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添加新股票

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jackyu66git
2025-12-16 18:10:51 +08:00
parent 5616ae4c32
commit ec2e16e1a0
4 changed files with 274 additions and 77 deletions
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ChanLun.py
+13 -9
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@@ -54,15 +54,19 @@ class ChanLun():
timeframe = timeframes[index] timeframe = timeframes[index]
interval = intervals[index] interval = intervals[index]
self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe) self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
def init_dataframes(self, dataframe_m, dataframe_h, dataframe_d, dataframe_M): def init_dataframes(self, dataframe_m=None, dataframe_h=None, dataframe_d=None, dataframe_M=None):
self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m') if dataframe_m is not None:
self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols) self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h') self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols) if dataframe_h is not None:
self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d') self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols) self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M') if dataframe_d is not None:
self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols) self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
if dataframe_M is not None:
self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
def get_ema52_dict(self): def get_ema52_dict(self):
if len(self.tf_df_dict) > 0: if len(self.tf_df_dict) > 0:
return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols} return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
strategies/ChanLun_BTC_15.py
+14 -68
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@@ -5,8 +5,6 @@ import sys
import os import os
# 添加父目录到系统路径 # 添加父目录到系统路径
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from ChanLun import ChanLun
from ChanLun_Classifier import ChanLunClassifier
from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
# -------------------------------- # --------------------------------
from technical.util import resample_to_interval, resampled_merge from technical.util import resample_to_interval, resampled_merge
@@ -17,6 +15,7 @@ from freqtrade.persistence import Trade
from typing import Optional from typing import Optional
import logging import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
from TF_DF import TF_DF
### Now you can use logger.info('asfd') to log ### 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 plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
@@ -88,76 +87,23 @@ class ChanLun_BTC_15(IStrategy):
time1d = 1440 time1d = 1440
time5 = 1440 time5 = 1440
last_time = datetime.now() last_time = datetime.now()
chan = ChanLun()
classifier = ChanLunClassifier(None)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
tf_df_5 = TF_DF(dataframe, self.time5, '5m')
tf_df_15 = TF_DF(dataframe, self.time15, '15m')
tf_df_30 = TF_DF(dataframe, self.time30, '30m')
tf_df_60 = TF_DF(dataframe, self.time60, '60m')
tf_df_4h = TF_DF(dataframe, self.time4h, '4h')
tf_df_1d = TF_DF(dataframe, self.time1d, '1d')
# 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 = resampled_merge(dataframe, tf_df_5.dataframe)
#dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200) dataframe = resampled_merge(dataframe, tf_df_15.dataframe)
dataframe = self.add_indicators(dataframe) dataframe = resampled_merge(dataframe, tf_df_30.dataframe)
dataframe_5 = self.add_indicators(dataframe_5) dataframe = resampled_merge(dataframe, tf_df_60.dataframe)
dataframe_30 = self.add_indicators(dataframe_30) dataframe = resampled_merge(dataframe, tf_df_4h.dataframe)
dataframe_60 = self.add_indicators(dataframe_60) dataframe = resampled_merge(dataframe, tf_df_1d.dataframe)
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 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, def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
entry_tag: str | None, side: str, **kwargs) -> float: entry_tag: str | None, side: str, **kwargs) -> float:
strategies/ChanLun_BTC_15_back.py
+245
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@@ -0,0 +1,245 @@
# --- 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 ChanLun import ChanLun
from ChanLun_Classifier import ChanLunClassifier
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
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])
web/cn_stock_data.py
+2
View File
@@ -77,6 +77,8 @@ class ChinaStockData:
def get_popular_stocks(self): def get_popular_stocks(self):
"""获取热门A股股票代码列表 - 扩展版本，按行业分类""" """获取热门A股股票代码列表 - 扩展版本，按行业分类"""
return [ return [
# 包装引印刷
{'symbol': '002836', 'name': '新宏泽', 'sector': '包装印刷'},
# 银行股 # 银行股
{'symbol': '600036', 'name': '招商银行', 'sector': '银行'}, {'symbol': '600036', 'name': '招商银行', 'sector': '银行'},
{'symbol': '000001', 'name': '平安银行', 'sector': '银行'}, {'symbol': '000001', 'name': '平安银行', 'sector': '银行'},