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Enhance trading workflow with new order flow management

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- Added dingdanliu_nb_mflow for improved order processing
- Updated related scripts and configurations to support new functionality
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2025-03-15 22:45:08 +08:00
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2.数据下载与处理/ssquant_download/ssquant_download.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from ssquant.SQDATA import TakeData\n",
"\n",
"#注意首先需要pip install ssquant\n",
"#否则链接不到数据库\n",
"#输入俱乐部的账号密码即可调用，注意保密。\n",
"#目前数据是2019年1月-至今\n",
"#每日下午收盘后3点30分录入当天数据。\n",
"#有任何疑问可以再群里提出，或者私信我(慕金龙)\n",
"#官网: quant789.com\n",
"#公众号：松鼠Quant\n",
"#客服微信: viquant01\n",
"\n",
"#只能调取分钟及以上数据，tick数据每月底更新到百度网盘下载\n",
"\n",
"'''\n",
"获取数据-\n",
"品种:symbol,不区分大小写\n",
"起始时间:start_date,\n",
"结束时间:end_date(包含当天),\n",
"周期kline_period:1M..5M..NM(分钟),1D(天),1W(周),1Y(月)\n",
"复权adjust_type:0(不复权)1(后复权)\n",
"注意：\n",
"1.请正确输入账号密码\n",
"2.不要挂代理访问数据库\n",
"3.暂时没有股指数据，下个月补齐。\n",
"'''\n",
" \n",
"# username='俱乐部账号' password='密码'\n",
"client = TakeData(username='77777@qq.com', password='7777')\n",
"data = client.get_data(\n",
" symbol='rb888',\n",
" start_date='2023-01-02',\n",
" end_date='2024-01-03',\n",
" kline_period='60M',\n",
" adjust_type=1\n",
")\n",
"print(data)\n",
"\n",
"\n",
"\n",
"'''\n",
"datetime:时间,\n",
"\n",
"symbol:品种,\n",
"\n",
"open:开盘价,\n",
"\n",
"high:最高价,\n",
"\n",
"low:最低价,\n",
"\n",
"close:收盘价,\n",
"\n",
"volume:成交量(单bar),\n",
"\n",
"amount:成交金额(单bar),\n",
"\n",
"openint:持仓量(单bar),\n",
"\n",
"cumulative_openint:累计持仓量，\n",
"\n",
"open_bidp , open_askp: K线第一个价格的买一价格和卖一价格\n",
"\n",
"close_bidp , close_askp: K线最后一个价格的买一价格和卖一价格\n",
"\n",
"\n",
" datetime symbol open high low close volume amount cumulative_openint openint open_bidp open_askp close_bidp close_askp\n",
"0 2023-01-03 10:00:00 rb2305 4081.0 4081.0 4016.0 4037.0 737537 29782187220 1883481 -48415 4081.0 4084.0 4037.0 4038.0\n",
"1 2023-01-03 11:00:00 rb2305 4038.0 4056.0 4037.0 4042.0 158548 6415696920 1887716 4235 4037.0 4038.0 4042.0 4044.0\n",
"2 2023-01-03 12:00:00 rb2305 4044.0 4054.0 4037.0 4051.0 67448 2728130300 1890125 2409 4043.0 4044.0 4050.0 4051.0\n",
"3 2023-01-03 14:00:00 rb2305 4055.0 4065.0 4045.0 4058.0 110181 4469698600 1895841 5723 4050.0 4051.0 4058.0 4059.0\n",
"4 2023-01-03 15:00:00 rb2305 4059.0 4074.0 4056.0 4063.0 167932 6824213940 1882723 -13125 4058.0 4059.0 4062.0 4063.0\n",
"... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n",
"1689 2024-01-03 12:00:00 rb2405 4055.0 4057.0 4044.0 4049.0 79745 3229361570 1597387 -6515 4054.0 4055.0 4049.0 4050.0\n",
"1690 2024-01-03 14:00:00 rb2405 4050.0 4056.0 4046.0 4049.0 55040 2229498750 1598566 1179 4050.0 4051.0 4049.0 4050.0\n",
"1691 2024-01-03 15:00:00 rb2405 4050.0 4064.0 4048.0 4055.0 148845 6038835190 1583796 -14770 4049.0 4050.0 4055.0 4056.0\n",
"1692 2024-01-03 22:00:00 rb2405 4054.0 4054.0 4040.0 4049.0 181753 7354584770 1582419 990 4053.0 4054.0 4048.0 4049.0\n",
"1693 2024-01-03 23:00:00 rb2405 4049.0 4057.0 4042.0 4049.0 104712 4240341050 1574287 -8132 4048.0 4049.0 4049.0 4050.0"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ssquant.SQDATA import TakeData"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"out_path = r'D:/data'\n",
"symbol_name = 'rb888' #主力连续888 次主力合约777\n",
"time_period = '1M'\n",
"start_time = '2000-01-01'\n",
"end_time = '2019-01-31'\n",
"adjust_k = 'Faj' #Naj:Non adjust,Faj:Forward adjust,后复权\n",
"\n",
"if adjust_k == 'Naj':\n",
" adjust_tmp = 0\n",
"elif adjust_k == 'Faj':\n",
" adjust_tmp = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client = TakeData(username='240884432@qq.com', password='Zj123!@#')\n",
"data = client.get_data(\n",
" symbol=symbol_name,\n",
" start_date=start_time,\n",
" end_date=end_time,\n",
" kline_period=time_period,\n",
" adjust_type= adjust_tmp\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"头部文件为：--------------------\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>datetime</th>\n",
" <th>symbol</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" <th>amount</th>\n",
" <th>cumulative_openint</th>\n",
" <th>openint</th>\n",
" <th>open_bidp</th>\n",
" <th>open_askp</th>\n",
" <th>close_bidp</th>\n",
" <th>close_askp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-01-02 09:01:00</td>\n",
" <td>rb1905</td>\n",
" <td>3399</td>\n",
" <td>3405</td>\n",
" <td>3389</td>\n",
" <td>3401</td>\n",
" <td>69562</td>\n",
" <td>2362607160</td>\n",
" <td>2383714</td>\n",
" <td>16864</td>\n",
" <td>3399.0</td>\n",
" <td>3400.0</td>\n",
" <td>3400.0</td>\n",
" <td>3401.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-01-02 09:02:00</td>\n",
" <td>rb1905</td>\n",
" <td>3401</td>\n",
" <td>3430</td>\n",
" <td>3401</td>\n",
" <td>3410</td>\n",
" <td>88696</td>\n",
" <td>3034283200</td>\n",
" <td>2399530</td>\n",
" <td>-12248</td>\n",
" <td>3401.0</td>\n",
" <td>3402.0</td>\n",
" <td>3409.0</td>\n",
" <td>3410.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-01-02 09:03:00</td>\n",
" <td>rb1905</td>\n",
" <td>3409</td>\n",
" <td>3414</td>\n",
" <td>3409</td>\n",
" <td>3412</td>\n",
" <td>22828</td>\n",
" <td>778740580</td>\n",
" <td>2387356</td>\n",
" <td>1180</td>\n",
" <td>3409.0</td>\n",
" <td>3410.0</td>\n",
" <td>3411.0</td>\n",
" <td>3412.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-01-02 09:04:00</td>\n",
" <td>rb1905</td>\n",
" <td>3412</td>\n",
" <td>3413</td>\n",
" <td>3403</td>\n",
" <td>3404</td>\n",
" <td>17378</td>\n",
" <td>592413220</td>\n",
" <td>2388158</td>\n",
" <td>54</td>\n",
" <td>3411.0</td>\n",
" <td>3412.0</td>\n",
" <td>3404.0</td>\n",
" <td>3405.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-01-02 09:05:00</td>\n",
" <td>rb1905</td>\n",
" <td>3405</td>\n",
" <td>3409</td>\n",
" <td>3405</td>\n",
" <td>3405</td>\n",
" <td>15770</td>\n",
" <td>537276980</td>\n",
" <td>2388190</td>\n",
" <td>1674</td>\n",
" <td>3405.0</td>\n",
" <td>3406.0</td>\n",
" <td>3405.0</td>\n",
" <td>3406.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime symbol open high low close volume amount \\\n",
"0 2019-01-02 09:01:00 rb1905 3399 3405 3389 3401 69562 2362607160 \n",
"1 2019-01-02 09:02:00 rb1905 3401 3430 3401 3410 88696 3034283200 \n",
"2 2019-01-02 09:03:00 rb1905 3409 3414 3409 3412 22828 778740580 \n",
"3 2019-01-02 09:04:00 rb1905 3412 3413 3403 3404 17378 592413220 \n",
"4 2019-01-02 09:05:00 rb1905 3405 3409 3405 3405 15770 537276980 \n",
"\n",
" cumulative_openint openint open_bidp open_askp close_bidp close_askp \n",
"0 2383714 16864 3399.0 3400.0 3400.0 3401.0 \n",
"1 2399530 -12248 3401.0 3402.0 3409.0 3410.0 \n",
"2 2387356 1180 3409.0 3410.0 3411.0 3412.0 \n",
"3 2388158 54 3411.0 3412.0 3404.0 3405.0 \n",
"4 2388190 1674 3405.0 3406.0 3405.0 3406.0 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('头部文件为：--------------------')\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
" \n",
"# 假设你有一个字符串，表示时间，格式为 'YYYY-MM-DD HH:MM:SS'\n",
"real_start_time = data.iloc[0,0]\n",
" \n",
"# 使用datetime.strptime将字符串转换为时间\n",
"time_obj = datetime.strptime(real_start_time, '%Y-%m-%d %H:%M:%S')\n",
" \n",
"# 获取年月日\n",
"year = time_obj.year\n",
"month = time_obj.month\n",
"day = time_obj.day\n",
" \n",
"print(f'年: {year}, 月: {month}, 日: {day}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('尾部文件为：--------------------')\n",
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import datetime\n",
"real_start_time = pd.to_datetime(data.iloc[0,0]).date().strftime('%Y-%m-%d')\n",
"real_end_time = pd.to_datetime(data.iloc[-1,0]).date().strftime('%Y-%m-%d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.to_csv('%s/%s_%s_%s(%s_%s).csv'%(out_path,symbol_name,time_period,adjust_k,real_start_time,real_end_time), index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}