139 lines
5.2 KiB
Python
139 lines
5.2 KiB
Python
import logging
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from functools import reduce
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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import numpy as np
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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logger = logging.getLogger(__name__)
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class freqai_test_classifier(IStrategy):
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"""
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Test strategy - used for testing freqAI functionalities.
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DO not use in production.
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"""
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minimal_roi = {"0": 0.1, "240": -1}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"target_roi": {
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"target_roi": {"color": "brown"},
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},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 300
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can_short = False
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linear_roi_offset = DecimalParameter(
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0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
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)
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max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
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informative_pairs = []
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for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
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for pair in whitelist_pairs:
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informative_pairs.append((pair, tf))
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for pair in corr_pairs:
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if pair in whitelist_pairs:
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continue # avoid duplication
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informative_pairs.append((pair, tf))
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return informative_pairs
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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coin = pair.split('/')[0]
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with self.freqai.lock:
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df['&s-up_or_down'] = np.where(df["close"].shift(-100) > df["close"], 'up', 'down')
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config["freqai"]
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dataframe = self.freqai.start(dataframe, metadata, self)
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [df['&s-up_or_down'] == 'up']
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
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] = (1, "long")
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enter_short_conditions = [df['&s-up_or_down'] == 'down']
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
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] = (1, "short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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return df
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