# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/FreqaiExampleStrategy_2.py import logging from functools import reduce 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 Github_remiotore_freqtrade__FreqaiExampleStrategy_2__20260111_210550(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": 0.1, "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 = True stoploss = -0.05 use_exit_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. """ with self.model.bridge.lock: if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) for t in self.freqai_info["feature_parameters"]["indicator_periods"]: t = int(t) informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) informative[f"{coin}20sma-period_{t}"] = ta.SMA(informative, timeperiod=t) informative[f"{coin}21ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"%-{coin}close_over_20sma-period_{t}"] = ( informative["close"] / informative[f"{coin}20sma-period_{t}"] ) informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(informative), window=t, stds=2.2 ) informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] informative[f"%-{coin}bb_width-period_{t}"] = ( informative[f"{coin}bb_upperband-period_{t}"] - informative[f"{coin}bb_lowerband-period_{t}"] ) / informative[f"{coin}bb_middleband-period_{t}"] informative[f"%-{coin}close-bb_lower-period_{t}"] = ( informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] ) informative[f"%-{coin}roc-period_{t}"] = ta.ROC( informative, timeperiod=t ) macd = ta.MACD(informative, timeperiod=t) informative[f"%-{coin}macd-period_{t}"] = macd["macd"] informative[f"%-{coin}relative_volume-period_{t}"] = ( informative["volume"] / informative["volume"].rolling(t).mean() ) informative[f"%-{coin}pct-change"] = informative["close"].pct_change() informative[f"%-{coin}raw_volume"] = informative["volume"] informative[f"%-{coin}raw_price"] = informative["close"] 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) 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) if pair == self.freqai_info['corr_pairlist'][0] and tf == self.timeframe: df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 df['&-s_close'] = ( df["close"] .shift(-self.freqai_info['feature_parameters']["period"]) .rolling(self.freqai_info['feature_parameters']["period"]) .mean() / df["close"] - 1 ) return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] self.pair = metadata["pair"] 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] + "-" ) dataframe = self.model.bridge.start(dataframe, metadata, self) dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > 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["&-s_close"] < 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["&-s_close"] < 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["&-s_close"] > 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() follow_mode = self.config.get('freqai', {}).get('follow_mode', False) if not follow_mode: pair_dict = self.model.bridge.data_drawer.pair_dict else: pair_dict = self.model.bridge.data_drawer.follower_dict entry_tag = trade.enter_tag if ('prediction' + entry_tag not in pair_dict[pair] or pair_dict[pair]['prediction' + entry_tag] > 0): with self.model.bridge.lock: pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['&-s_close']) if not follow_mode: self.model.bridge.data_drawer.save_drawer_to_disk() else: self.model.bridge.data_drawer.save_follower_dict_to_disk() roi_price = pair_dict[pair]['prediction' + entry_tag] 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_decay: return 'roi_custom_win' if current_profit < -roi_decay: return 'roi_custom_loss' def confirm_trade_exit( self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time, **kwargs ) -> bool: entry_tag = trade.enter_tag follow_mode = self.config.get("freqai", {}).get("follow_mode", False) if not follow_mode: pair_dict = self.model.bridge.data_drawer.pair_dict else: pair_dict = self.model.bridge.data_drawer.follower_dict with self.model.bridge.lock: pair_dict[pair]["prediction" + entry_tag] = 0 if not follow_mode: self.model.bridge.data_drawer.save_drawer_to_disk() else: self.model.bridge.data_drawer.save_follower_dict_to_disk() return True def confirm_trade_entry( self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag, side: str, **kwargs ) -> bool: df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = df.iloc[-1].squeeze() if side == "long": if rate > (last_candle["close"] * (1 + 0.0025)): return False else: if rate < (last_candle["close"] * (1 - 0.0025)): return False return True