Intratio
Quantitative Research
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Quantitative Equity Research

Systematic Alpha Generation Through Machine Intelligence

Intratio delivers institutional-grade equity forecasting and portfolio optimization. Our proprietary models process millions of data points daily to identify non-linear patterns across 4,000+ US-listed securities, enabling systematic alpha capture with rigorous risk management.

Universe Coverage
4,000+
US-listed equities
Forecast Horizon
90
Days forward
Signal Frequency
Daily
Post-market close
Data History
10+
Years fundamental data
Core Capabilities

Institutional-Grade Quantitative Infrastructure

Purpose-built for professional allocators who demand transparency, rigor, and reproducibility in their investment process.

Predictive Signal Generation

Proprietary machine learning models trained on decades of fundamental, technical, and alternative data to generate daily equity forecasts with measurable information coefficients.

Portfolio Construction

Mean-variance optimization with constraints on sector exposure, turnover, and position sizing. Efficient frontier computation via Modern Portfolio Theory with custom objective functions.

Risk Analytics

Comprehensive factor exposure analysis, correlation matrices, drawdown monitoring, and Value-at-Risk estimation to ensure portfolios remain within defined risk parameters.

Programmatic Access

RESTful API for seamless integration with existing trading infrastructure, order management systems, and proprietary analytics platforms. Full documentation and SDKs provided.

Fundamental Data Platform

Cleaned, normalized financial statements spanning 10+ years across all US-listed companies. Balance sheets, income statements, cash flows, and corporate event data updated daily.

Backtesting Framework

Out-of-sample validation with an embargo between training and test, on a factor-neutral long/short book charged with realised trading costs and tested for statistical significance.

Research Process

A Disciplined Approach to Signal Discovery

Our research pipeline is built on the same principles that govern institutional quantitative funds: hypothesis-driven feature engineering, strict walk-forward validation, and continuous model monitoring.

01
Data Ingestion & Normalization

Daily automated collection and cleaning of financial statements, market data, corporate events, and macroeconomic indicators across the full US equity universe.

02
Feature Engineering

A proprietary feature set drawn from several independent data families. Every candidate is hypothesis-driven and admitted only on evidence of out-of-sample contribution; names and definitions are not disclosed.

03
Model Training & Validation

Ensemble machine-learning models trained on the factor-neutral part of returns, with purged, embargoed, regime-aware cross-validation and a strictly posterior test window that is opened once, after every modelling choice is frozen.

04
Signal Delivery & Monitoring

Daily post-market generation of forecasts with continuous IC tracking, regime detection, and automated model degradation alerts.

Market Map

The Whole Market, Lit by Today's Signals

Every covered company, sized by market capitalisation and grouped by sector. Zoom from the whole market down to a single forecast.

Explore the map →
Platform

Designed for Professional Decision-Making

Clean, information-dense interfaces built for portfolio managers and research analysts who need clarity, not noise.

Daily Signal Dashboard

Ranked equity forecasts with directional conviction scores.

Security Deep Dive

Fundamental analysis, historical signals, and forecast accuracy tracking.

Portfolio Analytics

Performance attribution, risk decomposition, and exposure monitoring.

Systematic Screener

Multi-factor screening with customizable thresholds across the full US equity universe.

Optimization Engine

Efficient frontier computation with configurable constraints on concentration, sector limits, and turnover.

Model Performance

Transparent, Reproducible Results

Every performance figure is computed on the most recent block of history the model was never trained on, separated from training by an embargo, on a factor-neutral long/short book charged with realised trading costs. The full report is regenerated automatically from the artifacts of each training run.

Out-of-Sample Window
Latest run
Net Sharpe, 1M book
Newey-West t, net
Universe
4,000+
Book
Factor-neutral L/S
Costs
Realised, net

Validation Approach

  • Factor-neutral long/short book, net of realised trading costs, out of sample
  • Newey-West significance, drawdowns, monthly returns and the leverage table
  • Calibration by score ventile, per-date rank correlation, every raw prediction
  • Full artifact set and capacity study available to qualified allocators under NDA
Open the Model Validation

Out-of-sample, factor-neutral, net of costs — with the limitations stated

Get Started

Integrate Systematic Intelligence Into Your Investment Process

Whether you manage a multi-strategy fund or a single family office portfolio, our research infrastructure adapts to your workflow. Schedule a consultation to discuss your specific requirements.

Intratio provides quantitative research and analytical tools for informational purposes only. Nothing on this website constitutes investment advice, a solicitation, or a recommendation to buy or sell any security. Past performance of any model or strategy does not guarantee future results. All investments carry risk, including possible loss of principal. Users should consult with qualified financial advisors before making investment decisions. Intratio does not hold, manage, or have custody of client funds.