RL_kdog_spot

import logging
from functools import reduce
from datetime import datetime
from typing import Optional
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_spot(IStrategy):
    """
Here be stonks
1. freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy RL_kdog_spot --freqaimodel ReinforcementLearnerSpot --spaces roi stoploss --timerange "$(date --date='-1 week' '+%Y%m%d')"-"$(date '+%Y%m%d')" -e 1000
2. freqtrade trade --logfile ./logs --freqaimodel ReinforcementLearnerSpot --strategy RL_kdog_spot
    """

    minimal_roi = {"0": 0.1, "2400": -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"},
            },
        },
    }
    position_adjustment_enable = True
    max_entry_position_adjustment = 2
    max_dca_multiplier = 4
    process_only_new_candles = True
    stoploss = -0.3
    use_exit_signal = True
    startup_candle_count: int = 300
    can_short = False
    # Specific variables
    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 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)
            # Volume Weighted Average Price
            vwap = qtpylib.vwap(informative)
            # OBV - On Balance Volume
            informative[f"%-{coin}obv"] = ta.OBV(informative)
            # 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")

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
        if exit_long_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        return df
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: Optional[str], **kwargs) -> float:

        # We need to leave most of the funds for possible further DCA orders
        # This also applies to fixed stakes
        return proposed_stake / self.max_dca_multiplier

    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float, min_stake: float,
                              max_stake: float, **kwargs):
        """
        Custom trade adjustment logic, returning the stake amount that a trade should be increased.
        This means extra buy orders with additional fees.

        :param trade: trade object.
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Current buy rate.
        :param current_profit: Current profit (as ratio), calculated based on current_rate.
        :param min_stake: Minimal stake size allowed by exchange.
        :param max_stake: Balance available for trading.
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return float: Stake amount to adjust your trade
        """

        #if current_profit > -0.01:
            #return None
        # Don't rebuy for trades on hold
        is_backtest = self.dp.runmode.value == 'backtest'
        if (trade.open_date_utc.replace(tzinfo=None) < datetime(2022, 4, 6) and not is_backtest):
            return None
        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
        if(len(dataframe) < 2):
            return None
        last_candle = dataframe.iloc[-1].squeeze()
        previous_candle = dataframe.iloc[-2].squeeze()

        filled_buys = trade.select_filled_orders()
        count_of_buys = trade.nr_of_successful_entries
        try:
            stake_amount = filled_buys[0].cost
            stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
            if (
                    ((last_candle['do_predict'] == 1)
                    and (last_candle['&-action'] == 2)
                    and count_of_buys == 1)
                    or ((previous_candle['do_predict'] == 1)
                    and (previous_candle['&-action'] == 2)
                    and count_of_buys == 1)
            ):
                return stake_amount
            if (
                    ((last_candle['do_predict'] == 1)
                    and (last_candle['&-action'] == 3)
                    and count_of_buys == 2)
                    or ((previous_candle['do_predict'] == 1)
                    and (previous_candle['&-action'] == 3)
                    and count_of_buys == 2)
            ):
                return stake_amount
        except Exception as exception:
            logger.warning(f"{exception}")

        return None