Stars
IV regression using GMM, testing for weak instruments, Wooldridge-Hausman-Wu test for endogeneity, instrument validity-Sargan's J-stat test, IV regression with 2sls method, Testing for overidentify…
Identifying market regime through Gaussian Mixture Models and Markov Regression
Developed a model estimating VIX spike timing based on the Hawkes Process theory. Improved traditional mean-reverting model of VIX by adding the Hawkes model as jump-diffusion, estimated the parame…
Collection of links and example implementations how to get and use energy related data
Selenium-based webscraper that scrapes specified data from the eex website and sends it to an email address
Option pricing with various models (Black-Scholes, Heston, Merton jump diffusion, etc) and methods (Monte Carlo, finite difference, Fourier).
Option modelling in the Jump Diffusion Model
Master thesis on the Financial Mathematics topic of Option Pricing and Hedging under Jump-diffusion model
A Python-based Monte Carlo simulation of electricity forward curves with inherent delivery month correlation and jump diffusion behavior, complete with visualization, documentation, and supporting …
Thesis project on pricing financial derivatives for electricity prices.
Python client for the ENTSO-E API (european network of transmission system operators for electricity)
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 filte…
A tool to track mutual fund performance using Kalman Filter and similar tools. Feel free to add anything you know or comment on anything.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
used for Stock Prodiction&power prediction&Traffic prediction by ARIMA,xgboost,RNN,LSTM,TCN
Experiments using wavenet-like algorithms to predict BTC price fluctuations
BaseWavenet/Wavenet+ResidualBlock
This repository implements some popular neural network time series forcasting solution with comprehensive comments and tensor shape explanation
A simple implementation of the WaveNet model for time series forecasting
Implementation of "A CNN-LSTM-Based Model to Forecast Stock Prices" article with pytorch framework
Research on MLP LSTM CNN GAN for stock return prediction in Pytorch
Tensorflow and Pytorch stock prices forecasting
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
This is the the final project of the course: L330 Data Science: principles and practice at the University Of Cambridge. The task for this project is stock market prediction using a diverse set of v…
Determining stock price direction by using CNN on 1-D time series data encoded as 2-D Images
An attempt to implement the idea behind this paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212320
The code compares the performance of WaveNet architecture performance on forecasting CSI300 index.
Using Python and Tushare financial database