"""
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大单成交数据解析器
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"""
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import os
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import re
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from multiprocessing import Pool
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import pandas as pd
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from utils import tool
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class BigOrderDealParser:
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@classmethod
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def str_to_float(cls, s):
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try:
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# 移除单位并转换
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return round(float(s.split("@")[0]), 2)
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except:
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return float("nan")
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@classmethod
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def code_format(cls, s):
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try:
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code = "{0:0>6}".format(s)
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return code
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except:
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return ''
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def __pre_process_transactions_detail(args):
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def first_last(group):
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"""
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获取第一条数据与最后一条
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@param group:
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@return:
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"""
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return pd.Series({
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'TotalAmount': group['TradeAmount'].sum(),
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'TotalVolume': group['TradeVolume'].sum(),
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'StartTime': group['TradeTime'].iloc[0],
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'StartPrice': group['TradePrice'].iloc[0],
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'EndTime': group['TradeTime'].iloc[-1],
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'EndPrice': group['TradePrice'].iloc[-1]
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})
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chunk_index, chunk_data = args[0]
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csv_path = args[1]
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df = chunk_data.copy()
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index = chunk_index + 1
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children_dir = csv_path.replace(".csv", "")
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if not os.path.exists(children_dir):
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os.makedirs(children_dir)
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child_path = os.path.join(children_dir, f"{index}.csv")
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if os.path.exists(child_path):
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return
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print(f"处理Transaction第{index}批次", os.getpid())
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df["TradePrice"] = df["TradePrice"].apply(BigOrderDealParser.str_to_float)
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df["SecurityID"] = df["SecurityID"].apply(BigOrderDealParser.code_format)
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df = df[df["SecurityID"].str.startswith(("30", "00", "60"), na=False)]
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# 计算成交金额
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df['TradeAmount'] = df['TradePrice'] * df['TradeVolume']
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df = df[df["TradeAmount"] > 0]
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# 判断是否为空
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# print(df.empty)
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# 按SecurityID和BuyNo分组
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grouped = df.groupby(['SecurityID', 'BuyNo'])
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# 应用聚合函数
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chunk_result = grouped.apply(first_last)
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if not df.empty:
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chunk_result = chunk_result.reset_index()
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chunk_result.to_csv(child_path, index=False)
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def pre_process_transactions(csv_path, process_count=4):
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chunk_size = 200000
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# 创建DataFrame
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chunks = pd.read_csv(csv_path, chunksize=chunk_size)
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indexed_data = list(enumerate(chunks))
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args = [(x, csv_path) for x in indexed_data]
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# 新写法
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with Pool(processes=process_count) as pool:
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pool.map(__pre_process_transactions_detail, args)
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def __pre_process_ngtsticks(args):
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def first_last(group):
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return pd.Series({
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'TotalAmount': group['TradeMoney'].sum(),
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'TotalVolume': group['Volume'].sum(),
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'StartTime': group['TickTime'].iloc[0],
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'StartPrice': group['Price'].iloc[0],
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'EndTime': group['TickTime'].iloc[-1],
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'EndPrice': group['Price'].iloc[-1]
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})
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chunk_index, chunk_data = args[0]
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csv_path = args[1]
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df = chunk_data.copy()
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index = chunk_index + 1
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children_dir = csv_path.replace(".csv", "")
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if not os.path.exists(children_dir):
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os.makedirs(children_dir)
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child_path = os.path.join(children_dir, f"{index}.csv")
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if os.path.exists(child_path):
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return
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print(f"处理NGTSTick第{index}批次")
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df = df[df["TickType"] == 'T']
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df["Price"] = df["Price"].apply(BigOrderDealParser.str_to_float)
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df["SecurityID"] = df["SecurityID"].apply(BigOrderDealParser.code_format)
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df = df[df["SecurityID"].str.startswith(("30", "00", "60"), na=False)]
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# 计算成交金额
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df['TradeMoney'] = df["TradeMoney"].apply(BigOrderDealParser.str_to_float)
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# 按SecurityID和BuyNo分组
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grouped = df.groupby(['SecurityID', 'BuyNo'])
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# 应用聚合函数
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chunk_result = grouped.apply(first_last)
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if not df.empty:
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chunk_result = chunk_result.reset_index()
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chunk_result.to_csv(child_path, index=False)
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def pre_process_ngtsticks(csv_path, process_count=4):
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chunk_size = 200000
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# 创建DataFrame
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chunks = pd.read_csv(csv_path, chunksize=chunk_size)
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indexed_data = list(enumerate(chunks))
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args = [(x, csv_path) for x in indexed_data]
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# 新写法
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with Pool(processes=process_count) as pool:
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pool.map(__pre_process_ngtsticks, args)
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def __concat_pre_datas(dir_path):
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"""
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拼接数据
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@param dir_path:
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@return:
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"""
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combined_path = os.path.join(dir_path, 'combined.csv')
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if os.path.exists(combined_path):
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print("合并的目标文件已存在")
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return
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file_list = os.listdir(dir_path)
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file_list.sort(key=lambda x: int(re.findall(r'\d+', x)[0]))
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df_list = []
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for file in file_list:
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df = pd.read_csv(os.path.join(dir_path, file))
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if df.empty:
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continue
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df["SecurityID"] = df["SecurityID"].apply(BigOrderDealParser.code_format)
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df_list.append(df)
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print("准备合并的文件数量：", len(df_list))
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combined_df = pd.concat(df_list, ignore_index=True)
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print("合并完成，准备写入文件！")
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# 保存结果
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combined_df.to_csv(combined_path, index=False)
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print("写入文件完成！")
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def concat_pre_transactions(dir_path):
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__concat_pre_datas(dir_path)
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def concat_pre_ngtsticks(dir_path):
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__concat_pre_datas(dir_path)
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if __name__ == "__main__":
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pre_process_transactions("E:/测试数据/Transaction_Test.csv")
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# pre_process_ngtsticks("E:/测试数据/NGTSTick_Test.csv")
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concat_pre_transactions("E:/测试数据/Transaction_Test")
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