AKLite
Key Features
A super-fast request engine built in HTTPX and accelerated with Asyncio.
The ability to fetch multiple stocks with ease.
Access to historical data from main stream data source.
With AKLite, you’ll have all the tools you need to fetch data. Start using AKLite today and take your working to the next level!
A Quick Example
Get a glimpse of what fetching with AKLite looks like with these code snippets:
Fetching Stock Data:
import aklite as ai
stock_zh_a_hist_obj = ai.stock_zh_a_hist(symbols=["000001", "000002"],
period="daily",
start_date="20220101",
end_date="20230601",
adjust="hfq",
timeout=5,
proxies={})
print(stock_zh_a_hist_obj.data)
print(stock_zh_a_hist_obj.columns)
print(stock_zh_a_hist_obj.url)
print(stock_zh_a_hist_obj.desc)
print(stock_zh_a_hist_obj.symbols)
print(stock_zh_a_hist_obj.start_date)
print(stock_zh_a_hist_obj.end_date)
print(stock_zh_a_hist_obj.adjust)
To learn how to use AKLite, see the notebooks under the User Guide:
Recommended Reading
The following is a list of essential books that provide background information on quantitative finance and algorithmic trading:
Lingjie Ma, Quantitative Investing: From Theory to Industry
Timothy Masters, Testing and Tuning Market Trading Systems: Algorithms in C++
Stefan Jansen, Machine Learning for Algorithmic Trading, 2nd Edition
Ernest P. Chan, Machine Trading: Deploying Computer Algorithms to Conquer the Markets
Perry J. Kaufman, Trading Systems and Methods, 6th Edition