Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

Free, open-source crypto trading bot, automated bitcoin / cryptocurrency trading software, algorithmic trading bots. Visually design your crypto trading bot, leveraging an integrated charting system, data-mining, backtesting, paper trading, and multi-server crypto bot deployments.

A list of online resources for quantitative modeling, trading, portfolio management

Terminal dashboard for trading Bitcoin, predicting price movements, and losing all your money

A Comprehensive Collection of Everything Related to Tradingview Pine Script.

Open-source Rust framework for building event-driven live-trading & backtesting systems

QuickFIX/J is a full featured messaging engine for the FIX protocol. - This is the official project repository.

Framework for algorithmic trading

The Go FIX Protocol Library 🚀

Technical analysis library for Rust language

a light-weighted, integrated trading/backtesting system/platform（综合量化交易回测系统/平台）

Backtest and live trading in Python

A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine

A trading bot for automated algorithmic trading on Binance Futures, Bybit, BitMEX and FTX written in python.

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

Basic order matching engine written in Rust

Indicators and EA Snippets for MetaTrader 5

Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.

The apex of my CSE tenure at UIET Kurukshetra University in 2018, This project focuses on Zerodha, involving live online trading in the NSE-BSE with real money, utilizing Artificial Intelligence techniques. The project employs Python programming, incorporating live trading bots, indicator screeners, and back testers through REST API and websockets.

CryptoCurrency prediction using machine learning and deep learning