Implementation of Trading Strategies for Backtesting in MetaTrader 5 (MT5) Using MQL5 Programming Language – Free Open-Source Trading Bots (Expert Advisors) for MT5
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Updated
Nov 15, 2025 - MQL5
Implementation of Trading Strategies for Backtesting in MetaTrader 5 (MT5) Using MQL5 Programming Language – Free Open-Source Trading Bots (Expert Advisors) for MT5
QuantLib implementation in ImGui
A simple trading platform with separate server and client components
Demonstrates applied data science techniques (Jupyter Notebook) for financial or risk reporting, including visualization and statistical inference.
Fetches market data from the Alpha Vantage API, calculates daily returns for a portfolio of stocks, and computes the Value at Risk (VaR) at a 95% confidence level.
A backtesting and performance analysis package
Looks like nautilus trader is best for live trading, and bt is good for visualizing
High-performance Limit Order Book (LOB) matching engine in C++20
Project 1 of the 2020 Northwestern Financial Technology Bootcamp. We built a functional quantitative trading system that implements strategies researched and tested in Quantopian. Those strategies are then executed in Alpaca- the commission-free stock trading API.
Customer-side copy trading execution agent.
📈 A high-frequency trading engine designed to simulate ingesting market data and execute trades with microsecond latency.
A Python research lab for forex pairs trading using Engle-Granger cointegration, OLS hedge ratios, z-score mean reversion, walk-forward validation, and pair scanning.
Limit and market order book in Java with unit test
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