# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement 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 from freqtrade.strategy import merge_informative_pair import time class FreqGym_normalized(IStrategy): # # If you've used SimpleROIEnv then use this minimal_roi # minimal_roi = {"770": -10, "600": 0.00001, "60": 0.01, "30": 0.02, "0": 0.03} minimal_roi = { # "1440": -10 "0": 10, } stoploss = -0.10 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.017 trailing_only_offset_is_reached = True timeframe = "15m" informative_timeframe = "1h" use_sell_signal = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False startup_candle_count: int = 200 model = None window_size = None freqtrade_columns = ["date", "open", "close", "high", "low", "volume", "buy", "sell", "buy_tag", "exit_tag"] informative_freqtrade_columns = [f"{c}_1h" for c in freqtrade_columns] btc_freqtrade_columns = [f"{c}_btc_1h" for c in freqtrade_columns] indicators = None try: model = PPO.load("models/best_model") # 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 = [8, 16, 32] def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] informative_pairs += [("BTC/USDT", self.informative_timeframe)] return informative_pairs def generate_features(self, df): dataframe = df.copy() # Plus Directional Indicator / Movement dataframe["plus_di"] = normalize(ta.PLUS_DI(dataframe), 0, 100) # # Minus Directional Indicator / Movement dataframe["minus_di"] = normalize(ta.MINUS_DI(dataframe), 0, 100) # Ultimate Oscillator dataframe["uo"] = normalize(ta.ULTOSC(dataframe), 0, 100) # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe["htsine"] = normalize(hilbert["sine"], -1, 1) dataframe["htleadsine"] = normalize(hilbert["leadsine"], -1, 1) # BOP Balance Of Power dataframe["bop"] = normalize(ta.BOP(dataframe), -1, 1) # STOCH - Stochastic stoch = ta.STOCH(dataframe) dataframe["slowk"] = normalize(stoch["slowk"], 0, 100) dataframe["slowd"] = normalize(stoch["slowd"], 0, 100) # STOCHF - Stochastic Fast stochf = ta.STOCHF(dataframe) dataframe["fastk"] = normalize(stochf["fastk"], 0, 100) dataframe["fastk"] = normalize(stochf["fastk"], 0, 100) # Bollinger Bands 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: # ADX Average Directional Movement Index dataframe[f"adx_{period}"] = normalize(ta.ADX(dataframe, timeperiod=period), 0, 100) # Aroon, Aroon Oscillator 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) # CMO Chande Momentum Oscillator dataframe[f"cmo_{period}"] = normalize(ta.CMO(dataframe, timeperiod=period), -100, 100) # DX Directional Movement Index dataframe[f"dx_{period}"] = normalize(ta.DX(dataframe, timeperiod=period), 0, 100) # MFI Money Flow Index dataframe[f"mfi_{period}"] = normalize(ta.MFI(dataframe, timeperiod=period), 0, 100) # MINUS_DI Minus Directional Indicator dataframe[f"minus_di_{period}"] = normalize(ta.MINUS_DI(dataframe, timeperiod=period), 0, 100) # PLUS_DI Plus Directional Indicator dataframe[f"plus_di_{period}"] = normalize(ta.PLUS_DI(dataframe, timeperiod=period), 0, 100) # Williams %R dataframe[f"willr_{period}"] = normalize(ta.WILLR(dataframe, timeperiod=period), -100, 0) # RSI dataframe[f"rsi_{period}"] = normalize(ta.RSI(dataframe, timeperiod=period), 0, 100) # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) 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) # STOCHRSI - Stochastic Relative Strength Index 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) # # CORREL - Pearson's Correlation Coefficient (r) # dataframe[f'correl_{period}'] = normalize(ta.CORREL(dataframe, timeperiod=period), -1, 1) # this is buggy # LINEARREG_ANGLE - Linear Regression Angle dataframe[f"linangle_{period}"] = normalize(ta.LINEARREG_ANGLE(dataframe, timeperiod=period), -90, 90) return dataframe 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 = self.generate_features(dataframe) informative_df = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) informative_df = self.generate_features(informative_df) dataframe = merge_informative_pair( dataframe, informative_df, self.timeframe, self.informative_timeframe, ffill=True ) dataframe = dataframe[dataframe.columns[~dataframe.columns.isin(self.informative_freqtrade_columns)]] informative_btc = self.dp.get_pair_dataframe("BTC/USDT", timeframe=self.informative_timeframe) informative_btc = self.generate_features(informative_btc) informative_btc.rename(columns=lambda s: s if s == "date" else f"{s}_btc", inplace=True) dataframe = merge_informative_pair( dataframe, informative_btc, self.timeframe, self.informative_timeframe, ffill=True ) dataframe = dataframe[ dataframe.columns[~dataframe.columns.isin(self.btc_freqtrade_columns + self.informative_freqtrade_columns)] ] dataframe.fillna(0, inplace=True) self.indicators = dataframe[dataframe.columns[~dataframe.columns.isin(self.freqtrade_columns)]].to_numpy() assert ( self.indicators.max() < 1.00001 and self.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 """ # dataframe['buy'] = self.rl_model_predict(dataframe) action = self.rl_model_predict(dataframe) dataframe["buy"] = (action == 0).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 == 1).astype("int") return dataframe def rl_model_predict(self, dataframe): output = pd.DataFrame(np.full((len(dataframe), 1), 2)) # start_time = time.time() def _predict(window): observation = self.indicators[window.index] res, _ = self.model.predict(observation, deterministic=True) return res output = output.rolling(window=self.window_size).apply(_predict) # print("--- rolling %s seconds ---" % (time.time() - start_time)) # start_time = time.time() # for window in range(self.startup_candle_count, len(dataframe)): # start = window - self.window_size + 1 # end = window # observation = self.indicators[start:end+1] # res, _ = self.model.predict(observation, deterministic=True) # output.iloc[end] = res # print("--- forloop %s seconds ---" % (time.time() - start_time)) return output class FreqGym_normalizedDCA(FreqGym_normalized): # DCA position_adjustment_enable = True max_entry_position_adjustment = 2 max_dca_multiplier = 1.25 dca_stake_multiplier = 1.25 # This is called when placing the initial order (opening trade) def custom_stake_amount( self, pair: str, current_time, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs, ) -> float: if (self.config["position_adjustment_enable"] == True) and (self.config["stake_amount"] == "unlimited"): return self.wallets.get_total_stake_amount() / self.config["max_open_trades"] / self.max_dca_multiplier else: return proposed_stake def adjust_trade_position( self, trade, current_time, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs, ): if current_profit > -0.05: return None # Obtain pair dataframe (just to show how to access it) dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Only buy when not actively falling price. last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() # if previous_candle['close'] < last_candle['close']: # return None filled_buys = trade.select_filled_orders("buy") count_of_buys = len(filled_buys) if 0 < count_of_buys <= self.max_entry_position_adjustment: try: # This returns first order stake size stake_amount = filled_buys[0].cost # This then calculates current safety order size stake_amount = stake_amount * self.dca_stake_multiplier return stake_amount except Exception as exception: return None return None def normalize(data, min_value, max_value): return (data - min_value) / (max_value - min_value)