Quantitative Infrastructure

Infrastructure for Quantitative Research & Market Execution

Run large-scale quantitative workflows through programmable infrastructure. API-first, server-side execution, built for systematic research at scale.

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~200ms · 5 years · 15M · server-side

POST /api/public/v1/backtest

{
  "assetPair": "BTC-USDC",
  "initialDate": "2020-01-01",
  "finalDate": "2024-12-31",
  "strategySnapshotJson": {
    "configuration": { "timeframe": "1H" },
    "inputs": {
      "emaFast": "ema(close, 9)",
      "emaSlow": "ema(close, 21)",
      "rsi": "rsi(close, 14)"
    },
    "conditions": {
      "trend": "emaFast > emaSlow",
      "momentum": "rsi > 45 AND rsi < 70"
    },
    "score": { "trend": 30, "momentum": 20 },
    "decision": { "entry": "score >= 40" }
  }
}

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Platform

Programmable infrastructure. Not a trading platform.

EmidLabs is the infrastructure layer for quantitative research and market execution. Every capability is exposed as a programmable API — designed for systematic workflows, not manual operation.

Programmable by Default

Every capability is an API. Send JSON payloads, receive structured results. Compose workflows, automate research pipelines, integrate into any system.

Execution at Scale

~200ms for 5 years of market data server-side. Run thousands of concurrent workflows. Built for systematic iteration, not one-off analysis.

Rigorous by Design

Fixed risk model. Results in R-units. Full condition diagnostics and score distribution tracking — infrastructure-grade statistical output.

Built for Systematic Builders

For dev-traders, independent quants, small systematic teams, and developers building their own quantitative infrastructure — not for retail dashboards.

API-First

Every product is programmable by default

Server-Side

Execution happens in the infrastructure, not your machine

R-Unit Model

Focus on statistical edge, not dollar amounts

Products

Infrastructure modules for quantitative workflows.

Start with the Backtesting API. More modules are being built as the infrastructure evolves.

Live

Backtesting API

Quantitative research infrastructure

Execute backtests server-side via API. Define strategies using our DSL, send JSON payloads, and receive detailed quantitative results — trades, metrics, score distributions, and condition diagnostics.

View Documentation See Benchmarks

Capabilities

~200ms for 5 years of BTC at 15M timeframe

Fixed risk model — results in R-units

Condition frequency and score distribution tracking

Concurrent execution support

JSON payload input, structured response output

AI-assisted strategy generation workflows

Soon

Live Execution API

Coming Soon

Automated market execution infrastructure

Deploy validated strategies to live markets. Infrastructure for signal generation, automated execution, real-time monitoring, and deployment lifecycle management.

Join the Waitlist

Capabilities

Live signal infrastructure

Strategy deployment workflows

Real-time execution monitoring

Execution lifecycle management

Multi-strategy orchestration

Research Workflow

The quantitative research loop.

EmidLabs exposes the entire research lifecycle as a programmable API workflow — not a dashboard. Each step is an execution node. The loop runs until you have edge.

Hypothesis

Define what market behavior you're trying to capture. Frame it as a quantitative thesis, not a gut feeling.

outQuantitative thesis

Strategy DSL

Formalize the hypothesis using the EmidLabs DSL — or generate it with an AI model. Composable inputs, conditions, score weights, decision rules.

outStrategy object

JSON Payload

Serialize the strategy into a structured JSON payload. Specify asset pair, date range, and execution parameters.

outPOST /backtest

Iterate

Feed results back into the strategy. Tighten conditions. Adjust weights. Run concurrent variations. Loop until the edge is validated.

out→ back to 01

Metrics & Analysis

Receive structured quantitative output: trades, win rate, expectancy R, profit factor, condition diagnostics, score distribution.

outresult{}

API Execution

The infrastructure receives the payload, evaluates the strategy server-side against historical OHLCV data, candle by candle.

outBacktest ID

Research Loop

Hypothesis

Define what market behavior you're trying to capture. Frame it as a quantitative thesis, not a gut feeling.

outQuantitative thesis

Strategy DSL

Formalize the hypothesis using the EmidLabs DSL — or generate it with an AI model. Composable inputs, conditions, score weights, decision rules.

outStrategy object

JSON Payload

Serialize the strategy into a structured JSON payload. Specify asset pair, date range, and execution parameters.

outPOST /backtest

API Execution

The infrastructure receives the payload, evaluates the strategy server-side against historical OHLCV data, candle by candle.

outBacktest ID

Metrics & Analysis

Receive structured quantitative output: trades, win rate, expectancy R, profit factor, condition diagnostics, score distribution.

outresult{}

Iterate

Feed results back into the strategy. Tighten conditions. Adjust weights. Run concurrent variations. Loop until the edge is validated.

out→ back to 01

↩Research Loop

Strategy System

A composable DSL for machine-readable strategies.

Define quantitative strategies as structured JSON objects. Composable inputs, boolean conditions, scoring weights, and decision rules — designed to be generated, iterated, and executed programmatically.

Configuration

Set the execution timeframe for your strategy.

15M30M1H2H4H1D

Inputs

Reusable computed values from market data and built-in functions.

ema(close, 9)

rsi(close, 14)

atr(14)

volumeSpike(1.5)

Conditions

Boolean expressions evaluated per candle using inputs, operators, and market data.

><>=<===!=ANDOR

Score & Decision

Weight conditions and define the entry threshold. Focus on statistical edge over raw signals.

Built-in Functions

Indicators

ema(series, period)

sma(series, period)

rsi(series, period)

atr(period)

Signals

crossUp(a, b)

crossDown(a, b)

Series Operators

highest(series, n)

lowest(series, n)

change(series)

Volume

volumeSma(period)

volumeSpike(multiplier)

Price Action

body()

range()

upperWick()

lowerWick()

strategy.json

{
  "configuration": {
    "timeframe": "1H"
  },
  "inputs": {
    "emaFast": "ema(close, 9)",
    "emaSlow": "ema(close, 21)",
    "rsiValue": "rsi(close, 14)"
  },
  "conditions": {
    "trendUp": "emaFast > emaSlow",
    "rsiHealthy": "rsiValue > 40 AND rsiValue < 65"
  },
  "score": {
    "trendUp": 20,
    "rsiHealthy": 30
  },
  "decision": {
    "entry": "score >= 40"
  }
}

Benchmarks

Execution performance at infrastructure scale.

Latency, throughput, and concurrency numbers for the Backtesting API. Measured on production infrastructure. All results are reproducible via the API.

~200ms

Execution latency

5 years BTC/USDC · 15M · ~175k candles

1,000+

Concurrent workflows

Horizontal scaling · no queue blocking

<50ms

Daily timeframe

5 years BTC/USDC · 1D · ~1.8k candles

Linear

Throughput scaling

Execution time scales with candle count

Execution time by timeframe · BTC/USDC · 5 years

~45ms

~85ms

~110ms

~150ms

30M

~175ms

15M

~200ms

Benchmarks measured on production infrastructure using BTC/USDC data from Coinbase. Median of 100 consecutive executions. Results are reproducible via the API.

Execution Model

Rigorous by design.

The backtest engine enforces deterministic execution with a fixed risk model — no ambiguity, no lookahead bias.

Entry on close

Entries execute on the same candle close where the entry condition evaluates to true.

Fixed risk model

Stop-loss at 1% from entry. Take-profit at 1:3 risk-reward ratio. Results in R-units, not dollar amounts.

Multiple positions

Every valid entry signal opens a new independent trade. Positions can coexist simultaneously.

Conservative ambiguity

If stop-loss and take-profit hit in the same candle, stop-loss is assumed to trigger first.

No lookahead bias

Strict enforcement: all calculations use only past or current candle data. Randomness forbidden.

Strategy diagnostics

Condition frequency and score distribution tracked per candle for deep strategy quality analysis.

Pricing

Consumption-Based Quantitative Infrastructure

Pay only for executed quantitative workloads. Infrastructure consumption scales proportionally to processed market data execution.

Start Free

No credit card required

Get immediate access to the Backtesting API and begin building with included starter execution capacity.

API access
Console access
Documentation access
Starter execution capacity included
Full strategy DSL access

Start Building

Scale With Usage

Infrastructure consumption scales proportionally to executed quantitative workloads.

example workload

BTC/USD · 15m · 5 years

Estimated infrastructure usage calculated dynamically based on processed market data volume.

Scale execution capacity as needed. No mandatory subscriptions.

Estimate Usage

Enterprise Agreements

Custom infrastructure agreements for large-scale quantitative workloads.

High-volume execution support
Custom workload limits
Direct engineering contact
Infrastructure roadmap alignment

Contact Sales

Infrastructure usage is calculated proportionally to processed market data workloads. Consumption details available in the documentation.

Infrastructure FAQ

How is infrastructure usage calculated?

Usage is calculated proportionally to processed market workloads, including asset count, timeframe resolution, and execution range.

How does infrastructure consumption work?

Infrastructure consumption is metered proportionally to processed execution workloads. Usage accounting is handled internally based on quantitative throughput.

How do I get access to the platform?

Create an account at the console to get immediate access to the Backtesting API, documentation, and included starter execution capacity.

Roadmap

Where the infrastructure is going.

The Backtesting API is the first module. The vision is a complete quantitative infrastructure stack.

Current

Backtesting API

Server-side backtest execution. DSL strategy system. JSON API. Metrics and diagnostics. Pay-as-you-go pricing.

Live Execution API

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

Future

Signal Infrastructure

Programmable signal generation, distribution, and processing. Build signal pipelines as composable infrastructure.

Future

Quantitative Infrastructure Expansion

New markets, new datasets, portfolio management, multi-strategy orchestration, and automated quantitative pipelines.

Start building quantitative infrastructure.

Open the console. Run your first backtest. Iterate faster than you thought possible.

Access Console Explore Documentation

Or view Benchmarks