Guide: Building a Small-Cap Screening Engine with Alternative Data (2026 Edition)
Small caps need specialized signals. This guide shows how to build a screening engine that blends traditional fundamentals with alternative data sources for robust 2026 performance.
Guide: Building a Small-Cap Screening Engine with Alternative Data (2026 Edition)
Hook: Small-cap investing in 2026 is a data problem. The winners are teams that can collect sparse signals, normalize noise, and construct screening rules that survive regime changes.
Why small caps demand a different approach
Publicly available metrics are scarce for many small-cap companies. Alternative signals—web traffic, job postings, merchant-processor telemetry, and option-implied skew—fill gaps. But combining them requires normalization, backtesting, and careful attention to survivorship bias.
Core architecture
Your screening engine should have three layers:
- Ingestion: Connect to fundamentals feeds, filings, and alternative APIs.
- Normalization & feature store: Clean raw signals and store derived metrics (growth rates, z-scores).
- Screening & testing: Define filters and backtest across multiple regimes.
Feature ideas for 2026
- Quarterly revenue acceleration from payment-processor proxies.
- Hiring momentum using job-posting deltas.
- Supply-chain stress from shipping-delay indices.
- Retail sentiment and options skew for squeeze risk.
Backtesting & robustness checks
Run tests across at least three market regimes and include slippage assumptions. For scaling and query performance, engineering notes such as Scaling Mongoose are practical references. Also ensure your QA pipeline validates new signals in sandboxed environments, borrowing continuous-testing ideas from cloud QA writeups such as Play Store Cloud Update.
Trade construction and execution
Once screens identify names, construct baskets or ETFs to reduce single-name exposure. Large trades should be executed using liquidity-aware algorithms and pre-trade simulations.
Case study: screening for durable cash flow in micro-cap tech
A research team used payment-processor data plus job-posting momentum to identify micro-cap SaaS names with improving retention metrics. After applying liquidity filters and trading a pilot basket, they scaled to a 2% portfolio weight with strict slippage controls.
Operational considerations and compliance
Data privacy, vendor contracts, and provenance are non-negotiable. Ensure legal sign-off on third-party data and maintain reproducible pipelines for auditability.
Extensions and experimental ideas
Teams can combine screening engines with arbitrage bots in cross-market setups; for engineering of arbitrage systems, see practical guideposts like How to Build a Simple Arbitrage Bot Between Exchanges. For macro context or tail-hedge ideas, the Annual Outlook 2026 provides scenario planning that teams should incorporate into stress tests.
Data is only useful when it is reproducible, tested, and scaled with engineering discipline.
Suggested stack
- Event-driven ingestion (Kafka)
- Feature store (warehouse + vector-index for unstructured signals)
- Backtest engine with realistic transaction-cost modelling
- Execution orchestration using smart-order routers and algos
Author
Thomas Keller — Head of Quant Engineering. Thomas builds data platforms for systematic small-cap strategies.
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Thomas Keller
Head of Quant Engineering
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