# source: https://raw.githubusercontent.com/kyrypto/kdog-freqai-strats/84d45842d9978a3ae5e2da8061e5ee93e2b9fd56/user_data/strategies/RL_kdog_futures.py 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 Github_kyrypto_kdog_freqai_strats__RL_kdog_futures__20221013_001739(IStrategy): """ Here be stonks 1. freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy Github_kyrypto_kdog_freqai_strats__RL_kdog_futures__20221013_001739 --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 Github_kyrypto_kdog_freqai_strats__RL_kdog_futures__20221013_001739 """ 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