Is Julia the best language for quantitative finance? Nov 19 2019 13:22 languageMoneyScience
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Mario Emmanuel over at Towards Data Science spent some time earlier this year (2019) working on quantitative intraday strategies and in the process tested workflows for similar tasks in Python, C, Fortran and Julia.
To give a background on the nature of the projects that have been tested I will begin clarifying that:
- The projects are related to instruments trading (i.e. I design and simulate derivatives market algorithmic/quantitative strategies).
- I have not used Machine Learning or AI techniques in these strategies, just plain/vanilla statistics and simulation.
- Handled data sets are large but not huge, normally my simulations cover 30 million records data sets per asset/instrument, every data is used several times, and I do parametric and Monte Carlo analysis. This implies a large number of iterations.
- I am not an expert programmer and I am not interested in becoming one, I just want to focus on the market logic and the strategies that exploit profitable edges.
My quest is to find the right tool combination that performs well enough and simplify my workflow. Hence the review is based on the perspective of an end-user of these technologies.
This context has some implications:
- I need a language that can deal easily and without efforts with large data sets.
- I need speed.
- I do not need that much speed to require multi-core or parallel processing.
- I do not need —at this time— Machine Learning or AI libraries.
This post is the outcome from the journey I have done to find an optimal workflow. It is a subjective but still informed view of each language strengths and weaknesses for this particular endeavour. I hope you find it useful and enjoyable.