# source: https://raw.githubusercontent.com/kyrypto/kdog-freqai-strats/83f1d44b4f88c6e4708c5fc31bc1813ab7d5a697/user_data/strategies/RL_kdog_spot.py 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 Github_kyrypto_kdog_freqai_strats__RL_kdog_spot__20221013_001739(IStrategy): """ Here be stonks 1. freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy Github_kyrypto_kdog_freqai_strats__RL_kdog_spot__20221013_001739 --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 Github_kyrypto_kdog_freqai_strats__RL_kdog_spot__20221013_001739 """ 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