Merge pull request #8076 from freqtrade/new_release

New release 2023.1

.github/workflows/ci.yml

@@ -148,6 +148,19 @@ jobs:
       if: runner.os == 'macOS'
       run: |
         brew update
+        # homebrew fails to update python due to unlinking failures
+        # https://github.com/actions/runner-images/issues/6817
+        rm /usr/local/bin/2to3 || true
+        rm /usr/local/bin/2to3-3.11 || true
+        rm /usr/local/bin/idle3 || true
+        rm /usr/local/bin/idle3.11 || true
+        rm /usr/local/bin/pydoc3 || true
+        rm /usr/local/bin/pydoc3.11 || true
+        rm /usr/local/bin/python3 || true
+        rm /usr/local/bin/python3.11 || true
+        rm /usr/local/bin/python3-config || true
+        rm /usr/local/bin/python3.11-config || true
+
         brew install hdf5 c-blosc
         python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
@@ -347,6 +360,8 @@ jobs:
         pip install -e .
 
     - name: Tests incl. ccxt compatibility tests
+      env:
+        CI_WEB_PROXY: http://152.67.78.211:13128
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
 

.pre-commit-config.yaml

@@ -8,16 +8,16 @@ repos:
         # stages: [push]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v0.942"
+    rev: "v0.991"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
           - types-cachetools==5.2.1
           - types-filelock==3.2.7
-          - types-requests==2.28.11.5
+          - types-requests==2.28.11.8
           - types-tabulate==0.9.0.0
-          - types-python-dateutil==2.8.19.4
+          - types-python-dateutil==2.8.19.6
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort

README.md

@@ -1,6 +1,7 @@
 # ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
 
 [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Documentation](https://readthedocs.org/projects/freqtrade/badge/)](https://www.freqtrade.io)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)

build_helpers/publish_docker_arm64.sh

@@ -70,20 +70,21 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
 # Otherwise installation might fail.
 echo "create manifests"
 
-docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
+docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
 docker manifest push -p ${IMAGE_NAME}:${TAG}
 
-docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
+docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
 
-docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
 
-docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} ${CACHE_IMAGE}:${TAG_FREQAI_RL}
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
 
 # Tag as latest for develop builds
 if [ "${TAG}" = "develop" ]; then
+    echo 'Tagging image as latest'
     docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
     docker manifest push -p ${IMAGE_NAME}:latest
 fi

build_helpers/publish_docker_multi.sh

@@ -26,7 +26,10 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
         --cache-to=type=registry,ref=${CACHE_TAG} \
         -f docker/Dockerfile.armhf \
         --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG_PI} --push .
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
 else
     echo "event ${GITHUB_EVENT_NAME}: building with cache"
     # Build regular image
@@ -35,12 +38,16 @@ else
 
     # Pull last build to avoid rebuilding the whole image
     # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
+    # disable provenance due to https://github.com/docker/buildx/issues/1509
     docker buildx build \
         --cache-from=type=registry,ref=${CACHE_TAG} \
         --cache-to=type=registry,ref=${CACHE_TAG} \
         -f docker/Dockerfile.armhf \
         --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG_PI} --push .
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
 fi
 
 if [ $? -ne 0 ]; then
@@ -68,12 +75,10 @@ fi
 
 docker images
 
-docker push ${CACHE_IMAGE}
+docker push ${CACHE_IMAGE}:$TAG
 docker push ${CACHE_IMAGE}:$TAG_PLOT
 docker push ${CACHE_IMAGE}:$TAG_FREQAI
 docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
-docker push ${CACHE_IMAGE}:$TAG
-
 
 docker images
 

config_examples/config_binance.example.json

@@ -59,20 +59,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

config_examples/config_bittrex.example.json

@@ -56,20 +56,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

config_examples/config_freqai.example.json

@@ -21,8 +21,8 @@
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
-            "1INCH/USDT",
-            "ALGO/USDT"
+            "1INCH/USDT:USDT",
+            "ALGO/USDT:USDT"
         ],
         "pair_blacklist": []
     },
@@ -60,8 +60,8 @@
                 "1h"
             ],
             "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
+                "BTC/USDT:USDT",
+                "ETH/USDT:USDT"
             ],
             "label_period_candles": 20,
             "include_shifted_candles": 2,

config_examples/config_kraken.example.json

@@ -64,20 +64,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

docs/advanced-backtesting.md

@@ -32,7 +32,7 @@ To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-an
 with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
 
 ``` bash
-freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
 ```
 
 This command will read from the last backtesting results. The `--analysis-groups` option is
@@ -43,6 +43,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
 * 2: profit summaries grouped by enter_tag and exit_tag
 * 3: profit summaries grouped by pair and enter_tag
 * 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+* 5: profit summaries grouped by exit_tag
 
 More options are available by running with the `-h` option.
 

docs/advanced-hyperopt.md

@@ -75,7 +75,7 @@ This function needs to return a floating point number (`float`). Smaller numbers
 
 ## Overriding pre-defined spaces
 
-To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
+To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
 
 ```python
 from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
@@ -123,6 +123,12 @@ class MyAwesomeStrategy(IStrategy):
 
                 Categorical([True, False], name='trailing_only_offset_is_reached'),
         ]
+
+        # Define a custom max_open_trades space
+        def max_open_trades_space(self) -> List[Dimension]:
+            return [
+                Integer(-1, 10, name='max_open_trades'),
+            ]
 ```
 
 !!! Note

docs/backtesting.md

@@ -300,7 +300,11 @@ A backtesting result will look like that:
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
 - `CAGR %`: Compound annual growth rate.
+- `Sortino`: Annualized Sortino ratio.
+- `Sharpe`: Annualized Sharpe ratio.
+- `Calmar`: Annualized Calmar ratio.
 - `Profit factor`: profit / loss.
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.

docs/bot-basics.md

@@ -75,3 +75,7 @@ This loop will be repeated again and again until the bot is stopped.
 
 !!! Note
     Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
+
+!!! Warning "Callback call frequency"
+    Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
+    Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

docs/configuration.md

@@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
 
 You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
 
-If you used the [Quick start](installation.md/#quick-start) method for installing
+If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
 the bot, the installation script should have already created the default configuration file (`config.json`) for you.
 
 If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@@ -134,7 +134,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 
 |  Parameter | Description |
 |------------|-------------|
-| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
 | `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
 | `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
 | `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
@@ -263,6 +263,7 @@ Values set in the configuration file always overwrite values set in the strategy
 * `minimal_roi`
 * `timeframe`
 * `stoploss`
+* `max_open_trades`
 * `trailing_stop`
 * `trailing_stop_positive`
 * `trailing_stop_positive_offset`

docs/exchanges.md

@@ -75,6 +75,25 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
 * [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
 * [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
 
+### Binance RSA keys
+
+Freqtrade supports binance RSA API keys.
+
+We recommend to use them as environment variable.
+
+``` bash
+export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
+```
+
+They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
+
+``` json
+// ...
+ "key": "<someapikey>",
+ "secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
+// ...
+```
+
 ### Binance Futures
 
 Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.

docs/freqai-configuration.md

@@ -43,116 +43,113 @@ The FreqAI strategy requires including the following lines of code in the standa
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
-        # the model will return all labels created by user in `populate_any_indicators`
+        # the model will return all labels created by user in `set_freqai_labels()`
         # (& appended targets), an indication of whether or not the prediction should be accepted,
         # the target mean/std values for each of the labels created by user in
-        # `populate_any_indicators()` for each training period.
+        # `feature_engineering_*` for each training period.
 
         dataframe = self.freqai.start(dataframe, metadata, self)
 
         return dataframe
 
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + pair `
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
+        return dataframe
 
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
 
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
 
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
+        All features must be prepended with `%` to be recognized by FreqAI internals.
 
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
-
-        return df
-
-
 ```
 
-Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
-
-Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
+Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
 
 !!! Note
     The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
 
 !!! Note
-    Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
-    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
-    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
-
-    ```python
-        def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
-
-            ...
-
-            # Add generalized indicators here (because in live, it will call only this function to populate
-            # indicators for retraining). Notice how we ensure not to add them multiple times by associating
-            # these generalized indicators to the basepair/timeframe
-            if set_generalized_indicators:
-                df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
-                df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
-
-                # user adds targets here by prepending them with &- (see convention below)
-                # If user wishes to use multiple targets, a multioutput prediction model
-                # needs to be used such as templates/CatboostPredictionMultiModel.py
-                df["&-s_close"] = (
-                    df["close"]
-                    .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .mean()
-                    / df["close"]
-                    - 1
-                    )
-    ```
-
-    Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
+    Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
+    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
+    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
 
 ## Important dataframe key patterns
 
@@ -160,11 +157,11 @@ Below are the values you can expect to include/use inside a typical strategy dat
 
 |  DataFrame Key | Description |
 |------------|-------------|
-| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
+| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
 | `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
 | `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
 | `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
-| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
+| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
 
 ## Setting the `startup_candle_count`
 
@@ -239,20 +236,3 @@ If you want to predict multiple targets you must specify all labels in the same
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
 ```
-
-### Convolutional Neural Network model
-
-The `CNNPredictionModel` is a non-linear regression based on `Tensorflow` which follows very similar configuration to the other regressors. Feature engineering and label creation remains the same as highlighted [here](#building-a-freqai-strategy) and [here](#setting-model-targets). Control of the model is focused in the `model_training_parameters` configuration dictionary, which accepts any hyperparameter available to the CNN `fit()` function of Tensorflow [more here](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit). For example, this is where the `epochs` and `batch_size` are controlled:
-
-```json
-    "model_training_parameters" : {
-        "batch_size": 64,
-        "epochs": 10,
-        "verbose": "auto",
-        "shuffle": false,
-        "workers": 1,
-        "use_multiprocessing": false
-    }
-```
-
-Running the `CNNPredictionModel` is the same as other regressors: `--freqaimodel CNNPredictionModel`.

docs/freqai-feature-engineering.md

@@ -2,96 +2,130 @@
 
 ## Defining the features
 
-Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
+Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
 
-!!! Note
-    Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
+|  Function | Description |
+|---------------|-------------|
+| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
+| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g. day of the week).
+| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
 
 Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
 
-It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
+It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
 
 ```python
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        Function designed to automatically generate, name, and merge features
-        from user-indicated timeframes in the configuration file. The user controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + pair `
-        (see convention below). I.e., the user should not prepend any supporting metrics
-        (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            bollinger = qtpylib.bollinger_bands(
-                qtpylib.typical_price(informative), window=t, stds=2.2
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
-            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
-            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
-            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
-
-            informative[f"%-{pair}bb_width-period_{t}"] = (
-                informative[f"{pair}bb_upperband-period_{t}"]
-                - informative[f"{pair}bb_lowerband-period_{t}"]
-            ) / informative[f"{pair}bb_middleband-period_{t}"]
-            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
-                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
-            )
-
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
-            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-        return df
+        
+        return dataframe
 ```
 
 In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
@@ -118,13 +152,13 @@ After having defined the `base features`, the next step is to expand upon them u
     }
 ```
 
-The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
+The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
 
-You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
+You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
 
 `include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
 
-In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
+In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
  $= 3 * 3 * 3 * 2 * 2 = 108$.
 
 ### Returning additional info from training

docs/freqai-parameter-table.md

@@ -15,7 +15,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
 | `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
 | `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
-| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
+| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
 | `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
 | `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
@@ -29,12 +29,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |------------|-------------|
 |  |  **Feature parameters within the `freqai.feature_parameters` sub dictionary**
 | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
-| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
-| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
-| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
+| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
+| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
 | `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
 | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
-| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
+| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
 | `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
 | `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
 | `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
@@ -89,6 +89,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |  Parameter | Description |
 |------------|-------------|
 |  |  **Extraneous parameters**
-| `freqai.keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag should be activated so that the model save/loading follows Keras standards. If the the provided `CNNPredictionModel` is used, then this is handled automatically. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `freqai.keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
 | `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

docs/freqai-reinforcement-learning.md

@@ -34,65 +34,36 @@ Setting up and running a Reinforcement Learning model is the same as running a R
 freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
 ```
 
-where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
+where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
 
 ```python
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        More details about feature engineering available:
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
-
-        # The following raw price values are necessary for RL models
-        informative[f"%-{pair}raw_close"] = informative["close"]
-        informative[f"%-{pair}raw_open"] = informative["open"]
-        informative[f"%-{pair}raw_high"] = informative["high"]
-        informative[f"%-{pair}raw_low"] = informative["low"]
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-
-            # For RL, there are no direct targets to set. This is filler (neutral)
-            # until the agent sends an action.
-            df["&-action"] = 0
-
-        return df
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        # For RL, there are no direct targets to set. This is filler (neutral)
+        # until the agent sends an action.
+        dataframe["&-action"] = 0
 ```
 
 Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
 
 ```python
+    def feature_engineering_standard(self, dataframe, **kwargs):
         # The following features are necessary for RL models
-        informative[f"%-{pair}raw_close"] = informative["close"]
-        informative[f"%-{pair}raw_open"] = informative["open"]
-        informative[f"%-{pair}raw_high"] = informative["high"]
-        informative[f"%-{pair}raw_low"] = informative["low"]
+        dataframe[f"%-raw_close"] = dataframe["close"]
+        dataframe[f"%-raw_open"] = dataframe["open"]
+        dataframe[f"%-raw_high"] = dataframe["high"]
+        dataframe[f"%-raw_low"] = dataframe["low"]
 ```
 
 Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
@@ -272,15 +243,14 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
 !!! Note
     The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
 
-
 ### Choosing a base environment
 
-FreqAI provides two base environments, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 4 or 5 actions. In the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Meanwhile, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
+FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
 
 * the actions available in the `calculate_reward`
 * the actions consumed by the user strategy
 
-Both of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.
+All of the FreqAI provided environments inherit from an action/position agnostic environment object called the `BaseEnvironment`, which contains all shared logic. The architecture is designed to be easily customized. The simplest customization is the `calculate_reward()` (see details [here](#creating-a-custom-reward-function)). However, the customizations can be further extended into any of the functions inside the environment. You can do this by simply overriding those functions inside your `MyRLEnv` in the prediction model file. Or for more advanced customizations, it is encouraged to create an entirely new environment inherited from `BaseEnvironment`.
 
 !!! Note
-    FreqAI does not provide by default, a long-only training environment. However, creating one should be as simple as copy-pasting one of the built in environments and removing the `short` actions (and all associated references to those).
+    Only the `Base3ActionRLEnv` can do long-only training/trading (set the user strategy attribute `can_short = False`).

docs/freqai-running.md

@@ -67,6 +67,10 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
     *want* to retrain a new model with the same config file, you should simply change the `identifier`.
     This way, you can return to using any model you wish by simply specifying the `identifier`.
 
+!!! Note
+    Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
+    More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
+
 ---
 
 ### Saving prediction data
@@ -135,7 +139,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt
 `hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
 
 - The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
-- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
+- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
 - The backtesting instructions also apply to hyperopt.
 
 The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.

docs/freqai.md

@@ -72,11 +72,25 @@ pip install -r requirements-freqai.txt
 
 If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
-
 ### FreqAI position in open-source machine learning landscape
 
 Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 
 
+### Citing FreqAI
+
+FreqAI is [published in the Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.04864). If you find FreqAI useful in your research, please use the following citation:
+
+```bibtex
+@article{Caulk2022, 
+    doi = {10.21105/joss.04864},
+    url = {https://doi.org/10.21105/joss.04864},
+    year = {2022}, publisher = {The Open Journal},
+    volume = {7}, number = {80}, pages = {4864},
+    author = {Robert A. Caulk and Elin Törnquist and Matthias Voppichler and Andrew R. Lawless and Ryan McMullan and Wagner Costa Santos and Timothy C. Pogue and Johan van der Vlugt and Stefan P. Gehring and Pascal Schmidt},
+    title = {FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts},
+    journal = {Journal of Open Source Software} } 
+```
+
 ## Common pitfalls
 
 FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
@@ -99,6 +113,8 @@ Code review and software architecture brainstorming:
 
 Software development:
 Wagner Costa @wagnercosta
+Emre Suzen @aemr3
+Timothy Pogue @wizrds
 
 Beta testing and bug reporting:
-Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza, Timothy Pogue @wizrds
+Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza

docs/hyperopt.md

@@ -50,7 +50,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--eps] [--dmmp] [--enable-protections]
                           [--dry-run-wallet DRY_RUN_WALLET]
                           [--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
                           [--hyperopt-loss NAME] [--disable-param-export]
@@ -96,7 +96,7 @@ optional arguments:
                         Specify detail timeframe for backtesting (`1m`, `5m`,
                         `30m`, `1h`, `1d`).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
+  --spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
                         Specify which parameters to hyperopt. Space-separated
                         list.
   --print-all           Print all results, not only the best ones.
@@ -180,6 +180,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
 * `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
 * `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
 * `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
+* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
 
 !!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
     You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
@@ -365,7 +366,7 @@ class MyAwesomeStrategy(IStrategy):
     timeframe = '15m'
     minimal_roi = {
         "0":  0.10
-    },
+    }
     # Define the parameter spaces
     buy_ema_short = IntParameter(3, 50, default=5)
     buy_ema_long = IntParameter(15, 200, default=50)
@@ -400,7 +401,7 @@ class MyAwesomeStrategy(IStrategy):
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-          conditions = []
+        conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
             ))
@@ -643,6 +644,7 @@ Legal values are:
 * `roi`: just optimize the minimal profit table for your strategy
 * `stoploss`: search for the best stoploss value
 * `trailing`: search for the best trailing stop values
+* `trades`: search for the best max open trades values
 * `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
 * `default`: `all` except `trailing` and `protection`
 * space-separated list of any of the above values for example `--spaces roi stoploss`
@@ -916,5 +918,5 @@ Once the optimized strategy has been implemented into your strategy, you should
 To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
 
 Should results not match, please double-check to make sure you transferred all conditions correctly.
-Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
-You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
+Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy.
+You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).

docs/includes/pairlists.md

@@ -23,6 +23,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
 * [`ProducerPairList`](#producerpairlist)
+* [`RemotePairList`](#remotepairlist)
 * [`AgeFilter`](#agefilter)
 * [`OffsetFilter`](#offsetfilter)
 * [`PerformanceFilter`](#performancefilter)
@@ -173,6 +174,48 @@ You can limit the length of the pairlist with the optional parameter `number_ass
     `ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
     Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
 
+#### RemotePairList
+
+It allows the user to fetch a pairlist from a remote server or a locally stored json file within the freqtrade directory, enabling dynamic updates and customization of the trading pairlist.
+
+The RemotePairList is defined in the pairlists section of the configuration settings. It uses the following configuration options:
+
+```json
+"pairlists": [
+    {
+        "method": "RemotePairList",
+        "pairlist_url": "https://example.com/pairlist",
+        "number_assets": 10,
+        "refresh_period": 1800,
+        "keep_pairlist_on_failure": true,
+        "read_timeout": 60,
+        "bearer_token": "my-bearer-token"
+    }
+]
+```
+
+The `pairlist_url` option specifies the URL of the remote server where the pairlist is located, or the path to a local file (if file:/// is prepended). This allows the user to use either a remote server or a local file as the source for the pairlist.
+
+The user is responsible for providing a server or local file that returns a JSON object with the following structure:
+
+```json
+{
+    "pairs": ["XRP/USDT", "ETH/USDT", "LTC/USDT"],
+    "refresh_period": 1800,
+}
+```
+
+The `pairs` property should contain a list of strings with the trading pairs to be used by the bot. The `refresh_period` property is optional and specifies the number of seconds that the pairlist should be cached before being refreshed.
+
+The optional `keep_pairlist_on_failure` specifies whether the previous received pairlist should be used if the remote server is not reachable or returns an error. The default value is true.
+
+The optional `read_timeout` specifies the maximum amount of time (in seconds) to wait for a response from the remote source, The default value is 60.
+
+The optional `bearer_token` will be included in the requests Authorization Header.
+
+!!! Note
+    In case of a server error the last received pairlist will be kept if `keep_pairlist_on_failure` is set to true, when set to false a empty pairlist is returned.
+
 #### AgeFilter
 
 Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).

docs/index.md

@@ -1,6 +1,7 @@
 ![freqtrade](assets/freqtrade_poweredby.svg)
 
 [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.04864/status.svg)](https://doi.org/10.21105/joss.04864)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
 [![Maintainability](https://api.codeclimate.com/v1/badges/5737e6d668200b7518ff/maintainability)](https://codeclimate.com/github/freqtrade/freqtrade/maintainability)
 

docs/leverage.md

@@ -67,8 +67,6 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
 Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
 A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
 
-Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
-
 ### Margin mode
 
 On top of `trading_mode` - you will also have to configure your `margin_mode`.
@@ -92,6 +90,8 @@ One account is used to share collateral between markets (trading pairs). Margin
 "margin_mode": "cross"
 ```
 
+Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
+
 ## Set leverage to use
 
 Different strategies and risk profiles will require different levels of leverage.

docs/overrides/main.html

@@ -11,9 +11,6 @@
   {% endif %}
   <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" {{ hidden }}>
     <div class="md-sidebar__scrollwrap">
-      <div id="widget-wrapper">
-
-      </div>
       <div class="md-sidebar__inner">
         {% include "partials/nav.html" %}
       </div>
@@ -44,25 +41,4 @@
     <script src="https://code.jquery.com/jquery-3.4.1.min.js"
       integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
 
-    <!-- Load binance SDK -->
-    <script async defer src="https://public.bnbstatic.com/static/js/broker-sdk/broker-sdk@1.0.0.min.js"></script>
-
-    <script>
-      window.onload = function () {
-        var sidebar = document.getElementById('widget-wrapper')
-        var newDiv = document.createElement("div");
-        newDiv.id = "widget";
-        try {
-          sidebar.prepend(newDiv);
-
-          window.binanceBrokerPortalSdk.initBrokerSDK('#widget', {
-            apiHost: 'https://www.binance.com',
-            brokerId: 'R4BD3S82',
-            slideTime: 4e4,
-          });
-        } catch(err) {
-          console.log(err)
-        }
-      }
-    </script>
 {% endblock %}

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.4.2
-mkdocs-material==8.5.11
+mkdocs-material==9.0.5
 mdx_truly_sane_lists==1.3
-pymdown-extensions==9.9
+pymdown-extensions==9.9.1
 jinja2==3.1.2

docs/strategy-advanced.md

@@ -80,7 +80,7 @@ class AwesomeStrategy(IStrategy):
 ## Enter Tag
 
 When your strategy has multiple buy signals, you can name the signal that triggered.
-Then you can access you buy signal on `custom_exit`
+Then you can access your buy signal on `custom_exit`
 
 ```python
 def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

docs/strategy-callbacks.md

@@ -659,6 +659,7 @@ Position adjustments will always be applied in the direction of the trade, so a
 
 !!! Warning "Backtesting"
     During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
+    This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
 
 ``` python
 from freqtrade.persistence import Trade
@@ -827,7 +828,7 @@ class AwesomeStrategy(IStrategy):
 
         """
         # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
-        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
+        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
             # just cancel the order if it has been filled more than half of the amount
             if order.filled > order.remaining:
                 return None

docs/strategy-customization.md

@@ -989,38 +989,18 @@ from freqtrade.persistence import Trade
 The following example queries for the current pair and trades from today, however other filters can easily be added.
 
 ``` python
-if self.config['runmode'].value in ('live', 'dry_run'):
-    trades = Trade.get_trades([Trade.pair == metadata['pair'],
-                               Trade.open_date > datetime.utcnow() - timedelta(days=1),
-                               Trade.is_open.is_(False),
-                ]).order_by(Trade.close_date).all()
-    # Summarize profit for this pair.
-    curdayprofit = sum(trade.close_profit for trade in trades)
+trades = Trade.get_trades_proxy(pair=metadata['pair'],
+                                open_date=datetime.now(timezone.utc) - timedelta(days=1),
+                                is_open=False,
+            ]).order_by(Trade.close_date).all()
+# Summarize profit for this pair.
+curdayprofit = sum(trade.close_profit for trade in trades)
 ```
 
-Get amount of stake_currency currently invested in Trades:
-
-``` python
-if self.config['runmode'].value in ('live', 'dry_run'):
-    total_stakes = Trade.total_open_trades_stakes()
-```
-
-Retrieve performance per pair.
-Returns a List of dicts per pair.
-
-``` python
-if self.config['runmode'].value in ('live', 'dry_run'):
-    performance = Trade.get_overall_performance()
-```
-
-Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
-
-``` json
-{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
-```
+For a full list of available methods, please consult the [Trade object](trade-object.md) documentation.
 
 !!! Warning
-    Trade history is not available during backtesting or hyperopt.
+    Trade history is not available in `populate_*` methods during backtesting or hyperopt, and will result in empty results.
 
 ## Prevent trades from happening for a specific pair
 

docs/strategy_migration.md

@@ -477,3 +477,254 @@ after:
     "ignore_buying_expired_candle_after": 120
 }
 ```
+
+## FreqAI strategy
+
+The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
+
+For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
+As such, the definition of features becomes much simpler with the new logic.
+
+For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
+
+``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
+
+def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+
+            t = int(t)
+            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
+            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+
+            bollinger = qtpylib.bollinger_bands(
+                qtpylib.typical_price(informative), window=t, stds=2.2
+            )
+            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
+            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
+            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
+
+            informative[f"%-{pair}bb_width-period_{t}"] = (
+                informative[f"{pair}bb_upperband-period_{t}"]
+                - informative[f"{pair}bb_lowerband-period_{t}"]
+            ) / informative[f"{pair}bb_middleband-period_{t}"]
+            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
+                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
+            )
+
+            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+
+            informative[f"%-{pair}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            ) # (1)
+
+        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
+        informative[f"%-{pair}raw_volume"] = informative["volume"]
+        informative[f"%-{pair}raw_price"] = informative["close"]
+        # (2)
+
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # Add generalized indicators here (because in live, it will call this
+        # function to populate indicators during training). Notice how we ensure not to
+        # add them multiple times
+        if set_generalized_indicators:
+            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
+            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
+            # (3)
+
+            # user adds targets here by prepending them with &- (see convention below)
+            df["&-s_close"] = (
+                df["close"]
+                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                .mean()
+                / df["close"]
+                - 1
+            )  # (4)
+
+        return df
+```
+
+1. Features - Move to `feature_engineering_expand_all`
+2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
+3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
+4. Targets - Move this part to `set_freqai_targets()`.
+
+### freqai - feature engineering expand all
+
+Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
+
+``` python linenums="1"
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+```
+
+### Freqai - feature engineering basic
+
+Basic features. Make sure to remove the `{pair}` part from your features.
+
+``` python linenums="1"
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+```
+
+### FreqAI - feature engineering standard
+
+``` python linenums="1"
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+```
+
+### FreqAI - set Targets
+
+Targets now get their own, dedicated method.
+
+``` python linenums="1"
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+            )
+
+        return dataframe
+```

docs/stylesheets/ft.extra.css

@@ -11,18 +11,3 @@
 .rst-versions .rst-other-versions {
     color: white;
 }
-
-
-#widget-wrapper {
-    height: calc(220px * 0.5625 + 18px);
-    width: 220px;
-    margin: 0 auto 16px auto;
-    border-style: solid;
-    border-color: var(--md-code-bg-color);
-    border-width: 1px;
-    border-radius: 5px;
-}
-
-@media screen and (max-width: calc(76.25em - 1px)) {
-    #widget-wrapper { display: none; }
-}

docs/trade-object.md

@@ -0,0 +1,148 @@
+# Trade Object
+
+## Trade
+
+A position freqtrade enters is stored in a `Trade` object - which is persisted to the database.
+It's a core concept of freqtrade - and something you'll come across in many sections of the documentation, which will most likely point you to this location.
+
+It will be passed to the strategy in many [strategy callbacks](strategy-callbacks.md). The object passed to the strategy cannot be modified directly. Indirect modifications may occur based on callback results.
+
+## Trade - Available attributes
+
+The following attributes / properties are available for each individual trade - and can be used with `trade.<property>` (e.g. `trade.pair`).
+
+|  Attribute | DataType | Description |
+|------------|-------------|-------------|
+`pair`| string | Pair of this trade
+`is_open`| boolean | Is the trade currently open, or has it been concluded
+`open_rate`| float | Rate this trade was entered at (Avg. entry rate in case of trade-adjustments)
+`close_rate`| float | Close rate - only set when is_open = False
+`stake_amount`| float | Amount in Stake (or Quote) currency.
+`amount`| float | Amount in Asset / Base currency that is currently owned.
+`open_date`| datetime | Timestamp when trade was opened **use `open_date_utc` instead**
+`open_date_utc`| datetime | Timestamp when trade was opened - in UTC
+`close_date`| datetime | Timestamp when trade was closed **use `close_date_utc` instead**
+`close_date_utc`| datetime | Timestamp when trade was closed - in UTC
+`close_profit`| float | Relative profit at the time of trade closure. `0.01` == 1%
+`close_profit_abs`| float | Absolute profit (in stake currency) at the time of trade closure.
+`leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets.
+`enter_tag`| string | Tag provided on entry via the `enter_tag` column in the dataframe
+`is_short` | boolean | True for short trades, False otherwise
+`orders` | Order[] | List of order objects attached to this trade (includes both filled and cancelled orders)
+`date_last_filled_utc` | datetime | Time of the last filled order
+`entry_side` | "buy" / "sell" | Order Side the trade was entered
+`exit_side` | "buy" / "sell" | Order Side that will result in a trade exit / position reduction.
+`trade_direction` | "long" / "short" | Trade direction in text - long or short.
+`nr_of_successful_entries` | int | Number of successful (filled) entry orders
+`nr_of_successful_exits` | int | Number of successful (filled) exit orders
+
+## Class methods
+
+The following are class methods - which return generic information, and usually result in an explicit query against the database.
+They can be used as `Trade.<method>` - e.g. `open_trades = Trade.get_open_trade_count()`
+
+!!! Warning "Backtesting/hyperopt"
+    Most methods will work in both backtesting / hyperopt and live/dry modes.
+    During backtesting, it's limited to usage in [strategy callbacks](strategy-callbacks.md). Usage in `populate_*()` methods is not supported and will result in wrong results.
+
+### get_trades_proxy
+
+When your strategy needs some information on existing (open or close) trades - it's best to use `Trade.get_trades_proxy()`.
+
+Usage:
+
+``` python
+from freqtrade.persistence import Trade
+from datetime import timedelta
+
+# ...
+trade_hist = Trade.get_trades_proxy(pair='ETH/USDT', is_open=False, open_date=current_date - timedelta(days=2))
+
+```
+
+`get_trades_proxy()` supports the following keyword arguments. All arguments are optional - calling `get_trades_proxy()` without arguments will return a list of all trades in the database.
+
+* `pair` e.g. `pair='ETH/USDT'`
+* `is_open` e.g. `is_open=False`
+* `open_date` e.g. `open_date=current_date - timedelta(days=2)`
+* `close_date` e.g. `close_date=current_date - timedelta(days=5)`
+
+### get_open_trade_count
+
+Get the number of currently open trades
+
+``` python
+from freqtrade.persistence import Trade
+# ...
+open_trades = Trade.get_open_trade_count()
+```
+
+### get_total_closed_profit
+
+Retrieve the total profit the bot has generated so far.
+Aggregates `close_profit_abs` for all closed trades.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+profit = Trade.get_total_closed_profit()
+```
+
+### total_open_trades_stakes
+
+Retrieve the total stake_amount that's currently in trades.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+profit = Trade.total_open_trades_stakes()
+```
+
+### get_overall_performance
+
+Retrieve the overall performance - similar to the `/performance` telegram command.
+
+``` python
+from freqtrade.persistence import Trade
+
+# ...
+if self.config['runmode'].value in ('live', 'dry_run'):
+    performance = Trade.get_overall_performance()
+```
+
+Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
+
+``` json
+{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
+```
+
+## Order Object
+
+An `Order` object represents an order on the exchange (or a simulated order in dry-run mode).
+An `Order` object will always be tied to it's corresponding [`Trade`](#trade-object), and only really makes sense in the context of a trade.
+
+### Order - Available attributes
+
+an Order object is typically attached to a trade.
+Most properties here can be None as they are dependant on the exchange response.
+
+|  Attribute | DataType | Description |
+|------------|-------------|-------------|
+`trade` | Trade | Trade object this order is attached to
+`ft_pair` | string | Pair this order is for
+`ft_is_open` | boolean | is the order filled?
+`order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss
+`status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled
+`side` | string | Buy or Sell
+`price` | float | Price the order was placed at
+`average` | float | Average price the order filled at
+`amount` | float | Amount in base currency
+`filled` | float | Filled amount (in base currency)
+`remaining` | float | Remaining amount
+`cost` | float | Cost of the order - usually average * filled
+`order_date` | datetime | Order creation date **use `order_date_utc` instead**
+`order_date_utc` | datetime | Order creation date (in UTC)
+`order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead**
+`order_fill_date_utc` | datetime | Order fill date

freqtrade/__init__.py

@@ -1,19 +1,20 @@
 """ Freqtrade bot """
-__version__ = '2022.12.dev'
+__version__ = '2023.1'
 
 if 'dev' in __version__:
+    from pathlib import Path
     try:
         import subprocess
+        freqtrade_basedir = Path(__file__).parent
 
         __version__ = __version__ + '-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
-            stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+            stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
 
     except Exception:  # pragma: no cover
         # git not available, ignore
         try:
             # Try Fallback to freqtrade_commit file (created by CI while building docker image)
-            from pathlib import Path
             versionfile = Path('./freqtrade_commit')
             if versionfile.is_file():
                 __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

freqtrade/commands/cli_options.py

@@ -251,7 +251,8 @@ AVAILABLE_CLI_OPTIONS = {
     "spaces": Arg(
         '--spaces',
         help='Specify which parameters to hyperopt. Space-separated list.',
-        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
+        choices=['all', 'buy', 'sell', 'roi', 'stoploss',
+                 'trailing', 'protection', 'trades', 'default'],
         nargs='+',
         default='default',
     ),
@@ -632,10 +633,11 @@ AVAILABLE_CLI_OPTIONS = {
               "1: by enter_tag, "
               "2: by enter_tag and exit_tag, "
               "3: by pair and enter_tag, "
-              "4: by pair, enter_ and exit_tag (this can get quite large)"),
+              "4: by pair, enter_ and exit_tag (this can get quite large), "
+              "5: by exit_tag"),
         nargs='+',
         default=['0', '1', '2'],
-        choices=['0', '1', '2', '3', '4'],
+        choices=['0', '1', '2', '3', '4', '5'],
     ),
     "enter_reason_list": Arg(
         "--enter-reason-list",

freqtrade/commands/data_commands.py

@@ -14,6 +14,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import market_is_active, timeframe_to_minutes
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
+from freqtrade.util.binance_mig import migrate_binance_futures_data
 
 
 logger = logging.getLogger(__name__)
@@ -86,6 +87,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
                     "Please use `--dl-trades` instead for this exchange "
                     "(will unfortunately take a long time)."
                     )
+            migrate_binance_futures_data(config)
             pairs_not_available = refresh_backtest_ohlcv_data(
                 exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
                 datadir=config['datadir'], timerange=timerange,
@@ -145,6 +147,7 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
     if ohlcv:
+        migrate_binance_futures_data(config)
         candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
         for candle_type in candle_types:
             convert_ohlcv_format(config,

freqtrade/configuration/configuration.py

@@ -28,7 +28,7 @@ class Configuration:
     Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
     """
 
-    def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
+    def __init__(self, args: Dict[str, Any], runmode: Optional[RunMode] = None) -> None:
         self.args = args
         self.config: Optional[Config] = None
         self.runmode = runmode

freqtrade/configuration/load_config.py

@@ -6,7 +6,7 @@ import re
 import sys
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 import rapidjson
 
@@ -75,7 +75,8 @@ def load_config_file(path: str) -> Dict[str, Any]:
     return config
 
 
-def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
+def load_from_files(
+        files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
     """
     Recursively load configuration files if specified.
     Sub-files are assumed to be relative to the initial config.

freqtrade/constants.py

@@ -31,7 +31,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'CalmarHyperOptLoss',
                          'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
                          'ProfitDrawDownHyperOptLoss']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList',
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', 'RemotePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
@@ -636,7 +636,6 @@ SCHEMA_TRADE_REQUIRED = [
 
 SCHEMA_BACKTEST_REQUIRED = [
     'exchange',
-    'max_open_trades',
     'stake_currency',
     'stake_amount',
     'dry_run_wallet',
@@ -646,6 +645,7 @@ SCHEMA_BACKTEST_REQUIRED = [
 SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
     'stoploss',
     'minimal_roi',
+    'max_open_trades'
 ]
 
 SCHEMA_MINIMAL_REQUIRED = [
@@ -681,3 +681,4 @@ MakerTaker = Literal['maker', 'taker']
 BidAsk = Literal['bid', 'ask']
 
 Config = Dict[str, Any]
+IntOrInf = float

freqtrade/data/btanalysis.py

@@ -10,7 +10,7 @@ from typing import Any, Dict, List, Optional, Union
 import numpy as np
 import pandas as pd
 
-from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import json_load
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@@ -20,8 +20,8 @@ from freqtrade.persistence import LocalTrade, Trade, init_db
 logger = logging.getLogger(__name__)
 
 # Newest format
-BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
-                   'open_rate', 'close_rate',
+BT_DATA_COLUMNS = ['pair', 'stake_amount', 'max_stake_amount', 'amount',
+                   'open_date', 'close_date', 'open_rate', 'close_rate',
                    'fee_open', 'fee_close', 'trade_duration',
                    'profit_ratio', 'profit_abs', 'exit_reason',
                    'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
@@ -90,7 +90,8 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
         return 'hyperopt_results.pickle'
 
 
-def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
+def get_latest_hyperopt_file(
+        directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
     """
     Get latest hyperopt export based on '.last_result.json'.
     :param directory: Directory to search for last result
@@ -193,7 +194,7 @@ def get_backtest_resultlist(dirname: Path):
 
 
 def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
-                                 min_backtest_date: datetime = None) -> Dict[str, Any]:
+                                 min_backtest_date: Optional[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.
@@ -241,6 +242,33 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
     return results
 
 
+def _load_backtest_data_df_compatibility(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Compatibility support for older backtest data.
+    """
+    df['open_date'] = pd.to_datetime(df['open_date'],
+                                     utc=True,
+                                     infer_datetime_format=True
+                                     )
+    df['close_date'] = pd.to_datetime(df['close_date'],
+                                      utc=True,
+                                      infer_datetime_format=True
+                                      )
+    # Compatibility support for pre short Columns
+    if 'is_short' not in df.columns:
+        df['is_short'] = False
+    if 'leverage' not in df.columns:
+        df['leverage'] = 1.0
+    if 'enter_tag' not in df.columns:
+        df['enter_tag'] = df['buy_tag']
+        df = df.drop(['buy_tag'], axis=1)
+    if 'max_stake_amount' not in df.columns:
+        df['max_stake_amount'] = df['stake_amount']
+    if 'orders' not in df.columns:
+        df['orders'] = None
+    return df
+
+
 def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load backtest data file.
@@ -269,24 +297,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
         data = data['strategy'][strategy]['trades']
         df = pd.DataFrame(data)
         if not df.empty:
-            df['open_date'] = pd.to_datetime(df['open_date'],
-                                             utc=True,
-                                             infer_datetime_format=True
-                                             )
-            df['close_date'] = pd.to_datetime(df['close_date'],
-                                              utc=True,
-                                              infer_datetime_format=True
-                                              )
-            # Compatibility support for pre short Columns
-            if 'is_short' not in df.columns:
-                df['is_short'] = 0
-            if 'leverage' not in df.columns:
-                df['leverage'] = 1.0
-            if 'enter_tag' not in df.columns:
-                df['enter_tag'] = df['buy_tag']
-                df = df.drop(['buy_tag'], axis=1)
-            if 'orders' not in df.columns:
-                df['orders'] = None
+            df = _load_backtest_data_df_compatibility(df)
 
     else:
         # old format - only with lists.
@@ -322,7 +333,7 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
 
 
 def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
-                          max_open_trades: int) -> pd.DataFrame:
+                          max_open_trades: IntOrInf) -> pd.DataFrame:
     """
     Find overlapping trades by expanding each trade once per period it was open
     and then counting overlaps

freqtrade/data/dataprovider.py

@@ -281,7 +281,7 @@ class DataProvider:
     def historic_ohlcv(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         candle_type: str = ''
     ) -> DataFrame:
         """
@@ -333,7 +333,7 @@ class DataProvider:
     def get_pair_dataframe(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         candle_type: str = ''
     ) -> DataFrame:
         """
@@ -415,7 +415,7 @@ class DataProvider:
 
     def refresh(self,
                 pairlist: ListPairsWithTimeframes,
-                helping_pairs: ListPairsWithTimeframes = None) -> None:
+                helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
         """
         Refresh data, called with each cycle
         """
@@ -439,7 +439,7 @@ class DataProvider:
     def ohlcv(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         copy: bool = True,
         candle_type: str = ''
     ) -> DataFrame:

freqtrade/data/entryexitanalysis.py

@@ -52,7 +52,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
     return analysed_trades_dict
 
 
-def _analyze_candles_and_indicators(pair, trades, signal_candles):
+def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
     buyf = signal_candles
 
     if len(buyf) > 0:
@@ -120,7 +120,7 @@ def _do_group_table_output(bigdf, glist):
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
-                        'profit_ratio': ['sum', 'median', 'mean']}
+                        'profit_ratio': ['median', 'mean', 'sum']}
             agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
                         'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
                         'total_profit_pct']
@@ -141,6 +141,12 @@ def _do_group_table_output(bigdf, glist):
             # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
             if g == "4":
                 group_mask = ['pair', 'enter_reason', 'exit_reason']
+
+            # 5: profit summaries grouped by exit_tag
+            if g == "5":
+                group_mask = ['exit_reason']
+                sortcols = ['exit_reason']
+
             if group_mask:
                 new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
                 new.columns = group_mask + agg_cols

freqtrade/data/history/history_utils.py

@@ -28,8 +28,8 @@ def load_pair_history(pair: str,
                       fill_up_missing: bool = True,
                       drop_incomplete: bool = False,
                       startup_candles: int = 0,
-                      data_format: str = None,
-                      data_handler: IDataHandler = None,
+                      data_format: Optional[str] = None,
+                      data_handler: Optional[IDataHandler] = None,
                       candle_type: CandleType = CandleType.SPOT
                       ) -> DataFrame:
     """
@@ -69,7 +69,7 @@ def load_data(datadir: Path,
               fail_without_data: bool = False,
               data_format: str = 'json',
               candle_type: CandleType = CandleType.SPOT,
-              user_futures_funding_rate: int = None,
+              user_futures_funding_rate: Optional[int] = None,
               ) -> Dict[str, DataFrame]:
     """
     Load ohlcv history data for a list of pairs.
@@ -116,7 +116,7 @@ def refresh_data(*, datadir: Path,
                  timeframe: str,
                  pairs: List[str],
                  exchange: Exchange,
-                 data_format: str = None,
+                 data_format: Optional[str] = None,
                  timerange: Optional[TimeRange] = None,
                  candle_type: CandleType,
                  ) -> None:
@@ -189,7 +189,7 @@ def _download_pair_history(pair: str, *,
                            timeframe: str = '5m',
                            process: str = '',
                            new_pairs_days: int = 30,
-                           data_handler: IDataHandler = None,
+                           data_handler: Optional[IDataHandler] = None,
                            timerange: Optional[TimeRange] = None,
                            candle_type: CandleType,
                            erase: bool = False,
@@ -272,7 +272,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
                                 datadir: Path, trading_mode: str,
                                 timerange: Optional[TimeRange] = None,
                                 new_pairs_days: int = 30, erase: bool = False,
-                                data_format: str = None,
+                                data_format: Optional[str] = None,
                                 prepend: bool = False,
                                 ) -> List[str]:
     """

freqtrade/data/history/idatahandler.py

@@ -374,6 +374,21 @@ class IDataHandler(ABC):
                 logger.warning(f"{pair}, {candle_type}, {timeframe}, "
                                f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
 
+    def rename_futures_data(
+            self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType):
+        """
+        Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
+        Only used for binance to support the binance futures naming unification.
+        """
+
+        file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
+        # print(file_old, file_new)
+        if file_new.exists():
+            logger.warning(f"{file_new} exists already, can't migrate {pair}.")
+            return
+        file_old.rename(file_new)
+
 
 def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
     """
@@ -403,8 +418,8 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
         raise ValueError(f"No datahandler for datatype {datatype} available.")
 
 
-def get_datahandler(datadir: Path, data_format: str = None,
-                    data_handler: IDataHandler = None) -> IDataHandler:
+def get_datahandler(datadir: Path, data_format: Optional[str] = None,
+                    data_handler: Optional[IDataHandler] = None) -> IDataHandler:
     """
     :param datadir: Folder to save data
     :param data_format: dataformat to use

freqtrade/data/metrics.py

@@ -1,4 +1,6 @@
 import logging
+import math
+from datetime import datetime
 from typing import Dict, Tuple
 
 import numpy as np
@@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
     :return: CAGR
     """
     return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
+
+
+def calculate_expectancy(trades: pd.DataFrame) -> float:
+    """
+    Calculate expectancy
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: expectancy
+    """
+    if len(trades) == 0:
+        return 0
+
+    expectancy = 1
+
+    profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
+    loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
+    nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
+    nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
+
+    if (nb_win_trades > 0) and (nb_loss_trades > 0):
+        average_win = profit_sum / nb_win_trades
+        average_loss = loss_sum / nb_loss_trades
+        risk_reward_ratio = average_win / average_loss
+        winrate = nb_win_trades / len(trades)
+        expectancy = ((1 + risk_reward_ratio) * winrate) - 1
+    elif nb_win_trades == 0:
+        expectancy = 0
+
+    return expectancy
+
+
+def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                      starting_balance: float) -> float:
+    """
+    Calculate sortino
+    :param trades: DataFrame containing trades (requires columns profit_abs)
+    :return: sortino
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+
+    down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
+
+    if down_stdev != 0 and not np.isnan(down_stdev):
+        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+        sortino_ratio = -100
+
+    # print(expected_returns_mean, down_stdev, sortino_ratio)
+    return sortino_ratio
+
+
+def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate sharpe
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :return: sharpe
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+    up_stdev = np.std(total_profit)
+
+    if up_stdev != 0:
+        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+        sharp_ratio = -100
+
+    # print(expected_returns_mean, up_stdev, sharp_ratio)
+    return sharp_ratio
+
+
+def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate calmar
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: calmar
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'].sum() / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    # adding slippage of 0.1% per trade
+    # total_profit = total_profit - 0.0005
+    expected_returns_mean = total_profit / days_period * 100
+
+    # calculate max drawdown
+    try:
+        _, _, _, _, _, max_drawdown = calculate_max_drawdown(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+    except ValueError:
+        max_drawdown = 0
+
+    if max_drawdown != 0:
+        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
+    else:
+        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+        calmar_ratio = -100
+
+    # print(expected_returns_mean, max_drawdown, calmar_ratio)
+    return calmar_ratio

freqtrade/exchange/binance.py

@@ -11,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
-from freqtrade.exchange.types import Tickers
+from freqtrade.exchange.types import OHLCVResponse, Tickers
 from freqtrade.misc import deep_merge_dicts, json_load
 
 
@@ -28,10 +28,10 @@ class Binance(Exchange):
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
         "l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
-        "ccxt_futures_name": "future"
+        "ccxt_futures_name": "swap"
     }
     _ft_has_futures: Dict = {
-        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "stoploss_order_types": {"limit": "stop", "market": "stop_market"},
         "tickers_have_price": False,
     }
 
@@ -112,7 +112,7 @@ class Binance(Exchange):
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
         Does not work for other exchanges, which don't return the earliest data when called with "0"

freqtrade/exchange/exchange.py

@@ -3,7 +3,6 @@
 Cryptocurrency Exchanges support
 """
 import asyncio
-import http
 import inspect
 import logging
 from copy import deepcopy
@@ -36,7 +35,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
                                                price_to_precision, timeframe_to_minutes,
                                                timeframe_to_msecs, timeframe_to_next_date,
                                                timeframe_to_prev_date, timeframe_to_seconds)
-from freqtrade.exchange.types import Ticker, Tickers
+from freqtrade.exchange.types import OHLCVResponse, Ticker, Tickers
 from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -45,12 +44,6 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 logger = logging.getLogger(__name__)
 
 
-# Workaround for adding samesite support to pre 3.8 python
-# Only applies to python3.7, and only on certain exchanges (kraken)
-# Replicates the fix from starlette (which is actually causing this problem)
-http.cookies.Morsel._reserved["samesite"] = "SameSite"  # type: ignore
-
-
 class Exchange:
 
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
@@ -474,7 +467,7 @@ class Exchange:
         try:
             if self._api_async:
                 self.loop.run_until_complete(
-                    self._api_async.load_markets(reload=reload))
+                    self._api_async.load_markets(reload=reload, params={}))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
             logger.warning('Could not load async markets. Reason: %s', e)
@@ -483,7 +476,7 @@ class Exchange:
     def _load_markets(self) -> None:
         """ Initialize markets both sync and async """
         try:
-            self._markets = self._api.load_markets()
+            self._markets = self._api.load_markets(params={})
             self._load_async_markets()
             self._last_markets_refresh = arrow.utcnow().int_timestamp
             if self._ft_has['needs_trading_fees']:
@@ -501,7 +494,7 @@ class Exchange:
             return None
         logger.debug("Performing scheduled market reload..")
         try:
-            self._markets = self._api.load_markets(reload=True)
+            self._markets = self._api.load_markets(reload=True, params={})
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
             self._last_markets_refresh = arrow.utcnow().int_timestamp
@@ -682,7 +675,7 @@ class Exchange:
                 f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
             )
 
-    def get_option(self, param: str, default: Any = None) -> Any:
+    def get_option(self, param: str, default: Optional[Any] = None) -> Any:
         """
         Get parameter value from _ft_has
         """
@@ -1357,7 +1350,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def fetch_positions(self, pair: str = None) -> List[Dict]:
+    def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
         """
         Fetch positions from the exchange.
         If no pair is given, all positions are returned.
@@ -1705,7 +1698,7 @@ class Exchange:
                 return self._config['fee']
             # validate that markets are loaded before trying to get fee
             if self._api.markets is None or len(self._api.markets) == 0:
-                self._api.load_markets()
+                self._api.load_markets(params={})
 
             return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
                                            price=price, takerOrMaker=taker_or_maker)['rate']
@@ -1801,7 +1794,7 @@ class Exchange:
     def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int, candle_type: CandleType,
                            is_new_pair: bool = False,
-                           until_ms: int = None) -> List:
+                           until_ms: Optional[int] = None) -> List:
         """
         Get candle history using asyncio and returns the list of candles.
         Handles all async work for this.
@@ -1813,32 +1806,18 @@ class Exchange:
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
         :return: List with candle (OHLCV) data
         """
-        pair, _, _, data = self.loop.run_until_complete(
+        pair, _, _, data, _ = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, until_ms=until_ms,
                                            is_new_pair=is_new_pair, candle_type=candle_type))
         logger.info(f"Downloaded data for {pair} with length {len(data)}.")
         return data
 
-    def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
-                                 since_ms: int, candle_type: CandleType) -> DataFrame:
-        """
-        Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
-        :param pair: Pair to download
-        :param timeframe: Timeframe to get data for
-        :param since_ms: Timestamp in milliseconds to get history from
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: OHLCV DataFrame
-        """
-        ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
-        return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
-                                  drop_incomplete=self._ohlcv_partial_candle)
-
     async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Download historic ohlcv
         :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@@ -1869,15 +1848,16 @@ class Exchange:
                     continue
                 else:
                     # Deconstruct tuple if it's not an exception
-                    p, _, c, new_data = res
+                    p, _, c, new_data, _ = res
                     if p == pair and c == candle_type:
                         data.extend(new_data)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        return pair, timeframe, candle_type, data
+        return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
-    def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
-                         since_ms: Optional[int], cache: bool) -> Coroutine:
+    def _build_coroutine(
+            self, pair: str, timeframe: str, candle_type: CandleType,
+            since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
         not_all_data = cache and self.required_candle_call_count > 1
         if cache and (pair, timeframe, candle_type) in self._klines:
             candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@@ -1914,7 +1894,7 @@ class Exchange:
         """
         Build Coroutines to execute as part of refresh_latest_ohlcv
         """
-        input_coroutines = []
+        input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
         cached_pairs = []
         for pair, timeframe, candle_type in set(pair_list):
             if (timeframe not in self.timeframes
@@ -1978,7 +1958,6 @@ class Exchange:
         :return: Dict of [{(pair, timeframe): Dataframe}]
         """
         logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
-        drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
 
         # Gather coroutines to run
         input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@@ -1996,8 +1975,9 @@ class Exchange:
                 if isinstance(res, Exception):
                     logger.warning(f"Async code raised an exception: {repr(res)}")
                     continue
-                # Deconstruct tuple (has 4 elements)
-                pair, timeframe, c_type, ticks = res
+                # Deconstruct tuple (has 5 elements)
+                pair, timeframe, c_type, ticks, drop_hint = res
+                drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
                 ohlcv_df = self._process_ohlcv_df(
                     pair, timeframe, c_type, ticks, cache, drop_incomplete)
 
@@ -2025,7 +2005,7 @@ class Exchange:
         timeframe: str,
         candle_type: CandleType,
         since_ms: Optional[int] = None,
-    ) -> Tuple[str, str, str, List]:
+    ) -> OHLCVResponse:
         """
         Asynchronously get candle history data using fetch_ohlcv
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
@@ -2035,8 +2015,8 @@ class Exchange:
             # Fetch OHLCV asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
-                "Fetching pair %s, interval %s, since %s %s...",
-                pair, timeframe, since_ms, s
+                "Fetching pair %s, %s, interval %s, since %s %s...",
+                pair, candle_type, timeframe, since_ms, s
             )
             params = deepcopy(self._ft_has.get('ohlcv_params', {}))
             candle_limit = self.ohlcv_candle_limit(
@@ -2050,11 +2030,12 @@ class Exchange:
                     limit=candle_limit, params=params)
             else:
                 # Funding rate
-                data = await self._api_async.fetch_funding_rate_history(
-                    pair, since=since_ms,
-                    limit=candle_limit)
-                # Convert funding rate to candle pattern
-                data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+                data = await self._fetch_funding_rate_history(
+                    pair=pair,
+                    timeframe=timeframe,
+                    limit=candle_limit,
+                    since_ms=since_ms,
+                )
             # Some exchanges sort OHLCV in ASC order and others in DESC.
             # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
             # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@@ -2064,9 +2045,9 @@ class Exchange:
                     data = sorted(data, key=lambda x: x[0])
             except IndexError:
                 logger.exception("Error loading %s. Result was %s.", pair, data)
-                return pair, timeframe, candle_type, []
+                return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
             logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
-            return pair, timeframe, candle_type, data
+            return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
         except ccxt.NotSupported as e:
             raise OperationalException(
@@ -2082,6 +2063,24 @@ class Exchange:
             raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
                                        f'for pair {pair}. Message: {e}') from e
 
+    async def _fetch_funding_rate_history(
+        self,
+        pair: str,
+        timeframe: str,
+        limit: int,
+        since_ms: Optional[int] = None,
+    ) -> List[List]:
+        """
+        Fetch funding rate history - used to selectively override this by subclasses.
+        """
+        # Funding rate
+        data = await self._api_async.fetch_funding_rate_history(
+            pair, since=since_ms,
+            limit=limit)
+        # Convert funding rate to candle pattern
+        data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+        return data
+
     # Fetch historic trades
 
     @retrier_async
@@ -2668,7 +2667,7 @@ class Exchange:
         :param amount: Trade amount
         :param open_date: Open date of the trade
         :return: funding fee since open_date
-        :raies: ExchangeError if something goes wrong.
+        :raises: ExchangeError if something goes wrong.
         """
         if self.trading_mode == TradingMode.FUTURES:
             if self._config['dry_run']:
@@ -2745,11 +2744,16 @@ class Exchange:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!
         PERPETUAL:
-         gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
+         gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
+         > Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
+                                [ 1 ± (Maintenance Margin Ratio + Taker Rate)]
+            Wherein, "+" or "-" depends on whether the contract goes long or short:
+            "-" for long, and "+" for short.
+
          okex: https://www.okex.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
 
-        :param exchange_name:
+        :param pair: Pair to calculate liquidation price for
         :param open_rate: Entry price of position
         :param is_short: True if the trade is a short, false otherwise
         :param amount: Absolute value of position size incl. leverage (in base currency)
@@ -2789,7 +2793,7 @@ class Exchange:
     def get_maintenance_ratio_and_amt(
         self,
         pair: str,
-        nominal_value: float = 0.0,
+        nominal_value: float,
     ) -> Tuple[float, Optional[float]]:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!

freqtrade/exchange/exchange_utils.py

@@ -15,18 +15,19 @@ from freqtrade.util import FtPrecise
 CcxtModuleType = Any
 
 
-def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
+def is_exchange_known_ccxt(
+        exchange_name: str, ccxt_module: Optional[CcxtModuleType] = None) -> bool:
     return exchange_name in ccxt_exchanges(ccxt_module)
 
 
-def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
+def ccxt_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
     """
     Return the list of all exchanges known to ccxt
     """
     return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
 
 
-def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
+def available_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
     """
     Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
     """
@@ -86,7 +87,7 @@ def timeframe_to_msecs(timeframe: str) -> int:
     return ccxt.Exchange.parse_timeframe(timeframe) * 1000
 
 
-def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
+def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
     """
     Use Timeframe and determine the candle start date for this date.
     Does not round when given a candle start date.
@@ -102,7 +103,7 @@ def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
     return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
-def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
+def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
     """
     Use Timeframe and determine next candle.
     :param timeframe: timeframe in string format (e.g. "5m")

freqtrade/exchange/types.py

@@ -1,4 +1,6 @@
-from typing import Dict, Optional, TypedDict
+from typing import Dict, List, Optional, Tuple, TypedDict
+
+from freqtrade.enums import CandleType
 
 
 class Ticker(TypedDict):
@@ -14,3 +16,6 @@ class Ticker(TypedDict):
 
 
 Tickers = Dict[str, Ticker]
+
+# pair, timeframe, candleType, OHLCV, drop last?,
+OHLCVResponse = Tuple[str, str, CandleType, List, bool]

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -0,0 +1,125 @@
+import logging
+from enum import Enum
+
+from gym import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Buy = 1
+    Sell = 2
+
+
+class Base3ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 3 action environment
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action])
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            if action == Actions.Buy.value:
+                if self._position == Positions.Short:
+                    self._update_total_profit()
+                self._position = Positions.Long
+                trade_type = "long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and self.can_short:
+                if self._position == Positions.Long:
+                    self._update_total_profit()
+                self._position = Positions.Short
+                trade_type = "short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and not self.can_short:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {'price': self.current_price(), 'index': self._current_tick,
+                     'type': trade_type})
+
+        if (self._total_profit < self.max_drawdown or
+                self._total_unrealized_profit < self.max_drawdown):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            action=action,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
+        )
+
+        observation = self._get_observation()
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, info
+
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Buy while it is in a Positions.short
+        """
+        return (
+            (action == Actions.Buy.value and self._position == Positions.Neutral)
+            or (action == Actions.Sell.value and self._position == Positions.Long)
+            or (action == Actions.Sell.value and self._position == Positions.Neutral
+                and self.can_short)
+            or (action == Actions.Buy.value and self._position == Positions.Short
+                and self.can_short)
+        )
+
+    def _is_valid(self, action: int) -> bool:
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Sell while it is in a Positions.Long
+        """
+        if self.can_short:
+            return action in [Actions.Buy.value, Actions.Sell.value, Actions.Neutral.value]
+        else:
+            if action == Actions.Sell.value and self._position != Positions.Long:
+                return False
+            return True

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -88,7 +88,8 @@ class Base4ActionRLEnv(BaseEnvironment):
                     {'price': self.current_price(), 'index': self._current_tick,
                      'type': trade_type})
 
-        if self._total_profit < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
+        if (self._total_profit < self.max_drawdown or
+                self._total_unrealized_profit < self.max_drawdown):
             self._done = True
 
         self._position_history.append(self._position)

freqtrade/freqai/RL/BaseEnvironment.py

@@ -45,7 +45,7 @@ class BaseEnvironment(gym.Env):
     def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
                  reward_kwargs: dict = {}, window_size=10, starting_point=True,
                  id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
-                 fee: float = 0.0015):
+                 fee: float = 0.0015, can_short: bool = False):
         """
         Initializes the training/eval environment.
         :param df: dataframe of features
@@ -58,6 +58,7 @@ class BaseEnvironment(gym.Env):
         :param config: Typical user configuration file
         :param live: Whether or not this environment is active in dry/live/backtesting
         :param fee: The fee to use for environmental interactions.
+        :param can_short: Whether or not the environment can short
         """
         self.config = config
         self.rl_config = config['freqai']['rl_config']
@@ -73,6 +74,7 @@ class BaseEnvironment(gym.Env):
         # set here to default 5Ac, but all children envs can override this
         self.actions: Type[Enum] = BaseActions
         self.tensorboard_metrics: dict = {}
+        self.can_short = can_short
         self.live = live
         if not self.live and self.add_state_info:
             self.add_state_info = False

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -165,7 +165,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         env_info = {"window_size": self.CONV_WIDTH,
                     "reward_kwargs": self.reward_params,
                     "config": self.config,
-                    "live": self.live}
+                    "live": self.live,
+                    "can_short": self.can_short}
         if self.data_provider:
             env_info["fee"] = self.data_provider._exchange \
                 .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore
@@ -279,26 +280,36 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
+        # %-raw_volume_gen_shift-2_ETH/USDT_1h
         # price data for model training and evaluation
         tf = self.config['timeframe']
-        ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
-                     f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
-        rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
-                       f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+        rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
+                       '%-raw_high': ' high', '%-raw_close': 'close'}
+        rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
+                           f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+
+        prices_train = train_df.filter(rename_dict.keys(), axis=1)
+        prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
+        if prices_train.empty or not prices_train_old.empty:
+            if not prices_train_old.empty:
+                prices_train = prices_train_old
+                rename_dict = rename_dict_old
+            logger.warning('Reinforcement learning module didnt find the correct raw prices '
+                           'assigned in feature_engineering_standard(). '
+                           'Please assign them with:\n'
+                           'dataframe["%-raw_close"] = dataframe["close"]\n'
+                           'dataframe["%-raw_open"] = dataframe["open"]\n'
+                           'dataframe["%-raw_high"] = dataframe["high"]\n'
+                           'dataframe["%-raw_low"] = dataframe["low"]\n'
+                           'inside `feature_engineering_standard()')
+        elif prices_train.empty:
+            raise OperationalException("No prices found, please follow log warning "
+                                       "instructions to correct the strategy.")
 
-        prices_train = train_df.filter(ohlc_list, axis=1)
-        if prices_train.empty:
-            raise OperationalException('Reinforcement learning module didnt find the raw prices '
-                                       'assigned in populate_any_indicators. Please assign them '
-                                       'with:\n'
-                                       'informative[f"%-{pair}raw_close"] = informative["close"]\n'
-                                       'informative[f"%-{pair}raw_open"] = informative["open"]\n'
-                                       'informative[f"%-{pair}raw_high"] = informative["high"]\n'
-                                       'informative[f"%-{pair}raw_low"] = informative["low"]\n')
         prices_train.rename(columns=rename_dict, inplace=True)
         prices_train.reset_index(drop=True)
 
-        prices_test = test_df.filter(ohlc_list, axis=1)
+        prices_test = test_df.filter(rename_dict.keys(), axis=1)
         prices_test.rename(columns=rename_dict, inplace=True)
         prices_test.reset_index(drop=True)
 

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -2,8 +2,6 @@ import logging
 from time import time
 from typing import Any
 
-import numpy as np
-import tensorflow as tf
 from pandas import DataFrame
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -19,14 +17,6 @@ class BaseTensorFlowModel(IFreqaiModel):
     User *must* inherit from this class and set fit() and predict().
     """
 
-    def __init__(self, **kwargs):
-        super().__init__(config=kwargs['config'])
-        self.keras = True
-        # if self.ft_params.get("DI_threshold", 0):
-        #     self.ft_params["DI_threshold"] = 0
-        #     logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
-        self.dd.model_type = 'keras'
-
     def train(
         self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
@@ -43,6 +33,7 @@ class BaseTensorFlowModel(IFreqaiModel):
 
         start_time = time()
 
+        # filter the features requested by user in the configuration file and elegantly handle NaNs
         features_filtered, labels_filtered = dk.filter_features(
             unfiltered_df,
             dk.training_features_list,
@@ -50,9 +41,13 @@ class BaseTensorFlowModel(IFreqaiModel):
             training_filter=True,
         )
 
+        start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
+        logger.info(f"-------------------- Training on data from {start_date} to "
+                    f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -73,76 +68,3 @@ class BaseTensorFlowModel(IFreqaiModel):
                     f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
-
-
-class WindowGenerator:
-    def __init__(
-        self,
-        input_width,
-        label_width,
-        shift,
-        train_df=None,
-        val_df=None,
-        test_df=None,
-        train_labels=None,
-        val_labels=None,
-        test_labels=None,
-        batch_size=None,
-    ):
-        # Store the raw data.
-        self.train_df = train_df
-        self.val_df = val_df
-        self.test_df = test_df
-        self.train_labels = train_labels
-        self.val_labels = val_labels
-        self.test_labels = test_labels
-        self.batch_size = batch_size
-        self.input_width = input_width
-        self.label_width = label_width
-        self.shift = shift
-        self.total_window_size = input_width + shift
-        self.input_slice = slice(0, input_width)
-        self.input_indices = np.arange(self.total_window_size)[self.input_slice]
-
-    def make_dataset(self, data, labels=None):
-        data = np.array(data, dtype=np.float32)
-        if labels is not None:
-            labels = np.array(labels, dtype=np.float32)
-        ds = tf.keras.preprocessing.timeseries_dataset_from_array(
-            data=data,
-            targets=labels,
-            sequence_length=self.total_window_size,
-            sequence_stride=1,
-            sampling_rate=1,
-            shuffle=False,
-            batch_size=self.batch_size,
-        )
-
-        return ds
-
-    @property
-    def train(self):
-        return self.make_dataset(self.train_df, self.train_labels)
-
-    @property
-    def val(self):
-        return self.make_dataset(self.val_df, self.val_labels)
-
-    @property
-    def test(self):
-        return self.make_dataset(self.test_df, self.test_labels)
-
-    @property
-    def inference(self):
-        return self.make_dataset(self.test_df)
-
-    @property
-    def example(self):
-        """Get and cache an example batch of `inputs, labels` for plotting."""
-        result = getattr(self, "_example", None)
-        if result is None:
-            # No example batch was found, so get one from the `.train` dataset
-            result = next(iter(self.train))
-            # And cache it for next time
-            self._example = result
-        return result

freqtrade/freqai/data_kitchen.py

@@ -1,10 +1,11 @@
 import copy
+import inspect
 import logging
 import shutil
 from datetime import datetime, timezone
 from math import cos, sin
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import numpy.typing as npt
@@ -23,6 +24,7 @@ from freqtrade.constants import Config
 from freqtrade.data.converter import reduce_dataframe_footprint
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.strategy import merge_informative_pair
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -110,7 +112,7 @@ class FreqaiDataKitchen:
     def set_paths(
         self,
         pair: str,
-        trained_timestamp: int = None,
+        trained_timestamp: Optional[int] = None,
     ) -> None:
         """
         Set the paths to the data for the present coin/botloop
@@ -1145,9 +1147,9 @@ class FreqaiDataKitchen:
 
         for pair in pairs:
             pair = pair.replace(':', '')  # lightgbm doesnt like colons
-            valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
-            pair_cols = [col for col in dataframe.columns if
-                         any(substr in col for substr in valid_strs)]
+            pair_cols = [col for col in dataframe.columns if col.startswith("%")
+                         and f"{pair}_" in col]
+
             if pair_cols:
                 pair_cols.insert(0, 'date')
                 corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@@ -1176,6 +1178,103 @@ class FreqaiDataKitchen:
 
         return dataframe
 
+    def get_pair_data_for_features(self,
+                                   pair: str,
+                                   tf: str,
+                                   strategy: IStrategy,
+                                   corr_dataframes: dict = {},
+                                   base_dataframes: dict = {},
+                                   is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Get the data for the pair. If it's not in the dictionary, get it from the data provider
+        :param pair: str = pair to get data for
+        :param tf: str = timeframe to get data for
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = dataframe containing the pair data
+        """
+        if is_corr_pairs:
+            dataframe = corr_dataframes[pair][tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+        else:
+            dataframe = base_dataframes[tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+
+    def merge_features(self, df_main: DataFrame, df_to_merge: DataFrame,
+                       tf: str, timeframe_inf: str, suffix: str) -> DataFrame:
+        """
+        Merge the features of the dataframe and remove HLCV and date added columns
+        :param df_main: DataFrame = main dataframe
+        :param df_to_merge: DataFrame = dataframe to merge
+        :param tf: str = timeframe of the main dataframe
+        :param timeframe_inf: str = timeframe of the dataframe to merge
+        :param suffix: str = suffix to add to the columns of the dataframe to merge
+        :return: dataframe = merged dataframe
+        """
+        dataframe = merge_informative_pair(df_main, df_to_merge, tf, timeframe_inf=timeframe_inf,
+                                           append_timeframe=False, suffix=suffix, ffill=True)
+        skip_columns = [
+            (f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        dataframe = dataframe.drop(columns=skip_columns)
+        return dataframe
+
+    def populate_features(self, dataframe: DataFrame, pair: str, strategy: IStrategy,
+                          corr_dataframes: dict, base_dataframes: dict,
+                          is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Use the user defined strategy functions for populating features
+        :param dataframe: DataFrame = dataframe to populate
+        :param pair: str = pair to populate
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+        :param base_dataframes: dict = dict containing the current pair dataframes
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = populated dataframe
+        """
+        tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+
+        for tf in tfs:
+            informative_df = self.get_pair_data_for_features(
+                pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
+            informative_copy = informative_df.copy()
+
+            for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
+                df_features = strategy.feature_engineering_expand_all(
+                    informative_copy.copy(), t)
+                suffix = f"{t}"
+                informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
+
+            generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
+            suffix = "gen"
+
+            informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
+
+            indicators = [col for col in informative_df if col.startswith("%")]
+            for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
+                if n == 0:
+                    continue
+                df_shift = informative_df[indicators].shift(n)
+                df_shift = df_shift.add_suffix("_shift-" + str(n))
+                informative_df = pd.concat((informative_df, df_shift), axis=1)
+
+            dataframe = self.merge_features(dataframe.copy(), informative_df,
+                                            self.config["timeframe"], tf, f'{pair}_{tf}')
+
+        return dataframe
+
     def use_strategy_to_populate_indicators(
         self,
         strategy: IStrategy,
@@ -1188,7 +1287,87 @@ class FreqaiDataKitchen:
         """
         Use the user defined strategy for populating indicators during retrain
         :param strategy: IStrategy = user defined strategy object
-        :param corr_dataframes: dict = dict containing the informative pair dataframes
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param pair: str = pair to populate
+        :param prediction_dataframe: DataFrame = dataframe containing the pair data
+        used for prediction
+        :param do_corr_pairs: bool = whether to populate corr pairs or not
+        :return:
+        dataframe: DataFrame = dataframe containing populated indicators
+        """
+
+        # this is a hack to check if the user is using the populate_any_indicators function
+        new_version = inspect.getsource(strategy.populate_any_indicators) == (
+            inspect.getsource(IStrategy.populate_any_indicators))
+
+        if new_version:
+            tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+            pairs: List[str] = self.freqai_config["feature_parameters"].get(
+                "include_corr_pairlist", [])
+
+            for tf in tfs:
+                if tf not in base_dataframes:
+                    base_dataframes[tf] = pd.DataFrame()
+                for p in pairs:
+                    if p not in corr_dataframes:
+                        corr_dataframes[p] = {}
+                    if tf not in corr_dataframes[p]:
+                        corr_dataframes[p][tf] = pd.DataFrame()
+
+            if not prediction_dataframe.empty:
+                dataframe = prediction_dataframe.copy()
+            else:
+                dataframe = base_dataframes[self.config["timeframe"]].copy()
+
+            corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
+                "include_corr_pairlist", [])
+            dataframe = self.populate_features(dataframe.copy(), pair, strategy,
+                                               corr_dataframes, base_dataframes)
+
+            dataframe = strategy.feature_engineering_standard(dataframe.copy())
+            # ensure corr pairs are always last
+            for corr_pair in corr_pairs:
+                if pair == corr_pair:
+                    continue  # dont repeat anything from whitelist
+                if corr_pairs and do_corr_pairs:
+                    dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
+                                                       corr_dataframes, base_dataframes, True)
+
+            dataframe = strategy.set_freqai_targets(dataframe.copy())
+
+            self.get_unique_classes_from_labels(dataframe)
+
+            dataframe = self.remove_special_chars_from_feature_names(dataframe)
+
+            if self.config.get('reduce_df_footprint', False):
+                dataframe = reduce_dataframe_footprint(dataframe)
+
+            return dataframe
+
+        else:
+            # the user is using the populate_any_indicators functions which is deprecated
+
+            df = self.use_strategy_to_populate_indicators_old_version(
+                strategy, corr_dataframes, base_dataframes, pair,
+                prediction_dataframe, do_corr_pairs)
+            return df
+
+    def use_strategy_to_populate_indicators_old_version(
+        self,
+        strategy: IStrategy,
+        corr_dataframes: dict = {},
+        base_dataframes: dict = {},
+        pair: str = "",
+        prediction_dataframe: DataFrame = pd.DataFrame(),
+        do_corr_pairs: bool = True,
+    ) -> DataFrame:
+        """
+        Use the user defined strategy for populating indicators during retrain
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
                                 (for user defined timeframes)
         :param base_dataframes: dict = dict containing the current pair dataframes
                                 (for user defined timeframes)

freqtrade/freqai/freqai_interface.py

@@ -1,3 +1,4 @@
+import inspect
 import logging
 import threading
 import time
@@ -104,6 +105,9 @@ class IFreqaiModel(ABC):
         self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
         self.data_provider: Optional[DataProvider] = None
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
+        self.can_short = True  # overridden in start() with strategy.can_short
+
+        self.warned_deprecated_populate_any_indicators = False
 
         record_params(config, self.full_path)
 
@@ -133,6 +137,10 @@ class IFreqaiModel(ABC):
         self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
         self.dd.set_pair_dict_info(metadata)
         self.data_provider = strategy.dp
+        self.can_short = strategy.can_short
+
+        # check if the strategy has deprecated populate_any_indicators function
+        self.check_deprecated_populate_any_indicators(strategy)
 
         if self.live:
             self.inference_timer('start')
@@ -147,12 +155,9 @@ class IFreqaiModel(ABC):
         # the concatenated results for the full backtesting period back to the strategy.
         elif not self.follow_mode:
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
-            dataframe = self.dk.use_strategy_to_populate_indicators(
-                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
-            )
             if not self.config.get("freqai_backtest_live_models", False):
                 logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
-                dk = self.start_backtesting(dataframe, metadata, self.dk)
+                dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
                 dataframe = dk.remove_features_from_df(dk.return_dataframe)
             else:
                 logger.info(
@@ -253,7 +258,7 @@ class IFreqaiModel(ABC):
                     self.dd.save_metric_tracker_to_disk()
 
     def start_backtesting(
-        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
     ) -> FreqaiDataKitchen:
         """
         The main broad execution for backtesting. For backtesting, each pair enters and then gets
@@ -265,19 +270,22 @@ class IFreqaiModel(ABC):
         :param dataframe: DataFrame = strategy passed dataframe
         :param metadata: Dict = pair metadata
         :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :param strategy: Strategy to train on
         :return:
             FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
 
         self.pair_it += 1
         train_it = 0
+        pair = metadata["pair"]
+        populate_indicators = True
+        check_features = True
         # Loop enforcing the sliding window training/backtesting paradigm
         # tr_train is the training time range e.g. 1 historical month
         # tr_backtest is the backtesting time range e.g. the week directly
         # following tr_train. Both of these windows slide through the
         # entire backtest
         for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
-            pair = metadata["pair"]
             (_, _, _) = self.dd.get_pair_dict_info(pair)
             train_it += 1
             total_trains = len(dk.backtesting_timeranges)
@@ -299,18 +307,42 @@ class IFreqaiModel(ABC):
             dk.set_new_model_names(pair, timestamp_model_id)
 
             if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
-                self.dd.load_metadata(dk)
-                dk.find_features(dataframe)
-                self.check_if_feature_list_matches_strategy(dk)
+                if check_features:
+                    self.dd.load_metadata(dk)
+                    dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
+                    )
+                    dk.find_features(dataframe_dummy_features)
+                    self.check_if_feature_list_matches_strategy(dk)
+                    check_features = False
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
-                dataframe_train = dk.slice_dataframe(tr_train, dataframe)
-                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
+                if populate_indicators:
+                    dataframe = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+                    )
+                    populate_indicators = False
+
+                dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
+                dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
+                dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
+                dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
+
+                dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
+                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
+
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
-                    self.model = self.train(dataframe_train, pair, dk)
+
+                    try:
+                        self.model = self.train(dataframe_train, pair, dk)
+                    except Exception as msg:
+                        logger.warning(
+                            f"Training {pair} raised exception {msg.__class__.__name__}. "
+                            f"Message: {msg}, skipping.")
+
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         tr_train.stopts)
                     if self.plot_features:
@@ -347,7 +379,6 @@ class IFreqaiModel(ABC):
         :returns:
         dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
-
         # update follower
         if self.follow_mode:
             self.dd.update_follower_metadata()
@@ -911,9 +942,28 @@ class IFreqaiModel(ABC):
         dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
         dk.return_dataframe = pd.merge(
             dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
-        # dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
         return dk
 
+    def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
+        """
+        Check and warn if the deprecated populate_any_indicators function is used.
+        :param strategy: strategy object
+        """
+
+        if not self.warned_deprecated_populate_any_indicators:
+            self.warned_deprecated_populate_any_indicators = True
+            old_version = inspect.getsource(strategy.populate_any_indicators) != (
+                inspect.getsource(IStrategy.populate_any_indicators))
+
+            if old_version:
+                logger.warning("DEPRECATION WARNING: "
+                               "You are using the deprecated populate_any_indicators function. "
+                               "This function will raise an error on March 1 2023. "
+                               "Please update your strategy by using "
+                               "the new feature_engineering functions. See \n"
+                               "https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
+                               "for details.")
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqai/prediction_models/CNNPredictionModel.py

@@ -1,152 +0,0 @@
-import logging
-from typing import Any, Dict, Tuple
-
-import numpy as np
-import tensorflow as tf
-from pandas import DataFrame
-from tensorflow.keras.layers import Conv1D, Dense, Input
-from tensorflow.keras.models import Model
-
-from freqtrade.exceptions import OperationalException
-from freqtrade.freqai.base_models.BaseTensorFlowModel import BaseTensorFlowModel, WindowGenerator
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class CNNPredictionModel(BaseTensorFlowModel):
-    """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), fit().
-    """
-
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :params:
-        :data_dictionary: the dictionary constructed by DataHandler to hold
-        all the training and test data/labels.
-        """
-        train_df = data_dictionary["train_features"]
-        train_labels = data_dictionary["train_labels"]
-        test_df = data_dictionary["test_features"]
-        test_labels = data_dictionary["test_labels"]
-        n_labels = len(train_labels.columns)
-
-        if n_labels > 1:
-            raise OperationalException(
-                "Neural Net not yet configured for multi-targets. Please "
-                " reduce number of targets to 1 in strategy."
-            )
-
-        n_features = len(data_dictionary["train_features"].columns)
-        BATCH_SIZE = self.model_training_parameters.get("batch_size", 64)
-
-        # we need to remove batch_size from the model_training_params because
-        # we dont want fit() to get the incorrect assignment (we use the WindowGenerator)
-        # to handle our batches.
-        if 'batch_size' in self.model_training_parameters:
-            self.model_training_parameters.pop('batch_size')
-        input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
-
-        w1 = WindowGenerator(
-            input_width=self.CONV_WIDTH,
-            label_width=1,
-            shift=1,
-            train_df=train_df,
-            val_df=test_df,
-            train_labels=train_labels,
-            val_labels=test_labels,
-            batch_size=BATCH_SIZE,
-        )
-
-        model = self.create_model(input_dims, n_labels)
-
-        steps_per_epoch = np.ceil(len(test_df) / BATCH_SIZE)
-        lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
-            0.001, decay_steps=steps_per_epoch * 1000, decay_rate=1, staircase=False
-        )
-
-        early_stopping = tf.keras.callbacks.EarlyStopping(
-            monitor="loss", patience=3, mode="min", min_delta=0.0001
-        )
-
-        model.compile(
-            loss=tf.losses.MeanSquaredError(),
-            optimizer=tf.optimizers.Adam(lr_schedule),
-            metrics=[tf.metrics.MeanAbsoluteError()],
-        )
-
-        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
-            val_data = None
-        else:
-            val_data = w1.val
-
-        model.fit(
-            w1.train,
-            validation_data=val_data,
-            callbacks=[early_stopping],
-            **self.model_training_parameters,
-        )
-
-        return model
-
-    def predict(
-        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
-    ) -> Tuple[DataFrame, DataFrame]:
-        """
-        Filter the prediction features data and predict with it.
-        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
-        :return:
-        :predictions: np.array of predictions
-        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
-        data (NaNs) or felt uncertain about data (PCA and DI index)
-        """
-
-        dk.find_features(unfiltered_dataframe)
-        filtered_dataframe, _ = dk.filter_features(
-            unfiltered_dataframe, dk.training_features_list, training_filter=False
-        )
-        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
-        dk.data_dictionary["prediction_features"] = filtered_dataframe
-
-        # optional additional data cleaning/analysis
-        self.data_cleaning_predict(dk)
-
-        if first:
-            full_df = dk.data_dictionary["prediction_features"]
-
-            w1 = WindowGenerator(
-                input_width=self.CONV_WIDTH,
-                label_width=1,
-                shift=1,
-                test_df=full_df,
-                batch_size=len(full_df),
-            )
-
-            predictions = self.model.predict(w1.inference)
-            len_diff = len(dk.do_predict) - len(predictions)
-            if len_diff > 0:
-                dk.do_predict = dk.do_predict[len_diff:]
-
-        else:
-            data = dk.data_dictionary["prediction_features"]
-            data = tf.expand_dims(data, axis=0)
-            data = tf.convert_to_tensor(data)
-            predictions = self.model(data, training=False)
-
-        predictions = predictions[:, 0, 0]
-        pred_df = DataFrame(predictions, columns=dk.label_list)
-
-        pred_df = dk.denormalize_labels_from_metadata(pred_df)
-
-        return (pred_df, np.ones(len(pred_df)))
-
-    def create_model(self, input_dims, n_labels) -> Any:
-
-        input_layer = Input(shape=(input_dims[1], input_dims[2]))
-        Layer_1 = Conv1D(filters=32, kernel_size=(self.CONV_WIDTH,), activation="relu")(input_layer)
-        Layer_3 = Dense(units=32, activation="relu")(Layer_1)
-        output_layer = Dense(units=n_labels)(Layer_3)
-        return Model(inputs=input_layer, outputs=output_layer)

freqtrade/freqtradebot.py

@@ -33,6 +33,7 @@ from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.util import FtPrecise
+from freqtrade.util.binance_mig import migrate_binance_futures_names
 from freqtrade.wallets import Wallets
 
 
@@ -177,6 +178,8 @@ class FreqtradeBot(LoggingMixin):
         Called on startup and after reloading the bot - triggers notifications and
         performs startup tasks
         """
+        migrate_binance_futures_names(self.config)
+
         self.rpc.startup_messages(self.config, self.pairlists, self.protections)
         # Update older trades with precision and precision mode
         self.startup_backpopulate_precision()
@@ -374,7 +377,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             if not trade.is_open and not trade.fee_updated(trade.exit_side):
                 # Get sell fee
-                order = trade.select_order(trade.exit_side, False)
+                order = trade.select_order(trade.exit_side, False, only_filled=True)
                 if not order:
                     order = trade.select_order('stoploss', False)
                 if order:
@@ -390,7 +393,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             with self._exit_lock:
                 if trade.is_open and not trade.fee_updated(trade.entry_side):
-                    order = trade.select_order(trade.entry_side, False)
+                    order = trade.select_order(trade.entry_side, False, only_filled=True)
                     open_order = trade.select_order(trade.entry_side, True)
                     if order and open_order is None:
                         logger.info(
@@ -720,7 +723,7 @@ class FreqtradeBot(LoggingMixin):
             time_in_force=time_in_force,
             leverage=leverage
         )
-        order_obj = Order.parse_from_ccxt_object(order, pair, side)
+        order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
         order_id = order['id']
         order_status = order.get('status')
         logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
@@ -912,6 +915,7 @@ class FreqtradeBot(LoggingMixin):
             stake_amount=stake_amount,
             min_stake_amount=min_stake_amount,
             max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None,
         )
 
         return enter_limit_requested, stake_amount, leverage
@@ -1093,7 +1097,8 @@ class FreqtradeBot(LoggingMixin):
                 leverage=trade.leverage
             )
 
-            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
+            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss',
+                                                     trade.amount, stop_price)
             trade.orders.append(order_obj)
             trade.stoploss_order_id = str(stoploss_order['id'])
             trade.stoploss_last_update = datetime.now(timezone.utc)
@@ -1517,7 +1522,7 @@ class FreqtradeBot(LoggingMixin):
             *,
             exit_tag: Optional[str] = None,
             ordertype: Optional[str] = None,
-            sub_trade_amt: float = None,
+            sub_trade_amt: Optional[float] = None,
     ) -> bool:
         """
         Executes a trade exit for the given trade and limit
@@ -1594,7 +1599,7 @@ class FreqtradeBot(LoggingMixin):
             self.handle_insufficient_funds(trade)
             return False
 
-        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side)
+        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
         trade.orders.append(order_obj)
 
         trade.open_order_id = order['id']
@@ -1611,7 +1616,7 @@ class FreqtradeBot(LoggingMixin):
         return True
 
     def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
-                     sub_trade: bool = False, order: Order = None) -> None:
+                     sub_trade: bool = False, order: Optional[Order] = None) -> None:
         """
         Sends rpc notification when a sell occurred.
         """
@@ -1724,8 +1729,9 @@ class FreqtradeBot(LoggingMixin):
 # Common update trade state methods
 #
 
-    def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
-                           stoploss_order: bool = False, send_msg: bool = True) -> bool:
+    def update_trade_state(
+            self, trade: Trade, order_id: str, action_order: Optional[Dict[str, Any]] = None,
+            stoploss_order: bool = False, send_msg: bool = True) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
         Handles closing both buy and sell orders.

freqtrade/main.py

@@ -5,7 +5,7 @@ Read the documentation to know what cli arguments you need.
 """
 import logging
 import sys
-from typing import Any, List
+from typing import Any, List, Optional
 
 from freqtrade.util.gc_setup import gc_set_threshold
 
@@ -23,7 +23,7 @@ from freqtrade.loggers import setup_logging_pre
 logger = logging.getLogger('freqtrade')
 
 
-def main(sysargv: List[str] = None) -> None:
+def main(sysargv: Optional[List[str]] = None) -> None:
     """
     This function will initiate the bot and start the trading loop.
     :return: None

freqtrade/misc.py

@@ -6,7 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Mapping, Union
+from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -205,7 +205,7 @@ def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, d
     return default_value
 
 
-def plural(num: float, singular: str, plural: str = None) -> str:
+def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
 
@@ -269,6 +269,8 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
     def default(z):
         if isinstance(z, pd.Timestamp):
             return z.timestamp() * 1e3
+        if z is pd.NaT:
+            return 'NaT'
         raise TypeError
 
     return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')

freqtrade/optimize/backtesting.py

@@ -15,7 +15,7 @@ from pandas import DataFrame
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
-from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
 from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
@@ -37,6 +37,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util.binance_mig import migrate_binance_futures_data
 from freqtrade.wallets import Wallets
 
 
@@ -157,6 +158,7 @@ class Backtesting:
         self._can_short = self.trading_mode != TradingMode.SPOT
         self._position_stacking: bool = self.config.get('position_stacking', False)
         self.enable_protections: bool = self.config.get('enable_protections', False)
+        migrate_binance_futures_data(config)
 
         self.init_backtest()
 
@@ -573,26 +575,6 @@ class Backtesting:
         """ Rate is within candle, therefore filled"""
         return row[LOW_IDX] <= rate <= row[HIGH_IDX]
 
-    def _get_exit_trade_entry_for_candle(self, trade: LocalTrade,
-                                         row: Tuple) -> Optional[LocalTrade]:
-
-        # Check if we need to adjust our current positions
-        if self.strategy.position_adjustment_enable:
-            trade = self._get_adjust_trade_entry_for_candle(trade, row)
-
-        enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
-        exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
-        exits = self.strategy.should_exit(
-            trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(),  # type: ignore
-            enter=enter, exit_=exit_sig,
-            low=row[LOW_IDX], high=row[HIGH_IDX]
-        )
-        for exit_ in exits:
-            t = self._get_exit_for_signal(trade, row, exit_)
-            if t:
-                return t
-        return None
-
     def _get_exit_for_signal(
             self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
             amount: Optional[float] = None) -> Optional[LocalTrade]:
@@ -662,7 +644,7 @@ class Backtesting:
         return None
 
     def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
-                    close_rate: float, amount: float = None) -> Optional[LocalTrade]:
+                    close_rate: float, amount: Optional[float] = None) -> Optional[LocalTrade]:
         self.order_id_counter += 1
         exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
         order_type = self.strategy.order_types['exit']
@@ -692,11 +674,10 @@ class Backtesting:
         trade.orders.append(order)
         return trade
 
-    def _get_exit_trade_entry(
-            self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
+    def _check_trade_exit(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
         exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
 
-        if is_first and self.trading_mode == TradingMode.FUTURES:
+        if self.trading_mode == TradingMode.FUTURES:
             trade.funding_fees = self.exchange.calculate_funding_fees(
                 self.futures_data[trade.pair],
                 amount=trade.amount,
@@ -705,7 +686,22 @@ class Backtesting:
                 close_date=exit_candle_time,
             )
 
-        return self._get_exit_trade_entry_for_candle(trade, row)
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            trade = self._get_adjust_trade_entry_for_candle(trade, row)
+
+        enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
+        exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
+        exits = self.strategy.should_exit(
+            trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(),  # type: ignore
+            enter=enter, exit_=exit_sig,
+            low=row[LOW_IDX], high=row[HIGH_IDX]
+        )
+        for exit_ in exits:
+            t = self._get_exit_for_signal(trade, row, exit_)
+            if t:
+                return t
+        return None
 
     def get_valid_price_and_stake(
         self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@@ -769,6 +765,7 @@ class Backtesting:
             stake_amount=stake_amount,
             min_stake_amount=min_stake_amount,
             max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None
         )
 
         return propose_rate, stake_amount_val, leverage, min_stake_amount
@@ -778,6 +775,11 @@ class Backtesting:
                      trade: Optional[LocalTrade] = None,
                      requested_rate: Optional[float] = None,
                      requested_stake: Optional[float] = None) -> Optional[LocalTrade]:
+        """
+        :param trade: Trade to adjust - initial entry if None
+        :param requested_rate: Adjusted entry rate
+        :param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
+        """
 
         current_time = row[DATE_IDX].to_pydatetime()
         entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
@@ -803,7 +805,7 @@ class Backtesting:
             return trade
         time_in_force = self.strategy.order_time_in_force['entry']
 
-        if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
+        if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
             self.order_id_counter += 1
             base_currency = self.exchange.get_pair_base_currency(pair)
             amount_p = (stake_amount / propose_rate) * leverage
@@ -919,8 +921,9 @@ class Backtesting:
                 trade.close(exit_row[OPEN_IDX], show_msg=False)
                 LocalTrade.close_bt_trade(trade)
 
-    def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
+    def trade_slot_available(self, open_trade_count: int) -> bool:
         # Always allow trades when max_open_trades is enabled.
+        max_open_trades: IntOrInf = self.config['max_open_trades']
         if max_open_trades <= 0 or open_trade_count < max_open_trades:
             return True
         # Rejected trade
@@ -1050,7 +1053,8 @@ class Backtesting:
 
     def backtest_loop(
             self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
-            max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
+            open_trade_count_start: int, trade_dir: Optional[LongShort],
+            is_first: bool = True) -> int:
         """
         NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
 
@@ -1069,11 +1073,10 @@ class Backtesting:
         # max_open_trades must be respected
         # don't open on the last row
         # We only open trades on the main candle, not on detail candles
-        trade_dir = self.check_for_trade_entry(row)
         if (
             (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
             and is_first
-            and self.trade_slot_available(max_open_trades, open_trade_count_start)
+            and self.trade_slot_available(open_trade_count_start)
             and current_time != end_date
             and trade_dir is not None
             and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
@@ -1098,7 +1101,7 @@ class Backtesting:
 
                 # 4. Create exit orders (if any)
             if not trade.open_order_id:
-                self._get_exit_trade_entry(trade, row, is_first)  # Place exit order if necessary
+                self._check_trade_exit(trade, row)  # Place exit order if necessary
 
                 # 5. Process exit orders.
             order = trade.select_order(trade.exit_side, is_open=True)
@@ -1120,8 +1123,7 @@ class Backtesting:
         return open_trade_count_start
 
     def backtest(self, processed: Dict,
-                 start_date: datetime, end_date: datetime,
-                 max_open_trades: int = 0) -> Dict[str, Any]:
+                 start_date: datetime, end_date: datetime) -> Dict[str, Any]:
         """
         Implement backtesting functionality
 
@@ -1133,7 +1135,6 @@ class Backtesting:
         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
         :return: DataFrame with trades (results of backtesting)
         """
         self.prepare_backtest(self.enable_protections)
@@ -1163,7 +1164,15 @@ class Backtesting:
                 indexes[pair] = row_index
                 self.dataprovider._set_dataframe_max_index(row_index)
                 current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
-                if self.timeframe_detail and pair in self.detail_data:
+                trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
+
+                if (
+                    (trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
+                    and self.timeframe_detail and pair in self.detail_data
+                ):
+                    # Spread out into detail timeframe.
+                    # Should only happen when we are either in a trade for this pair
+                    # or when we got the signal for a new trade.
                     exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
 
                     detail_data = self.detail_data[pair]
@@ -1174,8 +1183,9 @@ class Backtesting:
                     if len(detail_data) == 0:
                         # Fall back to "regular" data if no detail data was found for this candle
                         open_trade_count_start = self.backtest_loop(
-                            row, pair, current_time, end_date, max_open_trades,
-                            open_trade_count_start)
+                            row, pair, current_time, end_date,
+                            open_trade_count_start, trade_dir)
+                        continue
                     detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
                     detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
                     detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
@@ -1186,13 +1196,14 @@ class Backtesting:
                     current_time_det = current_time
                     for det_row in detail_data[HEADERS].values.tolist():
                         open_trade_count_start = self.backtest_loop(
-                            det_row, pair, current_time_det, end_date, max_open_trades,
-                            open_trade_count_start, is_first)
+                            det_row, pair, current_time_det, end_date,
+                            open_trade_count_start, trade_dir, is_first)
                         current_time_det += timedelta(minutes=self.timeframe_detail_min)
                         is_first = False
                 else:
                     open_trade_count_start = self.backtest_loop(
-                        row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
+                        row, pair, current_time, end_date,
+                        open_trade_count_start, trade_dir)
 
             # Move time one configured time_interval ahead.
             self.progress.increment()
@@ -1224,13 +1235,11 @@ class Backtesting:
         self._set_strategy(strat)
 
         # Use max_open_trades in backtesting, except --disable-max-market-positions is set
-        if self.config.get('use_max_market_positions', True):
-            # Must come from strategy config, as the strategy may modify this setting.
-            max_open_trades = self.strategy.config['max_open_trades']
-        else:
+        if not self.config.get('use_max_market_positions', True):
             logger.info(
                 'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
-            max_open_trades = 0
+            self.strategy.max_open_trades = float('inf')
+            self.config.update({'max_open_trades': self.strategy.max_open_trades})
 
         # need to reprocess data every time to populate signals
         preprocessed = self.strategy.advise_all_indicators(data)
@@ -1253,7 +1262,6 @@ class Backtesting:
             processed=preprocessed,
             start_date=min_date,
             end_date=max_date,
-            max_open_trades=max_open_trades,
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({

freqtrade/optimize/hyperopt.py

@@ -74,6 +74,7 @@ class Hyperopt:
         self.roi_space: List[Dimension] = []
         self.stoploss_space: List[Dimension] = []
         self.trailing_space: List[Dimension] = []
+        self.max_open_trades_space: List[Dimension] = []
         self.dimensions: List[Dimension] = []
 
         self.config = config
@@ -117,11 +118,10 @@ class Hyperopt:
         self.current_best_epoch: Optional[Dict[str, Any]] = None
 
         # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
-        if self.config.get('use_max_market_positions', True):
-            self.max_open_trades = self.config['max_open_trades']
-        else:
+        if not self.config.get('use_max_market_positions', True):
             logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
-            self.max_open_trades = 0
+            self.backtesting.strategy.max_open_trades = float('inf')
+            config.update({'max_open_trades': self.backtesting.strategy.max_open_trades})
 
         if HyperoptTools.has_space(self.config, 'sell'):
             # Make sure use_exit_signal is enabled
@@ -209,6 +209,10 @@ class Hyperopt:
             result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
         if HyperoptTools.has_space(self.config, 'trailing'):
             result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
+        if HyperoptTools.has_space(self.config, 'trades'):
+            result['max_open_trades'] = {
+                'max_open_trades': self.backtesting.strategy.max_open_trades
+                if self.backtesting.strategy.max_open_trades != float('inf') else -1}
 
         return result
 
@@ -229,6 +233,8 @@ class Hyperopt:
                 'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
                 'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
             }
+        if not HyperoptTools.has_space(self.config, 'trades'):
+            result['max_open_trades'] = {'max_open_trades': strategy.max_open_trades}
         return result
 
     def print_results(self, results) -> None:
@@ -280,8 +286,13 @@ class Hyperopt:
             logger.debug("Hyperopt has 'trailing' space")
             self.trailing_space = self.custom_hyperopt.trailing_space()
 
+        if HyperoptTools.has_space(self.config, 'trades'):
+            logger.debug("Hyperopt has 'trades' space")
+            self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
+
         self.dimensions = (self.buy_space + self.sell_space + self.protection_space
-                           + self.roi_space + self.stoploss_space + self.trailing_space)
+                           + self.roi_space + self.stoploss_space + self.trailing_space
+                           + self.max_open_trades_space)
 
     def assign_params(self, params_dict: Dict, category: str) -> None:
         """
@@ -328,6 +339,20 @@ class Hyperopt:
             self.backtesting.strategy.trailing_only_offset_is_reached = \
                 d['trailing_only_offset_is_reached']
 
+        if HyperoptTools.has_space(self.config, 'trades'):
+            if self.config["stake_amount"] == "unlimited" and \
+                    (params_dict['max_open_trades'] == -1 or params_dict['max_open_trades'] == 0):
+                # Ignore unlimited max open trades if stake amount is unlimited
+                params_dict.update({'max_open_trades': self.config['max_open_trades']})
+
+            updated_max_open_trades = int(params_dict['max_open_trades']) \
+                if (params_dict['max_open_trades'] != -1
+                    and params_dict['max_open_trades'] != 0) else float('inf')
+
+            self.config.update({'max_open_trades': updated_max_open_trades})
+
+            self.backtesting.strategy.max_open_trades = updated_max_open_trades
+
         with self.data_pickle_file.open('rb') as f:
             processed = load(f, mmap_mode='r')
             if self.analyze_per_epoch:
@@ -337,8 +362,7 @@ class Hyperopt:
         bt_results = self.backtesting.backtest(
             processed=processed,
             start_date=self.min_date,
-            end_date=self.max_date,
-            max_open_trades=self.max_open_trades,
+            end_date=self.max_date
         )
         backtest_end_time = datetime.now(timezone.utc)
         bt_results.update({

freqtrade/optimize/hyperopt_auto.py

@@ -91,5 +91,8 @@ class HyperOptAuto(IHyperOpt):
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
 
+    def max_open_trades_space(self) -> List['Dimension']:
+        return self._get_func('max_open_trades_space')()
+
     def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
         return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

freqtrade/optimize/hyperopt_interface.py

@@ -191,6 +191,16 @@ class IHyperOpt(ABC):
             Categorical([True, False], name='trailing_only_offset_is_reached'),
         ]
 
+    def max_open_trades_space(self) -> List[Dimension]:
+        """
+        Create a max open trades space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            Integer(-1, 10, name='max_open_trades'),
+        ]
+
     # This is needed for proper unpickling the class attribute timeframe
     # which is set to the actual value by the resolver.
     # Why do I still need such shamanic mantras in modern python?

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
 from datetime import datetime
-from math import sqrt as msqrt
-from typing import Any, Dict
 
 from pandas import DataFrame
 
 from freqtrade.constants import Config
-from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_calmar
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(
-        results: DataFrame,
-        trade_count: int,
-        min_date: datetime,
-        max_date: datetime,
-        config: Config,
-        processed: Dict[str, DataFrame],
-        backtest_stats: Dict[str, Any],
-        *args,
-        **kwargs
-    ) -> float:
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Calmar Ratio calculation.
         """
-        total_profit = backtest_stats["profit_total"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period * 100
-
-        # calculate max drawdown
-        try:
-            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
-                results, value_col="profit_abs"
-            )
-        except ValueError:
-            max_drawdown = 0
-
-        if max_drawdown != 0:
-            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
-        else:
-            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
-            calmar_ratio = -20.0
-
+        starting_balance = config['dry_run_wallet']
+        calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, max_drawdown, calmar_ratio)
         return -calmar_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sharpe
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sharpe Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-        up_stdev = np.std(total_profit)
-
-        if up_stdev != 0:
-            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
-            sharp_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sortino
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sortino Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-
-        results['downside_returns'] = 0
-        results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
-        down_stdev = np.std(results['downside_returns'])
-
-        if down_stdev != 0:
-            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
-            sortino_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, down_stdev, sortino_ratio)
         return -sortino_ratio

freqtrade/optimize/hyperopt_tools.py

@@ -96,7 +96,7 @@ class HyperoptTools():
         Tell if the space value is contained in the configuration
         """
         # 'trailing' and 'protection spaces are not included in the 'default' set of spaces
-        if space in ('trailing', 'protection'):
+        if space in ('trailing', 'protection', 'trades'):
             return any(s in config['spaces'] for s in [space, 'all'])
         else:
             return any(s in config['spaces'] for s in [space, 'all', 'default'])
@@ -170,7 +170,7 @@ class HyperoptTools():
 
     @staticmethod
     def show_epoch_details(results, total_epochs: int, print_json: bool,
-                           no_header: bool = False, header_str: str = None) -> None:
+                           no_header: bool = False, header_str: Optional[str] = None) -> None:
         """
         Display details of the hyperopt result
         """
@@ -187,7 +187,8 @@ class HyperoptTools():
 
         if print_json:
             result_dict: Dict = {}
-            for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
+            for s in ['buy', 'sell', 'protection',
+                      'roi', 'stoploss', 'trailing', 'max_open_trades']:
                 HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
@@ -201,6 +202,8 @@ class HyperoptTools():
             HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
+            HyperoptTools._params_pretty_print(
+                params, 'max_open_trades', "Max Open Trades:", non_optimized)
 
     @staticmethod
     def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
@@ -239,7 +242,9 @@ class HyperoptTools():
             if space == "stoploss":
                 stoploss = safe_value_fallback2(space_params, no_params, space, space)
                 result += (f"stoploss = {stoploss}{appendix}")
-
+            elif space == "max_open_trades":
+                max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
+                result += (f"max_open_trades = {max_open_trades}{appendix}")
             elif space == "roi":
                 result = result[:-1] + f'{appendix}\n'
                 minimal_roi_result = rapidjson.dumps({
@@ -259,7 +264,7 @@ class HyperoptTools():
             print(result)
 
     @staticmethod
-    def _space_params(params, space: str, r: int = None) -> Dict:
+    def _space_params(params, space: str, r: Optional[int] = None) -> Dict:
         d = params.get(space)
         if d:
             # Round floats to `r` digits after the decimal point if requested

freqtrade/optimize/optimize_reports.py

@@ -8,9 +8,10 @@ from pandas import DataFrame, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
-                                 Config)
-from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
-                                    calculate_max_drawdown)
+                                 Config, IntOrInf)
+from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
+                                    calculate_expectancy, calculate_market_change,
+                                    calculate_max_drawdown, calculate_sharpe, calculate_sortino)
 from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 
@@ -190,7 +191,7 @@ def generate_tag_metrics(tag_type: str,
         return []
 
 
-def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
+def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
     """
     Generate small table outlining Backtest results
     :param max_open_trades: Max_open_trades parameter
@@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
         'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
         'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
         'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
+        'expectancy': calculate_expectancy(results),
+        'sortino': calculate_sortino(results, min_date, max_date, start_balance),
+        'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
+        'calmar': calculate_calmar(results, min_date, max_date, start_balance),
         'profit_factor': profit_factor,
         'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
         'backtest_start_ts': int(min_date.timestamp() * 1000),
@@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                   strat_results['stake_currency'])),
             ('Total profit %', f"{strat_results['profit_total']:.2%}"),
             ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
+            ('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
+            ('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
+            ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
             ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
                               in strat_results else 'N/A'),
+            ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
+                           in strat_results else 'N/A'),
             ('Trades per day', strat_results['trades_per_day']),
             ('Avg. daily profit %',
              f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

freqtrade/persistence/migrations.py

@@ -109,11 +109,10 @@ def migrate_trades_and_orders_table(
     else:
         is_short = get_column_def(cols, 'is_short', '0')
 
-    # Margin Properties
+    # Futures Properties
     interest_rate = get_column_def(cols, 'interest_rate', '0.0')
-
-    # Futures properties
     funding_fees = get_column_def(cols, 'funding_fees', '0.0')
+    max_stake_amount = get_column_def(cols, 'max_stake_amount', 'stake_amount')
 
     # If ticker-interval existed use that, else null.
     if has_column(cols, 'ticker_interval'):
@@ -162,7 +161,8 @@ def migrate_trades_and_orders_table(
             timeframe, open_trade_value, close_profit_abs,
             trading_mode, leverage, liquidation_price, is_short,
             interest_rate, funding_fees, realized_profit,
-            amount_precision, price_precision, precision_mode, contract_size
+            amount_precision, price_precision, precision_mode, contract_size,
+            max_stake_amount
             )
         select id, lower(exchange), pair, {base_currency} base_currency,
             {stake_currency} stake_currency,
@@ -190,7 +190,8 @@ def migrate_trades_and_orders_table(
             {is_short} is_short, {interest_rate} interest_rate,
             {funding_fees} funding_fees, {realized_profit} realized_profit,
             {amount_precision} amount_precision, {price_precision} price_precision,
-            {precision_mode} precision_mode, {contract_size} contract_size
+            {precision_mode} precision_mode, {contract_size} contract_size,
+            {max_stake_amount} max_stake_amount
             from {trade_back_name}
             """))
 
@@ -213,17 +214,22 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
     average = get_column_def(cols_order, 'average', 'null')
     stop_price = get_column_def(cols_order, 'stop_price', 'null')
     funding_fee = get_column_def(cols_order, 'funding_fee', '0.0')
+    ft_amount = get_column_def(cols_order, 'ft_amount', 'coalesce(amount, 0.0)')
+    ft_price = get_column_def(cols_order, 'ft_price', 'coalesce(price, 0.0)')
 
     # sqlite does not support literals for booleans
     with engine.begin() as connection:
         connection.execute(text(f"""
             insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
             status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
-            stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee)
+            stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee,
+            ft_amount, ft_price
+            )
             select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
             status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
             cost, {stop_price} stop_price, order_date, order_filled_date,
-            order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee
+            order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee,
+            {ft_amount} ft_amount, {ft_price} ft_price
             from {table_back_name}
             """))
 
@@ -310,8 +316,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
     # if ('orders' not in previous_tables
     # or not has_column(cols_orders, 'funding_fee')):
     migrating = False
-    # if not has_column(cols_trades, 'contract_size'):
-    if not has_column(cols_orders, 'funding_fee'):
+    # if not has_column(cols_trades, 'max_stake_amount'):
+    if not has_column(cols_orders, 'ft_price'):
         migrating = True
         logger.info(f"Running database migration for trades - "
                     f"backup: {table_back_name}, {order_table_bak_name}")

freqtrade/persistence/pairlock_middleware.py

@@ -30,8 +30,8 @@ class PairLocks():
             PairLocks.locks = []
 
     @staticmethod
-    def lock_pair(pair: str, until: datetime, reason: str = None, *,
-                  now: datetime = None, side: str = '*') -> PairLock:
+    def lock_pair(pair: str, until: datetime, reason: Optional[str] = None, *,
+                  now: Optional[datetime] = None, side: str = '*') -> PairLock:
         """
         Create PairLock from now to "until".
         Uses database by default, unless PairLocks.use_db is set to False,

freqtrade/persistence/trade_model.py

@@ -49,6 +49,8 @@ class Order(_DECL_BASE):
     ft_order_side: str = Column(String(25), nullable=False)
     ft_pair: str = Column(String(25), nullable=False)
     ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
+    ft_amount = Column(Float, nullable=False)
+    ft_price = Column(Float, nullable=False)
 
     order_id: str = Column(String(255), nullable=False, index=True)
     status = Column(String(255), nullable=True)
@@ -82,9 +84,13 @@ class Order(_DECL_BASE):
             self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
         )
 
+    @property
+    def safe_amount(self) -> float:
+        return self.amount or self.ft_amount
+
     @property
     def safe_price(self) -> float:
-        return self.average or self.price or self.stop_price
+        return self.average or self.price or self.stop_price or self.ft_price
 
     @property
     def safe_filled(self) -> float:
@@ -94,7 +100,7 @@ class Order(_DECL_BASE):
     def safe_remaining(self) -> float:
         return (
             self.remaining if self.remaining is not None else
-            self.amount - (self.filled or 0.0)
+            self.safe_amount - (self.filled or 0.0)
         )
 
     @property
@@ -140,7 +146,7 @@ class Order(_DECL_BASE):
                 # Assign funding fee up to this point
                 # (represents the funding fee since the last order)
                 self.funding_fee = self.trade.funding_fees
-            if (order.get('filled', 0.0) or 0.0) > 0:
+            if (order.get('filled', 0.0) or 0.0) > 0 and not self.order_filled_date:
                 self.order_filled_date = datetime.now(timezone.utc)
         self.order_update_date = datetime.now(timezone.utc)
 
@@ -227,11 +233,20 @@ class Order(_DECL_BASE):
             logger.warning(f"Did not find order for {order}.")
 
     @staticmethod
-    def parse_from_ccxt_object(order: Dict[str, Any], pair: str, side: str) -> 'Order':
+    def parse_from_ccxt_object(
+            order: Dict[str, Any], pair: str, side: str,
+            amount: Optional[float] = None, price: Optional[float] = None) -> 'Order':
         """
         Parse an order from a ccxt object and return a new order Object.
+        Optional support for overriding amount and price is only used for test simplification.
         """
-        o = Order(order_id=str(order['id']), ft_order_side=side, ft_pair=pair)
+        o = Order(
+            order_id=str(order['id']),
+            ft_order_side=side,
+            ft_pair=pair,
+            ft_amount=amount if amount else order['amount'],
+            ft_price=price if price else order['price'],
+            )
 
         o.update_from_ccxt_object(order)
         return o
@@ -293,6 +308,7 @@ class LocalTrade():
     close_profit: Optional[float] = None
     close_profit_abs: Optional[float] = None
     stake_amount: float = 0.0
+    max_stake_amount: float = 0.0
     amount: float = 0.0
     amount_requested: Optional[float] = None
     open_date: datetime
@@ -397,12 +413,6 @@ class LocalTrade():
     def close_date_utc(self):
         return self.close_date.replace(tzinfo=timezone.utc)
 
-    @property
-    def enter_side(self) -> str:
-        """ DEPRECATED, please use entry_side instead"""
-        # TODO: Please remove me after 2022.5
-        return self.entry_side
-
     @property
     def entry_side(self) -> str:
         if self.is_short:
@@ -475,8 +485,8 @@ class LocalTrade():
             'amount': round(self.amount, 8),
             'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
             'stake_amount': round(self.stake_amount, 8),
+            'max_stake_amount': round(self.max_stake_amount, 8) if self.max_stake_amount else None,
             'strategy': self.strategy,
-            'buy_tag': self.enter_tag,
             'enter_tag': self.enter_tag,
             'timeframe': self.timeframe,
 
@@ -513,7 +523,6 @@ class LocalTrade():
             'profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
             'profit_abs': self.close_profit_abs,
 
-            'sell_reason': self.exit_reason,  # Deprecated
             'exit_reason': self.exit_reason,
             'exit_order_status': self.exit_order_status,
             'stop_loss_abs': self.stop_loss,
@@ -790,7 +799,7 @@ class LocalTrade():
         else:
             return close_trade - fees
 
-    def calc_close_trade_value(self, rate: float, amount: float = None) -> float:
+    def calc_close_trade_value(self, rate: float, amount: Optional[float] = None) -> float:
         """
         Calculate the Trade's close value including fees
         :param rate: rate to compare with.
@@ -828,7 +837,8 @@ class LocalTrade():
             raise OperationalException(
                 f"{self.trading_mode.value} trading is not yet available using freqtrade")
 
-    def calc_profit(self, rate: float, amount: float = None, open_rate: float = None) -> float:
+    def calc_profit(self, rate: float, amount: Optional[float] = None,
+                    open_rate: Optional[float] = None) -> float:
         """
         Calculate the absolute profit in stake currency between Close and Open trade
         :param rate: close rate to compare with.
@@ -849,7 +859,8 @@ class LocalTrade():
         return float(f"{profit:.8f}")
 
     def calc_profit_ratio(
-            self, rate: float, amount: float = None, open_rate: float = None) -> float:
+            self, rate: float, amount: Optional[float] = None,
+            open_rate: Optional[float] = None) -> float:
         """
         Calculates the profit as ratio (including fee).
         :param rate: rate to compare with.
@@ -882,6 +893,7 @@ class LocalTrade():
         ZERO = FtPrecise(0.0)
         current_amount = FtPrecise(0.0)
         current_stake = FtPrecise(0.0)
+        max_stake_amount = FtPrecise(0.0)
         total_stake = 0.0  # Total stake after all buy orders (does not subtract!)
         avg_price = FtPrecise(0.0)
         close_profit = 0.0
@@ -923,7 +935,9 @@ class LocalTrade():
                     exit_rate, amount=exit_amount, open_rate=avg_price)
             else:
                 total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
+                max_stake_amount += (tmp_amount * price)
         self.funding_fees = funding_fees
+        self.max_stake_amount = float(max_stake_amount)
 
         if close_profit:
             self.close_profit = close_profit
@@ -959,11 +973,12 @@ class LocalTrade():
         return None
 
     def select_order(self, order_side: Optional[str] = None,
-                     is_open: Optional[bool] = None) -> Optional[Order]:
+                     is_open: Optional[bool] = None, only_filled: bool = False) -> Optional[Order]:
         """
         Finds latest order for this orderside and status
         :param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
         :param is_open: Only search for open orders?
+        :param only_filled: Only search for Filled orders (only valid with is_open=False).
         :return: latest Order object if it exists, else None
         """
         orders = self.orders
@@ -971,6 +986,8 @@ class LocalTrade():
             orders = [o for o in orders if o.ft_order_side == order_side]
         if is_open is not None:
             orders = [o for o in orders if o.ft_is_open == is_open]
+        if is_open is False and only_filled:
+            orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
         if len(orders) > 0:
             return orders[-1]
         else:
@@ -1044,8 +1061,9 @@ class LocalTrade():
         return self.exit_reason
 
     @staticmethod
-    def get_trades_proxy(*, pair: str = None, is_open: bool = None,
-                         open_date: datetime = None, close_date: datetime = None,
+    def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
+                         open_date: Optional[datetime] = None,
+                         close_date: Optional[datetime] = None,
                          ) -> List['LocalTrade']:
         """
         Helper function to query Trades.
@@ -1175,6 +1193,7 @@ class Trade(_DECL_BASE, LocalTrade):
     close_profit = Column(Float)
     close_profit_abs = Column(Float)
     stake_amount = Column(Float, nullable=False)
+    max_stake_amount = Column(Float)
     amount = Column(Float)
     amount_requested = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
@@ -1241,8 +1260,9 @@ class Trade(_DECL_BASE, LocalTrade):
         Trade.query.session.rollback()
 
     @staticmethod
-    def get_trades_proxy(*, pair: str = None, is_open: bool = None,
-                         open_date: datetime = None, close_date: datetime = None,
+    def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
+                         open_date: Optional[datetime] = None,
+                         close_date: Optional[datetime] = None,
                          ) -> List['LocalTrade']:
         """
         Helper function to query Trades.j

freqtrade/plot/plotting.py

@@ -436,11 +436,11 @@ def create_scatter(
     return None
 
 
-def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
-                               indicators1: List[str] = [],
-                               indicators2: List[str] = [],
-                               plot_config: Dict[str, Dict] = {},
-                               ) -> go.Figure:
+def generate_candlestick_graph(
+        pair: str, data: pd.DataFrame, trades: Optional[pd.DataFrame] = None, *,
+        indicators1: List[str] = [], indicators2: List[str] = [],
+        plot_config: Dict[str, Dict] = {},
+        ) -> go.Figure:
     """
     Generate the graph from the data generated by Backtesting or from DB
     Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators

freqtrade/plugins/pairlist/RemotePairList.py

@@ -0,0 +1,206 @@
+"""
+Remote PairList provider
+
+Provides pair list fetched from a remote source
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import requests
+from cachetools import TTLCache
+
+from freqtrade import __version__
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList
+
+
+logger = logging.getLogger(__name__)
+
+
+class RemotePairList(IPairList):
+
+    def __init__(self, exchange, pairlistmanager,
+                 config: Config, pairlistconfig: Dict[str, Any],
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        if 'number_assets' not in self._pairlistconfig:
+            raise OperationalException(
+                '`number_assets` not specified. Please check your configuration '
+                'for "pairlist.config.number_assets"')
+
+        if 'pairlist_url' not in self._pairlistconfig:
+            raise OperationalException(
+                '`pairlist_url` not specified. Please check your configuration '
+                'for "pairlist.config.pairlist_url"')
+
+        self._number_pairs = self._pairlistconfig['number_assets']
+        self._refresh_period: int = self._pairlistconfig.get('refresh_period', 1800)
+        self._keep_pairlist_on_failure = self._pairlistconfig.get('keep_pairlist_on_failure', True)
+        self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._pairlist_url = self._pairlistconfig.get('pairlist_url', '')
+        self._read_timeout = self._pairlistconfig.get('read_timeout', 60)
+        self._bearer_token = self._pairlistconfig.get('bearer_token', '')
+        self._init_done = False
+        self._last_pairlist: List[Any] = list()
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - {self._pairlistconfig['number_assets']} pairs from RemotePairlist."
+
+    def process_json(self, jsonparse) -> List[str]:
+
+        pairlist = jsonparse.get('pairs', [])
+        remote_refresh_period = int(jsonparse.get('refresh_period', self._refresh_period))
+
+        if self._refresh_period < remote_refresh_period:
+            self.log_once(f'Refresh Period has been increased from {self._refresh_period}'
+                          f' to minimum allowed: {remote_refresh_period} from Remote.', logger.info)
+
+            self._refresh_period = remote_refresh_period
+            self._pair_cache = TTLCache(maxsize=1, ttl=remote_refresh_period)
+
+        self._init_done = True
+
+        return pairlist
+
+    def return_last_pairlist(self) -> List[str]:
+        if self._keep_pairlist_on_failure:
+            pairlist = self._last_pairlist
+            self.log_once('Keeping last fetched pairlist', logger.info)
+        else:
+            pairlist = []
+
+        return pairlist
+
+    def fetch_pairlist(self) -> Tuple[List[str], float]:
+
+        headers = {
+            'User-Agent': 'Freqtrade/' + __version__ + ' Remotepairlist'
+        }
+
+        if self._bearer_token:
+            headers['Authorization'] = f'Bearer {self._bearer_token}'
+
+        try:
+            response = requests.get(self._pairlist_url, headers=headers,
+                                    timeout=self._read_timeout)
+            content_type = response.headers.get('content-type')
+            time_elapsed = response.elapsed.total_seconds()
+
+            if "application/json" in str(content_type):
+                jsonparse = response.json()
+
+                try:
+                    pairlist = self.process_json(jsonparse)
+                except Exception as e:
+
+                    if self._init_done:
+                        pairlist = self.return_last_pairlist()
+                        logger.warning(f'Error while processing JSON data: {type(e)}')
+                    else:
+                        raise OperationalException(f'Error while processing JSON data: {type(e)}')
+
+            else:
+                if self._init_done:
+                    self.log_once(f'Error: RemotePairList is not of type JSON: '
+                                  f' {self._pairlist_url}', logger.info)
+                    pairlist = self.return_last_pairlist()
+                else:
+                    raise OperationalException('RemotePairList is not of type JSON, abort.')
+
+        except requests.exceptions.RequestException:
+            self.log_once(f'Was not able to fetch pairlist from:'
+                          f' {self._pairlist_url}', logger.info)
+
+            pairlist = self.return_last_pairlist()
+
+            time_elapsed = 0
+
+        return pairlist, time_elapsed
+
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+
+        if self._init_done:
+            pairlist = self._pair_cache.get('pairlist')
+        else:
+            pairlist = []
+
+        time_elapsed = 0.0
+
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            if self._pairlist_url.startswith("file:///"):
+                filename = self._pairlist_url.split("file:///", 1)[1]
+                file_path = Path(filename)
+
+                if file_path.exists():
+                    with open(filename) as json_file:
+                        # Load the JSON data into a dictionary
+                        jsonparse = json.load(json_file)
+
+                        try:
+                            pairlist = self.process_json(jsonparse)
+                        except Exception as e:
+                            if self._init_done:
+                                pairlist = self.return_last_pairlist()
+                                logger.warning(f'Error while processing JSON data: {type(e)}')
+                            else:
+                                raise OperationalException('Error while processing'
+                                                           f'JSON data: {type(e)}')
+                else:
+                    raise ValueError(f"{self._pairlist_url} does not exist.")
+            else:
+                # Fetch Pairlist from Remote URL
+                pairlist, time_elapsed = self.fetch_pairlist()
+
+        self.log_once(f"Fetched pairs: {pairlist}", logger.debug)
+
+        pairlist = self._whitelist_for_active_markets(pairlist)
+        pairlist = pairlist[:self._number_pairs]
+
+        self._pair_cache['pairlist'] = pairlist.copy()
+
+        if time_elapsed != 0.0:
+            self.log_once(f'Pairlist Fetched in {time_elapsed} seconds.', logger.info)
+        else:
+            self.log_once('Fetched Pairlist.', logger.info)
+
+        self._last_pairlist = list(pairlist)
+
+        return pairlist
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        rpl_pairlist = self.gen_pairlist(tickers)
+        merged_list = pairlist + rpl_pairlist
+        merged_list = sorted(set(merged_list), key=merged_list.index)
+        return merged_list

freqtrade/plugins/pairlist/VolumePairList.py

@@ -135,7 +135,7 @@ class VolumePairList(IPairList):
                 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.get(self._sort_key) is not None)
                         and v['symbol'] in _pairlist)]
                 pairlist = [s['symbol'] for s in filtered_tickers]
             else:

freqtrade/plugins/pairlistmanager.py

@@ -23,7 +23,8 @@ logger = logging.getLogger(__name__)
 
 class PairListManager(LoggingMixin):
 
-    def __init__(self, exchange, config: Config, dataprovider: DataProvider = None) -> None:
+    def __init__(
+            self, exchange, config: Config, dataprovider: Optional[DataProvider] = None) -> None:
         self._exchange = exchange
         self._config = config
         self._whitelist = self._config['exchange'].get('pair_whitelist')
@@ -153,7 +154,8 @@ class PairListManager(LoggingMixin):
             return []
         return whitelist
 
-    def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
+    def create_pair_list(
+            self, pairs: List[str], timeframe: Optional[str] = None) -> ListPairsWithTimeframes:
         """
         Create list of pair tuples with (pair, timeframe)
         """

freqtrade/resolvers/iresolver.py

@@ -89,7 +89,8 @@ class IResolver:
             module = importlib.util.module_from_spec(spec)
             try:
                 spec.loader.exec_module(module)  # type: ignore # importlib does not use typehints
-            except (ModuleNotFoundError, SyntaxError, ImportError, NameError) as err:
+            except (AttributeError, ModuleNotFoundError, SyntaxError,
+                    ImportError, NameError) as err:
                 # Catch errors in case a specific module is not installed
                 logger.warning(f"Could not import {module_path} due to '{err}'")
                 if enum_failed:

freqtrade/resolvers/strategy_resolver.py

@@ -33,7 +33,7 @@ class StrategyResolver(IResolver):
     extra_path = "strategy_path"
 
     @staticmethod
-    def load_strategy(config: Config = None) -> IStrategy:
+    def load_strategy(config: Optional[Config] = None) -> IStrategy:
         """
         Load the custom class from config parameter
         :param config: configuration dictionary or None
@@ -76,6 +76,7 @@ class StrategyResolver(IResolver):
                       ("ignore_buying_expired_candle_after",  0),
                       ("position_adjustment_enable",      False),
                       ("max_entry_position_adjustment",      -1),
+                      ("max_open_trades",                    -1)
                       ]
         for attribute, default in attributes:
             StrategyResolver._override_attribute_helper(strategy, config,
@@ -110,7 +111,11 @@ class StrategyResolver(IResolver):
             val = getattr(strategy, attribute)
             # None's cannot exist in the config, so do not copy them
             if val is not None:
-                config[attribute] = val
+                # max_open_trades set to -1 in the strategy will be copied as infinity in the config
+                if attribute == 'max_open_trades' and val == -1:
+                    config[attribute] = float('inf')
+                else:
+                    config[attribute] = val
         # Explicitly check for None here as other "falsy" values are possible
         elif default is not None:
             setattr(strategy, attribute, default)
@@ -128,6 +133,8 @@ class StrategyResolver(IResolver):
                 key=lambda t: t[0]))
         if hasattr(strategy, 'stoploss'):
             strategy.stoploss = float(strategy.stoploss)
+        if hasattr(strategy, 'max_open_trades') and strategy.max_open_trades < 0:
+            strategy.max_open_trades = float('inf')
         return strategy
 
     @staticmethod

freqtrade/rpc/api_server/api_backtest.py

@@ -11,6 +11,7 @@ from freqtrade.configuration.config_validation import validate_config_consistenc
 from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException
+from freqtrade.misc import deep_merge_dicts
 from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
                                                   BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config, is_webserver_mode
@@ -37,10 +38,11 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
 
     btconfig = deepcopy(config)
     settings = dict(bt_settings)
+    if settings.get('freqai', None) is not None:
+        settings['freqai'] = dict(settings['freqai'])
     # Pydantic models will contain all keys, but non-provided ones are None
-    for setting in settings.keys():
-        if settings[setting] is not None:
-            btconfig[setting] = settings[setting]
+
+    btconfig = deep_merge_dicts(settings, btconfig, allow_null_overrides=False)
     try:
         btconfig['stake_amount'] = float(btconfig['stake_amount'])
     except ValueError:

freqtrade/rpc/api_server/api_schemas.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Union
 
 from pydantic import BaseModel
 
-from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.constants import DATETIME_PRINT_FORMAT, IntOrInf
 from freqtrade.enums import OrderTypeValues, SignalDirection, TradingMode
 
 
@@ -165,7 +165,7 @@ class ShowConfig(BaseModel):
     stake_amount: str
     available_capital: Optional[float]
     stake_currency_decimals: int
-    max_open_trades: int
+    max_open_trades: IntOrInf
     minimal_roi: Dict[str, Any]
     stoploss: Optional[float]
     trailing_stop: Optional[bool]
@@ -217,8 +217,8 @@ class TradeSchema(BaseModel):
     amount: float
     amount_requested: float
     stake_amount: float
+    max_stake_amount: Optional[float]
     strategy: str
-    buy_tag: Optional[str]  # Deprecated
     enter_tag: Optional[str]
     timeframe: int
     fee_open: Optional[float]
@@ -243,7 +243,6 @@ class TradeSchema(BaseModel):
     profit_pct: Optional[float]
     profit_abs: Optional[float]
     profit_fiat: Optional[float]
-    sell_reason: Optional[str]  # Deprecated
     exit_reason: Optional[str]
     exit_order_status: Optional[str]
     stop_loss_abs: Optional[float]
@@ -372,6 +371,10 @@ class StrategyListResponse(BaseModel):
     strategies: List[str]
 
 
+class FreqAIModelListResponse(BaseModel):
+    freqaimodels: List[str]
+
+
 class StrategyResponse(BaseModel):
     strategy: str
     code: str
@@ -410,15 +413,22 @@ class PairHistory(BaseModel):
         }
 
 
+class BacktestFreqAIInputs(BaseModel):
+    identifier: str
+
+
 class BacktestRequest(BaseModel):
     strategy: str
     timeframe: Optional[str]
     timeframe_detail: Optional[str]
     timerange: Optional[str]
-    max_open_trades: Optional[int]
+    max_open_trades: Optional[IntOrInf]
     stake_amount: Optional[str]
     enable_protections: bool
     dry_run_wallet: Optional[float]
+    backtest_cache: Optional[str]
+    freqaimodel: Optional[str]
+    freqai: Optional[BacktestFreqAIInputs]
 
 
 class BacktestResponse(BaseModel):

freqtrade/rpc/api_server/api_v1.py

@@ -13,12 +13,13 @@ from freqtrade.rpc import RPC
 from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, BlacklistPayload,
                                                   BlacklistResponse, Count, Daily,
                                                   DeleteLockRequest, DeleteTrade, ForceEnterPayload,
-                                                  ForceEnterResponse, ForceExitPayload, Health,
-                                                  Locks, Logs, OpenTradeSchema, PairHistory,
-                                                  PerformanceEntry, Ping, PlotConfig, Profit,
-                                                  ResultMsg, ShowConfig, Stats, StatusMsg,
-                                                  StrategyListResponse, StrategyResponse, SysInfo,
-                                                  Version, WhitelistResponse)
+                                                  ForceEnterResponse, ForceExitPayload,
+                                                  FreqAIModelListResponse, Health, Locks, Logs,
+                                                  OpenTradeSchema, PairHistory, PerformanceEntry,
+                                                  Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
+                                                  Stats, StatusMsg, StrategyListResponse,
+                                                  StrategyResponse, SysInfo, Version,
+                                                  WhitelistResponse)
 from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
 from freqtrade.rpc.rpc import RPCException
 
@@ -38,7 +39,9 @@ logger = logging.getLogger(__name__)
 # 2.17: Forceentry - leverage, partial force_exit
 # 2.20: Add websocket endpoints
 # 2.21: Add new_candle messagetype
-API_VERSION = 2.21
+# 2.22: Add FreqAI to backtesting
+# 2.23: Allow plot config request in webserver mode
+API_VERSION = 2.23
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -246,8 +249,18 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
 
 
 @router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
-def plot_config(rpc: RPC = Depends(get_rpc)):
-    return PlotConfig.parse_obj(rpc._rpc_plot_config())
+def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
+                rpc: Optional[RPC] = Depends(get_rpc_optional)):
+    if not strategy:
+        if not rpc:
+            raise RPCException("Strategy is mandatory in webserver mode.")
+        return PlotConfig.parse_obj(rpc._rpc_plot_config())
+    else:
+        config1 = deepcopy(config)
+        config1.update({
+            'strategy': strategy
+        })
+        return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
 
 
 @router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
@@ -279,6 +292,16 @@ def get_strategy(strategy: str, config=Depends(get_config)):
     }
 
 
+@router.get('/freqaimodels', response_model=FreqAIModelListResponse, tags=['freqai'])
+def list_freqaimodels(config=Depends(get_config)):
+    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+    strategies = FreqaiModelResolver.search_all_objects(
+        config, False)
+    strategies = sorted(strategies, key=lambda x: x['name'])
+
+    return {'freqaimodels': [x['name'] for x in strategies]}
+
+
 @router.get('/available_pairs', response_model=AvailablePairs, tags=['candle data'])
 def list_available_pairs(timeframe: Optional[str] = None, stake_currency: Optional[str] = None,
                          candletype: Optional[CandleType] = None, config=Depends(get_config)):

freqtrade/rpc/rpc.py

@@ -673,6 +673,7 @@ class RPC:
         if self._freqtrade.state == State.RUNNING:
             # Set 'max_open_trades' to 0
             self._freqtrade.config['max_open_trades'] = 0
+            self._freqtrade.strategy.max_open_trades = 0
 
         return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
 
@@ -944,7 +945,7 @@ class RPC:
         resp['errors'] = errors
         return resp
 
-    def _rpc_blacklist(self, add: List[str] = None) -> Dict:
+    def _rpc_blacklist(self, add: Optional[List[str]] = None) -> Dict:
         """ Returns the currently active blacklist"""
         errors = {}
         if add:
@@ -1126,12 +1127,12 @@ class RPC:
         return self._freqtrade.active_pair_whitelist
 
     @staticmethod
-    def _rpc_analysed_history_full(config, pair: str, timeframe: str,
+    def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
                                    timerange: str, exchange) -> Dict[str, Any]:
         timerange_parsed = TimeRange.parse_timerange(timerange)
 
         _data = load_data(
-            datadir=config.get("datadir"),
+            datadir=config["datadir"],
             pairs=[pair],
             timeframe=timeframe,
             timerange=timerange_parsed,
@@ -1156,6 +1157,16 @@ class RPC:
             self._freqtrade.strategy.plot_config['subplots'] = {}
         return self._freqtrade.strategy.plot_config
 
+    @staticmethod
+    def _rpc_plot_config_with_strategy(config: Config) -> Dict[str, Any]:
+
+        from freqtrade.resolvers.strategy_resolver import StrategyResolver
+        strategy = StrategyResolver.load_strategy(config)
+
+        if (strategy.plot_config and 'subplots' not in strategy.plot_config):
+            strategy.plot_config['subplots'] = {}
+        return strategy.plot_config
+
     @staticmethod
     def _rpc_sysinfo() -> Dict[str, Any]:
         return {

freqtrade/rpc/telegram.py

@@ -1605,7 +1605,7 @@ class Telegram(RPCHandler):
 
     def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
                   disable_notification: bool = False,
-                  keyboard: List[List[InlineKeyboardButton]] = None,
+                  keyboard: Optional[List[List[InlineKeyboardButton]]] = None,
                   callback_path: str = "",
                   reload_able: bool = False,
                   query: Optional[CallbackQuery] = None) -> None:

freqtrade/strategy/hyper.py

@@ -4,7 +4,7 @@ This module defines a base class for auto-hyperoptable strategies.
 """
 import logging
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Tuple, Type, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
@@ -36,7 +36,8 @@ class HyperStrategyMixin:
         self._ft_params_from_file = params
         # Init/loading of parameters is done as part of ft_bot_start().
 
-    def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
+    def enumerate_parameters(
+            self, category: Optional[str] = None) -> Iterator[Tuple[str, BaseParameter]]:
         """
         Find all optimizable parameters and return (name, attr) iterator.
         :param category:
@@ -80,6 +81,8 @@ class HyperStrategyMixin:
 
             self.stoploss = params.get('stoploss', {}).get(
                 'stoploss', getattr(self, 'stoploss', -0.1))
+            self.max_open_trades = params.get('max_open_trades', {}).get(
+                'max_open_trades', getattr(self, 'max_open_trades', -1))
             trailing = params.get('trailing', {})
             self.trailing_stop = trailing.get(
                 'trailing_stop', getattr(self, 'trailing_stop', False))

freqtrade/strategy/interface.py

@@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union
 import arrow
 from pandas import DataFrame
 
-from freqtrade.constants import Config, ListPairsWithTimeframes
+from freqtrade.constants import Config, IntOrInf, ListPairsWithTimeframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
                              SignalTagType, SignalType, TradingMode)
@@ -54,6 +54,9 @@ class IStrategy(ABC, HyperStrategyMixin):
     # associated stoploss
     stoploss: float
 
+    # max open trades for the strategy
+    max_open_trades:  IntOrInf
+
     # trailing stoploss
     trailing_stop: bool = False
     trailing_stop_positive: Optional[float] = None
@@ -595,9 +598,10 @@ class IStrategy(ABC, HyperStrategyMixin):
         return None
 
     def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
-                                informative: DataFrame = None,
+                                informative: Optional[DataFrame] = None,
                                 set_generalized_indicators: bool = False) -> DataFrame:
         """
+        DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
         Function designed to automatically generate, name and merge features
         from user indicated timeframes in the configuration file. User can add
         additional features here, but must follow the naming convention.
@@ -610,6 +614,98 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return df
 
+    def feature_engineering_expand_all(self, dataframe: DataFrame,
+                                       period: int, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        return dataframe
+
 ###
 # END - Intended to be overridden by strategy
 ###
@@ -663,7 +759,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return self.__class__.__name__
 
-    def lock_pair(self, pair: str, until: datetime, reason: str = None, side: str = '*') -> None:
+    def lock_pair(self, pair: str, until: datetime,
+                  reason: Optional[str] = None, side: str = '*') -> None:
         """
         Locks pair until a given timestamp happens.
         Locked pairs are not analyzed, and are prevented from opening new trades.
@@ -695,7 +792,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
 
-    def is_pair_locked(self, pair: str, *, candle_date: datetime = None, side: str = '*') -> bool:
+    def is_pair_locked(self, pair: str, *, candle_date: Optional[datetime] = None,
+                       side: str = '*') -> bool:
         """
         Checks if a pair is currently locked
         The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
@@ -866,7 +964,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         pair: str,
         timeframe: str,
         dataframe: DataFrame,
-        is_short: bool = None
+        is_short: Optional[bool] = None
     ) -> Tuple[bool, bool, Optional[str]]:
         """
         Calculates current exit signal based based on the dataframe
@@ -965,7 +1063,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
                     enter: bool, exit_: bool,
-                    low: float = None, high: float = None,
+                    low: Optional[float] = None, high: Optional[float] = None,
                     force_stoploss: float = 0) -> List[ExitCheckTuple]:
         """
         This function evaluates if one of the conditions required to trigger an exit order
@@ -1053,8 +1151,8 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def stop_loss_reached(self, current_rate: float, trade: Trade,
                           current_time: datetime, current_profit: float,
-                          force_stoploss: float, low: float = None,
-                          high: float = None) -> ExitCheckTuple:
+                          force_stoploss: float, low: Optional[float] = None,
+                          high: Optional[float] = None) -> ExitCheckTuple:
         """
         Based on current profit of the trade and configured (trailing) stoploss,
         decides to exit or not

freqtrade/templates/FreqaiExampleHybridStrategy.py

@@ -95,65 +95,132 @@ class FreqaiExampleHybridStrategy(IStrategy):
     short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
     exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
 
-    # FreqAI required function, user can add or remove indicators, but general structure
-    # must stay the same.
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        User feeds these indicators to FreqAI to train a classifier to decide
-        if the market will go up or down.
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
 
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
-            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
-            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
-            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
 
-        # FreqAI needs the following lines in order to detect features and automatically
-        # expand upon them.
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
 
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
 
-        # User can set the "target" here (in present case it is the
-        # "up" or "down")
-        if set_generalized_indicators:
-            # User "looks into the future" here to figure out if the future
-            # will be "up" or "down". This same column name is available to
-            # the user
-            df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
-                                           df["close"], 'up', 'down')
+        return dataframe
 
-        return df
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
+                                        dataframe["close"], 'up', 'down')
+
+        return dataframe
 
     # flake8: noqa: C901
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

freqtrade/templates/FreqaiExampleStrategy.py

@@ -1,12 +1,11 @@
 import logging
 from functools import reduce
 
-import pandas as pd
 import talib.abstract as ta
 from pandas import DataFrame
 from technical import qtpylib
 
-from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair
+from freqtrade.strategy import CategoricalParameter, IStrategy
 
 
 logger = logging.getLogger(__name__)
@@ -18,8 +17,8 @@ class FreqaiExampleStrategy(IStrategy):
     IFreqaiModel to the strategy. Namely, the user uses:
     self.freqai.start(dataframe, metadata)
 
-    to make predictions on their data. populate_any_indicators() automatically
-    generates the variety of features indicated by the user in the
+    to make predictions on their data. feature_engineering_*() automatically
+    generate the variety of features indicated by the user in the
     canonical freqtrade configuration file under config['freqai'].
     """
 
@@ -28,7 +27,7 @@ class FreqaiExampleStrategy(IStrategy):
     plot_config = {
         "main_plot": {},
         "subplots": {
-            "prediction": {"prediction": {"color": "blue"}},
+            "&-s_close": {"prediction": {"color": "blue"}},
             "do_predict": {
                 "do_predict": {"color": "brown"},
             },
@@ -40,133 +39,179 @@ class FreqaiExampleStrategy(IStrategy):
     use_exit_signal = True
     # this is the maximum period fed to talib (timeframe independent)
     startup_candle_count: int = 40
-    can_short = False
+    can_short = True
 
     std_dev_multiplier_buy = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
     std_dev_multiplier_sell = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
 
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `f'%-{pair}`
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
-            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
-            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
 
-            bollinger = qtpylib.bollinger_bands(
-                qtpylib.typical_price(informative), window=t, stds=2.2
-            )
-            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
-            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
-            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
 
-            informative[f"%-{pair}bb_width-period_{t}"] = (
-                informative[f"{pair}bb_upperband-period_{t}"]
-                - informative[f"{pair}bb_lowerband-period_{t}"]
-            ) / informative[f"{pair}bb_middleband-period_{t}"]
-            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
-                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
 
-            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+        # Classifiers are typically set up with strings as targets:
+        # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
+        #                                 df["close"], 'up', 'down')
 
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
+        # If user wishes to use multiple targets, they can add more by
+        # appending more columns with '&'. User should keep in mind that multi targets
+        # requires a multioutput prediction model such as
+        # freqai/prediction_models/CatboostRegressorMultiTarget.py,
+        # freqtrade trade --freqaimodel CatboostRegressorMultiTarget
 
-        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
-        informative[f"%-{pair}raw_volume"] = informative["volume"]
-        informative[f"%-{pair}raw_price"] = informative["close"]
+        # df["&-s_range"] = (
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .max()
+        #     -
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .min()
+        # )
 
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
-            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-
-            # user adds targets here by prepending them with &- (see convention below)
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-            # Classifiers are typically set up with strings as targets:
-            # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
-            #                                 df["close"], 'up', 'down')
-
-            # If user wishes to use multiple targets, they can add more by
-            # appending more columns with '&'. User should keep in mind that multi targets
-            # requires a multioutput prediction model such as
-            # templates/CatboostPredictionMultiModel.py,
-
-            # df["&-s_range"] = (
-            #     df["close"]
-            #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .max()
-            #     -
-            #     df["close"]
-            #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .min()
-            # )
-
-        return df
+        return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
-        # All indicators must be populated by populate_any_indicators() for live functionality
-        # to work correctly.
+        # All indicators must be populated by feature_engineering_*() functions
 
-        # the model will return all labels created by user in `populate_any_indicators`
+        # the model will return all labels created by user in `feature_engineering_*`
         # (& appended targets), an indication of whether or not the prediction should be accepted,
         # the target mean/std values for each of the labels created by user in
-        # `populate_any_indicators()` for each training period.
+        # `set_freqai_targets()` for each training period.
 
         dataframe = self.freqai.start(dataframe, metadata, self)
+
         for val in self.std_dev_multiplier_buy.range:
             dataframe[f'target_roi_{val}'] = (
                 dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val

freqtrade/templates/base_config.json.j2

@@ -41,20 +41,6 @@
     "pairlists": [
         {{ '{"method": "StaticPairList"}' if exchange_name == 'bittrex' else  volume_pairlist }}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": {{ telegram | lower }},
         "token": "{{ telegram_token }}",

freqtrade/util/binance_mig.py

@@ -0,0 +1,78 @@
+import logging
+
+from packaging import version
+
+from freqtrade.constants import Config
+from freqtrade.enums.tradingmode import TradingMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.persistence.pairlock import PairLock
+from freqtrade.persistence.trade_model import Trade
+
+
+logger = logging.getLogger(__name__)
+
+
+def migrate_binance_futures_names(config: Config):
+
+    if (
+        not (config.get('trading_mode', TradingMode.SPOT) == TradingMode.FUTURES
+             and config['exchange']['name'] == 'binance')
+    ):
+        # only act on new futures
+        return
+    import ccxt
+    if version.parse("2.6.26") > version.parse(ccxt.__version__):
+        raise OperationalException(
+            "Please follow the update instructions in the docs "
+            "(https://www.freqtrade.io/en/latest/updating/) to install a compatible ccxt version.")
+    _migrate_binance_futures_db(config)
+    migrate_binance_futures_data(config)
+
+
+def _migrate_binance_futures_db(config: Config):
+    logger.warning('Migrating binance futures pairs in database.')
+    trades = Trade.get_trades([Trade.exchange == 'binance', Trade.trading_mode == 'FUTURES']).all()
+    for trade in trades:
+        if ':' in trade.pair:
+            # already migrated
+            continue
+        new_pair = f"{trade.pair}:{trade.stake_currency}"
+        trade.pair = new_pair
+
+        for order in trade.orders:
+            order.ft_pair = new_pair
+            # Should symbol be migrated too?
+            # order.symbol = new_pair
+    Trade.commit()
+    pls = PairLock.query.filter(PairLock.pair.notlike('%:%'))
+    for pl in pls:
+        pl.pair = f"{pl.pair}:{config['stake_currency']}"
+    # print(pls)
+    # pls.update({'pair': concat(PairLock.pair,':USDT')})
+    Trade.commit()
+    logger.warning('Done migrating binance futures pairs in database.')
+
+
+def migrate_binance_futures_data(config: Config):
+
+    if (
+        not (config.get('trading_mode', TradingMode.SPOT) == TradingMode.FUTURES
+             and config['exchange']['name'] == 'binance')
+    ):
+        # only act on new futures
+        return
+
+    from freqtrade.data.history.idatahandler import get_datahandler
+    dhc = get_datahandler(config['datadir'], config.get('dataformat_ohlcv', 'json'))
+
+    paircombs = dhc.ohlcv_get_available_data(
+        config['datadir'],
+        config.get('trading_mode', TradingMode.SPOT)
+        )
+
+    for pair, timeframe, candle_type in paircombs:
+        if ':' in pair:
+            # already migrated
+            continue
+        new_pair = f"{pair}:{config['stake_currency']}"
+        dhc.rename_futures_data(pair, new_pair, timeframe, candle_type)

freqtrade/wallets.py

@@ -291,17 +291,22 @@ class Wallets:
         return self._check_available_stake_amount(stake_amount, available_amount)
 
     def validate_stake_amount(self, pair: str, stake_amount: Optional[float],
-                              min_stake_amount: Optional[float], max_stake_amount: float):
+                              min_stake_amount: Optional[float], max_stake_amount: float,
+                              trade_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 = min(max_stake_amount, self.get_available_stake_amount())
+        max_allowed_stake = min(max_stake_amount, self.get_available_stake_amount())
+        if trade_amount:
+            # if in a trade, then the resulting trade size cannot go beyond the max stake
+            # Otherwise we could no longer exit.
+            max_allowed_stake = min(max_allowed_stake, max_stake_amount - trade_amount)
 
-        if min_stake_amount is not None and min_stake_amount > max_stake_amount:
+        if min_stake_amount is not None and min_stake_amount > max_allowed_stake:
             if self._log:
                 logger.warning("Minimum stake amount > available balance. "
-                               f"{min_stake_amount} > {max_stake_amount}")
+                               f"{min_stake_amount} > {max_allowed_stake}")
             return 0
         if min_stake_amount is not None and stake_amount < min_stake_amount:
             if self._log:
@@ -320,11 +325,11 @@ class Wallets:
                 return 0
             stake_amount = min_stake_amount
 
-        if stake_amount > max_stake_amount:
+        if stake_amount > max_allowed_stake:
             if self._log:
                 logger.info(
                     f"Stake amount for pair {pair} is too big "
-                    f"({stake_amount} > {max_stake_amount}), adjusting to {max_stake_amount}."
+                    f"({stake_amount} > {max_allowed_stake}), adjusting to {max_allowed_stake}."
                 )
-            stake_amount = max_stake_amount
+            stake_amount = max_allowed_stake
         return stake_amount

freqtrade/worker.py

@@ -26,7 +26,7 @@ class Worker:
     Freqtradebot worker class
     """
 
-    def __init__(self, args: Dict[str, Any], config: Config = None) -> None:
+    def __init__(self, args: Dict[str, Any], config: Optional[Config] = None) -> None:
         """
         Init all variables and objects the bot needs to work
         """

mkdocs.yml

@@ -41,6 +41,7 @@ nav:
         - Backtest analysis: advanced-backtesting.md
     - Advanced Topics:
         - Advanced Post-installation Tasks: advanced-setup.md
+        - Trade Object: trade-object.md
         - Advanced Strategy: strategy-advanced.md
         - Advanced Hyperopt: advanced-hyperopt.md
         - Producer/Consumer mode: producer-consumer.md
@@ -58,7 +59,11 @@ theme:
   favicon: "images/logo.png"
   custom_dir: "docs/overrides"
   features:
+    - content.code.annotate
     - search.share
+    - content.code.copy
+    - navigation.top
+    - navigation.footer
   palette:
     - scheme: default
       primary: "blue grey"

pyproject.toml

@@ -31,7 +31,6 @@ asyncio_mode = "auto"
 [tool.mypy]
 ignore_missing_imports = true
 namespace_packages = false
-implicit_optional = true
 warn_unused_ignores = true
 exclude = [
     '^build_helpers\.py$'
@@ -41,6 +40,11 @@ exclude = [
 module = "tests.*"
 ignore_errors = true
 
+[[tool.mypy.overrides]]
+# Telegram does not use implicit_optional = false in the current version.
+module = "telegram.*"
+implicit_optional = true
+
 [build-system]
 requires = ["setuptools >= 46.4.0", "wheel"]
 build-backend = "setuptools.build_meta"
@@ -52,6 +56,3 @@ exclude = [
   "build_helpers/*.py",
 ]
 ignore = ["freqtrade/vendor/**"]
-
-# Align pyright to mypy config
-strictParameterNoneValue = false

requirements-dev.txt

@@ -10,24 +10,24 @@ coveralls==3.3.1
 flake8==6.0.0
 flake8-tidy-imports==4.8.0
 mypy==0.991
-pre-commit==2.20.0
-pytest==7.2.0
+pre-commit==2.21.0
+pytest==7.2.1
 pytest-asyncio==0.20.3
 pytest-cov==4.0.0
 pytest-mock==3.10.0
 pytest-random-order==1.1.0
-isort==5.10.1
+isort==5.11.4
 # For datetime mocking
-time-machine==2.8.2
+time-machine==2.9.0
 # fastapi testing
-httpx==0.23.1
+httpx==0.23.3
 
 # Convert jupyter notebooks to markdown documents
-nbconvert==7.2.6
+nbconvert==7.2.8
 
 # mypy types
 types-cachetools==5.2.1
 types-filelock==3.2.7
-types-requests==2.28.11.5
+types-requests==2.28.11.8
 types-tabulate==0.9.0.0
-types-python-dateutil==2.8.19.4
+types-python-dateutil==2.8.19.6

requirements-freqai-rl.txt

@@ -2,7 +2,7 @@
 -r requirements-freqai.txt
 
 # Required for freqai-rl
-torch==1.13.0
+torch==1.13.1
 stable-baselines3==1.6.2
 sb3-contrib==1.6.2
 # Gym is forced to this version by stable-baselines3.

requirements-freqai.txt

@@ -6,7 +6,6 @@
 scikit-learn==1.1.3
 joblib==1.2.0
 catboost==1.1.1; platform_machine != 'aarch64'
-lightgbm==3.3.3
-xgboost==1.7.2
-tensorboard==2.11.0
-tensorflow==2.11.0
+lightgbm==3.3.4
+xgboost==1.7.3
+tensorboard==2.11.2

requirements-hyperopt.txt

@@ -2,8 +2,8 @@
 -r requirements.txt
 
 # Required for hyperopt
-scipy==1.9.3
+scipy==1.10.0
 scikit-learn==1.1.3
 scikit-optimize==0.9.0
-filelock==3.8.2
+filelock==3.9.0
 progressbar2==4.2.0

requirements.txt

@@ -1,27 +1,26 @@
-numpy==1.23.5
-pandas==1.5.2
+numpy==1.24.1
+pandas==1.5.3
 pandas-ta==0.3.14b
 
-ccxt==2.2.92
+ccxt==2.7.12
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==38.0.1; platform_machine == 'armv7l'
-cryptography==38.0.4; platform_machine != 'armv7l'
+cryptography==39.0.0; platform_machine != 'armv7l'
 aiohttp==3.8.3
-SQLAlchemy==1.4.45
+SQLAlchemy==1.4.46
 python-telegram-bot==13.15
 arrow==1.2.3
 cachetools==4.2.2
-requests==2.28.1
-urllib3==1.26.13
+requests==2.28.2
+urllib3==1.26.14
 jsonschema==4.17.3
 TA-Lib==0.4.25
 technical==1.3.0
 tabulate==0.9.0
 pycoingecko==3.1.0
 jinja2==3.1.2
-tables==3.7.0
-blosc==1.10.6; platform_machine == 'arm64'
-blosc==1.11.0; platform_machine != 'arm64'
+tables==3.8.0
+blosc==1.11.1
 joblib==1.2.0
 pyarrow==10.0.1; platform_machine != 'armv7l'
 
@@ -31,14 +30,14 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.9
 # Properly format api responses
-orjson==3.8.3
+orjson==3.8.5
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.88.0
-pydantic==1.10.2
+fastapi==0.89.1
+pydantic==1.10.4
 uvicorn==0.20.0
 pyjwt==2.6.0
 aiofiles==22.1.0

scripts/rest_client.py

@@ -14,6 +14,7 @@ import logging
 import re
 import sys
 from pathlib import Path
+from typing import Optional
 from urllib.parse import urlencode, urlparse, urlunparse
 
 import rapidjson
@@ -36,7 +37,7 @@ class FtRestClient():
         self._session = requests.Session()
         self._session.auth = (username, password)
 
-    def _call(self, method, apipath, params: dict = None, data=None, files=None):
+    def _call(self, method, apipath, params: Optional[dict] = None, data=None, files=None):
 
         if str(method).upper() not in ('GET', 'POST', 'PUT', 'DELETE'):
             raise ValueError(f'invalid method <{method}>')
@@ -60,13 +61,13 @@ class FtRestClient():
         except ConnectionError:
             logger.warning("Connection error")
 
-    def _get(self, apipath, params: dict = None):
+    def _get(self, apipath, params: Optional[dict] = None):
         return self._call("GET", apipath, params=params)
 
-    def _delete(self, apipath, params: dict = None):
+    def _delete(self, apipath, params: Optional[dict] = None):
         return self._call("DELETE", apipath, params=params)
 
-    def _post(self, apipath, params: dict = None, data: dict = None):
+    def _post(self, apipath, params: Optional[dict] = None, data: Optional[dict] = None):
         return self._call("POST", apipath, params=params, data=data)
 
     def start(self):