# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/freqai/FreqaiBinaryClassStrategy.py from typing import Dict, List, Optional, Tuple, Union import logging from functools import reduce from turtle import update from h11 import Data from datetime import datetime, timedelta, timezone import pandas as pd import talib.abstract as ta from pandas_ta.trend import adx from pandas import DataFrame from technical import qtpylib import numpy as np from scipy.signal import argrelextrema from sklearn.metrics import precision_recall_curve from freqtrade.exchange import timeframe_to_prev_date from freqtrade.persistence import Trade from technical.util import resample_to_interval, resampled_merge from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy # from freqtrade.enums import DoPredictType logger = logging.getLogger(__name__) def find_support_levels(df: DataFrame) -> DataFrame: """ cond1 = df['Low'][i] < df['Low'][i-1] cond2 = df['Low'][i] < df['Low'][i+1] cond3 = df['Low'][i+1] < df['Low'][i+2] cond4 = df['Low'][i-1] < df['Low'][i-2] """ cond1 = df["low"] < df["low"].shift(1) cond2 = df["low"] < df["low"].shift(-1) cond3 = df["low"].shift(-1) < df["low"].shift(-2) cond4 = df["low"].shift(1) < df["low"].shift(2) return (cond1 & cond2 & cond3 & cond4) class Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626(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": { "do_predict": { "do_predict": { "color": "brown" } }, "DI_values": { "DI_values": { "color": "#8115a9", "type": "line" } }, "GTs": { "tp_max": { "color": "#69796a", "type": "bar" }, "tp_min": { "color": "#e2517f", "type": "bar" }, "max": { "color": "#69796a", "type": "line" }, "min": { "color": "#e2517f", "type": "line" }, "neutral": { "color": "#ffffff", "type": "line" } } } } position_adjustment_enable = False 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. :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. """ coin = pair.split('/')[0] 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) out = adx(informative["high"], informative["low"], informative["close"], window=t) informative[f"%-{coin}adx-period_{t}"] = out["ADX_14"] informative[f"%-{coin}diplus-period_{t}"] = out["DMP_14"] informative[f"%-{coin}diminus-period_{t}"] = out["DMN_14"] 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}"] ) 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("%")] # 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) # find support levels if tf == self.freqai_info["feature_parameters"]["include_timeframes"][-1]: informative_6h = resample_to_interval(informative, "6h") informative_6h["support_levels"] = find_support_levels(informative_6h) df = merge_informative_pair(df, informative_6h, self.config["timeframe"], "6h", ffill=True) 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 + 1) / 7 df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 # user adds targets here by prepending them with &- (see convention below) # If user wishes to use multiple targets, a multioutput prediction model # needs to be used such as templates/CatboostPredictionMultiModel.py #df["&s-minima"] = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_min_labels(df) #df["&s-maxima"] = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_max_labels(df) minmax = np.array(["neutral"] * len(df)) minmax[Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_min_labels(df) == 1] = "min" minmax[Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_max_labels(df) == 1] = "max" df["&s-minmax"] = np.array([str(x) for x in minmax]).astype(np.object0) return df @staticmethod def get_max_labels(df: DataFrame) -> DataFrame: labels = np.zeros(len(df), dtype=np.int32) col = "close" max_peaks = argrelextrema(df[col].values, np.greater, order=12)[0] nn = 2 out = adx(df["high"], df["low"], df["close"], window=12) diplus = out["DMP_14"] price = (df['high'] + df['low'] + df['close']) / 3 di_thr = min(35, np.nanmax(diplus[max_peaks])) for mp in max_peaks: ref_close = price.iloc[mp] start = max(0, mp-nn) end = min(df.shape[0], mp+nn+1) pct = np.abs(price[start:end] / ref_close - 1) is_close = np.where(pct <= 0.005)[0] left_idx = is_close[0] right_idx = is_close[-1] # locality labeling if diplus[mp-nn+left_idx:mp-nn+right_idx].mean() >= di_thr: labels[mp-nn+left_idx:mp-nn+right_idx] = 1 if labels.max() == 0: # if not any positive label is found, we force it idx = np.nanargmax(diplus[max_peaks]) labels[max_peaks[idx]] = 1 return labels @staticmethod def get_min_labels(df: DataFrame) -> DataFrame: labels = np.zeros(len(df), dtype=np.int32) col = "close" min_peaks = argrelextrema(df[col].values, np.less, order=12)[0] nn = 2 out = adx(df["high"], df["low"], df["close"], window=12) diminus = out["DMN_14"] price = (df['high'] + df['low'] + df['close']) / 3 di_thr = min(35, np.nanmax(diminus[min_peaks])) for mp in min_peaks: ref_close = price.iloc[mp] start = max(0, mp-nn) end = min(df.shape[0], mp+nn+1) pct = np.abs(price[start:end] / ref_close - 1) is_close = np.where(pct <= 0.005)[0] left_idx = is_close[0] right_idx = is_close[-1] # locality labeling if diminus[mp-nn+left_idx:mp-nn+right_idx].mean() >= di_thr: labels[mp-nn+left_idx:mp-nn+right_idx] = 1 # return np.array([str(x) for x in labels]).astype(np.object0) if labels.max() == 0: # if not any positive label is found, we force it idx = np.nanargmax(diminus[min_peaks]) labels[min_peaks[idx]] = 1 return labels @staticmethod def expand_labels(df: DataFrame, peaks: List[int]): col = "close" nn = 2 labels = np.zeros(len(df), dtype=np.int32) for p in peaks: ref_close = df[col][p] for idx in range(max(0, p - nn), min(p + nn, df.shape[0]-1)): if np.abs(df[col][idx] / ref_close - 1) <= 0.005: labels[idx] = 1 return np.array([str(x) for x in labels]).astype(np.object0) @staticmethod def get_labels(df: DataFrame, metadata: dict) -> DataFrame: col = "close" min_peaks = argrelextrema(df[col].values, np.less, order=24) min_peaks = min_peaks[0] max_peaks = argrelextrema(df[col].values, np.greater, order=24) max_peaks = max_peaks[0] # import epdb; epdb.set_trace() peaks = sorted(set(min_peaks).union(set(max_peaks))) updown = None max_peaks2 = [] min_peaks2 = [] for idx in peaks: if (idx in min_peaks and idx in max_peaks): continue if idx in min_peaks: if updown is None or updown == True: updown = False min_peaks2.append(idx) else: if df[col][min_peaks2[-1]] < df[col][idx]: continue else: min_peaks2[-1] = idx elif idx in max_peaks: if updown is None or updown == False: updown = True max_peaks2.append(idx) else: if df[col][max_peaks2[-1]] > df[col][idx]: continue else: max_peaks2[-1] = idx profits = [df["close"][y] / df["close"][x] - 1 for x, y in zip(min_peaks2, max_peaks2)] print(metadata, np.mean(profits)) #print(metadata, df["date"][min_peaks2]) #print(metadata, df["date"][max_peaks2]) min_peaks = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.expand_labels(df, min_peaks2) max_peaks = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.expand_labels(df, max_peaks2) return min_peaks, max_peaks def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] # 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 = self.freqai.start(dataframe, metadata, self) # dataframe["&s-minima"] = dataframe["&s-minima"].astype(np.float32) # dataframe["&s-maxima"] = dataframe["&s-maxima"].astype(np.float32) max_labels = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_max_labels(dataframe) min_labels = Github_hamidreza07_freqai_strategy__FreqaiBinaryClassStrategy__20231226_132626.get_min_labels(dataframe) self.maxima_threhsold = 0.4 self.minima_threhsold = 0.4 dataframe["tp_max"] = max_labels.astype(np.float32) dataframe["tp_min"] = min_labels.astype(np.float32) dataframe["di-"] = ta.MINUS_DI(dataframe, window=12) dataframe["di+"] = ta.PLUS_DI(dataframe, window=12) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") enter_missed_long_conditions = [df["do_predict"] == 1, df["tp_min"].shift(1) == 1] if enter_missed_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_missed_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long_missed") if self.can_short: enter_short_conditions = [df["do_predict"] == 1, df["max"] >= self.maxima_threhsold] if enter_short_conditions: df.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") enter_missed_short_conditions = [df["do_predict"] == 1, df["tp_max"].shift(1) == 1] if enter_missed_short_conditions: df.loc[ reduce(lambda x, y: x & y, enter_missed_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short_missed") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [df["do_predict"] == 1, df["max"] >= self.maxima_threhsold] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), ["exit_long", "exit_tag"]] = (1, "exit signal") exit_long_missed_conditions = [df["do_predict"] == 1, df["tp_max"].shift(1) == 1] if exit_long_missed_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_missed_conditions), ["exit_long", "exit_tag"]] = (1, "exit_long_missed") if self.can_short: exit_short_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold] if exit_short_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 exit_short_missed_conditions = [df["do_predict"] == 1, df["tp_min"].shift(1) == 1] if exit_short_missed_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_missed_conditions), ["exit_short", "exit_tag"]] = (1, "exit_short_missed") 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.dd.pair_dict else: pair_dict = self.model.bridge.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.model.bridge.lock: if entry_tag == "long": pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&s-maxima"]) else: pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"]) if not follow_mode: self.model.bridge.dd.save_drawer_to_disk() else: self.model.bridge.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 adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, **kwargs) -> Optional[float]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased. This means extra buy 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. :param max_stake: Balance available for trading. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: Stake amount to adjust your trade """ if not trade.is_short: if current_profit < -0.02: df, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) try: new_local_minima = [df["&s-minima"] > self.minima_threhsold, (df["close"] / current_rate - 1) < 1e-3] if df.shape[0] - df.loc[reduce(lambda x, y: x & y, new_local_minima)].index[-1] <= 10: return 20 except: pass return None