045 - Rob Hanna - Trading the VIX in a Diversified Portfolio
A Deep Dive into Rob's Work
For members of the Algo Collective (https://www.algoadvantage.io/collective) I’ll produce an even more detailed research report (it’s already 14 pages) on Rob’s strategies. Over time I’ll generate more actionable code in there as well, as I know that’s always in hot demand! See you on the inside! Oh, and the end of the interview is also published in the Collective!
1. Overview
Rob Hanna is a short-term systematic trader and researcher, founder of Quantifiable Edges and portfolio manager at Capital Advisors 360. He trades roughly 15 models across:
S&P-500/Nasdaq100 large-cap stocks
A diversified ETF universe
VIX options and VIX-linked ETFs
Tactical bond–equity rotation strategies
He describes himself as “90% systematic”, using research-driven indicators for entries/exits, with discretion mainly in position sizing and risk-off decisions around scheduled events (Fed, elections, etc.).
Core philosophy:
No trade without a quantified edge – he only trades setups whose historical behaviour he has studied in detail.
Research first, system second – he studies market behaviour (e.g., “what happens after a 5-day low in an uptrend?”) and only then wraps rules around robust edges, rather than optimizing indicators ex-post.
Multiple lenses on the market – breadth, seasonality, term-structure, Fed behaviour, volatility, etc., are combined into composite views.
Anxiety reduction via data – the research is explicitly there to “take the edge off anxiety” and help him stick with positions where the measured edge still exists.
2. Key Concepts and Definitions
2.1 Research-first edge and model construction
Definition / practice
Start from questions about market behaviour (e.g., after big sell-offs, before Fed days, under specific breadth conditions).
Run large numbers of historical studies (tools: TradeStation, AmiBroker, RealTest, Excel, Norgate data) to quantify outputs. Use metrics that fit the task and know your metrics in-depth. For example, the Sharpe ratio might be irrelevant to your trend following strategy. Profit factor is a key for many of Rob’s models.
Build models that exploit simple, robust conditional tendencies; avoid heavy parameter optimization and “curve-fit rescue missions”.
He explicitly criticizes the “classic rookie” approach: decide to build an RSI-2 system, optimize entry/exit thresholds, then bolt on filters to remove historical losers. Instead he prefers:
“Learn how the market has performed under situations that are repeatable… Then creating a model to take advantage of whatever edge you find becomes a lot easier.”
This is effectively research-driven feature discovery, followed by simple rule design.
Risk implications
Because the edge is defined first, robustness is baked in rather than retrofitted.
Still uses standard controls: out-of-sample tests, alternative parameter choices (“parameter ranges that all work”), and economic narrative (e.g., why institutional ownership supports mean reversion in large caps).
If there’s one area that the vast majority of traders could improve on, it’s this: research. Research takes time, so developing a process for research is the key. Once this process is in place, systematic trading can start to look and feel easy, but the newbie doesn’t see how long it took for the expert to develop that process. As an exercise, ask ChatGPT to analyse PHD-level research techniques and start applying these to your trading. Thank me later!
2.2 The Aggregator – multi-lens market bias filter
The Concept
The Quantifiable Edges Aggregator is a composite indicator that rolls up Rob’s active studies into a single short-term market bias measure. Each study expresses an expectation for SPX over the next few days; the Aggregator blends these into a net expectation series (plotted as a green line) where:
0 ⇒ bullish short-term expectation
< 0 ⇒ bearish short-term expectation
In practice, he shows the Aggregator every night in his newsletter and uses it to gate his stock and ETF strategies.
Trading logic
When Aggregator is bullish and the market isn’t already overbought
– he is willing to deploy long mean-reversion and other long-bias models; short setups are often disabled.When Aggregator is bearish
– long mean-reversion models stop taking new trades; short models activate.When Aggregator is neutral
– he typically stands aside from new equity entries, letting existing positions run down.
He explicitly frames this as “a rising tide lifts all boats”: if the broad market conditions are supportive, individual stock edges are more reliable; if not, you need to be a “really good stock picker”.
Educational angle
The Aggregator is a powerful example of:
Combining many weak but statistically significant edges into a single meta-signal.
Using market-regime conditioning to decide which strategy cluster (long, short, flat) should currently be active, rather than forcing all models to be agnostic to regime.
2.3 Breadth indicators and custom filters
Hanna leans heavily on breadth & filter logic, both via standard indicators and custom constructions.
2.3.1 Standard breadth thrusts
His upcoming breadth course uses and extends classic breadth tools:
Zweig Breadth Thrust, McClellan Oscillator, advance-decline lines, tick / TRIN, etc.
His Trip 70 breadth thrust looks at surges where a high proportion of stocks close above their 10-day moving average (70%+), a variant of classical thrust definitions.
These thrusts act as context filters: e.g., after powerful breadth thrusts out of oversold conditions, short setups in equities are de-emphasised.
2.3.2 Capitulative Breadth Indicator (CBI)
The Quantifiable Edges Capitulative Breadth Indicator (CBI) is Hanna’s flagship breadth tool:
Constructed from signals generated by his Catapult system, which buys individual S&P-100 stocks that have undergone “extreme (often capitulative) selling.”(Quantifiable Edges)
The CBI is simply the count of Catapult triggers currently active across the large-cap universe; readings thus measure how many big-cap stocks are in capitulation at once.(quantifiableedges.com)
Key levels (rules of thumb):
CBI ≥ 7 – “elevated” breadth capitulation.
CBI ≥ 10 – historically strong intermediate-term bullish edge for SPX, especially when the long-term trend is still up.(quantifiableedges.com)
Educationally, CBI is a clean example of building breadth from system triggers rather than raw up/down counts: you can define breadth over your signal logic, not just advances/declines.
2.3.3 Custom sector and universe filters
Stocks: S&P-100 + remaining S&P-500 names (split into “Swing 100” and “Swing 400” models), with no overlap – a stock can belong to one model only. This keeps position sizing in check, the key risk management technique for mean reversion models.
Sector caps: e.g., Swing 400 limits exposure to any one sector to 30% of portfolio, to avoid “all-in financials” or similar sector pile-ups. This kind of thing is easily implemented in Real Test with Norgate data.
ETF universes: baskets of ~46 liquid ETFs spanning cap-segments, sectors, and countries, deliberately avoiding near-duplicates (SPY vs IVV vs VOO).
2.4 Mean-reversion, momentum & seasonality models
2.4.1 Large-cap stock mean reversion
Universe:
S&P-500 / 100, Nasdaq-100 stocks only – he avoids small caps because the lack of institutional sponsorship makes them more prone to “falling knife” behaviour and structural problems (bankruptcy, dilutions, etc.).
Core ideas:
Simple price-based mean reversion: multi-day lows, short-term RSI, distance from moving average, etc.
Negative skew is acknowledged: “up up up big drop” is the standard equity mean-reversion pattern.
Risk management:
Position sizing – limits per-name and per-sector exposure; total daily new exposure is throttled so he doesn’t “load the boat” on one ugly day (accomplished through the deployment of multiple models to ‘spread the trading out’).
Scaling-in models – e.g., Catapult scales in as selling intensifies, smoothing entry prices and avoiding oversized bets at first touch.
Aggregator gating – he only deploys long MR when short-term market studies are bullish, and vice versa for shorts.
He also runs short stock MR (“Icarus”) that fades high-flyers extremely stretched to the upside, which tends to perform when long MR is struggling. Together with Catapult, this creates a pair of complementary MR engines.
2.4.2 ETF mean reversion
Logic is very similar:
A diversified ETF list (indices, sectors, countries). Care needs to be taken in constructing an ETF universe – pay attention to duplicates, costs, liquidity).
Model may hold up to four ETFs at once, typically 25% each, long or short based on the signal.
Uses similar overbought/oversold indicators, but parameter ranges don’t differ dramatically from stock MR – the main difference is volatility scale.
The ETF MR model is opportunistic, not always in: it is flat when conditions are neither stretched nor attractive.
2.4.3 Momentum and seasonality / tactical allocation
Hanna’s Momentum & Seasonality model:
Rotates among a small set: S&P (SPY), Nasdaq (QQQ), and bond ETFs, effectively a narrow tactical asset allocation system.
Drivers include:
Price momentum / trend filters
Seasonality (calendar patterns, holiday effects, etc.)
Simple cyclical indicators (from his Market Dynamics course)(quantifiableedges.com)
The model is deliberately simple – a small number of ETFs and signals – prioritising interpretability and robustness over fancy optimization.
2.5 VIX term structure & “volatility ladders”
Of particular interest to me were Rob’s VIX trading strategies due to their usefulness as a hedge in times of crises, and because they employ more than just price data (they look to the VIX futures curve - whether in backwardation or contango as a critical filter to his models). Trading volatility (through the futures, options or ETFs) can be extremely risky but given the strong edges that are present in trading a consistent down-trending market, it’s always of interest to me how traders find a way to profit while minimizing the risks inherent in these models.
2.5.1 Contango and Backwardation – the basics
Think of the VIX futures curve as prices for volatility at different expiries:
Typical (contango):
Dec 17 ──► Jan 19 ──► Feb 21 (upward-sloping curve)
Future months trade higher than near months.
VIX ETPs (e.g., VXX, UVIX) that roll daily from front month into second month bleed value because they are constantly selling cheaper and buying more expensive futures.
Crisis (backwardation):
Dec 30 ◄── Jan 27 ◄── Feb 24 (downward-sloping curve)
Near-term fear is extreme; the curve inverts.
Now the VIX ETP roll yields positive carry: as days pass, you roll from high-priced near futures into lower-priced longer ones.
Hanna uses this term-structure behaviour as a primary risk switch for short-volatility trades, and as a source of opportunity for relative-value trades.
2.5.2 Short-volatility and crisis risk
He runs several short-volatility models in VIX ETFs/futures, recognizing a strong long-run edge (vol is usually overpriced; term structure usually in contango). But:
VIX ETPs can absolutely explode in stress (5–10x moves), so position sizing and stepping aside in stressed regimes are non-negotiable.
He stresses two main controls:
Exit or cut risk when the curve flips from contango into backwardation.
Stand down when VIX levels or realized vs implied spreads pass certain thresholds (e.g., VIX > ~25–30, implied >> realized).
He also uses S&P overbought/oversold as an additional lens: his award-winning paper “Chicken & Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX?” shows that using SPX action to time VIX trades can be more effective than the reverse, and forms the basis for sample VIX-timing strategies.(quantifiableedges.com)
Simple rule of thumb from that work:
When SPX is strongly oversold, probability of a rebound is high → favourable time to short VIX.
When SPX is strongly overbought, short-VIX edge diminishes; adding long-vol exposure as protection can make sense(quantifiableedges.com).
2.5.3 Volatility ladders – options-based crisis alpha
His Volatility Ladders strategy trades UVIX options using:
Calendar and diagonal spreads (e.g., Jun vs Jul 140 calls)
Vertical call/put spreads when appropriate
Relative-value between different VIX maturities, especially around big events (tariff scares, election risk, carry unwinds).
He spent substantial effort building a historical database of VIX options and synthetic spreads to backtest these ideas, incorporating realistic assumptions about bid–ask slippage and achievable prices.
Key idea:
Use mispricings in deep-out-of-the-money options and curve dislocations to get paid to hold tail exposure:
Sell rich parts of the curve, buy cheap optionality further out.
As those relative mispricings mean-revert, profits from spreads can be recycled into maintaining a residual long-gamma/long-vega position.
This creates the possibility of crisis alpha: when his equity models are under pressure during volatility spikes, the long-vol legs in Volatility Ladders can pay off heavily.
2.6 Fed-day and macro-event filters
Hanna is well-known for his Fed-day research:
Long-term studies show that Fed days have historically produced returns several times larger than average SPX days.(Quantifiable Edges)
The edge is conditional: it is stronger when there has been fear going into the day (e.g., SPX down into short-term lows) and weak or absent when the market has rallied into the meeting.(quantifiableedges.com)
Crucially, he finds much of the edge occurs before the 2pm announcement (from prior close to early afternoon), not after – consistent with “insiders and positioning” doing the work.
These studies are examples of:
Event-driven filters that can be used to suspend certain models (e.g., short-vol, aggressive shorts) around Fed days.
Short-term tactical trades (overnight or intra-day) for those who want to exploit the quantified edge; much of this is compiled in his Guide to Fed Days.(quantifiableedges.com)
3. Debates, Controversies, and Best-Practice Themes
3.1 Mean-reversion vs trend following
Hanna is candidly biased toward mean reversion, especially in equities, because he can see clear, repeatable edges in extreme moves and capitulation. It also suits his contrarian personality and the return profile he’s looking for in his trading.
For education, this is a good case study in strategy–personality fit and in the dangers of forcing yourself into a style (e.g., chasing big multi-month winners like NVDA/TSLA) that your research doesn’t support.
3.2 Discretion vs pure automation
He is ~90% systematic, but reserves discretion mainly for:
Sizing and stepping aside (e.g., ahead of known binary events like elections; when volatility term-structure is doing something not often seen historically).
Some VIX trades where practical considerations (liquidity, strikes available) require judgment.
I guess the question is how much discretion is acceptable before you contaminate your edge? Hanna’s compromise is that entries/exits are always indicator-defined; discretion changes how hard he leans into them, not whether the signal exists.
3.3 Short-volatility ethics: edge vs blow-up risk
Short VIX ETPs are famously dangerous; “volmageddon” episodes have blown up products and retail accounts. Hanna’s approach is cautious:
He emphasises that the long-term edge is real but only if position sizes are modest and you stop playing when regime changes.
His more advanced implementations use spreads and ladders, explicitly limiting maximum loss and sometimes holding net long vol.
This sits squarely in the ongoing debate: is systematic short volatility a legitimate income strategy or just “picking up nickels in front of a steamroller”? His framework shows how to do it with explicit tail-risk budgeting rather than denial.
3.4 Overfitting and robustness
By starting from historical conditional behaviour (e.g., Fed days, breadth thrusts, capitulations) and then encoding simple rules, Hanna attempts to stay on the safer side of the overfitting line. But key best-practice themes in his work:
Use multiple independent edges (breadth, seasonality, vol term-structure) so performance isn’t reliant on any single anomaly.
Control exposures by model, asset, and sector.
Use breadth of evidence (many studies, long history) rather than a single impressive backtest equity curve.
His Aggregator is also a response to study collision: when many studies conflict, the Aggregator nets their expectations rather than cherry-picking the nicest ones.(quantifiableedges.com)
4. Practical Applications and Examples
Here are some lose examples of how this would actually look in a model.
4.1 Combining breadth filters with equity mean-reversion
Universe: S&P-500 large caps only.
Signal: Buy when a stock closes at a 5-day low, above its 200-day MA, with short-term RSI below X.
Breadth & Aggregator filter:
Only go long when:
Aggregator > 0 (market-wide bullish bias), and
CBI < 7 (no massive capitulation yet) – this version is “normal dip buying”, not crash-chasing.
Risk:
Max 30% per sector, fixed fractional position size per signal.
Limit to N new names per day; cap portfolio at e.g. 10–20 positions.
You can build a companion Crash MR model that only activates when CBI ≥ 10, with bigger, shorter-horizon bets – effectively a crisis-buying specialist.
4.2 ETF mean-reversion basket
Universe: ~40 liquid ETFs (broad indices, sectors, major-country ETFs).
Signal: Take long/short positions in ETFs whose Z-score vs 20-day mean exceeds threshold; overlay mild trend filter to avoid shorting in roaring bull markets or buying in entrenched bear trends.
Exposure control:
Up to four ETFs at a time; 25% capital each; can be all long, all short, or mixed.
Use Aggregator to throttle: scale down gross exposure when Aggregator is near zero.
This is close to Hanna’s described ETF swing model.
4.3 Simple SPX-to-VIX timing rule
Based on Chicken & Egg:
If SPX 3-day RSI < 20 (strongly oversold) and VIX term-structure still in contango, initiate a small short VIX ETF position (or equivalent options spread).
Exit when RSI crosses back above ~50, or when term-structure flips into backwardation.(quantifiableedges.com)
4.4 Fed-day overnight trade
Using his Fed studies:
When SPX closes at a multi-day low before a scheduled Fed meeting, buy SPY at close and hold until next day’s early afternoon (pre-announcement).
Avoid when SPX closes at 10- or 20-day highs into the meeting.(quantifiableedges.com)
This is a neat example of event-driven quant edges and of “edge playing out before the headline”.
5. Notable Quotes / References
On abandoning hunch trading: after tracking a “hunches” category, he found it consistently underperformed his quantified setups, teaching him to drop discretionary guesses.
On research and anxiety: researching “what the market typically does after X” allowed him to stick with trades where he knew he still had edge, and exit when he didn’t.
On Aggregator purpose: blog posts describe it as a way of turning many active studies into “a composite estimate of where the market is likely to go over the next few days,” resolving conflicting signals into a single bias.(quantifiableedges.com)
On CBI: he calls it an indicator born from his Catapult system, with readings ≥10 historically associated with powerful intermediate-term rallies after capitulation periods.(Quantifiable Edges)
On SPX vs VIX timing: the Chicken & Egg paper argues that SPX behaviour is often the better driver for VIX trades than VIX for SPX.(quantifiableedges.com)
6. Suggested Further Reading
Prioritising Quantifiable Edges and closely related sources:
How the Quantifiable Edges Aggregator uses expectations and risk/reward analysis to establish a reliable market bias – core explanation of the Aggregator, with charts.(quantifiableedges.com)
When Studies Collide – discussion of conflicting studies and how the Aggregator resolves them.(quantifiableedges.com)
My Capitulative Breadth Indicator and Introducing the Quantifiable Edges Catapult Exit Designer – background and stats on CBI and Catapult system.(quantifiableedges.com)
“Finding a quantifiable edge in capitulation selling analysis” – Proactive Advisor article summarising CBI and its use in practice.(Proactive Advisor Magazine)
CBI category archive on QuantifiableEdges.com – many real-time case studies of CBI spikes and subsequent market behaviour.(quantifiableedges.com)
Fed Study category and A Long-Term View of Fed Days – detailed work on Fed-day tendencies and conditional filters.(quantifiableedges.com)
Chicken & Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX? – the NAAIM-award-winning paper on SPX-driven VIX timing.(quantifiableedges.com)
Quantifiable Edges blog home and archives – ongoing short studies on breadth, seasonality, volatility, and event edges.(quantifiableedges.com)
Proactive Advisor pieces on Fed days and breadth – accessible write-ups of his research for advisers.(Proactive Advisor Magazine)
Get in Touch with Rob
For members of the Algo Collective (https://www.algoadvantage.io/collective) I’ll produce an even more detailed research report (it’s already 14 pages) on Rob’s strategies. See you on the inside!
Stay resilient,
Simon
Another excellent interview with a fascinating guest. Keep up the great work Simon!