Inspiration
The idea for HedgeMind came from a simple observation: quantitative researchers at hedge funds spend weeks manually backtesting trading strategies, only to discover fatal flaws after days of work. Meanwhile, AI chatbots can answer questions instantly but can't autonomously execute complex, multi-day research projects.When Google DeepMind announced the Gemini 3 "Action Era," we saw an opportunity to bridge this gap. What if an AI agent could take a single hypothesis—like "commodities hedge inflation"—and autonomously spend hours or even days researching it, the same way a human analyst would?We wanted to build something that truly embodied the Marathon Agent concept: a system that doesn't just respond to prompts, but independently orchestrates complex workflows, self-corrects errors, and produces professional-grade deliverables without constant human supervision.
What It Does
HedgeMind is an autonomous trading strategy researcher that operates across extended time periods (hours to days) to transform investment hypotheses into institutional-quality research.The workflow is fully autonomous: Hypothesis Parsing → The agent breaks down trading ideas into testable components Data Acquisition → Fetches decades of historical data from multiple APIs (Alpha Vantage, FRED, Yahoo Finance) Strategy Development → Writes Python code to implement the trading logic Backtesting → Tests strategies across multiple market regimes (bull markets, bear markets, volatility spikes) Validation → Runs out-of-sample tests, walk-forward optimization, and Monte Carlo simulations Risk Assessment → Calculates Sharpe ratios, drawdowns, tail risks, and position sizing Report Generation → Produces institutional-grade research memos with visualizations The key differentiator: HedgeMind runs for hours without human intervention, self-debugging code errors, detecting overfitting, and refining its analysis autonomously.Example output metrics:
Sharpe Ratio: $$SR = \frac{\bar{R_p} - R_f}{\sigma_p}$$ where $$\bar{R_p}$$ is average portfolio return Maximum Drawdown: $$MDD = \max_{t \in (0,T)} \left( \frac{\text{Peak}_t - \text{Trough}_t}{\text{Peak}_t} \right)$$ Win Rate, Recovery Time, Correlation Analysis 🛠️ How We Built ItTechnology Stack Gemini 3 Pro API via Google AI Studio Antigravity Framework for agent orchestration Python (pandas, numpy, matplotlib, scipy) for quantitative analysis Multiple Data APIs: Alpha Vantage, FRED, yfinance Autonomous browser verification for data validation ArchitectureWe implemented HedgeMind as a multi-phase autonomous agent using Gemini 3's extended context window and tool-calling capabilities:┌─────────────────────────────────────────┐ │ User Hypothesis Input │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 1: Research Planning │ │ - Parse hypothesis │ │ - Design methodology │ │ - Identify data sources │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 2: Data Acquisition │ │ - API calls (Alpha Vantage, FRED) │ │ - Data validation loops │ │ - Quality checks │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 3: Strategy Development │ │ - Write Python implementation │ │ - Autonomous debugging │ │ - Browser verification │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 4: Backtesting │ │ - Multi-regime testing │ │ - Performance metrics │ │ - Visualization generation │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 5: Validation & Stress Testing │ │ - Out-of-sample tests │ │ - Walk-forward optimization │ │ - Overfitting detection │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 6: Risk Assessment │ │ - Position sizing │ │ - Tail risk scenarios │ │ - Correlation analysis │ └──────────────┬──────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Phase 7: Report Generation │ │ - Institutional memo (PDF/Markdown) │ │ - Executable code files │ │ - Visualizations & charts │ └─────────────────────────────────────────┘
Log in or sign up for Devpost to join the conversation.