213 lines
9.8 KiB
Python
213 lines
9.8 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 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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### Now you can use logger.info('asfd') to log
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# freqtrade plot-dataframe --strategy ChanLun_BTC_1m --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
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# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20260501-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1m 1h 1d 1M --pairs BTC/USDT:USDT --timerange=20250405-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_1m.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20250721-
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# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
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# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies
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class ChanLun_BTC_1m_old(IStrategy):
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"""
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交易核心（缠论）：
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- 仅在缠论一/二/三类买卖点出现时交易。
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- 信号触发条件：前一笔被确认（bi.is_sure）时，该笔 end_klc 已被标记为 B1/B2/B3 或 S1/S2/S3。
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- 不使用未确认笔，不使用“状态猜测”列。
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"""
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INTERFACE_VERSION: int = 3
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timeframe = '1m'
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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minimal_roi = {
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"0": 100
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}
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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 # 兜底止损，实际由 custom_stoploss 基于中枢 zg/zd 控制
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use_custom_stoploss = True
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trailing_stop = False
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trailing_stop_positive = 0.03
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trailing_stop_positive_offset = 0.06
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trailing_only_offset_is_reached = False
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use_exit_signal = True
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position_adjustment_enable = True
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startup_candle_count = 500
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# 以 1m 为基础周期时，1h = 60 根K线（用于读取 resample_60_* 列并做确认延迟）
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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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# 多周期趋势共振：3个周期中至少2个同向（而非全部3个）
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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 get_trade_risk_ratio(self, pair: str, trade) -> float:
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risk_ratio = trade.get_custom_data('risk_ratio')
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if risk_ratio:
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return float(risk_ratio)
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risk_ratio = 0.001
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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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entry_candle = entry_rows.iloc[-1] if len(entry_rows) > 0 else dataframe.iloc[-1]
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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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risk_ratio = float(signal_candle['bsp_risk_ratio'])
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trade.set_custom_data('bsp_stop_price', float(signal_candle['bsp_stop_price']))
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trade.set_custom_data('bsp_zg', float(signal_candle['bsp_zg']))
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trade.set_custom_data('bsp_zd', float(signal_candle['bsp_zd']))
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else:
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risk_ratio = max(0.001, min(float(entry_candle['atr_ratio']), 0.005))
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except Exception:
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risk_ratio = 0.001
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trade.set_custom_data('risk_ratio', risk_ratio)
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return risk_ratio
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def adjust_trade_position(self, trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_stake: float | None, max_stake: float,
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current_entry_rate: float, current_exit_rate: float,
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current_entry_profit: float, current_exit_profit: float,
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**kwargs):
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risk_ratio = self.get_trade_risk_ratio(trade.pair, trade)
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if current_profit >= risk_ratio and trade.nr_of_successful_exits == 0:
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return -(trade.stake_amount / 2), 'take_half_1r'
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return None
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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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risk_ratio = self.get_trade_risk_ratio(pair, trade)
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if trade.nr_of_successful_exits > 0 and current_profit <= 0.001:
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return 'breakeven_after_1r'
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if current_profit >= risk_ratio * 2:
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return 'take_profit_2r'
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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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bsp_stop_price = trade.get_custom_data('bsp_stop_price')
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if bsp_stop_price:
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sl = stoploss_from_absolute(float(bsp_stop_price), 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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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]) |