zhoujie/Quant_Code
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

增加交易策略、交易指标、量化库代码等文件夹

Browse Source
This commit is contained in:
Win_home
2025-04-27 15:54:09 +08:00
parent ca3b209096
commit f57150dae8
589 changed files with 854346 additions and 1757 deletions
Download Patch File Download Diff File Expand all files Collapse all files

335
1.交易策略/999.其他策略/1.松鼠SF08_基于盘口数据的择时趋势策略/使用文档/过程文件/merged_tickdata.py Normal file
View File

@@ -0,0 +1,335 @@
import pandas as pd
import os
from datetime import time as s_time
from datetime import datetime
import chardet
# 日盘商品期货交易品种
commodity_day_dict = {'bb': s_time(15,00), 'jd': s_time(15,00), 'lh': s_time(15,00), 'l': s_time(15,00), 'fb': s_time(15,00), 'ec': s_time(15,00),
'AP': s_time(15,00), 'CJ': s_time(15,00), 'JR': s_time(15,00), 'LR': s_time(15,00), 'RS': s_time(15,00), 'PK': s_time(15,00),
'PM': s_time(15,00), 'PX': s_time(15,00), 'RI': s_time(15,00), 'ao': s_time(15,00), 'br': s_time(15,00), 'wr': s_time(15,00),}
# 夜盘商品期货交易品种
commodity_night_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
# 金融期货交易品种
financial_time_dict = {'IH': s_time(15,00), 'IF': s_time(15,00), 'IC': s_time(15,00), 'IM': s_time(15,00),'T': s_time(15,00), 'TS': s_time(15,00),
'TF': s_time(15,00), 'TL': s_time(15,00)}
# 所有已列入的筛选品种
all_dict = {k: v for d in [commodity_day_dict, commodity_night_dict, financial_time_dict] for k, v in d.items()}
# def has_common_keys(*dicts):
# keys_union = set().union(*dicts) # 计算所有字典键的并集
# keys_intersection = set().intersection(*dicts) # 计算所有字典键的交集
# return len(keys_intersection) > 0
# has_common_keys(commodity_day_dict, commodity_night_dict,financial_time_dict)
# import chardet
# # 假设file_path是你要读取的文件路径
# with open(file_path, 'rb') as file:
# data = file.read()
# # 使用chardet检测编码
# detected_encoding = chardet.detect(data)['encoding']
# # 如果检测到的编码不是gbk，可以尝试转换编码后再读取
# if detected_encoding and detected_encoding != 'gbk':
# with open(file_path, 'rb') as file:
# data = file.read().decode(detected_encoding)
def split_alpha_numeric(string):
alpha_chars = ""
numeric_chars = ""
for char in string:
if char.isalpha():
alpha_chars += char
elif char.isdigit():
numeric_chars += char
return alpha_chars, numeric_chars
def find_files(all_csv_files):
all_csv_files = sorted(all_csv_files)
sp_old_chars = ['_2019','_2020','_2021']
sp_old_chars = sorted(sp_old_chars)
sp_new_chars = ['_2022','_2023']
sp_new_chars = sorted(sp_new_chars)
csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]
csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]
return csv_old_files, csv_new_files
def merged_old_tickdata(all_csv_files, sp_char):
merged_up_df = pd.DataFrame()
merged_up_df = merged_old_unprocessed_tickdata(all_csv_files, sp_char)
# 获取当前目录下的所有文件名包含sp_char的csv文件
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
alpha_chars, numeric_chars = split_alpha_numeric(merged_up_df.loc[0,'合约代码'])
code_value = alpha_chars + "888"
print("code_value characters:", code_value)
merged_up_df.insert(loc=0,column="统一代码", value=code_value)
while alpha_chars not in all_dict.keys():
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
continue
# merged_df['时间'] = pd.to_datetime(merged_df['时间'])
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['时间'],'lastprice':merged_df['最新'],'volume':merged_df['成交量'],
'bid_p':merged_df['买一价'],'ask_p':merged_df['卖一价'],'bid_v':merged_df['买一量'],'ask_v':merged_df['卖一量']})
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
del merged_df['tmp_time']
if alpha_chars in financial_time_dict.keys():
drop_index1 = pd.DataFrame().index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照中金所交易时间筛选金融期货品种")
# else:
elif alpha_chars in commodity_night_dict.keys():
if commodity_night_dict[alpha_chars] == s_time(23,00):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(1,00):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(2,30):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
else:
print("夜盘截止交易时间未设置或者设置错误！！！")
elif alpha_chars in commodity_day_dict.keys():
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照无夜盘筛选商品期货品种")
else:
print("%s期货品种未列入筛选条件中!!!"%(code_value))
# 清理不在交易时间段的数据
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
merged_df.drop(drop_index2, axis=0, inplace=True)
merged_df.drop(drop_index3, axis=0, inplace=True)
merged_df.drop(drop_index4, axis=0, inplace=True)
del merged_df['time']
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], ascending=True)
# merged_df['datetime'] = pd.to_datetime(merged_df['datetime'])
merged_df['datetime'] = sorted(merged_df['datetime'])
print("%s%s数据生成成功!"%(code_value,sp_char))
return merged_df, code_value
def merged_new_tickdata(all_csv_files, sp_char):
# 获取当前目录下的所有文件名包含sp_char的csv文件
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
print("csv_files:", csv_files)
merged_df = pd.DataFrame()
dir = os.getcwd()
# 循环遍历每个csv文件
for file in csv_files:
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
try:
df = pd.read_csv(
file,
header=0,
usecols=[0, 1, 4, 11, 20, 21, 22, 23, 24, 25, 43],
names=[
"交易日",
"合约代码",
"最新价",
"数量",
"最后修改时间",
"最后修改毫秒",
"申买价一",
"申买量一",
"申卖价一",
"申卖量一",
"业务日期",
],
encoding='gbk',
# skiprows=0,
parse_dates=['业务日期','最后修改时间','最后修改毫秒'])#注意此处增加的排序，为了后面按时间排序
except:
# 假设file_path是你要读取的文件路径
file_path = os.path.join(dir, file)
with open(file_path, 'rb') as file:
data = file.read()
# 使用chardet检测编码
detected_encoding = chardet.detect(data)['encoding']
print("当前读取文件读取错误：", file)
print("当前读取文件正确解码格式", detected_encoding)
# 删除重复行
df.drop_duplicates(inplace=True)
# 将数据合并到新的DataFrame中
merged_df = pd.concat([merged_df, df], ignore_index=True)
# 删除重复列
merged_df.drop_duplicates(subset = merged_df.columns.tolist(), inplace=True)
# 重置行索引
merged_df.reset_index(inplace=True, drop=True)
#print("合约代码:", merged_df["合约代码"])
# 插入新的数据
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
# print("Alphabetical characters:", alpha_chars)
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
code_value = alpha_chars + "888"
print("code_value characters:", code_value)
merged_df.insert(loc=1, column="统一代码", value=code_value)
while alpha_chars not in all_dict.keys():
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
continue
#日期修正
#merged_df['业务日期'] = pd.to_datetime(merged_df['业务日期'])
merged_df['业务日期'] = merged_df['业务日期'].dt.strftime('%Y-%m-%d')
merged_df['datetime'] = merged_df['业务日期'] + ' '+merged_df['最后修改时间'].dt.time.astype(str) + '.' + merged_df['最后修改毫秒'].astype(str)
# 将'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
merged_df['datetime'] = pd.to_datetime(merged_df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
#计算瞬时成交量
merged_df['volume'] = merged_df['数量'] - merged_df['数量'].shift(1)
merged_df['volume'] = merged_df['volume'].fillna(0)
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['datetime'],'lastprice':merged_df['最新价'],'volume':merged_df['volume'],
'bid_p':merged_df['申买价一'],'ask_p':merged_df['申卖价一'],'bid_v':merged_df['申买量一'],'ask_v':merged_df['申卖量一']})
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
del merged_df['tmp_time']
if alpha_chars in financial_time_dict.keys():
drop_index1 = pd.DataFrame().index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照中金所交易时间筛选金融期货品种")
# else:
elif alpha_chars in commodity_night_dict.keys():
if commodity_night_dict[alpha_chars] == s_time(23,00):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(1,00):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
elif commodity_night_dict[alpha_chars] == s_time(2,30):
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
else:
print("夜盘截止交易时间未设置或者设置错误！！！")
elif alpha_chars in commodity_day_dict.keys():
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
drop_index4 = pd.DataFrame().index
print("按照无夜盘筛选商品期货品种")
else:
print("%s期货品种未列入筛选条件中!!!"%(code_value))
# 清理不在交易时间段的数据
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
merged_df.drop(drop_index2, axis=0, inplace=True)
merged_df.drop(drop_index3, axis=0, inplace=True)
merged_df.drop(drop_index4, axis=0, inplace=True)
del merged_df['time']
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], inplace=True)
merged_df['datetime'] = sorted(merged_df['datetime'])
print("%s%s数据生成成功!"%(code_value,sp_char))
return merged_df, code_value
def merged_old_unprocessed_tickdata(all_csv_files, sp_char):
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
print("csv_files:", csv_files)
merged_df = pd.DataFrame()
dir = os.getcwd()
# 循环遍历每个csv文件
for file in csv_files:
try:
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
df = pd.read_csv(file, header=0, encoding='gbk')
except:
file_path = os.path.join(dir, file)
with open(file_path, 'rb') as file:
data = file.read()
# 使用chardet检测编码
detected_encoding = chardet.detect(data)['encoding']
print("当前读取文件读取错误：", file)
print("当前读取文件正确解码格式", detected_encoding)
# 删除重复行
df.drop_duplicates(inplace=True)
# 将数据合并到新的DataFrame中
merged_df = pd.concat([merged_df, df], ignore_index=True)
# 删除重复列
merged_df.drop_duplicates(subset=merged_df.columns.tolist(), inplace=True)
# 重置行索引
merged_df.reset_index(inplace=True, drop=True)
# 插入新的数据
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
code_value = alpha_chars + "888"
print("code_value characters:", code_value)
merged_df.insert(loc=1,column="统一代码", value=code_value)
# 将合并后的数据保存到csv文件中
folder_path = "合成tick数据2019-2021"
if not os.path.exists(folder_path):
os.mkdir('合成tick数据2019-2021')
# sorted_merged_df = merged_df.sort_values(by= ['业务日期','最后修改时间','最后修改毫秒'], ascending=[True, True, True])
# sorted_merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False)
merged_df['时间'] = pd.to_datetime(merged_df['时间'])
sorted_merged_df = merged_df.sort_values(by = ['时间'], ascending=True)
sorted_merged_df.to_csv('./合成tick数据2019-2021/%s%s.csv'%(code_value,sp_char), index=False)
del merged_df
del sorted_merged_df
#merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False) #数据按照时间排序，前面文件夹按照时间修改好了可以直接用这里
# 打印提示信息
print("CSV文件合并成功！")