200 lines
9.0 KiB
Python
200 lines
9.0 KiB
Python
# --- Do not remove these libs ---
|
||
from statistics import median
|
||
from freqtrade.strategy import IStrategy, stoploss_from_absolute
|
||
import sys
|
||
import os
|
||
# 添加父目录到系统路径
|
||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
from ChanLun import ChanLun
|
||
from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
|
||
# --------------------------------
|
||
from technical.util import resample_to_interval, resampled_merge
|
||
import talib.abstract as ta
|
||
from pandas import DataFrame
|
||
import pandas as pd
|
||
from datetime import datetime, timedelta
|
||
from freqtrade.persistence import Trade, Order
|
||
from typing import Optional
|
||
import logging
|
||
logger = logging.getLogger(__name__)
|
||
"""
|
||
大周期：1h
|
||
小周期：15m，30m
|
||
大周期EMA156以下找做空机会
|
||
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
|
||
EMA24，EMA52，EMA104，EMA156成下跌趋势依次排列（EMA156 > EMA104 > EMA52 > EMA24）
|
||
做空
|
||
1. 做空开始点位条件：
|
||
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
|
||
止损放到最近的顶分型高点或者价格突破EMA156
|
||
2. 开始点位止盈策略
|
||
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
|
||
如果大周期遇到底背离可以平完所有仓位
|
||
3. 加仓点位
|
||
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
|
||
大周期顶分型+大周期macd归零轴可以加仓到最大仓位
|
||
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
|
||
小周期顶分型+大周期macd归零轴
|
||
"""
|
||
|
||
### Now you can use logger.info('asfd') to log
|
||
# freqtrade plot-dataframe --strategy ChanLun_BTC --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
|
||
|
||
# freqtrade trade -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies
|
||
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange=20260101-
|
||
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA_Align.json -t 1m 1m 1h 1d 1w 1M --pairs BTC/USDT:USDT --timerange=20240101-
|
||
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA_Align.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
|
||
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_EMA_Align.json -e 200 --timerange=20250201-20250901
|
||
# freqtrade edge -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
|
||
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
|
||
|
||
# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies --timerange=20250721-
|
||
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_EMA_Align.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
|
||
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_EMA_Align.json --strategy ChanLun_EMA_Align --strategy-path ./user_data/Chan/strategies
|
||
|
||
class ChanLun_EMA_Align(IStrategy):
|
||
INTERFACE_VERSION: int = 3
|
||
# Minimal ROI designed for the strategy.
|
||
# This attribute will be overridden if the config file contains "minimal_roi"
|
||
# 30m and 1h
|
||
|
||
minimal_roi = {
|
||
"0": 0.15,
|
||
"360": 0.2,
|
||
"640": 0.1,
|
||
"1200": 0
|
||
}
|
||
# 5m and 15m
|
||
minimal_roi_1 = {
|
||
"0": 0.1,
|
||
"60": 0.05,
|
||
"120": 0.02,
|
||
"240": 0
|
||
}
|
||
# 15m and 30m
|
||
minimal_roi_1 = {
|
||
"0": 0.1,
|
||
"240": 0.05,
|
||
"480": 0.03,
|
||
"600": 0
|
||
}
|
||
minimal_roi_1 = {
|
||
"0": 1.50,
|
||
"120": 0.05,
|
||
"240": 0.025,
|
||
"360": 0
|
||
}
|
||
|
||
can_short = True
|
||
lev = 1.0
|
||
stoploss = -0.3 # 设置为很大的负值，让custom_stoploss来控制
|
||
use_custom_stoploss = False # 启用自定义止损
|
||
|
||
trailing_stop = False
|
||
trailing_stop_positive = 0.03
|
||
trailing_stop_positive_offset = 0.06
|
||
trailing_only_offset_is_reached = False
|
||
|
||
# 关闭分批止盈/仓位调整
|
||
position_adjustment_enable = False
|
||
# startup_candle_count = 1600
|
||
time5 = 15
|
||
time15 = 15
|
||
time30 = 30
|
||
time60 = 60
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
dataframe = self.add_indicators(dataframe)
|
||
dataframe_5m = resample_to_interval(dataframe, self.get_ticker_indicator() * self.time5)
|
||
dataframe_5m = self.add_indicators(dataframe_5m)
|
||
dataframe = resampled_merge(dataframe, dataframe_5m)
|
||
return dataframe
|
||
def add_indicators(self, dataframe):
|
||
dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
|
||
dataframe['dir24'] = dataframe['close'] - dataframe['ema24']
|
||
dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
|
||
dataframe['dir52'] = dataframe['close'] - dataframe['ema52']
|
||
dataframe['ema104'] = ta.EMA(dataframe, timeperiod=104)
|
||
dataframe['dir104'] = dataframe['close'] - dataframe['ema104']
|
||
dataframe['ema156'] = ta.EMA(dataframe, timeperiod=156)
|
||
dataframe['dir156'] = dataframe['close'] - dataframe['ema156']
|
||
dataframe['dir52_156'] = dataframe['dir52'] - dataframe['dir156']
|
||
dataframe['dir52_104'] = dataframe['dir52'] - dataframe['dir104']
|
||
dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9)
|
||
dataframe['macdsignal'] = dataframe_macd['macdsignal']
|
||
dataframe['macd'] = dataframe_macd['macd']
|
||
dataframe['macdhist'] = dataframe_macd['macdhist']
|
||
dataframe['ema_align'] = (
|
||
((dataframe['ema24'] > dataframe['ema52']) & (dataframe['ema52'] > dataframe['ema104'])) |
|
||
((dataframe['ema24'] < dataframe['ema52']) & (dataframe['ema52'] < dataframe['ema104']))
|
||
)
|
||
return dataframe
|
||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||
new_entryprice = proposed_rate
|
||
if trade:
|
||
if trade.is_short:
|
||
new_entryprice = proposed_rate - 50
|
||
else:
|
||
new_entryprice = proposed_rate + 50
|
||
return new_entryprice
|
||
|
||
def custom_exit_price(self, pair: str, trade: Trade,
|
||
current_time: datetime, proposed_rate: float,
|
||
current_profit: float, exit_tag: str | None, **kwargs) -> float:
|
||
new_exitprice = proposed_rate
|
||
if trade:
|
||
if trade.is_short:
|
||
new_exitprice = proposed_rate + 50
|
||
else:
|
||
new_exitprice = proposed_rate - 50
|
||
return new_exitprice
|
||
|
||
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
||
current_rate: float, current_profit: float,
|
||
min_stake: Optional[float], max_stake: float,
|
||
current_entry_rate: float, current_exit_rate: float,
|
||
current_entry_profit: float, current_exit_profit: float,
|
||
**kwargs) -> Optional[float]:
|
||
# 关闭分批止盈，始终不调整仓位
|
||
return None
|
||
|
||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
|
||
current_profit: float, **kwargs):
|
||
# 不做分批止盈/最终止盈处理，退出由策略信号/ROI/止损决定
|
||
return None
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
resample_5m_align = 'resample_{}_ema_align'.format(self.get_ticker_indicator() * self.time5)
|
||
# 使用高周期的 dir52_156 方向作为多空判定依据
|
||
resample_5m_dir = 'resample_{}_dir52_104'.format(self.get_ticker_indicator() * self.time5)
|
||
resample_5m_signal = 'resample_{}_macdsignal'.format(self.get_ticker_indicator() * self.time5)
|
||
dataframe.loc[
|
||
(dataframe[resample_5m_align]) &
|
||
(dataframe[resample_5m_dir] < 0) &
|
||
(dataframe[resample_5m_signal] > 0),
|
||
['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
|
||
dataframe.loc[
|
||
(dataframe[resample_5m_align]) &
|
||
(dataframe[resample_5m_dir] > 0) &
|
||
(dataframe[resample_5m_signal] < 0),
|
||
['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
|
||
return dataframe
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
dataframe.loc[
|
||
(dataframe['dir156'] < 0) &
|
||
(dataframe['dir52_156'] < 0) &
|
||
(dataframe['macdhist'] < 0),
|
||
['exit_long', 'exit_tag']] = (1, 'long_exit_signal_chan')
|
||
dataframe.loc[
|
||
(dataframe['macd'] > 0) &
|
||
(dataframe['dir156'] > 0) &
|
||
(dataframe['dir52_156'] > 0) &
|
||
(dataframe['macdhist'] > 0),
|
||
['exit_short', 'exit_tag']] = (1, 'short_exit_signal_chan')
|
||
return dataframe
|
||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||
**kwargs) -> float:
|
||
return self.lev
|
||
def get_ticker_indicator(self):
|
||
return int(self.timeframe[:-1]) |