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import copy
import datetime
import os
from typing import List
import jqdatasdk as jq
import numpy as np
from meta.data_processors._base import _Base
class Joinquant(_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)
if "username" in kwargs.keys() and "password" in kwargs.keys():
jq.auth(kwargs["username"], kwargs["password"])
def download_data(
self, ticker_list: List[str], save_path: str = "./data/dataset.csv"
):
# joinquant supports: '1m', '5m', '15m', '30m', '60m', '120m', '1d', '1w', '1M'。'1w' denotes one week，‘1M' denotes one month。
count = len(self.get_trading_days(self.start_date, self.end_date))
df = jq.get_bars(
security=ticker_list,
count=count,
unit=self.time_interval,
fields=["date", "open", "high", "low", "close", "volume"],
end_dt=self.end_date,
)
df = df.reset_index().rename(columns={"level_0": "tic"})
self.dataframe = df
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):
n = len(stock_list)
N = df.shape[0]
assert N % n == 0
d = int(N / n)
stock1_ary = df.iloc[0:d, 1:].values
temp_ary = stock1_ary
for j in range(1, n):
stocki_ary = df.iloc[j * d : (j + 1) * d, 1:].values
temp_ary = np.hstack((temp_ary, stocki_ary))
return temp_ary
# start_day: str
# end_day: str
# output: list of str_of_trade_day, e.g., ['2021-09-01', '2021-09-02']
def get_trading_days(self, start_day: str, end_day: str) -> List[str]:
dates = jq.get_trade_days(start_day, end_day)
str_dates = []
for d in dates:
tmp = datetime.date.strftime(d, "%Y-%m-%d")
str_dates.append(tmp)
# str_dates = [date2str(dt) for dt in dates]
return str_dates
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