키움증권 REST/WebSocket 기반 AI 스캘핑 엔진 — 메인 봇·위젯·에피소드 매매와 장후 EV 자동 튜닝
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.
High-probability directional structures in price, backed by fundamentals.
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
Statistical arbitrage research platform in OCaml. Event-driven, paper trading only for now.
Advanced IDX Market Intelligence & Screener Platform featuring AI-powered Reasoning, Deep Broker Flow Detection, and Automated Trading Journal.
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.
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.
Quantitative AI hedge fund platform: Flask backend, ML/RL trading models, React web and React Native mobile clients.
Complete JavaScript & Node.js SDK for HTX's REST APIs & WebSockets, with TypeScript & browser support.
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.
Algorithmic trading framework with pluggable strategy
Collection of Python-based quantitative trading bots implementing systematic investment strategies, backtesting, risk analysis, and portfolio optimization.
AI-driven, paper-only research platform for finding and validating early on-chain token momentum across Ethereum and Robinhood. It prioritizes executable quotes, risk evidence, reproducible forward benchmarks, and honest expectancy over hype.
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.
Opening-range breakout on Nasdaq-100 futures with a full audit of how simulation conventions move the result.
ASRQuant is an open-source Python framework for auditable quantitative finance research, combining backtesting, Monte Carlo simulation, derivatives pricing, risk analytics, portfolio modelling, econometrics, machine learning, visualization, reproducibility, and implementation-sensitivity analysis.
Add a description, image, and links to the algorithmic-trading-quantitative topic page so that developers can more easily learn about it.
To associate your repository with the algorithmic-trading-quantitative topic, visit your repo's landing page and select "manage topics."