You read my mind! Got a few things in the oven:
I) High-Performance Numerical Computations:
Use Case: Developing and backtesting complex quantitative trading strategies.
Python Role: Python can be used for the rapid development and testing of trading strategies using libraries such as pandas, NumPy, and backtesting.py.
Rust Role: Rust can be used to optimize the performance of computationally intensive parts of the strategy, such as custom technical analysis indicators or simulations, ensuring faster execution and lower latency. (C/C++ has a clear monopoly on this now in banking space, some Java/Scala out there too)
II) Risk Management Systems:
Use Case: Real-time risk assessment and management for large portfolios.
Python Role: Python can be used to design the risk management framework, perform data analysis, and integrate various risk models.
Rust Role: Rust can be used to handle real-time data processing and high-frequency calculations, ensuring the system is robust and performs well under load.
(C++ winner here as well, but 'data science' aspects of this python has a clear advantage/edge.)
III) Pricing and Valuation Models:
Use Case: Implementing complex derivative pricing models.
Python Role: Python can be used to prototype and validate pricing models using libraries such as SciPy and QuantLib.
Rust Role: Rust can be used to implement the computationally intensive parts of these models, such as Monte Carlo simulations or partial differential equation solvers, to achieve higher performance and reliability.
(C++ Winner again, would love some feedback from our PDE expert
@Daniel Duffy if Rust even can hold a candle to C++ for this.)
IV) Market Data Processing:
Use Case: Real-time processing and analysis of market data feeds.
Python Role: Python can be used to manage the data ingestion pipeline, initial preprocessing, and feature extraction.
Rust Role: Rust can be used to implement the core data processing engine to handle high-throughput data streams, ensuring low latency and efficient memory usage.
(Here C/C++ is most common from what I've seen. Rust might have most application here as of right now from my thinking. Pure systems dev use case)
V) Algorithmic Trading Infrastructure:
Use Case: Building and deploying algorithmic trading systems.
Python Role: Python can be used for strategy development, parameter tuning, and integrating with various data sources and trading platforms.
Rust Role: Rust can be used to develop the execution engine, ensuring low-latency order execution, robust error handling, and secure memory management to prevent crashes and undefined behavior during live trading.
(Similar comment to above. Most C like/systems infra dev here)
Would love to get input from
@achirikhin @APalley @Daniel Duffy on this as well. I'll admit I'm quite biased toward C/C++ myself.
