- Joined
- 11/5/14
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- 294
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- 53
Hi guys,
I began reading some basic material on stochastic calculus and numerical methods. I am writing a toy Python library, where I hope to add analytics to support, for example, pricing a risk bond, a swap, CDS, CDswaptions, generate analytical risks, in my own personal free time, for fun.
Based on the C implementation here, I implemented the MT19937 PRNG algorithm - QuantPy/mt19937.py at master · quantophile/QuantPy. I know that this has a period of 2^19937 - 1, and it passes a number of statistical tests. I also know, that it caches 624 numbers.
Practical question: As a Quant, are you supposed to know, how this algorithm works, or it's pretty much a blackbox to practitioners? I couldn't make much sense of why the algorithm works mathematically - except, I could see computationally, it generated uniformly distributed numbers.
Thanks,
Quasar
I began reading some basic material on stochastic calculus and numerical methods. I am writing a toy Python library, where I hope to add analytics to support, for example, pricing a risk bond, a swap, CDS, CDswaptions, generate analytical risks, in my own personal free time, for fun.
Based on the C implementation here, I implemented the MT19937 PRNG algorithm - QuantPy/mt19937.py at master · quantophile/QuantPy. I know that this has a period of 2^19937 - 1, and it passes a number of statistical tests. I also know, that it caches 624 numbers.
Practical question: As a Quant, are you supposed to know, how this algorithm works, or it's pretty much a blackbox to practitioners? I couldn't make much sense of why the algorithm works mathematically - except, I could see computationally, it generated uniformly distributed numbers.
Thanks,
Quasar