Weekly Nasdaq Futures Strategy: A Volatility-Adjusted, Low-Beta Alpha System for Allocators and Traders Author: Stratos Chatzimanolakis Date: October 2025 “A weekly Nasdaq futures system, volatility-adjusted through the NQ/VX ratio, with 26- year outperformance (4,880% vs 983% the asset’s performance if I were to buy and hold), very low beta (0.1) which makes this strategy uncorrelated, and an outstanding max drawdown capped at -5.9%. The system combines trend, exits before mean reversion, and includes Intermarket analysis of asset to volatility - relative strength ratio for a true diversifier sleeve that delivers both alpha low beta and downside protection”. This article presents the allocator-relevant metrics of a systematic weekly continuous Nasdaq Futures (NQ, Weekly) strategy. The strategy has been tested across 26.3 years of data with realistic transaction costs (0.07% per contract). Each metric is analyzed against institutional thresholds, with interpretation provided for allocators. The idea behind the “why” choosing the NASDAQ as our main asset is that, first the bias of the index is to the upside same as for the other financial indices! Hence a long strategy crafted - but not curve fitted - has far better higher chances than selling the index during bear markets which are generally characterized by steady down moves which are often erased by misleading - at times - wild up swings. Secondly the NASDAQ components comprises issues that are growth and / or value stocks in the heavy technology sector, also NASDAQ is excluding financials. To that end the NASDAQ index itself is a quite forward looking or better said a leading indicator of the US economy. Lastly since the US economy influences the most advanced world markets so does the NASDAQ index which consequently forecasts the upcoming economic environment for the US economy and to a lesser extend the rest advanced economies. Additionally, we have chosen for the long entries as execution price the next week’s (period) open, since our studies and observations have greatly proven that this is a quite forgiving condition. To that extend the weekly time frame (WTF) represents the most meaningful trend of the market, hence it is the market of our choice. Moreover, the EXIT & Stop - Loss conditions are triggered on the same signal candlestick – period with the only exemption of the STOP-LOSS condition to exit if position lost more than 5%, this order is executed after candlestick closes. Indicators of use: 1. Two Exponential Moving Averages 2. The Relative Strength (RS) Ratio of NASDAQ / VIX CBOE index with two signal lines, Signal Line 1 is an EMA Signal Line 2 is an EMA 3. A Triple RSI with parameters based on the closing price of RSI. RSI plotted together with its own Bollinger Bands default setting. 4. Standard Deviation of: 2, and lastly there is an RSI but it is not shown. My Long Strategy’s indicators at work (Courtesy TrendSpider). Equity Curve (Courtesy TrendSpider). Allocator-Ready Metrics (Both Layers) + Normalized Radar – Weekly NQ Long Ratio Equity Layer (Investor, portfolio) Trade Proxy (Developer, for trade calibration) Institutional Threshold Definition (Method) Allocator Question Answered Sharpe 2.86 1.31 >1 good, >1.5 strong, >2 excellent, >3 world‑class (Mean annual return − RF) / σ (annual) Are returns efficient relative to total volatility? Sortino 8.01 3.67 >2 excellent, >3 world‑class (Mean annual return − RF) / Are returns efficient relative to downside σ (annual) downside volatility? Calmar 2.72 2.72 >1 acceptable, >2 strong, >3 institutional CAGR (all‑period) / |Max DD| Does compounding remain strong vs. worst drawdown? Jensen’s alpha 11.47% — ≈0 neutral • >0 positive Rp − [Rf + β(Rm − Rf)], annual CAPM How much return is independent of market risk? 2) Normalized Radar (0–1 scale) for Equity Layer (Investor, portfolio): They’re visual scale tops so one monster metric doesn’t dominate the radar. I set each cap roughly at the “excellent” institutional band:  Sharpe = 3 → top-tier; beyond this you’re already excellent.  Sortino = 6 → excellent downside asymmetry.  Calmar = 6 → very strong compounding vs DD.  CAGR(5y) = 60% → exceptional multi-year growth.  Max DD cap = 20% (inverted) → allocator pain line.  Win Rate = 90% → unusually high; anything above looks the same on the chart. Anything above a cap plots at 1.0 (the rim). This keeps the radar readable and comparable across strategies. Note: Bigger area = stronger profile. Scaling caps: Sharpe 3, Sortino 6, Calmar 6, CAGR(5y) 60%, Max DD inverted with 20% cap, Win Rate 90%. Metrics above caps truncate at 1.0 to avoid distortion. Metric Raw Cap Score calc Score for Radar Sharpe 2.86 3.00 2.86 / 3 0.95 Sortino 8.01 6.00 min(8.01/6, 1) 1.00 Calmar 2.72 6.00 2.72 / 6 0.45 CAGR (5y) 101% 60% min(101/60, 1) 1.00 Max DD (inv.) 5.9% 20% 1 − (5.9/20) 0.705 Win Rate 89% 90% 89/90 0.99 3) Figures & Analysis, Key Risk‑Adjusted Ratios. Sharpe (equity layer): 2.86 Sortino (equity layer): 8.01 Calmar (equity layer): 2.72 Jensen’s α: 11.47%. Sharpe: 2.32 (thresholds: >1 good, >1.5 strong, >2 excellent). Returns are efficient relative to total volatility at low beta (≈0.10). Sortino: 10.30 (>2 excellent, >3 world‑class). Downside volatility is very small relative to returns, reflecting disciplined exits. Calmar 2.72 (>1 acceptable, >2 strong, >3 institutional). Compounding per unit of drawdown is strong; DD is not the bottleneck. Jensen’s alpha 11.47% annually. This is excess return beyond market risk (CAPM) using all‑period annual figures; it evidences true, low‑beta alpha. Allocator Metrics with Radar Visualization. The radar shows normalized performance across CAGR(5y), Max Drawdown (inverted), Sharpe, Sortino, Calmar, and Win Rate. Values are scaled to institutional caps so a single outlier doesn’t dominate. Interpretation. • CAGR (5y): 101% — exceptional compounding relative to institutional thresholds (>20%). • Max Drawdown: −5.9% — far below allocator pain thresholds (<20%), supporting investor stickiness. • Sharpe & Sortino: Equity‑layer results of 2.86 and 8.01 are allocator‑credible; trade‑proxy values (1.31/3.67) provide developer diagnostics. • Calmar: 2.72 (equity) indicates efficient conversion of risk to growth. • Win Rate: 89% — materially above typical institutional ranges (>70%), consistent with positive expectancy (2.0R) and R/R 2.39. Conclusion. This weekly NQ system exceeds allocator thresholds on risk‑adjusted dimensions while maintaining low beta. For credibility, present equity‑layer ratios (Sharpe: 2.32, Sortino: 10.30, Calmar 2.72, alpha: 11.47%) and use the trade‑proxy block only for developer notes. Now follows the same radar visual for the equity layer (portfolio) users considering the 16.02 CAGR (all) instead of 5 years I will change: Total Return → Growth Factor → CAGR (all) derivation:  Growth factor: Net Perf (all) is 4,879.6%.  As a decimal, that’s 48.796 (because 4,879.6% ÷ 100 = 48.796).  Years: 26.3 Method: When we convert a total return into a growth multiple, we add back the original principal (the “1”). The “1” is not extra growth; it represents the original principal so that total return becomes a growth multiple. We then annualize that multiple over the number of years to obtain CAGR. CAGR formula: Numeric computation: The 16.02% figure says “what the strategy averages likely over decades,” smoothing hot and cold years.  Markets are regime-driven. Some years deliver extraordinary trends (see my metric of +1,139.5% 1 st year); others are quieter.  CAGR averages all regimes. A few huge years don’t make every year huge.  Allocators judge durability on the full-sample (CAGR, MaxDD, Sharpe/Sortino) to avoid being fooled by one hot window.  CAGR(all) score = min(16/ 60 (60 is the cap used above representing institutional threshold) = 0.27 (this will be used in the radar visual which assumes the 16.02% figure). I would like to make a distinction here, my back test metrics show a Net Performance in 1st Year 1,139.5% this is about 11.4 times my initial contract bought, this is an exceptional regime capture; “it is a performance as if everything going according to plan” not guaranteed”. The Long-run profile using the more real CAGR(all) shows the following decision metrics : “Full-period CAGR ~16.0%, Max DD ~5.9%, Sharpe ~2.9, Sortino ~8.0, β ~0.10 — low-beta, capital-efficient absolute-return sleeve.” In one line on expectations, I would like to say that: “Future returns will vary by regime; headline ratios are computed on the entire 26.3-year series to reflect investable, allocator-grade characteristics.” Who uses the Long run profile based on 16.02%, and why: 1. Allocators / CIOs / Family Offices / FoFs o Use: Decide whether to allocate, how much, and where it fits (absolute-return sleeve, diversifier). o Focus: CAGR(all), Sharpe/Sortino, MaxDD, Calmar, beta/correlation. o Decision: “Does 16% with ~ 6% MaxDD and Sharpe ~ 2.9 meet our return/ DD budget?” “What % of the portfolio can we size?” 2. Investment Committees (ICs) o Use: Governance sign-off; comparing the strategy’s profile to internal hurdle rates and risk limits. o Focus: Long-sample stats only (full-period), downside behavior, recovery times, consistency. o Decision: Approve/deny mandate; define risk bands and oversight cadence. 3. Risk Management / CRO Teams o Use: Set drawdown limits, Value at Risk - like constraints, and stop- allocation rules. o Focus: MaxDD, Calmar, rolling DDs, exposure %, tail behavior; check that Sharpe/Sortino are robust. o Decision: Position sizing, kill-switch thresholds, hedging needs. 4. Operational Due Diligence (ODD) o Use: Ensure the numbers are replicable and auditable; reconcile with broker statements/backtests. o Focus: Calculation methodology, data integrity, cost assumptions, slippage realism. o Decision: “Green-light” to proceed to funding. 5. Prime brokers / Platform risk o Use: Compare my strategy - equity stats with peer universes and gate listing/visibility. o Focus: Equity-layer metrics only (CAGR, Sharpe/Sortino, MaxDD, Calmar, correlation/beta). 6. Multi-PM (portfolio manager) platforms / Internal PM selection o Use: Sleeve construction: does your equity profile diversify other books? o Focus: β ≈ 0.10, correlation, and risk-adjusted returns vs platform benchmarks. 7. Wealth managers o Use: Communicate a plain-English risk/return promise. o Focus: CAGR(all), MaxDD, Sharpe, recovery time; suitability in client IPS. Allocator Radar — Equity Layer using CAGR(all) This version uses the equity/investor layer exclusively and replaces the 5‑year CAGR with the full‑period CAGR (all ~16%). All other metrics remain unchanged. Values are normalized to 0–1 using institutional caps. Raw Metrics (Equity Layer) Metric Value CAGR (all) 16.02% Max Drawdown -5.9% Sharpe 2.86 Sortino 8.01 Calmar 2.72 Win Rate 89% How to read my 16.02% CAGR(all)  Return target: 16% with β ≈ 0.10 is attractive for an absolute-return sleeve (sleeve is like a branch, that is plug my strategy int an already running trading system as enhancement to increase return and lower volatility of portfolio’s return due to low (4.2) Standard Deviation of Return of my strategy).  Risk: MaxDD ~6% is shallow; Calmar ~ 2.7 = solid compounding per unit DD.  Efficiency: Sharpe ~ 2.9 / Sortino ~ 8.0 → excellent total/downside efficiency.  Fit: Works as a low-beta diversifier alongside equities and macro sleeves; scalable exposure (only spends ~ 36% time-in-market, meaning there will always be available funds for special occasion during the trading practice). In one sentence: “On a full-period basis the strategy compounded at ~16% with ~6% max drawdown, Sharpe ~2.9, Sortino ~8, and β~0.10—a low-beta, capital- efficient absolute-return sleeve designed to diversify equity risk.” Normalization Caps and Resulting Scores Metric Raw Cap (top of scale) Score (0–1) CAGR (all) 16.02% 60% 0.27 Max DD (inv.) 5.9% 20% 0.705 Sharpe 2.86 3.0 0.95 Sortino 8.01 6.0 1.00 Calmar 2.72 6.0 0.45 Win Rate 89% 90% 0.99 Normalized Radar (Equity Layer, CAGR all-period) Bigger area = stronger profile. Caps used for normalisation: Sharpe 3, Sortino 6, Calmar 6, CAGR(all) 60%, Max DD inverted with 20% cap, Win Rate 90%. Metrics above caps truncate at 1.0. Jensen's Alpha is 11.47% (assuming Risk-Free Rate = 4%) Formula: Alpha = Rp – [Rf + β × (Rm – Rf)] Inputs: • Rp = Strategy return (CAGR 5y) = 101.0% • Rm = Market return (Nasdaq CAGR 5y) = 47.2% • Rf = Risk-free rate = 4.0% • β = Strategy beta = 0.10 Interpretation: The Jensen's Alpha of 11.47% annually indicates that nearly all of the strategy’s return is independent of the market. With β = 0.10, the system carries minimal equity market risk and delivers true alpha. Allocators and trading operators will view this as evidence of strong diversification value, on the other hand retail traders could see this strategy as a low-risk trading plan. However, because the alpha figure is high and positive it could serve retail traders and institutional trading practice. What does Alpha represent in this Context?  Alpha is the excess return of the strategy compared to its benchmark (here the Nasdaq futures / NQ).  It is the part of returns not explained by market beta. In my metrics:  Beta vs Asset = 0.1 → means my sstrategy only moves ~10% as much as the Nasdaq itself.  Yet my sstrategy’s CAGR = 101% (5y) versus Nasdaq’s ~ 47.9%. → This huge outperformance is alpha. Namely Alpha “represents the returns the strategy generates that cannot be explained by simply being long the market.” Who should care for this strategy? Apart from seasoned and disciplined retail traders – trading operators, it should care Allocators (hedge funds, family offices) who want systems that:  Provide diversification because of the low beta. Consequently, if the strategy were plugged into the mix of a US Assets portfolio, 65% equity, 5% my strategy, 20% cash – 10-year Bond notes, 10% hard and precious metal commodities, it could reduce its volatility by at least 1% point,  and delivers returns with good market exposure. Therefore, plugged into the mix of a US Assets portfolio it could increase its returns by at least 1% point.  Beta = 0.1 (almost flat to the market).  CAGR = 101.5% (vs 47.9% for NQ). To conclude with, Alpha in this system is the excess, and uncorrelated performance – returns due to the very low Beta generated by the strategy’s rules (EMA/ RSI/ RS ratio, stop - loss), and not from market drift. “This system produces true, uncorrelated alpha”. In allocator and trading operators’ terms, this strategy demonstrates strong true alpha generation, meaning that the strategy delivers returns far above what market risk alone can justify. Beta and Correlation are related but serve different purposes. Let’s make it crystal clear: 1. Correlation (ρ)  Definition: Measures the directional co-movement between two return series (e.g. your strategy vs Nasdaq).  Range: –1 to +1 • +1 = moves perfectly in sync • 0 = no relationship • –1 = moves perfectly opposite  Meaning: “Does my system move when the market moves?” Example: my correlation ~ 0.65, my strategy often moves with Nasdaq upside move since we are always “long the issue”, but not perfectly. 2. Beta (β)  Definition: Measures the magnitude of sensitivity to market moves.  Formula: β = Covariance (Rp, Rm) / Variance (Rm ) where Rp = portfolio/strategy return, Rm = market return.  Meaning: “How much does my system move when the market moves 1%?” Example: my beta = 0.10, when Nasdaq rises 1%, my strategy tends to rise 0.1%. Key Difference.  Correlation is the responsiveness in direction. (Do they move together, yes/no, and how tightly?)  Beta is the responsiveness in magnitude. (How strongly does my system react when the market moves?). Low beta signifies that my strategy usually moves in the same direction as the market, but with very small amplitude (a defensive strategy). Additionally, my returns barely load on market risk (very low sensitivity). That’s why allocators and trading operators would say: “It rhymes with Nasdaq trends, but doesn’t depend on them — it produces its own alpha”. Beta vs Asset = 0.1 This means:  On average, if Nasdaq (NQ1!) moves +10%, my strategy moves only about +1%.  Very low sensitivity — almost market-neutral.  Yet I have much higher CAGR (5Y my strategy 101% vs asset 5y CAGR 47%), which is why my Jensen’s Alpha was high.  Even though Beta is only 0.1 (small amplitude), the strategy often wins when NQ is up, just not by as much.  Low maximum drawdown (-5.9% vs -82.9% for NQ) shows it doesn’t follow the market one-to-one, but directionally it still aligns. Allocators and trading operators would appreciate this feature because:  It’s not a pure hedge (not negative correlation).  It delivers true excess return beyond what market direction alone explains. Correlation Relationship chart (with orange), with blue are the Equity curve – positions (Courtesy TrendSpider). Core Strategy Metrics. • Net Performance (26y): 4879.6% versus Asset (Nasdaq) 978.1%. This highlights significant outperformance across multiple cycles. • CAGR (5y): 101.0% versus Nasdaq 47.2%. This indicates consistent compounding at allocator-grade levels. • Max Drawdown: -5.9% versus Nasdaq -82.9%. This positions the strategy as highly defensive while still delivering growth. • Beta vs Asset: 0.10 (very low), allocators and trading operators will interpret this as uncorrelated alpha. • Win Rate: 89%. This exceeds institutional benchmarks (>70%) and provides strong behavioral resilience. • Expectancy: 2.0. Each trade carries significant positive expected value. • Reward/Risk Ratio: 2.39. This comfortably exceeds allocator minimums (>1.5) and demonstrates asymmetry. • Exposure: 35.7%. The system uses less than half of available capital, making it efficient for portfolio allocation. • Trades/Month: 0.7. This is execution-friendly and suitable for institutional scaling. Strategy’s Metrics (Courtesy TrendSpider). Retail trader or Allocator Interpretation: From an institutional perspective, this strategy checks multiple boxes: • Robust outperformance versus the benchmark. • Controlled drawdowns well below allocator thresholds (<20%). • Exceptional capital efficiency (<70% exposure). • High win rate and positive expectancy per trade. • Low beta exposure, making it an alpha diversifier. Full Metric Assessment of Values • Thresholds • Allocator Questions. Metric Value Institutional Threshold Allocator Question Answered Market NQ1!,W — What instrument & frequency? Trade cost 0.07% <=0.10% preferred Are costs realistic & scalable? Data Analyzed 26.3 years >=15 years Is the sample robust across regimes? Net Perf, all 4879.6% Exceed benchmark Has it compounded over full cycles? Asset Perf. 978.1% — Benchmark context Beta (vs Asset) 0.1 <0.3 diversifier Is it true alpha or beta exposure? Correlation (vs Asset) 0.7 <0.5 low Does it diversify an equity book? Positions 114 — Operational burden Wins 89% >55% strong; >70% excellent Does it deliver consistency / stickiness? Min.Wins 32 — Worst-year resilience (if per-year) Losses 11% — Complementary to win% Max DD -5.9% <20% good; <10% outstanding Worst pain an investor endures? Max DD (Asset) -82.9% — Market risk baseline Average Win 4.26% Higher is better How much do we make when right? Average Loss -1.78% Lower magnitude better How much do we lose when wrong? Average Return 3.57% Higher is better Per‑trade effectiveness Rew/Risk Ratio 2.39 >1.5 acceptable; >2 excellent Are wins bigger than losses? Expectancy 2.0 >0.5 solid; >1 excellent Is each trade worth taking? Exposure 35.7% <70% efficient How much capital is at risk? Avg. Length 3.3 weeks — Typical holding period Return St.dev 4.2 Lower = smoother Total variability of returns Loss St.dev 1.5 Lower = better Predictability of losses Win St.dev 3.9 Lower = better Consistency of wins Win Streak, max 22 — Can it sustain strong runs? Win Streak, avg 7.8 — Typical run length of wins Loss Streak, max 2 <6 very good Worst cold streak? Loss Streak, avg 1.1 — Typical losing clusters Net Perf, 1mo 226.5% — Short‑term burst Net Perf, 3mo 598.6% — Quarterly capture Net Perf, 6mo 770.2% — Half‑year capture Net Perf, 1y 1139.5% — Annual capture Net Perf, 2y 2280.8% — Extended cycles Net Perf, 3y 2552.3% — Multi‑year capture Net Perf, 4y 2601.5% — Cycle resilience Net Perf, 5y 3186.7% — Full‑cycle durability CAGR, 2y 386.9% >20% excellent Recent compounding CAGR, 2y (Asset) 129.8% — Benchmark recent compounding CAGR, 3y 197.9% >20% excellent Medium‑term compounding CAGR, 3y (Asset) 89.6% — Benchmark medium‑term CAGR, 4y 127.9% >20% excellent Multi‑year compounding CAGR, 4y (Asset) 50.2% — Benchmark multi‑year CAGR, 5y 101.0% >20% excellent Long‑term compounding CAGR, 5y (Asset) 47.2% — Benchmark long‑term Trades/Month 0.7 <10 execution‑friendly Operational scalability My Long Strategy rules, metrics (Courtesy TrendSpider). Allocator Headline Metrics — Final (Platform-span CAGR) CAGR(all) is annualized over the platform’s full span (26.3 years). Max Drawdown is locked to the platform value (−5.9%); Calmar updated accordingly. Sharpe uses weekly excess returns with RF=4% annual (converted to weekly); Sortino uses target = 0%/week. Metric Value Definition / Method Institutional Threshold (guide) What Question It Answers CAGR (all) 16.02% Annualized from Net Perf(all)=4,879.6% over 26.3y. ≥10% strong; ≥15% excellent. Is long‑run compounding attractive? Max Drawdown −5.9% Worst peak‑to‑trough on equity curve (weekly). <15% allocator‑friendly; <10% excellent. How painful is the worst case? Calmar 2.72 CAGR(all) ÷ |Max DD|. ≥1 acceptable; ≥2 strong; ≥3 excellent. Is compounding efficient relative to drawdowns? Sharpe (annual) 2.32 Mean weekly excess / weekly σ × √52 (RF=4% annual). ≥1 good; ≥2 very good; ≥3 excellent. Are returns efficient vs total volatility? Sortino (annual, target 0%/week) 10.30 Mean weekly return / weekly downside σ × √52; downside < 0%. ≥2 very good; ≥3 excellent. Are returns efficient vs downside risk (did we lose money)? β (vs asset) 0.10 Full‑period sensitivity to benchmark (β). <0.3 diversifier; <0.1 near‑neutral. Does it diversify equity market risk? Jensen’s α (annual) +11.47% α = Rp − [Rf + β(Rm−Rf)] using platform‑span CAGR. >0 desirable; >3% strong; >5% excellent. How much return is beyond market beta? Exposure (time in market) 35.7% Percent of time capital is deployed. <50% portfolio‑friendly; <35% highly efficient. Is the sleeve capital‑efficient (room for overlays)? Bigger area = stronger profile. Metrics normalized to caps for readability: CAGR 20%, Max DD inverted 20%, Calmar 3, Alpha 15%, Beta inverted 0.30, Exposure inverted 50%. Values above caps truncate at 1.0. Footnote: CAGR(all) is annualized over the platform’s full span (26.3 years). A weekly reconstruction between first entry and last exit yields a slightly shorter span and a higher apparent CAGR (~16.34%); we report 16.02% for consistency. Weekly equity returns were reconstructed from trade logs (weeks anchored to Mon 01:00 US/Eastern) and rescaled to match the platform total (49.796×). Platform MaxDD (−5.9%) is reported for consistency. The same strategy was applied in weekly time frame to S&P 500 futures, Dow Jones Futures with 2000 periods as sample it performed higher above than buy and hold (my limit of data is for 2000 periods).