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Add FreqAI migration documentation
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Matthias
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@ -477,3 +477,254 @@ after:
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"ignore_buying_expired_candle_after": 120
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}
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```
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## FreqAI strategy
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The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
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For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
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As such, the definition of features becomes much simpler with the new logic.
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For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
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``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
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informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(informative), window=t, stds=2.2
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)
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informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"%-{pair}bb_width-period_{t}"] = (
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informative[f"{pair}bb_upperband-period_{t}"]
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- informative[f"{pair}bb_lowerband-period_{t}"]
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) / informative[f"{pair}bb_middleband-period_{t}"]
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informative[f"%-{pair}close-bb_lower-period_{t}"] = (
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informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
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)
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informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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informative[f"%-{pair}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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) # (1)
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informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
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informative[f"%-{pair}raw_volume"] = informative["volume"]
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informative[f"%-{pair}raw_price"] = informative["close"]
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# (2)
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# (3)
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# user adds targets here by prepending them with &- (see convention below)
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df["&-s_close"] = (
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df["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ df["close"]
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- 1
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) # (4)
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return df
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```
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1. Features - Move to `feature_engineering_expand_all`
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2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
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3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
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4. Targets - Move this part to `set_freqai_targets()`.
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### freqai - feature engineering expand all
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Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
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``` python linenums="1"
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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
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`include_corr_pairs`. In other words, a single feature defined in this function
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will automatically expand to a total of
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`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
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`include_corr_pairs` numbers of features added to the model.
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
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https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
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:param df: strategy dataframe which will receive the features
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:param period: period of the indicator - usage example:
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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"""
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(dataframe), window=period, stds=2.2
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)
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dataframe["bb_lowerband-period"] = bollinger["lower"]
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dataframe["bb_middleband-period"] = bollinger["mid"]
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dataframe["bb_upperband-period"] = bollinger["upper"]
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dataframe["%-bb_width-period"] = (
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dataframe["bb_upperband-period"]
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- dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = (
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dataframe["close"] / dataframe["bb_lowerband-period"]
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)
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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dataframe["%-relative_volume-period"] = (
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dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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)
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return dataframe
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```
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### Freqai - feature engineering basic
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Basic features. Make sure to remove the `{pair}` part from your features.
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``` python linenums="1"
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def feature_engineering_expand_basic(self, dataframe, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
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In other words, a single feature defined in this function
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will automatically expand to a total of
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`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
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numbers of features added to the model.
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Features defined here will *not* be automatically duplicated on user defined
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`indicator_periods_candles`
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
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https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
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:param df: strategy dataframe which will receive the features
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
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"""
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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return dataframe
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```
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### FreqAI - feature engineering standard
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``` python linenums="1"
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def feature_engineering_standard(self, dataframe, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This optional function will be called once with the dataframe of the base timeframe.
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This is the final function to be called, which means that the dataframe entering this
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function will contain all the features and columns created by all other
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freqai_feature_engineering_* functions.
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This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
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This function is a good place for any feature that should not be auto-expanded upon
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(e.g. day of the week).
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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:param df: strategy dataframe which will receive the features
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usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
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"""
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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return dataframe
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```
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### FreqAI - set Targets
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Targets now get their own, dedicated method.
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``` python linenums="1"
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def set_freqai_targets(self, dataframe, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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Required function to set the targets for the model.
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All targets must be prepended with `&` to be recognized by the FreqAI internals.
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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:param df: strategy dataframe which will receive the targets
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usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
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"""
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dataframe["&-s_close"] = (
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dataframe["close"]
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.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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.mean()
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/ dataframe["close"]
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- 1
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)
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return dataframe
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```
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@ -59,6 +59,7 @@ theme:
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favicon: "images/logo.png"
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custom_dir: "docs/overrides"
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features:
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- content.code.annotate
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- search.share
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palette:
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- scheme: default
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