# source: https://raw.githubusercontent.com/jov0jova/vrach/828cff07f29c54058db6caf6ab7c811904f14e24/user_data/strategies/vrach_V1.py ''' __________________________________________________________________________________________________________________ VRACH Trading Strategy. __________________________________________________________________________________________________________________ Created by: jov0jova Live version : 1.0.0 __________________________________________________________________________________________________________________ Trading strategy : "scalp", "position", "daytrade", "swing", "long" __________________________________________________________________________________________________________________ ''' #Code # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib class Github_jov0jova_vrach__vrach_V1__20250523_103326(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' inf_timeframes = ['1h', '4h', '1d'] minimal_roi = {} stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.015 trailing_only_offset_is_reached = True use_custom_stoploss = False use_custom_exit = False can_short = False process_only_new_candles = True startup_candle_count = 288 def informative_pairs(self): pairs = [(pair, tf) for pair in self.dp.current_whitelist() for tf in self.inf_timeframes] return pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: ''' SELL: CLOSE > Close 1h CLOSE > CLOSE ROLLING 95% CLOSE > Close ROLLING 90% RSI 3 > RSI 3 Rolling 90% RSI 3 > RSI 3 Rolling 95% CLOSE > KAMA 25 KAMA 25 < KAMA 25 Shift 1 IF KAMA 25 GOING UP DONT EXIT MFI > MFI ROLLING 88,2 ''' lookback_5m = 288 lookback_1h = 144 lookback_4h = 72 lookback_1d = 36 # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ __ _____ __ __ _ // # // / __ \ _ __ ___ _____ / /____ _ ____ / ___/ / /_ __ __ ____/ /(_)___ _____ // # // / / / /| | / // _ \ / ___// // __ `// __ \ \__ \ / __// / / // __ // // _ \ / ___/ // # // / /_/ / | |/ // __// / / // /_/ // /_/ / ___/ // /_ / /_/ // /_/ // // __/(__ ) // # // \____/ |___/ \___//_/ /_/ \__,_// .___/ /____/ \__/ \__,_/ \__,_//_/ \___//____/ // # // /_/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #BBANDS Bollinger Bands dataframe['bb_upper'] = ta.BBANDS(dataframe, nbdevup=2.0, nbdevdn=2.0)['upperband'] dataframe['bb_middle'] = ta.BBANDS(dataframe, nbdevup=2.0, nbdevdn=2.0)['middleband'] dataframe['bb_lower'] = ta.BBANDS(dataframe, nbdevup=2.0, nbdevdn=2.0)['lowerband'] dataframe['bbands_breakout_down'] = dataframe['close'] < dataframe['bb_lower'] dataframe['bbands_breakout_up'] = dataframe['close'] > dataframe['bb_upper'] # #DEMA Double Exponential Moving Average # dataframe['dema_7'] = ta.DEMA(dataframe, timeperiod=7) # dataframe['dema_25'] = ta.DEMA(dataframe, timeperiod=25) # dataframe['dema_99'] = ta.DEMA(dataframe, timeperiod=99) # dataframe['dema_200'] = ta.DEMA(dataframe, timeperiod=200) # #EMA Exponential Moving Average dataframe['ema_7'] = ta.EMA(dataframe, timeperiod=7) dataframe['ema_25'] = ta.EMA(dataframe, timeperiod=25) dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema_99'] = ta.EMA(dataframe, timeperiod=99) dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_7_rolling_quartile_01'] = ta.EMA(dataframe, timeperiod=7).rolling(window=144).quantile(0.01) dataframe['ema_200_rolling_quartile_01'] = ta.EMA(dataframe, timeperiod=200).rolling(window=144).quantile(0.01) dataframe['ema_200_rolling_quartile_10'] = ta.EMA(dataframe, timeperiod=200).rolling(window=144).quantile(0.10) dataframe['ema_7_rolling_quartile_99'] = ta.EMA(dataframe, timeperiod=7).rolling(window=144).quantile(0.995) dataframe['ema_200_rolling_quartile_99'] = ta.EMA(dataframe, timeperiod=200).rolling(window=144).quantile(0.995) dataframe['ema_200_rolling_quartile_90'] = ta.EMA(dataframe, timeperiod=200).rolling(window=144).quantile(0.90) # #FEMA Fibonacci Exponenetial Moving Average # dataframe['fema_3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['fema_5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['fema_8'] = ta.EMA(dataframe, timeperiod=8) # dataframe['fema_13'] = ta.EMA(dataframe, timeperiod=13) # dataframe['fema_21'] = ta.EMA(dataframe, timeperiod=21) # dataframe['fema_34'] = ta.EMA(dataframe, timeperiod=34) # dataframe['fema_55'] = ta.EMA(dataframe, timeperiod=55) # dataframe['fema_89'] = ta.EMA(dataframe, timeperiod=89) # dataframe['fema_144'] = ta.EMA(dataframe, timeperiod=144) # dataframe['fema_233'] = ta.EMA(dataframe, timeperiod=233) # #HT_TRENDLINE Hilbert Transform - Instantaneous Trendline # dataframe['ht_trendline'] = ta.HT_TRENDLINE(dataframe) # #KAMA Kaufman Adaptive Moving Average dataframe['kama_7'] = ta.KAMA(dataframe, timeperiod=7) dataframe['kama_25'] = ta.KAMA(dataframe, timeperiod=25) dataframe['kama_99'] = ta.KAMA(dataframe, timeperiod=99) dataframe['kama_200'] = ta.KAMA(dataframe, timeperiod=200) dataframe['kama_trend_up'] = dataframe['kama_25'] > dataframe['kama_25'].shift(1) dataframe['kama_trend_down'] = dataframe['kama_25'] < dataframe['kama_25'].shift(1) # #MA Moving average # #MAMA MESA Adaptive Moving Average # dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe) # #MAVP Moving average with variable period # #MIDPOINT MidPoint over period # dataframe['midpoint_7'] = ta.MIDPOINT(dataframe, timeperiod=7) # dataframe['midpoint_25'] = ta.MIDPOINT(dataframe, timeperiod=25) # dataframe['midpoint_99'] = ta.MIDPOINT(dataframe, timeperiod=99) # dataframe['midpoint_200'] = ta.MIDPOINT(dataframe, timeperiod=200) # #MIDPRICE Midpoint Price over period # dataframe['midprice_7'] = ta.MIDPRICE(dataframe, timeperiod=7) # dataframe['midprice_25'] = ta.MIDPRICE(dataframe, timeperiod=25) # dataframe['midprice_99'] = ta.MIDPRICE(dataframe, timeperiod=99) # dataframe['midprice_200'] = ta.MIDPRICE(dataframe, timeperiod=200) # #SAR Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # #SAREXT Parabolic SAR - Extended # dataframe['sarext'] = ta.SAREXT(dataframe) # #SMA Simple Moving Average # dataframe['sma_7'] = ta.SMA(dataframe, timeperiod=7) # dataframe['sma_25'] = ta.SMA(dataframe, timeperiod=25) # dataframe['sma_99'] = ta.SMA(dataframe, timeperiod=99) # dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) # #T3 Triple Exponential Moving Average (T3) # dataframe['t3_7'] = ta.T3(dataframe, timeperiod=7) # dataframe['t3_25'] = ta.T3(dataframe, timeperiod=25) # dataframe['t3_99'] = ta.T3(dataframe, timeperiod=99) # dataframe['t3_200'] = ta.T3(dataframe, timeperiod=200) # #TEMA Triple Exponential Moving Average # dataframe['tema_7'] = ta.TEMA(dataframe, timeperiod=20) # dataframe['tema_25'] = ta.TEMA(dataframe, timeperiod=25) # dataframe['tema_99'] = ta.TEMA(dataframe, timeperiod=99) # dataframe['tema_200'] = ta.TEMA(dataframe, timeperiod=200) # #TRIMA Triangular Moving Average # dataframe['trima_7'] = ta.TRIMA(dataframe, timeperiod=7) # dataframe['trima_25'] = ta.TRIMA(dataframe, timeperiod=25) # dataframe['trima_99'] = ta.TRIMA(dataframe, timeperiod=99) # dataframe['trima_200'] = ta.TRIMA(dataframe, timeperiod=200) # #WMA Weighted Moving Average # dataframe['wma_7'] = ta.WMA(dataframe, timeperiod=7) # dataframe['wma_25'] = ta.WMA(dataframe, timeperiod=25) # dataframe['wma_99'] = ta.WMA(dataframe, timeperiod=99) # dataframe['wma_200'] = ta.WMA(dataframe, timeperiod=200) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // __ ___ __ ____ __ _ __ // # // / |/ /____ ____ ___ ___ ____ / /_ __ __ ____ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / /|_/ // __ \ / __ `__ \ / _ \ / __ \ / __// / / // __ `__ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / / / // /_/ // / / / / // __// / / // /_ / /_/ // / / / / / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // /_/ /_/ \____//_/ /_/ /_/ \___//_/ /_/ \__/ \__,_//_/ /_/ /_/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ADX Average Directional Movement Index dataframe['adx'] = ta.ADX(dataframe) dataframe['adx_rolling_quantile_25'] = dataframe['adx'].shift(1).rolling(window=lookback_5m).quantile(0.25) dataframe['adx_rolling_quantile_75'] = dataframe['adx'].shift(1).rolling(window=lookback_5m).quantile(0.75) # #ADXR Average Directional Movement Index Rating # dataframe['adxr'] = ta.ADXR(dataframe) # #APO Absolute Price Oscillator # dataframe['apo'] = ta.APO(dataframe) # #AROON Aroon # aroon_down, aroon_up = ta.AROON(dataframe) # dataframe['aroon_down'] = aroon_down # dataframe['aroon_up'] = aroon_up # #AROONOSC Aroon Oscillator # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # #BOP Balance Of Power # dataframe['bop'] = ta.BOP(dataframe) # #CCI Commodity Channel Index dataframe['cci'] = ta.CCI(dataframe) dataframe['cci_rolling_quantile_10'] = dataframe['cci'].shift(1).rolling(window=lookback_5m).quantile(0.10) # Za "oversold" uslove dataframe['cci_rolling_quantile_90'] = dataframe['cci'].shift(1).rolling(window=lookback_5m).quantile(0.90) # Za "overbought" uslove # #CMO Chande Momentum Oscillator # dataframe['cmo'] = ta.CMO(dataframe) # #DX Directional Movement Index # dataframe['dx'] = ta.DX(dataframe) # #MACD Moving Average Convergence/Divergence macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macd_signal'] = macd['macdsignal'] dataframe['macd_histogram'] = macd['macdhist'] # #MACDEXT MACD with controllable MA type # dataframe['macdext'], _, _ = ta.MACDEXT(dataframe) # #MACDFIX Moving Average Convergence/Divergence Fix 12/26 # dataframe['macdfix'], _, _ = ta.MACDFIX(dataframe) # #MFI Money Flow Index dataframe['mfi'] = ta.MFI(dataframe) dataframe['mfi_rolling_quantile_10'] = dataframe['mfi'].shift(1).rolling(window=lookback_5m).quantile(0.10) # Za "oversold" uslove (npr. < 20) dataframe['mfi_rolling_quantile_20'] = dataframe['mfi'].shift(1).rolling(window=lookback_5m).quantile(0.20) # Alternativni "oversold" dataframe['mfi_rolling_quantile_80'] = dataframe['mfi'].shift(1).rolling(window=lookback_5m).quantile(0.80) # Alternativni "overbought" dataframe['mfi_rolling_quantile_90'] = dataframe['mfi'].shift(1).rolling(window=lookback_5m).quantile(0.90) # Za "overbought" uslove (npr. > 80) # #MINUS_DI Minus Directional Indicator dataframe['minus_di'] = ta.MINUS_DI(dataframe) # #MINUS_DM Minus Directional Movement # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # #MOM Momentum # dataframe['mom'] = ta.MOM(dataframe) # #PLUS_DI Plus Directional Indicator dataframe['plus_di'] = ta.PLUS_DI(dataframe) # #PLUS_DM Plus Directional Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # #PPO Percentage Price Oscillator # dataframe['ppo'] = ta.PPO(dataframe) # #ROC Rate of change : ((price/prevPrice)-1)*100 dataframe['roc'] = ta.ROC(dataframe) # #ROCP Rate of change Percentage: (price-prevPrice)/prevPrice # dataframe['rocp'] = ta.ROCP(dataframe) # #ROCR Rate of change ratio: (price/prevPrice) # dataframe['rocr'] = ta.ROCR(dataframe) # #ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100 # dataframe['rocr100'] = ta.ROCR100(dataframe) # #RSI Relative Strength Index dataframe['rsi_3'] = ta.RSI(dataframe,timeperiod=3) dataframe['rsi_5'] = ta.RSI(dataframe,timeperiod=5) dataframe['rsi_8'] = ta.RSI(dataframe,timeperiod=8) dataframe['rsi_13'] = ta.RSI(dataframe,timeperiod=13) dataframe['rsi_21'] = ta.RSI(dataframe,timeperiod=21) dataframe['rsi_34'] = ta.RSI(dataframe,timeperiod=34) dataframe['rsi_3_rolling_quantile_10'] = dataframe['rsi_3'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_3_rolling_quantile_90'] = dataframe['rsi_3'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_3_rolling_quantile_5'] = dataframe['rsi_3'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_3_rolling_quantile_95'] = dataframe['rsi_3'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['rsi_5_rolling_quantile_10'] = dataframe['rsi_5'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_5_rolling_quantile_90'] = dataframe['rsi_5'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_5_rolling_quantile_5'] = dataframe['rsi_5'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_5_rolling_quantile_95'] = dataframe['rsi_5'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['rsi_8_rolling_quantile_10'] = dataframe['rsi_8'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_8_rolling_quantile_90'] = dataframe['rsi_8'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_8_rolling_quantile_5'] = dataframe['rsi_8'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_8_rolling_quantile_95'] = dataframe['rsi_8'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['rsi_13_rolling_quantile_10'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_13_rolling_quantile_90'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_13_rolling_quantile_5'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_13_rolling_quantile_95'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['rsi_13_rolling_quantile_1'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.01) dataframe['rsi_13_rolling_quantile_99'] = dataframe['rsi_13'].shift(1).rolling(window=lookback_5m).quantile(0.99) dataframe['rsi_21_rolling_quantile_10'] = dataframe['rsi_21'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_21_rolling_quantile_90'] = dataframe['rsi_21'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_21_rolling_quantile_5'] = dataframe['rsi_21'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_21_rolling_quantile_95'] = dataframe['rsi_21'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['rsi_34_rolling_quantile_10'] = dataframe['rsi_34'].shift(1).rolling(window=lookback_5m).quantile(0.15) dataframe['rsi_34_rolling_quantile_90'] = dataframe['rsi_34'].shift(1).rolling(window=lookback_5m).quantile(0.85) dataframe['rsi_34_rolling_quantile_5'] = dataframe['rsi_34'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['rsi_34_rolling_quantile_95'] = dataframe['rsi_34'].shift(1).rolling(window=lookback_5m).quantile(0.95) # #STOCH Stochastic # slowk, slowd = ta.STOCH(dataframe) # dataframe['stoch_k'] = slowk # dataframe['stoch_d'] = slowd # #STOCHF Stochastic Fast # fastk, fastd = ta.STOCHF(dataframe) # dataframe['stochf_k'] = fastk # dataframe['stochf_d'] = fastd # #STOCHRSI Stochastic Relative Strength Index # dataframe['stochrsi_k'], dataframe['stochrsi_d'] = ta.STOCHRSI(dataframe) # #TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA dataframe['trix'] = ta.TRIX(dataframe) dataframe['trix_rolling_quantile_10'] = dataframe['trix'].shift(1).rolling(window=lookback_5m).quantile(0.10) # Potencijalni signal za kupovinu (očekivanje rasta) dataframe['trix_rolling_quantile_90'] = dataframe['trix'].shift(1).rolling(window=lookback_5m).quantile(0.90) # Potencijalni signal za prodaju (očekivanje pada) # #ULTOSC Ultimate Oscillator # dataframe['ultosc'] = ta.ULTOSC(dataframe) # #WILLR Williams' %R # dataframe['willr'] = ta.WILLR(dataframe) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ ____ __ _ __ // # // | | / /____ / /__ __ ____ ___ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // / / // __ `__ \ / _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // / / / / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_//_/ /_/ /_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AD Chaikin A/D Line # dataframe['ad'] = ta.AD(dataframe) # #ADOSC Chaikin A/D Oscillator # dataframe['adosc'] = ta.ADOSC(dataframe) # #OBV On Balance Volume dataframe['obv'] = ta.OBV(dataframe) # Volume Mean dataframe['volume_mean_30'] = dataframe['volume'].rolling(window=30).mean() # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ __ _ __ _ __ ____ __ _ __ // # // | | / /____ / /____ _ / /_ (_)/ /(_)/ /_ __ __ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // __ `// __// // // // __// / / / / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // /_ / // // // /_ / /_/ / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_/ \__//_//_//_/ \__/ \__, / /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ATR Average True Range dataframe['atr'] = ta.ATR(dataframe) # #NATR Normalized Average True Range # dataframe['natr'] = ta.NATR(dataframe) # #TRANGE True Range # dataframe['trange'] = ta.TRANGE(dataframe) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ _ ______ ____ // # // / __ \ _____ (_)_____ ___ /_ __/_____ ____ _ ____ _____ / __/____ _____ ____ ___ // # // / /_/ // ___// // ___// _ \ / / / ___// __ `// __ \ / ___// /_ / __ \ / ___// __ `__ \ // # // / ____// / / // /__ / __/ / / / / / /_/ // / / /(__ )/ __// /_/ // / / / / / / / // # // /_/ /_/ /_/ \___/ \___/ /_/ /_/ \__,_//_/ /_//____//_/ \____//_/ /_/ /_/ /_/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AVGPRICE Average Price # dataframe['avgprice'] = ta.AVGPRICE(dataframe) # #MEDPRICE Median Price # dataframe['medprice'] = ta.MEDPRICE(dataframe) # #TYPPRICE Typical Price # dataframe['typprice'] = ta.TYPPRICE(dataframe) # #WCLPRICE Weighted Close Price # dataframe['wclprice'] = ta.WCLPRICE(dataframe) # Close Rolling dataframe['close_rolling_quantile_90'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.90) dataframe['close_rolling_quantile_10'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.10) dataframe['close_rolling_quantile_95'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.95) dataframe['close_rolling_quantile_5'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.05) dataframe['close_rolling_quantile_99'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.999) dataframe['close_rolling_quantile_1'] = dataframe['close'].shift(1).rolling(window=lookback_5m).quantile(0.01) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ______ __ ____ __ _ __ // # // / ____/__ __ _____ / /___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / / / / / // ___// // _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / /___ / /_/ // /__ / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // \____/ \__, / \___//_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #HT_DCPERIOD Hilbert Transform - Dominant Cycle Period dataframe['ht_dcperiod'] = ta.HT_DCPERIOD(dataframe) # #HT_DCPHASE Hilbert Transform - Dominant Cycle Phase # dataframe['ht_dcphase'] = ta.HT_DCPHASE(dataframe) # #HT_PHASOR Hilbert Transform - Phasor Components # dataframe['ht_phasor_inphase'], dataframe['ht_phasor_quadrature'] = ta.HT_PHASOR(dataframe) # #HT_SINE Hilbert Transform - SineWave # dataframe['ht_sine'], dataframe['ht_leadsine'] = ta.HT_SINE(dataframe) # #HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode # dataframe['ht_trendmode'] = ta.HT_TRENDMODE(dataframe) informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') informative_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='4h') informative_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d') # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _____ ____________ ____ __ ______ ___________ ________ _____ ______ _____________ __________ ____ _____ // # // / _/ | / / ____/ __ \/ __ \/ |/ / |/_ __/ _/ | / / ____/ / _/ | / / __ \/ _/ ____/ |/_ __/ __ \/ __ \/ ___/ // # // / // |/ / /_ / / / / /_/ / /|_/ / /| | / / / / | | / / __/ / // |/ / / / // // / / /| | / / / / / / /_/ /\__ \ // # // _/ // /| / __/ / /_/ / _, _/ / / / ___ |/ / _/ / | |/ / /___ _/ // /| / /_/ // // /___/ ___ |/ / / /_/ / _, _/___/ / // # // /___/_/ |_/_/ \____/_/ |_/_/ /_/_/ |_/_/ /___/ |___/_____/ /___/_/ |_/_____/___/\____/_/ |_/_/ \____/_/ |_|/____/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ __ _____ __ __ _ // # // / __ \ _ __ ___ _____ / /____ _ ____ / ___/ / /_ __ __ ____/ /(_)___ _____ // # // / / / /| | / // _ \ / ___// // __ `// __ \ \__ \ / __// / / // __ // // _ \ / ___/ // # // / /_/ / | |/ // __// / / // /_/ // /_/ / ___/ // /_ / /_/ // /_/ // // __/(__ ) // # // \____/ |___/ \___//_/ /_/ \__,_// .___/ /____/ \__/ \__,_/ \__,_//_/ \___//____/ // # // /_/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #BBANDS Bollinger Bands informative_1h['bb_upper'] = ta.BBANDS(informative_1h, nbdevup=2.0, nbdevdn=2.0)['upperband'] informative_1h['bb_middle'] = ta.BBANDS(informative_1h, nbdevup=2.0, nbdevdn=2.0)['middleband'] informative_1h['bb_lower'] = ta.BBANDS(informative_1h, nbdevup=2.0, nbdevdn=2.0)['lowerband'] informative_1h['bbands_breakout_down'] = informative_1h['close'] < informative_1h['bb_lower'] informative_1h['bbands_breakout_up'] = informative_1h['close'] > informative_1h['bb_upper'] # #DEMA Double Exponential Moving Average # informative_1h['dema_7'] = ta.DEMA(informative_1h, timeperiod=7) # informative_1h['dema_25'] = ta.DEMA(informative_1h, timeperiod=25) # informative_1h['dema_99'] = ta.DEMA(informative_1h, timeperiod=99) # informative_1h['dema_200'] = ta.DEMA(informative_1h, timeperiod=200) # #EMA Exponential Moving Average informative_1h['ema_7'] = ta.EMA(informative_1h, timeperiod=7) informative_1h['ema_25'] = ta.EMA(informative_1h, timeperiod=25) informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema_99'] = ta.EMA(informative_1h, timeperiod=99) informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200) # #FEMA Fibonacci Exponenetial Moving Average # informative_1h['fema_3'] = ta.EMA(informative_1h, timeperiod=3) # informative_1h['fema_5'] = ta.EMA(informative_1h, timeperiod=5) # informative_1h['fema_8'] = ta.EMA(informative_1h, timeperiod=8) # informative_1h['fema_13'] = ta.EMA(informative_1h, timeperiod=13) # informative_1h['fema_21'] = ta.EMA(informative_1h, timeperiod=21) # informative_1h['fema_34'] = ta.EMA(informative_1h, timeperiod=34) # informative_1h['fema_55'] = ta.EMA(informative_1h, timeperiod=55) # informative_1h['fema_89'] = ta.EMA(informative_1h, timeperiod=89) # informative_1h['fema_144'] = ta.EMA(informative_1h, timeperiod=144) # informative_1h['fema_233'] = ta.EMA(informative_1h, timeperiod=233) # #HT_TRENDLINE Hilbert Transform - Instantaneous Trendline # informative_1h['ht_trendline'] = ta.HT_TRENDLINE(informative_1h) # #KAMA Kaufman Adaptive Moving Average informative_1h['kama_7'] = ta.KAMA(informative_1h, timeperiod=7) informative_1h['kama_25'] = ta.KAMA(informative_1h, timeperiod=25) informative_1h['kama_99'] = ta.KAMA(informative_1h, timeperiod=99) informative_1h['kama_200'] = ta.KAMA(informative_1h, timeperiod=200) informative_1h['kama_trend_up'] = informative_1h['kama_25'] > informative_1h['kama_25'].shift(1) informative_1h['kama_trend_down'] = informative_1h['kama_25'] < informative_1h['kama_25'].shift(1) # #MA Moving average # #MAMA MESA Adaptive Moving Average # informative_1h['mama'], informative_1h['fama'] = ta.MAMA(informative_1h) # #MAVP Moving average with variable period # #MIDPOINT MidPoint over period # informative_1h['midpoint_7'] = ta.MIDPOINT(informative_1h, timeperiod=7) # informative_1h['midpoint_25'] = ta.MIDPOINT(informative_1h, timeperiod=25) # informative_1h['midpoint_99'] = ta.MIDPOINT(informative_1h, timeperiod=99) # informative_1h['midpoint_200'] = ta.MIDPOINT(informative_1h, timeperiod=200) # #MIDPRICE Midpoint Price over period # informative_1h['midprice_7'] = ta.MIDPRICE(informative_1h, timeperiod=7) # informative_1h['midprice_25'] = ta.MIDPRICE(informative_1h, timeperiod=25) # informative_1h['midprice_99'] = ta.MIDPRICE(informative_1h, timeperiod=99) # informative_1h['midprice_200'] = ta.MIDPRICE(informative_1h, timeperiod=200) # #SAR Parabolic SAR informative_1h['sar'] = ta.SAR(informative_1h) # #SAREXT Parabolic SAR - Extended # informative_1h['sarext'] = ta.SAREXT(informative_1h) # #SMA Simple Moving Average # informative_1h['sma_7'] = ta.SMA(informative_1h, timeperiod=7) # informative_1h['sma_25'] = ta.SMA(informative_1h, timeperiod=25) # informative_1h['sma_99'] = ta.SMA(informative_1h, timeperiod=99) # informative_1h['sma_200'] = ta.SMA(informative_1h, timeperiod=200) # #T3 Triple Exponential Moving Average (T3) # informative_1h['t3_7'] = ta.T3(informative_1h, timeperiod=7) # informative_1h['t3_25'] = ta.T3(informative_1h, timeperiod=25) # informative_1h['t3_99'] = ta.T3(informative_1h, timeperiod=99) # informative_1h['t3_200'] = ta.T3(informative_1h, timeperiod=200) # #TEMA Triple Exponential Moving Average # informative_1h['tema_7'] = ta.TEMA(informative_1h, timeperiod=20) # informative_1h['tema_25'] = ta.TEMA(informative_1h, timeperiod=25) # informative_1h['tema_99'] = ta.TEMA(informative_1h, timeperiod=99) # informative_1h['tema_200'] = ta.TEMA(informative_1h, timeperiod=200) # #TRIMA Triangular Moving Average # informative_1h['trima_7'] = ta.TRIMA(informative_1h, timeperiod=7) # informative_1h['trima_25'] = ta.TRIMA(informative_1h, timeperiod=25) # informative_1h['trima_99'] = ta.TRIMA(informative_1h, timeperiod=99) # informative_1h['trima_200'] = ta.TRIMA(informative_1h, timeperiod=200) # #WMA Weighted Moving Average # informative_1h['wma_7'] = ta.WMA(informative_1h, timeperiod=7) # informative_1h['wma_25'] = ta.WMA(informative_1h, timeperiod=25) # informative_1h['wma_99'] = ta.WMA(informative_1h, timeperiod=99) # informative_1h['wma_200'] = ta.WMA(informative_1h, timeperiod=200) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // __ ___ __ ____ __ _ __ // # // / |/ /____ ____ ___ ___ ____ / /_ __ __ ____ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / /|_/ // __ \ / __ `__ \ / _ \ / __ \ / __// / / // __ `__ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / / / // /_/ // / / / / // __// / / // /_ / /_/ // / / / / / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // /_/ /_/ \____//_/ /_/ /_/ \___//_/ /_/ \__/ \__,_//_/ /_/ /_/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ADX Average Directional Movement Index informative_1h['adx'] = ta.ADX(informative_1h) informative_1h['adx_rolling_quantile_25'] = informative_1h['adx'].shift(1).rolling(window=lookback_1h).quantile(0.25) informative_1h['adx_rolling_quantile_75'] = informative_1h['adx'].shift(1).rolling(window=lookback_1h).quantile(0.75) # #ADXR Average Directional Movement Index Rating # informative_1h['adxr'] = ta.ADXR(informative_1h) # #APO Absolute Price Oscillator # informative_1h['apo'] = ta.APO(informative_1h) # #AROON Aroon # aroon_down, aroon_up = ta.AROON(informative_1h) # informative_1h['aroon_down'] = aroon_down # informative_1h['aroon_up'] = aroon_up # #AROONOSC Aroon Oscillator # informative_1h['aroonosc'] = ta.AROONOSC(informative_1h) # #BOP Balance Of Power # informative_1h['bop'] = ta.BOP(informative_1h) # #CCI Commodity Channel Index informative_1h['cci'] = ta.CCI(informative_1h) informative_1h['cci_rolling_quantile_10'] = informative_1h['cci'].shift(1).rolling(window=lookback_1h).quantile(0.10) # Za "oversold" uslove informative_1h['cci_rolling_quantile_90'] = informative_1h['cci'].shift(1).rolling(window=lookback_1h).quantile(0.90) # Za "overbought" uslove # #CMO Chande Momentum Oscillator # informative_1h['cmo'] = ta.CMO(informative_1h) # #DX Directional Movement Index # informative_1h['dx'] = ta.DX(informative_1h) # #MACD Moving Average Convergence/Divergence macd = ta.MACD(informative_1h) informative_1h['macd'] = macd['macd'] informative_1h['macd_signal'] = macd['macdsignal'] informative_1h['macd_histogram'] = macd['macdhist'] # #MACDEXT MACD with controllable MA type # informative_1h['macdext'], _, _ = ta.MACDEXT(informative_1h) # #MACDFIX Moving Average Convergence/Divergence Fix 12/26 # informative_1h['macdfix'], _, _ = ta.MACDFIX(informative_1h) # #MFI Money Flow Index informative_1h['mfi'] = ta.MFI(informative_1h) informative_1h['mfi_rolling_quantile_10'] = informative_1h['mfi'].shift(1).rolling(window=lookback_1h).quantile(0.10) # Za "oversold" uslove (npr. < 20) informative_1h['mfi_rolling_quantile_20'] = informative_1h['mfi'].shift(1).rolling(window=lookback_1h).quantile(0.20) # Alternativni "oversold" informative_1h['mfi_rolling_quantile_80'] = informative_1h['mfi'].shift(1).rolling(window=lookback_1h).quantile(0.80) # Alternativni "overbought" informative_1h['mfi_rolling_quantile_90'] = informative_1h['mfi'].shift(1).rolling(window=lookback_1h).quantile(0.90) # Za "overbought" uslove (npr. > 80) # #MINUS_DI Minus Directional Indicator informative_1h['minus_di'] = ta.MINUS_DI(informative_1h) # #MINUS_DM Minus Directional Movement # informative_1h['minus_dm'] = ta.MINUS_DM(informative_1h) # #MOM Momentum # informative_1h['mom'] = ta.MOM(informative_1h) # #PLUS_DI Plus Directional Indicator informative_1h['plus_di'] = ta.PLUS_DI(informative_1h) # #PLUS_DM Plus Directional Movement # informative_1h['plus_dm'] = ta.PLUS_DM(informative_1h) # #PPO Percentage Price Oscillator # informative_1h['ppo'] = ta.PPO(informative_1h) # #ROC Rate of change : ((price/prevPrice)-1)*100 informative_1h['roc'] = ta.ROC(informative_1h) # #ROCP Rate of change Percentage: (price-prevPrice)/prevPrice # informative_1h['rocp'] = ta.ROCP(informative_1h) # #ROCR Rate of change ratio: (price/prevPrice) # informative_1h['rocr'] = ta.ROCR(informative_1h) # #ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100 # informative_1h['rocr100'] = ta.ROCR100(informative_1h) # #RSI Relative Strength Index informative_1h['rsi_3'] = ta.RSI(informative_1h,timeperiod=3) informative_1h['rsi_5'] = ta.RSI(informative_1h,timeperiod=5) informative_1h['rsi_8'] = ta.RSI(informative_1h,timeperiod=8) informative_1h['rsi_13'] = ta.RSI(informative_1h,timeperiod=13) informative_1h['rsi_21'] = ta.RSI(informative_1h,timeperiod=21) informative_1h['rsi_34'] = ta.RSI(informative_1h,timeperiod=34) informative_1h['rsi_3_rolling_quantile_10'] = informative_1h['rsi_3'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['rsi_3_rolling_quantile_90'] = informative_1h['rsi_3'].shift(1).rolling(window=lookback_1h).quantile(0.90) informative_1h['rsi_8_rolling_quantile_10'] = informative_1h['rsi_8'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['rsi_8_rolling_quantile_90'] = informative_1h['rsi_8'].shift(1).rolling(window=lookback_1h).quantile(0.90) informative_1h['rsi_13_rolling_quantile_10'] = informative_1h['rsi_13'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['rsi_13_rolling_quantile_90'] = informative_1h['rsi_13'].shift(1).rolling(window=lookback_1h).quantile(0.90) informative_1h['rsi_21_rolling_quantile_10'] = informative_1h['rsi_21'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['rsi_21_rolling_quantile_90'] = informative_1h['rsi_21'].shift(1).rolling(window=lookback_1h).quantile(0.90) informative_1h['rsi_34_rolling_quantile_10'] = informative_1h['rsi_34'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['rsi_34_rolling_quantile_90'] = informative_1h['rsi_34'].shift(1).rolling(window=lookback_1h).quantile(0.90) # #STOCH Stochastic # slowk, slowd = ta.STOCH(informative_1h) # informative_1h['stoch_k'] = slowk # informative_1h['stoch_d'] = slowd # #STOCHF Stochastic Fast # fastk, fastd = ta.STOCHF(informative_1h) # informative_1h['stochf_k'] = fastk # informative_1h['stochf_d'] = fastd # #STOCHRSI Stochastic Relative Strength Index # informative_1h['stochrsi_k'], informative_1h['stochrsi_d'] = ta.STOCHRSI(informative_1h) # #TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA informative_1h['trix'] = ta.TRIX(informative_1h) informative_1h['trix_rolling_quantile_10'] = informative_1h['trix'].shift(1).rolling(window=lookback_1h).quantile(0.10) # Potencijalni signal za kupovinu (očekivanje rasta) informative_1h['trix_rolling_quantile_90'] = informative_1h['trix'].shift(1).rolling(window=lookback_1h).quantile(0.90) # Potencijalni signal za prodaju (očekivanje pada) # #ULTOSC Ultimate Oscillator # informative_1h['ultosc'] = ta.ULTOSC(informative_1h) # #WILLR Williams' %R # informative_1h['willr'] = ta.WILLR(informative_1h) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ ____ __ _ __ // # // | | / /____ / /__ __ ____ ___ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // / / // __ `__ \ / _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // / / / / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_//_/ /_/ /_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AD Chaikin A/D Line # informative_1h['ad'] = ta.AD(informative_1h) # #ADOSC Chaikin A/D Oscillator # informative_1h['adosc'] = ta.ADOSC(informative_1h) # #OBV On Balance Volume informative_1h['obv'] = ta.OBV(informative_1h) # Volume Mean informative_1h['volume'] = informative_1h['volume'] informative_1h['volume_mean_30'] = informative_1h['volume'].rolling(window=30).mean() # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ __ _ __ _ __ ____ __ _ __ // # // | | / /____ / /____ _ / /_ (_)/ /(_)/ /_ __ __ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // __ `// __// // // // __// / / / / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // /_ / // // // /_ / /_/ / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_/ \__//_//_//_/ \__/ \__, / /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ATR Average True Range informative_1h['atr'] = ta.ATR(informative_1h) # #NATR Normalized Average True Range # informative_1h['natr'] = ta.NATR(informative_1h) # #TRANGE True Range # informative_1h['trange'] = ta.TRANGE(informative_1h) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ _ ______ ____ // # // / __ \ _____ (_)_____ ___ /_ __/_____ ____ _ ____ _____ / __/____ _____ ____ ___ // # // / /_/ // ___// // ___// _ \ / / / ___// __ `// __ \ / ___// /_ / __ \ / ___// __ `__ \ // # // / ____// / / // /__ / __/ / / / / / /_/ // / / /(__ )/ __// /_/ // / / / / / / / // # // /_/ /_/ /_/ \___/ \___/ /_/ /_/ \__,_//_/ /_//____//_/ \____//_/ /_/ /_/ /_/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AVGPRICE Average Price # informative_1h['avgprice'] = ta.AVGPRICE(informative_1h) # #MEDPRICE Median Price # informative_1h['medprice'] = ta.MEDPRICE(informative_1h) # #TYPPRICE Typical Price # informative_1h['typprice'] = ta.TYPPRICE(informative_1h) # #WCLPRICE Weighted Close Price # informative_1h['wclprice'] = ta.WCLPRICE(informative_1h) # Close Rolling informative_1h['close'] = informative_1h['close'] informative_1h['close_rolling_quantile_90_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.90) informative_1h['close_rolling_quantile_10_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.10) informative_1h['close_rolling_quantile_95_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.95) informative_1h['close_rolling_quantile_5_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.05) informative_1h['close_rolling_quantile_99_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.999) informative_1h['close_rolling_quantile_1_1h'] = informative_1h['close'].shift(1).rolling(window=lookback_1h).quantile(0.01) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ______ __ ____ __ _ __ // # // / ____/__ __ _____ / /___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / / / / / // ___// // _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / /___ / /_/ // /__ / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // \____/ \__, / \___//_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #HT_DCPERIOD Hilbert Transform - Dominant Cycle Period informative_1h['ht_dcperiod'] = ta.HT_DCPERIOD(informative_1h) # #HT_DCPHASE Hilbert Transform - Dominant Cycle Phase # informative_1h['ht_dcphase'] = ta.HT_DCPHASE(informative_1h) # #HT_PHASOR Hilbert Transform - Phasor Components # informative_1h['ht_phasor_inphase'], informative_1h['ht_phasor_quadrature'] = ta.HT_PHASOR(informative_1h) # #HT_SINE Hilbert Transform - SineWave # informative_1h['ht_sine'], informative_1h['ht_leadsine'] = ta.HT_SINE(informative_1h) # #HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode # informative_1h['ht_trendmode'] = ta.HT_TRENDMODE(informative_1h) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ __ _____ __ __ _ // # // / __ \ _ __ ___ _____ / /____ _ ____ / ___/ / /_ __ __ ____/ /(_)___ _____ // # // / / / /| | / // _ \ / ___// // __ `// __ \ \__ \ / __// / / // __ // // _ \ / ___/ // # // / /_/ / | |/ // __// / / // /_/ // /_/ / ___/ // /_ / /_/ // /_/ // // __/(__ ) // # // \____/ |___/ \___//_/ /_/ \__,_// .___/ /____/ \__/ \__,_/ \__,_//_/ \___//____/ // # // /_/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #BBANDS Bollinger Bands informative_4h['bb_upper'] = ta.BBANDS(informative_4h, nbdevup=2.0, nbdevdn=2.0)['upperband'] informative_4h['bb_middle'] = ta.BBANDS(informative_4h, nbdevup=2.0, nbdevdn=2.0)['middleband'] informative_4h['bb_lower'] = ta.BBANDS(informative_4h, nbdevup=2.0, nbdevdn=2.0)['lowerband'] informative_4h['bbands_breakout_down'] = informative_4h['close'] < informative_4h['bb_lower'] informative_4h['bbands_breakout_up'] = informative_4h['close'] > informative_4h['bb_upper'] # #DEMA Double Exponential Moving Average # informative_4h['dema_7'] = ta.DEMA(informative_4h, timeperiod=7) # informative_4h['dema_25'] = ta.DEMA(informative_4h, timeperiod=25) # informative_4h['dema_99'] = ta.DEMA(informative_4h, timeperiod=99) # informative_4h['dema_200'] = ta.DEMA(informative_4h, timeperiod=200) # #EMA Exponential Moving Average informative_4h['ema_7'] = ta.EMA(informative_4h, timeperiod=7) informative_4h['ema_25'] = ta.EMA(informative_4h, timeperiod=25) informative_4h['ema_50'] = ta.EMA(informative_4h, timeperiod=50) informative_4h['ema_99'] = ta.EMA(informative_4h, timeperiod=99) informative_4h['ema_200'] = ta.EMA(informative_4h, timeperiod=200) # #FEMA Fibonacci Exponenetial Moving Average # informative_4h['fema_3'] = ta.EMA(informative_4h, timeperiod=3) # informative_4h['fema_5'] = ta.EMA(informative_4h, timeperiod=5) # informative_4h['fema_8'] = ta.EMA(informative_4h, timeperiod=8) # informative_4h['fema_13'] = ta.EMA(informative_4h, timeperiod=13) # informative_4h['fema_21'] = ta.EMA(informative_4h, timeperiod=21) # informative_4h['fema_34'] = ta.EMA(informative_4h, timeperiod=34) # informative_4h['fema_55'] = ta.EMA(informative_4h, timeperiod=55) # informative_4h['fema_89'] = ta.EMA(informative_4h, timeperiod=89) # informative_4h['fema_144'] = ta.EMA(informative_4h, timeperiod=144) # informative_4h['fema_233'] = ta.EMA(informative_4h, timeperiod=233) # #HT_TRENDLINE Hilbert Transform - Instantaneous Trendline # informative_4h['ht_trendline'] = ta.HT_TRENDLINE(informative_4h) # #KAMA Kaufman Adaptive Moving Average informative_4h['kama_7'] = ta.KAMA(informative_4h, timeperiod=7) informative_4h['kama_25'] = ta.KAMA(informative_4h, timeperiod=25) informative_4h['kama_99'] = ta.KAMA(informative_4h, timeperiod=99) informative_4h['kama_200'] = ta.KAMA(informative_4h, timeperiod=200) informative_4h['kama_trend_up'] = informative_4h['kama_25'] > informative_4h['kama_25'].shift(1) informative_4h['kama_trend_down'] = informative_4h['kama_25'] < informative_4h['kama_25'].shift(1) # #MA Moving average # #MAMA MESA Adaptive Moving Average # informative_4h['mama'], informative_4h['fama'] = ta.MAMA(informative_4h) # #MAVP Moving average with variable period # #MIDPOINT MidPoint over period # informative_4h['midpoint_7'] = ta.MIDPOINT(informative_4h, timeperiod=7) # informative_4h['midpoint_25'] = ta.MIDPOINT(informative_4h, timeperiod=25) # informative_4h['midpoint_99'] = ta.MIDPOINT(informative_4h, timeperiod=99) # informative_4h['midpoint_200'] = ta.MIDPOINT(informative_4h, timeperiod=200) # #MIDPRICE Midpoint Price over period # informative_4h['midprice_7'] = ta.MIDPRICE(informative_4h, timeperiod=7) # informative_4h['midprice_25'] = ta.MIDPRICE(informative_4h, timeperiod=25) # informative_4h['midprice_99'] = ta.MIDPRICE(informative_4h, timeperiod=99) # informative_4h['midprice_200'] = ta.MIDPRICE(informative_4h, timeperiod=200) # #SAR Parabolic SAR informative_4h['sar'] = ta.SAR(informative_4h) # #SAREXT Parabolic SAR - Extended # informative_4h['sarext'] = ta.SAREXT(informative_4h) # #SMA Simple Moving Average # informative_4h['sma_7'] = ta.SMA(informative_4h, timeperiod=7) # informative_4h['sma_25'] = ta.SMA(informative_4h, timeperiod=25) # informative_4h['sma_99'] = ta.SMA(informative_4h, timeperiod=99) # informative_4h['sma_200'] = ta.SMA(informative_4h, timeperiod=200) # #T3 Triple Exponential Moving Average (T3) # informative_4h['t3_7'] = ta.T3(informative_4h, timeperiod=7) # informative_4h['t3_25'] = ta.T3(informative_4h, timeperiod=25) # informative_4h['t3_99'] = ta.T3(informative_4h, timeperiod=99) # informative_4h['t3_200'] = ta.T3(informative_4h, timeperiod=200) # #TEMA Triple Exponential Moving Average # informative_4h['tema_7'] = ta.TEMA(informative_4h, timeperiod=20) # informative_4h['tema_25'] = ta.TEMA(informative_4h, timeperiod=25) # informative_4h['tema_99'] = ta.TEMA(informative_4h, timeperiod=99) # informative_4h['tema_200'] = ta.TEMA(informative_4h, timeperiod=200) # #TRIMA Triangular Moving Average # informative_4h['trima_7'] = ta.TRIMA(informative_4h, timeperiod=7) # informative_4h['trima_25'] = ta.TRIMA(informative_4h, timeperiod=25) # informative_4h['trima_99'] = ta.TRIMA(informative_4h, timeperiod=99) # informative_4h['trima_200'] = ta.TRIMA(informative_4h, timeperiod=200) # #WMA Weighted Moving Average # informative_4h['wma_7'] = ta.WMA(informative_4h, timeperiod=7) # informative_4h['wma_25'] = ta.WMA(informative_4h, timeperiod=25) # informative_4h['wma_99'] = ta.WMA(informative_4h, timeperiod=99) # informative_4h['wma_200'] = ta.WMA(informative_4h, timeperiod=200) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // __ ___ __ ____ __ _ __ // # // / |/ /____ ____ ___ ___ ____ / /_ __ __ ____ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / /|_/ // __ \ / __ `__ \ / _ \ / __ \ / __// / / // __ `__ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / / / // /_/ // / / / / // __// / / // /_ / /_/ // / / / / / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // /_/ /_/ \____//_/ /_/ /_/ \___//_/ /_/ \__/ \__,_//_/ /_/ /_/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ADX Average Directional Movement Index informative_4h['adx'] = ta.ADX(informative_4h) informative_4h['adx_rolling_quantile_25'] = informative_4h['adx'].shift(1).rolling(window=lookback_4h).quantile(0.25) informative_4h['adx_rolling_quantile_75'] = informative_4h['adx'].shift(1).rolling(window=lookback_4h).quantile(0.75) # #ADXR Average Directional Movement Index Rating # informative_4h['adxr'] = ta.ADXR(informative_4h) # #APO Absolute Price Oscillator # informative_4h['apo'] = ta.APO(informative_4h) # #AROON Aroon # aroon_down, aroon_up = ta.AROON(informative_4h) # informative_4h['aroon_down'] = aroon_down # informative_4h['aroon_up'] = aroon_up # #AROONOSC Aroon Oscillator # informative_4h['aroonosc'] = ta.AROONOSC(informative_4h) # #BOP Balance Of Power # informative_4h['bop'] = ta.BOP(informative_4h) # #CCI Commodity Channel Index informative_4h['cci'] = ta.CCI(informative_4h) informative_4h['cci_rolling_quantile_10'] = informative_4h['cci'].shift(1).rolling(window=lookback_4h).quantile(0.10) # Za "oversold" uslove informative_4h['cci_rolling_quantile_90'] = informative_4h['cci'].shift(1).rolling(window=lookback_4h).quantile(0.90) # Za "overbought" uslove # #CMO Chande Momentum Oscillator # informative_4h['cmo'] = ta.CMO(informative_4h) # #DX Directional Movement Index # informative_4h['dx'] = ta.DX(informative_4h) # #MACD Moving Average Convergence/Divergence macd = ta.MACD(informative_4h) informative_4h['macd'] = macd['macd'] informative_4h['macd_signal'] = macd['macdsignal'] informative_4h['macd_histogram'] = macd['macdhist'] # #MACDEXT MACD with controllable MA type # informative_4h['macdext'], _, _ = ta.MACDEXT(informative_4h) # #MACDFIX Moving Average Convergence/Divergence Fix 12/26 # informative_4h['macdfix'], _, _ = ta.MACDFIX(informative_4h) # #MFI Money Flow Index informative_4h['mfi'] = ta.MFI(informative_4h) informative_4h['mfi_rolling_quantile_10'] = informative_4h['mfi'].shift(1).rolling(window=lookback_4h).quantile(0.10) # Za "oversold" uslove (npr. < 20) informative_4h['mfi_rolling_quantile_20'] = informative_4h['mfi'].shift(1).rolling(window=lookback_4h).quantile(0.20) # Alternativni "oversold" informative_4h['mfi_rolling_quantile_80'] = informative_4h['mfi'].shift(1).rolling(window=lookback_4h).quantile(0.80) # Alternativni "overbought" informative_4h['mfi_rolling_quantile_90'] = informative_4h['mfi'].shift(1).rolling(window=lookback_4h).quantile(0.90) # Za "overbought" uslove (npr. > 80) # #MINUS_DI Minus Directional Indicator informative_4h['minus_di'] = ta.MINUS_DI(informative_4h) # #MINUS_DM Minus Directional Movement # informative_4h['minus_dm'] = ta.MINUS_DM(informative_4h) # #MOM Momentum # informative_4h['mom'] = ta.MOM(informative_4h) # #PLUS_DI Plus Directional Indicator informative_4h['plus_di'] = ta.PLUS_DI(informative_4h) # #PLUS_DM Plus Directional Movement # informative_4h['plus_dm'] = ta.PLUS_DM(informative_4h) # #PPO Percentage Price Oscillator # informative_4h['ppo'] = ta.PPO(informative_4h) # #ROC Rate of change : ((price/prevPrice)-1)*100 informative_4h['roc'] = ta.ROC(informative_4h) # #ROCP Rate of change Percentage: (price-prevPrice)/prevPrice # informative_4h['rocp'] = ta.ROCP(informative_4h) # #ROCR Rate of change ratio: (price/prevPrice) # informative_4h['rocr'] = ta.ROCR(informative_4h) # #ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100 # informative_4h['rocr100'] = ta.ROCR100(informative_4h) # #RSI Relative Strength Index informative_4h['rsi_3'] = ta.RSI(informative_4h,timeperiod=3) informative_4h['rsi_5'] = ta.RSI(informative_4h,timeperiod=5) informative_4h['rsi_8'] = ta.RSI(informative_4h,timeperiod=8) informative_4h['rsi_13'] = ta.RSI(informative_4h,timeperiod=13) informative_4h['rsi_21'] = ta.RSI(informative_4h,timeperiod=21) informative_4h['rsi_34'] = ta.RSI(informative_4h,timeperiod=34) informative_4h['rsi_3_rolling_quantile_10'] = informative_4h['rsi_3'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['rsi_3_rolling_quantile_90'] = informative_4h['rsi_3'].shift(1).rolling(window=lookback_4h).quantile(0.90) informative_4h['rsi_8_rolling_quantile_10'] = informative_4h['rsi_8'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['rsi_8_rolling_quantile_90'] = informative_4h['rsi_8'].shift(1).rolling(window=lookback_4h).quantile(0.90) informative_4h['rsi_13_rolling_quantile_10'] = informative_4h['rsi_13'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['rsi_13_rolling_quantile_90'] = informative_4h['rsi_13'].shift(1).rolling(window=lookback_4h).quantile(0.90) informative_4h['rsi_21_rolling_quantile_10'] = informative_4h['rsi_21'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['rsi_21_rolling_quantile_90'] = informative_4h['rsi_21'].shift(1).rolling(window=lookback_4h).quantile(0.90) informative_4h['rsi_34_rolling_quantile_10'] = informative_4h['rsi_34'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['rsi_34_rolling_quantile_90'] = informative_4h['rsi_34'].shift(1).rolling(window=lookback_4h).quantile(0.90) # #STOCH Stochastic # slowk, slowd = ta.STOCH(informative_4h) # informative_4h['stoch_k'] = slowk # informative_4h['stoch_d'] = slowd # #STOCHF Stochastic Fast # fastk, fastd = ta.STOCHF(informative_4h) # informative_4h['stochf_k'] = fastk # informative_4h['stochf_d'] = fastd # #STOCHRSI Stochastic Relative Strength Index # informative_4h['stochrsi_k'], informative_4h['stochrsi_d'] = ta.STOCHRSI(informative_4h) # #TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA informative_4h['trix'] = ta.TRIX(informative_4h) informative_4h['trix_rolling_quantile_10'] = informative_4h['trix'].shift(1).rolling(window=lookback_4h).quantile(0.10) # Potencijalni signal za kupovinu (očekivanje rasta) informative_4h['trix_rolling_quantile_90'] = informative_4h['trix'].shift(1).rolling(window=lookback_4h).quantile(0.90) # Potencijalni signal za prodaju (očekivanje pada) # #ULTOSC Ultimate Oscillator # informative_4h['ultosc'] = ta.ULTOSC(informative_4h) # #WILLR Williams' %R # informative_4h['willr'] = ta.WILLR(informative_4h) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ ____ __ _ __ // # // | | / /____ / /__ __ ____ ___ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // / / // __ `__ \ / _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // / / / / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_//_/ /_/ /_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AD Chaikin A/D Line # informative_4h['ad'] = ta.AD(informative_4h) # #ADOSC Chaikin A/D Oscillator # informative_4h['adosc'] = ta.ADOSC(informative_4h) # #OBV On Balance Volume informative_4h['obv'] = ta.OBV(informative_4h) # Volume Mean informative_4h['volume'] = informative_4h['volume'] informative_4h['volume_mean_30'] = informative_4h['volume'].rolling(window=30).mean() # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ __ _ __ _ __ ____ __ _ __ // # // | | / /____ / /____ _ / /_ (_)/ /(_)/ /_ __ __ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // __ `// __// // // // __// / / / / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // /_ / // // // /_ / /_/ / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_/ \__//_//_//_/ \__/ \__, / /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ATR Average True Range informative_4h['atr'] = ta.ATR(informative_4h) # #NATR Normalized Average True Range # informative_4h['natr'] = ta.NATR(informative_4h) # #TRANGE True Range # informative_4h['trange'] = ta.TRANGE(informative_4h) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ _ ______ ____ // # // / __ \ _____ (_)_____ ___ /_ __/_____ ____ _ ____ _____ / __/____ _____ ____ ___ // # // / /_/ // ___// // ___// _ \ / / / ___// __ `// __ \ / ___// /_ / __ \ / ___// __ `__ \ // # // / ____// / / // /__ / __/ / / / / / /_/ // / / /(__ )/ __// /_/ // / / / / / / / // # // /_/ /_/ /_/ \___/ \___/ /_/ /_/ \__,_//_/ /_//____//_/ \____//_/ /_/ /_/ /_/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AVGPRICE Average Price # informative_4h['avgprice'] = ta.AVGPRICE(informative_4h) # #MEDPRICE Median Price # informative_4h['medprice'] = ta.MEDPRICE(informative_4h) # #TYPPRICE Typical Price # informative_4h['typprice'] = ta.TYPPRICE(informative_4h) # #WCLPRICE Weighted Close Price # informative_4h['wclprice'] = ta.WCLPRICE(informative_4h) # Close Rolling informative_4h['close_4h'] = informative_4h['close'] informative_4h['close_rolling_quantile_90_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.90) informative_4h['close_rolling_quantile_10_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.10) informative_4h['close_rolling_quantile_95_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.95) informative_4h['close_rolling_quantile_5_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.05) informative_4h['close_rolling_quantile_99_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.999) informative_4h['close_rolling_quantile_1_4h'] = informative_4h['close_4h'].shift(1).rolling(window=lookback_4h).quantile(0.01) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ______ __ ____ __ _ __ // # // / ____/__ __ _____ / /___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / / / / / // ___// // _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / /___ / /_/ // /__ / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // \____/ \__, / \___//_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #HT_DCPERIOD Hilbert Transform - Dominant Cycle Period informative_4h['ht_dcperiod'] = ta.HT_DCPERIOD(informative_4h) # #HT_DCPHASE Hilbert Transform - Dominant Cycle Phase # informative_4h['ht_dcphase'] = ta.HT_DCPHASE(informative_4h) # #HT_PHASOR Hilbert Transform - Phasor Components # informative_4h['ht_phasor_inphase'], informative_4h['ht_phasor_quadrature'] = ta.HT_PHASOR(informative_4h) # #HT_SINE Hilbert Transform - SineWave # informative_4h['ht_sine'], informative_4h['ht_leadsine'] = ta.HT_SINE(informative_4h) # #HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode # informative_4h['ht_trendmode'] = ta.HT_TRENDMODE(informative_4h) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ __ _____ __ __ _ // # // / __ \ _ __ ___ _____ / /____ _ ____ / ___/ / /_ __ __ ____/ /(_)___ _____ // # // / / / /| | / // _ \ / ___// // __ `// __ \ \__ \ / __// / / // __ // // _ \ / ___/ // # // / /_/ / | |/ // __// / / // /_/ // /_/ / ___/ // /_ / /_/ // /_/ // // __/(__ ) // # // \____/ |___/ \___//_/ /_/ \__,_// .___/ /____/ \__/ \__,_/ \__,_//_/ \___//____/ // # // /_/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #BBANDS Bollinger Bands informative_1d['bb_upper'] = ta.BBANDS(informative_1d, nbdevup=2.0, nbdevdn=2.0)['upperband'] informative_1d['bb_middle'] = ta.BBANDS(informative_1d, nbdevup=2.0, nbdevdn=2.0)['middleband'] informative_1d['bb_lower'] = ta.BBANDS(informative_1d, nbdevup=2.0, nbdevdn=2.0)['lowerband'] informative_1d['bbands_breakout_down'] = informative_1d['close'] < informative_1d['bb_lower'] informative_1d['bbands_breakout_up'] = informative_1d['close'] > informative_1d['bb_upper'] # #DEMA Double Exponential Moving Average # informative_1d['dema_7'] = ta.DEMA(informative_1d, timeperiod=7) # informative_1d['dema_25'] = ta.DEMA(informative_1d, timeperiod=25) # informative_1d['dema_99'] = ta.DEMA(informative_1d, timeperiod=99) # informative_1d['dema_200'] = ta.DEMA(informative_1d, timeperiod=200) # #EMA Exponential Moving Average informative_1d['ema_7'] = ta.EMA(informative_1d, timeperiod=7) informative_1d['ema_25'] = ta.EMA(informative_1d, timeperiod=25) informative_1d['ema_50'] = ta.EMA(informative_1d, timeperiod=50) informative_1d['ema_99'] = ta.EMA(informative_1d, timeperiod=99) informative_1d['ema_200'] = ta.EMA(informative_1d, timeperiod=200) # #FEMA Fibonacci Exponenetial Moving Average # informative_1d['fema_3'] = ta.EMA(informative_1d, timeperiod=3) # informative_1d['fema_5'] = ta.EMA(informative_1d, timeperiod=5) # informative_1d['fema_8'] = ta.EMA(informative_1d, timeperiod=8) # informative_1d['fema_13'] = ta.EMA(informative_1d, timeperiod=13) # informative_1d['fema_21'] = ta.EMA(informative_1d, timeperiod=21) # informative_1d['fema_34'] = ta.EMA(informative_1d, timeperiod=34) # informative_1d['fema_55'] = ta.EMA(informative_1d, timeperiod=55) # informative_1d['fema_89'] = ta.EMA(informative_1d, timeperiod=89) # informative_1d['fema_144'] = ta.EMA(informative_1d, timeperiod=144) # informative_1d['fema_233'] = ta.EMA(informative_1d, timeperiod=233) # #HT_TRENDLINE Hilbert Transform - Instantaneous Trendline # informative_1d['ht_trendline'] = ta.HT_TRENDLINE(informative_1d) # #KAMA Kaufman Adaptive Moving Average informative_1d['kama_7'] = ta.KAMA(informative_1d, timeperiod=7) informative_1d['kama_25'] = ta.KAMA(informative_1d, timeperiod=25) informative_1d['kama_99'] = ta.KAMA(informative_1d, timeperiod=99) informative_1d['kama_200'] = ta.KAMA(informative_1d, timeperiod=200) informative_1d['kama_trend_up'] = informative_1d['kama_25'] > informative_1d['kama_25'].shift(1) informative_1d['kama_trend_down'] = informative_1d['kama_25'] < informative_1d['kama_25'].shift(1) # #MA Moving average # #MAMA MESA Adaptive Moving Average # informative_1d['mama'], informative_1d['fama'] = ta.MAMA(informative_1d) # #MAVP Moving average with variable period # #MIDPOINT MidPoint over period # informative_1d['midpoint_7'] = ta.MIDPOINT(informative_1d, timeperiod=7) # informative_1d['midpoint_25'] = ta.MIDPOINT(informative_1d, timeperiod=25) # informative_1d['midpoint_99'] = ta.MIDPOINT(informative_1d, timeperiod=99) # informative_1d['midpoint_200'] = ta.MIDPOINT(informative_1d, timeperiod=200) # #MIDPRICE Midpoint Price over period # informative_1d['midprice_7'] = ta.MIDPRICE(informative_1d, timeperiod=7) # informative_1d['midprice_25'] = ta.MIDPRICE(informative_1d, timeperiod=25) # informative_1d['midprice_99'] = ta.MIDPRICE(informative_1d, timeperiod=99) # informative_1d['midprice_200'] = ta.MIDPRICE(informative_1d, timeperiod=200) # #SAR Parabolic SAR informative_1d['sar'] = ta.SAR(informative_1d) # #SAREXT Parabolic SAR - Extended # informative_1d['sarext'] = ta.SAREXT(informative_1d) # #SMA Simple Moving Average # informative_1d['sma_7'] = ta.SMA(informative_1d, timeperiod=7) # informative_1d['sma_25'] = ta.SMA(informative_1d, timeperiod=25) # informative_1d['sma_99'] = ta.SMA(informative_1d, timeperiod=99) # informative_1d['sma_200'] = ta.SMA(informative_1d, timeperiod=200) # #T3 Triple Exponential Moving Average (T3) # informative_1d['t3_7'] = ta.T3(informative_1d, timeperiod=7) # informative_1d['t3_25'] = ta.T3(informative_1d, timeperiod=25) # informative_1d['t3_99'] = ta.T3(informative_1d, timeperiod=99) # informative_1d['t3_200'] = ta.T3(informative_1d, timeperiod=200) # #TEMA Triple Exponential Moving Average # informative_1d['tema_7'] = ta.TEMA(informative_1d, timeperiod=20) # informative_1d['tema_25'] = ta.TEMA(informative_1d, timeperiod=25) # informative_1d['tema_99'] = ta.TEMA(informative_1d, timeperiod=99) # informative_1d['tema_200'] = ta.TEMA(informative_1d, timeperiod=200) # #TRIMA Triangular Moving Average # informative_1d['trima_7'] = ta.TRIMA(informative_1d, timeperiod=7) # informative_1d['trima_25'] = ta.TRIMA(informative_1d, timeperiod=25) # informative_1d['trima_99'] = ta.TRIMA(informative_1d, timeperiod=99) # informative_1d['trima_200'] = ta.TRIMA(informative_1d, timeperiod=200) # #WMA Weighted Moving Average # informative_1d['wma_7'] = ta.WMA(informative_1d, timeperiod=7) # informative_1d['wma_25'] = ta.WMA(informative_1d, timeperiod=25) # informative_1d['wma_99'] = ta.WMA(informative_1d, timeperiod=99) # informative_1d['wma_200'] = ta.WMA(informative_1d, timeperiod=200) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // __ ___ __ ____ __ _ __ // # // / |/ /____ ____ ___ ___ ____ / /_ __ __ ____ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / /|_/ // __ \ / __ `__ \ / _ \ / __ \ / __// / / // __ `__ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / / / // /_/ // / / / / // __// / / // /_ / /_/ // / / / / / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // /_/ /_/ \____//_/ /_/ /_/ \___//_/ /_/ \__/ \__,_//_/ /_/ /_/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ADX Average Directional Movement Index informative_1d['adx'] = ta.ADX(informative_1d) informative_1d['adx_rolling_quantile_25'] = informative_1d['adx'].shift(1).rolling(window=lookback_1d).quantile(0.25) informative_1d['adx_rolling_quantile_75'] = informative_1d['adx'].shift(1).rolling(window=lookback_1d).quantile(0.75) # #ADXR Average Directional Movement Index Rating # informative_1d['adxr'] = ta.ADXR(informative_1d) # #APO Absolute Price Oscillator # informative_1d['apo'] = ta.APO(informative_1d) # #AROON Aroon # aroon_down, aroon_up = ta.AROON(informative_1d) # informative_1d['aroon_down'] = aroon_down # informative_1d['aroon_up'] = aroon_up # #AROONOSC Aroon Oscillator # informative_1d['aroonosc'] = ta.AROONOSC(informative_1d) # #BOP Balance Of Power # informative_1d['bop'] = ta.BOP(informative_1d) # #CCI Commodity Channel Index informative_1d['cci'] = ta.CCI(informative_1d) informative_1d['cci_rolling_quantile_10'] = informative_1d['cci'].shift(1).rolling(window=lookback_1d).quantile(0.10) # Za "oversold" uslove informative_1d['cci_rolling_quantile_90'] = informative_1d['cci'].shift(1).rolling(window=lookback_1d).quantile(0.90) # Za "overbought" uslove # #CMO Chande Momentum Oscillator # informative_1d['cmo'] = ta.CMO(informative_1d) # #DX Directional Movement Index # informative_1d['dx'] = ta.DX(informative_1d) # #MACD Moving Average Convergence/Divergence macd = ta.MACD(informative_1d) informative_1d['macd'] = macd['macd'] informative_1d['macd_signal'] = macd['macdsignal'] informative_1d['macd_histogram'] = macd['macdhist'] # #MACDEXT MACD with controllable MA type # informative_1d['macdext'], _, _ = ta.MACDEXT(informative_1d) # #MACDFIX Moving Average Convergence/Divergence Fix 12/26 # informative_1d['macdfix'], _, _ = ta.MACDFIX(informative_1d) # #MFI Money Flow Index informative_1d['mfi'] = ta.MFI(informative_1d) informative_1d['mfi_rolling_quantile_10'] = informative_1d['mfi'].shift(1).rolling(window=lookback_1d).quantile(0.10) # Za "oversold" uslove (npr. < 20) informative_1d['mfi_rolling_quantile_20'] = informative_1d['mfi'].shift(1).rolling(window=lookback_1d).quantile(0.20) # Alternativni "oversold" informative_1d['mfi_rolling_quantile_80'] = informative_1d['mfi'].shift(1).rolling(window=lookback_1d).quantile(0.80) # Alternativni "overbought" informative_1d['mfi_rolling_quantile_90'] = informative_1d['mfi'].shift(1).rolling(window=lookback_1d).quantile(0.90) # Za "overbought" uslove (npr. > 80) # #MINUS_DI Minus Directional Indicator informative_1d['minus_di'] = ta.MINUS_DI(informative_1d) # #MINUS_DM Minus Directional Movement # informative_1d['minus_dm'] = ta.MINUS_DM(informative_1d) # #MOM Momentum # informative_1d['mom'] = ta.MOM(informative_1d) # #PLUS_DI Plus Directional Indicator informative_1d['plus_di'] = ta.PLUS_DI(informative_1d) # #PLUS_DM Plus Directional Movement # informative_1d['plus_dm'] = ta.PLUS_DM(informative_1d) # #PPO Percentage Price Oscillator # informative_1d['ppo'] = ta.PPO(informative_1d) # #ROC Rate of change : ((price/prevPrice)-1)*100 informative_1d['roc'] = ta.ROC(informative_1d) # #ROCP Rate of change Percentage: (price-prevPrice)/prevPrice # informative_1d['rocp'] = ta.ROCP(informative_1d) # #ROCR Rate of change ratio: (price/prevPrice) # informative_1d['rocr'] = ta.ROCR(informative_1d) # #ROCR100 Rate of change ratio 100 scale: (price/prevPrice)*100 # informative_1d['rocr100'] = ta.ROCR100(informative_1d) # #RSI Relative Strength Index informative_1d['rsi_3'] = ta.RSI(informative_1d,timeperiod=3) informative_1d['rsi_5'] = ta.RSI(informative_1d,timeperiod=5) informative_1d['rsi_8'] = ta.RSI(informative_1d,timeperiod=8) informative_1d['rsi_13'] = ta.RSI(informative_1d,timeperiod=13) informative_1d['rsi_21'] = ta.RSI(informative_1d,timeperiod=21) informative_1d['rsi_34'] = ta.RSI(informative_1d,timeperiod=34) informative_1d['rsi_3_rolling_quantile_10'] = informative_1d['rsi_3'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['rsi_3_rolling_quantile_90'] = informative_1d['rsi_3'].shift(1).rolling(window=lookback_1d).quantile(0.90) informative_1d['rsi_8_rolling_quantile_10'] = informative_1d['rsi_8'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['rsi_8_rolling_quantile_90'] = informative_1d['rsi_8'].shift(1).rolling(window=lookback_1d).quantile(0.90) informative_1d['rsi_13_rolling_quantile_10'] = informative_1d['rsi_13'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['rsi_13_rolling_quantile_90'] = informative_1d['rsi_13'].shift(1).rolling(window=lookback_1d).quantile(0.90) informative_1d['rsi_21_rolling_quantile_10'] = informative_1d['rsi_21'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['rsi_21_rolling_quantile_90'] = informative_1d['rsi_21'].shift(1).rolling(window=lookback_1d).quantile(0.90) informative_1d['rsi_34_rolling_quantile_10'] = informative_1d['rsi_34'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['rsi_34_rolling_quantile_90'] = informative_1d['rsi_34'].shift(1).rolling(window=lookback_1d).quantile(0.90) # #STOCH Stochastic # slowk, slowd = ta.STOCH(informative_1d) # informative_1d['stoch_k'] = slowk # informative_1d['stoch_d'] = slowd # #STOCHF Stochastic Fast # fastk, fastd = ta.STOCHF(informative_1d) # informative_1d['stochf_k'] = fastk # informative_1d['stochf_d'] = fastd # #STOCHRSI Stochastic Relative Strength Index # informative_1d['stochrsi_k'], informative_1d['stochrsi_d'] = ta.STOCHRSI(informative_1d) # #TRIX 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA informative_1d['trix'] = ta.TRIX(informative_1d) informative_1d['trix_rolling_quantile_10'] = informative_1d['trix'].shift(1).rolling(window=lookback_1d).quantile(0.10) # Potencijalni signal za kupovinu (očekivanje rasta) informative_1d['trix_rolling_quantile_90'] = informative_1d['trix'].shift(1).rolling(window=lookback_1d).quantile(0.90) # Potencijalni signal za prodaju (očekivanje pada) # #ULTOSC Ultimate Oscillator # informative_1d['ultosc'] = ta.ULTOSC(informative_1d) # #WILLR Williams' %R # informative_1d['willr'] = ta.WILLR(informative_1d) # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ ____ __ _ __ // # // | | / /____ / /__ __ ____ ___ ___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // / / // __ `__ \ / _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // / / / / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_//_/ /_/ /_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AD Chaikin A/D Line # informative_1d['ad'] = ta.AD(informative_1d) # #ADOSC Chaikin A/D Oscillator # informative_1d['adosc'] = ta.ADOSC(informative_1d) # #OBV On Balance Volume informative_1d['obv'] = ta.OBV(informative_1d) # Volume Mean informative_1d['volume'] = informative_1d['volume'] informative_1d['volume_mean_30'] = informative_1d['volume'].rolling(window=30).mean() # ''' # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // _ __ __ __ _ __ _ __ ____ __ _ __ // # // | | / /____ / /____ _ / /_ (_)/ /(_)/ /_ __ __ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // | | / // __ \ / // __ `// __// // // // __// / / / / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // | |/ // /_/ // // /_/ // /_ / // // // /_ / /_/ / _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // |___/ \____//_/ \__,_/ \__//_//_//_/ \__/ \__, / /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #ATR Average True Range informative_1d['atr'] = ta.ATR(informative_1d) # #NATR Normalized Average True Range # informative_1d['natr'] = ta.NATR(informative_1d) # #TRANGE True Range # informative_1d['trange'] = ta.TRANGE(informative_1d) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ____ _ ______ ____ // # // / __ \ _____ (_)_____ ___ /_ __/_____ ____ _ ____ _____ / __/____ _____ ____ ___ // # // / /_/ // ___// // ___// _ \ / / / ___// __ `// __ \ / ___// /_ / __ \ / ___// __ `__ \ // # // / ____// / / // /__ / __/ / / / / / /_/ // / / /(__ )/ __// /_/ // / / / / / / / // # // /_/ /_/ /_/ \___/ \___/ /_/ /_/ \__,_//_/ /_//____//_/ \____//_/ /_/ /_/ /_/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #AVGPRICE Average Price # informative_1d['avgprice'] = ta.AVGPRICE(informative_1d) # #MEDPRICE Median Price # informative_1d['medprice'] = ta.MEDPRICE(informative_1d) # #TYPPRICE Typical Price # informative_1d['typprice'] = ta.TYPPRICE(informative_1d) # #WCLPRICE Weighted Close Price # informative_1d['wclprice'] = ta.WCLPRICE(informative_1d) # Close Rolling informative_1d['close_1d'] = informative_1d['close'] informative_1d['close_rolling_quantile_90_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.90) informative_1d['close_rolling_quantile_10_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.10) informative_1d['close_rolling_quantile_95_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.95) informative_1d['close_rolling_quantile_5_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.05) informative_1d['close_rolling_quantile_99_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.999) informative_1d['close_rolling_quantile_1_1d'] = informative_1d['close_1d'].shift(1).rolling(window=lookback_1d).quantile(0.01) # ''' # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # // // # // ______ __ ____ __ _ __ // # // / ____/__ __ _____ / /___ / _/____ ____/ /(_)_____ ____ _ / /_ ____ _____ _____ // # // / / / / / // ___// // _ \ / / / __ \ / __ // // ___// __ `// __// __ \ / ___// ___/ // # // / /___ / /_/ // /__ / // __/ _/ / / / / // /_/ // // /__ / /_/ // /_ / /_/ // / (__ ) // # // \____/ \__, / \___//_/ \___/ /___//_/ /_/ \__,_//_/ \___/ \__,_/ \__/ \____//_/ /____/ // # // /____/ // # // // # ////////////////////////////////////////////////////////////////////////////////////////////////////////// # ''' # #HT_DCPERIOD Hilbert Transform - Dominant Cycle Period informative_1d['ht_dcperiod'] = ta.HT_DCPERIOD(informative_1d) # #HT_DCPHASE Hilbert Transform - Dominant Cycle Phase # informative_1d['ht_dcphase'] = ta.HT_DCPHASE(informative_1d) # #HT_PHASOR Hilbert Transform - Phasor Components # informative_1d['ht_phasor_inphase'], informative_1d['ht_phasor_quadrature'] = ta.HT_PHASOR(informative_1d) # #HT_SINE Hilbert Transform - SineWave # informative_1d['ht_sine'], informative_1d['ht_leadsine'] = ta.HT_SINE(informative_1d) # #HT_TRENDMODE Hilbert Transform - Trend vs Cycle Mode # informative_1d['ht_trendmode'] = ta.HT_TRENDMODE(informative_1d) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, '1h', ffill=True) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, '4h', ffill=True) dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, '1d', ffill=True) # dataframe['custom_stop_keep'] = ( # (dataframe['ema_25_1h'] > dataframe['ema_99_1h']) & # (dataframe['sar_1h'] < dataframe['close']) & # (dataframe['macd_1h'] > dataframe['macd_signal_1h']) & # (dataframe['adx_1h'] > dataframe['adx_rolling_quantile_25_1h']) & # (dataframe['plus_di_1h'] > dataframe['minus_di_1h']) & # (dataframe['rsi_13_1h'] > dataframe['rsi_13_rolling_quantile_10_1h']) # ).astype('bool') return dataframe # def assign_trend_type(self, dataframe: DataFrame) -> DataFrame: # dataframe['trend_type'] = 'none' # scalp_cond = ( # (dataframe['rsi_8'] < dataframe['rsi_8_rolling_quantile_10']) & (dataframe['rsi_8'] > dataframe['rsi_8'].shift(1)) & # (dataframe['ema_7'] > dataframe['ema_25']) & # (dataframe['macd'] > dataframe['macd_signal']) & # (dataframe['adx'] > dataframe['adx_rolling_quantile_25']) & (dataframe['plus_di'] > dataframe['minus_di']) # ) # swing_cond = ( # (dataframe['ema_50_4h'] > dataframe['close']) & # (dataframe['bbands_breakout_up_4h']) & # (dataframe['rsi_13_4h'] > 40) & # (dataframe['macd_4h'] > dataframe['macd_signal_4h']) # ) # long_cond = ( # (dataframe['ema_99_1d'] > dataframe['close']) & # (dataframe['macd_histogram_1d'] > 0) & # (dataframe['rsi_13_1d'] > 55) # ) # trend_cond = ( # (dataframe['ema_200_1d'] > dataframe['close']) & # (dataframe['adx_1d'] > 25) & # (dataframe['obv_1d'] > 0) # ) # dataframe.loc[scalp_cond, 'trend_type'] = 'scalp' # dataframe.loc[swing_cond, 'trend_type'] = 'swing' # dataframe.loc[trend_cond, 'trend_type'] = 'trend' # dataframe.loc[long_cond, 'trend_type'] = 'long' # return dataframe def is_price_above_max_buy(trade, current_rate): max_buy_price = max([o.price for o in trade.orders if o.is_buy]) return current_rate > max_buy_price def market_state(self,dataframe:DataFrame,metadata:dict) -> DataFrame: # --- Stanje Tržišta --- # Dnevni Timeframe (1d) - Šira Slika dataframe['1d_bullish_trend'] = (dataframe['ema_50_1d'] > dataframe['ema_200_1d']) & (dataframe['close_1d'] > dataframe['ema_50_1d']) & (dataframe['adx_1d'] > 20) & (dataframe['rsi_13_1d'] > 50) dataframe['1d_bearish_trend'] = (dataframe['ema_50_1d'] < dataframe['ema_200_1d']) & (dataframe['close_1d'] < dataframe['ema_50_1d']) & (dataframe['adx_1d'] > 20) & (dataframe['rsi_13_1d'] < 50) dataframe['1d_ranging'] = (dataframe['adx_1d'] < 20) | ((dataframe['close_1d'] > dataframe['ema_200_1d']) & (dataframe['close_1d'] < dataframe['ema_50_1d']) & (dataframe['ema_50_1d'] < dataframe['ema_200_1d'])) # Primer za kompleksnije ranging uslove # 4-satni Timeframe (4h) - Glavni Trend za Sesiju dataframe['4h_bullish_strong_trend'] = (dataframe['ema_50_4h'] > dataframe['ema_200_4h']) & (dataframe['close_4h'] > dataframe['ema_50_4h']) & (dataframe['adx_4h'] > 25) & (dataframe['rsi_13_4h'] > 55) & (dataframe['ema_200_4h'] > dataframe['ema_200_4h'].shift(1)) # EMA_200 raste dataframe['4h_bullish_trend'] = (dataframe['ema_50_4h'] > dataframe['ema_200_4h']) & (dataframe['close_4h'] > dataframe['ema_50_4h']) & (dataframe['adx_4h'] > 20) & (dataframe['rsi_13_4h'] > 50) dataframe['4h_bearish_strong_trend'] = (dataframe['ema_50_4h'] < dataframe['ema_200_4h']) & (dataframe['close_4h'] < dataframe['ema_50_4h']) & (dataframe['adx_4h'] > 25) & (dataframe['rsi_13_4h'] < 45) & (dataframe['ema_200_4h'] < dataframe['ema_200_4h'].shift(1)) # EMA_200 pada dataframe['4h_bearish_trend'] = (dataframe['ema_50_4h'] < dataframe['ema_200_4h']) & (dataframe['close_4h'] < dataframe['ema_50_4h']) & (dataframe['adx_4h'] > 20) & (dataframe['rsi_13_4h'] < 50) dataframe['4h_ranging'] = (dataframe['adx_4h'] < 20) | ( (dataframe['bb_upper_4h'] - dataframe['bb_lower_4h']) / dataframe['bb_middle_4h'] < 0.04 ) # Bollinger Band širina kao indikator range-a # 1-satni Timeframe (1h) - Primarni Setup Timeframe dataframe['1h_bullish_momentum'] = (dataframe['ema_25_1h'] > dataframe['ema_50_1h']) & (dataframe['close_1h'] > dataframe['ema_50_1h']) & (dataframe['adx_1h'] > 20) & (dataframe['plus_di_1h'] > dataframe['minus_di_1h']) & (dataframe['rsi_13_1h'] > 50) & (dataframe['macd_1h'] > dataframe['macd_signal_1h']) dataframe['1h_bearish_momentum'] = (dataframe['ema_25_1h'] < dataframe['ema_50_1h']) & (dataframe['close_1h'] < dataframe['ema_50_1h']) & (dataframe['adx_1h'] > 20) & (dataframe['plus_di_1h'] < dataframe['minus_di_1h']) & (dataframe['rsi_13_1h'] < 50) & (dataframe['macd_1h'] < dataframe['macd_signal_1h']) dataframe['1h_choppy'] = (dataframe['adx_1h'] < 18) & (dataframe['atr_1h'] / dataframe['close_1h'] < 0.005) # Nizak ADX i niska volatilnost # 5-minutni Timeframe (5m) - Ulazni Timeframe (koristimo postojeće indikatore na 'dataframe') dataframe['5m_strong_bullish_burst'] = (dataframe['ema_25'] > dataframe['ema_50']) & (dataframe['rsi_13'] > 60) & (dataframe['macd'] > dataframe['macd_signal']) & (dataframe['volume'] > dataframe['volume_mean_30'] * 1.5) dataframe['5m_strong_bearish_burst'] = (dataframe['ema_25'] < dataframe['ema_50']) & (dataframe['rsi_13'] < 40) & (dataframe['macd'] < dataframe['macd_signal']) & (dataframe['volume'] > dataframe['volume_mean_30'] * 1.5) dataframe['5m_pullback_to_support'] = (dataframe['close'] < dataframe['ema_50']) & (dataframe['close'] > dataframe['ema_200']) & (dataframe['rsi_13'] < 40) # Primer za pullback u uptrendu dataframe['5m_rejection_at_resistance'] = (dataframe['close'] > dataframe['ema_50']) & (dataframe['close'] < dataframe['ema_200']) & (dataframe['rsi_13'] > 60) # Primer za odbijanje u downtrendu return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.market_state(dataframe, metadata) ''' RSI 3 < RSI3 ROLLING 10 Quartile RSI 5 < RSI5 ROLLING 10 Quartile RSI 8 < RSI8 ROLLING 10 Quartile EMA 7 < EMA 25 < EMA 50 < EMA 99 < EMA 200 EMA 5 > EMA 5 Shift 1 EMA 8 > EMA 8 Shift 1 MACD HIST SHIFT 1 < 0 MACD HIST > MACD HIST Shift 1 ''' # Scalping Entry dataframe.loc[ ( (dataframe['rsi_13'] > dataframe['rsi_13'].shift(1)) & (dataframe['ema_200'].shift(2) < dataframe['ema_200_rolling_quartile_01'].shift(3)) & (dataframe['close'].shift(2) < dataframe['close_rolling_quantile_1'].shift(3)) & (dataframe['rsi_13'].shift(2) < dataframe['rsi_13_rolling_quantile_1'].shift(3)) ), ['enter_long', 'enter_tag']] = (1, 'scalp_max') # # Swing Entry # dataframe.loc[ # ( # (dataframe['trend_type'] == 'swing') & # (dataframe['rsi_13_4h'] < dataframe['rsi_13_rolling_quantile_10_4h']) & # (dataframe['macd_4h'] > dataframe['macd_signal_4h']) # ), # ['enter_long', 'swing_entry'] # ] = (1, 'swing_entry') # # Long-Term Entry # dataframe.loc[ # ( # (dataframe['trend_type'] == 'long') & # (dataframe['rsi_13_1d'] < dataframe['rsi_13_rolling_quantile_10_1d']) & # (dataframe['macd_histogram_1d'] > 0) # ), # ['enter_long', 'long_entry'] # ] = (1, 'long_entry') # # Trend-Following Entry # dataframe.loc[ # ( # (dataframe['trend_type'] == 'trend') & # (dataframe['obv_1d'] > dataframe['obv_1d'].rolling(5).mean()) & # (dataframe['adx_1d'] > dataframe['adx_rolling_quantile_25_1d']) # ), # ['enter_long', 'trend_entry'] # ] = (1, 'trend_entry') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # === Scalping Exit with Profit Check === dataframe = self.market_state(dataframe, metadata) dataframe.loc[ ( (dataframe['rsi_13'] < dataframe['rsi_13'].shift(1)) & (dataframe['ema_200'].shift(2) > dataframe['ema_200_rolling_quartile_99'].shift(3)) & (dataframe['close'].shift(2) > dataframe['close_rolling_quantile_99'].shift(3)) & (dataframe['close'] > dataframe['close_1d']) & (dataframe['rsi_13'].shift(2) > dataframe['rsi_13_rolling_quantile_99'].shift(3)) & (dataframe['ema_200_4h']> dataframe['ema_200_1d']) #(dataframe['ema_25'] < dataframe['ema_50_1h'].shift(1)) #(dataframe['ema_200_4h'] < dataframe['ema_200_4h'].shift(1)) #(dataframe['adx_1h'] > 20) & (dataframe['plus_di_1h'] < dataframe['minus_di_1h']) ), ['exit_long', 'exit_tag']] = (1, 'scalp_max') # # === Swing Exit === # dataframe.loc[ # ( # (dataframe['trend_type'] == 'swing') & # ( # (dataframe['ema_50_4h'] < dataframe['close']) | # EMA50 acting as resistance # (dataframe['bbands_breakout_down_4h']) | # BBANDS breakout down # (dataframe['kama_trend_down_4h']) | # KAMA okrenut nadole (pretpostavimo da postoji ta kolona) # (dataframe['rsi_13_4h'] < 60) | # (dataframe['macd_4h'] < dataframe['macd_signal_4h']) | # (dataframe['obv_4h'] < dataframe['obv_4h'].shift(1)) | # OBV pada # (dataframe['trix_4h'] < 0) # ) # ), # ['exit_long', 'swing_exit'] # ] = (1, 'swing_exit') # # === Long Term Exit === # dataframe.loc[ # ( # (dataframe['trend_type'] == 'long') & # ( # (dataframe['ema_200_1d'] < dataframe['close']) | # EMA200 kao otpor # (dataframe['sar_1d'] > dataframe['close']) | # (dataframe['bbands_breakout_down_1d']) | # (dataframe['mfi_1d'] < 80) | # (dataframe['macd_histogram_1d'] < 0) | # (dataframe['roc_1d'] < 0) # ) # ), # ['exit_long', 'long_exit'] # ] = (1, 'long_exit') # # === Trend Exit === # dataframe.loc[ # ( # (dataframe['trend_type'] == 'trend') & # ( # (dataframe['ema_200_1d'] < dataframe['close']) | # (dataframe['kama_trend_down_1d']) | # Ili MAMA — pretpostavimo da postoji # ((dataframe['adx_1d'] > 25) & (dataframe['minus_di_1d'] > dataframe['plus_di_1d'])) | # (dataframe['obv_1d'] < dataframe['obv_1d'].shift(1)) | # (dataframe['ht_dcperiod_1d'] > dataframe['ht_dcperiod_1d'].shift(1)) # momentum opada # ) # ), # ['exit_long', 'trend_exit'] # ] = (1, 'trend_exit') return dataframe # def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # # Dohvati poslednji poznati red iz DataProvider-a # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # if dataframe is None or len(dataframe) < 1: # return 1 # ne prekidaj # last_candle = dataframe.iloc[-1] # if last_candle['custom_stop_keep']: # return 1 # Drži poziciju # # Panic exit logika (dodaj ako želiš više signala) # if last_candle['rsi_13_1h'] < 30 and last_candle['macd_histogram_1h'] < 0: # return 0.98 # Stop-out na -2% # return 0.95 # Fallback SL