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Copy pathSSE_Stocks_traded_Byday_analysis.py
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SSE_Stocks_traded_Byday_analysis.py
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# -*- coding:utf-8 -*-
import datetime
from datetime import date
from chinese_calendar import is_workday,is_holiday
import akshare as ak
import pandas as pd
import numpy as np
import pymysql
from sqlalchemy import create_engine
#以下代码实现个股每日分笔成交数据初步分析，通过更改company code即可实现不同个股切换；
# Query data (only change code and database name is stock_deal_detail_daily)
company_code = 'sz000651'
# date（By day after stock Market Closed ）
# use akshare module
### DDD = Deal Detail Daily abbreviation
### 待补充case函数反馈抓取异常；
DDD = ak.stock_zh_a_tick_tx_js(code=company_code)
# query data is all string of the DataFrame formate
# pre handle to numeric and datatime and set datetimeindex
rowdata = DDD.apply(pd.to_numeric,errors= 'ignore')
rowdata['成交时间']=pd.to_datetime(rowdata['成交时间'])
rowdata.set_index('成交时间',inplace=True)
#print(rowdata)
#rowdata 初步处理，生成初步指标数据：
# 开盘价，收盘价 当日幅动等，详见附件思维导入或解析说明
# 1.open price
open_price = rowdata.iat[0,0]
# 2.close price
closed_price = rowdata.iat[len(rowdata.index)-1,0]
# 3.today_change_precent
today_change_precent = (closed_price-open_price)/open_price
# 4.day range
today_high = rowdata['成交价'].max()
today_low = rowdata['成交价'].min()
day_range = (today_high-today_low)/today_low
#print(today_change_precent,day_range)
# 5.turnover
today_turnover = rowdata['成交额'].sum()
# 6.Volume
today_volume = rowdata['成交量'].sum()
# 7.turn over > 500K ration
turnover_500K = rowdata.loc[rowdata['成交额']>500000]
turnover_500K_sum =turnover_500K['成交额'].sum()
#print(today_turnover,turnover_500K_sum)
#print(type(today_turnover),type(turnover_500K_sum))
turnover_500K_ration = turnover_500K_sum/today_turnover
#print(turnover_500K_ration)
# divide B/S
buyorder = rowdata.loc[(rowdata['性质']=='B')]
sellorder= rowdata.loc[(rowdata['性质']=='S')]
# 8.turn over active buy
buyorder_turnover = buyorder['成交额'].sum()
# 9.turn over postibve sell
sellorder_sellorder = sellorder['成交额'].sum()
# 10.net_inflows
net_inflows = buyorder_turnover-sellorder_sellorder
#print(net_inflows)
# By 成交金额By成交金额(区分B/S)
# >1000
# >500
# >200
# >100
# >50
#10. >10Million
buyorder_turnover_10M = buyorder.loc[buyorder['成交额']>10000000]
buyorder_turnover_10M_sum = buyorder_turnover_10M['成交额'].sum()
#print(buyorder_turnover_10M_sum)
sellorder_turnover_10M = sellorder.loc[sellorder['成交额']>10000000]
sellorder_turnover_10M_sum = sellorder_turnover_10M['成交额'].sum()
#print(sellorder_turnover_10M_sum)
net_inflows_10Million = buyorder_turnover_10M_sum-sellorder_turnover_10M_sum
# 10Millin<>5Million
buyorder_turnover_5M = buyorder.loc[(buyorder['成交额']>5000000)&(buyorder['成交额']<10000000)]
buyorder_turnover_5M_sum = buyorder_turnover_5M['成交额'].sum()
#print(buyorder_turnover_5M_sum)
sellorder_turnover_5M = sellorder.loc[(sellorder['成交额']>5000000)&(sellorder['成交额']<10000000)]
sellorder_turnover_5M_sum = sellorder_turnover_5M['成交额'].sum()
#print(sellorder_turnover_5M_sum)
net_inflows_5Million = buyorder_turnover_5M_sum-sellorder_turnover_5M_sum
# 5Millin<>2Million
buyorder_turnover_2M = buyorder.loc[(buyorder['成交额']>2000000)&(buyorder['成交额']<5000000)]
buyorder_turnover_2M_sum = buyorder_turnover_2M['成交额'].sum()
#print(buyorder_turnover_2M_sum)
sellorder_turnover_2M = sellorder.loc[(sellorder['成交额']>2000000)&(sellorder['成交额']<5000000)]
sellorder_turnover_2M_sum = sellorder_turnover_2M['成交额'].sum()
#print(sellorder_turnover_2M_sum)
net_inflows_2Million = buyorder_turnover_2M_sum-sellorder_turnover_2M_sum
# 2Millin<>1Million
buyorder_turnover_1M = buyorder.loc[(buyorder['成交额']>1000000)&(buyorder['成交额']<2000000)]
buyorder_turnover_1M_sum = buyorder_turnover_1M['成交额'].sum()
#print(buyorder_turnover_1M_sum)
sellorder_turnover_1M = sellorder.loc[(sellorder['成交额']>1000000)&(sellorder['成交额']<2000000)]
sellorder_turnover_1M_sum = sellorder_turnover_1M['成交额'].sum()
#print(sellorder_turnover_1M_sum)
net_inflows_1Million = buyorder_turnover_1M_sum-sellorder_turnover_1M_sum
# 1Millin<>500K
buyorder_turnover_500K = buyorder.loc[(buyorder['成交额']>500000)&(buyorder['成交额']<1000000)]
buyorder_turnover_500K_sum = buyorder_turnover_500K['成交额'].sum()
#print(buyorder_turnover_500K_sum)
sellorder_turnover_500K = sellorder.loc[(sellorder['成交额']>500000)&(sellorder['成交额']<1000000)]
sellorder_turnover_500K_sum = sellorder_turnover_500K['成交额'].sum()
#print(sellorder_turnover_500K_sum)
net_inflows_500K = buyorder_turnover_500K_sum-sellorder_turnover_500K_sum
# <500K
buyorder_turnover_500000 = buyorder.loc[buyorder['成交额']<500000]
buyorder_turnover_500000_sum = buyorder_turnover_500000['成交额'].sum()
#print(buyorder_turnover_500000_sum)
sellorder_turnover_500000 = sellorder.loc[sellorder['成交额']<500000]
sellorder_turnover_500000_sum = sellorder_turnover_500000['成交额'].sum()
#print(sellorder_turnover_500000_sum)
net_inflows_1Million = buyorder_turnover_500000_sum-sellorder_turnover_500000_sum
# By成交时间段(区分B/S)
# 9:25~9:45
# 9:45~10:00
# 10:00~10:30
# 10:30~11:30
# 13:00~14:30
# 14:30~15:00
# 9:25~9:45
buyorder_time_index_0945 = buyorder.index.indexer_between_time('09:25:00','9:44:59')
#print(data_num_time)
#print(data_num.iloc[data_num_time])
buyorder_time_0945 = buyorder.iloc[buyorder_time_index_0945]['成交额'].sum()
#print(buyorder_time_0945)
sellorder_time_index_0945 = sellorder.index.indexer_between_time('09:25:00','9:44:59')
sellorder_time_0945 = sellorder.iloc[sellorder_time_index_0945]['成交额'].sum()
#print(sellorder_time_0945)
# 9:46~10:00
buyorder_time_index_1000 = buyorder.index.indexer_between_time('09:45:00','09:59:59')
buyorder_time_1000 = buyorder.iloc[buyorder_time_index_1000]['成交额'].sum()
#print(buyorder_time_1000)
sellorder_time_index_1000 = sellorder.index.indexer_between_time('09:45:00','09:59:59')
sellorder_time_1000 = sellorder.iloc[sellorder_time_index_1000]['成交额'].sum()
#print(sellorder_time_1000)
# 10:00~10:30
buyorder_time_index_1030 = buyorder.index.indexer_between_time('10:00:00','10:29:59')
buyorder_time_1030 = buyorder.iloc[buyorder_time_index_1030]['成交额'].sum()
#print(buyorder_time_1030)
sellorder_time_index_1030 = sellorder.index.indexer_between_time('10:00:00','10:29:59')
sellorder_time_1030= sellorder.iloc[sellorder_time_index_1030]['成交额'].sum()
#print(sellorder_time_1030)
# 10:30~11:30
buyorder_time_index_1130 = buyorder.index.indexer_between_time('10:30:00','11:30:59')
buyorder_time_1130 = buyorder.iloc[buyorder_time_index_1130]['成交额'].sum()
#print(buyorder_time_1130)
sellorder_time_index_1130 = sellorder.index.indexer_between_time('10:30:00','11:30:59')
sellorder_time_1130 = sellorder.iloc[sellorder_time_index_1130]['成交额'].sum()
#print(sellorder_time_1130)
# 13:00~14:30
buyorder_time_index_1430 = buyorder.index.indexer_between_time('13:00:00','14:29:59')
buyorder_time_1430 = buyorder.iloc[buyorder_time_index_1430]['成交额'].sum()
#print(buyorder_time_1430)
sellorder_time_index_1430 = sellorder.index.indexer_between_time('13:00:00','14:29:59')
sellorder_time_1430 = sellorder.iloc[sellorder_time_index_1430]['成交额'].sum()
#print(sellorder_time_1430)
# 14:30~15:00
buyorder_time_index_1500 = buyorder.index.indexer_between_time('14:30:00','15:00:59')
buyorder_time_1500 = buyorder.iloc[buyorder_time_index_1500]['成交额'].sum()
#print(buyorder_time_1500)
sellorder_time_index_1500 = sellorder.index.indexer_between_time('14:30:00','15:00:59')
sellorder_time_1500 = sellorder.iloc[sellorder_time_index_1500]['成交额'].sum()
#print(sellorder_time_1500)
datatime=datetime.date.today()
#td=datetime.date.today()+datetime.timedelta(days=-3)
#trade_date_str =trade_date.strftime("%Y%m%d")
reviewdata_list=[datatime,open_price,closed_price,today_change_precent,today_turnover,today_volume,turnover_500K_ration,buyorder_turnover,sellorder_sellorder,net_inflows,
buyorder_turnover_10M_sum,sellorder_turnover_10M_sum,buyorder_turnover_5M_sum,sellorder_turnover_5M_sum,buyorder_turnover_2M_sum,sellorder_turnover_2M_sum,buyorder_turnover_1M_sum,sellorder_turnover_1M_sum,buyorder_turnover_500K_sum,sellorder_turnover_500K_sum,
buyorder_time_0945,sellorder_time_0945,buyorder_time_1000,sellorder_time_1000,buyorder_time_1030,sellorder_time_1030,buyorder_time_1000,sellorder_time_1000,buyorder_time_1130,sellorder_time_1130,buyorder_time_1430,sellorder_time_1430,buyorder_time_1500,sellorder_time_1500,]
#print(reviewdata_list)
reviewdata_dic={
'datatime':datatime,
'open_price':open_price,
'closed_price':closed_price,
'today_change_precent':today_change_precent,
'today_volume':today_volume,
'turnover_500K_ration':turnover_500K_ration,
'buyorder_turnover':buyorder_turnover,
'sellorder_sellorder':sellorder_sellorder,
'net_inflows':net_inflows,
'buyorder_turnover_10M_sum':buyorder_turnover_10M_sum,
'sellorder_turnover_10M_sum':sellorder_turnover_10M_sum,
'buyorder_turnover_5M_sum':buyorder_turnover_5M_sum,
'sellorder_turnover_5M_sum':sellorder_turnover_5M_sum,
'buyorder_turnover_2M_sum':buyorder_turnover_2M_sum,
'sellorder_turnover_2M_sum':sellorder_turnover_2M_sum,
'sellorder_turnover_1M_sum':sellorder_turnover_1M_sum,
'buyorder_turnover_500K_sum':buyorder_turnover_500K_sum,
'sellorder_turnover_500K_sum':sellorder_turnover_500K_sum,
'buyorder_time_0945':buyorder_time_0945,
'sellorder_time_0945':sellorder_time_0945,
'buyorder_time_1000':buyorder_time_1000,
'sellorder_time_1000':sellorder_time_1000,
'buyorder_time_1030':buyorder_time_1030,
'sellorder_time_1030':sellorder_time_1030,
'buyorder_time_1000':buyorder_time_1000,
'sellorder_time_1000':sellorder_time_1000,
'buyorder_time_1130':buyorder_time_1130,
'sellorder_time_1130':sellorder_time_1130,
'buyorder_time_1430':buyorder_time_1430,
'sellorder_time_1430':sellorder_time_1430,
'buyorder_time_1500':buyorder_time_1500,
'sellorder_time_1500':sellorder_time_1500,
}
#print(reviewdata_dic)
reviewdata=pd.DataFrame(reviewdata_dic,index=[0])
#reviewdata.set_index('datatime',inplace=True)
#print(reviewdata)
rowdata_datetime = DDD.apply(pd.to_numeric,errors= 'ignore')
rowdata_datetime['成交时间']=pd.to_datetime(rowdata_datetime['成交时间'])
#print(rowdata_datetime)
'''
# 创建数据库
conn = pymysql.connect(host='localhost',user='root',passwd='cucumber',charset='utf8')
cursor = conn.cursor()
cursor.execute("CREATE DATABASE IF NOT EXISTS stock_deal_detail_daily")
conn.close()
'''
# store ddd data to sql
from sqlalchemy import create_engine
tablename_ddd_rowdata = company_code+'_ddd_rowdata'
tablename_ddd_reviewdata = company_code+'_ddd_reviewdata'
#print(tablename_ddd_rowdata,tablename_ddd_reviewdata)
conn = create_engine('mysql+pymysql://root:cucumber@localhost/stock_deal_detail_daily',encoding='utf-8')
rowdata_datetime.to_sql(name=tablename_ddd_rowdata, con=conn, if_exists='append', index=False)
reviewdata.to_sql(name=tablename_ddd_reviewdata, con=conn, if_exists='append', index=False)