import logging from functools import reduce import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.exchange import timeframe_to_prev_date from freqtrade.freqai.strategy_bridge import CustomModel from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy logger = logging.getLogger(__name__) class FreqaiExampleStrategy(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.model = CustomModel(self.config) self.model.bridge.start(dataframe, metadata) to make predictions on their data. populate_any_indicators() automatically generates the variety of features indicated by the user in the canonical freqtrade configuration file under config['freqai']. """ minimal_roi = {"0": 0.01, "240": -1} plot_config = { "main_plot": {}, "subplots": { "prediction": {"prediction": {"color": "blue"}}, "target_roi": { "target_roi": {"color": "brown"}, }, "do_predict": { "do_predict": {"color": "brown"}, }, }, } process_only_new_candles = False stoploss = -0.05 use_sell_signal = True startup_candle_count: int = 300 can_short = False linear_roi_offset = DecimalParameter(0.00, 0.02, default=0.005, space='sell', optimize=False, load=True) max_roi_time_long = IntParameter(0, 800, default=400, space='sell', optimize=False, load=True) def informative_pairs(self): whitelist_pairs = self.dp.current_whitelist() corr_pairs = self.config["freqai"]["corr_pairlist"] informative_pairs = [] for tf in self.config["freqai"]["timeframes"]: for pair in whitelist_pairs: informative_pairs.append((pair, tf)) for pair in corr_pairs: if pair in whitelist_pairs: continue # avoid duplication informative_pairs.append((pair, tf)) return informative_pairs def bot_start(self): self.model = CustomModel(self.config) def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""): """ Function designed to automatically generate, name and merge features from user indicated timeframes in the configuration file. User controls the indicators passed to the training/prediction by prepending indicators with `'%-' + coin ` (see convention below). I.e. user should not prepend any supporting metrics (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the model. :params: :pair: pair to be used as informative :df: strategy dataframe which will receive merges from informatives :tf: timeframe of the dataframe which will modify the feature names :informative: the dataframe associated with the informative pair :coin: the name of the coin which will modify the feature names. """ if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) informative['%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14) informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) informative['%-' + coin + "adx"] = ta.ADX(informative, window=20) informative[coin + "20sma"] = ta.SMA(informative, timeperiod=20) informative[coin + "21ema"] = ta.EMA(informative, timeperiod=21) informative['%-' + coin + "bmsb"] = np.where( informative[coin + "20sma"].lt(informative[coin + "21ema"]), 1, 0 ) informative['%-' + coin + "close_over_20sma"] = informative["close"] / informative[ coin + "20sma"] informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25) informative[coin + "ema21"] = ta.EMA(informative, timeperiod=21) informative[coin + "sma20"] = ta.SMA(informative, timeperiod=20) stoch = ta.STOCHRSI(informative, 15, 20, 2, 2) informative['%-' + coin + "srsi-fk"] = stoch["fastk"] informative['%-' + coin + "srsi-fd"] = stoch["fastd"] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2) informative[coin + "bb_lowerband"] = bollinger["lower"] informative[coin + "bb_middleband"] = bollinger["mid"] informative[coin + "bb_upperband"] = bollinger["upper"] informative['%-' + coin + "bb_width"] = ( informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"] ) / informative[coin + "bb_middleband"] informative['%-' + coin + "close-bb_lower"] = ( informative["close"] / informative[coin + "bb_lowerband"] ) informative['%-' + coin + "roc"] = ta.ROC(informative, timeperiod=3) informative['%-' + coin + "adx"] = ta.ADX(informative, window=14) macd = ta.MACD(informative) informative['%-' + coin + "macd"] = macd["macd"] informative[coin + "pct-change"] = informative["close"].pct_change() informative['%-' + coin + "relative_volume"] = ( informative["volume"] / informative["volume"].rolling(10).mean() ) informative[coin + "pct-change"] = informative["close"].pct_change() # The following code automatically adds features according to the `shift` parameter passed # in the config. Do not remove indicators = [col for col in informative if col.startswith('%')] for n in range(self.freqai_info["feature_parameters"]["shift"] + 1): if n == 0: continue informative_shift = informative[indicators].shift(n) informative_shift = informative_shift.add_suffix("_shift-" + str(n)) informative = pd.concat((informative, informative_shift), axis=1) # The following code safely merges into the base timeframe. # Do not remove. df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]] df = df.drop(columns=skip_columns) # Add generalized indicators (not associated to any individual coin or timeframe) here # because in live, it will call this function to populate # indicators during training. Notice how we ensure not to add them multiple times if pair == metadata['pair'] and tf == self.timeframe: df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] self.pair = metadata['pair'] # the following loops are necessary for building the features # indicated by the user in the configuration file. # All indicators must be populated by populate_any_indicators() for live functionality # to work correctly. for tf in self.freqai_info["timeframes"]: dataframe = self.populate_any_indicators(metadata, self.pair, dataframe.copy(), tf, coin=self.pair.split("/")[0] + "-") for pair in self.freqai_info["corr_pairlist"]: if metadata['pair'] in pair: continue # do not include whitelisted pair twice if it is in corr_pairlist dataframe = self.populate_any_indicators( metadata, pair, dataframe.copy(), tf, coin=pair.split("/")[0] + "-" ) # the model will return 4 values, its prediction, an indication of whether or not the # prediction should be accepted, the target mean/std values from the labels used during # each training period. ( dataframe["prediction"], dataframe["do_predict"], dataframe["target_mean"], dataframe["target_std"], ) = self.model.bridge.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [ df['do_predict'] == 1, df['prediction'] > df["target_roi"] ] if enter_long_conditions: df.loc[reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]] = (1, 'long') enter_short_conditions = [ df['do_predict'] == 1, df['prediction'] < df["sell_roi"] ] if enter_short_conditions: df.loc[reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]] = (1, 'short') return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ df['do_predict'] == 1, df['prediction'] < df['sell_roi'] * 0.25 ] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 exit_short_conditions = [ df['do_predict'] == 1, df['prediction'] > df['target_roi'] * 0.25 ] if exit_short_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 return df def get_ticker_indicator(self): return int(self.config["timeframe"][:-1]) def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) trade_date = timeframe_to_prev_date(self.config['timeframe'], trade.open_date_utc) trade_candle = dataframe.loc[(dataframe['date'] == trade_date)] if trade_candle.empty: return None trade_candle = trade_candle.squeeze() pair_dict = self.model.bridge.data_drawer.pair_dict entry_tag = trade.enter_tag if 'prediction' + entry_tag not in pair_dict[pair]: with self.model.bridge.lock: self.model.bridge.data_drawer.pair_dict[pair][ 'prediction' + entry_tag] = abs(trade_candle['prediction']) self.model.bridge.data_drawer.save_drawer_to_disk() else: if pair_dict[pair]['prediction' + entry_tag] > 0: roi_price = abs(trade_candle['prediction' + entry_tag]) else: with self.model.bridge.lock: self.model.bridge.data_drawer.pair_dict[pair][ 'prediction' + entry_tag] = abs(trade_candle['prediction']) self.model.bridge.data_drawer.save_drawer_to_disk() roi_price = abs(trade_candle['prediction']) roi_time = self.max_roi_time_long.value roi_decay = roi_price * (1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)) if roi_decay < 0: roi_decay = self.linear_roi_offset.value else: roi_decay += self.linear_roi_offset.value if current_profit > roi_price: # roi_decay: with self.model.bridge.lock: self.model.bridge.data_drawer.pair_dict[pair]['prediction' + entry_tag] = 0 self.model.bridge.data_drawer.save_drawer_to_disk() return 'roi_custom_win'