# source: https://raw.githubusercontent.com/tonyblaze27/freqStrategies/30102ae7119fbf53b9ebfbdee2b838a83c53c3af/strategies/ZaratustraDCA2_mori.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import logging import math from datetime import datetime, timedelta, timezone from freqtrade.persistence import Trade import time from typing import Dict, List, Optional # --- import sys import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, DecimalParameter, IntParameter, \ CategoricalParameter import technical.indicators as ftt import pandas as pd import warnings warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) ######################################## Warning ######################################## # You won't get a lot of benefits by simply changing to this strategy # # with the HyperOpt values changed. # # # # You should test it closely, trying backtesting and dry running, and we recommend # # customizing the terms of sale and purchase as well. # # # # You should always be careful in real trading! # ######################################################################################### ######################################## Changelog ######################################## # 2025-02-12 # # - Added short position functionality (can_short=True) # # - Enhanced DCA implementation with hyperoptimizable parameters: # # * initial_safety_order_trigger # # * safety_order_step_scale # # * safety_order_volume_scale # # * max_so_multiplier_orig # # * partial_fill_compensation_scale # # - Added sell conditions with separate EWO and RSI parameters # # - Added sell signal tagging for better analysis # # - Implemented short position exit conditions # # - Optimized for futures trading # # # # Original strategy credits: # # - SMAOffsetProtectOptV1 by kkeue # # - DCA implementation by Stash86 # # - Partial fill compensation by jorikito#2815 # ########################################################################################### def EWO(dataframe, ema_length=5, ema2_length=35): # df = dataframe.copy() ema1 = ta.EMA(dataframe, timeperiod=ema_length) ema2 = ta.EMA(dataframe, timeperiod=ema2_length) emadif = (ema1 - ema2) / dataframe['close'] * 100 return emadif def lerp(a: float, b: float, t: float) -> float: """Linear interpolate on the scale given by a to b, using t as the point on that scale. Examples -------- 50 == lerp(0, 100, 0.5) 4.2 == lerp(1, 5, 0.8) """ return (1 - t) * a + t * b logger = logging.getLogger(__name__) class Github_tonyblaze27_freqStrategies__ZaratustraDCA2_mori__20250214_125621(IStrategy): # Original: SMAOffsetProtectOptV1 by kkeue, shared on the freqtrade discord at 2021-06-19 # Added dca of Stash86, modified and optimized it. # Added jorikito#2815 's partial fill compensation # Use this option with caution! # enables a full 1st slot and base+safety-order for the 2nd slot before you run out of money in your wallet. # (combo from the .ods-file: 4+3, see rows "Overbuy calculation") overbuy_factor = 1.295 can_short=True position_adjustment_enable = True #initial_safety_order_trigger = -0.02 #max_so_multiplier_orig = 7 #safety_order_step_scale = 2 #safety_order_volume_scale = 2 # DCA Parameters initial_safety_order_trigger = DecimalParameter(-0.02, -0.01, default=-0.015, space='buy', optimize=True) safety_order_step_scale = DecimalParameter(1.1, 2.0, default=1.5, space='buy', optimize=True) safety_order_volume_scale = DecimalParameter(0.5, 4.0, default=2.0, space='buy', optimize=True) max_so_multiplier_orig = IntParameter(1, 15, default=3, space='buy', optimize=True) partial_fill_compensation_scale = DecimalParameter(0.0, 1.0, default=0.5, space='buy', optimize=True) # just for initialization, now we calculate it... max_so_multiplier = max_so_multiplier_orig.value # We will store the size of stake of each trade's first order here cust_proposed_initial_stakes = {} # Amount the strategy should compensate previously partially filled orders for successive safety orders (0.0 - 1.0) #partial_fill_compensation_scale = 1 if (max_so_multiplier_orig.value > 0): if (safety_order_volume_scale.value > 1): firstLine = (safety_order_volume_scale.value * (math.pow(safety_order_volume_scale.value, (max_so_multiplier_orig.value - 1)) - 1)) divisor = (safety_order_volume_scale.value - 1) max_so_multiplier = (2 + firstLine / divisor) elif (safety_order_volume_scale.value < 1): firstLine = safety_order_volume_scale.value * \ (1 - math.pow(safety_order_volume_scale.value, (max_so_multiplier_orig.value - 1))) divisor = 1 - safety_order_volume_scale.value max_so_multiplier = (2 + firstLine / divisor) # Since stoploss can only go up and can't go down, if you set your stoploss here, your lowest stoploss will always be tied to the first buy rate # So disable the hard stoploss here, and use custom_sell or custom_stoploss to handle the stoploss trigger stoploss = -1 # Modified Buy / Sell params - 20210619 # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 16, "ewo_high_buy": 5.672, "ewo_low_buy": -19.931, "low_offset": 0.973, "rsi_buy": 59, } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 20, "high_offset": 1.010, "ewo_high_sell": 5.672, "ewo_low_sell": -19.931, "rsi_sell": 49, } INTERFACE_VERSION = 2 # Modified ROI - 20210620 # ROI table: minimal_roi = { "0": 0.028, "10": 0.018, "30": 0.010, "40": 0.005 } # SMAOffset base_nb_candles_buy = IntParameter( 5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter( 5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter( 0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter( 0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low_buy = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low_buy'], space='buy', optimize=True) ewo_high_buy = DecimalParameter( 2.0, 12.0, default=buy_params['ewo_high_buy'], space='buy', optimize=True) rsi_buy = IntParameter(50, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) ewo_low_sell = DecimalParameter(-20.0, -8.0, default=sell_params['ewo_low_sell'], space='sell', optimize=True) ewo_high_sell = DecimalParameter( 2.0, 12.0, default=sell_params['ewo_high_sell'], space='sell', optimize=True) rsi_sell = IntParameter(30,50, default=sell_params['rsi_sell'], space='sell', optimize=True) # Trailing stop: trailing_stop = False trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # Sell signal use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False # Optimal timeframe for the strategy timeframe = '5m' process_only_new_candles = True startup_candle_count: int = 1000 plot_config = \ { "main_plot": {}, "subplots": { "sub": { "rsi": { "color": "#80dea8", "type": "line" } }, "sub2": { "ma_buy_16": { "color": "#db1ea2", "type": "line" }, "ma_sell_20": { "color": "#645825", "type": "line" }, "EWO": { "color": "#1e5964", "type": "line" }, "missing_data": { "color": "#26b08d", "type": "line" } } } } use_custom_stoploss = False def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): tag = super().custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs) if tag: return tag entry_tag = 'empty' if hasattr(trade, 'entry_tag') and trade.entry_tag is not None: entry_tag = trade.entry_tag if current_profit <= -0.35: return f'stop_loss ({entry_tag})' return None def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # remove pair from custom initial stake dict only if full exit if trade.amount == amount and pair in self.cust_proposed_initial_stakes: del self.cust_proposed_initial_stakes[pair] return True # Let unlimited stakes leave funds open for DCA orders def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, **kwargs) -> float: custom_stake = proposed_stake / self.max_so_multiplier * self.overbuy_factor self.cust_proposed_initial_stakes[ pair] = custom_stake # Setting of first stake size just before each first order of a trade return custom_stake # set to static 10 to simulate partial fills of 10$, etc def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs) -> Optional[float]: """ Custom implementation of position adjustment (DCA). """ if current_profit > self.initial_safety_order_trigger.value: return None filled_buys = trade.select_filled_orders(trade.entry_side) count_of_buys = len(filled_buys) if 1 <= count_of_buys <= self.max_so_multiplier_orig.value: safety_order_trigger = (abs(self.initial_safety_order_trigger.value) * count_of_buys) # Calculate dynamic safety order trigger based on step scale if self.safety_order_step_scale.value > 1: safety_order_trigger = abs(self.initial_safety_order_trigger.value) + ( abs(self.initial_safety_order_trigger.value) * self.safety_order_step_scale.value * ( math.pow(self.safety_order_step_scale.value, (count_of_buys - 1)) - 1 ) / (self.safety_order_step_scale.value - 1) ) elif self.safety_order_step_scale.value < 1: safety_order_trigger = abs(self.initial_safety_order_trigger.value) + ( abs(self.initial_safety_order_trigger.value) * self.safety_order_step_scale.value * ( 1 - math.pow(self.safety_order_step_scale.value, (count_of_buys - 1)) ) / (1 - self.safety_order_step_scale.value) ) if current_profit <= (-1 * abs(safety_order_trigger)): try: actual_initial_stake = filled_buys[0].cost stake_amount = actual_initial_stake already_bought = sum(filled_buy.cost for filled_buy in filled_buys) if trade.pair in self.cust_proposed_initial_stakes: if self.cust_proposed_initial_stakes[trade.pair] > 0: # Calculate stake for current safety order with partial fill compensation proposed_initial_stake = self.cust_proposed_initial_stakes[trade.pair] current_actual_stake = already_bought * math.pow( self.safety_order_volume_scale.value, (count_of_buys - 1) ) current_stake_preposition = proposed_initial_stake * math.pow( self.safety_order_volume_scale.value, (count_of_buys - 1) ) current_stake_preposition_compensation = current_stake_preposition + abs( current_stake_preposition - current_actual_stake ) total_so_stake = self._lerp( current_actual_stake, current_stake_preposition_compensation, self.partial_fill_compensation_scale.value ) stake_amount = total_so_stake else: stake_amount = stake_amount * math.pow( self.safety_order_volume_scale.value, (count_of_buys - 1) ) else: stake_amount = stake_amount * math.pow( self.safety_order_volume_scale.value, (count_of_buys - 1) ) amount = stake_amount / current_rate # Log DCA action self.dp.send_msg( f"DCA buy #{count_of_buys} for {trade.pair}\n" f"Stake amount: {stake_amount:.4f}\n" f"Amount: {amount:.4f}\n" f"Previous total: {already_bought:.4f}\n" f"New total: {already_bought + stake_amount:.4f}" ) return stake_amount except Exception as exception: logger.info(f'DCA error for {trade.pair}: {str(exception)}') return None return None def _lerp(self, a: float, b: float, t: float) -> float: """Linear interpolation between a and b using t as weight""" return a + (b - a) * t # Add this to your populate_indicators method if you want to track DCA opportunities def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # ... (previous indicators remain the same) # Add DCA indicators dataframe['dca_trigger'] = ( (dataframe['close'].shift(1) / dataframe['close'] - 1) * 100 ) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_buy values # moved into entry / exit functions to not have redundant calculations being done. # backtesting will be slower but who cares about a few seconds during backtests right? # All that counts is dry / live performance, not backtesting speed imo ... # If you want to put in the work with conditions be my guest ;) for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) #Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # Elliot dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) candles_bidaily_of_5m: int = 576 # Check for 0 volume candles in the last day dataframe['missing_data'] = \ (dataframe['volume'] <= 0).rolling( window=candles_bidaily_of_5m, min_periods=candles_bidaily_of_5m).sum() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Speed optimization for dry / live runs, not looping through for ... values with it, nothing else. dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] = \ ta.EMA(dataframe, timeperiod=int(self.base_nb_candles_buy.value)) conditions_buy = [] conditions_buy.append( (dataframe['close'] < ( dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high_buy.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['missing_data'] < 1) ) conditions_buy.append( (dataframe['close'] < ( dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low_buy.value) & (dataframe['missing_data'] < 1) ) if conditions_buy: final_buy_condition = reduce(lambda x, y: x | y, conditions_buy) dataframe.loc[final_buy_condition, ["enter_long", "enter_tag"]] = (1, "BUY_SIGNAL") # dataframe.loc[ # reduce(lambda x, y: x | y, conditions_buy), # 'enter_long' # ] = 1 conditions_sell = [] conditions_sell.append( (dataframe['close'] > ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low_sell.value) & (dataframe['rsi'] > self.rsi_sell.value) & (dataframe['missing_data'] < 1) ) conditions_sell.append( (dataframe['close'] > ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['EWO'] > self.ewo_high_sell.value) & (dataframe['missing_data'] < 1) ) if conditions_sell: final_sell_condition = reduce(lambda x, y: x | y, conditions_sell) dataframe.loc[final_sell_condition, ["enter_short", "enter_tag"]] = (1, "SELL_SIGNAL") # dataframe.loc[ # reduce(lambda x, y: x | y, conditions_sell), # 'enter_short' # ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Speed optimization for dry / live runs, not looping through for ... values with it, nothing else. dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] = \ ta.EMA(dataframe, timeperiod=int(self.base_nb_candles_sell.value)) conditions_exit_buy = [] conditions_exit_buy.append( ( (dataframe['close'] > ( dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ) ) if conditions_exit_buy: final_sell_condition = reduce(lambda x, y: x | y, conditions_exit_buy) dataframe.loc[final_sell_condition, ["exit_long", "exit_tag"]] = (1, "LONG_EXIT_SIGNAL") # dataframe.loc[ # reduce(lambda x, y: x | y, conditions_exit_buy), # 'exit_long' # ] = 1 conditions_exit_sell = [] conditions_exit_sell.append( ( (dataframe['close'] < ( dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['volume'] > 0) ) ) if conditions_exit_sell: final_sell_condition = reduce(lambda x, y: x | y, conditions_exit_sell) dataframe.loc[final_sell_condition, ["exit_short", "exit_tag"]] = (1, "SELL_EXIT_SIGNAL") # dataframe.loc[ # reduce(lambda x, y: x | y, conditions_exit_sell), # 'exit_short' # ] = 1 return dataframe