Backtesting at Scale

Scalable Financial Backtesting Framework

Written by Sabr Research · May 2025

In quantitative finance, testing new ideas isn’t as simple as flipping a switch — it requires simulating how those ideas would have performed in the real world, across real market conditions. That’s where backtesting comes in. Much like A/B testing in tech, backtesting lets us compare strategies, measure impact, and build conviction before risking capital. But beneath its surface lies a web of hidden pitfalls and subtle traps. At Sabr Research, we’ve developed a scalable backtesting framework that allows us to iterate quickly, test thousands of model variations, and validate investment hypotheses without compromising rigor or robustness.

What is Backtesting?

The idea is simple: simulate how a strategy would have performed in the past, and use that simulation to estimate how it might behave in the future. In practice, though, it’s rarely simple. A typical backtest compares a new strategy against a baseline or benchmark (e.g., SP500). Each strategy is run on the same historical dataset, subjected to the same rules, and evaluated using common metrics — think returns, Sharpe ratios, drawdowns, turnover, and more. The hope is that one version consistently outperforms the other — without simply overfitting noise from the past. Backtesting serves as the first layer of validation for any quantitative idea. Before capital is put at risk, we want to see that a signal could have worked. It helps us answer key questions:

Does this strategy deliver consistent and significant alpha?
Is this strategy robust across different regimes?
How sensitive is it to transaction costs or liquidity assumptions?
Does it hold up out-of-sample, or only in hindsight?

But beyond just validation, backtesting allows us to scale our research efforts. With the right infrastructure, we can simulate hundreds — even thousands — of strategies across markets and timeframes in a fraction of the time it would take to do manually. Instead of testing one idea at a time, we can explore the entire research surface at once, uncovering patterns and insights we might have otherwise missed.

Why It’s Hard

But here’s the catch: just like an A/B test can be manipulated to show a “statistically significant” win by slicing the data the right way, it’s dangerously easy to make a backtest tell a good story. Beyond just rigor and data quality, consistency across experiments — especially when different team members are involved — is essential for the credibility and success of a quant organization. Here are some of the key challenges we face:

Overfitting: A strategy that performs brilliantly in backtests might be overly tuned to the noise in historical data. It looks great on paper but often collapses in live trading.
Lookahead & Survivorship Bias: A common pitfall is inadvertently using future information. For example, selecting stocks that are currently in the S&P 500 and using them in a backtest going back 10 years assumes you knew which companies would survive — unfairly boosting results.
Robustness: Any performance metric, such as the Sharpe ratio, is just an estimate — not a certainty. The observed value is subject to sampling error and should not be taken at face value. Confidence intervals help quantify this uncertainty, especially in short or noisy datasets.
Consistency: When different researchers run backtests independently, inconsistencies in assumptions, definitions, or methodology can lead to incomparable results. Standardizing how we test strategies is key to scaling insights and avoiding confusion.

At Sabr Research, we have built a scalable experimentation platform which helps us address challenges in a scalable and efficient way.

Engineering Discipline for Quant Agility

Figure 1: Scalable Backtesting Platform

Speed of experimentation and research matters, but so does discipline. From day one, we designed our backtesting framework with modularity, consistency and reproducibility at its core. Just like our data infrastructure, this system is built to scale with us, not be outgrown. Our architecture is grounded in a few key principles:

Unified & Automated Data Collection: Historical market data is fetched automatically through a shared interface, ensuring consistency and reducing manual overhead. This ensures that all historical events are properly taken into account without any forward looking bias.
Centralized Evaluation Engine: All performance metrics and statistical tests are defined in one place and reused across simulations, making results easier to compare and interpret.
Configurable & Scalable Workflows: Simulations are driven by high-level configuration functions and built to scale horizontally. This allows us to test hundreds of strategies in seconds.
Transparent & Modular Design: The framework is openly accessible and modular, making it easy for researchers to contribute and iterate quickly.

This modular approach is critical in a research environment where dozens of experiments may run each day. It allows researchers to focus on strategy development while streamlining the entire testing pipeline. Our framework also integrates seamlessly with internal dashboards, allowing researchers to quickly surface high-potential signals when analyzing large volumes of strategies. Beyond core performance metrics, the system automatically generates a comprehensive set of standardized diagnostics—such as sector exposures, regime consistency, signal decay, win rates, and more. These additional insights are critical for building conviction in a strategy. They not only confirm whether a model outperforms a benchmark, but also shed light on how and why that performance is achieved.

Closing the Loop Between Research and Execution

Backtesting isn't just a checkbox in the quantitative workflow—it's a foundational pillar of strategy development, risk control, and innovation at scale. To be truly effective, it must combine speed with rigor and experimentation with discipline. By investing in a modular, reproducible, and scalable backtesting framework, we’ve created a system that empowers our researchers to explore bold ideas while maintaining the standards necessary for production-grade deployment. As the pace of financial innovation accelerates, this kind of infrastructure isn’t just a nice-to-have—it’s a competitive advantage.