Скачать или смотреть Math for Quant Dev - Example

There is a lot of confusion around what is "quant" and what is "quant dev." When I explain quant dev is responsible for implementing models, people correct me and say that is really just "ML Ops" or "Dev Ops." What really separates a quant dev is the depth of math understanding. For quantitative finance optimization of implementation through mathematical simplification is critical. Solely relying on software and hardware optimization can only take you so far.

Another important difference is "quant" vs "quant dev." Quants build models and do not need efficiency of runtime however they do need efficiency in model development time. Quants focus on building models whereas quant devs focus on optimizing those models.

Today's example is a simple real world example for loans (credit risk). The goal is to get the ratio of principal to payment amount without calculating an amortization table. Amortization tables are the correct way of calculating these numbers from financial theory however it is inefficient when this scales to a large number of loans. A new table would need to be created for loan. Just imagine how much memory and processing power you would need to do say 100,000 loans. This is very inefficient. The computer science path would be to run this in parallel however it is cheaper to use a simplified math equation though I would argue it is more challenging to find equations that to simple do computer science optimization. Math typically reduces the probability of a bug (coding issue) as well and can be ran in almost any environment.

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