PorterandCursor
2c1232555e
将核心结构、指标与分析拆到 chan/{core,indicators,analysis,pipeline}；
根目录保留兼容 shim；strategies 改为从 chan 包导入；买卖点经 bsp_macd 与 MACD 接合。
Co-authored-by: Cursor <cursoragent@cursor.com>
260 lines
9.1 KiB
Python
260 lines
9.1 KiB
Python
# --- Do not remove these libs ---
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from freqtrade.strategy import IStrategy, stoploss_from_absolute
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from chan.pipeline.ChanLun import ChanLun
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from chan.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR
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# --------------------------------
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from technical.util import resample_to_interval, resampled_merge
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import talib.abstract as ta
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from pandas import DataFrame
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade, Order
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from typing import Optional
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import logging
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logger = logging.getLogger(__name__)
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# freqtrade trade -c ./user_data/Chan/config/BTC_Perpetual_Futures.json --strategy BTC_Perpetual_Futures --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/BTC_Perpetual_Futures.json --strategy BTC_Perpetual_Futures --strategy-path ./user_data/Chan/strategies --timerange=20260101-
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# freqtrade download-data -c ./user_data/Chan/config/BTC_Perpetual_Futures.json -t 1m --pairs BTC/USDT:USDT --timerange=20260101-
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class BTC_Perpetual_Futures(IStrategy):
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"""
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BTC永续合约交易策略 - 优化版
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- 基于缠论(ChanLun)技术分析 + RSI/MACD/布林带/ATR 多指标共振
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- 支持做多和做空
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- 基于ATR的动态止损
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- 成交量确认过滤
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"""
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INTERFACE_VERSION: int = 3
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# ROI配��� - 分阶段止盈
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minimal_roi = {
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"0": 0.08, # 立即: 8%止盈
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"60": 0.05, # 1小时后: 5%止盈
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"180": 0.02, # 3小时后: 2%止盈
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"360": 0 # 6小时后: 保本出场
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}
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can_short = True
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lev = 1.0 # 杠杆倍数，建议新手用1-3倍
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stoploss = -0.04 # 默认4%止损（custom_stoploss会覆盖）
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use_custom_stoploss = True
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trailing_stop = False
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position_adjustment_enable = False
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startup_candle_count = 1440 # 需要1440根1分钟K线预热
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# 时间框架常量
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time5 = 5
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time15 = 15
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time30 = 30
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time60 = 60
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chan = ChanLun()
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""添加多时间框架技术指标"""
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# 重采样到5分钟和30分钟
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dataframe_5m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time5)
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dataframe_30m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time30)
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# 添加技术指标
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dataframe = self._add_indicators(dataframe)
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dataframe_5m = self._add_indicators(dataframe_5m)
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dataframe_30m = self._add_indicators(dataframe_30m)
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# 缠论状态分析
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dataframe_5m['state'] = self.chan.get_klu_state(dataframe_5m)
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dataframe_30m['state'] = self.chan.get_klu_state(dataframe_30m)
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# 合并多时间框架数据
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dataframe = resampled_merge(dataframe, dataframe_5m)
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dataframe = resampled_merge(dataframe, dataframe_30m)
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return dataframe
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def _add_indicators(self, df: DataFrame) -> DataFrame:
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"""添加技术指标"""
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# MACD
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macd = ta.MACD(df, fastperiod=12, slowperiod=26, signalperiod=9)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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df['macdhist'] = macd['macdhist']
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# 布林带 (20周期, 2倍标准差)
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bb = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
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df['bb_upper'] = bb['upperband']
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df['bb_middle'] = bb['middleband']
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df['bb_lower'] = bb['lowerband']
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# ATR - 用于动态止损
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df['atr'] = ta.ATR(df, timeperiod=14)
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# EMA均线系统
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df['ema5'] = ta.EMA(df, timeperiod=5)
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df['ema10'] = ta.EMA(df, timeperiod=10)
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df['ema26'] = ta.EMA(df, timeperiod=26)
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df['ema52'] = ta.EMA(df, timeperiod=52)
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# RSI
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df['rsi'] = ta.RSI(df, timeperiod=14)
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# 成交量比（当前成交量 / 10周期均量）
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avg_vol = df['volume'].rolling(window=10).mean()
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df['volume_ratio'] = (df['volume'] / avg_vol).fillna(1.0)
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return df
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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进场信号定义 - 优化版
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- 做多: 缠论底部信号(-10/-20) + RSI<65 + 放量 + EMA确认
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- 做空: 缠论顶部信号(10/20) + RSI>35 + 放量 + EMA确认
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"""
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state_30m = 'resample_{}_state'.format(self.get_ticker_indicator() * self.time30)
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ema52_30m = 'resample_{}_ema52'.format(self.get_ticker_indicator() * self.time30)
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close_30m = 'resample_{}_close'.format(self.get_ticker_indicator() * self.time30)
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shift = self.time30
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# 做多信号：只在缠论-10信号 + 强势过滤
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dataframe.loc[
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(dataframe[state_30m].shift(shift) == "-10") &
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(dataframe['rsi'] < 65) &
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(dataframe['rsi'] > 20) & # RSI不过冷
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(dataframe['volume_ratio'] > 1.2) & # 放量确认
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(dataframe['close'] > dataframe['ema52']) & # 价格在EMA52上方
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(dataframe['macd'] > dataframe['macdsignal']), # MACD金叉
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['enter_long', 'enter_tag']] = (1, 'chan_long')
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# 做空信号：只在缠论10信号 + 强势过滤
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dataframe.loc[
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(dataframe[state_30m].shift(shift) == "10") &
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(dataframe['rsi'] > 35) &
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(dataframe['rsi'] < 80) & # RSI不过热
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(dataframe['volume_ratio'] > 1.2) & # 放量确认
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(dataframe['close'] < dataframe['ema52']) & # 价格在EMA52下方
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(dataframe['macd'] < dataframe['macdsignal']), # MACD死叉
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['enter_short', 'enter_tag']] = (1, 'chan_short')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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出场信号定义
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- 做多出场: 缠论顶部反转信号
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- 做空出场: 缠论底部反转信号
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"""
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state_30m = 'resample_{}_state'.format(self.get_ticker_indicator() * self.time30)
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shift = self.time30
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dataframe.loc[
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(dataframe[state_30m].shift(shift) == "10"),
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['exit_long', 'exit_tag']] = (1, 'chan_exit_long')
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dataframe.loc[
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(dataframe[state_30m].shift(shift) == "-10"),
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['exit_short', 'exit_tag']] = (1, 'chan_exit_short')
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return dataframe
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float, after_fill: bool,
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**kwargs) -> float | None:
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"""
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基于ATR的动态止损
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止损距离 = 开仓价 ± 1.5*ATR
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"""
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try:
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entry_atr = trade.get_custom_data(key="entry_atr")
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if entry_atr is None:
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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if dataframe is not None and len(dataframe) > 0 and 'atr' in dataframe.columns:
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entry_atr = float(dataframe.iloc[-1]['atr'])
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else:
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return -0.04
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if trade.is_short:
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stop_price = trade.open_rate + (float(entry_atr) * 1.5)
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else:
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stop_price = trade.open_rate - (float(entry_atr) * 1.5)
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return stoploss_from_absolute(stop_price, current_rate, is_short=trade.is_short)
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except Exception as e:
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logger.warning(f"custom_stoploss error: {e}")
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return None
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs):
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"""快速止盈：浮盈超过0.5%直接出场"""
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if current_profit > 0.005:
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return "quick_profit"
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return None
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: str | None,
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side: str, **kwargs) -> bool:
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"""进场前最终过滤：ATR太小或RSI极端时拒绝"""
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try:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if dataframe is None or len(dataframe) == 0:
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return False
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last = dataframe.iloc[-1]
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atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator() * self.time30)
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atr_val = float(last.get(atr_str, 0) or 0)
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if atr_val < 0.001:
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return False
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return True
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except Exception as e:
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logger.warning(f"confirm_trade_entry error: {e}")
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return True
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def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
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"""订单成交时保存ATR用于止损计算"""
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try:
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if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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if dataframe is not None and len(dataframe) > 0:
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atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator() * self.time30)
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last = dataframe.iloc[-1]
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entry_atr = float(last.get(atr_str, 0) or 0) * 3
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trade.set_custom_data(key="entry_atr", value=entry_atr)
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except Exception as e:
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logger.warning(f"order_filled error: {e}")
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def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
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entry_tag: str | None, side: str, **kwargs) -> float:
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"""入场价微调，减少滑点"""
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if trade:
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if trade.is_short:
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return proposed_rate - 50
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else:
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return proposed_rate + 50
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return proposed_rate
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def custom_exit_price(self, pair: str, trade: Trade,
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current_time: datetime, proposed_rate: float,
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current_profit: float, exit_tag: str | None, **kwargs) -> float:
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"""出场价微调，减少滑点"""
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if trade:
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if trade.is_short:
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return proposed_rate + 50
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else:
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return proposed_rate - 50
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return proposed_rate
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def leverage(self, pair: str, current_time: datetime, current_rate: float,
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proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
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**kwargs) -> float:
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return self.lev
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def get_ticker_indicator(self) -> int:
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return int(self.timeframe[:-1])
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