from datetime import datetime, timedelta import talib.abstract as ta import pandas_ta as pta from freqtrade.strategy import merge_informative_pair from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter from functools import reduce from freqtrade.persistence import Trade import pandas as pd import numpy as np import warnings from freqtrade.exchange import date_minus_candles import logging logger = logging.getLogger(__name__) ''' martin short long 策略 讨论群 https://discord.gg/hfxRh74mK7 策略整体思路： 1、主流币做多马丁，非主流币做空马丁，将原来的两个策略融合在一起，资金分开管理 使用建议： 建议至少使用4000刀 思路更新日志 20251028 1、主流币做多马丁，非主流币做空马丁，将原来的两个策略融合在一起，避免极端行情某个方向爆仓，资金分开管理-----✔ 20251030 1、使用dataframe['volume_mean'] > 400000辅助买入，和原版差距不大，估计400000这个阈值还需要再调整-------X 2、加仓限制次数，利润大幅降低，回撤也大幅降低-------X 3、做空使用动态止盈（1d数据），交易笔数下降很多，做空总利润几乎一致-------X 4、做多使用动态止盈（1d数据），交易笔数下降很多，做多总利润提升不大-------X 5、做多调整为5x（使做多盈利能力和做空盈利能力一致），初始仓位由200刀降低到10刀，做多和做空的加仓调整为10天固定时间加仓，金额波动更为温和，最后回测结束时，不再有1000多天的扛单，利润下降了一些-------✔ 20251112 1、添加历史最低利润统计，方便观察-----✔ 20251113 1、加仓改为标准的马丁加仓，long的买入金额改为3刀， 最低利润统计降低很多，利润提升2倍-----✔ 2、short的买入金额改为6/15，防止爆掉----✔ 20251114 1、做空买入改为第10-30名，利润降低，且会爆掉------X 2、long/short_profit_ratio改为-10或者-20,没有提升----X 20251121 1、使用自定义动态止盈（1h数据），效果不佳，利润大幅下降------X 20251124 1、原来的版本会爆仓，调整为间隔40天加仓，杠杆降低，仓位变高，利润相对杠杆相对提高-------✔ 20251125 1、在两个极端币同时存在的情况下，仍然会爆掉，添加-500%止损-------✔（重要优化，马丁不纯粹了，哈哈哈） 2、加了90天冷却时间（*****需要搭配E0的只亏损冷却文件使用*****），避免重复买入暴涨得币，导致频繁亏损-------✔ 3、杠杆调低到3x，仓位调高，调低利润百分比，降低风险，增加单笔利润金额，降低利润比例获利快速退出------✔ 4、加仓间隔调到90天，尽量不使用加仓可以退出，三个月长期横盘或者小涨的币，通过加仓达到退出的目的-----✔ 5、加仓后盈利马上退出，避免大幅亏损，调整加仓作为长期扛单退出的手段，不作为盈利的目的------✔ 从20251201开始实盘，截至20251215目前盈亏情况如下 ROI: Closed trades ∙ 426.373 USDT (16.68%) (11.14 Σ%) ∙ 426.373 USD ROI: All trades ∙ 251.96 USDT (7.85%) (6.58 Σ%) ∙ 251.96 USD Total Trade Count: 330 Bot started: 2025-12-01 13:35:34 First Trade opened: 13 days ago (2025-12-01 13:45:24) Latest Trade opened: an hour ago (2025-12-15 00:20:08) Win / Loss: 247 / 1 Winrate: 99.60% Expectancy (Ratio): 1.72 (0.03) Avg. Duration: 4 days, 8:45:33 Best Performing: AIOT/USDT:USDT: 16.853 USDT (84.72%) Trading volume: 17759.65 USDT Profit factor: 7.48 Max Drawdown: 1.59% (65.79 USDT) from 2025-12-11 00:05:22 (315.594 USDT) to 2025-12-11 00:05:34 (249.804 USDT) Current Drawdown: 0.00% (0 USDT) from 2025-12-15 00:18:33 (426.373 USDT) Day (count) USDT USD Profit % --------------- ------------ ---------- ---------- 2025-12-15 (49) 91.25 USDT 91.25 USD 2.19% 2025-12-14 (5) 6.606 USDT 6.61 USD 0.16% 2025-12-13 (14) 23.126 USDT 23.13 USD 0.56% 2025-12-12 (3) 3.868 USDT 3.87 USD 0.09% 2025-12-11 (32) -12.808 USDT -12.81 USD -0.31% 2025-12-10 (2) 6.451 USDT 6.45 USD 0.16% 2025-12-09 (8) 24.992 USDT 24.99 USD 0.61% ''' class MD_SL(IStrategy): def __init__(self, config): super().__init__(config) self.dry_run_wallet = config.get('dry_run_wallet', 4000) if hasattr(config, 'get') else 4000 self.total_short_usdt = 0 self.total_long_usdt = 0 self.min_history_profit_pct = 0 self.real_usdt = 0 @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 90 } ] can_short = True timeframe = '1d' # startup_candle_count = 90 # 需要至少90根K线 process_only_new_candles = True # 只处理新K线 stoploss = -5 use_custom_stoploss = False # 不用动态止损 # short交易参数 short_leverage = 3 short_entry_stake_amount = 10 short_profit_ratio = 0 short_fast_profit = 0.12 # long自定义参数 long_leverage = 3 long_entry_stake_amount = 20 long_profit_ratio = 0 long_fast_profit = 0.12 # dca加仓天数 dca_day = 60 # 避免仓位过重，加仓后有机会就卖出，不追求利润 Protection_days = dca_day + 1 position_adjustment_enable = True long_pair_list = [ "BTC/USDT:USDT", "ETH/USDT:USDT", "XRP/USDT:USDT", "BNB/USDT:USDT", "SOL/USDT:USDT", "DOGE/USDT:USDT", "ADA/USDT:USDT", "TRX/USDT:USDT", "WBTC/USDT:USDT", "SUI/USDT:USDT", "XLM/USDT:USDT", "LINK/USDT:USDT", "HBAR/USDT:USDT", "BCH/USDT:USDT", "AVAX/USDT:USDT" ] def bot_loop_start(self, current_time: datetime, **kwargs) -> None: # 在每个bot循环开始时更新资产情况 logger.info(f"当前日期:{current_time}") self.total_short_usdt, self.short_profit_ratio, self.total_long_usdt, self.long_profit_ratio, self.real_usdt,total_profit_pct, self.min_history_profit_pct = self.get_assets_in_usdt() def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: current_pair = metadata['pair'] whitelist = self.dp.current_whitelist() new_cols = {} for pair in whitelist: pair_df = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe) new_cols[f'{pair}_1d_per'] = (pair_df['close'] / pair_df['close'].shift(1)) - 1 # 合并所有新列 new_cols_df = pd.DataFrame(new_cols, index=dataframe.index) dataframe = pd.concat([dataframe, new_cols_df], axis=1) # 只保留实际存在的列 valid_cols = [col for col in [f'{pair}_1d_per' for pair in whitelist] if col in dataframe.columns] current_col = f'{current_pair}_1d_per' if not valid_cols or current_col not in valid_cols: dataframe['momentum_rank'] = None else: dataframe['momentum_rank'] = dataframe[valid_cols].rank(axis=1, ascending=False)[current_col] dataframe['ma5'] = ta.SMA(dataframe, timeperiod=5) # 计算5日均线 dataframe['ma90'] = ta.SMA(dataframe, timeperiod=60) # 计算90日均线 return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: enter_short_conditions = [ metadata['pair'] not in self.long_pair_list, dataframe['momentum_rank'] <= 20, dataframe['close'] < dataframe['ma90'], ] if enter_short_conditions: dataframe.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") enter_long_conditions = [ metadata['pair'] in self.long_pair_list, dataframe['close'] > dataframe['ma5'], ] if enter_long_conditions: dataframe.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") enter_long_conditions1 = [ metadata['pair'] in self.long_pair_list, dataframe['ma5'] > dataframe['ma90'], ] if enter_long_conditions1: dataframe.loc[ reduce(lambda x, y: x & y, enter_long_conditions1), ["enter_long", "enter_tag"] ] = (1, "long1") return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: dataframe['exit_long'] = 0 dataframe['exit_short'] = 0 return dataframe def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float | None, max_stake: float, leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: if side == 'short': return max(self.short_entry_stake_amount, self.real_usdt * (self.short_entry_stake_amount/self.dry_run_wallet) / 3) #return self.short_entry_stake_amount return max(self.long_entry_stake_amount, self.real_usdt * (self.long_entry_stake_amount/self.dry_run_wallet) / 3) #return self.long_entry_stake_amount def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, side: str, **kwargs) -> bool: if side == 'short': if self.short_profit_ratio < -10: return False if side == 'long': if self.long_profit_ratio < -10: return False return True def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float | None, max_stake: float, current_entry_rate: float, current_exit_rate: float, current_entry_profit: float, current_exit_profit: float, **kwargs ) -> float | None | tuple[float | None, str | None]: if trade.trade_direction == 'short': last_time = trade.date_last_filled_utc + timedelta(days=self.dca_day) if current_time > last_time: return (2 ** trade.nr_of_successful_entries) * self.short_entry_stake_amount elif trade.trade_direction == 'long': last_time = trade.date_last_filled_utc + timedelta(days=self.dca_day) if current_time > last_time: return (2 ** trade.nr_of_successful_entries) * self.long_entry_stake_amount return None def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): current_profit_100 = current_profit * 100 exit_time1 = trade.open_date_utc + timedelta(days=self.Protection_days) if trade.trade_direction == 'short': exit_time = trade.open_date_utc + timedelta(days=1) if current_time >= exit_time and current_profit > self.short_fast_profit: #logger.info(f"当前交易对{pair},利润{current_profit_100 :.2f}%") return "short_get_profit" if current_time >= exit_time1 and current_profit > 0: #logger.info(f"当前交易对{pair},利润{current_profit_100 :.2f}%") return "short_time_exit" elif trade.trade_direction == 'long': if current_profit > self.long_fast_profit: #logger.info(f"当前交易对{pair},利润{current_profit_100 :.2f}%") return "long_get_profit" if current_time >= exit_time1 and current_profit > 0: #logger.info(f"当前交易对{pair},利润{current_profit_100 :.2f}%") return "long_time_exit" 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, exit_tag: str = None, **kwargs) -> bool: return True def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: if side == 'short': return self.short_leverage return self.long_leverage def get_assets_in_usdt(self): initial_usdt = self.dry_run_wallet open_profit = 0 open_trades = Trade.get_trades_proxy(is_open=True) total_profit_amount = 0.0 for open_trade in open_trades: profit_ratio = 0.0 other_pair = open_trade.pair (dataframe, _) = self.dp.get_analyzed_dataframe(pair=other_pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1] rate_for_other_pair = last_candle['close'] if open_trade.is_short: # Short position profit ratio profit_ratio = (1 - (rate_for_other_pair / open_trade.open_rate)) else: # Long position profit ratio profit_ratio = ((rate_for_other_pair / open_trade.open_rate) - 1) profit_ratio *= open_trade.leverage profit_amount = open_trade.stake_amount * profit_ratio total_profit_amount += profit_amount open_profit = total_profit_amount close_profit = Trade.get_total_closed_profit() # 获取USDT本身余额 total_usdt = self.wallets.get_total('USDT') real_usdt = total_usdt + open_profit total_profit_pct = 100 * ((open_profit + close_profit) / initial_usdt) self.min_history_profit_pct = min(self.min_history_profit_pct, total_profit_pct ) long_close_trade_profit = 0 short_open_trade_profit = 0 short_close_trade_profit = 0 long_open_trade_profit = 0 short_open_stake_amount = 0 long_open_stake_amount = 0 all_close_trades = Trade.get_trades_proxy(is_open=False) close_long_trades = [trade for trade in all_close_trades if trade.trade_direction == 'long'] for trade in close_long_trades: long_close_trade_profit += float(trade.close_profit_abs) #logger.info(f"{trade.pair} 平仓盈亏: {trade.close_profit_abs}") all_open_trades = Trade.get_trades_proxy(is_open=True) open_short_trades = [trade for trade in all_open_trades if trade.trade_direction == 'short'] for trade in open_short_trades: profit_ratio = 0.0 open_trade = trade other_pair = open_trade.pair (dataframe, _) = self.dp.get_analyzed_dataframe(pair=other_pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1] rate_for_other_pair = last_candle['close'] if open_trade.is_short: # Short position profit ratio profit_ratio = (1 - (rate_for_other_pair / open_trade.open_rate)) else: # Long position profit ratio profit_ratio = ((rate_for_other_pair / open_trade.open_rate) - 1) profit_ratio *= open_trade.leverage profit_amount = open_trade.stake_amount * profit_ratio short_open_trade_profit += profit_amount short_open_stake_amount+= float(trade.stake_amount) close_short_trades = [trade for trade in all_close_trades if trade.trade_direction == 'short'] for trade in close_short_trades: short_close_trade_profit += float(trade.close_profit_abs) open_long_trades = [trade for trade in all_open_trades if trade.trade_direction == 'long'] for trade in open_long_trades: open_trade = trade profit_ratio = 0.0 other_pair = open_trade.pair (dataframe, _) = self.dp.get_analyzed_dataframe(pair=other_pair, timeframe=self.timeframe) last_candle = dataframe.iloc[-1] rate_for_other_pair = last_candle['close'] if open_trade.is_short: # Short position profit ratio profit_ratio = (1 - (rate_for_other_pair / open_trade.open_rate)) else: # Long position profit ratio profit_ratio = ((rate_for_other_pair / open_trade.open_rate) - 1) profit_ratio *= open_trade.leverage profit_amount = open_trade.stake_amount * profit_ratio long_open_trade_profit += profit_amount long_open_stake_amount += float(trade.stake_amount) total_short_usdt = 0.5 * initial_usdt + short_close_trade_profit + short_open_trade_profit total_long_usdt = 0.5 * initial_usdt + long_close_trade_profit + long_open_trade_profit short_profit_ratio = 100 * short_open_trade_profit / total_short_usdt short_profit_real_ratio = 100 * (short_close_trade_profit + short_open_trade_profit) / (0.5 * initial_usdt) long_profit_ratio = 100 * long_open_trade_profit / total_long_usdt long_profit_real_ratio = 100 * (long_close_trade_profit + long_open_trade_profit) / (0.5 * initial_usdt) logger.info(f"获取资产情况") logger.info(f"--short") logger.info(f"-----short开仓金额: {short_open_stake_amount}") logger.info(f"-----short持仓利润: {short_open_trade_profit}") logger.info(f"-----short关仓利润: {short_close_trade_profit}") logger.info(f"-----short账户实际余额: {total_short_usdt:.2f} ") logger.info(f"-----short账户利润百分比（持仓利润除以short实际余额）: {short_profit_ratio:.2f} %") logger.info(f"-----short账户真实盈利百分比（所有short订单利润除以short初始资金）: {short_profit_real_ratio:.2f} %") logger.info(f"--long") logger.info(f"-----long开仓金额: {long_open_stake_amount}") logger.info(f"-----long持仓利润: {long_open_trade_profit}") logger.info(f"-----long关仓利润: {long_close_trade_profit}") logger.info(f"-----long账户实际余额: {total_long_usdt:.2f} ") logger.info(f"-----long账户利润百分比（持仓利润除以long实际余额）: {long_profit_ratio:.2f} %") logger.info(f"-----long账户真实盈利百分比（所有long订单利润除以long初始资金）: {long_profit_real_ratio:.2f} %") logger.info(f"--总览") logger.info(f"-----当前账户实际余额(已包含持仓): {real_usdt:.2f} USDT") logger.info(f"-----当前持仓利润: {total_profit_amount:.2f} USDT") logger.info(f"-----当前账户真实盈利百分比（所有订单利润除以初始资金）: {total_profit_pct:.2f} %") logger.info(f"-----当前账户历史真实盈利最低百分比（所有订单利润除以初始资金）: {self.min_history_profit_pct:.2f} %") return total_short_usdt, short_profit_ratio, total_long_usdt, long_profit_ratio, real_usdt, total_profit_pct, self.min_history_profit_pct