# source: https://raw.githubusercontent.com/markdregan/FreqAI-Marcos-Lopez-De-Prado/8b5dec0e7741a53a7b8d20c6658d3dd7112c1819/user_data/strategies/deprecated/LitmusMinMaxStrategy.py from freqtrade.freqai.data_kitchen import FreqaiDataKitchen # from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair from functools import reduce from pandas import DataFrame from scipy.signal import argrelextrema from technical import qtpylib from freqtrade.litmus import indicator_helpers import logging import numpy as np import pandas as pd import talib.abstract as ta logger = logging.getLogger(__name__) class Github_markdregan_FreqAI_Marcos_Lopez_De_Prado__LitmusMinMaxStrategy__20240117_200530(IStrategy): """ Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.freqai.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, "600": -1} plot_config = { "main_plot": {}, "subplots": { "do_predict": { "do_predict": {"color": "brown"}, "DI_values": {"color": "grey"}, }, "Maxima": { "&s-maxima": {"color": "CornflowerBlue"}, "long_exit_target": {"color": "FireBrick"}, "short_entry_target": {"color": "DarkOliveGreen"}, "&s-maxima-shift-2": {"color": "LightPink"}, }, "Minima": { "&s-minima": {"color": "CornflowerBlue"}, "long_entry_target": {"color": "DarkOliveGreen"}, "short_exit_target": {"color": "FireBrick"}, "&s-minima-shift-2": {"color": "LightPink"}, }, "Max Forecast": { "&s-max-close": {"color": "MediumSeaGreen"}, }, "Real": { "real-minima": {"color": "blue"}, "real-maxima": {"color": "yellow"}, "price_forecast": {"color": "brown"} }, }, } process_only_new_candles = True stoploss = -0.05 use_exit_signal = True startup_candle_count: int = 300 can_short = True 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"]["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 """ coin = pair.split('/')[0] with self.freqai.lock: 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"%-{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}sma-period_{t}"] = ta.SMA(informative, timeperiod=t) informative[f"{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t) informative[f"%-{coin}close_over_sma-period_{t}"] = ( informative["close"] / informative[f"{coin}sma-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) 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("%")] # 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 # Max extreme value df["s-max-close"] = ( df["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"] + 1) .apply(indicator_helpers.max_extreme_value) / df["close"] - 1 ) # Define Min & Max binary indicators min_peaks = argrelextrema(df["close"].values, np.less, order=30) max_peaks = argrelextrema(df["close"].values, np.greater, order=30) df["&s-minima"] = 0 df["&s-maxima"] = 0 df["real-minima"] = 0 df["real-maxima"] = 0 # proximity_tolerance = 0.01 for mp in min_peaks[0]: df.at[mp, "real-minima"] = 1 df.at[mp, "&s-minima"] = 1 """min_peak_value = df.at[mp, "close"] if abs(df.at[mp - 1, "close"] / min_peak_value - 1) < proximity_tolerance: df.at[mp - 1, "&s-minima"] = 1 df.at[mp - 1, "real-minima"] = 0.5 if abs(df.at[mp + 1, "close"] / min_peak_value - 1) < proximity_tolerance: df.at[mp + 1, "&s-minima"] = 1 df.at[mp + 1, "real-minima"] = 0.5""" for mp in max_peaks[0]: df.at[mp, "real-maxima"] = 1 df.at[mp, "&s-maxima"] = 1 """max_peak_value = df.at[mp, "close"] if abs(df.at[mp - 1, "close"] / max_peak_value - 1) < proximity_tolerance: df.at[mp - 1, "&s-maxima"] = 1 df.at[mp - 1, "real-maxima"] = 0.5 if abs(df.at[mp + 1, "close"] / max_peak_value - 1) < proximity_tolerance: df.at[mp + 1, "&s-maxima"] = 1 df.at[mp + 1, "real-maxima"] = 0.5""" # Create shifted target for predicting missing max/min """lookback_period = 5 df["&s-minima-missed"] = ( df["&s-minima"] .shift(1) .rolling(lookback_period) .apply(some_func) ) df["&s-maxima-missed"] = ( df["&s-maxima"] .shift(1) .rolling(lookback_period) .apply(some_func) )""" return df def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] # All indicators must be populated by populate_any_indicators() for live functionality # to work correctly. # the model will return all labels created by user in `populate_any_indicators` # (& appended targets), an indication of whether or not the prediction should be accepted, # the target mean/std values for each of the labels created by user in # `populate_any_indicators()` for each training period. dataframe = self.freqai.start(dataframe, metadata, self) # Standard deviation entry / exists entry_std = 1.4 exit_std = 1.3 # Short dataframe["short_entry_target"] = ( dataframe["&s-maxima_mean"] + dataframe["&s-maxima_std"] * entry_std) dataframe["short_exit_target"] = ( dataframe["&s-minima_mean"] + dataframe["&s-minima_std"] * exit_std) # Long dataframe["long_entry_target"] = ( dataframe["&s-minima_mean"] + dataframe["&s-minima_std"] * entry_std) dataframe["long_exit_target"] = ( dataframe["&s-maxima_mean"] + dataframe["&s-maxima_std"] * exit_std) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [df["do_predict"] == 1, df["&s-minima"] > df["long_entry_target"]] 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-maxima"] > df["short_entry_target"]] 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 = [1 == 1, df["&s-maxima"] > df["long_exit_target"]] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 exit_short_conditions = [1 == 1, df["&s-minima"] > df["short_exit_target"]] 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.freqai.dd.pair_dict else: pair_dict = self.freqai.dd.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.freqai.lock: pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) if not follow_mode: self.freqai.dd.save_drawer_to_disk() else: self.freqai.dd.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.freqai.dd.pair_dict else: pair_dict = self.freqai.dd.follower_dict pair_dict[pair]["prediction" + entry_tag] = 0 if not follow_mode: self.freqai.dd.save_drawer_to_disk() else: self.freqai.dd.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 def analyze_trade_database(self, dk: FreqaiDataKitchen, pair: str) -> None: """ User analyzes the trade database here and returns summary stats which will be passed back to the strategy for reinforcement learning or for additional adaptive metrics for use in entry/exit signals. Store these metrics in dk.data['extra_returns_per_train'] and they will format themselves into the dataframe as an additional column in the user strategy. User has access to the current trade database in dk.trade_database_df. """ # total_profit = dk.trade_database_df['close_profit_abs'].sum() # dk.data['extra_returns_per_train']['total_profit'] = total_profit return