# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/FreqGym_normalized.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np # noqa import pandas as pd # noqa import talib.abstract as ta from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from stable_baselines3.ppo.ppo import PPO from stable_baselines3.a2c.a2c import A2C class Github_remiotore_freqtrade__FreqGym_normalized__20260111_210550(IStrategy): minimal_roi = {"0": 100} stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.017 trailing_only_offset_is_reached = True ticker_interval = "5m" use_sell_signal = True process_only_new_candles = False startup_candle_count: int = 200 model = None window_size = None try: model = PPO.load( "models/final_model_Github_remiotore_freqtrade__FreqGym_normalized__20260111_210550_FreqtradeEnv_PPO_20211226_191904" ) # Note: Make sure you use the same policy as the one used to train window_size = model.observation_space.shape[0] except Exception: pass timeperiods = [7, 14, 21] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ dataframe["plus_di"] = normalize(ta.PLUS_DI(dataframe), 0, 100) dataframe["minus_di"] = normalize(ta.MINUS_DI(dataframe), 0, 100) hilbert = ta.HT_SINE(dataframe) dataframe["htsine"] = normalize(hilbert["sine"], -1, 1) dataframe["htleadsine"] = normalize(hilbert["leadsine"], -1, 1) dataframe["bop"] = normalize(ta.BOP(dataframe), -1, 1) stoch = ta.STOCH(dataframe) dataframe["slowk"] = normalize(stoch["slowk"], 0, 100) dataframe["slowd"] = normalize(stoch["slowd"], 0, 100) stochf = ta.STOCHF(dataframe) dataframe["fastk"] = normalize(stochf["fastk"], 0, 100) dataframe["fastk"] = normalize(stochf["fastk"], 0, 100) bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3) dataframe["bb2_lower_gt_close"] = bollinger2["lower"].gt(dataframe["close"]).astype("int") dataframe["bb3_lower_gt_close"] = bollinger3["lower"].gt(dataframe["close"]).astype("int") for period in self.timeperiods: dataframe[f"adx_{period}"] = normalize(ta.ADX(dataframe, timeperiod=period), 0, 100) aroon = ta.AROON(dataframe, timeperiod=period) dataframe[f"aroonup_{period}"] = normalize(aroon["aroonup"], 0, 100) dataframe[f"aroondown_{period}"] = normalize(aroon["aroondown"], 0, 100) dataframe[f"aroonosc_{period}"] = normalize( ta.AROONOSC(dataframe, timeperiod=period), -100, 100 ) dataframe[f"cmo_{period}"] = normalize(ta.CMO(dataframe, timeperiod=period), -100, 100) dataframe[f"dx_{period}"] = normalize(ta.DX(dataframe, timeperiod=period), 0, 100) dataframe[f"mfi_{period}"] = normalize(ta.MFI(dataframe, timeperiod=period), 0, 100) dataframe[f"minus_di_{period}"] = normalize( ta.MINUS_DI(dataframe, timeperiod=period), 0, 100 ) dataframe[f"plus_di_{period}"] = normalize( ta.PLUS_DI(dataframe, timeperiod=period), 0, 100 ) dataframe[f"willr_{period}"] = normalize( ta.WILLR(dataframe, timeperiod=period), -100, 0 ) dataframe[f"rsi_{period}"] = normalize(ta.RSI(dataframe, timeperiod=period), 0, 100) rsi = 0.1 * (dataframe[f"rsi_{period}"] - 50) dataframe[f"fisher_rsi_{period}"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) dataframe[f"fisher_rsi_{period}"] = normalize(dataframe[f"fisher_rsi_{period}"], -1, 1) stoch_rsi = ta.STOCHRSI(dataframe, timeperiod=period) dataframe[f"stochrsi_k_{period}"] = normalize(stoch_rsi["fastk"], 0, 100) dataframe[f"stochrsi_d_{period}"] = normalize(stoch_rsi["fastd"], 0, 100) dataframe[f"linangle_{period}"] = normalize( ta.LINEARREG_ANGLE(dataframe, timeperiod=period), -90, 90 ) indicators = dataframe[ dataframe.columns[ ~dataframe.columns.isin( ["date", "open", "close", "high", "low", "volume", "buy", "sell", "buy_tag"] ) ] ] assert all(indicators.max() < 1.00001) and all( indicators.min() > -0.00001 ), "Error, values are not normalized!" return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ action = self.rl_model_predict(dataframe) dataframe["buy"] = (action == 1).astype("int") return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ action = self.rl_model_predict(dataframe) dataframe["sell"] = (action == 2).astype("int") return dataframe def rl_model_predict(self, dataframe): output = pd.DataFrame(np.zeros((len(dataframe), 1))) indicators = ( dataframe[ dataframe.columns[ ~dataframe.columns.isin( ["date", "open", "close", "high", "low", "volume", "buy", "sell", "buy_tag"] ) ] ] .fillna(0) .to_numpy() ) for window in range(self.window_size, len(dataframe)): start = window - self.window_size end = window observation = indicators[start:end] res, _ = self.model.predict(observation, deterministic=True) output.loc[end] = res return output def normalize(data, min_value, max_value): return (data - min_value) / (max_value - min_value)