Introduction

What EmidLabs is, who it's built for, and how the infrastructure is organized.

What is EmidLabs?

EmidLabs builds programmable infrastructure for quantitative intelligence and market execution. The focus is not on building another trading platform or visual backtesting interface — it is on building the infrastructure layer that powers quantitative workflows.

Every product is designed API-first. You interact with EmidLabs through HTTP requests, JSON payloads, and structured responses — not through dashboards or drag-and-drop builders.

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EmidLabs is built for people who think in code: dev-traders, independent quants, and systematic traders who need speed, scale, and programmatic control.

Infrastructure Philosophy

The infrastructure follows a strict API-first principle. This means:

  • Every capability is accessible via API before any UI exists.
  • Inputs and outputs are structured, machine-readable JSON.
  • Workflows are composable — you can pipe outputs into other systems, AI agents, or scripts.
  • The Console is an operational tool, not the primary interface for extracting value.

This philosophy is inspired by companies like Stripe (financial infrastructure), Vercel (deployment infrastructure), and Supabase (backend infrastructure) — but applied to quantitative research and market execution.

Who is it for?

Primary audience

  • Dev-traders — developers who build and automate their own trading strategies.
  • Independent quants — quantitative researchers working outside of institutional environments.
  • Systematic traders — traders who execute rule-based, algorithmic strategies.
  • Small quant teams — teams building quantitative infrastructure internally.
  • AI agent builders — developers building autonomous agents that generate and validate strategies.
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EmidLabs is not designed for retail traders who want a visual interface or manual trade copying. If you need to write code to use it, you are the right audience.

Current Products

Backtesting API — Available now

The first infrastructure module. Send a strategy definition as a JSON payload, specify an asset pair and date range, and receive structured quantitative results: trades, win rate, expectancy, profit factor, drawdown, and per-condition diagnostics.

Execution is server-side. You do not need to download data, set up a local engine, or manage infrastructure. The API handles it.

Live Execution API — Coming soon

Infrastructure for deploying validated strategies to live markets. Signal generation, execution management, real-time monitoring, and deployment lifecycle management.

Core Concepts

Strategy

A strategy is a JSON object that defines: a timeframe, computed inputs (indicators), boolean conditions, scoring weights, and an entry decision rule. The engine evaluates the strategy against historical OHLCV data, candle by candle.

R-Units

All results are expressed in R (risk units), not dollar amounts. R represents one unit of risk as defined by the fixed risk model (1% stop-loss per trade). This allows results to be compared across strategies independent of position sizing.

Score system

Each condition in a strategy has an associated score weight. On each candle, the engine evaluates all conditions and sums the score of the ones that are true. The entry triggers only when the total score meets or exceeds the decision threshold. This allows nuanced, multi-condition entry rules rather than binary all-or-nothing logic.