Beast code in Giters

Quoc hung's starred repositories

signature-regime-detection

Code accompanying the paper "Pathwise methods for non-parametric online market regime detection and regime clustering for multidimensional and non-Markovian data"

Language:Jupyter Notebook2000

TGC_torch

A Pytorch implementation of paper "Temporal Relational Ranking for Stock Prediction"

Language:Python3800

x-trend

X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

MIT4900

A high-frequency trading and market-making backtesting tool in Python and Rust, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books, with real-world crypto market-making examples for Binance Futures

Language:RustMIT162000

bayesian-stats-modelling-tutorial

How to do Bayesian statistical modelling using numpy and PyMC3

Language:Jupyter NotebookMIT65400

essays-on-data-science

In which I put together my thoughts on the practice of data science.

Language:Dockerfile27400

HFT-Races

Code package to analyze high-frequency trading (HFT) races using financial-exchange message data, following Aquilina, Budish and O'Neill (2021).

Language:PythonBSD-3-Clause4300

python-fcdata

Language:Python1500

Awesome-Time-Series-Explainability

A list of (post-hoc) XAI for time series

MIT6200

ShapTime

Language:Jupyter Notebook700

Vietnam_Power_Plants

Open data repository for all power plants in Vietnam. Information includes capacity, coordinates, commissioning year, and plant names.

Language:Python100

ListFold

Language:Python4100

Time-Series-Forecasting-and-Deep-Learning

Resources about time series forecasting and deep learning.

48900

Probability-of-Informed-Trading

Implementation of PIN ( Probability of Informed trading) on A-Share daily public data (based on Yan Y, Zhang S. An improved estimation method and empirical properties of the probability of informed trading[J]. Journal of Banking & Finance, 2012, 36(2): 454-467.)

Language:Jupyter Notebook3000

trials

Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.

Language:PythonMIT10400

ifrs9

The full scope of IFRS 9 Impairment models including PD, LGD and EAD are provided. It also covers ECL, which is the combination of those three parameters as well as staging criteria.

Language:Jupyter Notebook7000