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import copy
import os
import time
import warnings
warnings.filterwarnings("ignore")
from typing import List
import pandas as pd
from tqdm import tqdm
import stockstats
import talib
from meta.data_processors._base import _Base
import akshare as ak # pip install akshare
class Akshare(_Base):
def __init__(
self,
data_source: str,
start_date: str,
end_date: str,
time_interval: str,
**kwargs,
):
start_date = self.transfer_date(start_date)
end_date = self.transfer_date(end_date)
super().__init__(data_source, start_date, end_date, time_interval, **kwargs)
if "adj" in kwargs.keys():
self.adj = kwargs["adj"]
print(f"Using {self.adj} method.")
else:
self.adj = ""
if "period" in kwargs.keys():
self.period = kwargs["period"]
else:
self.period = "daily"
def get_data(self, id) -> pd.DataFrame:
return ak.stock_zh_a_hist(
symbol=id,
period=self.time_interval,
start_date=self.start_date,
end_date=self.end_date,
adjust=self.adj,
)
def download_data(
self, ticker_list: List[str], save_path: str = "./data/dataset.csv"
):
"""
`pd.DataFrame`
7 columns: A tick symbol, time, open, high, low, close and volume
for the specified stock ticker
"""
assert self.time_interval in [
"daily",
"weekly",
"monthly",
], "Not supported currently"
self.ticker_list = ticker_list
self.dataframe = pd.DataFrame()
for i in tqdm(ticker_list, total=len(ticker_list)):
nonstandard_id = self.transfer_standard_ticker_to_nonstandard(i)
df_temp = self.get_data(nonstandard_id)
df_temp["tic"] = i
# df_temp = self.get_data(i)
self.dataframe = pd.concat([self.dataframe, df_temp])
# self.dataframe = self.dataframe.append(df_temp)
# print("{} ok".format(i))
time.sleep(0.25)
self.dataframe.columns = [
"time",
"open",
"close",
"high",
"low",
"volume",
"amount",
"amplitude",
"pct_chg",
"change",
"turnover",
"tic",
]
self.dataframe.sort_values(by=["time", "tic"], inplace=True)
self.dataframe.reset_index(drop=True, inplace=True)
self.dataframe = self.dataframe[
["tic", "time", "open", "high", "low", "close", "volume"]
]
# self.dataframe.loc[:, 'tic'] = pd.DataFrame((self.dataframe['tic'].tolist()))
self.dataframe["time"] = pd.to_datetime(
self.dataframe["time"], format="%Y-%m-%d"
)
self.dataframe["day"] = self.dataframe["time"].dt.dayofweek
self.dataframe["time"] = self.dataframe.time.apply(
lambda x: x.strftime("%Y-%m-%d")
)
self.dataframe.dropna(inplace=True)
self.dataframe.sort_values(by=["time", "tic"], inplace=True)
self.dataframe.reset_index(drop=True, inplace=True)
self.save_data(save_path)
print(
f"Download complete! Dataset saved to {save_path}. \nShape of DataFrame: {self.dataframe.shape}"
)
def data_split(self, df, start, end, target_date_col="time"):
"""
split the dataset into training or testing using time
:param data: (df) pandas dataframe, start, end
:return: (df) pandas dataframe
"""
data = df[(df[target_date_col] >= start) & (df[target_date_col] < end)]
data = data.sort_values([target_date_col, "tic"], ignore_index=True)
data.index = data[target_date_col].factorize()[0]
return data
def transfer_standard_ticker_to_nonstandard(self, ticker: str) -> str:
# "600000.XSHG" -> "600000"
# "000612.XSHE" -> "000612"
# "600000.SH" -> "600000"
# "000612.SZ" -> "000612"
if "." in ticker:
n, alpha = ticker.split(".")
# assert alpha in ["XSHG", "XSHE"], "Wrong alpha"
return n
def transfer_date(self, time: str) -> str:
if "-" in time:
time = "".join(time.split("-"))
elif "." in time:
time = "".join(time.split("."))
elif "/" in time:
time = "".join(time.split("/"))
return time
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