def get_benchmark_data(benchmark, start_date, end_data):
if Settings.data_source == DataSource.DXDataCenter:
benchmark_data = api.GetIndexBarEOD(instrumentIDList=benchmark,
startDate=start_date,
endDate=end_data,
field=['closePrice'])
elif Settings.data_source == DataSource.DataYes:
import os
import tushare as ts
try:
ts.set_token(os.environ['DATAYES_TOKEN'])
except KeyError:
raise
mt = ts.Market()
benchmark_data = mt.MktIdxd(benchmark,
beginDate=start_date.replace('-', ''),
endDate=end_data.replace('-', ''),
field='closeIndex,tradeDate')
benchmark_data = benchmark_data.set_index('tradeDate')
benchmark_data = benchmark_data.rename(columns={'closeIndex': 'closePrice'})
benchmark_data.index = pd.to_datetime(benchmark_data.index, format="%Y-%m-%d")
return benchmark_data
def run():
ts.set_token(ct.DATA_YES_TOKEN)
st = ts.Market()
today = datetime.strftime(datetime.today(),"%Y%m%d")
stock_list = st.MktEqud(tradeDate="20160513",field="ticker,PE,secShortName")
if not isinstance(stock_list,pd.DataFrame) or stock_list.empty:
return
stock_list['ticker'] = stock_list['ticker'].map(lambda x: str(x).zfill(6))
result = []
mongo = Mongo()
db = mongo.getDB()
for i in stock_list.index:
code = stock_list.loc[i,'ticker']
pe = stock_list.loc[i,'PE']
name = stock_list.loc[i,'secShortName']
if np.isnan(pe):
continue
cursor = db.year_min_value.find({"ticker":code})
if cursor.count() <= 0:
continue
pe_list = []
for row in cursor:
pe_list.append(row['pe'])
min_pe = min(pe_list)
rate = (pe - min_pe)/min_pe
result.append({"code":code,"name":name,"pe":pe,"min_pe":min_pe,"rate":rate})
df = pd.DataFrame(result)
if db.lowest_pe_stock.find().count() > 0:
db.lowest_pe_stock.remove()
db.lowest_pe_stock.insert(json.loads(df.to_json(orient='records')))
def import_datayes_daily_data(start_date, end_date, cont_list = [], is_replace = False):
numdays = (end_date - start_date).days + 1
date_list = [start_date + datetime.timedelta(days=x) for x in range(0, numdays) ]
date_list = [ d for d in date_list if (d.weekday()< 5) and (d not in misc.CHN_Holidays)]
for d in date_list:
cnt = 0
dstring = d.strftime('%Y%m%d')
ts.set_token(misc.datayes_token)
mkt = ts.Market()
df = mkt.MktFutd(tradeDate = dstring)
if len(df.ticker) == 0:
continue
for cont in df.ticker:
if (len(cont_list) > 0) and (cont not in cont_list):
continue
data = df[df.ticker==cont]
if len(data) == 0:
print 'no data for %s for %s' % (cont, dstring)
else:
data_dict = {}
data_dict['date'] = d
data_dict['open'] = float(data.openPrice)
data_dict['close'] = float(data.closePrice)
data_dict['high'] = float(data.highestPrice)
data_dict['low'] = float(data.lowestPrice)
data_dict['volume'] = int(data.turnoverVol)
data_dict['openInterest'] = int(data.openInt)
if data_dict['volume'] > 0:
cnt += 1
db.insert_daily_data(cont, data_dict, is_replace = is_replace, dbtable = 'fut_daily')
print 'date=%s, insert count = %s' % (d, cnt)
def get_equity_eod(instruments, start_date, end_date):
if Settings.data_source == DataSource.DXDataCenter:
from DataAPI import api
data = api.GetEquityBarEOD(instrumentIDList=instruments,
startDate=start_date,
endDate=end_date,
field='closePrice',
instrumentIDasCol=True,
baseDate='end')
elif Settings.data_source == DataSource.DataYes:
import os
import tushare as ts
try:
ts.set_token(os.environ['DATAYES_TOKEN'])
except KeyError:
raise
mt = ts.Market()
res = []
for ins in instruments:
data = mt.MktEqud(ticker=ins,
beginDate=start_date.replace('-', ''),
endDate=end_date.replace('-', ''),
field='tradeDate,ticker,closePrice')
res.append(data)
data = pd.concat(res)
data['tradeDate'] = pd.to_datetime(data['tradeDate'], format='%Y-%m-%d')
data['ticker'] = data['ticker'].apply(lambda x: '{0:06d}'.format(x))
data.set_index(['tradeDate', 'ticker'], inplace=True, verify_integrity=True)
data = data.unstack(level=-1)
return data
def getNews(self):
token = '60517739976b768e07823056c6f9cb0fee33ed55a1709b3eaa14a76c6a1b7a56'
ts.set_token(token)
print(ts.get_token())
mkt = ts.Market()
df = mkt.TickRTSnapshot(securityID='000001.XSHE')
print(df)
def quanshan():
#Failed
ts.set_token('de0596189f600d1dc59c509e5b6a1387e4e29cb6225697a25ef9d5d2a425d854')
ts.get_token()
mt = ts.Market()
print(mt)
df = mt.TickRTSnapshot(securityID='000001.XSHE')
print(df)
def set_universe(code, refDate=None):
if Settings.data_source != DataSource.DXDataCenter:
import tushare as ts
ts.set_token('2bfc4b3b06efa5d8bba2ab9ef83b5d61f1c3887834de729b60eec9f13e1d4df8')
idx = ts.Idx()
return list(idx.IdxCons(secID=code, field='consID')['consID'])
else:
from DataAPI import api
data = api.GetIndexConstitutionInfo(code, refDate=refDate).sort_values('conSecurityID')
return list(data.conSecurityID)
def ipo():
ts.set_token('b4c94429dc00fee32d14c52507d9cd44c9621ca91eaa161fcec14041')
pro = ts.pro_api()
# 查询当前所有正常上市交易的股票列表
df = pro.stock_basic(exchange_id='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
print(df)
data = df.to_dict('index')
print(data.items())
for item, value in sorted(data.items()):
code = value['symbol']
ipo_date = value['list_date']
Stock.objects(code=code).update_one(code=code, ipo_date=ipo_date, upsert=True)
def set_token(token=None):
try:
if token is None:
# 从~/.quantaxis/setting/config.ini中读取配置
token = QASETTING.get_config('TSPRO', 'token', None)
else:
QASETTING.set_config('TSPRO', 'token', token)
ts.set_token(token)
except:
if token is None:
print('请设置tushare的token')
else:
print('请升级tushare 至最新版本 pip install tushare -U')
def set_universe(code, refDate=None):
if Settings.data_source != DataSource.DXDataCenter:
import os
import tushare as ts
try:
ts.set_token(os.environ['DATAYES_TOKEN'])
except KeyError:
raise
idx = ts.Idx()
return list(idx.IdxCons(secID=code, field='consID')['consID'])
else:
from DataAPI import api
data = api.GetIndexConstitutionInfo(code, refDate=refDate).sort_values('conSecurityID')
return list(data.conSecurityID)
def __init__(self, **kwargs):
super(DataYesMarketDataHandler, self).__init__(kwargs['logger'], kwargs['symbolList'])
if kwargs['token']:
ts.set_token(kwargs['token'])
else:
try:
token = os.environ['DATAYES_TOKEN']
ts.set_token(token)
except KeyError:
raise ValueError("Please input token or set up DATAYES_TOKEN in the envirement.")
self.idx = ts.Idx()
self.startDate = kwargs['startDate'].strftime("%Y%m%d")
self.endDate = kwargs['endDate'].strftime("%Y%m%d")
self._getDatas()
if kwargs['benchmark']:
self._getBenchmarkData(kwargs['benchmark'], self.startDate, self.endDate)
def get_stockholder(code, start, end):
# 十大非流通
global pro
try:
stockholder = pro.top10_holders(ts_code=code, start_date=start, end_date=end)
time.sleep(1)
stockfloat = pro.top10_floatholders(ts_code=code, start_date=start, end_date=end)
time.sleep(1)
except Exception as e:
print(e)
time.sleep(10)
ts.set_token(token)
pro = ts.pro_api()
else:
if stockholder.empty and stockfloat.empty:
return pd.DataFrame(), pd.DataFrame()
else:
return stockholder, stockfloat
def fetch_():
ts.set_token(cfg.get_datayes_key())
eq = ts.Equity()
df = eq.Equ(equTypeCD='A', listStatusCD='L', field='ticker')
df['ticker'] = df['ticker'].map(lambda x: str(x).zfill(6))
start, end = '20150901', '20160326'
# thread can not make full use of cpu
for i, row in df.iterrows():
csv = cfg.get_ratio_table_path(row['ticker'], start, end)
if os.path.exists(csv):
print("{0}/{1} {2} exists.".format(i, len(df.index), row['ticker']))
continue
# code_queue.put(row['ticker'])
# code_list.append(str(row['ticker']))
proc_queue.put(row['ticker'])
# for i in range(3):
# thread = ExportThread(thread_id, code_queue, start, end)
# thread.start()
# threads.append(thread)
# thread_id += 1
#
# get_code_and_export(0, code_queue, start, end)
#
# for t in threads:
# t.join()
#
# print("Exit main thread.")
processes = 4
for i in range(processes):
p = ExportProcess(i+1, start, end, proc_queue)
p.start()
procs.append(p)
for p in procs:
p.join()
print("Exit main")
def export_ratio_table(code, start, end, thread_id):
# queue_lock.acquire()
ts.set_token(cfg.get_datayes_key())
mkt = ts.Market()
# print("exporting " + code + " from " + start + " to " + end)
st = time.time()
df = mkt.MktEqud(ticker=code, beginDate=start, endDate=end,
field='ticker,tradeDate,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice')
print(" Thread {0} fetch online: {1}".format(thread_id, time.time()-st))
# queue_lock.release()
# df = ts.get_h_data(code, start, end)
# print(df)
wave_ratio_df = pd.DataFrame(columns=["max_ratio", "min_ratio"])
for i, row in df.iterrows():
dict = wv.calc_wave_ratio(row["preClosePrice"], row["openPrice"],
row["highestPrice"], row["lowestPrice"])
wave_ratio_df.loc[row["tradeDate"]] = dict
st = time.time()
idx_col = wv.calc_ratio_table_index_and_columns(max_ratio=0.03, min_ratio=-0.03)
index, columns = idx_col["index"], idx_col["columns"]
ratio_table = wv.calc_ratio_table(wave_ratio_df, index, columns)
print(" Thread {0} calc ratio table: {1}".format(thread_id, time.time()-st))
st = time.time()
length_ratio_df = wv.calc_length_ratio(ratio_table, len(wave_ratio_df.index))
print(" Thread {0} calc length ratio: {1}".format(thread_id, time.time()-st))
# write csv
st = time.time()
ratio_table.to_csv(cfg.get_ratio_table_path(code, start, end))
length_ratio_df.to_csv(cfg.get_length_ratio_path(code, start, end))
# print(" save csv: {0}".format(time.time()-st))
return length_ratio_df
def setToken(token):
ts.set_token(token)
print(ts.get_token())
import tushare as ts
import pymysql
import time
import datetime
from sqlalchemy import create_engine
thistbname = 'tb_moneyflowhsgt'
thistbinfo = '沪深港通资金流向'
ts.set_token('7eb4bc05a48bb2704d76c1b79c501053b58ad1b190b505faa9009d5c')
pro = ts.pro_api()
con = pymysql.connect(user='******',
password='******',
database='fundamentalplatform',
charset='utf8')
cu = con.cursor()
nowtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
nowdate = time.strftime("%Y%m%d", time.localtime())
'''
engine = create_engine("mysql+pymysql://{}:{}@{}/{}".format('root', 'lksjlksj', 'localhost', 'fundamentalplatform'))
con = engine.connect()
df = pro.moneyflow_hsgt(trade_date='20190315')
print(df)
df.to_sql(name=thistbname, con=con, if_exists='append', index=False)
'''
try:
cu.execute(
'select lastdate from tb_index where tbname = "{tbname}"'.format(
tbname=thistbname))
lastdate = cu.fetchall()[0][0]
import matplotlib.pyplot as plt
import tushare as ts
import datetime
dt=datetime.datetime.now().strftime('%Y%m%d')
symbol='300072'
symbol='300330'
symbol='600868'
symbol='002384'
symbol='002430'
symbol='002382'
# df1 = ts.get_today_ticks(symbol)
# df2 = ts.get_tick_data(symbol,date=dt)
# df3 = ts.get_hist_data(symbol)
df = ts.get_realtime_quotes(symbol)
ts.set_token('7e30dd0a070cd4306193a5925ec5b3c250a694f08ea390d7cc3af2d6')
pro = ts.pro_api()
df = pro.fina_mainbz(ts_code='000627.SZ', period='20171231', type='P')
# df = pro.index_basic('sw')
# df = pro.cashflow(ts_code='600000.SH', start_date='20180101', end_date='20180730')
# df =ts.moneyflow_hsgt()
# ts.cashflow(symbol)
print df.describe()
# ts.get_k_data
# ts.get_hist_data
# ts.get_today_all
# print df1,df2
# df2.to_csv('%s.today.csv'%symbol,encoding ='gbk' )
# print df
# exit()
return dd, hh, mm, ss
# 主函数
if __name__ == '__main__':
# 计时开始
time_start = time.time()
# 建立数据库连接,设置tushare的token,定义一些初始化参数
db = pymysql.connect(host='localhost',
user='******',
passwd='your password',
db='your dbname',
charset='utf8mb4')
cursor = db.cursor()
ts.set_token('your token')
pro = ts.pro_api()
# 选取回测区间
year = 2019
date_seq_start = str(year) + '-07-24'
date_seq_end = str(year) + '-08-23'
# 计算一个真实时间间隔, 用于折算Sharp Rate中对应的无风险利率
dt_start = datetime.datetime.strptime(date_seq_start, '%Y-%m-%d')
dt_end = datetime.datetime.strptime(date_seq_end, '%Y-%m-%d')
delta_dt = (dt_end - dt_start).days
# 设定需要进行回测的股票池, 取云计算相关的: 中国软件, 中兴通讯, 浪潮信息, 用友网络, 宝信软件
# 高端的策略在于选股, 目前没有能力完成
stock_pool = [
import tushare as ts
import pandas as pd
import os
import numpy as np
import time
from tqdm import tqdm
"""
获取历史数据
"""
mytoken = ''
ts.set_token(mytoken)
ts.set_token(mytoken)
save_path = 'stock'
pro = ts.pro_api()
def getNoramlData():
#获取基础信息数据，包括股票代码、名称、上市日期、退市日期等
pool = pro.stock_basic(exchange='',
list_status='L',
adj='qfq',
fields='ts_code,symbol,name,area,industry,fullname,list_date, market,exchange,is_hs')
#print(pool.head())
# 因为穷没开通创业板和科创板权限，这里只考虑主板和中心板
pool = pool[pool['market'].isin(['主板', '中小板'])].reset_index()
pool.to_csv(os.path.join(save_path, 'company_info.csv'), index=False, encoding='utf-8')
print('获得上市股票总数：', len(pool)-1)
def _login():
token = '0b23bc848cf9b8e79df3b0d90c2406ddff1ffd2cc30835b1782be1177f3804fb'
ts.set_token(token)
import tushare as ts
import quantdata.cons as ct
from quantdata.db.mongo import Mongo
from datetime import datetime
from quantdata import logger
import time
import json
import pymongo
ts.set_token(ct.DATA_YES_TOKEN)
def run():
'''get qoute data '''
#set log
LOGGER_NAME = "HISTORY_DATA"
mylog = logger.getLogger(LOGGER_NAME)
#get the stock list
today = datetime.strftime(datetime.today(),"%Y%m%d")
mongo = Mongo()
db = mongo.getDB()
cursor = db.stock_list.find({"listStatusCD":"L"})
for row in cursor:
ticker = str(row['ticker'])
mylog.info("update history data of %s"%(ticker))
exchangeCD = str(row['exchangeCD'])
listDate = str(row['listDate']).replace("-", "").replace("NaN", "")
if exchangeCD == 'XSHG' and not ticker.startswith("6"):
continue
# coding=utf-8
# Token=728e54671e5284ce12bb098ea5d481b266570256d5430b53750093af
import tushare as ts
ts.set_token('728e54671e5284ce12bb098ea5d481b266570256d5430b53750093af')
pro=ts.pro_api()
#df=pro.query('daily',ts_code='000835.SZ',start_date='20170101',end_date='20180801')
df=ts.get_hist_data('000835')
print(df)
#df.to_csv('..\\..\\Quant\\stock\\Data\\NewDown\\000835.sz.csv')
def get_tushare_api():
ts.set_token('9303ab9ddece253dc96ac6f4662f22a1d0d92579f1d18368f87aaf65')
return ts.pro_api()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tushare as ts
ts.set_token('c2ce9df72eb324c01e809da43379650bbc25f3a0f0c2ef9aa75f6baa')
#获取数据
s_pf = '600848'
s_gd = '601818'
sdate = '2016-01-01'
edate = '2016-12-31'
pro = ts.pro_api()
#print(ts.get_hist_data('600848')) #一次性获取全部日k线数据
# res=ts.pro_bar(ts_code='000001.SZ', adj='qfq', start_date='20180101', end_date='20181011')
# print(res.close)
df_pf = ts.pro_bar(ts_code='002407.SZ',
adj='qfq',
start_date='20180101',
end_date='20190521').sort_index(axis=0, ascending=True)
df_gd = ts.pro_bar(ts_code='002092.SZ',
adj='qfq',
start_date='20190101',
end_date='20190521').sort_index(axis=0, ascending=True)
# df_pf=ts.get_h_data(s_pf,start=sdate,end=edate)
# df_gd=ts.get_h_data(s_gd,start=sdate,end=edate).sort_index(axis=0,ascending=True)
# print(df_pf.close)
#df=pd.concat([df_pf.close,df_gd.close],axis=1,keys=['pf_close','gd_close'])
df = pd.concat([df_pf.close], axis=1, keys=['pf_close'])
df.ffill(axis=0, inplace=True) #填充数据
df.to_csv('pf_gd.csv')
def get_portfolio(stock_list,state_dt,para_window):
# 建数据库连接，设置Tushare的token
db = pymysql.connect(host='127.0.0.1', user='******', passwd='root', db='stock', charset='utf8')
cursor = db.cursor()
ts.set_token('519ade7742d87ada41e0aba865dbecfadeed0a4443f6f88ff6a300a2')
pro = ts.pro_api()
portfilio = stock_list
# 建评估时间序列, para_window参数代表回测窗口长度
model_test_date_start = (datetime.datetime.strptime(state_dt, '%Y-%m-%d') - datetime.timedelta(days=para_window)).strftime(
'%Y%m%d')
model_test_date_end = (datetime.datetime.strptime(state_dt, "%Y-%m-%d")).strftime('%Y%m%d')
df = pro.trade_cal(exchange_id='', is_open=1, start_date=model_test_date_start, end_date=model_test_date_end)
date_temp = list(df.iloc[:, 1])
model_test_date_seq = [(datetime.datetime.strptime(x, "%Y%m%d")).strftime('%Y-%m-%d') for x in date_temp]
list_return = []
for i in range(len(model_test_date_seq)-4):
ti = model_test_date_seq[i]
ri = []
for j in range(len(portfilio)):
sql_select = "select * from stock_all a where a.stock_code = '%s' and a.state_dt >= '%s' and a.state_dt <= '%s' order by state_dt asc" % (portfilio[j], model_test_date_seq[i], model_test_date_seq[i + 4])
cursor.execute(sql_select)
done_set = cursor.fetchall()
db.commit()
temp = [x[3] for x in done_set]
base_price = 0.00
after_mean_price = 0.00
if len(temp) <= 1:
r = 0.00
else:
base_price = temp[0]
after_mean_price = np.array(temp[1:]).mean()
r = (float(after_mean_price/base_price)-1.00)*100.00
ri.append(r)
del done_set
del temp
del base_price
del after_mean_price
list_return.append(ri)
# 求协方差矩阵
cov = np.cov(np.array(list_return).T)
# 求特征值和其对应的特征向量
ans = np.linalg.eig(cov)
# 排序，特征向量中负数置0，非负数归一
ans_index = copy.copy(ans[0])
ans_index.sort()
resu = []
for k in range(len(ans_index)):
con_temp = []
con_temp.append(ans_index[k])
content_temp1 = ans[1][np.argwhere(ans[0] == ans_index[k])[0][0]]
content_temp2 = []
content_sum = np.array([x for x in content_temp1 if x >= 0.00]).sum()
for m in range(len(content_temp1)):
if content_temp1[m] >= 0 and content_sum > 0:
content_temp2.append(content_temp1[m]/content_sum)
else:
content_temp2.append(0.00)
con_temp.append(content_temp2)
# 计算夏普率
sharp_temp = np.array(copy.copy(list_return)) * content_temp2
sharp_exp = sharp_temp.mean()
sharp_base = 0.04
sharp_std = np.std(sharp_temp)
if sharp_std == 0.00:
sharp = 0.00
else:
sharp = (sharp_exp - sharp_base) / sharp_std
con_temp.append(sharp)
resu.append(con_temp)
return resu
import datetime
import tushare as ts # 导入 tushare 模块
import pymysql # 导入 pymysql 模块
import numpy as np
# 连接 mysql 数据库 database ： stock
db = pymysql.connect(host='127.0.0.1',
user='******',
passwd='112358',
db='stock')
cursor = db.cursor()
# 设置tushare pro的token并获取连接
ts.set_token('ae9283163f806c321f2f4aa00a3c5cce84e69aa44eb2873d4863498e')
pro = ts.pro_api()
# 设定获取日线行情的初始日期和终止日期，其中终止日期设定为昨天。
start_dt = '20100101'
time_temp = datetime.datetime.now() - datetime.timedelta(days=1)
end_dt = time_temp.strftime('%Y%m%d')
stocks = pro.daily('ts_code', start_date=start_dt, trade_date=end_dt)
stock_pool = np.array(stocks['ts_code']) # list
total = len(stock_pool)
import json
import time
from concurrent.futures import ThreadPoolExecutor
import tushare as ts
from sqlalchemy import text
from clients import clients
from settings import API_TOKEN
ts.set_token(API_TOKEN)
pro = ts.pro_api()
def update_or_insert(data):
update_sql = "update stock_basic set ts_code=:ts_code, stock_name=:stock_name, area=:area, " \
"industry=:industry, list_date=:list_date where stock_id=:stock_id"
insert_sql = "insert into stock_basic(ts_code, stock_id, stock_name, area, industry, list_date) " \
"values (:ts_code, :stock_id, :stock_name, :area, :industry, :list_date)"
with clients.mysql_db.connect() as conn:
result = conn.execute(text(update_sql), **data)
stock_id = data.get('stock_id')
if result.rowcount > 0:
return '[update | {}]: {}'.format(
stock_id, json.dumps(data, ensure_ascii=False))
conn.execute(text(insert_sql), **data)
return '[update | {}]: {}'.format(stock_id,
json.dumps(data, ensure_ascii=False))
def set_universe(code):
import tushare as ts
ts.set_token('2bfc4b3b06efa5d8bba2ab9ef83b5d61f1c3887834de729b60eec9f13e1d4df8')
idx = ts.Idx()
return list(idx.IdxCons(secID=code, field='consID')['consID'])
import tushare as ts
import MySQLdb
from datetime import date
import datetime
print datetime.datetime.now()
conn = MySQLdb.connect(host="localhost", user="******", passwd="root", db="stock", charset="utf8")
cursor = conn.cursor()
ts.set_token('e8596c92be7248552f8fa6b4af32f5c8eed01e2044b0962313fdaec5e69e5d5c')
mt = ts.Master()
df = mt.TradeCal(exchangeCD='XSHG', beginDate='20160101', endDate='20161231', field='exchangeCD,calendarDate,isOpen,prevTradeDate,isWeekEnd,isMonthEnd,isQuarterEnd,isYearEnd')
if df is not None:
for idx in df.index:
temp = df.ix[idx]
sql = "insert into trade_cal(exchangeCD,calendarDate,isOpen,prevTradeDate,isWeekEnd,isMonthEnd,isQuarterEnd,isYearEnd) \
values(%s,%s,%s,%s,%s,%s,%s,%s)"
param = (temp['exchangeCD'],temp['calendarDate'],temp['isOpen'],temp['prevTradeDate'],temp['isWeekEnd'],temp['isMonthEnd'],temp['isQuarterEnd'],temp['isYearEnd'])
cursor.execute(sql, param)
conn.commit()
cursor.close()
conn.close()
print datetime.datetime.now()
def __init__(self):
ts.set_token('c44e067e293a18b4b6852036dbaf87979112fa2615bb2a8d1cdb3b63')
self.pro = ts.pro_api()
# @File : stockholder_info.py
# 股东信息获取
import pandas as pd
import time
import pymysql
import tushare as ts
import config
from setting import get_mysql_conn
conn = get_mysql_conn('db_stock', 'local')
cursor = conn.cursor()
token = config.token
ts.set_token(token)
pro = ts.pro_api()
def get_stock_list():
df = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
return dict(zip(list(df['ts_code'].values), list(df['name'].values)))
# 生产日期 2000到2018
def create_date():
start_date = '20{}0101'
end_date = '20{}1231'
date_list = []
for i in range(18, 0, -1):
def __apiConnect(self, config):
"""连接数据源"""
if config:
token = config.get('TOKEN', None)
else:
self.writeLog('tushare 连接参数读取错误，请检查')
return False
if token:
# 设置tushare数据令牌
ts.set_token(token)
try:
self._api = ts.pro_api()
# 构建合约信息字典
stock_df = self._api.stock_basic(
exchange='',
list_status='L',
fields=
'ts_code,symbol,name,exchange,list_status,list_date,delist_date'
)
if stock_df is None or stock_df.empty:
self.writeLog('未取得可交易的合约信息，请检查参数设置及网络连接状态')
self._api = None
return False
stock_df.set_index('ts_code', inplace=True)
self._contractDict = stock_df.T.to_dict()
# 构建指数信息字典
index_SSE_zh = self._api.index_basic(
market='SSE',
category='综合指数',
fields='ts_code,name,market,category,list_date,desc')
index_SSE_gm = self._api.index_basic(
market='SSE',
category='规模指数',
fields='ts_code,name,market,category,list_date,desc')
index_SZSE_zh = self._api.index_basic(
market='SZSE',
category='综合指数',
fields='ts_code,name,market,category,list_date,desc')
index_SZSE_gm = self._api.index_basic(
market='SZSE',
category='规模指数',
fields='ts_code,name,market,category,list_date,desc')
df = pd.concat(
[index_SSE_zh, index_SSE_gm, index_SZSE_zh, index_SZSE_gm],
ignore_index=True)
df.set_index('ts_code', inplace=True)
self._indexDict = df.T.to_dict()
# 构建交易日历
try:
self._calendar = pd.read_csv('etc/calendar.csv').trade_date
except Exception:
cal = self.tradeCalendar()
if cal is None:
self.writeLog('获取交易日历失败，请检查')
return False
else:
self._calendar = cal
self._calendar.to_csv('etc/calendar.csv',
header=True,
index=False)
return True
except Exception as e:
self.writeLog('程序错误： %s' % e)
self._api = None
return False
else:
self.writeLog('tushare 数据令牌未设置，请检查')
return False
def set_token():
if ts.get_token() is None:
ts.set_token('03d8d816cd281b447e2809dfbac371a992620752da35392f5ea41c1be5e3f827')
print('已设置token凭证码')
def main():
ts.set_token('267addf63a14adcfc98067fc253fbd72a728461706acf9474c0dae29')
pro = ts.pro_api()
dict_300 = {}
for i in range(14):
dict_300[str(2007+i)+'0101'] = list(pro.index_weight(index_code='399300.SZ',
start_date=str(2007+i)+'0101',
end_date=str(2007+i)+'0110')['con_code'].iloc[:300])
dict_300[str(2007+i)+'0701'] = list(pro.index_weight(index_code='399300.SZ',
start_date=str(2007+i)+'0625',
end_date=str(2007+i)+'0701')['con_code'].iloc[:300])
dict_500 = {}
for i in range(14):
dict_500[str(2007+i)+'0101'] = list(pro.index_weight(index_code='000905.SH',
start_date=str(2007+i)+'0101',
end_date=str(2007+i)+'0201')['con_code'].iloc[:500])
dict_500[str(2007+i)+'0701'] = list(pro.index_weight(index_code='000905.SH',
start_date=str(2007+i)+'0625',
end_date=str(2007+i)+'0710')['con_code'].iloc[:500])
calendar = pro.trade_cal(exchange='')
calendar = calendar[calendar['is_open'] == 1]['cal_date']
dict_industry = get_data.get_industry_stock_list()
stock_list = get_data.get_sql_key()
# prep_data_for_rf(stock_list, dict_industry, calendar, 1, dict_300, dict_500)
stock_list_list = []
length = int(len(stock_list) / 24)
for i in range(24):
if i == 23:
stock_list_list.append(stock_list[i*length:])
else:
stock_list_list.append(stock_list[i*length: (i+1)*length])
p = Pool()
for i in range(24):
p.apply_async(prep_data_for_rf, args=(stock_list_list[i], dict_industry, calendar, i, dict_300, dict_500, ))
p.close()
p.join()
data = pd.DataFrame()
for i in range(24):
con = db.connect('D:\\Data\\rf_temp_' + str(i) + '.sqlite')
cur = con.cursor()
data_temp = pd.read_sql_query(
sql="SELECT * FROM '" + str(i) + "'",
con=con
)
data = data.append(data_temp)
cur.close()
con.close()
con = db.connect('D:\\Data\\rf_data.sqlite')
cur = con.cursor()
data.to_sql(
name='All_Data',
con=con,
if_exists='replace',
index=False
)
con.commit()
data_test = data[data['date'] >= '20170101']
data_test.to_sql(
name='test_data',
con=con,
if_exists='replace',
index=False
)
data_train = data[data['date'] < '20170101']
data_train.to_sql(
name='train_data',
con=con,
if_exists='replace',
index=False
)
con.commit()
cur.close()
con.close()
growth = 'roe_yoy,q_gr_yoy,q_sales_yoy,q_op_yoy,q_profit_yoy,q_netprofit_yoy,ocf_yoy,equity_yoy'
balance_sheet = 'current_ratio,quick_ratio,cash_ratio,ca_to_assets,tbassets_to_totalassets,int_to_talcap,currentdebt_to_debt,longdeb_to_debt,ocf_to_shortdebt,debt_to_eqt,tangibleasset_to_debt,tangasset_to_intdebt,tangibleasset_to_netdebt,ocf_to_debt,ocf_to_interestdebt,longdebt_to_workingcapital,ebitda_to_debt'
cashflow = 'inv_turn,ar_turn,ca_turn,fa_turn,assets_turn,ocf_to_or,ocf_to_opincome,q_ocf_to_or,q_ocf_to_sales'
profit_quality = 'q_netprofit_margin,q_gsprofit_margin,q_exp_to_sales,q_profit_to_gr,q_saleexp_to_gr,q_adminexp_to_gr,q_finaexp_to_gr,q_impair_to_gr_ttm,q_gc_to_gr,q_op_to_gr,q_roe,q_dt_roe,q_opincome_to_ebt,q_investincome_to_ebt,q_dtprofit_to_profit,q_salescash_to_or'
fund_factor = list(set((growth+','+balance_sheet+','+cashflow+','+profit_quality).split(',')) & set(data.columns))\
+ ['tick', 'industry', 'stock_value_cat', 'date', 'fcfe', 'rd_exp_to_earning', 'pb', 'pe', 'ps',
'q_npta', 'cash_to_liqdebt', 'tax_to_ebt', 'cash_to_liqdebt_withinterest', 'return_rate']
tec_factor = list(set(data.columns)-set(fund_factor)) + ['tick', 'industry', 'stock_value_cat', 'date', 'return_rate']
industry_list = data_train['industry'].drop_duplicates().fillna('None')
con = db.connect('D:\\Data\\rf_data_industry.sqlite')
cur = con.cursor()
for industry in industry_list:
data_train[data_train['industry'] == industry][fund_factor].to_sql(
name='train_data_' + industry,
con=con,
if_exists='replace',
index=False
)
data_test[data_test['industry'] == industry][fund_factor].to_sql(
name='test_data_'+industry,
con=con,
if_exists='replace',
index=False
)
con.commit()
cur.close()
con.close()
stock_mv_cat_list = data_train['stock_value_cat'].drop_duplicates().fillna('None')
con = db.connect('D:\\Data\\rf_data_mv.sqlite')
cur = con.cursor()
for cat in stock_mv_cat_list:
data_train[data_train['stock_value_cat'] == cat][tec_factor].to_sql(
name='train_data_' + cat,
con=con,
if_exists='replace',
index=False
)
data_test[data_test['stock_value_cat'] == cat][tec_factor].to_sql(
name='test_data_' + cat,
con=con,
if_exists='replace',
index=False
)
con.commit()
cur.close()
con.close()
print('done')
return None
df = ts.pro_bar(ts_code=tscodes[i],
start_date=g_start_date,
end_date=g_end_date,
ma=[2, 3, 4, 5, 8, 10, 15, 20],
factors=['tor', 'vr'])
df.to_csv(save_path)
time.sleep(0.5)
def get_price(szcode, date=None):
global root
date = strtime_latest_trade_date(date)
date = int(strtime_convert(date))
df = pd.read_csv(root + 'stock/hdailydata/hdaily-data/' + szcode + '.csv')
pricedf = df.loc[df['trade_date'] == date, ['open', 'close']]
if len(pricedf['open'].tolist()) > 0:
return {
'date': date,
'open': pricedf['open'].tolist()[0],
'close': pricedf['close'].tolist()[0]
}
return {'date': -1, 'open': -1, 'close': -1}
if __name__ == '__main__':
ts.set_token('08aedc1cc54171e54a64bbe834ec1cb45026fa2ab39e9e4cb8208cad')
pro = ts.pro_api(
'08aedc1cc54171e54a64bbe834ec1cb45026fa2ab39e9e4cb8208cad')
#download(pro)
print(get_price('000010.SZ', '2019-08-30'))
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
#引入TA-Lib库
import talib as ta
#查看包含的技术指标和数学运算函数
#print(ta.get_functions())
#print(ta.get_function_groups())
ta_fun = ta.get_function_groups()
ta_fun.keys()
#使用tushare获取上证指数数据作为示例
import tushare as ts
ts.set_token('a119134c895dca96f7caedef1de1fcf51409888f8df48aabf62c0399')
pro = ts.pro_api()
#df=ts.get_k_data('sh',start='2000-01-01')
df = pro.query('daily',
ts_code='600519.SH',
start_date='20100101',
end_date='20211217')
#df.index=pd.to_datetime(df.date)
df.index = pd.to_datetime(df.trade_date)
df = df.sort_index()
# RSI
df["rsi"] = ta.RSI(df.close, timeperiod=14)
ax = df[["close", "rsi"]].plot(secondary_y=['rsi'],
figsize=(16, 8),
title='RSI',
# 最高、最低、开盘、收盘、成交量、换手率、
# 资金流量
# ps 简单利用 get hist_data 即可啥都有了 就是近3年的
###
import pandas as pd
import tushare as ts
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.finance as mfinance
from matplotlib.dates import YearLocator, MonthLocator,DayLocator, DateFormatter
ts.set_token('6cbc132dcf304322dc7b6f1d714d792fe224049b2038abf29e7711bf71334b52')
## ------------------------------------
# 获取数据
# 利用tushare 接口
# sh=上证指数 sz=深圳成指 hs300=沪深300指数 sz50=上证50 zxb=中小板 cyb=创业板
#
## ------------------------------------
# 以 牧原股份， 国轩高科， 哈尔斯，宁波港 为例
candi_stock=ts.get_hist_data('002074',start='2014-01-01',end='2015-12-31')
sh_index=ts.get_hist_data('sh',start='2014-01-01',end='2015-12-31')
zxb_index=ts.get_hist_data('zxb',start='2014-01-01',end='2015-12-31')
## 获取 交易所日历信息
#mt = ts.Master()
def require_data(self):
ts.set_token('6bf40c7acd89be6fadaba6ab8b06e22b988cb6fed50d552885df6f81')
pro = ts.pro_api()
self.df = pro.daily(self.share_dict['stack'], self.start_date, self.end_date)
self.df.sort_index(by=['trade_date'], ascending = True)
# -*- coding:utf-8 -*- import tushare as ts token = '0e693d9bddaad8bf493fd3f19a04741337bcfc9bd3b686415938f866' ts.set_token(token) pro = ts.pro_api() data = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date') # df = ts.get_realtime_quotes(['300059', '399006', 'sh']) # print(df['code'][2] + " " + df['name'][2] + " " + str(round((float(df['price'][2]) - float(df['pre_close'][2])) / float(df['pre_close'][2]) * 100, 2)) + "%" + " ") # print(df['code'][1] + " " + df['name'][1] + " " + str(round((float(df['price'][1]) - float(df['pre_close'][1])) / float(df['pre_close'][1]) * 100, 2)) + "%" + " ") # print(df['code'][0] + " " + df['name'][0] + " " + str(round((float(df['price'][0]) - float(df['pre_close'][0])) / float(df['pre_close'][0]) * 100, 2)) + "%" + " ")
import tushare as ts
from sqlalchemy import create_engine
ts.set_token("0b8f33e64a5558e84bd5b7499d0d0d6417d11d7db5d7ae960889f00e")
pro = ts.pro_api()
list = pro.stock_basic(
fields=
'ts_code,symbol,name,fullname,enname,exchange_id,curr_type,list_date,delist_date,is_hs,list_status'
)
list.to_json('./share_list.json', orient='records')
# engine = create_engine('mysql+pymysql://root:[email protected]/quantify?charset=utf8')
# list.to_sql('shares_list', engine, if_exists='append')
{
"pp": "0"，
"IF": "0"
}
2. 主力合约判断
运行程序后，点击‘合约初始化’按钮，程序会获取通联的期货数据，自动判断主力合约。并写入Contracts_init.json中。
注：通联选择的是持仓量判断主力，本程序选择的是昨日成交量判断，两者不同时会给出提示。
3. 合约订阅
4. Tick存储
'''
import json
import os
import pymongo
import tushare as ts
# ts.set_token('575593eb7696aec7339224c0fac2313780d8645f68b77369dcb35f8bcb419a0b')
ts.set_token('ced15aa738976abf2136cc9e197fbcd34776e0f8183c7660b7fdcd626a715b3b') # paolo
import time
from uiBasicWidget import QtGui, QtCore, BasicCell
from eventEngine import *
from ctaAlgo.ctaBase import *
from vtConstant import *
from vtGateway import VtSubscribeReq
########################################################################
class DataRecorder(QtGui.QFrame):
"""
用来记录历史数据的工具（基于CTA策略），
可单独运行，
本工具会记录Tick数据。
"""
def __init__(self):
ts.set_token(self.__TOKEN)
self.pro = ts.pro_api()
import pymongo
from datetime import datetime
import tushare as ts
import json
import time
import pandas as pd
import numpy as np
print('=======================11')
# 建立连接
client = pymongo.MongoClient(host='localhost', port=27017)
db_name = 'tushare_storage' #数据库名
database = client[db_name] #建立数据库
print('=======================22')
ts.set_token(
"1f5fcc75bfa0d0ddb8e3d7caab4c9623185529a5052e7671f3e9c7e2") #XXX为自己的token
#pro = ts.pro_api()
# 连接stock数据库，注意只有往数据库中插入了数据，数据库才会自动创建
#database = client.stock
# 创建一个daily集合，类似于MySQL中"表"的概念
#daily = database["daily"]
def MA(tsPrice, k): #MovingAverage计算
Sma = pd.Series(0.0, index=tsPrice.index)
for i in range(k - 1, len(tsPrice)):
Sma[i] = sum(tsPrice[(i - k + 1):(i + 1)]) / k
return (Sma)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
st_number = '300454.SZ'
first_money = 1000000
import tushare as ts
ts.set_token('a339e517ed9b1cb97cda578c2ee8fa829ef50d13ae3623a113227777')
pro = ts.pro_api()
df = pro.daily(ts_code=st_number, start_date='20100701', end_date='20210208')
df.to_csv(st_number + '.csv')
df = pd.read_csv(st_number + '.csv',
index_col='trade_date',
parse_dates=['trade_date'])[['open', 'close', 'low',
'high']].sort_index()
df['ma5'] = df['open'].rolling(5).mean()
df['ma30'] = df['open'].rolling(30).mean()
df = df.dropna()
# df[['open', 'ma5', 'ma30']].plot()
# plt.show()
sre1 = df['ma5'] < df['ma30']
sre2 = df['ma5'] >= df['ma30']
golden_cross = df[sre1 & sre2.shift(1)].index
# coding: 'utf-8'
__author__ = 'xlyang0211'
import tushare as ts
ts.set_token("efe5e687247788b99191f7fe13357d13b23e89a1df6989ec597d9b8c12a51403")
print ts.get_token()
print ts.get_industry_classified()
# print fd
$nin不在范围内{'age': {'$nin': [20, 23]}}
"""
import tushare as ts
import pymongo
import time
import sys
client = pymongo.MongoClient(host='localhost', port=27017)
db = client.ffgHarvester
col = db.stockDaily
calendarList = db.stockCalendar.find()
todayStr = time.strftime("%Y%m%d", time.localtime())
ts.set_token('495bd6a4d40acef11e6a222a1632889b27c60938aa9decba468c472b')
pro = ts.pro_api()
def main():
print('开始获取日线行情')
for dateObj in calendarList:
isGetDaily = dateObj['isGetDaily']
cal_date = dateObj['cal_date']
if isGetDaily == False: #若还未获取数据，则获取k线数据
if cal_date <= todayStr: #获取当天之前的数据
is_open = dateObj['is_open']
if is_open == 1:
df = pro.query("daily", trade_date=cal_date) #获取当日的全部股票数据
list = []
#!//bin/python
# -*- coding: utf-8 -*-
import types
import urllib2
import json
import datetime
import time
import DataAPI
import pandas as pd
import tushare as ts
benchmarkMap = {"SH":"000001", "SH50":"000016", "SH180":"000010", "ZZ500":"000905", "SZ":"399001", "ZXB":"399101", "HS300":"399300", "FUND_SH":"000011", "FUND_SZ"
:"399305"}
ts.set_token('8a72d90cf841a0dcc4d2d4cf55e48c603e0b3d21295c5b0f93f4be846158d903')
mt = ts.Master()
def registerUrl(url):
try:
data = urllib2.urlopen(url).read()
return data
except Exception,e:
print e
def isNum(value):
try:
x = int(value)
except TypeError:
return False
except ValueError:
return False
# 更新日线数据， 周线数据， 月线数据
import tushare as ts
from sqlalchemy import create_engine
import time
import datetime
import pandas as pd
from getStockDaily import getDailyOnDate
from getStockWeekly import getWeeklyOnDate
from getStockMonthly import getMonthlyOnDate
from getStockAdjustFactor import getAdjustFactorOnDate
ts.set_token('803f1548c1f25bf44c56644e4527a6d8cd3dbd8517e7c59e3aa1f6d0')
pro = ts.pro_api()
engine = create_engine(
"mysql+pymysql://root:4401821211@localhost:3306/stock?charset=utf8")
sqlstr = "SELECT max(trade_date) as maxdate FROM stock.daily2021"
maxDate = pd.read_sql_query(sqlstr, con=engine).loc[0, 'maxdate']
print("previous stock data crawle date is %s, start updating!" % maxDate)
begin = datetime.datetime.strptime(maxDate, '%Y%m%d')
end = datetime.datetime.now()
date = begin
delta = datetime.timedelta(days=1)
while date < end:
date += delta
getDailyOnDate(date, pro, engine)
time.sleep(1)
def connect(self):
ts.set_token(self.Token)
self._ts = ts.pro_api()
return 0
# -*- coding: utf-8 -*- """ Created on Mon Mar 4 09:39:53 2019 @author: Xzw E-mail: [email protected] """ import tushare as ts import pandas as pd import time # 退市股票序列 ts.set_token('############################################################') pro = ts.pro_api() delist_data = pro.stock_basic(exchange='', list_status='D', fields='ts_code,symbol,name,fullname,delist_date') delist_data.to_csv('./delist_data.csv', index=False) # 上市股票数据 list_data = pro.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,fullname,delist_date') # 财务风险股票数据 st_data = list_data.drop([i for i in range(len(list_data)) if 'ST' not in list_data['name'][i]]) st_data.to_csv('./st_data.csv', index=False) # 随机挑选相同数量的非财务危机股票 normal_data = list_data.drop([i for i in range(len(list_data)) if 'ST' in list_data['name'][i]])
import matplotlib as mpl
import matplotlib.pyplot as plt
import tushare as ts
# pip install mplfinance
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
from glob import glob
from os import path
from datetime import timedelta
# ggplot好看点
mpl.style.use("ggplot")
DATE_FORMAT = "%Y%m%d"
TS_TOKEN = "<你的Token>"
ts.set_token(TS_TOKEN)
pro = ts.pro_api(TS_TOKEN)
def ohlc_plot(df, ax=None):
if ax is None:
ax = plt.gca()
data_lst = []
for date, row in df.iterrows():
t = date2num(date)
data = (t, ) + tuple(row)
data_lst.append(data)
candlestick_ohlc(ax,
data_lst,
# -*- coding: utf-8 -*-
"""
Created on Mon May 23 09:27:16 2016
@author: Administrator
"""
from sqlalchemy import create_engine
import datetime
import tushare as ts
ts.set_token('601be43bd14269103d558400372fc2a18d752d999d4e78a167335353abf79e8e')
engine = create_engine('mysql://*****:*****@127.0.0.1/tstest?charset=utf8')
#equ_info_A = ts.Equity().Equ(equTypeCD='A',field='')
#equ_info_B = ts.Equity().Equ(equTypeCD='B',field='')
#equ_info_A.to_sql('equ_info_a',con=engine, if_exists = 'replace', index = False, index_label = 'secID')
#equ_info_B.to_sql('equ_info_b',con=engine, if_exists = 'replace', index = False, index_label = 'secID')
today = datetime.date.today().strftime('%Y%m%d')
mkt = ts.Market().MktEqud(tradeDate = today,field = '')
try:
mkt.to_sql('mkt_price', con=engine, if_exists = 'append', index = False)
except Exception:
pass
from datetime import datetime, timedelta
import pymongo
from pymongo.errors import ConnectionFailure
from time import time
from multiprocessing.pool import ThreadPool
import json
import os
import csv
from ctaBase import *
from vtConstant import *
from vtFunction import loadMongoSetting
from datayesClient import DatayesClient
import tushare as ts
ts.set_token('')
# 以下为vn.trader和通联数据规定的交易所代码映射
VT_TO_DATAYES_EXCHANGE = {}
VT_TO_DATAYES_EXCHANGE[EXCHANGE_CFFEX] = 'CCFX' # 中金所
VT_TO_DATAYES_EXCHANGE[EXCHANGE_SHFE] = 'XSGE' # 上期所
VT_TO_DATAYES_EXCHANGE[EXCHANGE_CZCE] = 'XZCE' # 郑商所
VT_TO_DATAYES_EXCHANGE[EXCHANGE_DCE] = 'XDCE' # 大商所
DATAYES_TO_VT_EXCHANGE = {v:k for k,v in VT_TO_DATAYES_EXCHANGE.items()}
########################################################################
class HistoryDataEngine(object):
"""CTA模块用的历史数据引擎"""
#----------------------------------------------------------------------
def __init__(self,parent=None):
logging.debug("begin init")
super(DatayesThread,self).__init__(parent)
ts.set_token('edc37d879a4757aae38b00cf49cc2dffe936bf3efb0f700c3cbb1f798ec82d5d')
import tushare as ts
ts.set_token('577c33e4d462eba6c110d77e39408eaa08f6e91c7e2cb4275fad192e374b1ddb')
print('this is just only a test')
st = ts.Market()
df = st.MktEqud(tradeDate='20151009', field='ticker,secShortName,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,turnoverVol,turnoverRate')
df['ticker'] = df['ticker'].map(lambda x: str(x).zfill(6))
df1 = st.MktEqud(tradeDate='20151008', field='ticker,secShortName,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,turnoverVol,turnoverRate')
df1['ticker'] = df1['ticker'].map(lambda x: str(x).zfill(6))
good_stock = [0 for i in range(3000)]
def is_star(open_price, close_price, lowest_price,highest_price):
is_star = 0
if(open_price <= close_price):
diff = (close_price - open_price)/open_price
if(diff < 0.03):
if(((open_price/lowest_price) > 1.01)and ((highest_price/close_price)> 1.01)):
is_star = 1
else:
diff = (open_price - close_price)/close_price
if(diff < 0.03):
if(((close_price/lowest_price) > 1.01)and ((highest_price/open_price)> 1.01)):
is_star = 1
return is_star
i = 0
for index,row in df1.iterrows():
openPrice = row['openPrice']
closePrice = row['closePrice']
lowestPrice = row['lowestPrice']
highestPrice = row['highestPrice']
if(row['openPrice'] == 0):
exp_norisk = 0.04 * (5.0 / 12.0)
sharp_rate = (exp_portfolio - exp_norisk) / (std)
return sharp_rate, std
if __name__ == '__main__':
# 建立数据库连接,设置tushare的token,定义一些初始化参数
db = pymysql.connect(host='127.0.0.1',
user='******',
passwd='admin',
db='stock',
charset='utf8')
cursor = db.cursor()
ts.set_token(const.TUSHARE_TOKEN)
pro = ts.pro_api()
year = 2018
date_seq_start = str(year) + '-03-01'
date_seq_end = str(year) + '-03-10'
stock_pool = [
'603912.SH', '300666.SZ', '300618.SZ', '002049.SZ', '300672.SZ'
]
# 先清空之前的测试记录,并创建中间表
sql_wash1 = 'delete from my_capital where seq != 1'
cursor.execute(sql_wash1)
db.commit()
sql_wash3 = 'truncate table my_stock_pool'
cursor.execute(sql_wash3)
db.commit()
def __init__(self, *args, **kwargs):
config = ReadConfig().getConfig()
self._token = config['tushare']['token']
ts.set_token(self._token)
self.tusharePro = ts.pro_api()
import tushare as ts
ts.set_token('af3678578bf2abfcbcd2e98c1825e665c64c44ebe51332e561203abe3a993d96')
# update tushare database
import sys
import time
from os import path
sys.path.append('/Users/linhua/PycharmProjects/Fupan')
from private import tushare_token
import tushare as ts
import sqlite3
import pandas as pd
# prepare Tushare data interface
ts.set_token(tushare_token.tushare_token)
pro = ts.pro_api()
#
# Database design:
# table: concept_list
# | concept_code | concept_name | src |
# table: concept_dashboard
# | concept_name (1) | concept_name (2) |
#
def update_concept_database(dpath):
concept_list_df = pro.concept()
concept_dashboard_code = pd.DataFrame()
concept_dashboard_name = pd.DataFrame()
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 9 23:17:43 2019
@author: 骨灰盒
"""
import tushare as ts
import numpy as np
import time
ts.set_token('7d6ac63764d82662169992641f582aab920fc6a0564f3147a773e8bf')
pro = ts.pro_api()
TODAY = time.strftime("%Y%m%d", time.localtime())
START_DATE = '20190801'
#------------------------------------------------------------------------------
codes = pro.query('stock_basic',
exchange='',
list_status='L',
market='',
fields='ts_code,symbol,name,area,industry,list_date')
my_codes = codes.ts_code
my_codes.to_csv('code_list.csv')
my_codes = my_codes[0:3]
for code in my_codes:
#----------------------策略区--------------------------------------------------
try:
tmp_df = pro.query(
'daily',