Multi-var Calc (part 2) + quantpylib improved + bounties
moving on from this post;
this week - we finish off multivariable calculus to the market notes, part 2. Next post will be the follow up to:
which is implementing a C-level LimitOrderBook in Python.
Finally, over the span of 4 posts, we have added treatment to single and multivariable calculus. We will add ODEs, and PDEs, which will give us valuable techniques at our disposable. The HJB equation, for instance, comes from PDEs - appear in many market making papers, including the seminal work from Gueant, and Stoikov. We will finally have complete references, all the way from undergraduate mathematics!
In the meantime, we have incorporated Limit Order Book and RingBuffer implementations to quantpylib, which offer significant performance benefits to previous versions. This is already documented, and share the same interface as the original libraries, so no change of code is required if you are using it for market making:
https://hangukquant.github.io/hft/lob/
By the way, the quantpylib’s bounties are active and may be claimed. The pool has increased, including 69$ from donations.
https://hangukquant.github.io/bounties/
Quantpylib is only going to expand, and become a powerhouse for quant trading, learning and research implementation. Obtain the repo pass here.
Now, the market notes (977 pages, paid)