QuantLab

Quantitative Research Infrastructure for Systematic Trading

QuantLab is an extensible quantitative research platform for developing, testing, and validating systematic trading strategies.

It was built from the bottom up around a simple principle: the research environment should remain stable while the trading hypothesis changes.

Rather than coupling strategy logic to a particular backtester, broker, or data source, QuantLab separates the research process into independently usable layers for data, strategy construction, execution, analytics, optimization, and validation.

The Research Problem

A backtest can produce a precise-looking result while still depending on fragile assumptions about data, execution, transaction costs, parameter selection, or sampling.

QuantLab treats these assumptions as part of the experiment.

The system therefore provides a common research pipeline in which strategies can be tested under consistent execution and cost assumptions, evaluated out of sample, and subjected to resampling and robustness analysis before being considered for paper or live execution.

The objective is not simply to produce a profitable backtest.

It is to make quantitative hypotheses testable, comparable, and reproducible.

Architecture

Market Data
     ↓
Strategy
     ↓
Backtest
     ↓
Performance Analytics
     ↓
Validation & Robustness
     ↓
Paper Trading
     ↓
Live Execution

Each layer has its own abstraction and can be used independently.

This allows research components to evolve without rewriting the entire system.

Data Layer

quantlab.data provides a common data contract across different sources.

Current data interfaces include:

• Synthetic market data
• CSV datasets
• Yahoo Finance
• Parquet storage

The purpose of the abstraction is to prevent individual strategies from becoming coupled to a particular data source or storage format.

A strategy should consume a standardized dataset rather than know where that dataset originated.

Strategy Layer

Strategies implement a common interface for generating target positions.

Initial implementations include:

• Simple Moving Average
• Momentum
• Buy and Hold

The strategy layer is deliberately separated from portfolio execution.

A strategy expresses what position it wants to hold. The execution layer determines how that position is actually applied.

This separation makes it possible to use the same strategy logic across historical backtests, simulated brokerage, and future live execution.

Backtesting

QuantLab provides vectorized historical backtesting with explicit execution assumptions.

The backtester incorporates:

• Transaction costs
• Position changes
• Portfolio accounting
• One-bar position lag
• Look-ahead prevention

The one-bar lag is particularly important: signals are generated from information available at the decision point rather than allowing the backtest to implicitly trade on information from the same completed bar.

Transaction costs are treated as part of the simulation rather than an afterthought applied to the final performance number.

Performance Analytics

QuantLab provides standardized performance analysis across experiments.

Current metrics include:

• Sharpe ratio
• Sortino ratio
• Calmar ratio
• Maximum drawdown
• Return statistics
• Performance reports

The purpose of the analytics layer is to provide a consistent basis for comparing experiments rather than relying on a single headline metric.

Optimization

Strategy parameters can be explored through systematic parameter search.

The current optimization layer supports grid search and provides an abstraction for extending the system to other optimization methods, including random and Bayesian search.

Optimization is treated separately from strategy implementation so that parameter-search methods can change without changing the underlying strategy.

Walk-Forward Validation

QuantLab supports both anchored and rolling walk-forward validation.

Instead of evaluating a strategy only on the data used to develop it, walk-forward experiments repeatedly separate historical information used for research from subsequent observations used for evaluation.

This provides a more realistic test of whether a strategy's behavior persists outside its development sample.

Monte Carlo and Robustness Analysis

Historical returns represent one realization of a stochastic process.

QuantLab therefore includes resampling tools for examining how sensitive observed results are to the particular sequence of observations.

Current methods include:

• Bootstrap resampling
• Moving-block bootstrap

The moving-block approach preserves local temporal dependence better than independently resampling individual observations, making it useful for time-series experiments where observations are not exchangeable.

Paper Trading

QuantLab includes a simulated broker for paper-trading experiments.

The paper broker uses the same cost model as the historical backtester.

This is an important architectural constraint:

Moving from backtesting to paper trading should change the execution environment, not the economic assumptions of the experiment.

The strategy therefore does not need to be rewritten simply because the research moves from historical data to simulated execution.

Live Execution

The execution layer is defined behind a broker abstraction that can support live implementations.

The intended progression is:

Historical Data
      ↓
Backtest
      ↓
Walk-Forward Validation
      ↓
Monte Carlo / Robustness
      ↓
Paper Trading
      ↓
Live Broker

The live interface is therefore an extension point rather than a claim that QuantLab is currently a production trading system.

Design Principles

Separation of Concerns

Data, strategy logic, execution, analytics, optimization, and validation remain independent components.

Consistent Experimental Assumptions

Transaction costs and execution rules are carried across research and paper-trading environments rather than being redefined for each experiment.

No Look-Ahead

Information availability is treated as part of the experimental design.

Out-of-Sample Evaluation

A strategy is not treated as validated simply because it performs well on its development sample.

Replaceable Infrastructure

Data sources, optimization methods, and brokers can be exchanged through stable interfaces.

What QuantLab Is

QuantLab is quantitative research infrastructure.

It is designed to answer questions such as:

• Does a trading hypothesis survive out-of-sample evaluation?
• How sensitive is its performance to transaction costs?
• Does the observed edge persist under different parameter choices?
• How dependent is the result on a particular sequence of observations?
• Does the strategy retain its behavior when moved from historical simulation to paper execution?

The platform provides the machinery for conducting those experiments without rebuilding the research environment for every strategy.

Current Scope

QuantLab currently provides the core infrastructure for:

• Market-data ingestion
• Strategy development
• Historical backtesting
• Transaction-cost modeling
• Performance analytics
• Parameter optimization
• Walk-forward validation
• Bootstrap and block-bootstrap analysis
• Paper trading
• Broker abstraction for future live execution

The system is intentionally extensible. Future research can add strategies, datasets, optimization methods, analytics, and execution implementations without changing the underlying architecture.

Research Position

QuantLab is separate from my Scarcity research programme.

Where Scarcity investigates how structural relationships can be discovered and used under data scarcity, QuantLab provides a controlled environment for quantitative trading research.

It is the experimental machinery around a different class of question:

When a quantitative hypothesis appears to work, how much of that result survives when the experiment is made more realistic?