Research & Insights

Quantitative trading strategies, AI agents, and the infrastructure behind them. All open source.

Running Autonomous AI Agents with Curupira

How Curupira orchestrates autonomous AI trading agents — from research to execution. Architecture overview, agent lifecycle, monitoring, and how to build your own agent on the platform.

Feb 11, 2026

curupiraagentsinfrastructure

AI Trading

Markets Are Languages, Not Physics

Why treating financial markets as natural language problems — not physics problems — yields better predictive models. The theoretical framework behind our AI trading approach, inspired by Renaissance Technologies and modern NLP.

Feb 9, 2026

thesisnlpai

AI Trading

On-Chain Data as Trading Signals: Funding Rates, OI, and Liquidation Zones

How to use on-chain perpetual futures data — funding rates, open interest, and liquidation heatmaps — as quantitative trading signals. Python implementations and backtest results on crypto markets.

Feb 6, 2026

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AI Trading

Building a $1.30/Day LLM Trading Agent on Hyperliquid

How we built an autonomous LLM-powered trading agent that runs on Hyperliquid for $1.30/day in API costs. Complete architecture, cost breakdown, prompt engineering, and live results.

Feb 4, 2026

llmtrading-agenthyperliquid

Quant Research

31 Strategies Tested, 4 Survived

The complete results of testing 31 systematic trading strategies across forex and crypto. What worked, what failed, and the patterns that separate robust strategies from curve-fitted illusions.

Feb 2, 2026

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Quant Research

Hurst Exponents for Detecting Mean Reversion in Forex

Using the Hurst exponent to dynamically identify mean-reverting vs. trending regimes in forex markets. Complete theory, Python implementation, and backtest results for a regime-adaptive trading system.

Jan 26, 2026

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Quant Research

FVG Magnetism: Do Fair Value Gaps Really Fill?

A rigorous statistical analysis of fair value gap magnetism in price action trading. We test the claim that FVGs act as magnets for price, introduce the FVG Wall specification, and present backtest results across forex and crypto markets.

Jan 19, 2026

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Quant Research

Entropy Collapse as a Volatility Timing Signal

How we use Shannon entropy of price returns to detect volatility regime changes before they happen. Full methodology, Python implementation, and backtest results showing a 1.44 profit factor on EURUSD.

Jan 12, 2026

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Why We Open-Source Our Quant Research

The Curupira manifesto — why we believe open-sourcing quantitative trading research is the future of retail alpha generation, and how radical transparency makes us better traders.

Jan 5, 2026

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