# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement import logging import time from datetime import timedelta from functools import partial import numpy as np # noqa import pandas as pd # noqa from freqtrade.strategy.interface import IStrategy from keras import Sequential from keras.models import load_model from pandas import DataFrame from time_series_to_gaf.cnn_model import create_cnn from time_series_to_gaf.constants import REPO from time_series_to_gaf.preprocess import quick_gaf, tensor_transform logger = logging.getLogger(__name__) COLUMNS_FILTER = [ 'date', 'open', 'close', 'high', 'low', 'buy', 'sell', 'volume', 'buy_tag', 'exit_tag', ] from scipy import stats stats.zscore = partial(stats.zscore, nan_policy='omit') class SagesGymCNN(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 = '1h' use_sell_signal = True # Run "populate_indicators()" only for new candle. process_only_new_candles = True startup_candle_count: int = 504 models: dict[str, Sequential] = {} window_size = 504 def __init__(self, config: dict) -> None: super().__init__(config) # self.custom_strategy = load_strategy('BatsContest', self.config) model_map = { 'BTC/USDT': ('20220403125044', 'RandomUniform.h5'), 'ETH/USDT': ('20220403113648', 'none.h5'), 'LTC/USDT': ('20220403120134', 'LecunUniform.h5'), } for pair, model_info in model_map.items(): try: self.models[pair] = self.load_model(pair, *model_info) except Exception as e: raise RuntimeError(f'Could not load model: {e}') from e else: logger.info(f'Loaded model: {model_info}') # @informative('1h') # def informative_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # return dataframe def load_model(self, pair: str, time: str, model_name: str) -> Sequential: # model = create_cnn(224) model_to_load = REPO / pair.replace('/', '_') / time / 'models' / model_name return load_model(model_to_load) 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 """ # logger.info(f'Calculating TA indicators for {metadata["pair"]}') # # # indicators = dataframe[dataframe.columns[~dataframe.columns.isin(COLUMNS_FILTER)]] # # assert all(indicators.max() < 1.00001) and all( # # indicators.min() > -0.00001 # # ), "Error, values are not normalized!" # # logger.info(f'{metadata["pair"]} - indicators populated!') # # dataframe = self.custom_strategy.populate_indicators(dataframe, metadata) # # rsi # dataframe[f'rsi'] = stats.zscore(ta.RSI(dataframe['close'])) # # awesome oscillator # dataframe['ao'] = stats.zscore( # pta.ao(dataframe['high'], dataframe['low'], fast=12, slow=26) # ) # # # macd # macd, macdsignal, macdhist = ta.MACD(dataframe['close']) # dataframe['macd'] = stats.zscore(macd) # dataframe['macdsignal'] = stats.zscore(macdsignal) # dataframe['macdhist'] = stats.zscore(macdhist) # # # aroon # dataframe['aroonup'], dataframe['aroondown'] = stats.zscore( # ta.AROON(dataframe['high'], dataframe['low'], timeperiod=25) # ) # dataframe['current_price'] = stats.zscore(dataframe['close']) 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) assert self.models.get(metadata['pair']) is not None, 'Model is not loaded.' logger.info(f'Populating buy signal for {metadata["pair"]}') action = self.rl_model_predict(dataframe, metadata['pair']) dataframe['buy'] = (action[0] > 0.50).astype('int') dataframe['sell'] = (action[1] > 0.50).astype('int') print(dataframe['buy'].value_counts(), 'buy signals') logger.info(f'{metadata["pair"]} - buy signal populated!') 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 """ logger.info(f'Populating sell signal for {metadata["pair"]}') # action = self.rl_model_predict(dataframe, metadata['pair']) # dataframe['sell'] = (action == 2).astype('int') # print number of sell signals print(dataframe['sell'].value_counts(), 'sell signals') logger.info(f'{metadata["pair"]} - sell signal populated!') return dataframe def preprocess(self, indicators: pd.DataFrame): t1 = time.perf_counter() images = quick_gaf(indicators)[0] images = tensor_transform(images[-1]) print('preprocess() -> Elapsed time:', timedelta(seconds=time.perf_counter() - t1)) return images def rl_model_predict(self, dataframe: DataFrame, pair: str): action_output = pd.DataFrame(np.zeros((len(dataframe), 2))) # multiplier_output = pd.DataFrame(np.zeros((len(dataframe), 1))) # indicators = # for c in COLUMNS_FILTER: # # remove every column that contains a substring of c # indicators = indicators.drop(columns=[col for col in indicators.columns if c in col]) indicators = dataframe.copy()[['date', 'open', 'close']] # start index where all indicators are available # print(f'{indicators.shape}') # TODO: This is slow and ugly, must use .rolling for window in range(self.window_size, len(dataframe), 24): start = window - self.window_size end = window observation = self.preprocess(indicators[start:end]) t1 = time.perf_counter() res = self.models[pair].predict(observation)[0] print('model.predict() -> Elapsed time:', timedelta(seconds=time.perf_counter() - t1)) action_output.loc[end] = res return action_output # 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: # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # last_candle = dataframe.iloc[-1].squeeze() # pob = self.percent_of_balance_dict[last_candle.name.astype('int')] # if pob > 0: # return pob / 10 * self.wallets.get_available_stake_amount()