Targeting Risk; Volatility and Leverage Management -28 Pages (With Code)

In this paper - we talk volatility targeting and leverage targeting. We provide code implementation in Python. We compare variants and include interesting discussions.

A Preview:

The Report (28 Pages) (for ALL):

Targeting Risk; Volatility and Leverage Management (Interim Report) With Code

1.5MB ∙ PDF file

Download

Download

Notes for Paid Readers:

We originally had another discussion in mind when writing the pieces of code demonstrated here - we intended to discuss the implementation of a robust, efficient testing engine. However, as we typed up the code, we realised there were some interesting points of discussion, and after making some interesting observations created a (interim) report. While we did not explicitly discuss the Alpha class in detail, we intend to dive deeper into this testing engine in the weeks/months to come. In particular, we believe that this is the minimum requirement any systematic approach needs to address - the computation of edge, the relative allocations of edge and the overall allocations of edge.

However, we can do much better. We want to decrease the latency between the birth of an hypothesis and evaluation of its merits by increasing the robustness of our quant tools. We intend to discuss in great detail (for paid readers) how to make this fundamentally ‘solid’ vanilla implementation into a high performance quant tool that we can use to test any trading ideas within minutes and go from programmer to quant.

We include discussions in code profiling, under the hood optimizations, data buses, Cython-ic and JIT compilers to achieve C-like perfomances in our Python application. We also want to include things like FX converters to allow us to trade multi-FX products. Join us as a paid reader to get access.

As we mentioned, this year, another of HangukQuant’s objective is to go beyond alpha discussions to talk about creating your own quantitative systems and tools to take you to the next level in your quantitative journey. Look forward to seeing you there.