KunQuant is a optimizer, code generator and executor for financial expressions and factors. The initial aim of it is to generate efficient implementation code for Alpha101 of WorldQuant. Some existing implementations of Alpha101 is straightforward but too simple. Hence we are developing KunQuant to provide optimizated code on a batch of general factors.
This project has mainly two parts: KunQuant and KunRunner. KunQuant is an optimizer & code generator written in Python. It takes a batch of financial expressions as the input and it generates highly optimized C++ code for computing these expressions. KunRunner is a supporting runtime library and Python wrapper to load and run the generated C++ code from KunQuant.
Experiments show that KunQuant-generated code can be more than 100x faster than naive implementation based on Pandas. We ran Alpha001~Alpha020 with Pandas-based code and our optimized code. See results below:
| Pandas-based | KunQuant 1-core | KunQuant 4-cores |
|---|---|---|
| 3.26s | 0.10s | 0.029s |
The data was collected on 4-core i7-7700HQ, running synthetic data of 64 stocks with 1000 days.
- KunQuant parallelized the computation factors and we use SIMD (AVX2) to vectorize them.
- Redundant computation among factors are eliminated: Think what we can do with
sum(x),avg(x),stddev(x)? The result ofsum(x)is needed by all these factors. KunQuant also automatically finds if a internal result of a factor is used by other factors. - Temp buffers are minimized by operator-fusion. For a factor like
(a+b)/2, pandas and numpy will first compute the result of(a+b)and collect all the result in a buffer. Then,/2opeator is applied on each element of the temp buffer of(a+b). This will result in large memory usage and bandwidth. KunQuant will generate C++ code to compute(a[i]+b[i])/2in the same loop, to avoid the need to access and allocate temp memory.