# source: https://raw.githubusercontent.com/AlexBabescu/freqgym/fe1c5f91cbda6c0234d64732c6b52d4b0f21239a/user_data/strategies/FreqGym.py # 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, Series from stable_baselines3.ppo.ppo import PPO from stable_baselines3.a2c.a2c import A2C from ta import add_all_ta_features class github_AlexBabescu_freqgym__FreqGym__20211031_081005(IStrategy): # If you've used SimpleROIEnv then use this minimal_roi minimal_roi = { "720": -10, "600": 0.00001, "60": 0.01, "30": 0.02, "0": 0.03 } # minimal_roi = { # "0": 100 # } stoploss = -0.99 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.017 trailing_only_offset_is_reached = True timeframe = '5m' 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 try: model = PPO.load('models/best_model_SimpleROIEnv_PPO_20211028_104631') # 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 """ # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Awesome Oscillator dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # Ultimate Oscillator dataframe['uo'] = ta.ULTOSC(dataframe) # EWO dataframe['ewo'] = EWO(dataframe, 50, 200) # # Hilbert Transform Indicator - SineWave # hilbert = ta.HT_SINE(dataframe) # dataframe['htsine'] = hilbert['sine'] # dataframe['htleadsine'] = hilbert['leadsine'] # # # Heikin Ashi Strategy # heikinashi = qtpylib.heikinashi(dataframe) # dataframe['ha_open'] = heikinashi['open'] # dataframe['ha_close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] for period in self.timeperiods: # ADX dataframe[f'adx_{period}'] = ta.ADX(dataframe, timeperiod=period) # Williams %R dataframe[f'wr_{period}'] = williams_r(dataframe, timeperiod=period) # CCI dataframe[f'cci_{period}'] = ta.CCI(dataframe, timeperiod=period) # RSI dataframe[f'rsi_{period}'] = ta.RSI(dataframe, timeperiod=period) # 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) # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe[f'fisher_rsi_norma_{period}'] = 50 * (dataframe[f'fisher_rsi_{period}'] + 1) # Aroon, Aroon Oscillator aroon = ta.AROON(dataframe, timeperiod=period) dataframe[f'aroonup_{period}'] = aroon['aroonup'] dataframe[f'aroondown_{period}'] = aroon['aroondown'] dataframe[f'aroonosc_{period}'] = ta.AROONOSC(dataframe, timeperiod=period) # Chande Momentum Oscillator dataframe[f'cmo_{period}'] = ta.CMO(dataframe, timeperiod=period) # Money Flow Index dataframe[f'mfi_{period}'] = ta.MFI(dataframe, timeperiod=period) # # EMA - Exponential Moving Average # dataframe[f'ema_{period}'] = ta.EMA(dataframe, timeperiod=period) # # SMA - Simple Moving Average # dataframe[f'sma_{period}'] = ta.SMA(dataframe, timeperiod=period) # # TEMA - Triple Exponential Moving Average # dataframe[f'tema_{period}'] = ta.TEMA(dataframe, timeperiod=period) # All other # dataframe = add_all_ta_features(dataframe, 'open', 'high', 'low', 'close', 'volume', fillna=True) 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 == 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() # TODO: This is slow and ugly, must use .rolling 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 EWO(dataframe, sma1_length=5, sma2_length=35): df = dataframe.copy() sma1 = ta.EMA(df, timeperiod=sma1_length) sma2 = ta.EMA(df, timeperiod=sma2_length) smadif = (sma1 - sma2) / df['close'] * 100 return smadif def williams_r(dataframe: DataFrame, timeperiod: int = 14) -> Series: """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). """ highest_high = dataframe["high"].rolling(center=False, window=timeperiod).max() lowest_low = dataframe["low"].rolling(center=False, window=timeperiod).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{timeperiod} Williams %R", ) return WR * -100