def add_margin_info(today=None):
"""
插入融资融券信息
:param today:
:return:
"""
if today is not None:
"""
插入今日信息
"""
pro = tushare_data.get_tushare_pro()
trade_date = (datetime.datetime.now() -
datetime.timedelta(days=1)).strftime('%Y%m%d')
df = pro.margin(trade_date=trade_date)
if df is not None and df.size != 0:
try:
tables.margin_info_add(trade_date, df['rzye'].sum(),
df['rzmre'].sum(), df['rzche'].sum(),
df['rqye'].sum(), df['rqmcl'].sum(),
df['rzrqye'].sum())
print('{0}融资余额插入成功'.format(trade_date))
except Exception as err:
print('{0}融资余额插入失败'.format(trade_date))
print(err)
else:
"""
插入历史信息
"""
pro = tushare_data.get_tushare_pro()
money_flow_info = tables.get_moneyflow_info()
money_flow_info_date_sets = money_flow_info['date'].drop_duplicates()
for trade_date in money_flow_info_date_sets:
df = pro.margin(trade_date=trade_date)
time.sleep(1)
if df is not None and df.size != 0:
try:
tables.margin_info_add(trade_date, df['rzye'].sum(),
df['rzmre'].sum(),
df['rzche'].sum(), df['rqye'].sum(),
df['rqmcl'].sum(),
df['rzrqye'].sum())
print('{0}融资余额插入成功'.format(trade_date))
except Exception as err:
print('{0}融资余额插入失败'.format(trade_date))
print(err)
def __init__(self) -> object:
self.pro = tushare_data.get_tushare_pro()
trade_date = datetime.datetime.now().strftime('%Y%m%d')
df = self.pro.index_weight(index_code='399300.SZ')
df = df.sort_values(by='trade_date', ascending=False)
df = df.head(300)
self.hs300_ts_code = df['con_code']
self.selected_ts = []
self.std = []
def __data_prepare(self):
pro = tushare_data.get_tushare_pro()
self.financing_date = pro.margin_detail(ts_code=self.ts_code)
if not self.__data_check():
raise Exception("无融资信息")
self.financing_date = self.financing_date.sort_values(by='trade_date', ascending=False)
self.financing_date = pandas.DataFrame(self.financing_date.head(self.recent_days)[self.financing_type])
if not self.__data_check():
raise Exception("数据处理失败")
print(self.financing_date.size, self.ts_code)
self.financing_rolling()
def margin_info_figure():
pro = tushare_data.get_tushare_pro()
df = pro.margin(start_date='20140922')
rziy = df.groupby(['trade_date'])['rzye'].sum()
print(rziy)
draw_single(df['trade_date'].drop_duplicates(), rziy, '211', '融资余额')
hs300_daily_info = tushare_data.get_index_daily('000300.SH')
hs300_daily_info = hs300_daily_info.loc[hs300_daily_info['trade_date'] > '20140922']
hs300_close_rolling = date_rolling(hs300_daily_info['close'])
draw_single(hs300_daily_info['trade_date'], hs300_close_rolling, '212', '沪深300指数')
figure = plt.gcf() # 获取当前图片
figure.set_size_inches(21.6, 24)
plt.savefig('./result/' + datetime.now().strftime('%Y%m%d') + 'yzye.png', dpi=100)
def hsgt_add(today=None):
"""
插入沪港通资金流向信息
:return:
"""
pro = tushare_data.get_tushare_pro()
if today is None:
hs300_daily_info = tushare_data.get_index_daily('000300.SH')
hs300_daily_info = hs300_daily_info.loc[
hs300_daily_info['trade_date'] > '20180630']
for date in hs300_daily_info['trade_date']:
hsgt_info = pro.moneyflow_hsgt(trade_date=date)
if hsgt_info is None or hsgt_info.size == 0:
continue
try:
north_money = hsgt_info['north_money'][0]
south_money = hsgt_info['south_money'][0]
tables.money_flow_hsgt_add(trade_date=date,
north_money=north_money,
south_money=south_money)
print(date, ':沪港通现金流信息插入成功')
except Exception as err:
print(date, ':沪港通现金流信息插入失败', err)
print('沪港通现金流信息插入完成')
else:
date = datetime.datetime.now().strftime('%Y%m%d')
hsgt_info = pro.moneyflow_hsgt(trade_date=date)
if hsgt_info is None or hsgt_info.size == 0:
return
try:
north_money = hsgt_info['north_money'][0]
south_money = hsgt_info['south_money'][0]
tables.money_flow_hsgt_add(trade_date=date,
north_money=north_money,
south_money=south_money)
print(date, ':沪港通现金流信息插入成功')
except Exception as err:
print(date, ':沪港通现金信息插入失败', err)
def add_money_flow_today():
"""
沪深300当日生成小单统计数据
tips:成交明细列表中的买盘/卖盘：“买盘”表示以比市价高的价格进行委托买入，并已经“主动成交”，代表外盘；
“卖盘”表示以比市价低的价格进行委托卖出，并已经“主动成交”，代表内盘
:return:
"""
print('插入当日现金流信息开始......')
zz500 = tushare.get_zz500s()
hs300_index = tushare.get_hs300s()
zz800_ts_codes = hs300_index.append(zz500)
ts_codes = zz800_ts_codes['code'].apply(util.stock_code_change)
for ts_code in set(ts_codes):
time.sleep(1)
try:
date = datetime.datetime.now().strftime('%Y%m%d')
daily_info = tushare_data.get_tushare_pro().daily(ts_code=ts_code,
trade_date=date)
close = daily_info['close']
df = tushare.get_tick_data(str(ts_code[:6]), date=date, src='tt')
if df is None:
print("未获取到股票{0}-{1}的信息".format(ts_code, date))
continue
total_amt = df['amount'].sum()
total_vol = df['volume'].sum()
sell_trade = df.loc[(df['type'] == '卖盘')]
total_sell_vol = sell_trade['volume'].sum()
total_sell_amt = sell_trade['amount'].sum()
small_trade_amount = util.trade_scale(close)
sell_sm_trade = df.loc[(df['type'] == '卖盘')
& (df['amount'] < small_trade_amount)]
sell_sm_vol = sell_sm_trade['volume'].sum()
sell_sm_amt = sell_sm_trade['amount'].sum()
buy_trade = df.loc[(df['type'] == '买盘')]
total_buy_vol = buy_trade['volume'].sum()
total_buy_amt = buy_trade['amount'].sum()
buy_sm_trade = df.loc[(df['type'] == '买盘')
& (df['amount'] < small_trade_amount)]
buy_sm_vol = buy_sm_trade['volume'].sum()
buy_sm_amt = buy_sm_trade['amount'].sum()
total_sm_trade = df.loc[df['amount'] < small_trade_amount]
total_sm_amt = total_sm_trade['amount'].sum()
total_sm_vol = total_sm_trade['volume'].sum()
id = ts_code[:6] + date
tables.add_money_flow(id=id,
code=str(ts_code[:6]),
date=date,
sell_sm_vol=sell_sm_vol,
sell_sm_amt=sell_sm_amt,
buy_sm_vol=buy_sm_vol,
buy_sm_amt=buy_sm_amt,
total_sell_vol=total_sell_vol,
total_sell_amt=total_sell_amt,
total_buy_vol=total_buy_vol,
total_buy_amt=total_buy_amt,
total_amt=total_amt,
total_vol=total_vol,
total_sm_amt=total_sm_amt,
total_sm_vol=total_sm_vol)
except Exception as err:
print('插入当日现金流信息失败......')
raise err
print('插入当日现金流信息完成......')
def add_money_flow():
"""
沪深300生成小单统计数据
tips:成交明细列表中的买盘/卖盘：“买盘”表示以比市价高的价格进行委托买入，并已经“主动成交”，代表外盘；
“卖盘”表示以比市价低的价格进行委托卖出，并已经“主动成交”，代表内盘
:return:
"""
print('插入历史现金流信息开始......')
try:
zz500 = tushare.get_zz500s()
hs300_index = tushare.get_hs300s()
zz800_ts_codes = hs300_index.append(zz500)
ts_codes = zz800_ts_codes['code'].apply(util.stock_code_change)
for ts_code in ts_codes:
daily_info = tushare.pro_bar(ts_code,
api=tushare_data.get_tushare_pro())
if daily_info is None:
continue
for index, row in daily_info.iterrows():
date = row['trade_date']
date = date[:4] + '-' + date[4:6] + '-' + date[6:]
code = row['ts_code'][:6]
if date > '2018-06-30':
df = tushare.get_tick_data(code, date=date, src='tt')
if df is None:
continue
total_amt = df['amount'].sum()
total_vol = df['volume'].sum()
sell_trade = df.loc[(df['type'] == '卖盘')]
total_sell_vol = sell_trade['volume'].sum()
total_sell_amt = sell_trade['amount'].sum()
small_trade_amount = util.trade_scale(row['close'])
sell_sm_trade = df.loc[(df['type'] == '卖盘') &
(df['amount'] < small_trade_amount)]
sell_sm_vol = sell_sm_trade['volume'].sum()
sell_sm_amt = sell_sm_trade['amount'].sum()
buy_trade = df.loc[(df['type'] == '买盘')]
total_buy_vol = buy_trade['volume'].sum()
total_buy_amt = buy_trade['amount'].sum()
buy_sm_trade = df.loc[(df['type'] == '买盘') &
(df['amount'] < small_trade_amount)]
buy_sm_vol = buy_sm_trade['volume'].sum()
buy_sm_amt = buy_sm_trade['amount'].sum()
total_sm_trade = df.loc[df['amount'] < small_trade_amount]
total_sm_amt = total_sm_trade['amount'].sum()
total_sm_vol = total_sm_trade['volume'].sum()
id = code + row['trade_date']
tables.add_money_flow(id=id,
code=code,
date=row['trade_date'],
sell_sm_vol=sell_sm_vol,
sell_sm_amt=sell_sm_amt,
buy_sm_vol=buy_sm_vol,
buy_sm_amt=buy_sm_amt,
total_sell_vol=total_sell_vol,
total_sell_amt=total_sell_amt,
total_buy_vol=total_buy_vol,
total_buy_amt=total_buy_amt,
total_amt=total_amt,
total_vol=total_vol,
total_sm_amt=total_sm_amt,
total_sm_vol=total_sm_vol)
print('插入历史现金流信息完成......')
except Exception as err:
print('插入历史现金流信息失败......')
raise err
test.var_clc()
plt.plot(X, self.financing_date)
plt.plot(X, self.__model.predict(X))
plt.title(self.ts_code)
plt.show()
self._financing_gradient = self.__model.coef_
@property
def financing_gradient(self):
return self._financing_gradient
def var_clc(self):
financing_date_predict = self.__model.predict(self.financing_date)
rt = financing_date_predict - self.financing_date
self.var = np.dot(rt.T, rt)/np.power(self.recent_days, 2)
print('var:', self.var)
pro = tushare_data.get_tushare_pro()
df = pro.index_weight(index_code='399300.SZ')
df = df.sort_values(by='trade_date', ascending=False)
df = df.head(300)
hs300_ts_code = df['con_code']
for ts_code in hs300_ts_code:
test = Margin(ts_code=ts_code, financing_type='rzye')
try:
test.linear_model_learning()
print(test.financing_gradient)
except Exception as err:
print(err)