Spaces:
Runtime error
Runtime error
File size: 2,362 Bytes
de6e775 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
from typing import List
from meta.config import BINANCE_BASE_URL
from meta.config import TIME_ZONE_BERLIN
from meta.config import TIME_ZONE_JAKARTA
from meta.config import TIME_ZONE_PARIS
from meta.config import TIME_ZONE_SELFDEFINED
from meta.config import TIME_ZONE_SHANGHAI
from meta.config import TIME_ZONE_USEASTERN
from meta.config import USE_TIME_ZONE_SELFDEFINED
from meta.data_processors._base import _Base
# from basic_processor import _Base
## The code of this file is used in website, not locally.
class Quantconnect(_Base):
def __init__(
self,
data_source: str,
start_date: str,
end_date: str,
time_interval: str,
**kwargs,
):
super().__init__(data_source, start_date, end_date, time_interval, **kwargs)
# def data_fetch(start_time, end_time, stock_list, resolution=Resolution.Daily) :
# #resolution: Daily, Hour, Minute, Second
# qb = QuantBook()
# for stock in stock_list:
# qb.AddEquity(stock)
# history = qb.History(qb.Securities.Keys, start_time, end_time, resolution)
# return history
def download_data(
self, ticker_list: List[str], save_path: str = "./data/dataset.csv"
):
# self.time_zone = calc_time_zone(ticker_list, TIME_ZONE_SELFDEFINED, USE_TIME_ZONE_SELFDEFINED)
# start_date = pd.Timestamp(start_date, tz=self.time_zone)
# end_date = pd.Timestamp(end_date, tz=self.time_zone) + pd.Timedelta(days=1)
qb = QuantBook()
for stock in ticker_list:
qb.AddEquity(stock)
history = qb.History(
qb.Securities.Keys,
self.start_date,
self.end_date,
self.time_interval,
)
self.dataframe = history
self.save_data(save_path)
print(
f"Download complete! Dataset saved to {save_path}. \nShape of DataFrame: {self.dataframe.shape}"
)
# def preprocess(df, stock_list):
# df = df[['open','high','low','close','volume']]
# if_first_time = True
# for stock in stock_list:
# if if_first_time:
# ary = df.loc[stock].values
# if_first_time = False
# else:
# temp = df.loc[stock].values
# ary = np.hstack((ary,temp))
# return ary
|