Optimizing Low Latency Data Processing in Python for Algorithmic Trading
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This video discusses techniques to optimize Python code for low latency data processing, specifically in the context of trading strategies. The speaker highlights the challenges of executing computations within small time windows to avoid slippage. Key optimization techniques include avoiding loops, using vectorized operations, broadcasting, and NumPy operations. The video compares sequential processing with asynchronous processing using async I/O and websockets, demonstrating the performance benefits of the latter. Further optimization is achieved by using the Numba library's Just-In-Time (JIT) compilation, which generates machine code to minimize Python overhead. Additional tips include using CPython, raw NumPy or Pandas for large datasets, multiprocessing for complex strategies, and considering compiled languages like C++ or Rust for ultra-low latency requirements.
Chapters:
Introduction (00:00:00 - 00:01:03)
Sequential Processing and Its Pitfalls (00:01:03 - 00:02:53)
Asynchronous Processing with Async I/O and Websockets (00:02:53 - 00:04:21)
Further Optimization with Numba's JIT Compilation (00:04:21 - 00:06:22)
Additional Optimization Techniques (00:06:22 - 00:08:09)
Conclusion (00:08:09 - 00:08:37)
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