# source: https://raw.githubusercontent.com/nateemma/strategies/4e743136b7489224c967d42e0f22a90732890a88/ftx/KalmanSIMD.py import numpy as np import scipy.fft from scipy.fft import rfft, irfft import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import arrow from freqtrade.strategy import (IStrategy, merge_informative_pair, stoploss_from_open, IntParameter, DecimalParameter, CategoricalParameter) from typing import Dict, List, Optional, Tuple, Union from pandas import DataFrame, Series from functools import reduce from datetime import datetime, timedelta from freqtrade.persistence import Trade # Get rid of pandas warnings during backtesting import pandas as pd pd.options.mode.chained_assignment = None # default='warn' # Strategy specific imports, files must reside in same folder as strategy import sys from pathlib import Path sys.path.append(str(Path(__file__).parent)) import logging import warnings log = logging.getLogger(__name__) # log.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) import custom_indicators as cta from simdkalman import KalmanFilter """ #################################################################################### github_nateemma_strategies__KalmanSIMD__20230130_035719 - use a Kalmqn Filter to estimate future price movements, but *without* Fisher/Williams/Bollinger buy/sell signals Uses simdkalman package #################################################################################### """ class github_nateemma_strategies__KalmanSIMD__20230130_035719(IStrategy): # Do *not* hyperopt for the roi and stoploss spaces # ROI table: minimal_roi = { "0": 10 } # Stoploss: stoploss = -0.10 # Trailing stop: trailing_stop = False trailing_stop_positive = None trailing_stop_positive_offset = 0.0 trailing_only_offset_is_reached = False timeframe = '5m' inf_timeframe = '1h' use_custom_stoploss = True # Recommended use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = True # Required startup_candle_count: int = 32 process_only_new_candles = True custom_trade_info = {} ################################### # Strategy Specific Variable Storage kf_window = startup_candle_count filter_list = {} filter_init_list = {} kalman_filter = KalmanFilter( state_transition=1.0, process_noise=2.0, observation_model=1.0, observation_noise=0.5 ) current_pair = "" ## Hyperopt Variables # Kalman hyperparams enter_kf_diff = DecimalParameter(0.0, 5.0, decimals=1, default=1.0, space='buy', load=True, optimize=True) exit_kf_diff = DecimalParameter(-5.0, 0.0, decimals=1, default=-0.1, space='sell', load=True, optimize=True) # # FBB_ hyperparams # buy_bb_gain = DecimalParameter(0.01, 0.50, decimals=2, default=0.09, space='buy', load=True, optimize=True) # buy_fisher_wr = DecimalParameter(-0.99, -0.75, decimals=2, default=-0.75, space='buy', load=True, optimize=True) # # buy_force_fisher_wr = DecimalParameter(-0.99, -0.85, decimals=2, default=-0.99, space='buy', load=True, optimize=True) # # exit_bb_gain = DecimalParameter(0.7, 1.5, decimals=2, default=0.8, space='sell', load=True, optimize=True) # exit_fisher_wr = DecimalParameter(0.75, 0.99, decimals=2, default=0.9, space='sell', load=True, optimize=True) # # exit_force_fisher_wr = DecimalParameter(0.85, 0.99, decimals=2, default=0.99, space='sell', load=True, optimize=True) # Custom Sell Profit (formerly Dynamic ROI) cexit_roi_type = CategoricalParameter(['static', 'decay', 'step'], default='step', space='sell', load=True, optimize=True) cexit_roi_time = IntParameter(720, 1440, default=720, space='sell', load=True, optimize=True) cexit_roi_start = DecimalParameter(0.01, 0.05, default=0.01, space='sell', load=True, optimize=True) cexit_roi_end = DecimalParameter(0.0, 0.01, default=0, space='sell', load=True, optimize=True) cexit_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any', 'none'], default='any', space='sell', load=True, optimize=True) cexit_pullback = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True) cexit_pullback_amount = DecimalParameter(0.005, 0.03, default=0.01, space='sell', load=True, optimize=True) cexit_pullback_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) cexit_endtrend_respect_roi = CategoricalParameter([True, False], default=False, space='sell', load=True, optimize=True) # Custom Stoploss cstop_loss_threshold = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell', load=True, optimize=True) cstop_bail_how = CategoricalParameter(['roc', 'time', 'any', 'none'], default='none', space='sell', load=True, optimize=True) cstop_bail_roc = DecimalParameter(-5.0, -1.0, default=-3.0, space='sell', load=True, optimize=True) cstop_bail_time = IntParameter(60, 1440, default=720, space='sell', load=True, optimize=True) cstop_bail_time_trend = CategoricalParameter([True, False], default=True, space='sell', load=True, optimize=True) cstop_max_stoploss = DecimalParameter(-0.30, -0.01, default=-0.10, space='sell', load=True, optimize=True) ################################### """ Informative Pair Definitions """ def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.inf_timeframe) for pair in pairs] return informative_pairs ################################### """ Indicator Definitions """ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Base pair informative timeframe indicators curr_pair = metadata['pair'] informative = self.dp.get_pair_dataframe(pair=curr_pair, timeframe=self.inf_timeframe) # Kalman Filter # get filter for current pair self.current_pair = curr_pair # create if not already done if not curr_pair in self.filter_list: self.filter_list[curr_pair] = kalman_filter = KalmanFilter( state_transition=1.0, process_noise=2.0, observation_model=1.0, observation_noise=0.5 ) self.filter_init_list[curr_pair] = False # set current filter (can't pass parameter to apply()) self.kalman_filter = self.filter_list[curr_pair] informative['kf_model'] = informative['close'].rolling(window=self.kf_window).apply(self.model) # informative['kf_predict'] = informative['kf_model'].rolling(window=self.kf_window).apply(self.predict) # merge into normal timeframe dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True) # calculate predictive indicators in shorter timeframe (not informative) dataframe['kf_model'] = dataframe[f"kf_model_{self.inf_timeframe}"] # dataframe['kf_predict'] = dataframe[f"kf_predict_{self.inf_timeframe}"] dataframe['kf_model_diff'] = 100.0 * (dataframe['kf_model'] - dataframe['close']) / dataframe['close'] # Custom Stoploss if not metadata['pair'] in self.custom_trade_info: self.custom_trade_info[metadata['pair']] = {} if not 'had-trend' in self.custom_trade_info[metadata["pair"]]: self.custom_trade_info[metadata['pair']]['had-trend'] = False # RMI: https://www.tradingview.com/script/kwIt9OgQ-Relative-Momentum-Index/ dataframe['rmi'] = cta.RMI(dataframe, length=24, mom=5) # MA Streak: https://www.tradingview.com/script/Yq1z7cIv-MA-Streak-Can-Show-When-a-Run-Is-Getting-Long-in-the-Tooth/ dataframe['mastreak'] = cta.mastreak(dataframe, period=4) # Trends, Peaks and Crosses dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['open'], 1, 0) dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3, 1, 0) dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3, 1, 0) dataframe['rmi-dn'] = np.where(dataframe['rmi'] <= dataframe['rmi'].shift(), 1, 0) dataframe['rmi-dn-count'] = dataframe['rmi-dn'].rolling(8).sum() # Indicators used only for ROI and Custom Stoploss ssldown, sslup = cta.SSLChannels_ATR(dataframe, length=21) dataframe['sroc'] = cta.SROC(dataframe, roclen=21, emalen=13, smooth=21) dataframe['ssl-dir'] = np.where(sslup > ssldown, 'up', 'down') return dataframe ################################### def model(self, a: np.ndarray) -> np.float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std scaled.fillna(0, inplace=True) # init filter if needed if not self.filter_init_list[self.current_pair]: self.filter_init_list[self.current_pair] = True self.filter_list[self.current_pair] = self.filter_list[self.current_pair].em(scaled, n_iter=6) # get the Kalman model restored_sig = self.kalmanModel(scaled, self.kalman_filter) # re-trend model = (restored_sig * w_std) + w_mean length = len(model) return model[length-1] def scaledModel(self, a: np.ndarray) -> np.float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std scaled.fillna(0, inplace=True) # get the Fourier model model = self.kalmanModel(scaled, self.kalman_filter) length = len(model) return model[length-1] def scaledData(self, a: np.ndarray) -> np.float: # scale the data standardized = a.copy() w_mean = np.mean(standardized) w_std = np.std(standardized) scaled = (standardized - w_mean) / w_std scaled.fillna(0, inplace=True) length = len(scaled) return scaled.ravel()[length-1] def kalmanModel(self, data, kfilter: KalmanFilter): n = len(data) x = np.array(data) # kfilter = kfilter.em(data, n_iter=6) # mean, cov = kfilter.filter(x) # kfilter.filter_update(mean[0], cov[0]) # # mean = mean.squeeze() # #print("model(", len(mean), "): ", mean) # predict next close smoothed = kfilter.smooth(x) pr_mean = smoothed.observations.mean restored_sig = pr_mean.squeeze() # print ("Predict(", len(restored_sig), "): ", restored_sig) ldiff = len(restored_sig) - len(x) model = restored_sig[ldiff:] return model def predict(self, a: np.ndarray) -> np.float: # predicts the next value using polynomial extrapolation # a.fillna(0) # fit the supplied data # Note: extrapolation is notoriously fickle. Be careful length = len(a) x = np.arange(length) f = scipy.interpolate.UnivariateSpline(x, a, k=3) predict = f(length) return predict # ################################### # # # Williams %R # def williams_r(self, dataframe: DataFrame, period: int = 14) -> Series: # """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low # of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams. # Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between, # of its recent trading range. # The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest). # """ # # highest_high = dataframe["high"].rolling(center=False, window=period).max() # lowest_low = dataframe["low"].rolling(center=False, window=period).min() # # WR = Series( # (highest_high - dataframe["close"]) / (highest_high - lowest_low), # name=f"{period} Williams %R", # ) # # return WR * -100 ################################### """ Buy Signal """ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' # conditions.append(dataframe['volume'] > 0) # Kalman triggers kf_cond = ( qtpylib.crossed_above(dataframe['kf_model_diff'], self.enter_kf_diff.value) ) conditions.append(kf_cond) # Model will spike on big gains, so try to constrain spike_cond = ( dataframe['kf_model_diff'] < 2.0 * self.enter_kf_diff.value ) conditions.append(spike_cond) # set buy tags dataframe.loc[kf_cond, 'enter_tag'] += 'kf_buy ' if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 return dataframe ################################### """ Sell Signal """ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'exit_tag'] = '' # Kalman triggers kf_cond = ( qtpylib.crossed_below(dataframe['kf_model_diff'], self.exit_kf_diff.value) ) conditions.append(kf_cond) # DWTs will spike on big gains, so try to constrain spike_cond = ( dataframe['kf_model_diff'] > 2.0 * self.exit_kf_diff.value ) conditions.append(spike_cond) # set sell tags dataframe.loc[kf_cond, 'exit_tag'] += 'kf_sell ' if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe ################################### """ Custom Stoploss """ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) in_trend = self.custom_trade_info[trade.pair]['had-trend'] # limit stoploss if current_profit < self.cstop_max_stoploss.value: return 0.01 # Determine how we sell when we are in a loss if current_profit < self.cstop_loss_threshold.value: if self.cstop_bail_how.value == 'roc' or self.cstop_bail_how.value == 'any': # Dynamic bailout based on rate of change if last_candle['sroc'] <= self.cstop_bail_roc.value: return 0.01 if self.cstop_bail_how.value == 'time' or self.cstop_bail_how.value == 'any': # Dynamic bailout based on time, unless time_trend is true and there is a potential reversal if trade_dur > self.cstop_bail_time.value: if self.cstop_bail_time_trend.value == True and in_trend == True: return 1 else: return 0.01 return 1 ################################### """ Custom Sell """ 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=pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1].squeeze() trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60) max_profit = max(0, trade.calc_profit_ratio(trade.max_rate)) pullback_value = max(0, (max_profit - self.cexit_pullback_amount.value)) in_trend = False # Determine our current ROI point based on the defined type if self.cexit_roi_type.value == 'static': min_roi = self.cexit_roi_start.value elif self.cexit_roi_type.value == 'decay': min_roi = cta.linear_decay(self.cexit_roi_start.value, self.cexit_roi_end.value, 0, self.cexit_roi_time.value, trade_dur) elif self.cexit_roi_type.value == 'step': if trade_dur < self.cexit_roi_time.value: min_roi = self.cexit_roi_start.value else: min_roi = self.cexit_roi_end.value # Determine if there is a trend if self.cexit_trend_type.value == 'rmi' or self.cexit_trend_type.value == 'any': if last_candle['rmi-up-trend'] == 1: in_trend = True if self.cexit_trend_type.value == 'ssl' or self.cexit_trend_type.value == 'any': if last_candle['ssl-dir'] == 'up': in_trend = True if self.cexit_trend_type.value == 'candle' or self.cexit_trend_type.value == 'any': if last_candle['candle-up-trend'] == 1: in_trend = True # Don't sell if we are in a trend unless the pullback threshold is met if in_trend == True and current_profit > 0: # Record that we were in a trend for this trade/pair for a more useful sell message later self.custom_trade_info[trade.pair]['had-trend'] = True # If pullback is enabled and profit has pulled back allow a sell, maybe if self.cexit_pullback.value == True and (current_profit <= pullback_value): if self.cexit_pullback_respect_roi.value == True and current_profit > min_roi: return 'intrend_pullback_roi' elif self.cexit_pullback_respect_roi.value == False: if current_profit > min_roi: return 'intrend_pullback_roi' else: return 'intrend_pullback_noroi' # We are in a trend and pullback is disabled or has not happened or various criteria were not met, hold return None # If we are not in a trend, just use the roi value elif in_trend == False: if self.custom_trade_info[trade.pair]['had-trend']: if current_profit > min_roi: self.custom_trade_info[trade.pair]['had-trend'] = False return 'trend_roi' elif self.cexit_endtrend_respect_roi.value == False: self.custom_trade_info[trade.pair]['had-trend'] = False return 'trend_noroi' elif current_profit > min_roi: return 'notrend_roi' else: return None