algorithmic-trading-quantitative

15-strategy algorithmic paper trading platform on AWS EC2 — systemd-supervised Python services, risk engine with kill-lines, market regime detection, and automated analytics pipeline

A high-performance algorithmic trading system built in Rust for backtesting, live trading, and strategy optimization with Binance & MT5 support, parallel execution, advanced risk management, and extensible architecture.

A Python framework for testing trading strategies against the ways backtests mislead: look-ahead audits, matched-exposure controls, and block-bootstrap significance tests. The tester is itself tested - a property fuzzer plus mutation testing (4 planted engine bugs, all caught). Includes three case studies of rejected ideas.

Quantitative strategy validation pipeline HMM regimes, walk forward cost aware backtesting

Survivability-first quantitative research system. An AI council debates every architecture decision before code; deterministic, tested strategies do the trading. Walk-forward + purged CV + deflated Sharpe. LLMs never place trades.

AI multi-agent system for stock market signal generation using LangGraph, GPT-4, and Qdrant vector search. Achieved 42.8% backtest return vs. 24.5% buy-and-hold, 78% win rate on high-consensus signals. 🥇 Best Use of AI/ML, UB Hacking 2024.

Advanced IDX Market Intelligence & Screener Platform featuring AI-powered Reasoning, Deep Broker Flow Detection, and Automated Trading Journal.

Quantitative AI hedge fund platform: Flask backend, ML/RL trading models, React web and React Native mobile clients.

End-to-end automated crypto trading workflow featuring market scanning, signal generation, paper trading, risk management, Telegram alerts, PostgreSQL analytics, and Google Sheets reporting.

Cost-aware time-series momentum on a $20 IBKR account

Collection of Python-based quantitative trading bots implementing systematic investment strategies, backtesting, risk analysis, and portfolio optimization.

AI-powered multi-agent quant signal generation engine. Uses LangGraph to orchestrate 4 LLM agents (News Analyst, Trading Analyst, Risk Analyst, Manager) that collaborate to generate risk-adjusted BUY/SELL/HOLD signals using real-time news, vector memory, and backtesting.

Personal research project combining software development, behavioural analysis and quantitative review to transform discretionary trading decisions into an auditable dataset.

A reusable framework for validating systematic trading signals before risking capital — walk-forward CV, Monte Carlo tail-risk simulation, sensitivity analysis, and a fail-closed guardrail engine. No real strategy or data included.

Complete JavaScript & Node.js SDK for HTX's REST APIs & WebSockets, with TypeScript & browser support.

Opening-range breakout on Nasdaq-100 futures with a full audit of how simulation conventions move the result.

Systematic multi-factor equity strategy using momentum, liquidity and volatility signals with reproducible backtesting and Fama–French validation.

Backtesting Engine 2026 – Test trading strategies on historical data. RSI, MACD, SMA, Bollinger Bands, and custom strategies. No real money involved. Setup.exe included.

Algorithmic trading framework with pluggable strategy

Small-account systematic trading bot for Alpaca — built live, diagnosed a losing strategy with real backtests, and rebuilt it.