Most beginners eyeball equity curves and skip benchmarking (use the information ratio to prove skill against QQQ)

You’re not alone if your backtests look fine until you compare them to a passive ETF.

That gap creates false confidence and wasted time because raw returns hide whether you beat beta after risk.

And it gets worse as you add parameters that overfit charts but don’t improve risk-adjusted results.

Here’s the reality:

On real desks, we review excess return versus a benchmark and the tracking error before we talk trades. Teams compute daily active returns, annualize over 252 trading days, and fix alignment and adjusted prices.

There’s a straightforward way to move from “looks good” to “adds value” and it starts with a clean, repeatable benchmark check.

You’ll use that habit as the first gate in your strategy pipeline so only ideas with positive, stable information ratios move forward.

Here's a Python example you can use now.

Assumes you already have two pandas Series: port_rets and bench_rets with daily returns aligned to trading days.

This function aligns your portfolio and benchmark returns on the same dates, forms daily active returns, and returns the information ratio. It annualizes by default using 252 trading days.

You’ll get a single number that tells you how much excess return you earned per unit of active risk.

Use it as a hard gate in research and stop promoting strategies that trail QQQ on a risk-adjusted basis. That one habit cuts false positives and points your time at ideas that actually clear the benchmark.

Take it further

Students who join Getting Started With Python for Quant Finance use this style of metric early in the workflow so they stop overfitting equity curves and start building strategies that survive contact with reality.

The program helps you move from ad hoc plots to a research pipeline that measures edge, tests it across regimes, and only then considers deployment.

Inside Getting Started With Python for Quant Fianance:

• Vectorbt. Run fast parameter sweeps so you see whether an idea holds up across settings, not just one cherry-picked case.
• Zipline Reloaded. Execute event-driven backtests that match live behavior and avoid lookahead.
• PyFolio and AlphaLens. Quantify drawdowns and factor decay so you keep only signals with durable edge.

These pieces turn scattered experiments into a coherent process that measures and manages risk from day one.

Your next steps

Start with clean daily returns and a benchmark, compute the information ratio, and set a minimum threshold before you explore parameters.

Add rolling windows to watch stability and drop ideas when the ratio breaks. When a strategy clears your gate, promote it to a full backtest with risk reports and only then consider live execution.

​Build strategies that beat the benchmark after risk 👉​

Learn to measure, validate, and automate your edge with the same tools pros use.

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Jason Strimpel, founder of PyQuant News Say 👋 on X/Twitter, LinkedIn, or book a call with me. You can always reply to these emails. I check them all