Starred repositories
Using Unsupervised learning, K-means, to determine stock support and resistance levels. Great for trading algorithms/bots using time serial analysis.
Hello there! 👋 Welcome to my GitHub repository. Here, I explore various aspects of trading and share my experience in developing a risk-tolerance-based trading framework. As a former retail trader,…
Optimal numerical differentiation of noisy time series data in python.
fatpack provides functions and classes for fatigue analysis of data series.
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are commi…
High-Performance Fractal & Econophysics Tools for Financial Time Series using JAX (GPU-Accelerated).
Here I test a simple ML trading strategy
Nonlinear measures for dynamical systems (based on one-dimensional time series)
Thermodynamic cycle modeling library, built on top of OpenMDAO
A Python-powered stock analysis tool that visualizes price trends, rolling averages, and key market peaks/troughs.
A machine learning model that tests multiple algorithms to determine which would provide the most accurate results for a buy or sell signal of a crypto or stock. We then used dimensionality reducti…
Real-Time Stock Forecasting & Trading Signal Dashboard (Facebook Prophet)
Inspired by https://sarem-seitz.com/posts/with-pytorch-i-can-gradient-boost-anything.html I have built a Gradient Boosting Machine in a similar approach. I have to get deeper into the theory to kno…
CodeGeeX2: A More Powerful Multilingual Code Generation Model
Cycle indicators from “Cybernetic Analysis for Stocks and Futures” and “Cycle Analytics for Traders”
Cycle-by-cycle analysis of neural oscillations.
A TD Ameritrade API client for Python. Includes historical data for equities and ETFs, options chains, streaming order book data, complex order construction, and more.
Detect peaks in realtime timeseries data using z-scores. This is a Golang implementation for the algorithm described by: https://stackoverflow.com/a/22640362/14797322
Python implementation of the Kuramoto model
Example of Empirical Mode Decomposition algorithm
Zigzag persistence studies the topological behavior of point-cloud data by generalizing the setting of persistent homology. We provide a way to compute by using extended persistence.
Python implementation of the Automatic Multiscale Peak Detection (AMPD) by Felix Scholkmann et al., 2012
This project implements a sophisticated machine learning pipeline for predicting buy and sell signals in the forex market. The system utilizes multiple models and techniques to generate accurate pr…