Apps: {Portfolio Construction, Options Pricing, MacroNow Nowcasting, L/S Ranking} Better Tools → Better Insights
Joined October 2023
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Beyond the Models Everyone Else Still Uses
The models are not wrong because nobody noticed. They are wrong because replacing foundations is harder than adding parameters.
❌ Mean-variance assumes quadratic utility.
❌ Pearson correlation reduces all dependence to a single linear number.
❌ Parametric distributions impose structure markets do not naturally satisfy.
At OVVO Labs, we asked a different question:
What if quantitative finance were rebuilt without forcing markets into assumptions they do not naturally satisfy?
The result is a unified framework built on partial moments and nonlinear nonparametric statistics. Mean, variance, skewness, kurtosis, covariance, and even the empirical CDF are all special cases of one broader mathematical structure. Not a collection of tools. A coherent alternative to the parametric foundations the entire industry inherited by default.
That single foundation powers three production-grade applications.
✳️ Options Pricing
Call = upper partial moment.
Put = lower partial moment.
No lognormality assumption. No dependence on a calibrated volatility surface. Honest confidence intervals from the actual empirical distribution tested live against real market prices, including on the days when assumptions matter most.
✳️ Portfolio Optimizer
Embeds your utility preferences directly into the covariance structure using four directional quadrants of dependence. Captures crash dependence and asymmetric tail behavior that Pearson correlation was never designed to see. Two assets can show zero correlation and still crash together every time. Standard optimization misses it. This one does not.
✳️ MacroNow
Nonparametric vector autoregression across 30+ Federal Reserve variables. No arbitrary lag selection. No stationarity assumptions imposed on data that does not satisfy them. Built for live nowcasting, not retrospective fit.
Harry Markowitz reviewed this work over multiple years of correspondence and wrote:
"I agree that your approach is more general than old-fashioned mean variance... I wish you the best in getting your ideas out."
That is not a marketing slogan.
That is the founder of modern portfolio theory acknowledging that mean-variance (the framework every institution on Earth still uses) is a special case of something more general.
The full suite is priced at a fraction of a single Bloomberg terminal. A fundamentally different way to approach pricing, portfolio construction, and macro forecasting, built on decades of original research, unified under one mathematical framework, and available today.
Try the live tools here → ovvolabs.com
Markets don’t care about your assumptions. Neither do we.
* Quantitative finance cannot be done successfully without partial moments.
* Utility theory matters, a lot.
* The statistical notion of populations does not exist in finance (there is no underlying DGP).
* Risk, for all intents and purposes, is certainty in an uncertain world.
See why we believe these points, and more: ovvolabs.com/media
My father had seat on NYSE during this time, and had shirts made to remember the event.
He used to tell stories about how the Friday prior was really bad and how he was flat during the crash.
#BlackMonday
OVVO Labs retweeted
Last few weeks the number of bearish suggested positions were ~5:1 for Dec expiration with indices ($SPY $QQQ ) near ATH.
Beyond grabbing deltas, the options dashboard works as a real-time filter on an existing L/S book.
We posted the full tool here:
xitter.cf/OVVOLabs/status/210106…
On September 17th, the dashboard ranked $ANET at the extreme of the bearish side (score –100). The subsequent move was sharp. Same process flagged $COHR on the put side when the structure was still cheap relative to the ranking and IV regime.
The ranking supplies the directional bias. The put/call IV ratio supplies the tail-geometry condition. The dashboard turns both into defined-risk debit spreads with strikes, expiration, and sizing already attached so it can sit alongside a cash equity book without requiring a separate options process.
If you want to see how it maps onto your universe, send a DM via LinkedIn (no anons).
ANET: xitter.cf/OVVOLabs/status/210060…
COHR: xitter.cf/OVVOLabs/status/210060…
🚨 New Options Feature Alert 🚨
We've had success trading options positions that combine insights from both of our tools.
We previously shared how our options tool separates put and call IVs, and how the resulting ratio identifies conditional tail geometries.
👉 lnkd.in/p/gaKG--ik
We also demonstrated our expected partial-moments ranking tool and its effectiveness.
👉linkedin.com/feed/update/urn…
Using both, we formalized the strategy and ran it out of sample for the first time this quarter.
Results, June 1 to September 18, 2026, across 60 tickers and 1,109 debit spreads (bear put + bull call):
* 71.1% win rate
* 41.7% return on capital just this quarter, not annualized!
Bear put spreads on bearish-ranked names outperformed bull call spreads on bullish-ranked names by a wide margin: 48.5% ROC and 72.6% win rate vs. 28.7% ROC and 68.4% win rate. Notable given the market has spent most of the window near highs.
The IV ratio flags when tail risk is asymmetric, the ranking tool flags which names. The strategy is what happens when you combine them.
If interested in learning more, find us and send a DM through LinkedIn (no anons).
• Past performance is not indicative of future results
• Not investment advice
• Not an offer to sell or a solicitation to buy securities
OVVO Labs retweeted
Did your portable alpha (market timing) have your $SPY beta reduced from last week, or are you still waiting for the CTAs to catch the reversal?
So there’s been a lot of chatter about portable alpha.
The standard framing is simple:
Keep your beta allocation.
Then overlay a separate alpha engine.
In practice, that alpha engine is often a CTA, managed futures, trend-following strategy, or some other systematic directional program.
And what is that, really?
Market timing.
Systematic market timing, yes.
Rules-based, diversified, liquid, and mechanically executed.
But still market timing.
CTAs enter, exit, and size positions based on price action across equity indices, rates, currencies, and commodities. The industry has been comfortable calling that portable alpha for decades because the process is systematic rather than discretionary.
So the real issue is not market timing.
The real issue is whether the timing rule is disciplined, liquid, scalable, and checkable.
That raises a more interesting question:
If allocators are comfortable using systematic timing as an alpha overlay, why should they be uncomfortable using systematic timing to manage beta directly?
Most portfolios treat beta as static.
You own the S&P 500, so your beta is approximately 1.
You lever it, your beta is greater than 1.
You hedge it, your beta is lower.
You short it, your beta is negative.
But why should beta be fixed?
If market structure changes continuously, then exposure to that market should be allowed to change as well.
That is the role of the OVVO scalar.
The scalar is a rules-based exposure value derived from observed market structure. It determines whether the portfolio should be long, short, neutral, defensive, or aggressive — all through a single continuously updated beta decision.
This is different from a traditional CTA overlay.
A CTA is usually added beside the core beta book for diversification.
Direct beta management changes the exposure of the beta instrument itself.
That distinction matters.
But the intellectual standard should be the same:
Do not ask whether it is timing.
It is.
Ask whether the timing process is systematic, transparent enough to evaluate, liquid enough to implement, and checkable against actual execution.
Portable alpha asks:
“What alpha stream can I add to my beta book?”
Direct beta management asks:
“What should my beta be right now?”
Static beta is an assumption.
Dynamic beta is a decision.
The OVVO scalar is that decision rule.
More on the mechanism ovvolabs.com/media
🚨PSA🚨
Need macro forecasts, options pricing, portfolio construction, security rankings, beta exposures ($SPY $SPX), and trade setup alerts in one place?
OVVO Labs delivers all of it through the NNS framework, built on partial-moment statistics and nonlinear dependence rather than parametric assumptions, and backed by 30 years of trading experience.
Starting at the beginning of the year, new subscribers will subscribe to the entire suite. The a la carte app option remains open until then.
All current subscribers are grandfathered into their existing setups.
Our story and what differentiates us from the rest of the fintech landscape: ovvolabs.com/media
Last few weeks the number of bearish suggested positions were ~5:1 for Dec expiration with indices ($SPY $QQQ ) near ATH.
Beyond grabbing deltas, the options dashboard works as a real-time filter on an existing L/S book.
We posted the full tool here:
xitter.cf/OVVOLabs/status/210106…
On September 17th, the dashboard ranked $ANET at the extreme of the bearish side (score –100). The subsequent move was sharp. Same process flagged $COHR on the put side when the structure was still cheap relative to the ranking and IV regime.
The ranking supplies the directional bias. The put/call IV ratio supplies the tail-geometry condition. The dashboard turns both into defined-risk debit spreads with strikes, expiration, and sizing already attached so it can sit alongside a cash equity book without requiring a separate options process.
If you want to see how it maps onto your universe, send a DM via LinkedIn (no anons).
ANET: xitter.cf/OVVOLabs/status/210060…
COHR: xitter.cf/OVVOLabs/status/210060…
🚨 New Options Feature Alert 🚨
We've had success trading options positions that combine insights from both of our tools.
We previously shared how our options tool separates put and call IVs, and how the resulting ratio identifies conditional tail geometries.
👉 lnkd.in/p/gaKG--ik
We also demonstrated our expected partial-moments ranking tool and its effectiveness.
👉linkedin.com/feed/update/urn…
Using both, we formalized the strategy and ran it out of sample for the first time this quarter.
Results, June 1 to September 18, 2026, across 60 tickers and 1,109 debit spreads (bear put + bull call):
* 71.1% win rate
* 41.7% return on capital just this quarter, not annualized!
Bear put spreads on bearish-ranked names outperformed bull call spreads on bullish-ranked names by a wide margin: 48.5% ROC and 72.6% win rate vs. 28.7% ROC and 68.4% win rate. Notable given the market has spent most of the window near highs.
The IV ratio flags when tail risk is asymmetric, the ranking tool flags which names. The strategy is what happens when you combine them.
If interested in learning more, find us and send a DM through LinkedIn (no anons).
• Past performance is not indicative of future results
• Not investment advice
• Not an offer to sell or a solicitation to buy securities
Did your portable alpha (market timing) have your $SPY beta reduced from last week, or are you still waiting for the CTAs to catch the reversal?
So there’s been a lot of chatter about portable alpha.
The standard framing is simple:
Keep your beta allocation.
Then overlay a separate alpha engine.
In practice, that alpha engine is often a CTA, managed futures, trend-following strategy, or some other systematic directional program.
And what is that, really?
Market timing.
Systematic market timing, yes.
Rules-based, diversified, liquid, and mechanically executed.
But still market timing.
CTAs enter, exit, and size positions based on price action across equity indices, rates, currencies, and commodities. The industry has been comfortable calling that portable alpha for decades because the process is systematic rather than discretionary.
So the real issue is not market timing.
The real issue is whether the timing rule is disciplined, liquid, scalable, and checkable.
That raises a more interesting question:
If allocators are comfortable using systematic timing as an alpha overlay, why should they be uncomfortable using systematic timing to manage beta directly?
Most portfolios treat beta as static.
You own the S&P 500, so your beta is approximately 1.
You lever it, your beta is greater than 1.
You hedge it, your beta is lower.
You short it, your beta is negative.
But why should beta be fixed?
If market structure changes continuously, then exposure to that market should be allowed to change as well.
That is the role of the OVVO scalar.
The scalar is a rules-based exposure value derived from observed market structure. It determines whether the portfolio should be long, short, neutral, defensive, or aggressive — all through a single continuously updated beta decision.
This is different from a traditional CTA overlay.
A CTA is usually added beside the core beta book for diversification.
Direct beta management changes the exposure of the beta instrument itself.
That distinction matters.
But the intellectual standard should be the same:
Do not ask whether it is timing.
It is.
Ask whether the timing process is systematic, transparent enough to evaluate, liquid enough to implement, and checkable against actual execution.
Portable alpha asks:
“What alpha stream can I add to my beta book?”
Direct beta management asks:
“What should my beta be right now?”
Static beta is an assumption.
Dynamic beta is a decision.
The OVVO scalar is that decision rule.
More on the mechanism ovvolabs.com/media
OVVO Labs retweeted
Thank you for the cheap $COHR puts today!
🤯
Knew each of the component tools were performant (Ranking and the IV-ratio), but did not know the synthesis would work this well!
65 settled days. 40.6% mean ROC. 98.5% of those days green. Worst day −17.6%. Best day 82%.
ovvolabs.com
Not advice. Past quarter is not the next one.
Tickers used this quarter:
SMCI COHR VRT CVNA TTD TPL MU AMAT EXE CIEN HOOD PLTR VST LRCX ADBE KLAC WDC TSLA NEM EL TER ARES LITE IRM STX APP MRNA CEG APTV GLW DASH MPWR NOW SW WDAY NRG EME APH FCX KKR MCHP DECK FIX MOS SWKS GEV CCL TECH ON CRWD RDDT DXCM MRVL NVDA FSLR ALB NCLH AXON ANET APA
Random single position from the same daily books:
ovvolabs.com
Not advice. Past performance is not indicative of future results.
🤯
Knew each of the component tools were performant (Ranking and the IV-ratio), but did not know the synthesis would work this well!
65 settled days. 40.6% mean ROC. 98.5% of those days green. Worst day −17.6%. Best day 82%.
ovvolabs.com
Not advice. Past quarter is not the next one.
Tickers used this quarter:
SMCI COHR VRT CVNA TTD TPL MU AMAT EXE CIEN HOOD PLTR VST LRCX ADBE KLAC WDC TSLA NEM EL TER ARES LITE IRM STX APP MRNA CEG APTV GLW DASH MPWR NOW SW WDAY NRG EME APH FCX KKR MCHP DECK FIX MOS SWKS GEV CCL TECH ON CRWD RDDT DXCM MRVL NVDA FSLR ALB NCLH AXON ANET APA