# source: https://raw.githubusercontent.com/markdregan/FreqAI-Marcos-Lopez-De-Prado/8b5dec0e7741a53a7b8d20c6658d3dd7112c1819/user_data/strategies/LitmusSimpleStrategy.py import logging import numpy as np import pandas as pd import pandas_ta as pta import talib.abstract as ta from cointanalysis import CointAnalysis from datetime import datetime, timedelta from feature_engine.creation import CyclicalFeatures from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, merge_informative_pair from functools import reduce from freqtrade.litmus import bet_sizing from freqtrade.litmus.label_helpers import tripple_barrier from freqtrade.litmus import indicator_helpers as ih from freqtrade.litmus import nilux_indicator_helpers as nih from pandas import DataFrame from technical import qtpylib from typing import Optional, Dict logger = logging.getLogger(__name__) # Config for visual display pandas (for debugging) pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 2000) class Github_markdregan_FreqAI_Marcos_Lopez_De_Prado__LitmusSimpleStrategy__20240117_200530(IStrategy): """ to run this: freqtrade trade --strategy Github_markdregan_FreqAI_Marcos_Lopez_De_Prado__LitmusSimpleStrategy__20240117_200530 --config user_data/strategies/config.LitmusMLDP.json --freqaimodel LitmusMLDPClassifier --verbose """ plot_config = { "main_plot": { "mm_bb_lowerband": {"color": "grey"}, "mm_bb_upperband": {"color": "grey"}, }, "subplots": { "do_predict": { "do_predict": {"color": "brown"}, "DI_values": {"color": "grey"} }, "Meta": { "a_win_long": {"color": "PaleGreen"}, "a_win_short": {"color": "Salmon"}, "meta_enter_long_threshold": {"color": "ForestGreen"}, "meta_enter_short_threshold": {"color": "FireBrick"}, }, "GT": { "primary_enter_long_tbm": {"color": "PaleGreen"}, "primary_enter_short_tbm": {"color": "Salmon"}, }, "Returns": { "value_meta_long_max_returns_&-meta_target": {"color": "PaleGreen"}, "value_meta_short_max_returns_&-meta_target": {"color": "Salmon"} }, "Diag": { "total_time_&-meta_target": {"color": "Pink"}, "num_features_selected_&-meta_target": {"color": "Orange"} }, "F1": { "value_meta_f1_score_&-meta_target": {"color": "Yellow"} } }, } """prot_cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=True) prot_stoploss_enabled = BooleanParameter(default=False, space="protection", optimize=False) prot_stoploss_duration = IntParameter(1, 60, default=17, space="protection", optimize=False)""" # ROI table: minimal_roi = { "0": 1.0, "1000": 0 } # Stoploss: stoploss = -0.05 # Other strategy flags process_only_new_candles = True use_exit_signal = True can_short = True startup_candle_count = 120 """@property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.prot_cooldown_lookback.value }) if self.prot_stoploss_enabled.value: prot.append({ "method": "StoplossGuard", "lookback_period": 60, "trade_limit": 2, "stop_duration_candles": self.prot_stoploss_duration.value, "required_profit": 0.0, "only_per_pair": False, "only_per_side": False }) return prot""" def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs): """ Will expand: `indicator_periods_candles` *`include_timeframes` * `include_shifted_candles` * `include_corr_pairs` """ dataframe["%-rsi"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema"] = ta.EMA(dataframe, timeperiod=period) dataframe["%-cci"] = ta.CCI(dataframe, timeperiod=period) dataframe["%-er"] = pta.er(dataframe['close'], length=period) dataframe["%-rocr"] = ta.ROCR(dataframe, timeperiod=period) dataframe["%-cmf"] = ih.chaikin_mf(dataframe, periods=period) dataframe["%-tcp"] = ih.top_percent_change(dataframe, period) dataframe["%-cti"] = pta.cti(dataframe['close'], length=period) dataframe["%-chop"] = qtpylib.chopiness(dataframe, period) dataframe["%-linear"] = ta.LINEARREG_ANGLE(dataframe['close'], timeperiod=period) dataframe["%-obv"] = ta.OBV(dataframe) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] dataframe["%-bb_width"] = ( dataframe["bb_upperband"] - dataframe["bb_lowerband"] ) / dataframe["bb_middleband"] dataframe["%-close-bb_lower"] = ( dataframe["close"] / dataframe["bb_lowerband"] ) dataframe["%-roc"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) # Absolute Price Oscillator dataframe["%-apo"] = ta.APO( dataframe["close"], fastperiod=int(period / 2), slowperiod=period, matype=0) # PPO (Percentage Price Oscilator) dataframe["%-ppo"] = ta.PPO( dataframe["close"], fastperiod=int(period / 2), slowperiod=period, matype=0) # MACD (macd, macdsignal, macdhist) _, _, macdhist = ta.MACD( dataframe["close"], fastperiod=int(period / 2), slowperiod=period, signalperiod=int(3 * period / 4)) dataframe["%-macdhist"] = macdhist # Average True Range dataframe["%-atr"] = ta.ATR( dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=period) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: Dict, **kwargs) -> pd.DataFrame: """ Will expand: `include_timeframes` * `include_shifted_candles` * `include_corr_pairs` Will not expand: `indicator_periods_candles` """ t = 20 dataframe["%-raw_price"] = dataframe["close"] dataframe["%-price_change"] = dataframe["close"].pct_change() dataframe["%-volatility_price"] = \ dataframe["%-price_change"].rolling(t).std() * np.sqrt(t) dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-volume_change"] = np.log(dataframe['volume'] / dataframe['volume'].shift()) dataframe["%-volatility_volume"] = \ dataframe["%-volume_change"].rolling(t).std() * np.sqrt(t) # Hurst Exp dataframe["%-hurst-exp"] = nih.hurst_exponent(dataframe, lookback=200) # Stochastic Momentum Index dataframe["%-smi"] = nih.smi_momentum(dataframe[["high", "low", "close"]]) dataframe["%-smi-direction"] = np.where( dataframe["%-smi"] > dataframe["%-smi"].shift(1), 1, 0) dataframe["%-smi"] = nih.smi_momentum(dataframe[["high", "low", "close"]]) # Nilux Indicators """dataframe[['fisher_cg', 'fisher_sig']] = nih.fisher_cg(dataframe[['high', 'low']]) dataframe = nih.exhaustion_bars(dataframe) dataframe = nih.pinbar(dataframe, dataframe["smi"]) dataframe = nih.breakouts(dataframe)""" return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: Dict, **kwargs) -> pd.DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns created by all other freqai_feature_engineering_* functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is a good place for any feature that should not be auto-expanded upon (e.g. day of the week). All features must be prepended with `%` to be recognized by FreqAI internals. More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param df: strategy dataframe which will receive the features usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 """ # Time features dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour cyclical_transform = CyclicalFeatures( variables=["%-day_of_week", "%-hour_of_day"], max_values={"%-day_of_week": 7, "%-hour_of_day": 24}, drop_original=True ) dataframe = cyclical_transform.fit_transform(dataframe) informative = self.dp.historic_ohlcv(pair="BTC/USDT:USDT", timeframe="5m") # Informative features coint_df = merge_informative_pair( dataframe[["date", "close"]], informative[["date", "close"]], self.timeframe, self.timeframe, ffill=True) coint_df = coint_df[["close", "close_5m"]] # Simple BTC derivative features dataframe["%-close_div_btc"] = coint_df["close"] / coint_df["close_5m"] dataframe["%-close_div_btc_change"] = dataframe["%-close_div_btc"].pct_change() dataframe["%-close_div_btc_change_std"] = \ dataframe["%-close_div_btc_change"].rolling(50).std() * np.sqrt(50) dataframe["%-close_div_btc_ppo"] = ta.PPO( dataframe["%-close_div_btc"], fastperiod=10, slowperiod=20, matype=0) # Cointegration with BTC features coint_params = self.freqai_info["feature_engineering"]["cointegration"] if coint_params["enabled"]: coint = CointAnalysis() coint_pvalue = coint.test(coint_df).pvalue_ dataframe["%%-coint_spread_btc"] = coint.fit_transform(coint_df).reshape(-1, 1) dataframe["coint_pvalue"] = coint_pvalue logger.info(f"Cointegration pvalue for {metadata['pair']} is {coint_pvalue}") # TBM params secondary_target_params = self.freqai_info["secondary_target_parameters"] window = secondary_target_params["tmb_long_window"] # Secondary Model: TBM target features params = { "upper_pct": secondary_target_params["tmb_long_upper"], "lower_pct": secondary_target_params["tmb_long_lower"], "result": "side" } dataframe["primary_enter_long_tbm"] = ( dataframe["close"] .shift(-window) .rolling(window + 1) .apply(tripple_barrier, kwargs=params) ) params = { "upper_pct": secondary_target_params["tmb_long_upper"], "lower_pct": secondary_target_params["tmb_long_lower"], "result": "value" } dataframe["primary_enter_long_tbm_value"] = ( dataframe["close"] .shift(-window) .rolling(window + 1) .apply(tripple_barrier, kwargs=params) ) params = { "upper_pct": secondary_target_params["tmb_short_upper"], "lower_pct": secondary_target_params["tmb_short_lower"], "result": "side" } dataframe["primary_enter_short_tbm"] = ( dataframe["close"] .shift(-window) .rolling(window + 1) .apply(tripple_barrier, kwargs=params) ) params = { "upper_pct": secondary_target_params["tmb_short_upper"], "lower_pct": secondary_target_params["tmb_short_lower"], "result": "value" } dataframe["primary_enter_short_tbm_value"] = ( dataframe["close"] .shift(-window) .rolling(window + 1) .apply(tripple_barrier, kwargs=params) ) # Compute expected win/loss for kelly criterion bet sizing secondary_target_params = self.freqai_info["secondary_target_parameters"] window = secondary_target_params["tmb_long_window"] # Compute trade return when TBM barrier crossing occurs dataframe["!-trade_return_long"] = ( (dataframe["primary_enter_long_tbm_value"] - dataframe["close"]) / dataframe["close"] ) dataframe["!-trade_return_short"] = ( (dataframe["primary_enter_short_tbm_value"] - dataframe["close"]) / dataframe["close"] ) # Mask down to long/short entry triggers only (above proba threshold) dataframe["!-trade_return_long_masked"] = np.where( (dataframe["primary_enter_long"] == True), dataframe["!-trade_return_long"], np.nan) dataframe["!-trade_return_short_masked"] = np.where( (dataframe["primary_enter_short"] == True), dataframe["!-trade_return_short"], np.nan) # Compute expected win for long/short trades dataframe["!-trade_return_long_expected_win"] = dataframe.loc[ dataframe["!-trade_return_long_masked"] > 0, "!-trade_return_long_masked"].rolling( 1000, min_periods=1).mean() dataframe["!-trade_return_long_expected_win"] = dataframe[ "!-trade_return_long_expected_win"].fillna(method="backfill").fillna(method="ffill") dataframe["!-trade_return_long_expected_loss"] = dataframe.loc[ dataframe["!-trade_return_long_masked"] <= 0, "!-trade_return_long_masked"].rolling( 1000, min_periods=1).mean() dataframe["!-trade_return_long_expected_loss"] = dataframe[ "!-trade_return_long_expected_loss"].fillna(method="backfill").fillna(method="ffill") dataframe["!-trade_return_short_expected_win"] = dataframe.loc[ dataframe["!-trade_return_short_masked"] <= 0, "!-trade_return_short_masked"].rolling( 1000, min_periods=1).mean() dataframe["!-trade_return_short_expected_win"] = dataframe[ "!-trade_return_short_expected_win"].fillna(method="backfill").fillna(method="ffill") dataframe["!-trade_return_short_expected_loss"] = dataframe.loc[ dataframe["!-trade_return_short_masked"] > 0, "!-trade_return_short_masked"].rolling( 1000, min_periods=1).mean() dataframe["!-trade_return_short_expected_loss"] = dataframe[ "!-trade_return_short_expected_loss"].fillna(method="backfill").fillna(method="ffill") # Simple trade return measure for sample_weights dataframe["!-trade_return"] = dataframe["close"].pct_change(window).shift(-window) # Compute median max price change within window to enable auto TBM threshold setting dataframe["!-trade_return_max_price_change"] = (( dataframe["high"] .shift(-window) .rolling(window + 1) .max() ) - dataframe["close"]) / dataframe["close"] dataframe["!-trade_return_min_price_change"] = ( dataframe["low"] - dataframe["close"] .shift(-window) .rolling(window + 1) .min() ) / dataframe["close"] # Mask down to long/short entry triggers only dataframe["!-trade_return_max_price_change_masked"] = np.where( dataframe["primary_enter_long"], dataframe["!-trade_return_max_price_change"], np.nan) dataframe["!-trade_return_min_price_change_masked"] = np.where( dataframe["primary_enter_short"], dataframe["!-trade_return_min_price_change"], np.nan) # Compute rolling median over masked data dataframe["!-trade_return_median_max_price_change"] = dataframe.loc[ dataframe["!-trade_return_max_price_change_masked"] > 0, "!-trade_return_max_price_change_masked"].rolling( 5000, min_periods=1).median() dataframe["!-trade_return_median_max_price_change"] = dataframe[ "!-trade_return_median_max_price_change"].fillna( method="backfill").fillna(method="ffill") dataframe["!-trade_return_median_min_price_change"] = dataframe.loc[ dataframe["!-trade_return_min_price_change_masked"] > 0, "!-trade_return_min_price_change_masked"].rolling( 5000, min_periods=1).median() dataframe["!-trade_return_median_min_price_change"] = dataframe[ "!-trade_return_median_min_price_change"].fillna( method="backfill").fillna(method="ffill") return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> pd.DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals. More details about feature engineering available: https://www.freqtrade.io/en/latest/freqai-feature-engineering :param df: strategy dataframe which will receive the targets usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] """ # Long long_tbm_map = {1: "win_long", 0: "loss_long", -1: "loss_long"} dataframe["long_outcome_tbm"] = dataframe["primary_enter_long_tbm"].map(long_tbm_map) conditions = [dataframe["primary_enter_long"]] choices = [dataframe["long_outcome_tbm"]] dataframe["&-meta_target_binary_long"] = np.select(conditions, choices, default="drop-row") dataframe["&-meta_target_binary_long"] = dataframe["&-meta_target_binary_long"].fillna( value="drop-row") # Short short_tbm_map = {1: "loss_short", 0: "loss_short", -1: "win_short"} dataframe["short_outcome_tbm"] = dataframe["primary_enter_short_tbm"].map(short_tbm_map) conditions = [dataframe["primary_enter_short"]] choices = [dataframe["short_outcome_tbm"]] dataframe["&-meta_target_binary_short"] = np.select(conditions, choices, default="drop-row") dataframe["&-meta_target_binary_short"] = dataframe["&-meta_target_binary_short"].fillna( value="drop-row") #print(dataframe.groupby("&-meta_target_binary_long").size()) #print(dataframe.groupby("&-meta_target_binary_short").size()) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> pd.DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) # Trigger thresholds smoothing_window = self.freqai_info["entry_parameters"].get("smoothing_window", 30) dataframe["win_long_enter_threshold"] = dataframe[ "win_long_threshold_&-meta_target_binary_long"].rolling(smoothing_window).mean() dataframe["win_short_enter_threshold"] = dataframe[ "win_short_threshold_&-meta_target_binary_short"].rolling(smoothing_window).mean() # Indicator to confirm ML model trained dataframe["long_model_ready"] = (dataframe["win_long"].mean() != 0) dataframe["short_model_ready"] = (dataframe["win_short"].mean() != 0) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> pd.DataFrame: # Long Entry conditions = [df["primary_enter_long"], df["long_model_ready"], df["win_long"] >= df["win_long_enter_threshold"]] if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["enter_long", "enter_tag"] ] = (1, "meta_enter_long") # Short Entry conditions = [df["primary_enter_short"], df["short_model_ready"], df["win_short"] >= df["win_short_enter_threshold"]] if conditions: df.loc[ reduce(lambda x, y: x & y, conditions), ["enter_short", "enter_tag"] ] = (1, "meta_enter_short") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> pd.DataFrame: # Use custom_exit() instead for more control return df def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() exit_params = self.freqai_info["exit_parameters"] weak_profit_threshold = 0.005 # When not in strong profit, exit when weak opposing entry detected if exit_params["weak_opposing_entry_trigger_enabled"]: if 0 < current_profit < weak_profit_threshold: if ((last_candle["primary_enter_short"] == True) and (trade.trade_direction == "long")): return "exit_long_weak_opposing_signal" elif ((last_candle["primary_enter_long"] == True) and (trade.trade_direction == "short")): return "exit_short_weak_opposing_signal" # When in strong profit, only exit when strong opposing entry detected if exit_params["strong_opposing_entry_trigger_enabled"]: if weak_profit_threshold < current_profit: if ((last_candle["primary_enter_short"] == True) and (last_candle["short_model_ready"] == True) and (last_candle["win_short"] >= last_candle["win_short_enter_threshold"]) and (trade.trade_direction == "long")): return "exit_long_strong_opposing_signal" elif ((last_candle["primary_enter_long"] == True) and (last_candle["long_model_ready"] == True) and (last_candle["win_long"] >= last_candle["win_long_enter_threshold"]) and (trade.trade_direction == "short")): return "exit_short_strong_opposing_signal" # When in strong profit, only exit when strong opposing entry detected if exit_params["exit_trigger_enabled"]: if ((last_candle["primary_exit_long"] == True) and (trade.trade_direction == "long")): return "exit_long_signal" elif ((last_candle["primary_exit_short"] == True) and (trade.trade_direction == "short")): return "exit_short_signal" def get_ticker_indicator(self): return int(self.config["timeframe"][:-1]) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: bet_sizing_params = self.freqai_info["bet_sizing"] if bet_sizing_params["enabled"]: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() # Get available balance to bet total_stake_amount = max_stake # Calculate kelly criterion bet size if side == "long": pred_proba = current_candle["win_long"] expected_win = current_candle["!-trade_return_long_expected_win"] expected_loss = current_candle["!-trade_return_long_expected_loss"] #print(current_candle[["win_long", "!-trade_return_long_expected_win", "!-trade_return_long_expected_loss"]]) else: pred_proba = current_candle["win_short"] expected_win = current_candle["!-trade_return_short_expected_win"] expected_loss = current_candle["!-trade_return_short_expected_loss"] #print(current_candle[["win_short", "!-trade_return_short_expected_win", "!-trade_return_short_expected_loss"]]) kelly_fraction = 0.2 kelly_factor = bet_sizing.kelly_bet_size( p=pred_proba, win=expected_win, loss=expected_loss, kelly_fraction=kelly_fraction ) bet_amount = kelly_factor * total_stake_amount self.dp.send_msg("*Kelly Criterion Bet Sizing:* \n" f"*Pair:* {pair} \n" f"*Side:* {side} \n" f"*Total available funds:* {np.round(total_stake_amount, 4)} \n" f"*Bet amount:* {np.round(bet_amount, 4)} \n" f"*Kelly factor:* {kelly_factor} \n" f"*Prediction proba:* {np.round(pred_proba, 4)} \n" f"*Expected win:* {np.round(expected_win, 4)} \n" f"*Expected loss:* {np.round(expected_loss, 4)} \n" f"*Kelly fraction:* {kelly_fraction}") return bet_amount else: return proposed_stake 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 custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: if current_profit <= 0.00: return -1 elif current_profit > 0.005: return 0.025 elif current_profit > 0.01: desired_stoploss = current_profit min_sl = 0.01 max_sl = 0.02 new_sl = max(min(desired_stoploss, max_sl), min_sl) return new_sl return -1 def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased or decreased. This means extra buy or sell orders with additional fees. Only called when `position_adjustment_enable` is set to True. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns None :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Current buy rate. :param current_profit: Current profit (as ratio), calculated based on current_rate. :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). :param current_entry_rate: Current rate using entry pricing. :param current_exit_rate: Current rate using exit pricing. :param current_entry_profit: Current profit using entry pricing. :param current_exit_profit: Current profit using exit pricing. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: Stake amount to adjust your trade, Positive values to increase position, Negative values to decrease position. Return None for no action. """ # Only allow trade adjustment once every N minutes if ((current_time - trade.date_last_filled_utc > timedelta(minutes=15)) & (trade.nr_of_successful_entries <= self.max_entry_position_adjustment) & (current_profit < -0.005)): df, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = df.iloc[-1].squeeze() # Long Entry enter_long = np.where( ((last_candle["primary_enter_long"] == True) and (last_candle["long_model_ready"] == True) and (last_candle["win_long"] >= last_candle["win_long_enter_threshold"])), True, False) # Short Entry enter_short = np.where( ((last_candle["primary_enter_short"] == True) & (last_candle["short_model_ready"] == True) & (last_candle["win_short"] >= last_candle["win_short_enter_threshold"])), True, False) if enter_long or enter_short: # This returns first order stake size filled_entries = trade.select_filled_orders(trade.entry_side) stake_amount = filled_entries[0].cost logger.info(f"Trade adjustment made adding {stake_amount} to {trade.pair}") return stake_amount / 3.0 return None