False)}`"
-                       f" `({result['starting_capital_ratio']:.2%})`\n"
+                       f"{stake_improve}\n"
                        f"\t`{result['symbol']}: "
                        f"{round_coin_value(result['value'], result['symbol'], False)}`"
-                       f" `({result['starting_capital_fiat_ratio']:.2%})`\n")
+                       f"{fiat_val}\n")
             self._send_msg(output, reload_able=True, callback_path="update_balance",
                            query=update.callback_query)
         except RPCException as e:
@@ -1343,6 +1347,14 @@ class Telegram(RPCHandler):
         else:
             sl_info = f"*Stoploss:* `{val['stoploss']}`\n"
 
+        if val['position_adjustment_enable']:
+            pa_info = (
+                f"*Position adjustment:* On\n"
+                f"*Max enter position adjustment:* `{val['max_entry_position_adjustment']}`\n"
+            )
+        else:
+            pa_info = "*Position adjustment:* Off\n"
+
         self._send_msg(
             f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
             f"*Exchange:* `{val['exchange']}`\n"
@@ -1352,6 +1364,7 @@ class Telegram(RPCHandler):
             f"*Ask strategy:* ```\n{json.dumps(val['ask_strategy'])}```\n"
             f"*Bid strategy:* ```\n{json.dumps(val['bid_strategy'])}```\n"
             f"{sl_info}"
+            f"{pa_info}"
             f"*Timeframe:* `{val['timeframe']}`\n"
             f"*Strategy:* `{val['strategy']}`\n"
             f"*Current state:* `{val['state']}`"

freqtrade/strategy/interface.py

@@ -106,6 +106,10 @@ class IStrategy(ABC, HyperStrategyMixin):
     sell_profit_offset: float
     ignore_roi_if_buy_signal: bool
 
+    # Position adjustment is disabled by default
+    position_adjustment_enable: bool = False
+    max_entry_position_adjustment: int = -1
+
     # Number of seconds after which the candle will no longer result in a buy on expired candles
     ignore_buying_expired_candle_after: int = 0
 
@@ -185,7 +189,17 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return dataframe
 
-    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+    def bot_loop_start(self, **kwargs) -> None:
+        """
+        Called at the start of the bot iteration (one loop).
+        Might be used to perform pair-independent tasks
+        (e.g. gather some remote resource for comparison)
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        pass
+
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict,
+                          current_time: datetime, **kwargs) -> bool:
         """
         Check buy timeout function callback.
         This method can be used to override the buy-timeout.
@@ -198,12 +212,14 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param pair: Pair the trade is for
         :param trade: trade object.
         :param order: Order dictionary as returned from CCXT.
+        :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the buy-order is cancelled.
         """
         return False
 
-    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
+                           current_time: datetime, **kwargs) -> bool:
         """
         Check sell timeout function callback.
         This method can be used to override the sell-timeout.
@@ -216,22 +232,15 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param pair: Pair the trade is for
         :param trade: trade object.
         :param order: Order dictionary as returned from CCXT.
+        :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the sell-order is cancelled.
         """
         return False
 
-    def bot_loop_start(self, **kwargs) -> None:
-        """
-        Called at the start of the bot iteration (one loop).
-        Might be used to perform pair-independent tasks
-        (e.g. gather some remote resource for comparison)
-        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        """
-        pass
-
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            **kwargs) -> bool:
         """
         Called right before placing a buy order.
         Timing for this function is critical, so avoid doing heavy computations or
@@ -247,6 +256,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param rate: Rate that's going to be used when using limit orders
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
         :param current_time: datetime object, containing the current datetime
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the buy-order is placed on the exchange.
             False aborts the process
@@ -304,7 +314,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         return self.stoploss
 
     def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
-                           **kwargs) -> float:
+                           entry_tag: Optional[str], **kwargs) -> float:
         """
         Custom entry price logic, returning the new entry price.
 
@@ -315,6 +325,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param pair: Pair that's currently analyzed
         :param current_time: datetime object, containing the current datetime
         :param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return float: New entry price value if provided
         """
@@ -366,7 +377,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: float, max_stake: float,
-                            **kwargs) -> float:
+                            entry_tag: Optional[str], **kwargs) -> float:
         """
         Customize stake size for each new trade. This method is not called when edge module is
         enabled.
@@ -377,10 +388,34 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param proposed_stake: A stake amount proposed by the bot.
         :param min_stake: Minimal stake size allowed by exchange.
         :param max_stake: Balance available for trading.
+        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
         :return: A stake size, which is between min_stake and max_stake.
         """
         return proposed_stake
 
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs) -> Optional[float]:
+        """
+        Custom trade adjustment logic, returning the stake amount that a trade should be increased.
+        This means extra buy orders with additional fees.
+        Only called when `position_adjustment_enable` is set to True.
+
+        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+        When not implemented by a strategy, returns None
+
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Current buy rate.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param min_stake: Minimal stake size allowed by exchange.
+        :param max_stake: Balance available for trading.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return float: Stake amount to adjust your trade
+        """
+        return None
+
     def informative_pairs(self) -> ListPairsWithTimeframes:
         """
         Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -629,6 +664,9 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
         exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
+        # Tags can be None, which does not resolve to False.
+        buy_tag = buy_tag if isinstance(buy_tag, str) else None
+        exit_tag = exit_tag if isinstance(exit_tag, str) else None
 
         logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
                      latest['date'], pair, str(buy), str(sell))
@@ -703,23 +741,21 @@ 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}")
@@ -826,6 +862,29 @@ class IStrategy(ABC, HyperStrategyMixin):
         else:
             return current_profit > roi
 
+    def ft_check_timed_out(self, side: str, trade: Trade, order: Dict,
+                           current_time: datetime) -> bool:
+        """
+        FT Internal method.
+        Check if timeout is active, and if the order is still open and timed out
+        """
+        timeout = self.config.get('unfilledtimeout', {}).get(side)
+        ordertime = arrow.get(order['datetime']).datetime
+        if timeout is not None:
+            timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
+            timeout_kwargs = {timeout_unit: -timeout}
+            timeout_threshold = current_time + timedelta(**timeout_kwargs)
+            timedout = (order['status'] == 'open' and order['side'] == side
+                        and ordertime < timeout_threshold)
+            if timedout:
+                return True
+        time_method = self.check_sell_timeout if order['side'] == 'sell' else self.check_buy_timeout
+
+        return strategy_safe_wrapper(time_method,
+                                     default_retval=False)(
+                                        pair=trade.pair, trade=trade, order=order,
+                                        current_time=current_time)
+
     def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         """
         Populates indicators for given candle (OHLCV) data (for multiple pairs)

freqtrade/templates/base_config.json.j2

@@ -15,7 +15,8 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30,
+        "sell": 10,
+        "exit_timeout_count": 0,
         "unit": "minutes"
     },
     "bid_strategy": {

freqtrade/templates/subtemplates/strategy_methods_advanced.j2

@@ -12,9 +12,47 @@ def bot_loop_start(self, **kwargs) -> None:
     """
     pass
 
+def custom_entry_price(self, pair: str, current_time: 'datetime', proposed_rate: float,
+                       entry_tag: 'Optional[str]', **kwargs) -> float:
+    """
+    Custom entry price logic, returning the new entry price.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None, orderbook is used to set entry price
+
+    :param pair: Pair that's currently analyzed
+    :param current_time: datetime object, containing the current datetime
+    :param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: New entry price value if provided
+    """
+    return proposed_rate
+
+def custom_exit_price(self, pair: str, trade: 'Trade',
+                      current_time: 'datetime', proposed_rate: float,
+                      current_profit: float, **kwargs) -> float:
+    """
+    Custom exit price logic, returning the new exit price.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None, orderbook is used to set exit price
+
+    :param pair: Pair that's currently analyzed
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
+    :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: New exit price value if provided
+    """
+    return proposed_rate
+
 def custom_stake_amount(self, pair: str, current_time: 'datetime', current_rate: float,
                         proposed_stake: float, min_stake: float, max_stake: float,
-                        **kwargs) -> float:
+                        entry_tag: 'Optional[str]', **kwargs) -> float:
     """
     Customize stake size for each new trade. This method is not called when edge module is
     enabled.
@@ -25,6 +63,7 @@ def custom_stake_amount(self, pair: str, current_time: 'datetime', current_rate:
     :param proposed_stake: A stake amount proposed by the bot.
     :param min_stake: Minimal stake size allowed by exchange.
     :param max_stake: Balance available for trading.
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
     :return: A stake size, which is between min_stake and max_stake.
     """
     return proposed_stake
@@ -78,7 +117,8 @@ def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', curre
     return None
 
 def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                        time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
+                        time_in_force: str, current_time: 'datetime', entry_tag: 'Optional[str]',
+                        **kwargs) -> bool:
     """
     Called right before placing a buy order.
     Timing for this function is critical, so avoid doing heavy computations or
@@ -94,6 +134,7 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
     :param rate: Rate that's going to be used when using limit orders
     :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
     :param current_time: datetime object, containing the current datetime
+    :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
     :return bool: When True is returned, then the buy-order is placed on the exchange.
         False aborts the process
@@ -167,3 +208,26 @@ def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -
     :return bool: When True is returned, then the sell-order is cancelled.
     """
     return False
+
+def adjust_trade_position(self, trade: 'Trade', current_time: 'datetime',
+                          current_rate: float, current_profit: float, min_stake: float,
+                          max_stake: float, **kwargs) -> 'Optional[float]':
+    """
+    Custom trade adjustment logic, returning the stake amount that a trade should be increased.
+    This means extra buy orders with additional fees.
+    Only called when `position_adjustment_enable` is set to True.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, returns None
+
+    :param trade: trade object.
+    :param current_time: datetime object, containing the current datetime
+    :param current_rate: Current buy rate.
+    :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param min_stake: Minimal stake size allowed by exchange.
+    :param max_stake: Balance available for trading.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return float: Stake amount to adjust your trade
+    """
+    return None

freqtrade/wallets.py

@@ -3,7 +3,7 @@
 
 import logging
 from copy import deepcopy
-from typing import Any, Dict, NamedTuple
+from typing import Any, Dict, NamedTuple, Optional
 
 import arrow
 
@@ -238,14 +238,15 @@ class Wallets:
 
         return self._check_available_stake_amount(stake_amount, available_amount)
 
-    def validate_stake_amount(self, pair, stake_amount, min_stake_amount):
+    def validate_stake_amount(
+            self, pair: str, stake_amount: Optional[float], min_stake_amount: Optional[float]):
         if not stake_amount:
             logger.debug(f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.")
             return 0
 
         max_stake_amount = self.get_available_stake_amount()
 
-        if min_stake_amount > max_stake_amount:
+        if min_stake_amount is not None and min_stake_amount > max_stake_amount:
             if self._log:
                 logger.warning("Minimum stake amount > available balance.")
             return 0

pyproject.toml

@@ -23,6 +23,10 @@ exclude = '''
 line_length = 100
 multi_line_output=0
 lines_after_imports=2
+skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*"]
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
 
 [build-system]
 requires = ["setuptools >= 46.4.0", "wheel"]

requirements-dev.txt

@@ -5,25 +5,25 @@
 
 coveralls==3.3.1
 flake8==4.0.1
-flake8-tidy-imports==4.5.0
-mypy==0.930
+flake8-tidy-imports==4.6.0
+mypy==0.931
 pytest==6.2.5
-pytest-asyncio==0.16.0
+pytest-asyncio==0.17.2
 pytest-cov==3.0.0
-pytest-mock==3.6.1
+pytest-mock==3.7.0
 pytest-random-order==1.0.4
 isort==5.10.1
 # For datetime mocking
-time-machine==2.5.0
+time-machine==2.6.0
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==6.3.0
+nbconvert==6.4.1
 
 # mypy types
-types-cachetools==4.2.6
-types-filelock==3.2.1
-types-requests==2.26.2
-types-tabulate==0.8.3
+types-cachetools==4.2.9
+types-filelock==3.2.5
+types-requests==2.27.7
+types-tabulate==0.8.5
 
 # Extensions to datetime library
-types-python-dateutil==2.8.4
+types-python-dateutil==2.8.9

requirements-hyperopt.txt

@@ -7,5 +7,4 @@ scikit-learn==1.0.2
 scikit-optimize==0.9.0
 filelock==3.4.2
 joblib==1.1.0
-psutil==5.8.0
-progressbar2==3.55.0
+progressbar2==4.0.0

requirements.txt

@@ -1,24 +1,24 @@
-numpy==1.21.5
-pandas==1.3.5
+numpy==1.22.1
+pandas==1.4.0
 pandas-ta==0.3.14b
 
-ccxt==1.65.25
+ccxt==1.71.73
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==36.0.1
 aiohttp==3.8.1
-SQLAlchemy==1.4.29
-python-telegram-bot==13.9
-arrow==1.2.1
+SQLAlchemy==1.4.31
+python-telegram-bot==13.10
+arrow==1.2.2
 cachetools==4.2.2
-requests==2.26.0
-urllib3==1.26.7
-jsonschema==4.3.2
-TA-Lib==0.4.22
+requests==2.27.1
+urllib3==1.26.8
+jsonschema==4.4.0
+TA-Lib==0.4.24
 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
@@ -31,16 +31,16 @@ python-rapidjson==1.5
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.70.1
-uvicorn==0.16.0
+fastapi==0.73.0
+uvicorn==0.17.1
 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
 # Building config files interactively
 questionary==1.10.0
-prompt-toolkit==3.0.24
+prompt-toolkit==3.0.26
 # Extensions to datetime library
 python-dateutil==2.8.2

setup.cfg

@@ -14,9 +14,9 @@ classifiers =
     Environment :: Console
     Intended Audience :: Science/Research
     License :: OSI Approved :: GNU General Public License v3 (GPLv3)
-    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

@@ -10,7 +10,6 @@ hyperopt = [
     'filelock',
     'joblib',
     'progressbar2',
-    'psutil',
     ]
 
 develop = [
@@ -43,7 +42,7 @@ setup(
         ],
     install_requires=[
         # from requirements.txt
-        'ccxt>=1.60.11',
+        'ccxt>=1.66.32',
         'SQLAlchemy',
         'python-telegram-bot>=13.4',
         'arrow>=0.17.0',
@@ -69,6 +68,7 @@ setup(
         'blosc',
         'fastapi',
         'uvicorn',
+        'psutil',
         'pyjwt',
         'aiofiles'
     ],

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
     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 3.8 - 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
@@ -247,7 +246,7 @@ function help() {
     echo "	-p,--plot       Install dependencies for Plotting scripts."
 }
 
-# Verify if 3.7 or 3.8 is installed
+# Verify if 3.8+ is installed
 check_installed_python
 
 case $* in

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')
-    # and we want to match line against foobar in the tuple
-    return reduce(lambda a, b: a or b,
-                  filter(lambda x: x[2] == line, logs.record_tuples),
-                  False)
+    """Check if line is found on some caplog's message."""
+    return any(line == message for message in logs.messages)
 
 
 def log_has_re(line, logs):
-    return reduce(lambda a, b: a or b,
-                  filter(lambda x: re.match(line, x[2]), logs.record_tuples),
-                  False)
+    """Check if line matches some caplog's message."""
+    return any(re.match(line, message) for message in logs.messages)
+
+
+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 +8,15 @@ 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,
-                                       analyze_trade_parallelism, calculate_csum,
+from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism, calculate_csum,
                                        calculate_market_change, calculate_max_drawdown,
-                                       combine_dataframes_with_mean, create_cum_profit,
-                                       extract_trades_of_period, get_latest_backtest_filename,
-                                       get_latest_hyperopt_file, load_backtest_data, load_trades,
+                                       calculate_underwater, combine_dataframes_with_mean,
+                                       create_cum_profit, extract_trades_of_period,
+                                       get_latest_backtest_filename, get_latest_hyperopt_file,
+                                       load_backtest_data, load_backtest_metadata, 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
 
@@ -40,7 +41,7 @@ def test_get_latest_backtest_filename(testdatadir, mocker):
         get_latest_backtest_filename(testdatadir)
 
 
-def test_get_latest_hyperopt_file(testdatadir, mocker):
+def test_get_latest_hyperopt_file(testdatadir):
     res = get_latest_hyperopt_file(testdatadir / 'does_not_exist', 'testfile.pickle')
     assert res == testdatadir / 'does_not_exist/testfile.pickle'
 
@@ -50,21 +51,32 @@ def test_get_latest_hyperopt_file(testdatadir, mocker):
     res = get_latest_hyperopt_file(str(testdatadir.parent))
     assert res == testdatadir.parent / "hyperopt_results.pickle"
 
+    # Test with absolute path
+    with pytest.raises(
+        OperationalException,
+            match="--hyperopt-filename expects only the filename, not an absolute path."):
+        get_latest_hyperopt_file(str(testdatadir.parent), str(testdatadir.parent))
 
-def test_load_backtest_data_old_format(testdatadir):
 
-    filename = testdatadir / "backtest-result_test.json"
-    bt_data = load_backtest_data(filename)
-    assert isinstance(bt_data, DataFrame)
-    assert list(bt_data.columns) == BT_DATA_COLUMNS_OLD + ['profit_abs', 'profit_ratio']
-    assert len(bt_data) == 179
+def test_load_backtest_metadata(mocker, testdatadir):
+    res = load_backtest_metadata(testdatadir / 'nonexistant.file.json')
+    assert res == {}
 
-    # Test loading from string (must yield same result)
-    bt_data2 = load_backtest_data(str(filename))
-    assert bt_data.equals(bt_data2)
+    mocker.patch('freqtrade.data.btanalysis.get_backtest_metadata_filename')
+    mocker.patch('freqtrade.data.btanalysis.json_load', side_effect=Exception())
+    with pytest.raises(OperationalException,
+                       match=r"Unexpected error.*loading backtest metadata\."):
+        load_backtest_metadata(testdatadir / 'nonexistant.file.json')
 
-    with pytest.raises(ValueError, match=r"File .* does not exist\."):
-        load_backtest_data(str("filename") + "nofile")
+
+def test_load_backtest_data_old_format(testdatadir, mocker):
+
+    filename = testdatadir / "backtest-result_test222.json"
+    mocker.patch('freqtrade.data.btanalysis.load_backtest_stats', return_value=[])
+
+    with pytest.raises(OperationalException,
+                       match=r"Backtest-results with only trades data are no longer supported."):
+        load_backtest_data(filename)
 
 
 def test_load_backtest_data_new_format(testdatadir):
@@ -72,7 +84,7 @@ 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 +108,7 @@ 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 +179,8 @@ 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):
-    filename = testdatadir / "backtest-result_test.json"
+def test_analyze_trade_parallelism(testdatadir):
+    filename = testdatadir / "backtest-result_new.json"
     bt_data = load_backtest_data(filename)
 
     res = analyze_trade_parallelism(bt_data, "5m")
@@ -234,8 +246,15 @@ 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 +270,7 @@ 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 +292,31 @@ 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 lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
+    assert hdate == Timestamp('2018-01-25 01:30:00', 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 +344,13 @@ 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_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!!
@@ -1018,6 +1018,7 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
     assert order_book_l2_usd.call_count == 1
     assert order_closed['status'] == 'open'
     assert not order['fee']
+    assert order_closed['filled'] == 0
 
     order_book_l2_usd.reset_mock()
     order_closed['price'] = endprice
@@ -1025,6 +1026,8 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
     order_closed = exchange.fetch_dry_run_order(order['id'])
     assert order_closed['status'] == 'closed'
     assert order['fee']
+    assert order_closed['filled'] == 1
+    assert order_closed['filled'] == order_closed['amount']
 
     # Empty orderbook test
     mocker.patch('freqtrade.exchange.Exchange.fetch_l2_order_book',
@@ -1064,6 +1067,7 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amou
     assert order["type"] == "market"
     assert order["symbol"] == "LTC/USDT"
     assert order['status'] == 'closed'
+    assert order['filled'] == amount
     assert round(order["average"], 4) == round(endprice, 4)
 
 
@@ -1740,6 +1744,44 @@ 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,
-    trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
+    stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.002, use_sell_signal=True,
+    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
@@ -10,6 +11,7 @@ import pandas as pd
 import pytest
 from arrow import Arrow
 
+from freqtrade import constants
 from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_backtesting
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
@@ -19,6 +21,7 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import get_timerange
 from freqtrade.enums import RunMode, SellType
 from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.misc import get_strategy_run_id
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import LocalTrade
 from freqtrade.resolvers import StrategyResolver
@@ -648,7 +651,7 @@ 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,
@@ -760,6 +763,8 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
     # While buy-signals are unrealistic, running backtesting
     # over and over again should not cause different results
     for [contour, numres] in tests:
+        # Debug output for random test failure
+        print(f"{contour}, {numres}")
         assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
 
 
@@ -887,7 +892,7 @@ 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 +914,7 @@ 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,
@@ -1238,3 +1243,130 @@ def test_backtest_start_multi_strat_nomock_detail(default_conf, mocker,
     assert 'BACKTESTING REPORT' in captured.out
     assert 'SELL REASON STATS' in captured.out
     assert 'LEFT OPEN TRADES REPORT' in captured.out
+
+
+@pytest.mark.filterwarnings("ignore:deprecated")
+@pytest.mark.parametrize('run_id', ['2', 'changed'])
+@pytest.mark.parametrize('start_delta', [{'days': 0}, {'days': 1}, {'weeks': 1}, {'weeks': 4}])
+@pytest.mark.parametrize('cache', constants.BACKTEST_CACHE_AGE)
+def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testdatadir, run_id,
+                                            start_delta, cache):
+    default_conf.update({
+        "use_sell_signal": True,
+        "sell_profit_only": False,
+        "sell_profit_offset": 0.0,
+        "ignore_roi_if_buy_signal": False,
+    })
+    patch_exchange(mocker)
+    backtestmock = MagicMock(return_value={
+        'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
+        'config': default_conf,
+        'locks': [],
+        'rejected_signals': 20,
+        'final_balance': 1000,
+    })
+    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
+                 PropertyMock(return_value=['UNITTEST/BTC']))
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
+    mocker.patch('freqtrade.optimize.backtesting.show_backtest_results', MagicMock())
+
+    now = min_backtest_date = datetime.now(tz=timezone.utc)
+    start_time = now - timedelta(**start_delta) + timedelta(hours=1)
+    if cache == 'none':
+        min_backtest_date = now + timedelta(days=1)
+    elif cache == 'day':
+        min_backtest_date = now - timedelta(days=1)
+    elif cache == 'week':
+        min_backtest_date = now - timedelta(weeks=1)
+    elif cache == 'month':
+        min_backtest_date = now - timedelta(weeks=4)
+    load_backtest_metadata = MagicMock(return_value={
+        'StrategyTestV2': {'run_id': '1', 'backtest_start_time': now.timestamp()},
+        'TestStrategyLegacyV1': {'run_id': run_id, 'backtest_start_time': start_time.timestamp()}
+    })
+    load_backtest_stats = MagicMock(side_effect=[
+        {
+            'metadata': {'StrategyTestV2': {'run_id': '1'}},
+            'strategy': {'StrategyTestV2': {}},
+            'strategy_comparison': [{'key': 'StrategyTestV2'}]
+        },
+        {
+            'metadata': {'TestStrategyLegacyV1': {'run_id': '2'}},
+            'strategy': {'TestStrategyLegacyV1': {}},
+            'strategy_comparison': [{'key': 'TestStrategyLegacyV1'}]
+        }
+    ])
+    mocker.patch('pathlib.Path.glob', return_value=[
+        Path(datetime.strftime(datetime.now(), 'backtest-result-%Y-%m-%d_%H-%M-%S.json'))])
+    mocker.patch.multiple('freqtrade.data.btanalysis',
+                          load_backtest_metadata=load_backtest_metadata,
+                          load_backtest_stats=load_backtest_stats)
+    mocker.patch('freqtrade.optimize.backtesting.get_strategy_run_id', side_effect=['1', '2', '2'])
+
+    patched_configuration_load_config_file(mocker, default_conf)
+
+    args = [
+        'backtesting',
+        '--config', 'config.json',
+        '--datadir', str(testdatadir),
+        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
+        '--timeframe', '1m',
+        '--timerange', '1510694220-1510700340',
+        '--enable-position-stacking',
+        '--disable-max-market-positions',
+        '--cache', cache,
+        '--strategy-list',
+        'StrategyTestV2',
+        'TestStrategyLegacyV1',
+    ]
+    args = get_args(args)
+    start_backtesting(args)
+
+    # check the logs, that will contain the backtest result
+    exists = [
+        'Parameter -i/--timeframe detected ... Using timeframe: 1m ...',
+        'Parameter --timerange detected: 1510694220-1510700340 ...',
+        f'Using data directory: {testdatadir} ...',
+        'Loading data from 2017-11-14 20:57:00 '
+        'up to 2017-11-14 22:58:00 (0 days).',
+        'Parameter --enable-position-stacking detected ...',
+    ]
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+    if cache == 'none':
+        assert backtestmock.call_count == 2
+        exists = [
+            'Running backtesting for Strategy StrategyTestV2',
+            'Running backtesting for Strategy TestStrategyLegacyV1',
+            'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
+            'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
+        ]
+    elif run_id == '2' and min_backtest_date < start_time:
+        assert backtestmock.call_count == 0
+        exists = [
+            'Reusing result of previous backtest for StrategyTestV2',
+            'Reusing result of previous backtest for TestStrategyLegacyV1',
+        ]
+    else:
+        exists = [
+            'Reusing result of previous backtest for StrategyTestV2',
+            'Running backtesting for Strategy TestStrategyLegacyV1',
+            'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
+            'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
+        ]
+        assert backtestmock.call_count == 1
+
+    for line in exists:
+        assert log_has(line, caplog)
+
+
+def test_get_strategy_run_id(default_conf_usdt):
+    default_conf_usdt.update({
+        'strategy': 'StrategyTestV2',
+        'max_open_trades': float('inf')
+        })
+    strategy = StrategyResolver.load_strategy(default_conf_usdt)
+    x = get_strategy_run_id(strategy)
+    assert isinstance(x, str)

tests/optimize/test_backtesting_adjust_position.py

@@ -0,0 +1,82 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+
+from copy import deepcopy
+
+import pandas as pd
+from arrow import Arrow
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data import history
+from freqtrade.data.history import get_timerange
+from freqtrade.enums import SellType
+from freqtrade.optimize.backtesting import Backtesting
+from tests.conftest import patch_exchange
+
+
+def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf['use_sell_signal'] = False
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
+    patch_exchange(mocker)
+    default_conf.update({
+        "stake_amount": 100.0,
+        "dry_run_wallet": 1000.0,
+        "strategy": "StrategyTestV2"
+    })
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = 'UNITTEST/BTC'
+    timerange = TimeRange('date', None, 1517227800, 0)
+    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
+                             timerange=timerange)
+    backtesting.strategy.position_adjustment_enable = True
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    result = backtesting.backtest(
+        processed=deepcopy(processed),
+        start_date=min_date,
+        end_date=max_date,
+        max_open_trades=10,
+        position_stacking=False,
+    )
+    results = result['results']
+    assert not results.empty
+    assert len(results) == 2
+
+    expected = pd.DataFrame(
+        {'pair': [pair, pair],
+         'stake_amount': [500.0, 100.0],
+         'amount': [4806.87657523, 970.63960782],
+         'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
+                                      Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
+                                     ),
+         'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 00, 0).datetime,
+                                       Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
+         'open_rate': [0.10401764894444211, 0.10302485],
+         'close_rate': [0.10453904066847439, 0.103541],
+         'fee_open': [0.0025, 0.0025],
+         'fee_close': [0.0025, 0.0025],
+         'trade_duration': [200, 40],
+         'profit_ratio': [0.0, 0.0],
+         'profit_abs': [0.0, 0.0],
+         'sell_reason': [SellType.ROI.value, SellType.ROI.value],
+         'initial_stop_loss_abs': [0.0940005, 0.09272236],
+         'initial_stop_loss_ratio': [-0.1, -0.1],
+         'stop_loss_abs': [0.0940005, 0.09272236],
+         'stop_loss_ratio': [-0.1, -0.1],
+         'min_rate': [0.10370188, 0.10300000000000001],
+         'max_rate': [0.10481985, 0.1038888],
+         'is_open': [False, False],
+         'buy_tag': [None, None],
+         })
+    pd.testing.assert_frame_equal(results, expected)
+    data_pair = processed[pair]
+    for _, t in results.iterrows():
+        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
+        # Check open trade rate alignes to open rate
+        assert ln is not None
+        # check close trade rate alignes to close rate or is between high and low
+        ln = data_pair.loc[data_pair["date"] == t["close_date"]]
+        assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
+                round(ln.iloc[0]["low"], 6) < round(
+                t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))

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)
 
@@ -168,8 +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:
-    hyperopt_conf['user_data_dir'] = Path("tests")
+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(
@@ -178,7 +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',
@@ -222,14 +244,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     hyperopt.print_results(
         {
             'loss': 1,
-            'results_metrics':
-                {
-                    'trade_count': 1,
-                    'avg_profit': 0.1,
-                    'total_profit': 0.001,
-                    'profit': 1.0,
-                    'duration': 20.0
-                },
+            'results_metrics': generate_result_metrics(),
             'total_profit': 0,
             'current_epoch': 2,  # This starts from 1 (in a human-friendly manner)
             'is_initial_point': False,
@@ -238,7 +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:
@@ -295,14 +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':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            },
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -359,7 +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)
 
@@ -528,14 +536,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
                 'roi': {}, 'stoploss': {'stoploss': None},
                 'trailing': {'trailing_stop': None}
             },
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -584,14 +585,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
                 'sell': {'sell-mfi-value': None},
                 'roi': {}, 'stoploss': {'stoploss': None}
             },
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -629,14 +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':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -676,14 +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':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -756,14 +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':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -805,14 +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':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)

tests/optimize/test_hyperopt_tools.py

@@ -10,7 +10,7 @@ import rapidjson
 from freqtrade.constants import FTHYPT_FILEVERSION
 from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
-from tests.conftest import log_has
+from tests.conftest import log_has, log_has_re
 
 
 # Functions for recurrent object patching
@@ -24,6 +24,7 @@ def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
     hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
 
     hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
+    assert log_has_re("Hyperopt file .* not found.", caplog)
     assert hyperopt_epochs == ([], 0)
 
     # Test writing to temp dir and reading again