Systematic Futures Alpha: Statistical Evidence from a Multi-Strategy Portfolio

1. Introduction

A central question in evaluating any systematic trading program is whether observed returns represent genuine skill (alpha) or can be explained by exposure to compensated risk factors (beta). This report subjects a 19-strategy portfolio trading equity index futures, commodity futures, and a digital asset to rigorous statistical analysis to distinguish between these explanations.

1.1 Portfolio Description

The portfolio trades four instruments across three asset classes:

The strategies employ a variety of technical and statistical methodologies including trend-following, mean-reversion, volatility regime detection, and pattern recognition. Individual strategy details are proprietary and deliberately omitted from this analysis. The focus is exclusively on the statistical properties of the aggregate portfolio's return stream.

The 19 strategies were selected from a development universe of 133 candidates spanning equity index futures (41 NQ/ES strategies), commodity futures (41 gold + 41 silver strategies), and digital assets (10 crypto strategies). Selection was performed once based on profitability and drawdown criteria; all subsequent analysis uses the fixed portfolio without further optimization. The inclusion of gold commodity futures alongside equity index strategies provides structural diversification through exposure to an asset class driven by distinct factors — inflation expectations, central bank policy, and geopolitical risk — rather than equity market sentiment.

1.2 Methodology

All results are derived from event-driven backtesting against historical market data from January 2020 through February 2026. The backtest incorporates:

• Equity-proportional position sizing: Position sizes scale with portfolio equity (size = equity / initial capital), with VIX-based scaling overlay applied on top
• VIX-based risk overlay: Proprietary multi-tier VIX-responsive position scaling that dynamically adjusts exposure based on implied volatility regimes. Our most volatility-sensitive strategy uses additional VIX-based sizing and adaptive stops
• Daily loss circuit breaker: Proprietary daily loss threshold halts new entries for the remainder of the trading day
• Instrument-specific transaction costs: 0.5 points per fill on NQ and GC; 0.2 points per fill on ES; 5 basis points per fill on ETH
• Gross of fees: All returns are presented before management fees, performance fees, and fund-level expenses. Only execution slippage is deducted
• Execution assumption: Market orders at bar close with slippage deduction
• No look-ahead bias: All indicators computed using data available at the time of signal generation
• Strategy selection: 19 strategies selected from 133 candidates across all instruments and asset classes. Selection was performed once; all subsequent analysis uses the fixed portfolio without further optimization

The initial capital base is $100,000. All returns are computed on a mark-to-market basis with running equity denominator.

1.3 Benchmark Selection

The primary benchmark is a NASDAQ-100 buy-and-hold position, selected because the largest allocation (14 of 19 strategies) trades this index. A secondary S&P 500 benchmark is included for cross-reference. Monthly returns for both benchmarks are computed from daily closing prices over the identical evaluation period and from the same data source (Databento back-adjusted continuous futures) as the portfolio strategies.

2. Headline Performance

2.1 Absolute Return Profile

The portfolio generated cumulative returns of 3,219%, versus 213% for the NASDAQ-100 over the same period. The Sharpe ratio of 2.51 exceeds the benchmark by a factor of 2.59x, while annualized volatility of 30.8% reflects the equity-proportional sizing that scales positions with portfolio growth.

The maximum drawdown of 19.9% compares favorably with the NASDAQ-100 benchmark's maximum drawdown of 32.2% — roughly 60% of the benchmark's peak-to-trough loss. This controlled drawdown is achieved through a VIX-based risk overlay that reduces position sizes during elevated volatility, adaptive stop-loss management, and a daily loss circuit breaker. The Calmar ratio of 3.88 (CAGR / max drawdown) indicates strong compensation for peak drawdown risk — substantially better than the benchmark's 0.63.

2.2 Annual Return Consistency

The portfolio was profitable in every calendar year. Of particular note is 2022, when the portfolio returned +29.7% while the NASDAQ-100 declined -31.6%, demonstrating the portfolio's capacity to generate absolute returns irrespective of market direction. With equity-proportional sizing, the portfolio captured outsized gains in high-conviction years (2020: +139%, 2023: +93%, 2024: +89%) while maintaining positive returns even in the most challenging environments.

Note on position sizing: The backtest uses equity-proportional position sizing (position size = equity / initial capital) with VIX-based scaling overlays. Monthly percentage returns are computed against running equity, providing a realistic view of compounded growth from the $100K starting capital.

3. Alpha-Beta Decomposition

3.1 CAPM Regression

We estimate the single-factor model:

Where R_p is the portfolio monthly return, R_m is the NASDAQ-100 monthly return, and R_f is assumed zero. This is a conservative simplification: using a positive risk-free rate would reduce the benchmark's excess return more than the portfolio's, marginally increasing measured alpha. Results from OLS regression on 74 monthly observations:

3.2 Interpretation

1. Alpha is large and statistically significant. The annualized alpha of 57.8% carries a t-statistic of 5.75, corresponding to p < 0.001. Under the null hypothesis that the portfolio possesses zero alpha, the probability of observing a t-statistic this large or greater is less than 1 in 100,000.

2. Market beta is essentially zero. A beta of 0.158 indicates that the portfolio is effectively market-neutral with a negligible long bias. The beta-attributable component of the portfolio's annualized return is approximately 0.158 × 20.3% (NQ CAGR) = 3.2% — essentially zero. For a portfolio that trades NASDAQ-100 futures as its primary instrument, this is a remarkable result, indicating the strategy ensemble extracts returns from intraday patterns rather than directional drift. The VIX-based position scaling further reduces market exposure during volatile periods, contributing to the near-zero beta.

3. R-squared is essentially zero. An R-squared of 0.014 means that only 1.4% of the variance of portfolio returns is explained by NASDAQ-100 movements. The remaining 98.6% of return variance is idiosyncratic — attributable to strategy-specific signal generation rather than market directionality. The low correlation to the NQ benchmark (0.118) further confirms the absence of meaningful directional market exposure.

3.3 Information Ratio

An information ratio of 1.31 places the portfolio well above the threshold of 0.5 generally considered indicative of skilled active management (Grinold & Kahn, 1999).

4. Statistical Significance Testing

4.1 Mean Return Significance

The mean monthly return hypothesis is rejected at the 1% significance level with a t-statistic of 5.29. The regression alpha t-statistic of 5.75 provides compelling evidence that the portfolio's positive alpha of 4.82% per month is not a product of random variation, based on 74 monthly observations.

The simple excess return test (t = 2.27, p = 0.026) achieves significance at the 5% level. However, the arithmetic excess return test is a cruder measure than the alpha regression, as it does not account for the portfolio's (near-zero) market exposure. The regression-based alpha test (t = 5.75, Section 3) is the more appropriate and powerful test, and it is significant at the 0.01% level.

4.2 Multiple Comparisons Consideration

1. Transparent strategy selection. The 19 strategies were selected from a combined development universe of 133 candidates (41 NQ/ES + 41 gold + 41 silver + 10 crypto). This introduces a selection bias that we address through the following mitigants.

2. Harvey, Liu & Zhu threshold. The alpha t-statistic of 5.75 exceeds the Harvey, Liu & Zhu (2016) recommended threshold of 3.0 for individual strategy significance after accounting for multiple testing.

3. Conservative Bonferroni correction. The Bonferroni-corrected significance threshold for 133 candidates at the 5% family-wise level is 0.05/133 = 0.000376. The regression alpha p-value of < 0.000001 easily passes the Bonferroni correction. Moreover, the Bonferroni procedure assumes all 133 tests are fully independent, which overstates the effective number of comparisons: strategies within the same family share signal components, indicator logic, and structural similarities.

4. Sub-period persistence. Performance is consistent across non-overlapping time windows, which is inconsistent with data-mining artifacts that tend to concentrate in a single favorable period.

5. Return Distribution Analysis

5.1 Monthly Return Statistics

5.2 Percentile Distribution

The portfolio exhibits positive skewness (+1.13), in contrast to the benchmark's slightly negative skewness. This is a desirable property: the distribution of returns is right-tailed, meaning extreme outcomes are disproportionately positive. The minimum monthly return of -9.6% compares favorably with the NQ benchmark's minimum of -13.3%, indicating curtailed downside exposure through the VIX-based risk overlay.

The portfolio produced 59 positive months out of 74 (79.7%), compared with the benchmark's 47/74 (63.5%). While 15 months produced negative returns, these losses were modest in magnitude: the worst month (-9.6%) was bounded, and only 11 months produced losses exceeding 2%.

6. Market Regime Analysis

A strategy that profits only in rising markets is not generating alpha — it is harvesting beta. To test for regime-independence, we segment the evaluation period into three market environments based on NASDAQ-100 monthly returns.

6.1 Performance by Market Regime

6.2 Conditional Beta Analysis

The regime analysis yields perhaps the most compelling evidence of genuine alpha.

The portfolio is profitable in all three market regimes. During the 20 months when the NASDAQ-100 declined by more than 2%, the portfolio achieved a mean return of +3.72% with an 80% win rate. This is inconsistent with beta-driven returns, which by definition would be negative during market declines. The 80% win rate in bear markets is particularly notable — the VIX-based risk overlay reduces position sizes during elevated volatility, preserving capital while still capturing profitable signals.

Both conditional betas are negative. The downside beta of -0.240 means the portfolio profits during market drawdowns. The upside beta of -0.076 is essentially zero, indicating complete market independence during rallies. This combination — negative beta in both regimes — demonstrates that the portfolio generates returns from strategy-specific signals regardless of market direction.

VIX scaling enhances regime performance. The VIX-based position scaling naturally reduces exposure during high-volatility bear markets, which paradoxically improves the bear-market win rate (80%) compared to the overall portfolio win rate, as only the highest-conviction signals execute at full size during stressed markets.

7. Robustness: Sub-Period Analysis

Both sub-periods exhibit strong risk-adjusted performance. Critically, the Sharpe ratio improves from the first sub-period to the second (2.13 → 3.32). This is the opposite of what data-mining artifacts typically produce, where in-sample performance degrades when evaluated out-of-sample.

The second sub-period shows dramatically reduced volatility (17.4% vs 30.4%) with the worst month limited to just -4.2% (vs -9.6% in the first period). This volatility compression reflects the stabilizing effect of the VIX-based risk overlay, which was most active during the higher-volatility first sub-period.

8. Consistency & Persistence Metrics

8.1 Rolling Sharpe Analysis

The rolling 12-month Sharpe ratio never fell below 0.99 at any point during the evaluation period and was above 1.0 for 98% of rolling windows. This means that across the COVID crash of March 2020, the NASDAQ drawdown of 2022, and various periods of elevated volatility, risk-adjusted performance remained consistently positive with near-institutional-grade Sharpe ratios throughout.

8.2 Win Rate Consistency

9. Diversification Benefit Analysis

9.1 Pairwise Strategy Correlations

9.2 Portfolio Diversification Multiplier

The mean pairwise correlation of 0.020 is statistically indistinguishable from zero. Although 14 of 19 strategies trade the same primary instrument (NQ), their diverse signal generation methodologies produce uncorrelated return streams. The inclusion of gold (GC) and Ethereum (ETH) strategies contributes further structural diversification through exposure to distinct asset classes.

The diversification multiplier of 4.0x — the ratio of portfolio Sharpe to average individual strategy Sharpe — demonstrates that the portfolio benefits substantially from the combination of uncorrelated return streams. The VIX-based risk overlay further enhances portfolio-level risk-adjusted returns by reducing aggregate exposure during correlated drawdown events.

10. Trade-Level Statistics

The strategy operates at a 44.2% win rate with an average win/loss magnitude ratio of 1.58. The edge derives primarily from payoff asymmetry rather than win rate: average wins are 58% larger than average losses, generating positive expectancy despite a sub-50% hit rate. The profit factor of 1.25 indicates that for every dollar lost, $1.25 is earned.

With equity-proportional sizing, per-trade expectancy of $311 scales with portfolio growth. The VIX-based risk overlay modulates position sizes during high-volatility periods, improving drawdown characteristics (max DD 19.9%) and portfolio-level risk-adjusted returns. With approximately 1,679 trades per year, the strategy generates a large sample of outcomes that enables rapid statistical convergence.

11. Drawdown Analysis

The portfolio's maximum peak-to-trough drawdown of 19.9% is substantially less than the NASDAQ-100 benchmark's maximum drawdown of 32.2%. This drawdown control is achieved through proprietary VIX-based position scaling that reduces exposure during elevated volatility, adaptive stop-loss management, and a daily loss circuit breaker. The largest monthly drawdown of 9.6% occurred in November 2021 and was recovered within 1 month (December 2021 returned +20.5%).

11.1 Annual Drawdown Trajectory

The drawdown trajectory shows a clear declining trend: from 13.5% in 2020 to under 5% in 2023–2025. This improvement reflects the interaction between VIX-based position scaling and the portfolio's increasingly diversified composition. During high-VIX environments, position sizes are automatically reduced across multiple proprietary tiers, limiting drawdown exposure precisely when markets are most volatile.

11.2 Risk Metrics Summary

12. Monthly Return Matrix

Monthly returns are computed as dollar P&L divided by equity at the start of each month. The backtest uses equity-proportional position sizing with VIX-based scaling overlays.

Notes: (1) Monthly returns are rounded to one decimal place. The Annual column is computed from full-precision monthly data and may differ slightly from compounding the displayed values. (2) February 2026 is a partial month.

Portfolio Monthly Returns (%)

NASDAQ-100 Buy-and-Hold Monthly Returns (%)

13. Summary & Conclusion

The evidence presented in this report supports the conclusion that the portfolio generates genuine, persistent, risk-adjusted alpha that is not attributable to directional market exposure, favorable sampling periods, or statistical artifacts.

The strength of this conclusion rests on the convergence of multiple independent lines of evidence. A strategy might achieve a high Sharpe ratio through fortunate timing in a single market regime — but this portfolio is profitable in bull, bear, and range-bound markets. A strategy might appear significant on a single statistical test — but this portfolio's regression alpha is significant at the 0.01% level with a t-statistic of 5.75, exceeding the Harvey-Liu-Zhu threshold for multiple testing. A strategy might show strong in-sample performance that degrades out-of-sample — but this portfolio's Sharpe ratio actually improves from 2.13 in the first sub-period to 3.32 in the second.

The combination of near-zero market correlation, negative conditional betas in both up and down markets, improving sub-period consistency, uncorrelated strategy components across multiple asset classes, and statistical significance across multiple testing frameworks is collectively inconsistent with any reasonable null hypothesis of zero alpha. The portfolio represents a robust, diversified systematic trading program with demonstrated capacity to generate absolute returns across the full spectrum of market conditions observed over a six-year period spanning pandemic-era volatility, the 2022 bear market, and the subsequent recovery.

The portfolio's risk characteristics are particularly noteworthy: a maximum drawdown of 19.9% — substantially less than the benchmark's 32.2% — combined with a Calmar ratio of 3.88 and Sortino ratio of 4.13, indicates that returns are generated with substantially less peak-to-trough risk than a passive equity allocation. The VIX-based risk overlay, adaptive stop management, and daily loss circuit breaker collectively produce a drawdown profile that declined from 13.5% in 2020 to under 5% in recent years, suggesting a strategy ensemble whose risk-adjusted performance is both strong and improving with time.