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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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371
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
}

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2.数据下载与处理/ssquant_download/数据库使用示例.py Normal file
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from ssquant.SQDATA import TakeData
#注意首先需要pip install ssquant
#否则链接不到数据库
#输入俱乐部的账号密码即可调用，注意保密。
#目前数据是2019年1月-至今
#每日下午收盘后3点30分录入当天数据。
#有任何疑问可以再群里提出，或者私信我(慕金龙)
#官网: quant789.com
#公众号：松鼠Quant
#客服微信: viquant01
#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
'''
获取数据-
品种:symbol,不区分大小写
起始时间:start_date,
结束时间:end_date(包含当天),
周期kline_period:1M..5M..NM(分钟),1D(天),1W(周),1Y(月)
复权adjust_type:0(不复权)1(后复权)
注意：
1.请正确输入账号密码
2.不要挂代理访问数据库
3.暂时没有股指数据，下个月补齐。
'''
# username='俱乐部账号' password='密码'
client = TakeData(username='77777@qq.com', password='7777')
data = client.get_data(
symbol='rb888',
start_date='2023-01-02',
end_date='2024-01-03',
kline_period='60M',
adjust_type=1
)
print(data)
'''
datetime:时间,
symbol:品种,
open:开盘价,
high:最高价,
low:最低价,
close:收盘价,
volume:成交量(单bar),
amount:成交金额(单bar),
openint:持仓量(单bar),
cumulative_openint:累计持仓量，
open_bidp , open_askp: K线第一个价格的买一价格和卖一价格
close_bidp , close_askp: K线最后一个价格的买一价格和卖一价格
datetime symbol open high low close volume amount cumulative_openint openint open_bidp open_askp close_bidp close_askp
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
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
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
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
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
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
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
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
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
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
'''

65
2.数据下载与处理/ssquant_download/数据库读取_生产K线图.py Normal file
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from ssquant.SQDATA import TakeData
from pyecharts import options as opts
from pyecharts.charts import Kline, Bar, Grid
import pandas as pd
def plotK(data):
# 示例数据（您需要替换为您的实际数据）
kline_data = data[['open', 'close', 'low', 'high']].values.tolist()
dates = data.index.strftime('%Y-%m-%d %H:%M:%S').tolist()
symbol_data = data['symbol'].values.tolist()
# 标记 symbol 变化的位置
markline_data = []
for i in range(1, len(symbol_data)):
if symbol_data[i] != symbol_data[i-1]:
# 当前 symbol 与前一个不同时，添加红色竖线
markline_data.append(opts.MarkLineItem(x=dates[i], name=f'前一个合约{symbol_data[i-1]},当前合约{symbol_data[i]}'))
# 数据缩放组件配置
datazoom_slider = opts.DataZoomOpts(type_="slider", xaxis_index=[0, 1, 2, 3,4], range_start=50, range_end=100)
datazoom_inside = opts.DataZoomOpts(type_="inside", xaxis_index=[0, 1, 2, 3,4])
# 创建 K 线图
kline = (
Kline(init_opts=opts.InitOpts(width="100%", height="900px"))
.add_xaxis(dates)
.add_yaxis('K线图表', kline_data,markline_opts=opts.MarkLineOpts(data=markline_data, symbol='none', linestyle_opts=opts.LineStyleOpts(color="red")))#"ssss",
.set_global_opts(
datazoom_opts=[datazoom_slider, datazoom_inside],
toolbox_opts=opts.ToolboxOpts(is_show=True, pos_top="0%", pos_right="80%"),
legend_opts=opts.LegendOpts(pos_left='40%'), # 调整图例位置到底部
)
)
kline.render('K线图.html')
'''
获取数据-
品种:symbol,
起始时间:start_date,
结束时间:end_date(包含当天),
周期kline_period:1M..5M..NM(分钟),1D(天),1W(周),1Y(月)
复权adjust_type:0(不复权)1(后复权)
'''
# 请在下方输入你的俱乐部账号密码，username='俱乐部账号' password='密码'
client = TakeData(username='1234@qq.com', password='123')
data = client.get_data(
symbol='rb888',
start_date='2023-12-28',
end_date='2024-01-17',
kline_period='60M',
adjust_type=1
)
data.set_index("datetime", inplace=True)
data.index = pd.to_datetime(data.index)
print(data)
#生产K线图表到脚本同目录下
plotK(data)

249
2.数据下载与处理/ssquant_download/松鼠数据下载脚本.ipynb Normal file
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@@ -0,0 +1,249 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1a846b12",
"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='240884432@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\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "65b4b7aa",
"metadata": {},
"outputs": [],
"source": [
"from ssquant.SQDATA import TakeData\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "edd4f1e5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" datetime symbol open high low close volume \\\n",
"0 2023-01-03 10:00:00 rb2305 4081 4081 4016 4037 737537 \n",
"1 2023-01-03 11:00:00 rb2305 4038 4056 4037 4042 158548 \n",
"2 2023-01-03 12:00:00 rb2305 4044 4054 4037 4051 67448 \n",
"3 2023-01-03 14:00:00 rb2305 4055 4065 4045 4058 110181 \n",
"4 2023-01-03 15:00:00 rb2305 4059 4074 4056 4063 167932 \n",
".. ... ... ... ... ... ... ... \n",
"112 2023-02-01 12:00:00 rb2305 4126 4129 4105 4107 193291 \n",
"113 2023-02-01 14:00:00 rb2305 4108 4117 4100 4109 137182 \n",
"114 2023-02-01 15:00:00 rb2305 4109 4114 4075 4084 378930 \n",
"115 2023-02-01 22:00:00 rb2305 4092 4104 4087 4103 207519 \n",
"116 2023-02-01 23:00:00 rb2305 4102 4109 4075 4098 189724 \n",
"\n",
" amount cumulative_openint openint open_bidp open_askp \\\n",
"0 29782187220 1883481 -48415 4081 4084 \n",
"1 6415696920 1887716 4235 4037 4038 \n",
"2 2728130300 1890125 2409 4043 4044 \n",
"3 4469698600 1895841 5723 4050 4051 \n",
"4 6824213940 1882723 -13125 4058 4059 \n",
".. ... ... ... ... ... \n",
"112 7954826320 1984919 3490 4125 4126 \n",
"113 5634834380 1998312 13394 4108 4109 \n",
"114 15503896450 1994915 -3398 4109 4110 \n",
"115 8500232870 1988628 -5587 4091 4092 \n",
"116 7757206650 1973544 -15099 4101 4102 \n",
"\n",
" close_bidp close_askp \n",
"0 4037 4038 \n",
"1 4042 4044 \n",
"2 4050 4051 \n",
"3 4058 4059 \n",
"4 4062 4063 \n",
".. ... ... \n",
"112 4106 4107 \n",
"113 4108 4109 \n",
"114 4084 4085 \n",
"115 4102 4103 \n",
"116 4098 4099 \n",
"\n",
"[117 rows x 14 columns]\n"
]
}
],
"source": [
"client = TakeData(username='240884432@qq.com', password='Zj123!@#')\n",
"data = client.get_data(\n",
" symbol='rb888',\n",
" start_date='2023-01-01',\n",
" end_date='2023-02-01',\n",
" kline_period='60M',\n",
" adjust_type=1\n",
")\n",
"print(data)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "25c70609",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" symbol open high low close volume amount \\\n",
"datetime \n",
"2023-01-03 10:00:00 rb2305 4081 4081 4016 4037 737537 29782187220 \n",
"2023-01-03 11:00:00 rb2305 4038 4056 4037 4042 158548 6415696920 \n",
"2023-01-03 12:00:00 rb2305 4044 4054 4037 4051 67448 2728130300 \n",
"2023-01-03 14:00:00 rb2305 4055 4065 4045 4058 110181 4469698600 \n",
"2023-01-03 15:00:00 rb2305 4059 4074 4056 4063 167932 6824213940 \n",
"... ... ... ... ... ... ... ... \n",
"2023-02-01 12:00:00 rb2305 4126 4129 4105 4107 193291 7954826320 \n",
"2023-02-01 14:00:00 rb2305 4108 4117 4100 4109 137182 5634834380 \n",
"2023-02-01 15:00:00 rb2305 4109 4114 4075 4084 378930 15503896450 \n",
"2023-02-01 22:00:00 rb2305 4092 4104 4087 4103 207519 8500232870 \n",
"2023-02-01 23:00:00 rb2305 4102 4109 4075 4098 189724 7757206650 \n",
"\n",
" cumulative_openint openint open_bidp open_askp \\\n",
"datetime \n",
"2023-01-03 10:00:00 1883481 -48415 4081 4084 \n",
"2023-01-03 11:00:00 1887716 4235 4037 4038 \n",
"2023-01-03 12:00:00 1890125 2409 4043 4044 \n",
"2023-01-03 14:00:00 1895841 5723 4050 4051 \n",
"2023-01-03 15:00:00 1882723 -13125 4058 4059 \n",
"... ... ... ... ... \n",
"2023-02-01 12:00:00 1984919 3490 4125 4126 \n",
"2023-02-01 14:00:00 1998312 13394 4108 4109 \n",
"2023-02-01 15:00:00 1994915 -3398 4109 4110 \n",
"2023-02-01 22:00:00 1988628 -5587 4091 4092 \n",
"2023-02-01 23:00:00 1973544 -15099 4101 4102 \n",
"\n",
" close_bidp close_askp \n",
"datetime \n",
"2023-01-03 10:00:00 4037 4038 \n",
"2023-01-03 11:00:00 4042 4044 \n",
"2023-01-03 12:00:00 4050 4051 \n",
"2023-01-03 14:00:00 4058 4059 \n",
"2023-01-03 15:00:00 4062 4063 \n",
"... ... ... \n",
"2023-02-01 12:00:00 4106 4107 \n",
"2023-02-01 14:00:00 4108 4109 \n",
"2023-02-01 15:00:00 4084 4085 \n",
"2023-02-01 22:00:00 4102 4103 \n",
"2023-02-01 23:00:00 4098 4099 \n",
"\n",
"[117 rows x 13 columns]\n"
]
}
],
"source": [
"data.set_index(\"datetime\", inplace=True)\n",
"data.index = pd.to_datetime(data.index)\n",
"print(data)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": 5
}

2
2.数据下载与处理/ssquant_download/说明.txt Normal file
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