title	Quant AI - Advanced Quant Trading Engine & Backtest Simulator
emoji	📈
colorFrom	blue
colorTo	indigo
sdk	docker
app_port	7860
pinned	false

📈 Quant.ai — Institutional Alpha Research & Live/Paper Trading Platform

🚀 Interactive Terminal Demo: Experience the deployed terminal live on Hugging Face Spaces 👉 huggingface.co/spaces/Ypeng12/quant-ai

Quant.ai is an end-to-end, Point-in-Time Consistent Quantitative Trading & Paper/Live Execution Platform. Designed following Hudson River Trading (HRT) and Quantitative Hedge Fund engineering standards, it bridges the gap between rigorous signal research, microstructure execution, and real-time broker integration (Alpaca API).

---

🔥 Key Platform Highlights

⚡ 1. Real-Time Live & Paper Trading (Alpaca Integration)

• Direct Broker Connectivity: Seamless REST & WebSocket integration with Alpaca Markets for real-time order routing, position tracking, and market streaming.
• Paper & Live Mode Switch: Flexible environment toggling (https://paper-api.alpaca.markets for risk-free simulation vs. live brokerage execution).
• Execution Algorithmic Suite: Smart order routing with TWAP, VWAP, and Implementation Shortfall (IS) execution algorithms.
• Dynamic Portfolio Risk Controls:
  • Real-time position tracking and ledger recording.
  • Trailing stop-loss triggers and volatility-based position sizing.
  • Drawdown-contingent risk multipliers & consecutive loss protections.

🔬 2. HRT-Standard Alpha Research Engine

• Point-in-Time Data Hygiene: Strict timestamp truncation at date $t$ to eliminate lookahead bias across U.S. Equity & ETF universes.
• Purged Walk-Forward Cross Validation + Embargo: Eliminates overlap leakage across rolling 3-year train / 6-month validation / 6-month OOS test windows with a 5-day embargo.
• Novel Risk-Adjusted Signals:
  • Sortino Momentum: $\text{Return}{20d} / \text{DownsideVol}{20d}$
  • Residual Momentum: 60-day rolling OLS stripping SPY Market Beta: $R_i(\tau) = \alpha_i + \beta_i R_{\text{SPY}}(\tau) + \epsilon_i(\tau)$
  • Robust Z-Score Normalization: Cross-sectional Median/MAD standardization.
• Multiple Testing Correction: Deflated Sharpe Ratio (DSR) and Stationary Bootstrap 95% Confidence Intervals.

📊 3. Microstructure & Orderbook Engine

• Order Flow Imbalance (OFI): Real-time L2/L3 orderbook imbalance calculation for high-frequency price impact prediction.
• Friction & Shortfall Modeling: Multi-tiered transaction cost models with 2–15 bps slippage sensitivity checks.
• Low-Latency Engine Core: C++ accelerated order matching engine headers (orderbook.hpp).

🤖 4. Natural Language Alpha Hypothesis Parser

• LLM-assisted hypothesis compilation translating natural language ideas into validated, executable Pydantic model configurations.

---

🎯 Core Research Hypothesis

"Can volatility-adjusted and market-beta-residualized cross-sectional momentum signals across liquid U.S. ETFs predict short-term excess returns after transaction costs and execution friction?"

---

⚡ Quick Start & Setup

1. Clone & Install Dependencies

git clone https://github.com/ypeng12/Quant.ai.git
cd Quant.ai

# Install Python requirements
pip install -r requirements.txt

2. Configure Environment & Alpaca Credentials

Create a .env file in the project root:

# Alpaca Broker Credentials (Use Paper Trading for risk-free testing)
ALPACA_API_KEY=your_alpaca_paper_key
ALPACA_SECRET_KEY=your_alpaca_paper_secret
ALPACA_BASE_URL=https://paper-api.alpaca.markets

# Server Configuration
PORT=7860

3. Run Unit Tests (Zero Future Leakage Verification)

python -m pytest tests/ -v

4. Execute Out-of-Sample Alpha Experiment (make oos)

python run_experiment.py

Outputs:

================================================================================
  QUANT.AI OUT-OF-SAMPLE EXPERIMENT RUNNER
  Hypothesis: Volatility-Adjusted Momentum across Liquid U.S. ETFs
  Lookback: 20d | Holding: 5d | Transaction Cost: 5.0 bps
================================================================================
  --> Raw_Momentum_Baseline        | OOS Rank IC: -0.0182 | Net Sharpe: 0.33 | MaxDD: -55.2%
  --> Vol_Adj_Momentum_Baseline    | OOS Rank IC: -0.0196 | Net Sharpe: 0.33 | MaxDD: -59.4%
  --> Ridge_Linear                 | OOS Rank IC:  0.0102 | Net Sharpe: 1.58 | MaxDD: -23.5%

5. Launch Local Dashboard / FastAPI Backend

# Launch FastAPI Backend
uvicorn backend.app.main:app --host 0.0.0.0 --port 7860 --reload

Navigate to http://localhost:7860 in your browser.

---

📁 Repository Architecture

Quant.ai/
├── .github/
│   └── workflows/
│       └── sync_to_hf.yml        # CI/CD GitHub Action auto-syncing to Hugging Face
├── backend/
│   └── app/
│       ├── trading_engine.py     # Live/Paper Trading Engine & Portfolio Risk Control
│       ├── simulator.py          # Execution Simulator & Implementation Shortfall Model
│       ├── orderbook_ofi.py      # Microstructure Order Flow Imbalance (OFI) Engine
│       ├── execution_algo.py     # TWAP / VWAP Order Execution Algorithms
│       ├── low_latency_engine.py # High-Frequency Matching Engine Adapter
│       └── cpp_engine/           # C++ Orderbook & Level-2 Matching Headers
├── frontend/                     # React / TypeScript Institutional Dashboard UI
├── src/
│   ├── data/
│   ├── features/
│   ├── labels/
│   ├── validation/
│   ├── models/
│   └── portfolio/
├── tests/                        # Zero Future Leak & Purged CV Unit Tests
├── Dockerfile                    # Hugging Face Space Docker Deployment spec
├── run_experiment.py             # One-command OOS research executable
└── requirements.txt              # Production Python dependencies

---

🔄 CI/CD & Hugging Face Auto-Sync Workflow

This repository features automated GitHub Actions CI/CD (.github/workflows/sync_to_hf.yml).

Whenever changes are merged into the main branch, GitHub automatically mirrors and deploys the latest codebase directly to the Hugging Face Space, guaranteeing 24/7 live deployment without manual intervention.

---

🤝 Collaborative Development & Branching Model

To ensure platform stability:

• Feature Branches: Developers work on dedicated feature branches (e.g. ypeng12, lxc).
• Pull Requests (PR): Code is tested locally and submitted via PRs to main.
• Deployment Trigger: Merging to main automatically triggers Hugging Face live deployment.

---

Developed with ❤️ for Quantitative Finance, Machine Learning, and Automated Execution Research.