stable/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.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy


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.model = CustomModel(self.config)
    self.model.bridge.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"}},
            "target_roi": {
                "target_roi": {"color": "brown"},
            },
            "do_predict": {
                "do_predict": {"color": "brown"},
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.05
    use_exit_signal = True
    startup_candle_count: int = 300
    can_short = True

    linear_roi_offset = DecimalParameter(
        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
    )
    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)

    def informative_pairs(self):
        whitelist_pairs = self.dp.current_whitelist()
        corr_pairs = self.config["freqai"]["corr_pairlist"]
        informative_pairs = []
        for tf in self.config["freqai"]["timeframes"]:
            for pair in whitelist_pairs:
                informative_pairs.append((pair, tf))
            for pair in corr_pairs:
                if pair in whitelist_pairs:
                    continue  # avoid duplication
                informative_pairs.append((pair, tf))
        return informative_pairs

    def bot_start(self):
        self.model = CustomModel(self.config)

    def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
        """
        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.
        :params:
        :pair: pair to be used as informative
        :df: strategy dataframe which will receive merges from informatives
        :tf: timeframe of the dataframe which will modify the feature names
        :informative: the dataframe associated with the informative pair
        :coin: the name of the coin which will modify the feature names.
        """

        with self.model.bridge.lock:
            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"]:

                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, window=t)
                informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
                informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
                informative[f"%-{coin}close_over_20sma-period_{t}"] = (
                    informative["close"] / informative[coin + "20sma-period_{t}"]
                )

                informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(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
                )
                macd = ta.MACD(informative, timeperiod=t)
                informative[f"%-{coin}macd-period_{t}"] = macd["macd"]

                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"]["shift"] + 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 pair == self.freqai_info['corr_pairlist'][0] and tf == self.timeframe:
                df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
                df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25

        return df

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

        self.freqai_info = self.config["freqai"]
        self.pair = metadata["pair"]

        # the following loops are necessary for building the features
        # indicated by the user in the configuration file.
        # All indicators must be populated by populate_any_indicators() for live functionality
        # to work correctly.
        for tf in self.freqai_info["timeframes"]:
            dataframe = self.populate_any_indicators(
                metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-"
            )
            for pair in self.freqai_info["corr_pairlist"]:
                if metadata["pair"] in pair:
                    continue  # do not include whitelisted pair twice if it is in corr_pairlist
                dataframe = self.populate_any_indicators(
                    metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-"
                )

        # the model will return 4 values, its prediction, an indication of whether or not the
        # prediction should be accepted, the target mean/std values from the labels used during
        # each training period.
        dataframe = self.model.bridge.start(dataframe, metadata, self)

        dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.25
        dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.25
        return dataframe

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

        enter_long_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"]]

        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["prediction"] < df["sell_roi"]]

        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["prediction"] < df["sell_roi"] * 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["prediction"] > df["target_roi"] * 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 custom_exit(self, pair: str, trade: Trade, current_time, current_rate,
                    current_profit, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)

        trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc)
        trade_candle = dataframe.loc[(dataframe['date'] == trade_date)]

        if trade_candle.empty:
            return None
        trade_candle = trade_candle.squeeze()

        follow_mode = self.config.get('freqai', {}).get('follow_mode', False)

        if not follow_mode:
            pair_dict = self.model.bridge.data_drawer.pair_dict
        else:
            pair_dict = self.model.bridge.data_drawer.follower_dict

        entry_tag = trade.enter_tag

        if ('prediction' + entry_tag not in pair_dict[pair] or
                pair_dict[pair]['prediction' + entry_tag] > 0):
            with self.model.bridge.lock:
                pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
                if not follow_mode:
                    self.model.bridge.data_drawer.save_drawer_to_disk()
                else:
                    self.model.bridge.data_drawer.save_follower_dict_to_disk()

        roi_price = pair_dict[pair]['prediction' + entry_tag]
        roi_time = self.max_roi_time_long.value

        roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) /
                                 (roi_time * 60))
        if roi_decay < 0:
            roi_decay = self.linear_roi_offset.value
        else:
            roi_decay += self.linear_roi_offset.value

        if current_profit > roi_decay:
            return 'roi_custom_win'

        if current_profit < -roi_decay:
            return 'roi_custom_loss'

    def confirm_trade_exit(
        self,
        pair: str,
        trade: Trade,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        exit_reason: str,
        current_time,
        **kwargs
    ) -> bool:

        entry_tag = trade.enter_tag
        follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
        if not follow_mode:
            pair_dict = self.model.bridge.data_drawer.pair_dict
        else:
            pair_dict = self.model.bridge.data_drawer.follower_dict

        with self.model.bridge.lock:
            pair_dict[pair]["prediction" + entry_tag] = 0
            if not follow_mode:
                self.model.bridge.data_drawer.save_drawer_to_disk()
            else:
                self.model.bridge.data_drawer.save_follower_dict_to_disk()

        return True

    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