Popular GitHub Repositories for Indicators

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

Tensorflow's Fairness Evaluation and Visualization Toolkit

?? EA31337 framework (MQL library for writing trading Expert Advisors, indicators and scripts)

Extract indicators of compromise from text, including "escaped" ones.

Machine learning Options Trading Algorithm. API calls to collect the data from Yahoo Finance, Sentiment Investor, Finta. Encoding financial indicators. Building Logistic regression and Multinomial Machine Lerning models for evaluation. P2.

LLM4Data is a Python library designed to facilitate the application of large language models (LLMs) and artificial intelligence for development data and knowledge discovery.

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

TradeGPT is an intelligent trading bot built with ChatGPT and AI to automate and optimize trading strategies. It analyzes market data, predicts trends, and executes trades in real-time, providing traders with tools to enhance efficiency and profitability.

Python library to implement advanced trading strategies using machine learning and perform backtesting.

AI-Powered Financial Asset Forecasting: Predicts long-term US stock prices using AI. Integrates news sentiment, technical indicators, candlestick patterns, and fundamental analysis. Provides comprehensive insights for informed financial decision-making. Customizable data collection and analysis for investors and researchers.

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

Algorithmic trading using heterogeneous graph neural network and reinforcement learning, pre-alpha

Rule-based Algorithmic Trading using a Genetic Algorithm and Machine Learning Signals for the Cryptocurrency Market.

Deep Reinforcement Learning (DRL) stock trading system with LSTM-PPO, integrating technical indicators and FinBERT-based news sentiment analysis.

Stock closing and opening forecasting using Deep neural network and LSTM(technical indicators included)

A project that tries to influence buying and selling of stocks using an algorithmic model built using an ensemble of KNN, Decision Tree, Random forest and SVM. The model depicts an ideal scenario for maximizing profits from a trade. A momentum strategy that is used to predict trading signal has been modelled based on a set of rules using various technical indicators

A general text classifier based on BERT. Multi-process data processing, multi-gpu parallel training, rich monitoring indicators.

Technical analysis using indicators and building a ML based trading strategy