freqtrade_origin/freqtrade/templates/FreqaiExampleStrategy.py

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


logger = logging.getLogger(__name__)


class FreqaiExampleStrategy(IStrategy):
    """
    Example strategy showing how the user connects their own
    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
    canonical freqtrade configuration file under config['freqai'].
    """

    minimal_roi = {"0": 0.1, "240": -1}

    plot_config = {
        "main_plot": {},
        "subplots": {
            "prediction": {"prediction": {"color": "blue"}},
            "do_predict": {
                "do_predict": {"color": "brown"},
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.05
    use_exit_signal = True
    # this is the maximum period fed to talib (timeframe independent)
    startup_candle_count: int = 40
    can_short = False

    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(
        [0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)

    def populate_any_indicators(
        self, pair, df, tf, informative=None, set_generalized_indicators=False
    ):
        """
        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 `'%-' + coin `
        (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
        """

        coin = pair.split('/')[0]

        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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
            informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
            informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)

            bollinger = qtpylib.bollinger_bands(
                qtpylib.typical_price(informative), window=t, stds=2.2
            )
            informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
            informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
            informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]

            informative[f"%-{coin}bb_width-period_{t}"] = (
                informative[f"{coin}bb_upperband-period_{t}"]
                - informative[f"{coin}bb_lowerband-period_{t}"]
            ) / informative[f"{coin}bb_middleband-period_{t}"]
            informative[f"%-{coin}close-bb_lower-period_{t}"] = (
                informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
            )

            informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)

            informative[f"%-{coin}relative_volume-period_{t}"] = (
                informative["volume"] / informative["volume"].rolling(t).mean()
            )

        informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
        informative[f"%-{coin}raw_volume"] = informative["volume"]
        informative[f"%-{coin}raw_price"] = informative["close"]

        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

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # All indicators must be populated by populate_any_indicators() for live functionality
        # to work correctly.

        # the model will return all labels created by user in `populate_any_indicators`
        # (& 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.

        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
        for val in self.std_dev_multiplier_sell.range:
            dataframe[f'sell_roi_{val}'] = dataframe["&-s_close_mean"] - \
                dataframe["&-s_close_std"] * val
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"]
                                 > df[f"target_roi_{self.std_dev_multiplier_buy.value}"]]

        if enter_long_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
            ] = (1, "long")

        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"]
                                  < df[f"sell_roi_{self.std_dev_multiplier_sell.value}"]]

        if enter_short_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
            ] = (1, "short")

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] <
                                df[f"sell_roi_{self.std_dev_multiplier_sell.value}"] * 0.25]
        if exit_long_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] >
                                 df[f"target_roi_{self.std_dev_multiplier_buy.value}"] * 0.25]
        if exit_short_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1

        return df

    def get_ticker_indicator(self):
        return int(self.config["timeframe"][:-1])

    def confirm_trade_entry(
        self,
        pair: str,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        current_time,
        entry_tag,
        side: str,
        **kwargs,
    ) -> bool:

        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = df.iloc[-1].squeeze()

        if side == "long":
            if rate > (last_candle["close"] * (1 + 0.0025)):
                return False
        else:
            if rate < (last_candle["close"] * (1 - 0.0025)):
                return False

        return True