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AgentQuant: Autonomous Quantitative Research Agent

A fully autonomous AI agent that researches, generates, and validates trading strategies.

🚀 Update (Nov 2025): Now powered by Google Gemini 2.5 Flash. The agent is fully functional and no longer uses random simulation. It actively analyzes market regimes and proposes context-aware strategies.

🎯 What This Project Is

AgentQuant is an AI-powered research platform that automates the quantitative workflow. It replaces the manual work of a junior quant researcher:

  1. Market Analysis: Detects regimes (Bull, Bear, Crisis) using VIX and Momentum.
  2. Strategy Generation: Uses Gemini 2.5 Flash to propose mathematical strategy parameters optimized for the current regime.
  3. Validation: Runs rigorous Walk-Forward Analysis and Ablation Studies to prove strategy robustness.
  4. Backtesting: Executes vectorized backtests to verify performance.

🏗️ System Architecture

graph TD
    subgraph "User Interface"
        UI[Streamlit Dashboard]
        Config[config.yaml]
    end

    subgraph "Data Layer"
        Ingest[Data Ingestion<br/>yfinance]
        Features[Feature Engine<br/>Indicators]
        Regime[Regime Detection<br/>VIX/Momentum]
    end

    subgraph "Agent Core (Gemini 2.5 Flash)"
        Planner[Strategy Planner]
        Context[Market Context<br/>Analysis]
    end

    subgraph "Execution Layer"
        Strategies[Strategy Registry<br/>Momentum, MeanRev, etc.]
        Backtest[Backtest Engine<br/>VectorBT/Pandas]
    end

    subgraph "Validation"
        WalkForward[Walk-Forward<br/>Validation]
        Ablation[Ablation<br/>Study]
    end

    UI --> Config
    Config --> Ingest
    Ingest --> Features
    Features --> Regime
    
    Regime --> Context
    Features --> Context
    Context --> Planner
    
    Planner -->|Proposes Params| Strategies
    Strategies --> Backtest
    
    Backtest --> UI
    Backtest --> WalkForward
    Backtest --> Ablation
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🧠 The "Brain" (Gemini 2.5 Flash)

The agent uses a sophisticated prompt engineering framework to:

  • Analyze technical indicators (RSI, MACD, Volatility).
  • Understand market context (e.g., "High Volatility Bear Market").
  • Propose specific parameters (e.g., "Use a shorter 20-day lookback for momentum in this volatile regime").

🔬 Scientific Validation

We have implemented rigorous experiments to validate the agent's intelligence:

1. Ablation Study (experiments/ablation_study.py)

  • Hypothesis: Does giving the AI "Market Context" improve performance?
  • Method: Compare an agent with access to market data vs. a "blind" agent.
  • Result: Context-aware agents significantly outperform blind agents in Sharpe Ratio.

2. Walk-Forward Validation (experiments/walk_forward.py)

  • Hypothesis: Can the agent adapt to changing markets over time?
  • Method: The agent re-trains every 6 months, looking only at past data to predict the next 6 months.
  • Result: The agent successfully adapts parameters (e.g., switching from long-term trend following to short-term mean reversion) as regimes change.

🚀 Quick Start

Prerequisites: Python 3.10+ and a Google Gemini API Key.

  1. Clone the repo

    git clone https://github.com/OnePunchMonk/AgentQuant.git
cd AgentQuant

  2. Install dependencies

    pip install -r requirements.txt

  3. Set up API Key Create a .env file:

    GOOGLE_API_KEY=your_gemini_api_key_here

  4. Run the Experiments

    # Run the Walk-Forward Validation
python experiments/walk_forward.py

# Run the Ablation Study
python experiments/ablation_study.py

  5. Run the Dashboard

    streamlit run run_app.py

📂 Project Structure

AgentQuant/
├── src/
│   ├── agent/          # LLM Planner (Gemini 2.5 Flash)
│   ├── data/           # Data fetching (yfinance wrapper)
│   ├── features/       # Technical indicators & Regime detection
│   ├── backtest/       # Vectorized backtesting engine
│   └── strategies/     # Multi-strategy logic (Momentum, Mean Reversion, etc.)
├── experiments/        # Validation scripts (Walk-Forward, Ablation)
├── config.yaml         # Configuration (Tickers, Dates)
└── run_app.py          # Main entry point

This software is for educational purposes only.