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>
245 lines
10 KiB
Python
245 lines
10 KiB
Python
# --- Do not remove these libs ---
|
|
from statistics import median
|
|
from freqtrade.strategy import IStrategy
|
|
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 chan.pipeline.ChanLun import ChanLun
|
|
from chan.analysis.ChanLun_Classifier import ChanLunClassifier
|
|
from chan.core.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
|
|
from datetime import datetime, timedelta
|
|
from freqtrade.persistence import Trade
|
|
from typing import Optional
|
|
import logging
|
|
logger = logging.getLogger(__name__)
|
|
### Now you can use logger.info('asfd') to log
|
|
# freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
|
|
|
|
# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
|
|
# freqtrade backtesting --export none -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
|
|
# freqtrade lookahead-analysis --export none -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
|
|
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
|
|
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250401
|
|
|
|
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
|
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
|
|
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
|
|
|
|
class ChanLun_BTC_15(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": 0.60,
|
|
"360": 0.2,
|
|
"640": 0.1,
|
|
"1200": 0
|
|
}
|
|
# 5m and 15m
|
|
minimal_roi = {
|
|
"0": 0.1,
|
|
"60": 0.05,
|
|
"120": 0.02,
|
|
"240": 0
|
|
}
|
|
# 5m and 15m
|
|
minimal_roi_1 = {
|
|
"0": 0.05,
|
|
"120": 0.02,
|
|
"240": 0.01,
|
|
"360": 0
|
|
}
|
|
# 15m and 30m
|
|
minimal_roi_1 = {
|
|
"0": 0.1,
|
|
"240": 0.05,
|
|
"480": 0.03,
|
|
"600": 0
|
|
}
|
|
minimal_roi_2 = {
|
|
"0": 0.10,
|
|
"1200": 0.05,
|
|
"2400": 0.025,
|
|
"3600": 0
|
|
}
|
|
can_short = True
|
|
lev = 1.0
|
|
stoploss = -0.3
|
|
bsp_offset = 2
|
|
trailing_stop = False
|
|
trailing_stop_positive = 0.025
|
|
trailing_stop_positive_offset = 0.045
|
|
trailing_only_offset_is_reached = False
|
|
|
|
position_adjustment_enable = True
|
|
startup_candle_count = 100
|
|
|
|
time5 = 5
|
|
time15 = 15
|
|
time30 = 30
|
|
time60 = 60
|
|
time4h = 240
|
|
time1d = 1440
|
|
time5 = 1440
|
|
last_time = datetime.now()
|
|
chan = ChanLun()
|
|
classifier = ChanLunClassifier(None)
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
|
|
# resample our dataframes
|
|
dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5)
|
|
dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
|
|
dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30)
|
|
dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
|
|
dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240)
|
|
|
|
#dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
|
|
#dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080)
|
|
#dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200)
|
|
|
|
dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440)
|
|
#dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080)
|
|
#dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200)
|
|
dataframe = self.add_indicators(dataframe)
|
|
dataframe_5 = self.add_indicators(dataframe_5)
|
|
dataframe_30 = self.add_indicators(dataframe_30)
|
|
dataframe_60 = self.add_indicators(dataframe_60)
|
|
dataframe_4h = self.add_indicators(dataframe_4h)
|
|
dataframe_1d = self.add_indicators(dataframe_1d)
|
|
#self.chan.plot_dual(dataframe_5, dataframe_30)
|
|
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
|
|
|
|
state_list, fx_list = self.chan.get_klc_strength_list(dataframe_15)
|
|
dataframe_15['state'] = state_list
|
|
dataframe_15['fx'] = fx_list
|
|
dataframe_15['bsp'] = self.chan.get_klc_bsp_list(dataframe_15)
|
|
klc_list = self.chan.get_klc_list(dataframe_15)
|
|
bi_list = self.chan.cal_bi_list(klc_list)
|
|
if self.last_time + timedelta(minutes=1) < datetime.now():
|
|
print(state_list[-1], state_list[-2], state_list[-3], state_list[-4], state_list[-5])
|
|
print(fx_list[-1], fx_list[-2], fx_list[-3], fx_list[-4], fx_list[-5])
|
|
print(klc_list[-1].klc_fx_type, klc_list[-2].klc_fx_type, klc_list[-3].klc_fx_type, klc_list[-4].klc_fx_type, klc_list[-5].klc_fx_type)
|
|
print("-------------------------------------------------------------------------------")
|
|
self.last_time = datetime.now()
|
|
#dataframe = resampled_merge(dataframe, dataframe_5)
|
|
dataframe = resampled_merge(dataframe, dataframe_15)
|
|
#dataframe = resampled_merge(dataframe, dataframe_30)
|
|
#dataframe = resampled_merge(dataframe, dataframe_60)
|
|
#dataframe = resampled_merge(dataframe, dataframe_4h)
|
|
return dataframe
|
|
|
|
def add_indicators(self, df):
|
|
fast = 8
|
|
slow = 16
|
|
period = 6
|
|
macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
|
|
df['macd'] = macd['macd']
|
|
df['macdsignal'] = macd['macdsignal']
|
|
df['macdhist'] = macd['macdhist']
|
|
df['ma5'] = ta.MA(df, timeperiod=5)
|
|
df['ma10'] = ta.MA(df, timeperiod=10)
|
|
df['ma30'] = ta.EMA(df, timeperiod=30)
|
|
df['ma250'] = ta.MA(df, timeperiod=250)
|
|
df['rsi'] = ta.RSI(df, timeperiod=14)
|
|
df['volume_ratio'] = self.cal_volume_ratio(df)
|
|
return df
|
|
def cal_volume_ratio(self, dataframe, window=10):
|
|
df = dataframe.copy()
|
|
# 计算过去N根K线的平均成交量
|
|
df['avg_volume'] = df['volume'].rolling(window=window).mean()
|
|
# 计算量比
|
|
df['volume_ratio'] = df['volume'] / df['avg_volume']
|
|
# 填充缺失值（前N根K线）
|
|
df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
|
|
return df['volume_ratio']
|
|
|
|
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 populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
|
|
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
|
|
bsp_str = 'resample_{}_bsp'.format(self.get_ticker_indicator()*self.time5)
|
|
shift = self.time5*self.bsp_offset
|
|
dataframe.loc[
|
|
(
|
|
#(dataframe['state'] == "-30")
|
|
#(dataframe[state_str].shift(shift) > 1.0) &
|
|
#(dataframe[fx_str].shift(shift) == -1)
|
|
(dataframe[bsp_str].shift(shift) == -1)
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
|
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
|
|
),
|
|
['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
|
|
dataframe.loc[
|
|
(
|
|
#(dataframe['state'] == "-30")
|
|
#(dataframe[state_str].shift(shift) > 1.0) &
|
|
#(dataframe[fx_str].shift(shift) == 1)
|
|
(dataframe[bsp_str].shift(shift) == 1)
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
|
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
|
|
),
|
|
['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
|
|
return dataframe
|
|
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
|
|
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
|
|
bsp_str = 'resample_{}_bsp'.format(self.get_ticker_indicator()*self.time5)
|
|
shift = self.time5*self.bsp_offset
|
|
dataframe.loc[
|
|
(
|
|
#(dataframe['state']== "30")
|
|
#(dataframe[state_str].shift(shift) > 1.0) &
|
|
#(dataframe[fx_str].shift(shift) == 1)
|
|
(dataframe[bsp_str].shift(shift) == 1)
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
|
),
|
|
['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
|
|
dataframe.loc[
|
|
(
|
|
#(dataframe['state']== "30")
|
|
#(dataframe[state_str].shift(shift) > 1.0) &
|
|
#(dataframe[fx_str].shift(shift) == -1)
|
|
(dataframe[bsp_str].shift(shift) == -1)
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
|
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
|
),
|
|
['exit_short', 'exit_tag']] = (1, 'short_close_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]) |