13-strategy algorithmic paper trading platform on AWS EC2 — systemd-supervised Python services, risk engine with kill-lines, market regime detection, and automated analytics pipeline
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Updated
Jul 11, 2026 - Python
13-strategy algorithmic paper trading platform on AWS EC2 — systemd-supervised Python services, risk engine with kill-lines, market regime detection, and automated analytics pipeline
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
Quantitative strategy validation pipeline HMM regimes, walk forward cost aware backtesting
Sanitized public case study of AlphaQuant V12: systematic trading architecture, risk governance, QMS testing, safe demo code, and CI.
Quantitative AI hedge fund platform: Flask backend, ML/RL trading models, React web and React Native mobile clients.
Pairs trading strategy using Engle-Granger cointegration and a Kalman-filtered dynamic hedge ratio, with walk-forward out-of-sample backtesting.
Algorithmic trading framework with pluggable strategy
Crypto trading bot for Kraken: Optuna-tuned strategies, walk-forward backtesting, live Fly.io deployment. Concluded research project.
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