Derivatives Pricing Engine
Project description
DerivX
V0.2.7-Beta Build 20210816
© 2021-2021 Xu Rendong. All Rights Reserved.
Project Summary
Derivatives Pricing Engine.
Install
C++:
Python:
pip install derivx
JavaScript:
npm install derivx
Usage
C++:
Python:
import derivx
print(derivx.Version())
For more usages please refer to test files in derivx_py folder.
DerivX is not dependent on numpy, pandas and matplotlib, but if you want to run tests, you'd better install them with:
pip install numpy pandas matplotlib
JavaScript:
const derivx = require('derivx')
console.log(derivx.Version())
For more usages please refer to test files in derivx_js folder.
DerivX is not dependent on numjs, but if you want to run tests, you'd better install it with:
npm install git+https://github.com/nicolaspanel/numjs.git
Benchmark
Environment:
- CPU: Intel Core i7-4790 4C/8T 3.60GHz
- RAM: 16.0GB
- SYS: Windows 10 Pro 64-bit 20H2 19042.1110
Parameters:
- rand_rows: 500000
- rand_cols: 250
- rand_seed: { 0, 1, 2, 3, 4, 5, 6, 7 }
- dual_smooth: true
- runs_size: 1000000
- runs_step: 244
- run_from: 0
- run_days: 1
C++:
- InitRand: 461ms - 550ms
- InitPath: 1575ms - 1961ms
Autocall Snowball:
- CalcCoupon: 468ms - 564ms (there is still 200ms optimization space)
- CalcPayoff: 965ms - 983ms per price
- CalcDelta: 1932ms - 1983ms per price
- CalcGamma: 2898ms - 2918ms per price
- CalcVega: 2047ms - 2073ms per price
- CalcTheta: 1636ms - 1647ms per price (as run_days increases this tends to CalcPayoff's time consumption)
- CalcRho: 2106ms - 2161ms per price
- The difference between CalcCoupon and CalcPayoff shows that different prices and barriers will cause different time consumption.
Contact Information
QQ: 277195007, WeChat: xrd_ustc, E-mail: xrd@ustc.edu
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