σ

Elastic Sigma

Volatility Regime Mean-Reversion Model

Live Trading — Real Capital Since January 2026

Now Trading Live

Elastic Sigma is now trading live with real capital as of January 2026.
After extensive research, historical simulation, and out-of-sample validation, this systematic volatility mean-reversion model moved to live deployment in January 2026 and is building a real-money track record. The live account runs the strategy's core configuration. Partners interested in model documentation should contact us directly.

Strategy Philosophy

Elastic Sigma is a systematic volatility mean-reversion strategy on the US equity volatility complex. Once per day, at the close, it commits to one of three regimes—short volatility, long volatility, or cash—expressed entirely in liquid, exchange-traded volatility products, at roughly five trades a month, with no intraday trading and no options held directly.

Short volatility is the default carry stance: it harvests the persistent premium option buyers pay for near-term crash protection—the volatility risk premium—when the regime is calm. Long volatility is a tactical, convex position, taken only when independent signals show that owning volatility into potential stress offers asymmetric reward. Cash is the disciplined refusal of uncompensated carry—held whenever the stress layer flags strain in the volatility market, and in practice the most common state (roughly half the time), prioritizing capital preservation.

Model Characteristics

4.6

Avg Trades/Month

39%

of time Short Vol

13%

of time Long Vol

48%

of time Bonds/Cash

1x

Once Daily (MOC)

How It Works

Elastic Sigma identifies the prevailing volatility regime systematically, reading the term structure of volatility—contango versus backwardation—as the primary filter for whether it earns carry, owns convexity, or steps aside.

σ

Mean-Reversion Framework

Volatility is mean-reverting: it tends to return to its long-run average after periods of extreme elevation or suppression. Elastic Sigma's core engine reads the shape of the volatility term structure—contango when calm, backwardation under stress—to judge the regime and position accordingly: short volatility to harvest carry when calm, cash when the term structure signals strain, and a tactical long-volatility, positive-convexity stance when conditions favor owning volatility.

Adaptive overlays refine that core read using intraday repricing of short-dated SPXW options, adding exposure only on days their signals independently confirm. Regime switches use hysteresis—separate entry and exit conditions—so the book does not whipsaw when the market hovers near a boundary. Every position is expressed in liquid volatility ETPs and executed at the close.

1

Term Structure Analysis

A stress detector reads the shape of the volatility term structure—contango (normal) versus backwardation (inverted)—to gauge whether the regime rewards carry or demands defense.

2

Three-Regime Allocation

Each day the model commits to one of three regimes—short volatility (carry), long volatility (convexity), or cash—based on the confluence of its short-dated signal and independent confirmation layers.

3

Defensive Bonds/Cash

When the stress layer flags strain in the volatility market—roughly half the time—the model refuses uncompensated carry and steps aside to cash or ultra-short bonds, preserving capital until the regime clears.

4

ETF Execution

All positions are held in liquid, exchange-traded volatility products, executed once daily at the close (MOC)—roughly five trades per month, with no intraday trading and no options held directly.

Key Characteristics

Elastic Sigma is designed with institutional-grade risk management and transparent execution at its core.

📊

Three-Regime System

The strategy rotates between short-volatility, long-volatility, and cash/short-duration bond positions—harvesting carry, owning convexity, or standing aside. No naked option selling and no complex derivatives.

Daily Regime Rotation

Signals are calculated just before market close with regime rotation executed at the close, averaging roughly five trades per month.

🔄

Mean-Reversion Focus

Rather than chasing trends, the strategy exploits volatility's tendency to revert to equilibrium after extremes—selling rich near-term insurance and owning convexity into stress.

📈

ETF-Based Execution

All positions are implemented through liquid, exchange-traded volatility products with transparent pricing and minimal slippage.

⚙️

Systematic Discipline

100% rules-based execution eliminates emotional decision-making and ensures consistent application of the strategy methodology.

Advanced Configuration

Research Overlays

Two adaptive overlays extend the core three-regime engine, adding exposure only on days their signals independently confirm. They are included in the advanced backtest below and remain in live validation; the live account trades the core engine alone.

📉

Intraday Fear-Drain Detection

The model reads intraday repricing of short-dated SPXW options to detect tail-risk premium deflating through the session—crash protection bleeding value into the close, an overnight fear spike being absorbed, skew normalizing. On otherwise-ambiguous days that show this pattern, it re-enters the short-volatility carry position at the close, always subject to a term-structure stress veto.

📈

Spike-Accumulation Detection

The mirror signal: when intraday repricing of short-dated SPXW options shows crash protection being accumulated—skew firming, hedging flow consistent with institutional protection demand, the front of the term structure signalling building stress—on a day the core model would otherwise stand aside, the strategy takes a tactical long-volatility, positive-convexity position to monetize the potential spike. This overlay converts volatility events from the strategy's largest risk into some of its largest gains.

Both overlays add exposure only on days independently confirmed by their signals; the core three-regime engine is unchanged.

Backtest Results

Hypothetical historical simulation of the most advanced version of the Elastic Sigma model across the full sample period.

Hypothetical backtested performance. The figures below are derived from a historical simulation, not actual trading, and do not reflect real capital. They do reflect IBKR execution costs; because all transactions are executed Market-on-Close, the simulation assumes no slippage or liquidity constraints under any conditions. Hypothetical results have inherent limitations and benefit from hindsight. Past performance — whether actual or hypothetical — is not indicative of future results. Provided for informational purposes to licensed partners only.

Strategy Profile

Strategy class Systematic volatility regime rotation
Universe Short-vol ETP / Long-vol ETP / Cash
Rebalance Daily — signal evaluated about 8 minutes before the close, executed Market-on-Close (MOC)
Backtest period May 13, 2022 → Aug 14, 2026 (4.25 years)

Performance

Advanced configuration — hypothetical backtest

CAGR+367.1%
Total return+70,405%
Annualized volatility47.9%
Sharpe ratio3.47
Sortino ratio8.19
Max drawdown−25.2%
Calmar ratio14.55

Consistency

Advanced configuration — hypothetical backtest

Positive months82.7%
Best month+65.2%
Worst month−16.4%
Avg winning month+19.0%
Avg losing month−6.2%

Unlike conventional short-volatility carry, Elastic Sigma's return profile is positively skewed (daily skew +3.1; its best day, +38.3%, is three times the size of its worst, −11.8%): its fattest tail is the right one. Naive short-vol carries the opposite, negative skew—picking up pennies in front of a steamroller—the failure mode the regime engine is built to avoid.

Exposure Profile

RegimeTime
Short volatility (ETP)39%
Long volatility (ETP)13%
Cash (defensive)48%

Annual Returns

Advanced configuration — hypothetical backtest

YearReturn
2022 (from May 13)+71.2%
2023+804.1%
2024+479.3%
2025+238.4%
2026 (to Aug 14)+132.4%

Trade Frequency

Market-on-Close execution · May 2022 – Aug 2026

Round-trip trades / month4.6
Avg holding period3.7 days

Monthly Returns (%)

Advanced configuration — hypothetical backtest

Year JanFebMarAprMayJunJulAugSepOctNovDec EOY
2022 +14.4+7.5+17.5−2.6+3.2+2.7+14.5+0.1 +71.2
2023 +2.0−5.5+18.5+24.9+16.3+20.0+31.7+65.2+28.8+31.4+12.3+9.7 +804.1
2024 +17.6−15.7+12.7+43.1+12.3+4.8−3.7+41.1+47.3+48.8−9.1+13.8 +479.3
2025 +16.1+4.6+5.6−0.5+24.9+19.5−16.4+11.9+14.3+15.0+44.7−0.1 +238.4
2026 +8.2+21.4−1.8+13.4+17.2+26.0+4.5+3.0* +132.4

2022 is a partial year, measured from May 13, 2022 (first available market data). 2026 runs through August 14, 2026; August is a partial month (*).

Live · Real Capital

Real-Money Live Tracking

The performance above is a hypothetical backtest of the most advanced version of the strategy. The money actually at risk runs the core configuration.

Live with Real Capital Since January 2026

The live broker account runs the strategy's core configuration—the base three-regime engine—not the full set of research overlays shown in the backtest above.

Real-Money Result to Date

Since January 2026, the live account has accumulated a bit more than half of the advanced version's same-period (January–August 2026) backtest figure of +132.4%.

Why the gap? The live account trades the core configuration, while the headline backtest includes additional overlays still in live validation—and real results carry execution frictions the hypothetical figures do not fully capture. We show both deliberately: the advanced backtest is the research ceiling; the live account is the conservative, real result, tracking a bit above half of it.

Concepts & Definitions

Plain-language background on the public market concepts behind the strategy.

Volatility Risk Premium

Option buyers consistently pay a little more for protection than realized volatility later justifies. Sellers of that insurance earn the difference on average—the volatility risk premium—in exchange for bearing occasional sharp losses.

0DTE Options & Ultra-Short Volatility

Zero-days-to-expiration (0DTE) options expire the same day they trade. Their explosive growth created, for the first time, rich real-time data on volatility expected over the next few hours—a measurement horizon that barely existed a few years ago.

Why Short-Dated Prices Lead

Because ultra-short options re-price instantly with sentiment, the volatility they imply shifts hours or days before month-horizon gauges catch up. Reading the short end can therefore anticipate moves in the broader volatility complex.

Contango vs Backwardation

In calm markets, longer-dated volatility trades above near-dated (contango). When fear spikes, the curve inverts—near-dated rises above longer-dated (backwardation)—a classic signature of market stress.

Volatility ETPs

Exchange-traded products that package long or short exposure to volatility into a single listed instrument, giving transparent, liquid access without trading options or futures directly.

Hysteresis in Signal Design

Using different conditions to enter and to exit a position, so a signal that merely wobbles around a boundary does not trigger constant back-and-forth trading. It adds stability at the cost of slightly slower reactions.

Risk Considerations

Important Risk Factors

Volatility Product Risks

Volatility ETPs are complex instruments that may not track their intended benchmarks precisely. Short-volatility products in particular can decay over time through roll costs and contango, and can lose value sharply when the term structure inverts into backwardation.

Regime Transition Risk

Short-volatility carry is structurally exposed to volatility regime shifts and fat-tailed (high-kurtosis) markets. A regime can turn faster than the model can re-position, producing losses before it rotates to convexity or cash. The regime discipline and long-volatility convexity overlay mitigate this hazard but cannot eliminate it.

Model Limitations

No quantitative model can forecast markets with certainty. Historical relationships may not persist, and the strategy can underperform in volatility regimes unlike those in its sample.

Leverage Considerations

Some volatility ETPs employ leverage, which amplifies both gains and losses. Partners should understand the mechanics of leveraged products before implementation.

Data Dependency

Signals derived from short-dated SPXW option pricing depend on the availability and quality of intraday market data. Gaps, delays, or errors in that data can degrade signal accuracy.

Holidays & Shortened Sessions

Short-dated volatility measures can behave erratically around holidays and half-day sessions, when option liquidity thins and pricing becomes less reliable.

Volatility trading is not suitable for all investors. Partners should conduct thorough due diligence and consider their risk tolerance before implementation.

Development Status

Model Design

Core signal generation logic and regime detection framework completed

Historical Analysis

Extensive historical simulation and stress testing completed

Live Trading

Trading live with real capital since January 2026, building a verified track record

Partner Integration

API documentation and partner onboarding pending completion

Product Launch

ETI certificate issuance with exchange listing planned

Public Availability

General availability through licensed partner network

Interested in Elastic Sigma?

Partners interested in early access to the Elastic Sigma model documentation and development updates are invited to register their interest.

Register Interest

Early partners will receive priority access to model documentation, signal validation data, and integration support upon launch.