# source: https://raw.githubusercontent.com/mashiroissocute/freqtrade-grid/5bc342096507b3b5c58a11eb7c59e61fb0bb289b/user_data/strategies/ADX_PRICE_DCA_Strategy_Future.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy,merge_informative_pair from typing import Dict, List from functools import reduce from pandas import DataFrame from datetime import datetime, timedelta, timezone from typing import Optional from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from freqtrade.persistence import PairLocks import logging # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from typing import Optional, Tuple, Union from freqtrade.strategy import stoploss_from_open logger = logging.getLogger(__name__) class Github_mashiroissocute_freqtrade_grid__ADX_PRICE_DCA_Strategy_Future__20240716_023718(IStrategy): INTERFACE_VERSION: int = 3 can_short = True position_adjustment_enable = True max_entry_position_adjustment = -1 minimal_roi = { "0": 1 } stoploss = -1 # trailing_stop = True # trailing_stop_positive = 0.05 # trailing_stop_positive_offset = 0.25 # trailing_only_offset_is_reached = True order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': True } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } adxWindow = IntParameter(7, 21, default=14, space="buy") adxThr = IntParameter(15, 35, default=25, space="buy") emaThr = IntParameter(5, 55, default=24, space="buy") upGridPercent = 1.02 downGridPercent = 0.98 # Optimal timeframe for the strategy timeframe = '1m' inf_tf = '4h' def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, self.inf_tf) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: adxWindow = self.adxWindow.value informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_tf) informative['plus_di'] = ta.PLUS_DI(informative,adxWindow) informative['minus_di'] = ta.MINUS_DI(informative,adxWindow) informative['ema'] = ta.EMA(informative, timeperiod=self.emaThr.value) # n1 = 10 # n2 = 21 # ap = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 # esa = ap.ewm(span=n1, min_periods=n1).mean() # d = ap.sub(esa).abs().ewm(span=n1, min_periods=n1).mean() # ci = (ap - esa) / (0.015 * d) # tci = ci.ewm(span=n2, min_periods=n2).mean() # dataframe['wt1'] = tci # dataframe['wt2'] = dataframe['wt1'].rolling(window=4).mean() dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_tf, ffill=True) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['close'] < dataframe[f'ema_{self.inf_tf}']) & (dataframe[f'plus_di_{self.inf_tf}'] > dataframe[f'minus_di_{self.inf_tf}']) & (dataframe[f'plus_di_{self.inf_tf}']>self.adxThr.value) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe['close'] > dataframe[f'ema_{self.inf_tf}']) & (dataframe[f'plus_di_{self.inf_tf}'] < dataframe[f'minus_di_{self.inf_tf}']) & (dataframe[f'minus_di_{self.inf_tf}']>self.adxThr.value) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( ), 'exit_long'] = 0 dataframe.loc[ ( ), 'exit_short'] = 0 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 5 # DCA 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: return 10.0 # GRID ORDERs 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 ) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]: filled_entries = trade.select_filled_orders(trade.entry_side) last_order_price = filled_entries[-1].safe_price dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() last_candle_price = last_candle['close'] # ---------------- DCA increse position --------------- # long trade increase postion where curPrice < lastPrice*0.98 if trade.entry_side == 'buy' : if last_candle_price <= last_order_price * self.downGridPercent: try: # This returns first order stake size stake_amount = filled_entries[0].stake_amount return stake_amount except Exception as exception: return None # short trade increase postion where curPrice > lastPrice*1.02 if trade.entry_side == 'sell' : if last_candle_price >= last_order_price * self.upGridPercent: try: # This returns first order stake size stake_amount = filled_entries[0].stake_amount return stake_amount except Exception as exception: return None # ---------------- DCA decrease position -------------- # long trade decrese postion where curPrice > lastPrice*1.02 if current_profit >= 0.1: try: # This returns first order stake size stake_amount = trade.stake_amount /2 return -stake_amount except Exception as exception: return None if current_profit >= 0.2: try: # This returns first order stake size stake_amount = trade.stake_amount return -stake_amount except Exception as exception: return None return None