# source: https://raw.githubusercontent.com/bustillo/freqtrade-strategies/29512a7daa07505cf7d1852cdd19b21a25413fdf/ZaratustraDCA/ZaratustraDCA2_06.py import logging import numpy as np import pandas as pd from technical import qtpylib from pandas import DataFrame from datetime import datetime, timezone from typing import Optional from functools import reduce import talib.abstract as ta import pandas_ta as pta from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair) import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade logger = logging.getLogger(__name__) class Github_bustillo_freqtrade_strategies__ZaratustraDCA2_06__20250222_074233(IStrategy): """ Personalized Trading Strategy with Risk Management, DCA, and Technical Indicators This automated strategy combines advanced risk management techniques with Dollar Cost Averaging (DCA) to optimize market operations. The strategy employs technical indicators (e.g., ADX, PDI, MDI) to define entry and exit conditions for both long and short positions. It also includes dynamic stoploss logic based on ATR and protections like cooldown periods and loss guards. Key features: - Long and short trading enabled with dynamic position adjustments. - Protections against overtrading using cooldown mechanisms. - Use of technical indicators to track market trends and trigger signals. - Supports dynamic stake adjustments based on successful trade entries. NOTE: This strategy is under development, and some functions may be disabled or incomplete. Parameters like ROI targets and advanced stoploss logic are subject to future optimization. Test thoroughly before using it in live trading. Telegram Profile: https://t.me/bustillo Choose your coffee style: - BTC (Classic): bc1qfq46qqhurg8ps73506rtqsr26mfhl9t6vp2ltc - ETH/ERC-20 & BSC/BEP-20 (Smart): 0x486Ef431878e2a240ea2e7A6EBA42e74632c265c (Supports ETH, BNB, USDT, and tokens on: Ethereum, Binance Smart Chain, and EVM-compatible networks.) - SOL (Speed): 2nrYABUJLjHtUdVTXkcY8ELUK7q3HH4iWXQxQMQDdZa8 - XMR (Privacy): 45kQh8n23AgiY2yEDbMmJdcMGTaHmpn6vFfhECs7EwtPZ7pbyCQAyzDCehtDZSGsWzaDGir1LfA4EGDQP3dtPStsMdrzUG5 """ ### Strategy parameters ### exit_profit_only = True use_custom_stoploss = False trailing_stop = False ignore_roi_if_entry_signal = True can_short = True use_exit_signal = True stoploss = -0.10 startup_candle_count: int = 100 timeframe = '5m' # DCA Parameters position_adjustment_enable = True max_entry_position_adjustment = 2 max_dca_multiplier = 1 # Maximum DCA multiplier # ROI table: minimal_roi = {} ### Hyperopt ### # protections cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True) stop_duration = IntParameter(12, 120, default=72, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) ### Protections ### @property def protections(self): """ Defines the protections to apply during trading operations. """ prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": 1, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False }) return prot ### Dollar Cost Averaging (DCA) ### # This is called when placing the initial order (opening trade) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Calculates the stake amount to use for a trade, adjusted dynamically based on the DCA multiplier. - The proposed stake is divided by the maximum DCA multiplier (`self.max_dca_multiplier`) to determine the adjusted stake. - If the adjusted stake is lower than the allowed minimum (`min_stake`), it is automatically increased to meet the minimum stake requirement. """ # Calculates the adjusted stake amount based on the DCA multiplier. adjusted_stake = proposed_stake / self.max_dca_multiplier # Automatically adjusts to the minimum stake if it is too low. if adjusted_stake < min_stake: adjusted_stake = min_stake return adjusted_stake def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: Optional[float], max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs) -> Optional[float]: """ Custom trade adjustment logic, returning the stake amount that a trade should be increased or decreased. This means extra buy or sell orders with additional fees. Only called when `position_adjustment_enable` is set to True. """ if trade.entry_side == "buy": # For longs if current_profit > 0.10 and trade.nr_of_successful_exits == 0: return -(trade.stake_amount / 2) if current_profit > -0.04 and trade.nr_of_successful_entries == 1: return None if current_profit > -0.06 and trade.nr_of_successful_entries == 2: return None if current_profit > -0.08 and trade.nr_of_successful_entries == 3: return None else: # For shorts if current_profit > 0.10 and trade.nr_of_successful_exits == 0: return -(trade.stake_amount / 2) if current_profit > -0.04 and trade.nr_of_successful_entries == 1: return None if current_profit > -0.06 and trade.nr_of_successful_entries == 2: return None if current_profit > -0.08 and trade.nr_of_successful_entries == 3: return None # Obtain pair dataframe (just to show how to access it) dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) filled_entries = trade.select_filled_orders(trade.entry_side) count_of_entries = trade.nr_of_successful_entries try: if not filled_entries: logger.warning(f"No executed orders for {trade.pair}. Aborting DCA.") return None stake_amount = filled_entries[0].cost if count_of_entries > 1: stake_amount = stake_amount * (1 + (count_of_entries - 1) * 0.5) # 50% increase per additional entry return stake_amount except Exception as exception: logger.error(f"Error adjusting DCA position for the pair {trade.pair}: {exception}") return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Calculates technical indicators used to define entry and exit signals. """ dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['dx'] = ta.SMA(ta.DX(dataframe) * dataframe['volume']) / ta.SMA(dataframe['volume']) dataframe['adx'] = ta.SMA(ta.ADX(dataframe) * dataframe['volume']) / ta.SMA(dataframe['volume']) dataframe['pdi'] = ta.SMA(ta.PLUS_DI(dataframe) * dataframe['volume']) / ta.SMA(dataframe['volume']) dataframe['mdi'] = ta.SMA(ta.MINUS_DI(dataframe) * dataframe['volume']) / ta.SMA(dataframe['volume']) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: """ Defines the conditions for long/short entries based on technical indicators such as ADX, PDI, and MDI. """ df.loc[ ( (qtpylib.crossed_above(df['dx'], df['pdi'])) & (df['adx'] > df['mdi']) & (df['pdi'] > df['mdi']) ), ['enter_long', 'enter_tag'] ] = (1, 'ZaratustraDCA Entry Long') df.loc[ ( (qtpylib.crossed_above(df['dx'], df['mdi'])) & (df['adx'] > df['pdi']) & (df['mdi'] > df['pdi']) ), ['enter_short', 'enter_tag'] ] = (1, 'ZaratustraDCA Entry Short') return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: """ Defines exit conditions for trades based on the ADX indicator: - Exit Long: Triggered when 'dx' crosses below 'adx' and ADX is strong (>25). - Exit Short: Triggered when 'dx' crosses below 'adx' and ADX is weak (≤25). """ df.loc[ ( (qtpylib.crossed_below(df['dx'], df['adx'])) & (df['adx'] > 25) # Strong ADX (above 25, to confirm trend) ), ['exit_long', 'exit_tag'] ] = (1, 'ZaratustraDCA Exit Long') df.loc[ ( (qtpylib.crossed_below(df['dx'], df['adx'])) & (df['adx'] <= 25) # Weak ADX (below 25, lacks trend strength) ), ['exit_short', 'exit_tag'] ] = (1, 'ZaratustraDCA Exit Short') return df