184 lines
7.3 KiB
Python
184 lines
7.3 KiB
Python
import logging
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from functools import reduce
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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 pandas import DataFrame
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from technical import qtpylib
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from freqtrade.freqai.strategy_bridge import CustomModel
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from freqtrade.strategy import merge_informative_pair
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from freqtrade.strategy.interface import IStrategy
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logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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"""
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Example 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.model = CustomModel(self.config)
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self.model.bridge.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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"""
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minimal_roi = {"0": 0.01, "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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stoploss = -0.05
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use_sell_signal = True
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startup_candle_count: int = 1000
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def informative_pairs(self):
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pairs = self.freqai_info["corr_pairlist"]
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informative_pairs = []
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for tf in self.timeframes:
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informative_pairs.append([(pair, tf) for pair in pairs])
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return informative_pairs
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def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
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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 can add
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additional features here, but must follow the naming convention.
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:params:
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:pair: pair to be used as informative
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:df: strategy dataframe which will receive merges from informatives
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:tf: timeframe of the dataframe which will modify the feature names
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:informative: the dataframe associated with the informative pair
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:coin: the name of the coin which will modify the feature names.
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"""
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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informative[coin + "rsi"] = ta.RSI(informative, timeperiod=14)
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin + "adx"] = ta.ADX(informative, window=20)
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informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20)
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informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "bmsb"] = np.where(
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informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0
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)
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informative[coin + "close_over_20sma"] = informative["close"] / informative[coin + "20sma"]
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informative[coin + "mfi"] = ta.MFI(informative, timeperiod=25)
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informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21)
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informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20)
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stoch = ta.STOCHRSI(informative, 15, 20, 2, 2)
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informative[coin + "srsi-fk"] = stoch["fastk"]
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informative[coin + "srsi-fd"] = stoch["fastd"]
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
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informative[coin + "bb_lowerband"] = bollinger["lower"]
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informative[coin + "bb_middleband"] = bollinger["mid"]
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informative[coin + "bb_upperband"] = bollinger["upper"]
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informative[coin + "bb_width"] = (
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informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
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) / informative[coin + "bb_middleband"]
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informative[coin + "close-bb_lower"] = (
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informative["close"] / informative[coin + "bb_lowerband"]
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)
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informative[coin + "roc"] = ta.ROC(informative, timeperiod=3)
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informative[coin + "adx"] = ta.ADX(informative, window=14)
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macd = ta.MACD(informative)
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informative[coin + "macd"] = macd["macd"]
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informative[coin + "pct-change"] = informative["close"].pct_change()
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informative[coin + "relative_volume"] = (
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informative["volume"] / informative["volume"].rolling(10).mean()
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)
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informative[coin + "pct-change"] = informative["close"].pct_change()
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indicators = [col for col in informative if col.startswith(coin)]
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for n in range(self.freqai_info["feature_parameters"]["shift"] + 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 = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]]
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df = df.drop(columns=skip_columns)
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# the configuration file parameters are stored here
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self.freqai_info = self.config["freqai"]
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# the model is instantiated here
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self.model = CustomModel(self.config)
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print("Populating indicators...")
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# the following loops are necessary for building the features
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# indicated by the user in the configuration file.
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for tf in self.freqai_info["timeframes"]:
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dataframe = self.populate_any_indicators(metadata["pair"], dataframe.copy(), tf)
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for i in self.freqai_info["corr_pairlist"]:
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dataframe = self.populate_any_indicators(
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i, dataframe.copy(), tf, coin=i.split("/")[0] + "-"
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)
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# the model will return 4 values, its prediction, an indication of whether or not the
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# prediction should be accepted, the target mean/std values from the labels used during
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# each training period.
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(
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dataframe["prediction"],
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dataframe["do_predict"],
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dataframe["target_mean"],
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dataframe["target_std"],
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) = self.model.bridge.start(dataframe, metadata)
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dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5
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dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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buy_conditions = [
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(dataframe["prediction"] > dataframe["target_roi"]) & (dataframe["do_predict"] == 1)
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]
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if buy_conditions:
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dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
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sell_conditions = [
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(dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1)
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]
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if sell_conditions:
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dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1
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return dataframe
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def get_ticker_indicator(self):
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return int(self.config["timeframe"][:-1])
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