378 lines
15 KiB
Python
378 lines
15 KiB
Python
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import logging
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from datetime import datetime, timedelta
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from functools import reduce
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from typing import Optional
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from freqtrade.exchange import timeframe_to_prev_date
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from freqtrade.persistence import Trade
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from freqtrade.strategy import (DecimalParameter, IntParameter, IStrategy,
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merge_informative_pair)
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from numpy.lib import math
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from pandas import DataFrame
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from technical import qtpylib
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logger = logging.getLogger(__name__)
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class FreqaiExampleHybridStrategy(IStrategy):
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"""
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Example classifier hybrid strategy showing how the user connects their own
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IFreqaiModel to the strategy. Namely, the user uses:
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self.freqai.start(dataframe, metadata)
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to make predictions on their data. populate_any_indicators() automatically
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generates the variety of features indicated by the user in the
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canonical freqtrade configuration file under config['freqai'].
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The underlying original supertrend strat is authored by @juankysoriano (Juan Carlos Soriano)
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* github: https://github.com/juankysoriano/
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This strategy is not designed to be used live
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"""
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minimal_roi = {"0": 0.1, "30": 0.75, "60": 0.05, "120": 0.025, "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 = -0.1
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use_exit_signal = True
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startup_candle_count: int = 300
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can_short = True
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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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buy_params = {
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"buy_m1": 4,
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"buy_m2": 7,
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"buy_m3": 1,
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"buy_p1": 8,
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"buy_p2": 9,
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"buy_p3": 8,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_m1": 1,
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"sell_m2": 3,
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"sell_m3": 6,
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"sell_p1": 16,
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"sell_p2": 18,
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"sell_p3": 18,
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}
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buy_m1 = IntParameter(1, 7, default=1)
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buy_m2 = IntParameter(1, 7, default=3)
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buy_m3 = IntParameter(1, 7, default=4)
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buy_p1 = IntParameter(7, 21, default=14)
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buy_p2 = IntParameter(7, 21, default=10)
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buy_p3 = IntParameter(7, 21, default=10)
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sell_m1 = IntParameter(1, 7, default=1)
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sell_m2 = IntParameter(1, 7, default=3)
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sell_m3 = IntParameter(1, 7, default=4)
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sell_p1 = IntParameter(7, 21, default=14)
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sell_p2 = IntParameter(7, 21, default=10)
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sell_p3 = IntParameter(7, 21, default=10)
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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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"""
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Function designed to automatically generate, name and merge features
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from user indicated timeframes in the configuration file. User controls the indicators
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passed to the training/prediction by prepending indicators with `'%-' + coin `
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(see convention below). I.e. user should not prepend any supporting metrics
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(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
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model.
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:param pair: pair to be used as informative
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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"""
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coin = pair.split('/')[0]
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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}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(informative), window=t, stds=2.2
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)
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informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
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informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
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informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
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informative[f"%-{coin}bb_width-period_{t}"] = (
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informative[f"{coin}bb_upperband-period_{t}"]
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- informative[f"{coin}bb_lowerband-period_{t}"]
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) / informative[f"{coin}bb_middleband-period_{t}"]
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informative[f"%-{coin}close-bb_lower-period_{t}"] = (
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informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
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)
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informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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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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# Classifiers are typically set up with strings as targets:
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df['&s-up_or_down'] = np.where( df["close"].shift(-50) >
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df["close"], 'up', 'down')
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# REGRESSOR Model: Can use single or multi traget
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# user adds targets here by prepending them with &- (see convention below)
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#df["&-s_close"] = (
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .mean()
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# / df["close"]
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# - 1
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#)
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# If user wishes to use multiple targets, they can add more by
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# appending more columns with '&'. User should keep in mind that multi targets
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# requires a multioutput prediction model such as
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# templates/CatboostPredictionMultiModel.py,
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# df["&-s_range"] = (
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .max()
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# -
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# df["close"]
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# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
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# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
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# .min()
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# )
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# All indicators must be populated by populate_any_indicators() for live functionality
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# to work correctly.
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# the model will return all labels created by user in `populate_any_indicators`
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# (& appended targets), an indication of whether or not the prediction should be accepted,
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# the target mean/std values for each of the labels created by user in
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# `populate_any_indicators()` for each training period.
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for multiplier in self.buy_m1.range:
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for period in self.buy_p1.range:
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dataframe[f"supertrend_1_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.buy_m2.range:
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for period in self.buy_p2.range:
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dataframe[f"supertrend_2_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.buy_m3.range:
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for period in self.buy_p3.range:
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dataframe[f"supertrend_3_buy_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m1.range:
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for period in self.sell_p1.range:
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dataframe[f"supertrend_1_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m2.range:
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for period in self.sell_p2.range:
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dataframe[f"supertrend_2_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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for multiplier in self.sell_m3.range:
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for period in self.sell_p3.range:
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dataframe[f"supertrend_3_sell_{multiplier}_{period}"] = self.supertrend(
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dataframe, multiplier, period
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)["STX"]
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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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df.loc[
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(df[f"supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}"] == "up") &
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(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up") &
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(df[f"supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}"] == "up") &
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(df["do_predict"] == 1) &
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(df['&s-up_or_down'] == 'up'),
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"enter_long",
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] = 1
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df.loc[
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(df[f"supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}"] == "down") &
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(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down") &
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(df[f"supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}"] == "down") &
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(df["do_predict"] == 1) &
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(df['&s-up_or_down'] == 'down'),
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"enter_short",
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] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(df[f"supertrend_2_sell_{self.sell_m2.value}_{self.sell_p2.value}"] == "down"),
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"exit_long",
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] = 1
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df.loc[
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(df[f"supertrend_2_buy_{self.buy_m2.value}_{self.buy_p2.value}"] == "up"),
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"exit_short",
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] = 1
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return df
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def get_ticker_indicator(self):
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return int(self.config["timeframe"][:-1])
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
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rate: float, time_in_force: str, current_time, entry_tag, side: str,
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**kwargs, ) -> bool:
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.0025)):
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return False
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else:
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if rate < (last_candle["close"] * (1 - 0.0025)):
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return False
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return True
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return 1
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"""
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Supertrend Indicator; adapted for freqtrade, optimized by the math genius.
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from: Perkmeister#2394
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"""
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def supertrend(self, dataframe: DataFrame, multiplier, period):
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df = dataframe.copy()
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last_row = dataframe.tail(1).index.item()
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df['TR'] = ta.TRANGE(df)
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|
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df['ATR'] = ta.SMA(df['TR'], period)
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||
|
|
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||
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st = 'ST_' + str(period) + '_' + str(multiplier)
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||
|
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stx = 'STX_' + str(period) + '_' + str(multiplier)
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||
|
|
|
||
|
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# Compute basic upper and lower bands
|
||
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BASIC_UB = ((df['high'] + df['low']) / 2 + multiplier * df['ATR']).values
|
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|
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BASIC_LB = ((df['high'] + df['low']) / 2 - multiplier * df['ATR']).values
|
||
|
|
FINAL_UB = np.zeros(last_row + 1)
|
||
|
|
FINAL_LB = np.zeros(last_row + 1)
|
||
|
|
ST = np.zeros(last_row + 1)
|
||
|
|
CLOSE = df['close'].values
|
||
|
|
|
||
|
|
# Compute final upper and lower bands
|
||
|
|
for i in range(period, last_row + 1):
|
||
|
|
FINAL_UB[i] = BASIC_UB[i] if BASIC_UB[i] < FINAL_UB[i - 1] or CLOSE[i - 1] > FINAL_UB[i - 1] else FINAL_UB[i - 1]
|
||
|
|
FINAL_LB[i] = BASIC_LB[i] if BASIC_LB[i] > FINAL_LB[i - 1] or CLOSE[i - 1] < FINAL_LB[i - 1] else FINAL_LB[i - 1]
|
||
|
|
|
||
|
|
# Set the Supertrend value
|
||
|
|
for i in range(period, last_row + 1):
|
||
|
|
ST[i] = FINAL_UB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] <= FINAL_UB[i] else \
|
||
|
|
FINAL_LB[i] if ST[i - 1] == FINAL_UB[i - 1] and CLOSE[i] > FINAL_UB[i] else \
|
||
|
|
FINAL_LB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] >= FINAL_LB[i] else \
|
||
|
|
FINAL_UB[i] if ST[i - 1] == FINAL_LB[i - 1] and CLOSE[i] < FINAL_LB[i] else 0.00
|
||
|
|
df_ST = pd.DataFrame(ST, columns=[st])
|
||
|
|
df = pd.concat([df, df_ST],axis=1)
|
||
|
|
|
||
|
|
# Mark the trend direction up/down
|
||
|
|
df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down', 'up'), np.NaN)
|
||
|
|
|
||
|
|
df.fillna(0, inplace=True)
|
||
|
|
|
||
|
|
return DataFrame(index=df.index, data={
|
||
|
|
'ST' : df[st],
|
||
|
|
'STX' : df[stx]
|
||
|
|
})
|