Walk-Forward Backtest Results

Walk-forward validation is the gold standard for testing quantitative investment strategies. The concept is straightforward: you split your data into two non-overlapping periods. The first period (in-sample) is used to design, calibrate, and optimize your model. The second period (out-of-sample) is used to test it on data the model has never seen.

This approach directly addresses the most dangerous problem in quantitative finance: overfitting. With enough parameters, any model can be made to look spectacular on historical data. A model that “predicts the past” with 99% accuracy might be completely useless going forward because it memorized noise rather than discovering genuine patterns.

Walk-forward validation prevents this by separating discovery from confirmation. If a strategy performs well on data it was specifically designed for, that tells you very little. If it performs well on data it has never seen, that is meaningful evidence that the underlying signals are real.

Most retail trading tools skip this step entirely. They show you one set of backtested numbers without disclosing whether the model was designed on the same data being used to evaluate it. That is like a student writing the test questions and then claiming a perfect score proves intelligence.

AlphaStocks split the January 2021 through February 2026 period into two distinct windows:

The in-sample period includes the post-COVID rally, the 2022 bear market, and the 2023 recovery — a diverse set of market conditions that stress-tested the formula across multiple regimes.

We evaluate the scoring on the held-out out-of-sample window, which it was not tuned on. That period (2024 through early 2026) includes the AI-driven tech rally, sector rotations, and several correction events — the formula runs forward across all of them unchanged.

The backtest uses the v22 composite scoring formula, which blends four axes into a single 0–10 rating for every S&P 500 stock:

The headline test: a portfolio of the 10 highest-scoring S&P 500 stocks, rebalanced monthly and equally weighted, using only point-in-time data available on each scoring date (no look-ahead). Over the full January 2021 through February 2026 window it returned +140.3% versus +100.3% for the S&P 500.

The top-10 portfolio meaningfully outpaced the S&P 500 over the same period. An annual alpha of +4.3% means that each year, on average, the portfolio outperformed the benchmark by 4.3 percentage points. Over five years, that compounds into a substantial gap (+140.3% versus +100.3%).

This result reflects disciplined monthly reselection: each month the formula re-ranks the S&P 500 on point-in-time data and holds the top 10. The scoring consistently surfaced high-quality companies at attractive prices with strong momentum across the five-year window.

This table breaks the same top-10 portfolio down by period. Each month, the 10 highest-scoring S&P 500 stocks are selected on point-in-time data, equally weighted, and held until the next rebalance. Splitting the window into in-sample and out-of-sample tests whether the formula keeps working on data it was not tuned on.

Several observations stand out from this data. The top-10 portfolio outperformed the S&P 500 in both the in-sample and out-of-sample periods. Notably, the out-of-sample annual alpha (+6.7%) was higher than the in-sample alpha (+2.9%) — the opposite of the pattern you see with overfit strategies, whose edge shrinks or vanishes on unseen data. That the alpha held up, rather than decaying, on data the formula was not tuned on is the single strongest signal here.

The full-period annual alpha of +4.3% compounds into a roughly +40 percentage-point gap in cumulative return (+140.3% versus +100.3%), demonstrating the power of consistent, modest annual outperformance over multiple years.

Raw returns tell only half the story. The Sharpe ratio measures return per unit of risk. A higher Sharpe means the portfolio delivered more return for each unit of volatility endured.

Over the full 2021–2026 period, the top-10 portfolio's Sharpe ratio was 0.81versus 0.73 for the S&P 500 — a modest edge, broadly in line with the market rather than dramatically better. Risk-adjusted, the portfolio delivered its higher returns without taking on materially more volatility.

We want to be precise here: in the out-of-sample window specifically, the market was actually the stronger risk-adjusted performer (SPY Sharpe 1.65 versus the portfolio's 1.39). The portfolio's out-of-sample edge came from higher raw returns, not from a better Sharpe. The anti-overfitting evidence lives in the return alpha — which grew from +2.9% in-sample to +6.7% out-of-sample — not in the Sharpe ratio.

For context, a Sharpe ratio above 1.0 is generally considered good for a long-only equity strategy. The S&P 500's historical Sharpe ratio typically falls between 0.3 and 0.7 depending on the measurement period.

Maximum drawdown measures the largest peak-to-trough decline during the test period. It answers the question every investor actually cares about: how bad did it get at the worst point?

Over the full period the top-10 portfolio's worst peak-to-trough decline was -21.6% versus -23.9%for the S&P 500 — a slightly shallower drawdown, broadly comparable to the market rather than dramatically better. We should be candid: this is a modest edge, and it was not uniform. In the 2024–2026 out-of-sample window the market's worst drawdown was actually shallower than the portfolio's (-7.7% for the S&P 500 versus -10.8% for the portfolio).

This matters more than most investors realize. A 23.9% drawdown requires a 31.4% recovery to break even; a 21.6% drawdown requires about a 27.6% recovery. Smaller drawdowns mean faster recoveries and less temptation to panic-sell at the bottom.

The modestly lower full-period drawdown is likely a consequence of the Quality axis receiving the largest weight (40%). High-quality companies with strong balance sheets and consistent profitability tend to decline less during market stress. The formula is inherently biased toward resilient businesses, and that bias shows up most clearly during corrections.

The distinction between in-sample and out-of-sample performance is the single most important concept in quantitative finance testing. Many backtests you encounter online are exclusively in-sample: the model was designed and tested on the same data. This guarantees nothing about future performance.

Here is what our walk-forward split reveals:

Consider what an overfit strategy would look like: spectacular in-sample returns followed by zero or negative out-of-sample alpha. Our results show the opposite pattern — the return alpha grew on unseen data rather than collapsing. Risk-adjusted, the picture is more even-handed: over the full period the portfolio's Sharpe and drawdown were broadly in line with the market's, and in the out-of-sample window the market was actually the stronger risk-adjusted performer. The honest headline is stronger returns at comparable risk, with the anti-overfitting signal carried by the alpha.

The backtest results suggest — but do not prove — that the AlphaStocks composite formula identifies genuinely useful investment signals. Specifically:

The combination of quality, value, momentum, and timing factors appears to produce returns above the benchmark across multiple market conditions. The formula outperformed in the post-COVID rally, the 2022 bear market, and the 2024–2025 AI-driven bull run. It can lag in sideways periods.

The value-trap killer (Timing = min(Value, Momentum)) may help contain drawdowns. By requiring both cheapness and rising prices, the formula avoids the classic trap of buying stocks that are cheap and getting cheaper. This may contribute to the portfolio's modestly lower full-period drawdown, though risk was broadly comparable to the market overall.

The out-of-sample return alpha was actually stronger than in-sample, not weaker. A strategy whose return edge holds up — even grows — on data it was not tuned on is a more credible signal than spectacular in-sample numbers that collapse forward. Risk-adjusted, the out-of-sample market was competitive; the durable-alpha claim rests on returns, not the Sharpe ratio.

These results do not mean the formula will continue to outperform. They mean it has demonstrated the ability to do so in a controlled, honestly disclosed test.

Backtest results are a starting point, not a conclusion. Here is how to incorporate them into a sound investment process: