def get_datas(s):
init('a', s)
symbol = '002192'
exchange = Exchange('SZ')
day = Interval.DAILY
start = datetime(2019, 1, 1)
end = datetime(2019, 6, 3)
data = MongoManager()
datas = data.load_bar_data(symbol, exchange, day, start, end)
bars = pd.DataFrame(columns=['open', 'close', 'low', 'high', 'volume', 'openInterest'])
opens = [data.open_price for data in datas]
closes = [data.close_price for data in datas]
low = [data.low_price for data in datas]
high = [data.high_price for data in datas]
volume = [data.volume for data in datas]
datetimes=[data.datetime for data in datas]
bars['open'] = opens
bars['close'] = closes
bars['low'] = low
bars['high'] = high
bars['volume'] = volume
bars['openInterest'] = 0
bars['datetime'] = datetimes
return bars
def generate_bar_from_row(row, symbol, exchange):
""""""
bar = BarData(symbol=symbol,
exchange=Exchange(exchange),
interval=Interval.DAILY,
open_price=row["open"],
high_price=row["high"],
low_price=row["low"],
close_price=row["close"],
volume=row["vol"],
datetime=str2datatime(row['trade_date']),
gateway_name="DB")
return bar
def to_tick(self):
"""
Generate TickData object from DbTickData.
"""
tick = TickData(
symbol=self.symbol,
exchange=Exchange(self.exchange),
datetime=self.datetime,
name=self.name,
volume=self.volume,
last_price=self.last_price,
last_volume=self.last_volume,
limit_up=self.limit_up,
limit_down=self.limit_down,
open_price=self.open_price,
high_price=self.high_price,
low_price=self.low_price,
pre_close=self.pre_close,
bid_price_1=self.bid_price_1,
ask_price_1=self.ask_price_1,
bid_volume_1=self.bid_volume_1,
ask_volume_1=self.ask_volume_1,
gateway_name="DB",
)
if self.bid_price_2:
tick.bid_price_2 = self.bid_price_2
tick.bid_price_3 = self.bid_price_3
tick.bid_price_4 = self.bid_price_4
tick.bid_price_5 = self.bid_price_5
tick.ask_price_2 = self.ask_price_2
tick.ask_price_3 = self.ask_price_3
tick.ask_price_4 = self.ask_price_4
tick.ask_price_5 = self.ask_price_5
tick.bid_volume_2 = self.bid_volume_2
tick.bid_volume_3 = self.bid_volume_3
tick.bid_volume_4 = self.bid_volume_4
tick.bid_volume_5 = self.bid_volume_5
tick.ask_volume_2 = self.ask_volume_2
tick.ask_volume_3 = self.ask_volume_3
tick.ask_volume_4 = self.ask_volume_4
tick.ask_volume_5 = self.ask_volume_5
return tick
def to_bar(self):
"""
Generate BarData object from DbBarData.
"""
bar = BarData(
symbol=self.symbol,
exchange=Exchange(self.exchange),
datetime=self.datetime,
interval=Interval(self.interval),
volume=self.volume,
open_price=self.open_price,
open_interest=self.open_interest,
high_price=self.high_price,
low_price=self.low_price,
close_price=self.close_price,
build_time=self.build_time,
remove_time=self.remove_time,
gateway_name="DB",
)
return bar
def getReturn(stocks, startday, endday, peried=1):
if isinstance(startday, datetime):
start_day = startday
else:
start_day = datetime.strptime(startday, '%Y%m%d').date()
if isinstance(endday, datetime):
end_day = endday
else:
end_day = datetime.strptime(endday, '%Y%m%d').date()
results = pd.DataFrame()
for code in stocks:
code, exchange = code.split('.')
datas = dbm.load_bar_dataframe_data(code, Exchange(exchange),
Interval.DAILY, start_day, end_day)
close = datas['close_price']
lagclose = close.shift(peried)
results[code] = (close - lagclose) / lagclose
# returns = datas.close_price.pct_change().dropna()
# print('%s: %f' % (code, annualize(returns, 1)))
return fillInfNaN(results)
def set_parameters(self,
vt_symbol: str,
interval: Interval,
start: datetime,
rate: float,
slippage: float,
size: float,
pricetick: float,
capital: int = 0,
end: datetime = None,
mode: BacktestingMode = BacktestingMode.BAR,
days: int = 30):
""""""
self.mode = mode
self.vt_symbol = vt_symbol
self.interval = Interval(interval)
self.rate = rate
self.slippage = slippage
self.size = size
self.pricetick = pricetick
self.start = start
self.symbol, exchange_str = self.vt_symbol.split(".")
self.exchange = Exchange(exchange_str)
if capital:
self.capital = capital
if end:
self.end = end
if mode:
self.mode = mode
if days:
self.days = days
import pprint
from datetime import datetime
from base_database.database_mongo import init, MongoManager
from base_utils.constant import Interval, Exchange
from settings.setting import Settings
s = Settings()
init('a', s)
symbol = '002192'
data = MongoManager()
exchange = Exchange('SZ')
day = Interval.DAILY
# 最新价
# newest_bar = data.get_newest_bar_data('002192',Exchange.SZ,Interval.DAILY)
# print(newest_bar)
# 指定日期价
start = datetime(2019, 1, 1)
end = datetime(2019, 6, 3)
# date = start.strftime('%Y-%m-%d')
# print(date)
datas = data.load_bar_data(symbol, exchange, day, start, end)
# # print(datas)
#
#
# # d =d
# print(datas[0])
print(pprint.pformat(datas))
s = Settings()
dbm = init('_', s)
'''
'''
start = datetime(2019, 1, 1)
end = datetime(2020, 1, 1)
bank_path = s['PATH_BANK']
codes = pd.read_csv(bank_path)
codes.columns = ['name', 'code']
result_list = []
result_name = []
# 整合bank的收盘价
for index, codes in codes.iterrows():
code, exchange = codes[1].split('.')
result = dbm.load_bar_dataframe_data(code, Exchange(exchange),
Interval.DAILY, start, end)
result_name.append(codes[1])
result_list.append(result['close_price'])
all_banks = pd.concat(result_list, axis=1, keys=result_name)
fill_banks = all_banks.dropna()
st = PairTrading()
# up_ssd,down_ssd = st.calBound('SSD')
# print(up_ssd)
# print(down_ssd)
st.calAllCointegration(fill_banks)
print(st.resultCoint)
print(st.resultNotCoint)
st.to_csv()
def get_closeprice(codes, start, end):
code, exchange = codes.split('.')
price = dbm.load_bar_dataframe_data(code, Exchange(exchange),
Interval.DAILY, start, end)
return price['close_price']
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
from base_database.initialize import database_manager
from base_utils.constant import Interval, Exchange
from settings.setting import Settings
"""
多对象的相关系数计算
"""
s = Settings()
start = datetime(2018, 1, 1)
end = datetime(2020, 3, 31)
results = dict()
stocks_TMT = { '蓝思':'300433.SZ',
'立讯':'002475.SZ', #
'歌尔':'002241.SZ' #
}
for name, codes in stocks_TMT.items():
code,exchange = codes.split('.')
result = database_manager.load_bar_dataframe_data(code,Exchange(exchange),Interval.DAILY,start, end)
results[name] = result['close_price']
df = pd.DataFrame(results)
plt.scatter(df['歌尔'],df['蓝思'])
plt.legend()
plt.show()
corr = df.corr(method = 'pearson', min_periods = 1)
print(corr)