# source: https://raw.githubusercontent.com/markdregan/FreqAI-Marcos-Lopez-De-Prado/8b5dec0e7741a53a7b8d20c6658d3dd7112c1819/user_data/strategies/deprecated/LitmusEntryRollClassificationStrategy.py from datetime import datetime from feature_engine.creation import CyclicalFeatures from freqtrade.strategy import (IStrategy, merge_informative_pair, BooleanParameter, CategoricalParameter) from functools import reduce from freqtrade.litmus.label_helpers import nearby_extremes from pandas import DataFrame from technical import qtpylib from typing import Optional import logging import pandas as pd import talib.abstract as ta import zigzag logger = logging.getLogger(__name__) class Github_markdregan_FreqAI_Marcos_Lopez_De_Prado__LitmusEntryRollClassificationStrategy__20240117_200530(IStrategy): """ to run this: freqtrade trade --strategy Github_markdregan_FreqAI_Marcos_Lopez_De_Prado__LitmusEntryRollClassificationStrategy__20240117_200530 --config user_data/strategies/config.LitmusEntryRollClassification.json --freqaimodel LitmusMultiTargetClassifier --verbose """ minimal_roi = { "0": 1, "1000": -1 } plot_config = { "main_plot": {}, "subplots": { "do_predict": { "do_predict": {"color": "brown"}, "DI_values": {"color": "grey"}, "DI_no_outlier_detected": {"color": "#1e5780"}, }, "Long": { "minima_0": {"color": "PaleGreen"}, "long_entry_target": {"color": "ForestGreen"}, "maxima_1": {"color": "Salmon"}, "long_exit_target": {"color": "Crimson"}, }, "Short": { "maxima_0": {"color": "PaleGreen"}, "short_entry_target": {"color": "ForestGreen"}, "minima_1": {"color": "Salmon"}, "short_exit_target": {"color": "Crimson"}, }, "Labels": { "raw_peaks_1": {"color": "#ffffa3"}, "nearby_peaks_1": {"color": "#e0ce38"}, "raw_peaks_0": {"color": "#a47ebc"}, "nearby_peaks_0": {"color": "#700CBC"} }, "Other": { "total_time": {"color": "Pink"}, "num_trees_&target_0": {"color": "Orange"}, "num_trees_&target_1": {"color": "#65ceff"}, "maxima_0_mean": {"color": "grey"}, "maxima_1_mean": {"color": "grey"}, }, }, } # Configs for hyperopt std_entry = 2.5 std_exit = 1.5 # Hyperopt parameters do_predict_enabled = BooleanParameter( default=True, space="buy", optimize=True) outlier_detected_enabled = BooleanParameter( default=True, space="buy", optimize=True) long_entry_std_mul = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0], default=1.75, space="buy", optimize=True) long_exit_std_mul = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0], default=1.25, space="sell", optimize=True) short_entry_std_mul = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0], default=1.75, space="buy", optimize=True) short_exit_std_mul = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0], default=1.25, space="sell", optimize=True) # Stop loss config stoploss = -0.02 trailing_stop = True """trailing_stop_positive_offset = 0.02 trailing_stop_positive = 0.015 trailing_only_offset_is_reached = True""" process_only_new_candles = True use_exit_signal = True startup_candle_count = 300 can_short = True def informative_pairs(self): whitelist_pairs = self.dp.current_whitelist() corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] informative_pairs = [] for tf in self.config["freqai"]["feature_parameters"]["include_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 populate_any_indicators( self, pair, df, tf, informative=None, set_generalized_indicators=False ): """ 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. :param pair: pair to be used as informative :param df: strategy dataframe which will receive merges from informatives :param tf: timeframe of the dataframe which will modify the feature names :param informative: the dataframe associated with the informative pair """ if informative is None: informative = self.dp.get_pair_dataframe(pair, tf) # first loop is automatically duplicating indicators for time periods for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: t = int(t) informative[f"%%-{pair}-rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) informative[f"%%-{pair}-mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) informative[f"%%-{pair}-adx-period_{t}"] = ta.ADX(informative, window=t) informative[f"{pair}-sma-period_{t}"] = ta.SMA(informative, timeperiod=t) informative[f"{pair}-ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"%-{pair}-close_over_sma-period_{t}"] = ( informative["close"] / informative[f"{pair}-sma-period_{t}"] ) informative[f"%-{pair}-mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(informative), window=t, stds=2.2 ) informative[f"{pair}-bb_lowerband-period_{t}"] = bollinger["lower"] informative[f"{pair}-bb_middleband-period_{t}"] = bollinger["mid"] informative[f"{pair}-bb_upperband-period_{t}"] = bollinger["upper"] informative[f"%-{pair}-bb_width-period_{t}"] = ( informative[f"{pair}-bb_upperband-period_{t}"] - informative[f"{pair}-bb_lowerband-period_{t}"] ) / informative[f"{pair}-bb_middleband-period_{t}"] informative[f"%-{pair}-close-bb_lower-period_{t}"] = ( informative["close"] / informative[f"{pair}-bb_lowerband-period_{t}"] ) informative[f"%-{pair}-roc-period_{t}"] = ta.ROC(informative, timeperiod=t) informative[f"%-{pair}-relative_volume-period_{t}"] = ( informative["volume"] / informative["volume"].rolling(t).mean() ) # Absolute Price Oscillator informative[f"%-{pair}-apo-period_{t}"] = ta.APO( informative["close"], fastperiod=int(t / 2), slowperiod=t, matype=0) # PPO (Percentage Price Oscilator) informative[f"%-{pair}-ppo-period_{t}"] = ta.PPO( informative["close"], fastperiod=int(t / 2), slowperiod=t, matype=0) # MACD (macd, macdsignal, macdhist) _, _, macdhist = ta.MACD( informative["close"], fastperiod=int(t / 2), slowperiod=t, signalperiod=int(3 * t / 4)) informative[f"%-{pair}-macdhist-period_{t}"] = macdhist # Average True Range informative[f"%-{pair}-atr-period_{t}"] = ta.ATR( informative["high"], informative["low"], informative["close"], timeperiod=t) informative[f"%-{pair}-pct-change"] = informative["close"].pct_change() informative[f"%-{pair}-raw_volume"] = informative["volume"] informative[f"%-{pair}-raw_price"] = informative["close"] indicators = [col for col in informative if col.startswith("%")] # This loop duplicates and shifts all indicators to add a sense of recency to data for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 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) # Add generalized indicators 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 set_generalized_indicators: df["day_of_week"] = df["date"].dt.dayofweek df["hour_of_day"] = df["date"].dt.hour cyclical_transform = CyclicalFeatures( variables=["day_of_week", "hour_of_day"], max_values=None, drop_original=True ) df = cyclical_transform.fit_transform(df) # Zigzag min/max for pivot positions for i, g in enumerate(self.freqai_info["labeling_parameters"]["zigzag_min_growth"]): logger.info(f"Starting zigzag labeling method ({i})") min_growth = self.freqai_info["labeling_parameters"]["zigzag_min_growth"][i] peaks = zigzag.peak_valley_pivots( df["close"].values, min_growth, -min_growth) peaks[0] = 0 # Set first value of peaks = 0 peaks[-1] = 0 # Set last value of peaks = 0 name_map = {0: f"not_minmax_{i}", 1: f"maxima_{i}", -1: f"minima_{i}"} # Shift target for benefit of hindsight predictions target_offset = self.freqai_info["labeling_parameters"]["target_offset"][i] df[f"raw_peaks_{i}"] = peaks df[f"raw_peaks_{i}"] = df[f"raw_peaks_{i}"].shift(target_offset).fillna(value=0) # Smear label to values nearby within threshold nearby_threshold = self.freqai_info["labeling_parameters"]["nearby_threshold"][i] df[f"nearby_peaks_{i}"] = nearby_extremes( df[["close", f"raw_peaks_{i}"]], threshold=nearby_threshold, forward_pass=self.freqai_info["labeling_parameters"]["forward_pass"][i], reverse_pass=self.freqai_info["labeling_parameters"]["reverse_pass"][i]) df[f"&target_{i}"] = df[f"nearby_peaks_{i}"].map(name_map) df[f"real_peaks_{i}"] = peaks """# Cointegration features df["%-compare-BTC-log-returns_3m"] = ( np.log(df[f"%-{coin}-raw_price_3m"]) - np.log(df["%-BTC-raw_price_3m"]) ) df["%%-compare-BTC-log-returns-zscore_3m"] = zscore(df["%-compare-BTC-log-returns_3m"]) """ return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] dataframe = self.freqai.start(dataframe, metadata, self) # Long entry dataframe["long_entry_target"] = ( dataframe["minima_0_mean"] + dataframe["minima_0_std"] * float(self.long_entry_std_mul.value) ) # Long exit dataframe["long_exit_target"] = ( dataframe["maxima_1_mean"] + dataframe["maxima_1_std"] * float(self.long_exit_std_mul.value) ) # Short entry dataframe["short_entry_target"] = ( dataframe["maxima_0_mean"] + dataframe["maxima_0_std"] * float(self.short_entry_std_mul.value) ) # Short exit dataframe["short_exit_target"] = ( dataframe["minima_1_mean"] + dataframe["minima_1_std"] * float(self.short_exit_std_mul.value) ) # Dissimilarity Index (Rolling Calculation Method) DI_window = self.freqai_info["feature_parameters"].get("DI_window", 100) DI_std_mul = self.freqai_info["feature_parameters"].get("DI_std_mul", 1) dataframe["DI_outliers"] = ( dataframe["DI_values"].rolling(DI_window).mean() + dataframe["DI_values"].rolling(DI_window).std() * DI_std_mul ) dataframe["DI_no_outlier_detected"] = dataframe["DI_values"] < dataframe["DI_outliers"] return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: # Long Entry conditions = [] if self.do_predict_enabled.value: conditions.append(df["do_predict"] == 1) if self.outlier_detected_enabled.value: conditions.append(df["DI_no_outlier_detected"]) conditions.append( qtpylib.crossed_above( df["minima_0"], df["minima_0_mean"] + df["minima_0_std"] * float(self.long_entry_std_mul.value) ) ) if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"] ] = (1, "minima_entry") # Short Entry conditions = [] if self.do_predict_enabled.value: conditions.append(df["do_predict"] == 1) if self.outlier_detected_enabled.value: conditions.append(df["DI_no_outlier_detected"]) conditions.append( qtpylib.crossed_above( df["minima_0"], df["minima_0_mean"] + df["minima_0_std"] * float(self.short_entry_std_mul.value) ) ) if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["enter_short", "enter_tag"] ] = (1, "maxima_entry") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: # Long Exit conditions = [] conditions.append( qtpylib.crossed_above( df["maxima_1"], df["maxima_1_mean"] + df["maxima_1_std"] * float(self.long_exit_std_mul.value) ) ) if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["exit_long", "exit_tag"] ] = (1, "maxima_exit") # Short Exit conditions = [] conditions.append( qtpylib.crossed_above( df["minima_1"], df["minima_1_mean"] + df["minima_1_std"] * float(self.short_exit_std_mul.value) ) ) if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["exit_short", "exit_tag"] ] = (1, "minima_exit") return df def get_ticker_indicator(self): return int(self.config["timeframe"][:-1]) @property def protections(self): return [ { "method": "StoplossGuard", "lookback_period": 60, "trade_limit": 1, "stop_duration": 15, "required_profit": 0.0, "only_per_pair": True, "only_per_side": True }, { "method": "LowProfitPairs", "lookback_period": 60, "trade_limit": 2, "stop_duration": 60, "required_profit": 0.005, "only_per_pair": True, "only_per_side": True }, { "method": "MaxDrawdown", "lookback_period": 120, "trade_limit": 10, "stop_duration": 60, "max_allowed_drawdown": 0.05 } ] def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ fixed_leverage = self.freqai_info.get("fixed_leverage", 0) if fixed_leverage > 0: return fixed_leverage else: return 1.0 def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], side: str, **kwargs) -> bool: """open_trades = Trade.get_trades(trade_filter=Trade.is_open.is_(True)) # Balance longs vs shorts to help protect against black swan event max_open_trades = self.config.get("max_open_trades", 0) if max_open_trades > 0: num_shorts, num_longs = 0, 0 for trade in open_trades: if trade.enter_tag == "short": num_shorts += 1 elif trade.enter_tag == "long": num_longs += 1 if side == "long" and num_longs >= max_open_trades / 2.0: return False if side == "short" and num_shorts >= max_open_trades / 2.0: return False""" # Prevent taking trades that have already moved too far in predicted direction """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)): logger.info(f"Trade entry blocked (long) for {pair}") return False else: if rate < (last_candle["close"] * (1 - 0.0025)): logger.info(f"Trade entry blocked (short) for {pair}") return False""" return True """use_custom_stoploss = False def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = df.iloc[-1].squeeze() # Long Exit if last_candle["maxima_1"] > last_candle["long_exit_target"]: # Tighten stop loss under latest close return 0.02 # Short Exit if last_candle["minima_1"] > last_candle["short_exit_target"]: return 0.02 # Otherwise keep current stoploss return -1""" """def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, leverage: float, entry_tag: str, side: str, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() bid = self.wallets.get_available_stake_amount() * current_candle["missed_long_entry"] return bid """