172 lines
7.3 KiB
Python
172 lines
7.3 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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"""
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使用EMA周期52
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1. 检查当前price是否穿越，如果穿越时，MACD也是归零轴反转，则开仓
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2. 接近某个EMA周期后反转，此时MACD归零轴反转，则开仓
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止损放到顶底分型的高低点
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1. 从大周期开始找到价格接近ema52，MACD也接近零轴的周期，需要看这个周期的长级别是否高位空，大趋势方向
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2. 然后去小于这个时间周期的周期找买卖点，小级趋势方向和大趋势相反并且开始反向，小级别需要检查MACD是否归零轴反转，同时价格是否接近EMA52
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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 1M --pairs BTC/USDT:USDT --timerange=20250405-
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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 = 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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pair = 'BTC/USDT:USDT'
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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['rsi'] = ta.RSI(dataframe, timeperiod=14)
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if self.last_time + timedelta(minutes=1) < 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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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 = ChanLun()
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self.chan.init_dataframes(dataframe_1m, dataframe_15m,dataframe_1h, dataframe_1d, dataframe_1w, dataframe_1M)
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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['rsi'] < 30),
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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['rsi'] > 70),
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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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