jackyu66gitandCursor
74dec4e50b
将根目录引擎迁入 chanlun/ 并保留兼容 shim；拆分 TF_DF 与 web 服务； 前端模块化；strategies 改用 chanlun 导入；补充 ESS 文档与 golden 回归。 Co-authored-by: Cursor <cursoragent@cursor.com>
202 lines
7.5 KiB
Python
202 lines
7.5 KiB
Python
# --- Do not remove these libs ---
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from statistics import median
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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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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from chanlun import ChanLun
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from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX, Chan_BSP_TYPE
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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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import pandas as pd
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from datetime import datetime, timedelta
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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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class ChanLun_BTC_5m(IStrategy):
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"""ChanLun_BTC_5m: 5m B3 signals with trailing stop exit."""
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INTERFACE_VERSION: int = 3
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timeframe = '5m'
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minimal_roi = {"0": 100}
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can_short = True
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enable_long = True
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enable_short = False
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lev = 1.0
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stoploss = -0.3
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use_custom_stoploss = True
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trailing_stop = False
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use_exit_signal = True
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position_adjustment_enable = False
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startup_candle_count = 500
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chan = ChanLun()
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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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bsp_signal_data = self.chan.get_bsp_signal_data(dataframe)
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for column, values in bsp_signal_data.items():
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dataframe[column] = values
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return dataframe
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def add_indicators(self, df):
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df = self.add_base_indicators(df)
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base_interval = self.get_ticker_indicator()
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for interval in (5, 15, 60):
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if interval <= base_interval:
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df = self.copy_base_indicators_to_resample(df, interval)
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continue
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resampled = resample_to_interval(df, interval)
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resampled = self.add_base_indicators(resampled)
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df = resampled_merge(df, resampled)
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return df
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def copy_base_indicators_to_resample(self, df, interval):
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prefix = f'resample_{interval}_'
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for column in (
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'date', 'open', 'high', 'low', 'close', 'volume',
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'atr', 'macd', 'macdsignal', 'macdhist', 'ema24', 'ema52',
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'atr_ratio', 'resistance_240', 'support_240', 'trend'
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):
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if column in df.columns:
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df[f'{prefix}{column}'] = df[column]
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return df
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def add_base_indicators(self, df):
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fast = 12
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slow = 26
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period = 9
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macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
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df['atr'] = ta.ATR(df, timeperiod=14)
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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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df['ema24'] = ta.EMA(df, timeperiod=24)
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df['ema52'] = ta.EMA(df, timeperiod=52)
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df['atr_ratio'] = df['atr'] / df['close']
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df['resistance_240'] = df['high'].rolling(240).max().shift(1)
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df['support_240'] = df['low'].rolling(240).min().shift(1)
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df['trend'] = 0
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df.loc[(df['close'] > df['ema52']) & (df['ema24'] >= df['ema52']), 'trend'] = 1
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df.loc[(df['close'] < df['ema52']) & (df['ema24'] <= df['ema52']), 'trend'] = -1
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return df
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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min_atr_ratio = 0.0005
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long_min_sr_distance_r = 1.0
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short_min_sr_distance_r = 0.8
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long_space_ratio = (dataframe['resistance_240'].shift(1) - dataframe['close'].shift(1)) / dataframe['close'].shift(1)
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short_space_ratio = (dataframe['close'].shift(1) - dataframe['support_240'].shift(1)) / dataframe['close'].shift(1)
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long_tf_aligned = (
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(dataframe['resample_5_trend'].shift(1) == 1).astype(int) +
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(dataframe['resample_15_trend'].shift(1) == 1).astype(int) +
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(dataframe['resample_60_trend'].shift(1) == 1).astype(int)
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) >= 2
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short_tf_aligned = (
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(dataframe['resample_5_trend'].shift(1) == -1).astype(int) +
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(dataframe['resample_15_trend'].shift(1) == -1).astype(int) +
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(dataframe['resample_60_trend'].shift(1) == -1).astype(int)
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) >= 2
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dataframe.loc[
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(
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self.enable_long &
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(dataframe['bsp_state'].shift(1) == -1) &
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(dataframe['bsp_risk_ratio'].shift(1) > 0) &
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(long_space_ratio >= dataframe['bsp_risk_ratio'].shift(1) * long_min_sr_distance_r) &
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(dataframe['macdhist'].shift(1) > 0) &
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(dataframe['atr_ratio'].shift(1) >= min_atr_ratio) &
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(dataframe['trend'].shift(1) == 1) &
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long_tf_aligned
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),
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['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
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dataframe.loc[
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(
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self.enable_short &
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(dataframe['bsp_state'].shift(1) == 1) &
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(dataframe['bsp_risk_ratio'].shift(1) > 0) &
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(short_space_ratio >= dataframe['bsp_risk_ratio'].shift(1) * short_min_sr_distance_r) &
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(dataframe['macdhist'].shift(1) < 0) &
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(dataframe['atr_ratio'].shift(1) >= min_atr_ratio) &
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(dataframe['trend'].shift(1) == -1) &
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short_tf_aligned
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),
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['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
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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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return dataframe
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def custom_exit(self, pair: str, trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs):
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# Time-based exit only - trailing stop handles profit taking
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elapsed = current_time - trade.open_date_utc
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if elapsed >= timedelta(hours=72) and current_profit < 0.005:
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return 'time_stop_72h'
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return None
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def custom_stoploss(self, pair: str, trade, current_time: datetime, current_rate: float,
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current_profit: float, after_fill: bool, **kwargs) -> float | None:
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# Initialize stored state
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if not trade.get_custom_data('trail_activated'):
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trade.set_custom_data('trail_activated', False)
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trade.set_custom_data('max_profit', 0.0)
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# Read bsp_stop_price from signal
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try:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if len(dataframe) > 0:
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entry_rows = dataframe[dataframe['date'] <= trade.open_date_utc]
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signal_rows = entry_rows.tail(3)
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signal_rows = signal_rows[signal_rows['bsp_risk_ratio'] > 0]
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if len(signal_rows) > 0:
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signal_candle = signal_rows.iloc[-1]
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trade.set_custom_data('bsp_stop_price', float(signal_candle['bsp_stop_price']))
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except Exception:
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pass
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max_profit = max(float(trade.get_custom_data('max_profit')), current_profit)
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trade.set_custom_data('max_profit', max_profit)
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trail_activated = trade.get_custom_data('trail_activated')
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# Stage 1: Initial stop at bsp_stop with -5% floor
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if not trail_activated:
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if max_profit >= 0.02:
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# Activate trail: move stop to breakeven
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trade.set_custom_data('trail_activated', True)
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sl = stoploss_from_absolute(trade.open_rate, current_rate, is_short=trade.is_short)
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return max(sl, -0.005)
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else:
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bsp_stop = trade.get_custom_data('bsp_stop_price')
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if bsp_stop:
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sl = stoploss_from_absolute(float(bsp_stop), current_rate, is_short=trade.is_short)
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return min(sl, -0.05)
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return -0.05
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else:
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# Stage 2: Trail from max profit
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if max_profit >= 0.04:
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trail_offset = 0.02 # Trail 2% behind max
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trail_price = trade.open_rate * (1 + max_profit - trail_offset)
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sl = stoploss_from_absolute(trail_price, current_rate, is_short=trade.is_short)
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return max(sl, -0.02)
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elif max_profit >= 0.02:
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# Breakeven to 1% trail
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sl = stoploss_from_absolute(trade.open_rate * 1.005, current_rate, is_short=trade.is_short)
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return max(sl, -0.005)
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else:
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sl = stoploss_from_absolute(trade.open_rate * 0.998, current_rate, is_short=trade.is_short)
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return max(sl, -0.02)
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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):
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return int(self.timeframe[:-1])
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