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An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
Code repository for demos of the article 'Arbitrage-Free Implied Volatility Surface Generation with Variational Autoencoders'.
Avellaneda-Stoikov HFT market making algorithm implementation
Golub, Glattfelder and Olsen, ''The Alpha Engine: Designing an Automated Trading Algorithm''
上海期货交易所CTP接口 Shanghai Future CTP Interface CTP 6.3.15 Python API Wrapper
Here I will store some of my research work done on the data analysis.
A replica of the AlphaZero methodology for deep reinforcement learning in Python
Fooled by Randomness Chinese Version
Optimal control of risk aversion in Avellaneda Stoikov high frequency market making model with Soft Actor Critic reinforcement learning
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
A collection of homeworks of market microstructure models.
This project consists in the implementation of a Neural Network using TensorFlow in order to calibrate the SABR model. The final goal consists in predicting a volatility surface, as described in "Deep Learning Volatility" (2019).
Options market making using a stochastic control approach
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Some implementations from the paper robust risk aware reinforcement learning
Construction of local volatility surface by using SABR
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Surface SVI parameterisation and corresponding local volatility
For code and snippets for STA 2536: Data Science for Risk Modeling
Course materials for the Coursera MOOC: Stochastic Processes from National Research University Higher School of Economics
three stochastic volatility model: Heston, SABR, SVI
Exploring inference in variants of a stochastic blockmodel for (directed) network data
Extension of original open-sourced math library, SuanShu.
SVI volatility surface model and an example of China 50ETF option
Balanced Baskets: A New Approach to Trading and Hedging Risks (Bailey and de Prado)
Risk management Tools based on Intrinsic Time and Scaling Laws
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.