# source: https://raw.githubusercontent.com/jov0jova/vrach/63445349a8df742bc4fe97ae798b0448768d6d58/user_data/strategies/vrach_V1.py ''' __________________________________________________________________________________________________________________ VRACH Trading Strategy. __________________________________________________________________________________________________________________ Created by: jov0jova Live version : 1.0.0 __________________________________________________________________________________________________________________ Trading strategy : "scalp", "position", "daytrade", "swing", "long" __________________________________________________________________________________________________________________ ''' import numpy as np import pandas as pd import pandas_ta as pta 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__20250619_133128(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' minimal_roi = {} stoploss = -0.99 trailing_stop = False use_custom_stoploss = True use_custom_exit = True can_short = False process_only_new_candles = True startup_candle_count = 288 inf_timeframes = ['1h', '4h', '1d'] inf_1h = '1h' inf_4h = '4h' inf_1d = '1d' def informative_pairs(self): pairs = [(pair, tf) for pair in self.dp.current_whitelist() for tf in self.inf_timeframes] return pairs def informative_1d_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1d) #TA-Lib # Momentum Indicators # ULTOSC Ultimate Oscillator informative_1d['ultosc'] = ta.ULTOSC(informative_1d) # Volume Indicators # OBV On Balance Volume informative_1d['obv'] = ta.OBV(informative_1d) # Volatility Indicators # ATR Average True Range informative_1d['atr'] = ta.ATR(informative_1d) # NATR Normalized Average True Range informative_1d['natr'] = ta.NATR(informative_1d) #Pandas TA #Zero Lag Moving Average: zlma informative_1d['zlma_12'] = pta.zlma(informative_1d['close'], length=12) informative_1d['zlma_20'] = pta.zlma(informative_1d['close'], length=20) informative_1d['zlma_26'] = pta.zlma(informative_1d['close'], length=26) informative_1d['zlma_50'] = pta.zlma(informative_1d['close'], length=50) informative_1d['zlma_100'] = pta.zlma(informative_1d['close'], length=100) informative_1d['zlma_200'] = pta.zlma(informative_1d['close'], length=200) #Vortex: vortex vortex = pta.vortex(informative_1d['high'],informative_1d['low'],informative_1d['close'],14) informative_1d['vtx_p'] = vortex['VTXP_14'] informative_1d['vtx_m'] = vortex['VTXM_14'] informative_1d['vtx_gap'] = informative_1d['vtx_p'] - informative_1d['vtx_m'] #Donchian donchian = pta.donchian(informative_1d['high'],informative_1d['low'],20,20) informative_1d['DCL_20'] = donchian['DCL_20_20'] informative_1d['DCM_20'] = donchian['DCM_20_20'] informative_1d['DCU_20'] = donchian['DCU_20_20'] #Massi massi = pta.massi(informative_1d['high'],informative_1d['low'],9,25) informative_1d['massi'] = massi #RVI Indicator: Relative Volatility Index (RVI) informative_1d['rvi_14'] = pta.rvi(informative_1d,lenght=14) #CMF Chaikin Money Flow informative_1d['cmf_20'] = pta.cmf(informative_1d['high'],informative_1d['low'],informative_1d['close'],informative_1d['volume'],lenght=20) #EBSW Even Better SineWave (EBSW) informative_1d['ebsw'] = pta.ebsw(informative_1d,lenght=40,bars=10) return dataframe def informative_4h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_4h) #TA-Lib # Momentum Indicators # ULTOSC Ultimate Oscillator informative_4h['ultosc'] = ta.ULTOSC(informative_4h) # Volume Indicators # OBV On Balance Volume informative_4h['obv'] = ta.OBV(informative_4h) # Volatility Indicators # ATR Average True Range informative_4h['atr'] = ta.ATR(informative_4h) # NATR Normalized Average True Range informative_4h['natr'] = ta.NATR(informative_4h) #Pandas TA #Zero Lag Moving Average: zlma informative_4h['zlma_12'] = pta.zlma(informative_4h['close'], length=12) informative_4h['zlma_20'] = pta.zlma(informative_4h['close'], length=20) informative_4h['zlma_26'] = pta.zlma(informative_4h['close'], length=26) informative_4h['zlma_50'] = pta.zlma(informative_4h['close'], length=50) informative_4h['zlma_100'] = pta.zlma(informative_4h['close'], length=100) informative_4h['zlma_200'] = pta.zlma(informative_4h['close'], length=200) #Vortex: vortex vortex = pta.vortex(informative_4h['high'],informative_4h['low'],informative_4h['close'],14) informative_4h['vtx_p'] = vortex['VTXP_14'] informative_4h['vtx_m'] = vortex['VTXM_14'] informative_4h['vtx_gap'] = informative_4h['vtx_p'] - informative_4h['vtx_m'] #Donchian donchian = pta.donchian(informative_4h['high'],informative_4h['low'],20,20) informative_4h['DCL_20'] = donchian['DCL_20_20'] informative_4h['DCM_20'] = donchian['DCM_20_20'] informative_4h['DCU_20'] = donchian['DCU_20_20'] #Massi massi = pta.massi(informative_4h['high'],informative_4h['low'],9,25) informative_4h['massi'] = massi #RVI Indicator: Relative Volatility Index (RVI) informative_4h['rvi_14'] = pta.rvi(informative_4h,lenght=14) #CMF Chaikin Money Flow informative_4h['cmf_20'] = pta.cmf(informative_4h['high'],informative_4h['low'],informative_4h['close'],informative_4h['volume'],lenght=20) #EBSW Even Better SineWave (EBSW) informative_4h['ebsw'] = pta.ebsw(informative_4h,lenght=40,bars=10) return dataframe def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h) #TA-Lib # Momentum Indicators # ULTOSC Ultimate Oscillator informative_1h['ultosc'] = ta.ULTOSC(informative_1h) # Volume Indicators # OBV On Balance Volume informative_1h['obv'] = ta.OBV(informative_1h) # Volatility Indicators # ATR Average True Range informative_1h['atr'] = ta.ATR(informative_1h) # NATR Normalized Average True Range informative_1h['natr'] = ta.NATR(informative_1h) #Pandas TA #Zero Lag Moving Average: zlma informative_1h['zlma_12'] = pta.zlma(informative_1h['close'], length=12) informative_1h['zlma_20'] = pta.zlma(informative_1h['close'], length=20) informative_1h['zlma_26'] = pta.zlma(informative_1h['close'], length=26) informative_1h['zlma_50'] = pta.zlma(informative_1h['close'], length=50) informative_1h['zlma_100'] = pta.zlma(informative_1h['close'], length=100) informative_1h['zlma_200'] = pta.zlma(informative_1h['close'], length=200) #Vortex: vortex vortex = pta.vortex(informative_1h['high'],informative_1h['low'],informative_1h['close'],14) informative_1h['vtx_p'] = vortex['VTXP_14'] informative_1h['vtx_m'] = vortex['VTXM_14'] informative_1h['vtx_gap'] = informative_1h['vtx_p'] - informative_1h['vtx_m'] #Donchian donchian = pta.donchian(informative_1h['high'],informative_1h['low'],20,20) informative_1h['DCL_20'] = donchian['DCL_20_20'] informative_1h['DCM_20'] = donchian['DCM_20_20'] informative_1h['DCU_20'] = donchian['DCU_20_20'] #Massi massi = pta.massi(informative_1h['high'],informative_1h['low'],9,25) informative_1h['massi'] = massi #RVI Indicator: Relative Volatility Index (RVI) informative_1h['rvi_14'] = pta.rvi(informative_1h,lenght=14) #CMF Chaikin Money Flow informative_1h['cmf_20'] = pta.cmf(informative_1h['high'],informative_1h['low'],informative_1h['close'],informative_1h['volume'],lenght=20) #EBSW Even Better SineWave (EBSW) informative_1h['ebsw'] = pta.ebsw(informative_1h,lenght=40,bars=10) return dataframe def timeframe_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #TA-Lib # Momentum Indicators # ULTOSC Ultimate Oscillator dataframe['ultosc'] = ta.ULTOSC(dataframe) # Volume Indicators # OBV On Balance Volume dataframe['obv'] = ta.OBV(dataframe) # Volatility Indicators # ATR Average True Range dataframe['atr'] = ta.ATR(dataframe) # NATR Normalized Average True Range dataframe['natr'] = ta.NATR(dataframe) #Pandas TA #Zero Lag Moving Average: zlma dataframe['zlma_12'] = pta.zlma(dataframe['close'], length=12) dataframe['zlma_20'] = pta.zlma(dataframe['close'], length=20) dataframe['zlma_26'] = pta.zlma(dataframe['close'], length=26) dataframe['zlma_50'] = pta.zlma(dataframe['close'], length=50) dataframe['zlma_100'] = pta.zlma(dataframe['close'], length=100) dataframe['zlma_200'] = pta.zlma(dataframe['close'], length=200) #Vortex: vortex vortex = pta.vortex(dataframe['high'],dataframe['low'],dataframe['close'],14) dataframe['vtx_p'] = vortex['VTXP_14'] dataframe['vtx_m'] = vortex['VTXM_14'] dataframe['vtx_gap'] = dataframe['vtx_p'] - dataframe['vtx_m'] #Donchian donchian = pta.donchian(dataframe['high'],dataframe['low'],20,20) dataframe['DCL_20'] = donchian['DCL_20_20'] dataframe['DCM_20'] = donchian['DCM_20_20'] dataframe['DCU_20'] = donchian['DCU_20_20'] #Massi massi = pta.massi(dataframe['high'],dataframe['low'],9,25) dataframe['massi'] = massi #RVI Indicator: Relative Volatility Index (RVI) dataframe['rvi_14'] = pta.rvi(dataframe,lenght=14) #CMF Chaikin Money Flow dataframe['cmf_20'] = pta.cmf(dataframe['high'],dataframe['low'],dataframe['close'],dataframe['volume'],lenght=20) #EBSW Even Better SineWave (EBSW) dataframe['ebsw'] = pta.ebsw(dataframe,lenght=40,bars=10) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: informative_1h = self.informative_1h_indicators(dataframe, metadata) informative_4h = self.informative_4h_indicators(dataframe,metadata) informative_1d = self.informative_1d_indicators(dataframe,metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) dataframe = merge_informative_pair(dataframe, informative_4h, self.timeframe, self.inf_4h, ffill=True) dataframe = merge_informative_pair(dataframe, informative_1d, self.timeframe, self.inf_1d, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.timeframe_indicators(dataframe, metadata) return dataframe def populate_position_score(self, dataframe: DataFrame) -> DataFrame: dataframe['score'] = 0 # Macro trend alignment (daily ZLMA > 50) dataframe.loc[(dataframe['close_1d'] > dataframe['zlma_50_1d']), 'score'] += 2 dataframe.loc[(dataframe['vtx_gap_4h'] > 0), 'score'] += 1 dataframe.loc[(dataframe['ultosc'] > 50), 'score'] += 1 dataframe.loc[(dataframe['cmf_20'] > 0), 'score'] += 1 dataframe.loc[(dataframe['obv'] > dataframe['obv'].shift(1)), 'score'] += 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: Optional[float] = None, **kwargs) -> float: """ Dynamic leverage manager — adjusts exposure based on score. """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) score = dataframe.iloc[-1]['score'] if score >= 5: return 3.0 # Max risk elif score >= 3: return 2.0 return 1.0 def position_adjustment(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, amount: float, **kwargs) -> Optional[float]: """ Risk manager — adjusts size based on performance. """ max_position = 100 # Cap per trade risk_factor = min(max(trade.pnl_percentage / 10, 0.5), 2.0) # Adaptive risk control adjusted_size = min(amount * risk_factor, max_position) return adjusted_size def market_regime(self,dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Scalping Entry # dataframe.loc[ # ( # (dataframe['close'] < dataframe['close_quant_low_01']) & # #(dataframe['adx'] < 20) & # znači da je range # (dataframe['rsi_13'] < dataframe['rsi_13_quant_low_02']) & # preprodato # (dataframe['volume'] > dataframe['volume'].rolling(20).mean()) # ), # ['enter_long', 'enter_tag'] # ] = (1, 'scalp_max_confirmed') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Scalping Exit # dataframe.loc[ # ( # (dataframe['close'] > dataframe['close_quant_high_99']) & # (dataframe['close'] > dataframe['high']*0.995) # ), # ['exit_long', 'exit_tag'] # ] = (1, 'scalp_max_confirmed') return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[str]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] # Exit condition 1: price reached upper donchian band if current_rate >= last_candle['DCU_20']: return 'donchian_tp' # Exit condition 2: RVI shows volatility reversal if last_candle['rvi_14'] < 50: return 'rvi_exit' # Exit condition 3: Score collapsed if last_candle['score'] < 2: return 'score_drop_exit' return None 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, self.timeframe) entry_index = dataframe.index.get_loc(trade.open_date_utc, method='nearest') atr = dataframe['atr'].iloc[entry_index] entry_low = dataframe['low'].iloc[entry_index] stop_price = entry_low - 1.5 * atr if current_rate <= stop_price: return 0.99 # Full stop return 1 # Hold position