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>
241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
"""
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ChanLun Wave Strategy for BTC Perpetual Futures
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基于缠论波浪策略 V8
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核心逻辑:
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- 使用Chan库计算KLC-based缠论分型
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- 只做空头（在下跌趋势中做空反弹）
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- 顶分型确认 + RSI > 55 + 趋势确认 → 做空
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- 空头出场：底分型 + RSI < 40
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策略设计:
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- 短周期(5m)为主，长周期(1h/1d)确认趋势
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- 使用Chan库KLC分型确认入场
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- RSI > 55 做空条件，RSI < 40 出场条件
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- 不做多头（下跌趋势中做多风险太大）
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作者: AI Assistant
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"""
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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 freqtrade.strategy import IStrategy
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from pandas import DataFrame
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import pandas as pd
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import numpy as np
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import talib.abstract as ta
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import logging
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from datetime import datetime
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from typing import Optional
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logger = logging.getLogger(__name__)
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class ElliottWaveBTCStrategy(IStrategy):
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INTERFACE_VERSION = 3
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can_short = True
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stoploss = -0.02
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minimal_roi = {
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"0": 0.06,
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"120": 0.03,
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"360": 0.01
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}
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trailing_stop = True
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trailing_stop_positive = 0.02
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trailing_stop_positive_offset = 0.08
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trailing_only_offset_is_reached = True
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startup_candle_count = 500
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position_adjustment_enable = False
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pair = 'BTC/USDT:USDT'
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timeframe = '5m'
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chan = ChanLun()
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def informative_pairs(self):
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return [
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(self.pair, '5m'),
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(self.pair, '1h'),
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(self.pair, '1d'),
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]
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def _add_indicators(self, df: DataFrame) -> DataFrame:
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df['ema20'] = ta.EMA(df, timeperiod=20)
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df['ema50'] = ta.EMA(df, timeperiod=50)
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df['ema200'] = ta.EMA(df, timeperiod=200)
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df['rsi'] = ta.RSI(df, timeperiod=14)
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df['atr'] = ta.ATR(df, timeperiod=14)
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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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# Chan库指标
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df['ema52'] = ta.EMA(df, timeperiod=52)
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df['ema104'] = ta.EMA(df, timeperiod=104)
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df['ema24'] = ta.EMA(df, timeperiod=24)
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df['ema26'] = ta.EMA(df, timeperiod=26)
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df['volume_sma'] = ta.SMA(df, timeperiod=20)
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df['volume_ratio'] = df['volume'] / df['volume_sma']
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bb = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
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df['bb2633upper'] = bb['upperband']
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df['bb2633lower'] = bb['lowerband']
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df['bb2633middle'] = bb['middleband']
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return df
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def _get_dataframe(self, timeframe: str) -> DataFrame:
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return self.dp.get_pair_dataframe(pair=self.pair, timeframe=timeframe)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = self._add_indicators(dataframe)
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df_1h = self._get_dataframe('1h')
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df_1d = self._get_dataframe('1d')
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if len(df_1h) > 0:
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df_1h = self._add_indicators(df_1h)
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df_1h['chan_state'] = self.chan.get_klu_state(df_1h)
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dataframe['1h_ema200'] = df_1h['ema200'].reindex(dataframe.index, method='ffill')
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dataframe['1h_trend_up'] = (df_1h['close'] > df_1h['ema200']).reindex(dataframe.index, method='ffill')
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dataframe['1h_trend_down'] = (df_1h['close'] < df_1h['ema200']).reindex(dataframe.index, method='ffill')
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dataframe['1h_chan_state'] = df_1h['chan_state'].reindex(dataframe.index, method='ffill')
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else:
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dataframe['1h_ema200'] = dataframe['ema200']
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dataframe['1h_trend_up'] = True
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dataframe['1h_trend_down'] = True
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dataframe['1h_chan_state'] = '00'
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if len(df_1d) > 0:
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df_1d = self._add_indicators(df_1d)
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df_1d['chan_state'] = self.chan.get_klu_state(df_1d)
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dataframe['1d_ema200'] = df_1d['ema200'].reindex(dataframe.index, method='ffill')
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dataframe['1d_trend_up'] = (df_1d['close'] > df_1d['ema200']).reindex(dataframe.index, method='ffill')
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dataframe['1d_trend_down'] = (df_1d['close'] < df_1d['ema200']).reindex(dataframe.index, method='ffill')
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dataframe['1d_rsi'] = df_1d['rsi'].reindex(dataframe.index, method='ffill')
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dataframe['1d_chan_state'] = df_1d['chan_state'].reindex(dataframe.index, method='ffill')
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else:
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dataframe['1d_ema200'] = dataframe['ema200']
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dataframe['1d_trend_up'] = True
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dataframe['1d_trend_down'] = True
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dataframe['1d_rsi'] = 50
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dataframe['1d_chan_state'] = '00'
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# 缠论分型（使用Chan库）
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dataframe['chan_state'] = self.chan.get_klu_state(dataframe)
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dataframe = self._generate_signals(dataframe)
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return dataframe
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def _generate_signals(self, df: DataFrame) -> DataFrame:
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"""缠论分型 + 趋势确认 - 做空为主"""
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n = len(df)
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if n < 10:
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return df
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# 延迟分型状态（避免未来数据）
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df['_fx'] = df['chan_state'].shift(1).fillna('00')
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# 1h趋势
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hourly_down = df['1h_trend_down'].fillna(False)
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hourly_up = df['1h_trend_up'].fillna(False)
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# MACD方向
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macd_cross_down = (df['macd'] < df['macdsignal']) & (df['macd'].shift(1) >= df['macdsignal'].shift(1))
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# === 空头信号（下跌趋势中做空）===
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# 条件1: 顶分型 + RSI > 55 + 1h下跌趋势
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short_cond1 = (
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(df['_fx'] == '10') &
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(df['rsi'] > 55) &
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hourly_down
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)
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# 条件2: 1h共振顶分型 + RSI > 55
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short_cond2 = (
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(df['_fx'] == '10') &
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(df['1h_chan_state'].fillna('00') == '10') &
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(df['rsi'] > 55)
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)
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# 条件3: 顶分型 + MACD死叉 + RSI > 60
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short_cond3 = (
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(df['_fx'] == '10') &
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macd_cross_down &
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(df['rsi'] > 60)
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)
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df['chan_short'] = (short_cond1 | short_cond2 | short_cond3).astype(bool)
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# === 多头信号（仅在1h上涨趋势中做多，且很少）===
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# 只在1d和1h同时上涨时才做多，且需要强确认
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daily_up = df['1d_trend_up'].fillna(False)
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long_cond = (
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(df['_fx'] == '-10') &
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(df['rsi'] < 30) & # 极低RSI才做多
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hourly_up &
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daily_up
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)
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# 1h和1d共振底分型
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long_cond2 = (
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(df['_fx'] == '-10') &
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(df['rsi'] < 30) &
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(df['1h_chan_state'].fillna('00') == '-10') &
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(df['1d_chan_state'].fillna('00') == '-10')
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)
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df['chan_long'] = (long_cond | long_cond2).astype(bool)
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return df
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['enter_long'] = 0
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dataframe['enter_short'] = 0
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dataframe['enter_tag'] = ''
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if 'chan_long' not in dataframe.columns:
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return dataframe
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dataframe.loc[dataframe['chan_long'], 'enter_long'] = 1
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dataframe.loc[dataframe['chan_long'], 'enter_tag'] = 'chan_long'
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dataframe.loc[dataframe['chan_short'], 'enter_short'] = 1
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dataframe.loc[dataframe['chan_short'], 'enter_tag'] = '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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dataframe['exit_long'] = 0
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dataframe['exit_short'] = 0
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if len(dataframe) < 2:
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return dataframe
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if 'chan_state' not in dataframe.columns:
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return dataframe
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df = dataframe.copy()
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df['_fx'] = df['chan_state'].shift(1).fillna('00')
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# 空头出场：底分型 + RSI < 40（仅在明显反弹时出场）
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dataframe['exit_short'] = ((df['_fx'] == '-10') & (df['rsi'] < 40)).astype(int)
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# 多头出场：顶分型 + RSI > 60
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dataframe['exit_long'] = ((df['_fx'] == '10') & (df['rsi'] > 60)).astype(int)
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return dataframe
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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 2.0
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