Description
Introducing how to quickly and efficiently simulate stock price information in the Python ecosystem. Backtesting is a necessary process to verify the validity of a strategy, but it can take a considerable amount of time as the time-frequency of the data becomes more precise and the complexity of the strategy increases.
I believe that efficient and accurate backtesting is not only a tool to speed up the computation time, but also to quickly operate a better strategy and enhance profitability. In this session,
- Improving the IO speed of daily OHLC ~ tick
- Comparison of the computation time of pandas vectorization compared to for loop
- Backtesting package structure
- Distributed backtesting using Ray
- Optimizing trading strategies using Ray Cluster
I would like to share my experience.
Kim Tae-wan I am majoring in Computer Science at SUNY Korea SBU, and I like analysis in chaotic environments such as pattern exploration of market data. I am interested in data engineering and optimization, and I would like to contribute to quickly responding to the complexity of the financial market and creating robust alpha through this.