# source: https://raw.githubusercontent.com/ch99q/freqtrade_dataset/82703b5331ec7e0c5e7a563403d722420b8dc8b0/user_data/strategies/strategy.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame import catboost import pickle from freqtrade.strategy import IStrategy import technical.indicators as ftt # -------------------------------- # Add your lib to import here from freqtrade.strategy import CategoricalParameter, RealParameter, DecimalParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # This class is a sample. Feel free to customize it. # SSL Channels def SSLChannels(dataframe, length = 7): df = dataframe.copy() df['ATR'] = ta.ATR(df, timeperiod=14) df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR'] df['smaLow'] = df['low'].rolling(length).mean() - df['ATR'] df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN)) df['hlv'] = df['hlv'].ffill() df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow']) df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh']) return df['sslDown'], df['sslUp'] class github_ch99q_freqtrade_dataset__strategy__20220210_114951(IStrategy): INTERFACE_VERSION = 2 # For the pkl file, it is at: https://filen.io/d/5728cb6b-2ec7-446d-9166-dd760614c366#!0BC2FH2jNxmDPDCae3bNCDORFU7mNWjb # Buy hyperspace params: buy_params = { "buy_val": 0.4281607787074923, "buy_trigger": "pred4", # value loaded from strategy } # Sell hyperspace params: sell_params = { "sell_val": 0.3494876619363504, "sell_trigger": "pred4", # value loaded from strategy } # ROI table: minimal_roi = { "0": 0.24, "446": 0.218, "1154": 0.09, "2438": 0 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.9 # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.248 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '1h' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 240 # HO buy_trigger = CategoricalParameter(['pred0', 'pred1', 'pred2', 'pred3', 'pred4'], default=buy_params['buy_trigger'], space='buy', optimize=False, load=True) sell_trigger = CategoricalParameter(['pred0', 'pred1', 'pred2', 'pred3', 'pred4'], default=sell_params['sell_trigger'], space='sell', optimize=False, load=True) buy_val = DecimalParameter(low=0.35, high=0.58, default=buy_params['buy_val'], space="buy", optimize=True, load=True) sell_val = DecimalParameter(low=0.10, high=0.35, default=sell_params['sell_val'], space="sell", optimize=True, load=True) # Open model def __init__(self, **kwargs) -> None: super().__init__(**kwargs) with open('/kaggle/working/model_1h_fgi_Acc_6c_val_05_08_v1.pkl', 'rb') as f: model = pickle.load(f) self.model = model[0] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: verbose = False col_use = [ 'volume', 'smadiff_3', 'maxdiff_3', 'std_3', 'ma_3', 'smadiff_5', 'maxdiff_5', 'std_5', 'ma_5', 'smadiff_8', 'maxdiff_8', 'std_8', 'ma_8', 'smadiff_13', 'maxdiff_13', 'std_13', 'ma_13', 'smadiff_21', 'maxdiff_21', 'std_21', 'ma_21', 'smadiff_34', 'maxdiff_34', 'std_34', 'ma_34', 'smadiff_55', 'maxdiff_55', 'std_55', 'ma_55', 'smadiff_89', 'maxdiff_89', 'std_89', 'ma_89', 'smadiff_120', 'maxdiff_120', 'std_120', 'ma_120', 'smadiff_240', 'maxdiff_240', 'std_240', 'ma_240', 'z_score_120', 'time_hourmin', 'time_dayofweek', 'time_hour', 'uo', 'cci', 'rsi', 'adx', 'sar', 'fisher_rsi', 'fisher_rsi_norma', 'ssl_down', 'ssl_up'] # Starting create features for i in [3, 5, 8, 13, 21, 34, 55, 89, 120, 240]: # smadiff dataframe[f"smadiff_{i}"] = (dataframe['close'].rolling(i).mean() - dataframe['close']) # max diff dataframe[f"maxdiff_{i}"] = (dataframe['close'].rolling(i).max() - dataframe['close']) # min diff dataframe[f"maxdiff_{i}"] = (dataframe['close'].rolling(i).min() - dataframe['close']) # volatiliy dataframe[f"std_{i}"] = dataframe['close'].rolling(i).std() # Return dataframe[f"ma_{i}"] = dataframe['close'].pct_change(i).rolling(i).mean() dataframe['z_score_120'] = ((dataframe.ma_13 - dataframe.ma_13.rolling(21).mean() + 1e-9) / (dataframe.ma_13.rolling(21).std() + 1e-9)) dataframe["date"] = pd.to_datetime(dataframe["date"], unit='ms') dataframe['time_hourmin'] = dataframe.date.dt.hour * 60 + dataframe.date.dt.minute dataframe['time_dayofweek'] = dataframe.date.dt.dayofweek dataframe['time_hour'] = dataframe.date.dt.hour # Ultimate Oscillator dataframe['uo'] = ta.ULTOSC(dataframe) # Commodity Channel Index: values [Oversold:-100, Overbought:100] dataframe['cci'] = ta.CCI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) # SAR dataframe['sar'] = ta.SAR(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (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['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # SSL ssl_down_1h, ssl_up_1h = SSLChannels(dataframe, 12) dataframe['ssl_down'] = ssl_down_1h dataframe['ssl_up'] = ssl_up_1h # Model predictions preds = pd.DataFrame(self.model.predict_proba(dataframe[col_use])) preds.columns = [f"pred{i}" for i in range(5)] dataframe = dataframe.reset_index(drop=True) dataframe = pd.concat([dataframe, preds], axis=1) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe[self.buy_trigger.value] > self.buy_val.value) & # (dataframe["time_hour"].isin([23,2,5,8,11,14,17,20])) & (dataframe['ssl_up'] > dataframe['ssl_down']) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # if self.config['runmode'].value == 'hyperopt': dataframe.loc[ ( (dataframe[self.sell_trigger.value] < self.sell_val.value) # & (dataframe["time_hour"].isin([23,2,5,8,11,14,17,20])) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'sell'] = 1 return dataframe