292 lines
11 KiB
Python
292 lines
11 KiB
Python
import logging
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from functools import reduce
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import talib.abstract as ta
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.strategy import CategoricalParameter, IStrategy
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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"""
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Example strategy showing how the user connects their own
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IFreqaiModel to the strategy. Namely, the user uses:
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self.freqai.start(dataframe, metadata)
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to make predictions on their data. feature_engineering_*() automatically
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generate the variety of features indicated by the user in the
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canonical freqtrade configuration file under config['freqai'].
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"""
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minimal_roi = {"0": 0.1, "240": -1}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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# this is the maximum period fed to talib (timeframe independent)
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startup_candle_count: int = 40
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can_short = False
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std_dev_multiplier_buy = CategoricalParameter(
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[0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
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std_dev_multiplier_sell = CategoricalParameter(
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[0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
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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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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_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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# Classifiers are typically set up with strings as targets:
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# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
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# df["close"], 'up', 'down')
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# If user wishes to use multiple targets, they can add more by
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# appending more columns with '&'. User should keep in mind that multi targets
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# requires a multioutput prediction model such as
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# templates/CatboostPredictionMultiModel.py,
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# df["&-s_range"] = (
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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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# .max()
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# -
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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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# .min()
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# )
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# All indicators must be populated by feature_engineering_*() functions
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# the model will return all labels created by user in `feature_engineering_*`
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# (& appended targets), an indication of whether or not the prediction should be accepted,
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# the target mean/std values for each of the labels created by user in
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# `set_freqai_targets()` for each training period.
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dataframe = self.freqai.start(dataframe, metadata, self)
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for val in self.std_dev_multiplier_buy.range:
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dataframe[f'target_roi_{val}'] = (
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dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val
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)
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for val in self.std_dev_multiplier_sell.range:
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dataframe[f'sell_roi_{val}'] = (
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dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * val
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)
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"],
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]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
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] = (1, "long")
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enter_short_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"],
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]
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
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] = (1, "short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25,
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]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [
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df["do_predict"] == 1,
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df["&-s_close"] > df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25,
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]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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def get_ticker_indicator(self):
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return int(self.config["timeframe"][:-1])
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def confirm_trade_entry(
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self,
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pair: str,
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order_type: str,
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amount: float,
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rate: float,
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time_in_force: str,
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current_time,
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entry_tag,
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side: str,
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**kwargs,
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) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.0025)):
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return False
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else:
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if rate < (last_candle["close"] * (1 - 0.0025)):
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return False
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return True
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