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>
276 lines
12 KiB
Python
276 lines
12 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 chan.pipeline.ChanLun import ChanLun
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from chan.core.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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"""
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大周期：1h
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小周期：15m，30m
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大周期EMA156以下找做空机会
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找到最近的中枢，中枢下跌以后穿过EMA156，EMA52均线，形成死叉，macd黄白线穿越0轴
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EMA24，EMA52，EMA104，EMA156成下跌趋势依次排列（EMA156 > EMA104 > EMA52 > EMA24）
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做空
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1. 做空开始点位条件：
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确定下跌周期，价格在大于大周期的时间周期找到MACD归零轴+EMA52阻力线，按照K线动能理论，小周期确认是否背驰，背驰则开仓并且MACD穿零轴
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止损放到最近的顶分型高点或者价格突破EMA156
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2. 开始点位止盈策略
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计算盈亏比方式：至少1:2，到达1:2后平仓一半，移动止损到开仓价，1:3再平仓剩下的一半仓位，依次类推
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如果大周期遇到底背离可以平完所有仓位
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3. 加仓点位
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小周期顶分型+价格接近或突破大周期EMA24但是不突破EMA52后下跌可以加仓到最大仓位+大周期黄白线归零轴/小周期顶分型+小周期EMA52归零轴
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大周期顶分型+大周期macd归零轴可以加仓到最大仓位
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大周期顶分型或顶分型后，macd穿零轴后价格和macd红绿柱背驰可以加仓到最大仓位
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小周期顶分型+大周期macd归零轴
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"""
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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_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies
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# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange=20260101-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA52.json -t 1m 1m 1h 1d 1w 1M --pairs BTC/USDT:USDT --timerange=20240101-
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# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA52.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_EMA52.json -e 200 --timerange=20250201-20250901
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# freqtrade edge -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
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# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --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_EMA52.json --strategy ChanLun_EMA52 --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_EMA52.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_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies
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class ChanLun_EMA52(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 = False # 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 = 1600
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big_tf = '1h'
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small_tf = '15m'
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last_time = None
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chan = ChanLun()
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last_order = None
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last_trade = None
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pair = 'BTC/USDT:USDT'
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long_tf = '1h'
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short_tf = '15m'
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long_time = 60
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short_time = 15
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def informative_pairs(self):
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return [(self.pair, "1h"),
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(self.pair, "1d"),
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#(self.pair, "1M"),
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(self.pair, "15m"),
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#(self.pair, "1w"),
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]
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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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long_df = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
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long_df = self.add_indicators(long_df)
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dataframe['rsi'] = ta.RSI(long_df, timeperiod=14)
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if self.last_time is None or self.last_time + timedelta(seconds=10) < datetime.now():
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self.last_time = datetime.now()
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logger.info("init_dataframes----------------------------")
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last_price = dataframe.iloc[-1]['close']
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date = dataframe.iloc[-1]['date']
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tf_ema52_list = self.chan.check_price_ema52(last_price)
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self.init_dataframes(dataframe)
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logger.info("Date: " + date.strftime('%Y-%m-%d %H:%M:%S') + " Price: " + str(last_price) + " EMA52_list: " + str(tf_ema52_list))
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#print(long_df.iloc[-1])
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dataframe = resampled_merge(dataframe, long_df)
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return dataframe
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def ema_dir(self, dataframe):
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"""
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趋势方向综合判断，分为三个维度：
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1. ema_dir: 主趋势方向 (基于MACD零轴 + 价格与EMA52/EMA156关系)
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- 3: 强多（价格在EMA156上方 + MACD在零轴上方 + 价格在EMA24上方）
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- 2: 中多（价格在EMA156上方 + MACD在零轴上方）
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- 1: 弱多（价格在EMA52上方 或 MACD在零轴上方，满足其一）
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- -1: 弱空（价格在EMA52下方 或 MACD在零轴下方，满足其一）
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- -2: 中空（价格在EMA156下方 + MACD在零轴下方）
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- -3: 强空（价格在EMA156下方 + MACD在零轴下方 + 价格在EMA24下方）
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- 0: 盘整（无明确方向）
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2. ema_align: EMA排列状态（辅助确认趋势强度）
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- 1: 多头排列 (EMA24 > EMA52 > EMA104 > EMA156)
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- -1: 空头排列 (EMA24 < EMA52 < EMA104 < EMA156)
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- 0: 交叉/纠缠
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3. ema_slope: EMA52斜率方向（趋势加速/减速判断）
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- 正值: EMA52向上倾斜，趋势加速
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- 负值: EMA52向下倾斜，趋势减速
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"""
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close = dataframe['close']
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ema24 = dataframe['ema24']
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ema52 = dataframe['ema52']
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ema104 = dataframe['ema104']
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ema156 = dataframe['ema156']
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macd_signal = dataframe['macdsignal'] # 黄线（慢线），用于判断零轴
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# === 1. 主趋势方向 ===
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# 核心条件：价格与EMA156的关系（大趋势）+ MACD黄线与零轴的关系
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above_ema156 = close > ema156
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below_ema156 = close < ema156
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above_ema52 = close > ema52
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below_ema52 = close < ema52
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above_ema24 = close > ema24
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below_ema24 = close < ema24
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macd_above_zero = macd_signal > 0
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macd_below_zero = macd_signal < 0
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dataframe['ema_dir'] = 0
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# 强多：价格在EMA156上方 + MACD零轴上方 + 价格在EMA24上方（超强势结构）
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dataframe.loc[above_ema156 & macd_above_zero & above_ema24, 'ema_dir'] = 3
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# 中多：价格在EMA156上方 + MACD零轴上方
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dataframe.loc[above_ema156 & macd_above_zero & ~above_ema24, 'ema_dir'] = 2
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# 弱多：满足其一（价格在EMA52上方 或 MACD零轴上方）
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dataframe.loc[(above_ema52 & ~macd_above_zero) | (macd_above_zero & ~above_ema156), 'ema_dir'] = 1
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# 弱空：满足其一（价格在EMA52下方 或 MACD零轴下方）
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dataframe.loc[(below_ema52 & ~macd_below_zero) | (macd_below_zero & ~below_ema156), 'ema_dir'] = -1
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# 中空：价格在EMA156下方 + MACD零轴下方
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dataframe.loc[below_ema156 & macd_below_zero & ~below_ema24, 'ema_dir'] = -2
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# 强空：价格在EMA156下方 + MACD零轴下方 + 价格在EMA24下方（超强空势结构）
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dataframe.loc[below_ema156 & macd_below_zero & below_ema24, 'ema_dir'] = -3
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# === 2. EMA排列状态（辅助参考）===
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bull_align = (ema24 > ema52) & (ema52 > ema104) & (ema104 > ema156)
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bear_align = (ema24 < ema52) & (ema52 < ema104) & (ema104 < ema156)
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dataframe['ema_align'] = 0
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dataframe.loc[bull_align, 'ema_align'] = 1
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dataframe.loc[bear_align, 'ema_align'] = -1
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# === 3. EMA52斜率（趋势加速/减速）===
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# 用EMA52的变化率判断趋势是否在加速
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dataframe['ema_slope'] = (ema52 - ema52.shift(3)) / ema52.shift(3) * 100
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return dataframe
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def add_indicators(self, dataframe):
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dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
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dataframe['dir24'] = dataframe['close'] - dataframe['ema24']
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dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
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dataframe['dir52'] = dataframe['close'] - dataframe['ema52']
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dataframe['ema104'] = ta.EMA(dataframe, timeperiod=104)
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dataframe['dir104'] = dataframe['close'] - dataframe['ema104']
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dataframe['ema156'] = ta.EMA(dataframe, timeperiod=156)
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dataframe['dir156'] = dataframe['close'] - dataframe['ema156']
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dataframe['dir52_156'] = dataframe['dir52'] - dataframe['dir156']
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dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9)
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dataframe['macdsignal'] = dataframe_macd['macdsignal']
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dataframe['macd'] = dataframe_macd['macd']
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dataframe['macdhist'] = dataframe_macd['macdhist']
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return dataframe
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def init_dataframes(self, dataframe_1m):
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dataframe_15m = self.dp.get_pair_dataframe(pair=self.pair, timeframe='15m')
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dataframe_1h = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
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dataframe_1d = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1d')
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#dataframe_1w = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1w')
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#dataframe_1M = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1M')
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self.chan.init_dataframes(dataframe_1m, dataframe_15m,dataframe_1h, dataframe_1d)
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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_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 populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(dataframe['dir156'] > 0) &
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(dataframe['dir52_156'] > 0) &
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(dataframe['macdhist'] > 0),
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'enter_long'] = 1
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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.loc[
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(dataframe['dir156'] < 0) &
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(dataframe['dir52_156'] < 0) &
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(dataframe['macdhist'] < 0),
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'exit_long'] = 1
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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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