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增加交易策略、交易指标、量化库代码等文件夹

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2025-04-27 15:54:09 +08:00
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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/转换合并tick数据(2022-2023).ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"os.chdir('E:/data/ag')\n",
"all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]\n",
"all_csv_files = sorted(all_csv_files)\n",
"print(\"文件中所有CSV文件:\",all_csv_files)\n",
"\n",
"sp_chars = ['_2023','_2022']\n",
"sp_chars = sorted(sp_chars)\n",
"print(\"需要筛选的文件名关键字:\",sp_chars)\n",
"\n",
"csv_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_chars)]\n",
"print(\"筛选结果后的CSV文件:\",csv_files)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame()\n",
"for f in csv_files:\n",
" df_temp = pd.read_csv(\n",
" f,\n",
" usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
" names=[\n",
" \"交易日\",\n",
" \"统一代码\",\n",
" \"合约代码\",\n",
" \"最新价\",\n",
" \"数量\",\n",
" \"最后修改时间\",\n",
" \"最后修改毫秒\",\n",
" \"申买价一\",\n",
" \"申买量一\",\n",
" \"申卖价一\",\n",
" \"申卖量一\",\n",
" \"业务日期\",\n",
" ],\n",
" skiprows=1,\n",
" encoding=\"utf-8\",\n",
" )\n",
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
" # 'datetime', 'volume'])\n",
" df = pd.concat([df, df_temp])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 查看数据的头部和尾部：head()、tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 查看dataframe的基本情况\n",
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 等比复权,先不考虑\n",
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
"df['复权因子'] = df['复权因子'].fillna(1)\n",
"# df['复权因子'].loc[0] = 1\n",
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
"# df['high_adj'] = df['high'] * adjust.cumprod()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 等差复权\n",
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['申卖价一'].shift() - df['申买价一'], 0)\n",
"df['复权因子'] = df['复权因子'].fillna(0)\n",
"# df['复权因子'].loc[0] = 1\n",
"df['申买价一_adj'] = df['申买价一'] + df['复权因子'].cumsum()\n",
"df['申卖价一_adj'] = df['申卖价一'] + df['复权因子'].cumsum()\n",
"df['最新价_adj'] = df['最新价'] + df['复权因子'].cumsum()\n",
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 查找换期需要复权的索引\n",
"non_zero_indices = df[df['复权因子'] != 0].index\n",
"print(non_zero_indices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 查看未调整买价、卖价和最新价的数据\n",
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 将调整后的数值替换原来的值\n",
"df['申买价一'] = df['申买价一_adj']\n",
"df['申卖价一'] = df['申卖价一_adj']\n",
"df['最新价'] = df['最新价_adj']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 查看调整买价、卖价和最新价的数据\n",
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 删除多余的值\n",
"del df['复权因子']\n",
"del df['申买价一_adj']\n",
"del df['申卖价一_adj']\n",
"del df['最新价_adj']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('./ag888_2022_2023.csv', index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "orderflow",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}