RL_kdog_futures

import logging
from functools import reduce
from datetime import datetime
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.persistence import Trade

from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair


logger = logging.getLogger(__name__)


class RL_kdog_futures(IStrategy):
    """
Here be stonks
1. freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy RL_kdog_futures --freqaimodel ReinforcementLearner --spaces sell roi stoploss --timerange "$(date --date='-1 month' '+%Y%m%d')"-"$(date '+%Y%m%d')" -e 5000
2. freqtrade trade --logfile ./logs --freqaimodel ReinforcementLearner --strategy RL_kdog_futures
    """

    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"},
            },
            "&-action": {
                "&-action": {"color": "green"},
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.3
    use_exit_signal = True
    startup_candle_count: int = 300
    can_short = True
    # Specific variables
    leverage_optimize = True
    leverage_num = DecimalParameter(1, 20, default=5, decimals=2, space='sell', optimize=leverage_optimize)
    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)
        # This is called when placing the initial order (opening trade)
    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: str, side: str,
                 **kwargs) -> float:
        """
        Customize leverage for each new trade. This method is only called in futures mode.

        :param pair: Pair that's currently analyzed
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Rate, calculated based on pricing settings in exit_pricing.
        :param proposed_leverage: A leverage proposed by the bot.
        :param max_leverage: Max leverage allowed on this pair
        :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
        :param side: 'long' or 'short' - indicating the direction of the proposed trade
        :return: A leverage amount, which is between 1.0 and max_leverage.
        """
        return self.leverage_num.value
    def informative_pairs(self):
        whitelist_pairs = self.dp.current_whitelist()
        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
        informative_pairs = []
        for tf in self.config["freqai"]["feature_parameters"]["include_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 populate_any_indicators(
        self, pair, df, tf, informative=None, set_generalized_indicators=False
    ):

        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)
            # MIN - Lowest value over a specified period
            # informative[f"%-{coin}min-period_{t}"] = ta.MIN(informative, timeperiod=t)
            # MAX - Highest value over a specified period
            # informative[f"%-{coin}max-period_{t}"] = ta.MAX(informative, timeperiod=t)
            # DEMA - Double Exponential Moving Average
            # informative[f"%-{coin}dema-period_{t}"] = ta.DEMA(informative, timeperiod=t)
            # Linear Regression
            # informative[f"%-{coin}linearreg-period_{t}"] = ta.LINEARREG(informative, timeperiod=t)
            # informative[f"%-{coin}linearreg_angle-period_{t}"] = ta.LINEARREG_ANGLE(informative, timeperiod=t)
            # informative[f"%-{coin}linearreg_intercept-period_{t}"] = ta.LINEARREG_INTERCEPT(informative, timeperiod=t)
            # informative[f"%-{coin}linearreg_slope-period_{t}"] = ta.LINEARREG_SLOPE(informative, timeperiod=t)
            # CORREL - Pearson's Correlation Coefficient (r)
            # informative[f"%-{coin}correl-period_{t}"] = ta.CORREL(informative, timeperiod=t)
            # STDDEV - Standard Deviation
            # informative[f"%-{coin}stddev-period_{t}"] = ta.STDDEV(informative, timeperiod=t)
            # TSF - Time Series Forecast
            informative[f"%-{coin}tsf-period_{t}"] = ta.TSF(informative, timeperiod=t)
            # VAR - Variance
            # informative[f"%-{coin}var-period_{t}"] = ta.VAR(informative, timeperiod=t)
            # Momentum
            informative[f"%-{coin}mom-period_{t}"] = ta.MOM(informative, timeperiod=t)
            # RSI
            informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
            # Commodity Channel Index
            # informative[f"%-{coin}cci-period_{t}"] = ta.CCI(informative, timeperiod=t)
            # Minus Directional Indicator / Movement
            # informative[f"%-{coin}minus_di-period_{t}"] = ta.MINUS_DI(informative, timeperiod=t)
            # informative[f"%-{coin}minus_dm-period_{t}"] = ta.MINUS_DM(informative, timeperiod=t)
            # Plus Directional Indicator / Movement
            # informative[f"%-{coin}plus_di-period_{t}"] = ta.PLUS_DI(informative, timeperiod=t)
            # informative[f"%-{coin}plus_dm-period_{t}"] = ta.PLUS_DM(informative, timeperiod=t)
            # MFI
            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
            # ADX
            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
            # TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
            # informative[f"%-{coin}trix-period_{t}"] = ta.TRIX(informative, timeperiod=t)
            # WILLR - Williams' %R
            # informative[f"%-{coin}willr-period_{t}"] = ta.WILLR(informative, timeperiod=t)
            # Kaufman's Adaptive Moving Average (KAMA)
            # informative[f"%-{coin}kama-period_{t}"] = ta.KAMA(informative, window=t)
            # SMA
            informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
            # EMA
            informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
            # TEMA
            # informative[f"%-{coin}tema-period_{t}"] = ta.TEMA(informative, timeperiod=t)
            # ATR - Average True Range
            informative[f"%-{coin}atr-period_{t}"] = ta.ATR(informative, timeperiod=t)
            # Stoch
            # stoch = ta.STOCH(informative)
            # informative[f"%-{coin}slowd"] = stoch["slowd"]
            # informative[f"%-{coin}slowk"] = stoch["slowk"]
            # Stoch Fast
            # stochf = ta.STOCHF(informative)
            # informative[f"%-{coin}fastd"] = stochf["fastd"]
            # informative[f"%-{coin}fastk"] = stochf["fastk"]
            # Stoch RSI
            stoch_rsi = ta.STOCHRSI(informative)
            informative[f"%-{coin}fastd"] = stoch_rsi["fastd"]
            informative[f"%-{coin}fastk"] = stoch_rsi["fastk"]
            # Hilbert
            # hilbert = ta.HT_SINE(informative)
            # informative[f"%-{coin}htsine"] = hilbert["sine"]
            # informative[f"%-{coin}htleadsine"] = hilbert["leadsine"]
            # Bollinger bands
            # 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"]

        # FIXME: add these outside the user strategy?
        # The following columns are necessary for RL models.
        informative[f"%-{coin}raw_close"] = informative["close"]
        informative[f"%-{coin}raw_open"] = informative["open"]
        informative[f"%-{coin}raw_high"] = informative["high"]
        informative[f"%-{coin}raw_low"] = informative["low"]

        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

            # For RL, there are no direct targets to set. This is filler (neutral)
            # until the agent sends an action.
            df["&-action"] = 0

        return df

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

        dataframe = self.freqai.start(dataframe, metadata, self)

        return dataframe

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

        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]

        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["&-action"] == 3]

        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["&-action"] == 2]
        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["&-action"] == 4]
        if exit_short_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1

        return df