328 lines
14 KiB
Python
328 lines
14 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
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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 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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### Now you can use logger.info('asfd') to log
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# freqtrade plot-dataframe --strategy ChanLun_BTC --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_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange=20250901-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --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_30.json --strategy ChanLun_BTC --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_30.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_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies
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class ChanLun_BTC(IStrategy):
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INTERFACE_VERSION: int = 3
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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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# 30m and 1h
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minimal_roi = {
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"0": 0.15,
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"360": 0.2,
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"640": 0.1,
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"1200": 0
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}
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# 5m and 15m
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minimal_roi_1 = {
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"0": 0.1,
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"60": 0.05,
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"120": 0.02,
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"240": 0
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}
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# 15m and 30m
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minimal_roi_1 = {
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"0": 0.1,
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"240": 0.05,
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"480": 0.03,
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"600": 0
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}
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minimal_roi_1 = {
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"0": 1.50,
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"120": 0.05,
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"240": 0.025,
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"360": 0
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}
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can_short = True
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lev = 1.0
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stoploss = -0.3 # 设置为很大的负值，让custom_stoploss来控制
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use_custom_stoploss = True # 启用自定义止损
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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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# 关闭分批止盈/仓位调整
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position_adjustment_enable = False
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startup_candle_count = 2880
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time3 = 3
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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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time2h = 120
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time4h = 240
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time1d = 1440
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last_time = datetime.now()
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chan = ChanLun()
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last_order = None
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last_trade = None
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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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# 仅保留15m(用于BSP)与60m(用于ATR过滤/止损)两个重采样
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dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
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dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
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# 计算多周期BSP（以15m为基准），并合并到15m数据上
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# 先给重采样帧补指标
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dataframe_15 = self.add_indicators(dataframe_15)
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dataframe_60 = self.add_indicators(dataframe_60)
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# 计算15m BSP
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bsp_15 = self.chan.cal_bsp(dataframe, self.get_ticker_indicator())
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# 合并15m与60m到主DF，生成 resample_*_* 列
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dataframe = resampled_merge(dataframe, dataframe_15)
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dataframe = resampled_merge(dataframe, dataframe_60)
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return dataframe
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def add_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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bb365 = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
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bb120 = ta.BBANDS(df, timeperiod=120, nbdevup=3.0, nbdevdn=3.0, matype=0)
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bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
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bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
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bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
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bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
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# 计算布林带中轨（移动平均线）
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bb30_middle = ta.SMA(df, timeperiod=90)
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# 手动计算布林带 %B 指标 (BBP)
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# %B = (Price - Lower Band) / (Upper Band - Lower Band)
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bbp365 = (df['close'] - bb365['lowerband']) / (bb365['upperband'] - bb365['lowerband'])
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bbp120 = (df['close'] - bb120['lowerband']) / (bb120['upperband'] - bb120['lowerband'])
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bbp30 = (df['close'] - bb30['lowerband']) / (bb30['upperband'] - bb30['lowerband'])
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bbp302 = (df['close'] - bb302['lowerband']) / (bb302['upperband'] - bb302['lowerband'])
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df['atr'] = ta.ATR(df, timeperiod=14)
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df['bbup365'] = bb365['upperband']
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df['bblow365'] = bb365['lowerband']
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df['bbp365'] = bbp365
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df['bbup120'] = bb120['upperband']
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df['bblow120'] = bb120['lowerband']
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df['bbp120'] = bbp120
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df['bbup30'] = bb30['upperband']
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df['bblow30'] = bb30['lowerband']
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df['bbmiddle30'] = bb30_middle # 添加bb30中轨
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df['bbp30'] = bbp30
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df['bbup302'] = bb302['upperband']
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df['bblow302'] = bb302['lowerband']
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df['bbp302'] = bbp302
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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['ema5'] = ta.EMA(df, timeperiod=5)
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df['ema10'] = ta.EMA(df, timeperiod=10)
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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['ema52'] = ta.EMA(df, timeperiod=52)
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df['rsi'] = ta.RSI(df, timeperiod=14)
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df['volume_ratio'] = self.cal_volume_ratio(df)
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return df
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def cal_volume_ratio(self, dataframe, window=10):
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df = dataframe.copy()
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# 计算过去N根K线的平均成交量
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df['avg_volume'] = df['volume'].rolling(window=window).mean()
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# 计算量比
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df['volume_ratio'] = df['volume'] / df['avg_volume']
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# 填充缺失值（前N根K线）
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df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
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return df['volume_ratio']
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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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new_entryprice = proposed_rate
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if trade:
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if trade.is_short:
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new_entryprice = proposed_rate - 50
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else:
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new_entryprice = proposed_rate + 50
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return new_entryprice
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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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new_exitprice = proposed_rate
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if trade:
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if trade.is_short:
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new_exitprice = proposed_rate + 50
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else:
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new_exitprice = proposed_rate - 50
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return new_exitprice
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def adjust_trade_position(self, trade: Trade, current_time: datetime,
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current_rate: float, current_profit: float,
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min_stake: Optional[float], 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) -> Optional[float]:
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# 关闭分批止盈，始终不调整仓位
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return None
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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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止损 = 开仓价 ± 1 * ATR（开仓时的ATR）。
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多单: 开仓价 - ATR；空单: 开仓价 + ATR。
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"""
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# 保本止损：当浮盈达到或超过 1% 时，将止损提至开仓价
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#if current_profit is not None and current_profit >= 0.14:
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#return stoploss_from_absolute(trade.open_rate, current_rate, is_short=trade.is_short)
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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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# 回退：取当前数据的 ATR 估算
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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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# 最保守的回退：5%
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return -0.05
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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ema52_str = 'resample_{}_ema52'.format(self.get_ticker_indicator()*self.time15)
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ema52_val = float(last_candle.get(ema52_str, 0) or 0)
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close_str = 'resample_{}_close'.format(self.get_ticker_indicator()*self.time15)
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close_val = float(last_candle.get(close_str, 0) or 0)
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if close_val < ema52_val:
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return -0.01
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if trade.is_short:
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stop_price = trade.open_rate + float(entry_atr)
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else:
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stop_price = trade.open_rate - float(entry_atr)
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return stoploss_from_absolute(stop_price, current_rate, is_short=trade.is_short)
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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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# 不做分批止盈/最终止盈处理，退出由策略信号/ROI/止损决定
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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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"""
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ATR 过滤：atr < 100 不开单。
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"""
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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.time60)
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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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#logger.info(f"ATR过滤：atr={atr_val:.2f} < 100, 拒绝进场 {pair}")
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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 异常: {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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"""
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Called right after an order fills.
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Will be called for all order types (entry, exit, stoploss, position adjustment).
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:param pair: Pair for trade
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:param trade: trade object.
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:param order: Order object.
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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"""
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# Obtain pair dataframe (just to show how to access it)
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dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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atr_str = 'resample_{}_atr'.format(self.get_ticker_indicator()*self.time15)
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# 保存开仓时的ATR值用于止损计算
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if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
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entry_atr = last_candle[atr_str] * 4
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trade.set_custom_data(key="entry_atr", value=entry_atr)
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#logger.info(f"保存开仓时ATR值: {entry_atr}")
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return None
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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shift15 = self.time15
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shift60 = self.time60
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bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
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score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
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macdh_col = 'resample_{}_macdhist'.format(self.get_ticker_indicator()*shift15)
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c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
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e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
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# 强化过滤：15m BSP + 分数阈值 + 60m 趋势同向 + 15m MACD柱同向
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if all(col in dataframe.columns for col in [bsp_col, score_col, macdh_col, c60_col, e60_col]):
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dataframe.loc[
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(
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(dataframe[bsp_col].shift(shift15) == 1) &
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(dataframe[score_col].shift(shift15) >= 1.2) &
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(dataframe[c60_col].shift(shift60) >= dataframe[e60_col].shift(shift60)) &
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(dataframe[macdh_col].shift(shift15) > 0)
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),
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['enter_long', 'enter_tag']] = (1, 'long_bsp15_v2')
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dataframe.loc[
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(
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(dataframe[bsp_col].shift(shift15) == -1) &
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(dataframe[score_col].shift(shift15) <= -1.2) &
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(dataframe[c60_col].shift(shift60) <= dataframe[e60_col].shift(shift60)) &
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(dataframe[macdh_col].shift(shift15) < 0)
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),
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['enter_short', 'enter_tag']] = (1, 'short_bsp15_v2')
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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shift15 = self.time15
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shift60 = self.time60
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bsp_col = 'resample_{}_bsp_mtf'.format(self.get_ticker_indicator()*shift15)
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score_col = 'resample_{}_mtf_score'.format(self.get_ticker_indicator()*shift15)
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c60_col = 'resample_{}_close'.format(self.get_ticker_indicator()*shift60)
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e60_col = 'resample_{}_ema52'.format(self.get_ticker_indicator()*shift60)
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# 反向强信号或60m趋势反向时平仓
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if all(col in dataframe.columns for col in [bsp_col, score_col, c60_col, e60_col]):
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dataframe.loc[
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(
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((dataframe[bsp_col].shift(shift15) == -1) & (dataframe[score_col].shift(shift15) <= -0.8)) |
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(dataframe[c60_col].shift(shift60) < dataframe[e60_col].shift(shift60))
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),
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['exit_long', 'exit_tag']] = (1, 'long_close_bsp15')
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dataframe.loc[
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(
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((dataframe[bsp_col].shift(shift15) == 1) & (dataframe[score_col].shift(shift15) >= 0.8)) |
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(dataframe[c60_col].shift(shift60) > dataframe[e60_col].shift(shift60))
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),
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['exit_short', 'exit_tag']] = (1, 'short_close_bsp15')
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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 self.lev
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def get_ticker_indicator(self):
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return int(self.timeframe[:-1]) |