R & M Trade Desk

Defined-Risk Volatility-Risk-Premium Harvesting · Robert Lanni and Mario Treviño · FinTech 533 · Duke MEng FinTech

The strategy in one sentence

A 4-instrument cross-asset put-credit-spread basket (AAPL, MSFT, WMT, GLD) with a 3-layer halt framework, sized at 16-delta short strikes with weekly Monday entries, harvesting the volatility risk premium across single-stock idiosyncratic vol and commodity-ETF cross-asset vol simultaneously.

Headline result

Excess Sharpe +0.371 over OOS 2018-2024, beating the single-instrument SPX put-only baseline (+0.286) by 0.085 with cross-asset diversification and 2× the trade sample.

Metric	Value
Sharpe ratio (excess of risk-free, ML overlay engaged)	+0.371
Risk-free baseline (avg IRX 2018-2024)	2.33%
Geometric mean return (GMRR, annualized)	+2.41%
Annualized volatility	0.21%
Alpha vs SPY (annualized OLS)	+2.37%
Beta vs SPY (OLS slope, daily)	+0.0014
Max drawdown over 7 years	-0.12%
Trades total · Trades per year · Avg return per trade	437 · 62.8 · +8.25%
DSR (PSR)	1.0000 ≥ 0.95 ✓
PBO via CSCV	0.0402 ≤ 0.30 ✓

Both acceptance gates pass. See Results for the full breakdown.

What’s behind the number

4-instrument cross-asset basket, three single-stocks (AAPL, MSFT, WMT) for idiosyncratic short-vol exposure plus GLD for commodity-vol exposure. Selected from a 12-instrument tested universe; selection bias corrected via DSR (N̂=9 implied independent trials).
3-layer halt framework, tail-event halts on extreme single-day market moves, drawdown gate on a trailing 90-day window, vol-regime auto-resume with a 60-day time fallback.
Per-trade defined risk, 16-delta short put, 5-pt long-put protection, weekly Monday entries, gap-aware exit fills, VIX-conditional slippage model.
Exogenous-factor inputs, VIX/VIX3M/VVIX/SKEW for regime classification, IRX for risk-free baseline + cash accrual, HYG-LQD spread + rolling SPY-Treasury correlation for stress detection. See Mechanics for the full list.
Honest aggregation, book Sharpe computed from the equal-weighted aggregate equity curve, not from per-instrument Sharpe averaging. Cross-instrument correlation 0.26 (low) drives the diversification benefit.

Portfolio fit

This strategy is engineered as a risk-managed defensive sleeve, not a directional alpha generator. The headline profile, +2.41% annualized return, 0.21% volatility, -0.12% max drawdown across seven years including COVID, the 2022 bear, and the 2023 banking stress, is the signature of a structurally short-volatility insurance harvester rather than a stock-picking edge.

The natural deployment is alongside a directional alpha generator. A long-only equity factor sleeve, a momentum overlay, or a discretionary fundamental book all carry positive expected return at single-digit Sharpe with double-digit drawdown risk. Combining a directional alpha source with this defensive overlay does three things:

Smooths the equity curve during stress events. The defensive sleeve continued generating premium income through every named drawdown in the OOS sample, including weeks where broad-market equity exposure experienced material drawdowns.
Releases alpha budget for higher-conviction directional bets. A portfolio manager who would otherwise hold cash to manage drawdown can instead allocate that cash to this sleeve and earn a premium on the dry powder.
Adds a low-correlated return stream. Cross-instrument correlation inside the basket is 0.26; correlation against a broad-market directional book is materially lower because the strategy harvests the volatility premium rather than the equity premium.

The 0.371 excess Sharpe is the result of a tight volatility denominator, not a large numerator. That is the design intent. A defensive sleeve should be measured by its consistency and downside containment, not by raw return magnitude. Read the +0.085 outperformance versus the SPX baseline as evidence the architecture (cross-asset basket, halt framework, ML stress overlay) compounds risk-adjusted advantage over a single-instrument baseline.

Answering the two questions every trading-system writeup needs

Trading-system writeups in this course are expected to answer two questions explicitly:

How will you know your strategy is performing as expected? How will you quantify when the strategy stops working?

Both are answered by one framework: a Hoeffding-bound live monitor that quantifies, in real time, the probability the underlying win rate has shifted away from the OOS baseline of μ = 0.730. The mechanic is straightforward: roll a 60-trade window of the realized win rate, compare it to the pre-set μ, and apply the Hoeffding inequality to get a distribution-free probability bound on whether the observed underperformance is real or random.

For the question how will you know it’s performing as expected: the bound stays at or above 50% (the green band) and the strategy continues at full size. Performance is plausibly within the OOS regime.

For the question how will you quantify when it stops working: the bound crossing below 10% is the trip-wire. At that point, observed underperformance has dropped to a probability where random chance is an implausible explanation for the gap to baseline. The threshold is dated, quantitative, distribution-free, and pre-set before live deployment.

Backtested over the full 1,760 OOS trading days, the bound stayed at or above 50% on 88% of post-warmup trades and registered no critical signal across the 7-year window. The full framework, threshold table, worked example, and chart are on the Live Monitoring page.

What was tested but not used

The headline is one of six configurations tested. The other five (SPX-only put-credit-spread, 3-ETF basket, full 6-instrument book, and iron condor variants on the 3-instrument cluster and SPX-only) underperformed the SPX baseline. They are reported transparently on the Variants page; the iron-condor variant is particularly informative because adding the call wing systematically lost in the 2018-2024 trending-equity regime.

Future work: treasury management on undeployed capital

The current backtest accrues idle cash at the realized 13-week T-bill yield (IRX averaged 2.33% over OOS). This is the conservative baseline. A natural next step is active treasury management on the undeployed $200,000 of basket capital that backs the options positions.

Replacing the passive T-bill accrual with an ultra-short Treasury ETF (SGOV, BIL) or a short-duration corporate bond fund (VCSH, BSV) targets an additional 100-200 bps of carry with minimal duration or credit risk. The trade-off is a small increase in drawdown sensitivity during credit-spread blowouts, but the headline -0.12% maximum drawdown leaves substantial room before this becomes a constraint.

The deliberate exclusion is broad-equity exposure (SPY, QQQ) for the undeployed capital. Adding equity beta to the cash sleeve introduces correlation risk: in the stress events the strategy is designed to absorb (Volmageddon, COVID, 2022 bear, 2023 banking), the put-credit-spread portfolio is under maximum pressure exactly when an equity cash sleeve would also be drawing down. This double-correlation would destroy the maximum-drawdown profile that makes the strategy useful as a portfolio defensive sleeve.

Recommended treasury overlay for production deployment: 80% allocation to SGOV (0-3M Treasuries, near-zero duration, currently yielding ~5%) and 20% to short-investment-grade-corporate (VCSH, ~1y duration, currently yielding ~5%). Expected blended carry roughly 100-150 bps above passive IRX accrual, raising the strategy’s annualized return from 2.41% to approximately 3.5-4%, while keeping the maximum-drawdown floor near -0.12%. This is a low-risk capital-efficiency improvement, not a return-chasing one.

--- title: "R & M Trade Desk" subtitle: "Defined-Risk Volatility-Risk-Premium Harvesting · Robert Lanni and Mario Treviño · FinTech 533 · Duke MEng FinTech" --- ## The strategy in one sentence A 4-instrument cross-asset put-credit-spread basket (AAPL, MSFT, WMT, GLD) with a 3-layer halt framework, sized at 16-delta short strikes with weekly Monday entries, harvesting the volatility risk premium across single-stock idiosyncratic vol and commodity-ETF cross-asset vol simultaneously. ## Headline result {#headline} **Excess Sharpe +0.371** over OOS 2018-2024, beating the single-instrument SPX put-only baseline (+0.286) by 0.085 with cross-asset diversification and 2× the trade sample. | Metric | Value | |---|---:| | **Sharpe ratio (excess of risk-free, ML overlay engaged)** | **+0.371** | | Risk-free baseline (avg IRX 2018-2024) | 2.33% | | **Geometric mean return (GMRR, annualized)** | **+2.41%** | | Annualized volatility | 0.21% | | Alpha vs SPY (annualized OLS) | +2.37% | | Beta vs SPY (OLS slope, daily) | +0.0014 | | Max drawdown over 7 years | -0.12% | | Trades total · Trades per year · Avg return per trade | 437 · 62.8 · +8.25% | | **DSR (PSR)** | **1.0000** ≥ 0.95 ✓ | | **PBO via CSCV** | **0.0402** ≤ 0.30 ✓ | Both acceptance gates pass. See [Results](results.qmd) for the full breakdown. ## What's behind the number - **4-instrument cross-asset basket**, three single-stocks (AAPL, MSFT, WMT) for idiosyncratic short-vol exposure plus GLD for commodity-vol exposure. Selected from a 12-instrument tested universe; selection bias corrected via DSR (N̂=9 implied independent trials). - **3-layer halt framework**, tail-event halts on extreme single-day market moves, drawdown gate on a trailing 90-day window, vol-regime auto-resume with a 60-day time fallback. - **Per-trade defined risk**, 16-delta short put, 5-pt long-put protection, weekly Monday entries, gap-aware exit fills, VIX-conditional slippage model. - **Exogenous-factor inputs**, VIX/VIX3M/VVIX/SKEW for regime classification, IRX for risk-free baseline + cash accrual, HYG-LQD spread + rolling SPY-Treasury correlation for stress detection. See [Mechanics](mechanics.qmd) for the full list. - **Honest aggregation**, book Sharpe computed from the equal-weighted aggregate equity curve, not from per-instrument Sharpe averaging. Cross-instrument correlation 0.26 (low) drives the diversification benefit. ## Portfolio fit This strategy is engineered as a **risk-managed defensive sleeve**, not a directional alpha generator. The headline profile, +2.41% annualized return, 0.21% volatility, -0.12% max drawdown across seven years including COVID, the 2022 bear, and the 2023 banking stress, is the signature of a structurally short-volatility insurance harvester rather than a stock-picking edge. The natural deployment is alongside a directional alpha generator. A long-only equity factor sleeve, a momentum overlay, or a discretionary fundamental book all carry positive expected return at single-digit Sharpe with double-digit drawdown risk. Combining a directional alpha source with this defensive overlay does three things: - **Smooths the equity curve** during stress events. The defensive sleeve continued generating premium income through every named drawdown in the OOS sample, including weeks where broad-market equity exposure experienced material drawdowns. - **Releases alpha budget** for higher-conviction directional bets. A portfolio manager who would otherwise hold cash to manage drawdown can instead allocate that cash to this sleeve and earn a premium on the dry powder. - **Adds a low-correlated return stream**. Cross-instrument correlation inside the basket is 0.26; correlation against a broad-market directional book is materially lower because the strategy harvests the volatility premium rather than the equity premium. The 0.371 excess Sharpe is the result of a tight volatility denominator, not a large numerator. That is the design intent. A defensive sleeve should be measured by its consistency and downside containment, not by raw return magnitude. Read the +0.085 outperformance versus the SPX baseline as evidence the architecture (cross-asset basket, halt framework, ML stress overlay) compounds risk-adjusted advantage over a single-instrument baseline. ## Answering the two questions every trading-system writeup needs Trading-system writeups in this course are expected to answer two questions explicitly: > *How will you know your strategy is performing as expected?* > *How will you quantify when the strategy stops working?* Both are answered by one framework: a **Hoeffding-bound live monitor** that quantifies, in real time, the probability the underlying win rate has shifted away from the OOS baseline of μ = 0.730. The mechanic is straightforward: roll a 60-trade window of the realized win rate, compare it to the pre-set μ, and apply the Hoeffding inequality to get a distribution-free probability bound on whether the observed underperformance is real or random. For the question **how will you know it's performing as expected**: the bound stays at or above 50% (the green band) and the strategy continues at full size. Performance is plausibly within the OOS regime. For the question **how will you quantify when it stops working**: the bound crossing below 10% is the trip-wire. At that point, observed underperformance has dropped to a probability where random chance is an implausible explanation for the gap to baseline. The threshold is dated, quantitative, distribution-free, and pre-set before live deployment. Backtested over the full 1,760 OOS trading days, the bound stayed at or above 50% on 88% of post-warmup trades and registered no critical signal across the 7-year window. The full framework, threshold table, worked example, and chart are on the [Live Monitoring](monitoring.qmd) page. ## What was tested but not used The headline is one of six configurations tested. The other five (SPX-only put-credit-spread, 3-ETF basket, full 6-instrument book, and iron condor variants on the 3-instrument cluster and SPX-only) underperformed the SPX baseline. They are reported transparently on the [Variants](ablations.qmd) page; the iron-condor variant is particularly informative because adding the call wing systematically lost in the 2018-2024 trending-equity regime. ## Future work: treasury management on undeployed capital The current backtest accrues idle cash at the realized 13-week T-bill yield (IRX averaged 2.33% over OOS). This is the conservative baseline. A natural next step is active treasury management on the undeployed $200,000 of basket capital that backs the options positions. Replacing the passive T-bill accrual with an ultra-short Treasury ETF (SGOV, BIL) or a short-duration corporate bond fund (VCSH, BSV) targets an additional 100-200 bps of carry with minimal duration or credit risk. The trade-off is a small increase in drawdown sensitivity during credit-spread blowouts, but the headline -0.12% maximum drawdown leaves substantial room before this becomes a constraint. The deliberate exclusion is broad-equity exposure (SPY, QQQ) for the undeployed capital. Adding equity beta to the cash sleeve introduces correlation risk: in the stress events the strategy is designed to absorb (Volmageddon, COVID, 2022 bear, 2023 banking), the put-credit-spread portfolio is under maximum pressure exactly when an equity cash sleeve would also be drawing down. This double-correlation would destroy the maximum-drawdown profile that makes the strategy useful as a portfolio defensive sleeve. Recommended treasury overlay for production deployment: 80% allocation to SGOV (0-3M Treasuries, near-zero duration, currently yielding ~5%) and 20% to short-investment-grade-corporate (VCSH, ~1y duration, currently yielding ~5%). Expected blended carry roughly 100-150 bps above passive IRX accrual, raising the strategy's annualized return from 2.41% to approximately 3.5-4%, while keeping the maximum-drawdown floor near -0.12%. This is a low-risk capital-efficiency improvement, not a return-chasing one.