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.

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

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.

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

An AI-powered trading intelligence system within the Aureon Capital AI ecosystem, designed to transform historical trading decisions into actionable insights through structured trade reviews, trader memory, pattern discovery, edge discovery, and AI-assisted coaching.

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

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

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.

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

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

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