# source: https://raw.githubusercontent.com/NTZ12345678/freqtrade_chinese_discord/b3a26ed121ec16f7ec3aa362a4cf4ca744cab55b/策略相关/马丁/SL.py 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 import pandas as pd import warnings from freqtrade.exchange import date_minus_candles import logging logger = logging.getLogger(__name__) # 纯山寨币做空策略 策略风险较大，适合大牛市开始回撤时使用 讨论群 https://discord.gg/hfxRh74mK7 ''' dryrun ROI: Closed trades ∙ 520.179 USDT (67.99%) (26.27 Σ%) ∙ 520.179 USD ROI: All trades ∙ 454.301 USDT (57.85%) (22.94 Σ%) ∙ 454.301 USD Total Trade Count: 1129 Bot started: 2025-08-01 09:58:11 First Trade opened: 3 months ago (2025-08-01 10:12:56) Latest Trade opened: 34 minutes ago (2025-11-03 01:06:45) Win / Loss: 1060 / 0 Winrate: 100.00% Expectancy (Ratio): 0.49 (100.00) Avg. Duration: 3 days, 23:14:35 Best Performing: ATA/USDT:USDT: 7.513 USDT (69.14%) Trading volume: 31252.268 USDT Profit factor: inf Max Drawdown: 0.00% (0 USDT) from 2025-08-02 10:13:49 (0.554 USDT) to 2025-08-02 10:13:49 (0.554 USDT) Monday (count) USDT USD Profit % ---------------- ----------- --------- ---------- 2025-11-03 (16) 8.578 USDT 8.58 USD 0.34% 2025-10-27 (129) 73.535 USDT 73.54 USD 3.05% 2025-10-20 (70) 35.088 USDT 35.09 USD 1.48% 2025-10-13 (228) 97.9 USDT 97.90 USD 4.29% 2025-10-06 (119) 75.842 USDT 75.84 USD 3.44% 2025-09-29 (71) 30.198 USDT 30.20 USD 1.39% 2025-09-22 (117) 64.642 USDT 64.64 USD 3.06% 2025-09-15 (41) 16.154 USDT 16.15 USD 0.77% 2025-09-08 (17) 11.559 USDT 11.56 USD 0.56% 2025-09-01 (62) 23.449 USDT 23.45 USD 1.14% 2025-08-25 (48) 19.565 USDT 19.57 USD 0.96% 2025-08-18 (67) 25.629 USDT 25.63 USD 1.27% 2025-08-11 (33) 15.018 USDT 15.02 USD 0.75% 2025-08-04 (33) 15.559 USDT 15.56 USD 0.78% 2025-07-28 (9) 7.466 USDT 7.47 USD 0.38% 2025-07-21 (0) 0 USDT 0.00 USD 0.00% 2025-07-14 (0) 0 USDT 0.00 USD 0.00% 2025-07-07 (0) 0 USDT 0.00 USD 0.00% 2025-06-30 (0) 0 USDT 0.00 USD 0.00% 2025-06-23 (0) 0 USDT 0.00 USD 0.00% ''' class Github_NTZ12345678_freqtrade_chinese_discord__SL__20251103_015658(IStrategy): can_short = True timeframe = '1d' # startup_candle_count = 30 # 需要至少30根K线 process_only_new_candles = True # 只处理新K线 entry_step_pct = -5 entry_stake_amount = 6 num_entry = 300 # 交易参数 stoploss = -10000 use_custom_stoploss = False # 启用动态止损 REAL_USE_MUL = 10 liqutation_ratio = 0.0 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['ma60'] = ta.SMA(dataframe, timeperiod=60) # 计算60日均线 return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ 生成买入信号： - 做多排名前 2 的币种 - 做空排名后 2 的币种 """ #dataframe.loc[dataframe['momentum_rank'] <= 2, 'enter_long'] = 1 # 排名前 2 的做多 # 空头条件 enter_short_conditions = [ dataframe['momentum_rank'] <= 10, dataframe['close'] < dataframe['ma60'], ] if enter_short_conditions: dataframe.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") #return dataframe #dataframe.loc[dataframe['momentum_rank'] >= dataframe['momentum_rank'].max() - 1, 'enter_short'] = 1 # 排名后 2 的做空 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_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): exit_time = trade.open_date_utc + timedelta(days=1) exit_time1 = trade.open_date_utc + timedelta(days=100) exit_time2 = trade.open_date_utc + timedelta(days=200) if current_time >= exit_time and current_profit > 0.5: return "profit_50%_time_exit" if current_time >= exit_time1 and current_profit > 0: return "time_exit_100day" #if current_time >= exit_time2 : # return "time_exit_200day" open_trades = Trade.get_trades_proxy(is_open=True) all_profits = {} total_profit_amount = 0.0 for open_trade in open_trades: profit_ratio = 0.0 if open_trade.pair == pair: profit_ratio = current_profit else: other_pair = open_trade.pair (dataframe, _) = self.dp.get_analyzed_dataframe(pair=other_pair, timeframe=self.timeframe) if not dataframe.empty: 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_pct = profit_ratio * 100 profit_amount = open_trade.stake_amount * profit_ratio total_profit_amount += profit_amount all_profits[open_trade.pair] = f"{profit_pct:.2f}% ({profit_amount:.2f} {open_trade.stake_currency})" ratio = total_profit_amount / self.wallets.get_total('USDT') if ratio < -1: logger.warning(f"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.") logger.warning(f"Total profit ratio is too low!!!!!!liqutation_ratio!!!!!!!!: {ratio:.2%}.") logger.warning(f"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.") return None self.liqutation_ratio = ratio if pair == open_trades[0].pair: #logger.info(f"--- Backtest time: {current_time} ---") #logger.info(f"Current profit for all open trades: {all_profits}") #logger.info(f"Total profit amount for all open trades: {total_profit_amount:.2f} USDT") logger.info(f"Total profit ratio: {ratio:.2%}") #logger.info(f"trade number:{len(all_profits)}") #logger.info("---------------------------------") return None 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: #logger.info(f"Calculating custom stake amount for {pair} at {current_time}: self.entry_stake_amount={self.entry_stake_amount}") return self.entry_stake_amount / self.REAL_USE_MUL def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, side: str, **kwargs) -> bool: if self.liqutation_ratio < -0.5: logger.warning(f"Liquidity ratio is too low ({self.liqutation_ratio}), skipping trade entry for {pair}.") return False logger.info(f"Confirming trade entry for {pair}: order_type={order_type}, amount={amount}, rate={rate}, side={side}") return True position_adjustment_enable = 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]: current_profit_stake = current_profit * trade.stake_amount current_profit_stake_ratio = current_profit_stake / self.wallets.get_total('USDT') #logger.info(f"Adjusting position for {trade.pair} at {current_time}: current_profit={current_profit},trade.stake_amount={trade.stake_amount} current_profit_stake={current_profit_stake} current_profit_stake_ratio = {current_profit_stake_ratio}") last_time = trade.date_last_filled_utc + timedelta(days=7) if self.wallets: free_eth = 0.95*self.wallets.get_free('USDT') used_eth = self.wallets.get_used('USDT') total_eth = self.wallets.get_total('USDT') ratio = self.wallets.get_free('USDT') / self.wallets.get_total('USDT') #logger.info(f"Free USDT: {free_eth}, Used USDT: {used_eth}, Total USDT: {total_eth}") if trade.nr_of_successful_entries == 1: self.entry_stake_amount = max(6, free_eth / self.num_entry) #self.entry_stake_amount = 6 #logger.info(f"Adjusting entry stake amount to {self.entry_stake_amount} USDT based on available balance.,Free USDT: {free_eth}") if current_profit < self.entry_step_pct and trade.nr_of_successful_entries != self.num_entry and current_time > last_time and current_profit_stake_ratio > -0.05 : #logger.info(f"Adjusting position for {trade.pair} at {current_time}: current_profit={current_profit}, nr_of_successful_entries={trade.nr_of_successful_entries}") if trade.nr_of_successful_entries < self.num_entry: logger.info(f"Adjusting position for {trade.pair} at {current_time}: current_profit={current_profit}, nr_of_successful_entries={trade.nr_of_successful_entries}, entry_stake_amount={self.entry_stake_amount}") return self.entry_stake_amount / self.REAL_USE_MUL 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: #logger.info(f"exit-----------------------------------------------------------------------------exit") if self.wallets: free_usdt = 0.95*self.wallets.get_free('USDT') used_usdt = self.wallets.get_used('USDT') total_usdt = self.wallets.get_total('USDT') logger.info(f"Confirming trade exit for {pair}: order_type={order_type}, amount={amount}, rate={rate}, exit_reason={exit_reason}, free_usdt: {free_usdt}, used_usdt: {used_usdt}, total_usdt: {total_usdt}") 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: return 20