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config.py
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# -*- coding=utf-8 -*-
# @File : myconfig.py
# @Time : 2023/8/2 18:57
# @Author : EvanHong
# @Email : [email protected]
# @Project : 2023.06.08超高频上证50指数计算
# @Description: 回测系统的config，包括费率等超参的设定
from datetime import timedelta
import os
from backtest.support import str2timedelta, create_dirs
code_dict = {
# '浦发银行': '600000.XSHG',
# 沪深300
# '贵州茅台': '600519.XSHG',
# '宁德时代': '300750.XSHE',
# '招商银行': '600036.XSHG',
# '中国平安': '601318.XSHG',
# '隆基绿能': '601012.XSHG',
# '五粮液': '000858.XSHE',
# '比亚迪': '002594.XSHE',
# '美的集团': '000333.XSHE',
# '兴业银行': '601166.XSHG',
# '东方财富': '300059.XSHE',
# 上证50
'贵州茅台': '600519.XSHG',
'中国平安': '601318.XSHG',
'招商银行': '600036.XSHG',
'兴业银行': '601166.XSHG',
'中信证券': '600030.XSHG',
'紫金矿业': '601899.XSHG',
'长江电力': '600900.XSHG',
'恒瑞医药': '600276.XSHG',
'万华化学': '600309.XSHG',
'伊利股份': '600887.XSHG',
'隆基绿能': '601012.XSHG',
'工商银行': '601398.XSHG',
'药明康德': '603259.XSHG',
'中国建筑': '601668.XSHG',
'中国中免': '601888.XSHG',
'中国石化': '600028.XSHG',
'山西汾酒': '600809.XSHG',
'农业银行': '601288.XSHG',
'三一重工': '600031.XSHG',
'保利发展': '600048.XSHG', # <-
'中国联通': '600050.XSHG',
'国电南瑞': '600406.XSHG',
'通威股份': '600438.XSHG',
'中国神华': '601088.XSHG',
'海尔智家': '600690.XSHG',
'中国石油': '601857.XSHG',
'中国电信': '601728.XSHG',
'片仔癀': '600436.XSHG',
'韦尔股份': '603501.XSHG',
'特变电工': '600089.XSHG',
'中国中铁': '601390.XSHG',
'海天味业': '603288.XSHG',
'三峡能源': '600905.XSHG',
'兆易创新': '603986.XSHG',
'金山办公': '688111.XSHG',
'上汽集团': '600104.XSHG',
'华友钴业': '603799.XSHG',
'中远海控': '601919.XSHG',
'陕西煤业': '601225.XSHG',
'北方稀土': '600111.XSHG',
'中国人寿': '601628.XSHG',
'航发动力': '600893.XSHG',
'包钢股份': '600010.XSHG',
'中国电建': '601669.XSHG',
'天合光能': '688599.XSHG',
'闻泰科技': '600745.XSHG',
'复星医药': '600196.XSHG',
'长城汽车': '601633.XSHG',
'中信建投': '601066.XSHG',
'合盛硅业': '603260.XSHG',
# 测试
# '恒瑞医药': '600276.XSHG',
# '紫金矿业': '601899.XSHG',
# '浦发银行': '600000.XSHG',
# '海通证券': '600837.XSHG',
# '沪深300ETF': '510300.XSHG',
# '涨停股测试': '600219.XSHG',
# lobster
# "AAPL":"AAPL",
# "AMZN": "AMZN",
# "GOOG": "GOOG",
# "INTC": "INTC",
# "MSFT": "MSFT",
}
complete_status = {}
stk_name_dict = {v: k for k, v in code_dict.items()}
exclude = []
root='/Users/hongyifan/Desktop/work/internship/citic_futures/20231226bid-ask-spread/'
data_root = root + 'data/'
detail_data_root = data_root + '个股交易细节/'
res_root = root + 'res/'
model_root = root + 'models/'
scaler_root = root + 'scalers/'
sub_dirs = [d + '10min_10min/' for d in [res_root, model_root, scaler_root]]
# create_dirs(sub_dirs)
# 函数比formatted字符串更灵活
FILE_FMT_order_book_history = "{}_{}_order_book_history.csv" # 默认买卖各10档
FILE_FMT_order_book_history_dict = "{}_{}_order_book_history.pkl" # 默认买卖各10档
FILE_FMT_price_history = "{}_{}_price_history.csv"
FILE_FMT_clean_obh = "{}_{}_clean_obh.csv" # 默认买卖各5档
FILE_FMT_my_trade_details = "{}_{}_my_trade_details.csv"
FILE_FMT_vol_tov = "{}_{}_vol_tov.csv"
FILE_FMT_model = "{}_period{}_{}.pkl" # "{[stkname|'general']}_period{period}_{extract_model_name(model)}.pkl"
FILE_FMT_scaler = "{}_scaler_{}_{}.pkl" # "{stkname}_scaler_{period}_{Any:default:'_'}.pkl"
FILE_FMT_events = "{}_{}_events.csv"
FILE_FMT_feature = "{}_{}_feature{}.csv"
y = None
m = None
d = None
date = None
date1 = None
start = None
end = None
important_times = None
ranges = None
# ==========prediction settings===========
# ret
# target = Target.vol.name # ret,current
# min_freq='10ms' # 最小精度，agg前data的频率，load data默认将数据asfreq至该freq
# agg_freq=min_freq # 计算feature agg的时候，用多少的数据去agg出一个数据点，也即agg后feature的freq
# freq = agg_freq
# freq_minutes=1
# pred_n_steps = 200 # 预测1个1min
# use_n_steps = 3 # 利用use_n_steps个steps的数据去预测pred_n_steps之后的涨跌幅
# drop_current = False # 是否将当前股价作为因子输入给模型
# use_level = 5
# strip_time='5min' # 去掉开收盘5min数据
# # vol
# target = Target.vol.name # vol
# min_freq='1s' # 最小精度，agg前data的频率，load data默认将数据asfreq至该freq
# agg_freq='1min' # 计算feature agg的时候，用多少的数据去agg出一个数据点，也即agg后feature的freq
# freq = agg_freq
# # freq_minutes=1
# pred_n_steps = 10 # 预测1个1min
# use_n_steps = 10 # 利用use_n_steps个steps的数据去预测pred_n_steps之后的涨跌幅
# drop_current = False # 是否将当前股价作为因子输入给模型
# use_level = 5
# strip_time='5min' # 去掉开收盘5min数据
# --------若要使用海通时期的代码，请取消注释以下片段----------
# # vol_chg
# target = Target.vol_chg.name # vol_chg
# min_freq='3s' # 最小精度，agg前data的频率，calc_features将数据asfreq至该freq
# agg_freq='10min' # 计算feature agg的时候，用多少的数据去agg出一个数据点，也即agg后feature的freq
# freq = agg_freq
# # freq_minutes=1
# pred_n_steps = 1 # 预测1个10min
# use_n_steps = 1 # 利用use_n_steps个steps的数据去预测pred_n_steps之后的涨跌幅
# drop_current = False # 是否将当前股价作为因子输入给模型
# use_level = 5
# strip_time='5min' # 去掉开收盘5min数据
strip_time=None
# min_timedelta=str2timedelta(min_freq)
# agg_timedelta=str2timedelta(agg_freq,use_n_steps)
# pred_timedelta=str2timedelta(agg_freq,pred_n_steps)
# strip_timedelta=str2timedelta(strip_time)
# ------------------------------------------------------
# ====================================
# init
# update_date(yyyy='2022', mm='06', dd='29')