remove add pair to column from docs, fix keyerror bug and adjust hybrid strategy example
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Wagner Costa
@@ -95,65 +95,132 @@ class FreqaiExampleHybridStrategy(IStrategy):
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short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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# FreqAI required function, user can add or remove indicators, but general structure
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# must stay the same.
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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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def feature_engineering_expand_all(self, dataframe, period, **kwargs):
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"""
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User feeds these indicators to FreqAI to train a classifier to decide
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if the market will go up or down.
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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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:param pair: pair to be used as informative
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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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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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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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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# 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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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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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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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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)
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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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# FreqAI needs the following lines in order to detect features and automatically
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# expand upon them.
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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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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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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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dataframe["%-relative_volume-period"] = (
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dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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)
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# User can set the "target" here (in present case it is the
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# "up" or "down")
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if set_generalized_indicators:
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# User "looks into the future" here to figure out if the future
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# will be "up" or "down". This same column name is available to
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# the user
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df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
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df["close"], 'up', 'down')
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return dataframe
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return df
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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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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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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-up_or_down'] = np.where(dataframe["close"].shift(-50) >
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dataframe["close"], 'up', 'down')
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return dataframe
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# flake8: noqa: C901
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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