Quantpedia API
The On-Demand Factor Library & Strategy Intelligence Engine
Stop wasting hundreds of hours on manual strategy curation. Programmatically access, benchmark, and decompose systematic alpha factors in seconds.
Why Institutional Quant Teams Rely on Quantpedia API
Quantpedia API transforms the world’s most comprehensive encyclopedia of quantitative trading strategies into a machine-readable, institutional-grade research platform. Whether you are building next-generation AI quant agents, performing due diligence on black-box managers, or validating new trading ideas, Quantpedia API delivers structured data directly into your research environment.
1. Data & Infrastructure for LLM Model Training & AI Quant Agents
- Natural Language Queries: Query complex strategy parameters using natural language prompts (e.g., “Retrieve commodity multi-asset strategies with Sharpe > 1.0 and Max Drawdown < 15%”).
- Rich Context Ingestion: Feed your LLMs clean academic abstracts, trading rules, risk classifications, out-of-sample performance metrics, and source code snippets.
- Automated Repertoires: Enable AI agents to screen, summarize, and rank investment opportunities autonomously.
2. On-Demand Factor Database & Alternative Alpha Factors
- Black-Box Returns Analysis: Decompose unknown fund equity curves into underlying style drivers without needing holdings-level transparency.
- Multi-Factor Style Attribution: Run linear regressions, LASSO, and blend optimization against 500+ accessible strategy equity curves to isolate momentum, value, carry, trend-following, and defensive overlays.
- Custom Factor Exposure: Identify hidden drivers of performance across equities, fixed income, currencies, commodities, and digital assets.
3. Comprehensive Trading Strategy Library
- 700+ Documented Systems: Fully cataloged systematic models covering momentum, mean-reversion, seasonal effects, market timing, cross-asset arbitrage, and volatility strategies.
- Ready-to-Deploy Code Snippets: Access executable backtest code (QuantConnect / Python) and exact mathematical formulas.
- In-Sample & Out-of-Sample Metadata: Evaluate historical robustness with rigorous out-of-sample tracking and live-like performance updates.
4. Peer Group Strategy Analysis & Benchmarking
- Like-for-Like Comparison: Filter strategies by market universe, rebalancing frequency, and execution rules to construct custom equal-weighted peer group benchmarks.
- Distributional Insight: Evaluate where a model stands within its competitive universe using percentile rankings for CAGR, Sharpe Ratio, Volatility, Calmar Ratio, and Maximum Drawdown.
- Manager Due Diligence: Determine if a fund’s performance is genuinely alpha or simply the average return of its strategy category.
Core Institutional Use Cases
How quantitative portfolio managers and risk teams deploy Quantpedia API
| Use Case | Workflow | Key Benefit |
|---|---|---|
| AI Quant Copilot | Connect Quantpedia API with Cursor AI / OpenAI via HTTP Basic Auth to build a conversational quant assistant. | Reduces research literature review time from days to seconds. |
| Peer Group Benchmark Creation | Select 10–20 matching multi-asset futures strategies, aggregate their daily returns, and compound them into a custom benchmark curve. | Replaces irrelevant equity benchmarks with meaningful strategy-level peers. |
| Black-Box Curve Decomposition | Align an undisclosed manager’s returns with Quantpedia strategy curves to run multi-factor blend fitting (R² ≈ 0.85). | Uncovers hidden factor exposures and style drift without holdings transparency. |
| Strategy Uniqueness Check | Export daily equity curves from Python/Backtrader and pass them through Quantpedia’s nearest-neighbor and clustering models. | Prevents capital allocation to redundant or crowded factor strategies. |
Technical Specifications & Integration
Built to institutional security standards with high availability REST endpoints
Data Formats
JSON outputs for strategy metadata, descriptions, academic citations, and performance metrics; CSV/time-series objects for daily equity curves and returns.
Authentication
Standard HTTP Basic Auth using proprietary API credentials with enterprise token refresh support.
Asset Coverage
Equity factor data, systematic factors, multi-asset futures, commodities, FX, fixed income, and crypto anomalies.
Compatibility
Seamless integration with Python (requests, pandas), R, MATLAB, QuantConnect, and LLM coding assistants.
Transform Your Quant Workflow Today
Move away from isolated backtesting and step into benchmarked, AI-native quantitative research.
