Options are one of the most powerful tools in the market — but also one of the fastest ways to blow up an account.
Some traders buy options, hoping for explosive returns, but end up overpaying for premium. Others sell options, chasing steady income, but underestimate the risk when volatility spikes.The truth is, most options traders don’t understand the flows that drive options pricing. That’s where MenthorQ changes the game.
Check out our full video tutorial.
The Challenges of trading Options
Here’s the brutal truth about options trading:
Premium buyers get seduced by big upside, but most of the time, they overpay for volatility and watch their options decay to zero.
Premium sellers love the steady drip of income, but they blow up when volatility spikes and the market rips through their strikes.
In both cases, traders fail because they ignore flows, positioning, and volatility.
These are some of the mistakes traders typically make when trading options:
For Buyers of Premium (long calls/puts):
Overpaying when implied volatility is already high.
Buying breakouts that stall at gamma walls.
Losing money even when ‘directionally right’ because theta and IV crush premium.
For Sellers of Premium (short calls/puts/spreads):
Selling options in calm markets, then getting steamrolled when volatility spikes.
Mispricing risk because they don’t see dealer positioning.
Confusing premium collection with “free money.”
Most options traders look at charts and option chains in isolation. But institutions look at:
Gamma exposure — where dealers hedge and how that moves the underlying.
Volatility surfaces — whether IV is cheap or expensive relative to realized.
Risk regimes — whether today is calm or fragile.
How to build an Edge with MenthorQ
Now let’s look at how to build an edge with MenthorQ and how Options Traders can use our platform.
Q-Score
Check the Q-Score (Momentum, Seasonality, Volatility, Options) on the Dashboard to assess overall strength and sentiment. A high Momentum and Option Score with moderate Volatility can indicate strong positioning.
How to trade Options with MenthorQ 6
Net Gamma Exposure
Analyze Gamma Exposure (GEX) to identify key strike prices where market makers have significant hedging exposure, which often act as support/resistance zones. Here we want to see if we are in positive or negative gamma, the IV vs HV, IV Rank and the key levels.
We want to monitor All Expiration and also Multi Expiry Net GEX. The MenthorQ Option Matrix aggregates key options data for a ticker, including gamma exposure (GEX), delta exposure (DEX), open interest, and expected moves, broken down by individual expiration dates.
How to trade Options with MenthorQ 7
Swing Trading Model
I then want to look at our Swing Trading Models to understand the bias, historical backtest and the areas to watch for.
How to trade Options with MenthorQ 8
Volatility Models
The Volatility Risk Premium (VRP) shows whether options on a single asset are overpriced or underpriced by comparing implied volatility (what traders expect) with realized volatility (what actually happened).
How to trade Options with MenthorQ 9
For example, on August 7, Tesla’s implied volatility was just 45% while realized volatility was higher at 49% — a low VRP signal that options were undervalued. Traders buying premium then would have benefited as, by September 17, implied volatility surged to 61%, pushing VRP into overvalued territory.
This rise in IV made options significantly more expensive, rewarding those who acted early when the premium was cheap.
August 7, 2025:
IV: 45%
VRP: Very Low → Options were undervalued.
Interpretation: Market was underpricing risk → Buy premium.
September 17, 2025:
IV: 61%
VRP: +10.8% (Overvalued, 80th percentile) → Options had become expensive.
Outcome: The rise in IV inflated option premiums. Traders who bought premium when it was cheap had a significant edge.
For most traders, stocks are the starting point. They’re accessible, familiar, and exciting. But for many, stocks are also where progress stalls. Why? Because trading stocks without context often leads to the same painful mistakes:
Chasing price moves — buying breakouts that fade, or selling into false breakdowns.
Following headlines and hype — mistaking news or social media buzz for actual tradeable signals.
Ignoring positioning and flows — forgetting that stocks don’t move on fundamentals alone, but also on hedging and institutional flows.
Bad timing — buying the highs, selling the lows, and missing the invisible dynamics that drive price.
The truth is simple: most retail traders look only at charts. Institutions, meanwhile, look at flows, positioning, and risk regimes. That’s the gap MenthorQ fills.
To succeed in stock trading, you need more than technical indicators:
Clarity on key levels – real support and resistance shaped by positioning, not just chart lines.
Awareness of risk regimes – knowing when the market environment is calm vs. when it’s fragile and volatile.
Insight into flows – understanding whether dealers and funds are absorbing risk or amplifying it.
Without this context, you’re trading price — not the forces behind price.
How MenthorQ Gives Stock Traders an Edge
MenthorQ brings the institutional playbook into retail hands:
Options Positioning & Gamma Levels – uncover where hedging flows may pin or break stocks.
Q-Score – quickly assess whether the environment is stable or dangerous.
Swing Trading Levels – high-probability support and resistance zones based on flows, not just charts.
Volatility Models (Smile, VRP, Skew) – decode whether volatility is cheap, expensive, or likely to expand.
Check out our Video Tutorial:
Stock Trading
Swing Trading / Position Trading
Case Study 1: The Overhyped Breakout
Apple reports strong earnings. Headlines scream “bullish.” Social media is buzzing. Price action looks like a breakout. Every chart-based indicator says momentum is here.
But MenthorQ shows the hidden reality:
A call wall just overhead — dealer positioning acting as resistance.
Implied volatility overpriced — making calls expensive to chase.
Q-Score shifting bearish — fragility is building under the surface.
Instead of buying the hype, you size down, structure a defined-risk spread, or wait for a clean break. The result: you’re not chasing blindly — you’re trading with flow context.
Case Study 2: The Quiet Before the Storm
Microsoft’s chart looks boring. Tight range. Low realized volatility. Most traders ignore it.
But MenthorQ reveals what the chart can’t:
Implied volatility creeping higher, even while realized vol looks calm.
Dealers shifting into negative gamma, a setup for amplified moves.
Q-Score flashing caution, warning of fragility.
Retail traders get lulled into complacency and blindsided by sudden moves. MenthorQ traders see the buildup and prepare — hedging, positioning early, or simply staying alert.
Most retail stock traders rely on a top-down approach: market news, earnings buzz, or broad technicals. MenthorQ flips this into a bottom-up approach, grounded in flows, positioning, and volatility. You see the invisible forces shaping each stock — and trade with the same context institutions use.
How to find the Edge Trading Stocks
Now let’s look at how to build an edge with MenthorQ and how Stock Traders can use our platform.
We are going to start by using a bottom up approach vs a top down approach. Let’s begin with your watchlist of companies. For each stock you can leverage these models.
Q-Score
The Q-Score isn’t just a market-wide gauge — it can also be applied at the single-stock level to spot where real alpha hides. A bullish Q-Score signals supportive positioning, where dealer hedging flows and volatility conditions create a tailwind for upside moves. A bearish Q-Score, on the other hand, highlights fragility — negative gamma, expensive options, or skew that signals risk of sharp downside.
By scanning for stocks with extreme Q-Scores, traders can filter the noise, zero in on names with asymmetric setups, and position ahead of the crowd. Instead of chasing headlines, you’re targeting stocks where flows and volatility regimes align with directional opportunity. Learn more about Q-Score here.
Now let’s look at some examples:
TSLA: From Fragility to Momentum. Tesla’s Option Score dipped to 0, reflecting fragile positioning and bearish flow pressure. Soon after, the score flipped sharply higher, climbing to 4–5 and holding steady. That transition marked the start of a strong uptrend, with price breaking out and sustaining momentum.
How to Trade Stocks with MenthorQ 21
LYFT: Quiet Build, Explosive Move. Lyft’s score started at 0, but within days surged to 4–5 and stayed elevated. While the chart looked quiet at first, the bullish score was a signal that dealer positioning was shifting in favor of upside. The result: a powerful breakout rally that caught most traders off guard.
How to Trade Stocks with MenthorQ 22
BABA: The Turnaround Signal. Alibaba showed the same pattern — a bearish 0 score during a period of weakness, followed by a sustained move to 5. That sharp improvement in flow conditions foreshadowed a major trend reversal, with price ripping higher in the weeks that followed.
How to Trade Stocks with MenthorQ 23
Net Gamma Exposure (GEX)
We can use Gamma Exposure (GEX) to identify key strike prices where market makers have significant hedging exposure, which often act as support/resistance zones. Here we want to see if we are in positive or negative gamma, the IV vs HV, IV Rank and the key levels. We want to monitor All Expiration and also Multi Expiry Net GEX.
How to Trade Stocks with MenthorQ 24
Swing Trading Models
The Swing Trading Models provide a structured framework for positioning beyond the intraday noise. They define the directional bias, highlight historical backtest performance, and map out critical zones to watch for support, resistance, or risk triggers. Instead of guessing whether a rally has legs or a pullback is just noise, these models ground your decisions in data-driven levels tested over time. By combining bias with statistically validated levels, traders can anticipate where momentum is likely to continue, where reversals may occur, and how to size risk appropriately.
Analyzing multiple volatility models within MenthorQ is valuable because each model offers a unique lens on market sentiment, risk, and opportunity, enabling a richer, more nuanced understanding.
SKEW
Skew measures the difference in implied volatility across strike prices, revealing market biases toward puts or calls. For example, elevated put skew can indicate demand for downside protection, reflecting bearish sentiment or hedging activity. Learn more about the MenthorQ Skew here.
How to Trade Stocks with MenthorQ 26
Volatility Risk Premium (VRP)
The Volatility Risk Premium (VRP) model measures the difference between implied volatility (market’s expected future volatility) and historical volatility (actual past moves).
VRP bars above zero indicate implied volatility is rich (overvalued), while bars below zero show it is cheap (undervalued). Percentile ranks contextualize current VRP relative to recent history.
Traders use VRP to assess whether volatility is priced attractively for premium selling (when VRP is high) or premium buying (when VRP is low).
Term Structure shows how implied volatility varies across option expirations, highlighting expectations for volatility over different time horizons. This helps identify if near-term volatility is expected to spike or calm relative to longer-term expectations.
The chart shows the ATM term structure of implied volatility for GLD, comparing today’s curve (green) with prior snapshots. Implied volatility is not only higher across the curve compared to one month ago (yellow), but especially elevated in the front end, where short-dated options are pricing above 20%.
This steep front-end premium signals that the market is expecting a potential move in the near term, even as longer-dated expiries remain more anchored. In other words, traders are paying up for short-term protection and directional bets, reflecting heightened uncertainty or event risk in the immediate horizon.
How to Trade Stocks with MenthorQ 28
Gamma Levels and Blind Spots
Next, we can move to our Gamma Levels charts to see where positioning may create intraday support, resistance, or breakout zones on individual stocks. For broader market context, we also leverage our Blind Spots Levels on the MAG7 names, which highlight areas where dealer positioning is thin and the market is more vulnerable to outsized moves. By combining stock-specific Gamma Levels with Blind Spots across the biggest tech leaders, traders can anticipate both localized setups and systemic risks that often drive the broader indices.
How to Trade Stocks with MenthorQ 29
Options Screeners
Now that we’ve worked through a bottom-up view, we can shift to a top-down analysis using our Screeners. The screeners give us a market-wide perspective, highlighting where positioning and volatility are shifting most aggressively.
We start with Gamma changes — spotting where a surge or drop in gamma exposure could alter dealer hedging flows. Next, we look at Gamma Levels, using both end-of-day and intraday TrendSpider screeners to identify key strikes driving price behavior. We then scan Volatility and Open Interest, uncovering where option activity is clustering and signaling potential catalysts.
Finally, we apply the Q-Score, filtering for names where positioning creates either supportive or fragile conditions. This layered process helps traders quickly surface high-probability setups across the entire market, before drilling down into individual opportunities.
The new MenthorQ TradingView Scanner is a powerful indicator designed to help traders set real-time alerts on MenthorQ Gamma Levels across multiple assets. Check out the full Tutorial Video.
Below are the key features and details you need to know:
Scanner Capacity
The Scanner can handle up to 40 tickers.
Preloaded Tickers
20 tickers are automatically refreshed daily.
Our team updates the indicator every evening with end-of-day Gamma Levels for a predefined list of assets.
These are the 20 tickers: ES1!, NQ1!, RTY1!, SPX, NDX, VIX, SPY, QQQ, DIA, IWM, CL(most active contract), GC (most active contract), BTCUSD, NVDA, TSLA, AAPL, AMZN, GOOG, META, MSFT
Custom Tickers
You can add up to 20 custom tickers of your choice.
Upload either end-of-day or intraday levels for any asset.
Simply copy and paste the TradingView text provided in the Dashboard or Bot.
Additional MenthorQ Data
The Scanner also displays:
Gamma Condition
Implied Volatility
Q-Score
Alert Customization
Choose which levels you want to be alerted on.
Alerts can be set for Breakouts and Breakdowns:
Breakout: Price crosses the level from below to above.
Breakdown: Price crosses the level from above to below.
TradingView Notifications
Receive alerts via popup, sound, or email using TradingView’s native alerting system.
Update Levels
Here are some steps to follow to always alerted on the most updated levels:
EOD Levels will be uploaded each day by MenthorQ for the 20 Default tickers
Custom Levels would need to be updated by the user each day
After selecting the Scanner Settings within the indicator users would need to create an alert. The alert will refer to the selected settings at the time
If settings are changed during the day users will need to create a new alert to reflect the settings
To get the updated levels users will need to remove and re-add the scanner each morning
New Alerts needs to be set up each day to reflect the latest levels
Indicator Settings
Inputs
Levels Input 1–3: These boxes allow you to upload your own levels — either intraday or end-of-day (EOD). Simply paste the TradingView text from the Dashboard or Bot. You can upload up to 20 custom tickers.
Default Tickers: When selected, only the 20 predefined tickers (updated daily with EOD levels) will be displayed.
Custom Tickers: When selected, only the tickers you upload manually will be shown.
Default + Custom: If both are selected, the scanner will display both the default 20 tickers and your custom tickers together.
TradingView Scanner 36
Scanner Visual
Toggle which data points are shown in the scanner:
Show Price – Display the current asset price.
Show IV – Show implied volatility.
Show Gamma Condition – Show current gamma regime (positive/negative).
Show Q-Scores – Display MenthorQ proprietary scores:
Option Q-Score
Volatility Q-Score
Momentum Q-Score
Seasonality Q-Score
TradingView Scanner 37
Levels Alerts
You can choose which levels trigger alerts:
Call Resistance – Key resistance based on call positioning.
Put Support – Key support based on put positioning.
HVL – High Volatility Level.
1D Min / 1D Max – Daily minimum and maximum levels.
Break Out Alerts – Triggered when price crosses a level from below to above.
Break Down Alerts – Triggered when price crosses a level from above to below.
TradingView Scanner 38
Table Settings
Dark/Light Mode: Switch between Dark or Light display themes.
Table Text Size: Adjust the font size in the table (e.g., Small, Medium, Large).
Panel Position: Choose where the scanner panel is displayed on your chart (e.g., Middle/Right).
TradingView Scanner 39
How to Set Up Alerts on TradingView
Step 1 — Open the Alerts Menu
Click on the “+” alert icon in the top panel (see arrow 1 in screenshot).
This opens the TradingView alert creation window.
Step 2 – Select the Condition
Under Condition select MenthorQ Scanner. This links the alert to your configured scanner settings.
Make sure “Any alert() function call” is selected.
This allows the scanner to trigger alerts based on your chosen levels (Breakouts, Breakdowns, Call/Put levels, etc.).
Choose the interval for alert evaluation: “Same as chart” is usually best (e.g., 5 minutes in your example).
Set Expiration. Define how long the alert should remain active (e.g., until a set date like October 17, 2025).
Go to the Notifications tab and select how you want to be notified: Popup inside TradingView, Sound alert, Email notification or App push notification.
Once everything is set, click Create. The new alert will now appear in your Alerts panel.
Another important model to spot Volatility Premium across assets is our Cross Asset Volatility Tracker.
This scatter plot compares implied volatility (IV) and historical volatility (HV) percentiles for multiple assets over the past three months.
X-axis (3 Month HV Percentile): Shows where realized volatility sits compared to its past three months. Higher values = markets have actually been more volatile recently.
Y-axis (3 Month IV Percentile): Shows where implied volatility sits relative to its past three months. Higher values = options markets are pricing in more expected volatility.
Cross Asset Volatility Tracker 42
How to Read the Quadrants
The chart is divided into two colored zones by the diagonal line:
Red Zone (Above the Diagonal)
IV is running ahead of HV → This signals that options may be expensive and favors a selling options bias.
Example: VIX, GLD, XLC, and TLT are in this zone—where the 3-month percentile of Implied Volatility is above that of Historical Volatility. In other words, as of today, Implied Volatility occupies a higher portion of its range than Historical Volatility.
To illustrate:
Suppose GLD’s 3-month Implied Volatility sits at the 75th percentile of its own range, while its 3-month Historical Volatility is only at the 45th percentile.
For a trader, this can signal that options may be overpriced relative to actual market movement. In this case, selling premium could be attractive, since implied volatility suggests the market will move more than it realistically has been, allowing traders to capture elevated option prices.
Green Zone (Below the Diagonal)
HV is ahead of IV → This suggests that options may be cheap and favors a buying options bias.
Example: IWM, XLP, QQQ, and XBI are in this zone—where the 3-month percentile of Historical Volatility is above that of Implied Volatility. In other words, as of today, Historical Volatility sits in a higher portion of its range than Implied Volatility.
To put this in perspective:
Suppose over the last 3 months, Historical Volatility (HV) for QQQ is at the 70th percentile of its own range, while Implied Volatility (IV) is only at the 40th percentile. This means realized market moves are relatively large, but option prices (which reflect IV) are not keeping up.
For a trader, this can signal that options may be underpriced relative to actual market movement. For instance, buying a straddle or strangle could be attractive, since past realized swings suggest the market has the potential to move more than what is currently being priced in.
Why This Matters
This model allows traders to quickly see where volatility is being overpriced vs underpriced across assets:
Equity Indices (SPX, QQQ, IWM, DIA): Spot divergences in how volatility is being priced across major benchmarks.
Sectors (XLK, XLF, XLE, etc.): Identify rotations — e.g., tech may be pricing in higher fear than staples or vice versa.
Macro Assets (GLD, TLT, USO, SLV): Gauge risk sentiment in gold, bonds, and commodities, which often move differently than equities.
By comparing implied vs realized volatility directly, the Cross Asset Volatility Tracker helps traders decide whether the options market is overpaying for protection (red zone → selling bias) or underpricing actual risk (green zone → buying bias). It’s a simple, visual way to align strategy with volatility mispricing across the market.
Volatility doesn’t exist in isolation. While most traders focus on the S&P 500 or Nasdaq, risk is constantly shifting across equities, bonds, commodities, and volatility products themselves. By looking at volatility risk premiums (VRP) across multiple assets, traders gain a 360 degrees view of how the market is pricing risk — where volatility is cheap, where it’s expensive, and where opportunities lie. This is essential in understanding the VRP Cross Asset Monitor.
This is the idea behind our new VRP Cross Asset Monitor: a dashboard that ranks implied volatility versus realized volatility across major indices (SPX, QQQ, IWM, DIA), sectors (XLF, XLK, XLE, etc.), and key macro assets (GLD, TLT, USO, SLV).
How to read the Cross Asset Monitor
Each bar in the chart shows the rank of today’s VRP relative to its yearly range:
Red zone (>50%) → Implied volatility is expensive vs. history, signaling a selling options bias.
Green zone (≤50%) → Implied volatility is cheap, favoring a buying options bias.
VRP Cross Asset Monitor 44
For example:
SPX (78.4%), QQQ (81.8%), and XLK (81.4%) are all positioned in the high VRP zone, indicating that current VRP levels are elevated relative to their one-year historical range. Such high readings suggest that, based on historical patterns, selling options may offer a more favorable strategy in today’s market environment.
By contrast, XLP (36.4%) and XBI (41.1%) sit in the low VRP zone, indicating that options are priced cheaply relative to history and could present appealing opportunities for initiating long premium strategies.
This framework allows traders to quickly spot where the market is paying too much for risk versus discounting volatility.
In summary, the VRP Cross Asset Monitor represents a pivotal tool for traders seeking to navigate the intricate web of market volatility. Its multifaceted approach allows for a comprehensive understanding of how various assets are priced relative to risk, aiding traders in identifying optimal strategies. As markets continue to evolve, leveraging the insights provided by the monitor will be essential for those looking to gain an edge in their trading endeavors. Continuous monitoring and adjustment of strategies based on the VRP will not only enhance trading outcomes but also foster a more disciplined trading approach.
Why It’s Powerful
The Cross Asset Monitor gives traders a relative playbook across markets:
Equity Index Traders can compare volatility conditions across SPX, QQQ, IWM, and DIA to see which index offers the best setup for option buying or selling.
Sector Traders can identify divergences — for instance, when technology (XLK) shows high VRP while consumer staples (XLP) show low VRP, highlighting rotation and sentiment skews.
Macro Traders can track VRP in bonds (TLT), gold (GLD), and oil (USO) to understand how volatility is being priced in safe havens and commodities.
By putting all assets on the same scale, the monitor transforms volatility into a cross-market signal rather than a siloed metric.
The takeaway is simple:
High VRP (red zone) → Consider premium-selling strategies (credit spreads, iron condors, covered calls) where options are overpriced.
Low VRP (green zone) → Consider premium-buying strategies (long calls/puts, debit spreads, straddles) where options are underpriced.
This doesn’t replace broader macro analysis, but it gives traders a systematic framework for where the volatility edge is most attractive at any given time.
The model is divided into two sections:
Red Labels (High VRP ↑, Selling Option Bias). The red zone highlights assets where today’s Volatility Risk Premium is ranked above 50% of its yearly range. In these cases, the difference between implied volatility and historical volatility is high. The market is paying more than usual for protection or speculation. This environment favors option-selling strategies, since traders can capture elevated premiums by taking the other side of overpriced risk.
Green Labels (Low VRP ↓, Buying Option Bias). The green zone highlights assets where today’s VRP is ranked at or below 50% of its yearly range. Here, the difference between implied volatility and historical volatility is low. VRP is discounted suggesting the market is underpricing risk. This favors option-buying strategies, since traders can enter long premium trades (calls, puts, straddles) at historically lower costs.
For traders, volatility is more than just noise — it’s the heartbeat of pricing, risk, and opportunity. Whether you’re trading futures, options, or equities, understanding whether today’s implied volatility (IV) is fairly priced is critical. Too often, traders ask: Is volatility cheap or expensive right now? Our new Volatility Risk Premium (VRP) model provides a data-driven way to answer this.
Why Volatility Matters
Volatility is the lifeblood of the options market. It doesn’t just influence option pricing — it shapes how traders assess risk, manage exposure, and capture opportunity. The challenge is that volatility is not absolute; a 20% implied volatility reading might be expensive in one regime and cheap in another.
Without context, traders risk overpaying for protection or underselling premium. That’s why understanding whether volatility is rich or cheap relative to its own history is so important.
By putting today’s implied volatility in perspective, we can identify when markets are offering opportunity, when caution is warranted, and how best to position strategies around the volatility risk premium.
Why look at Volatility Risk Premium?
Volatility sits at the core of every market. It doesn’t just dictate the price of options — it defines the rhythm of stocks, futures, and risk-taking across asset classes. The problem is that volatility can’t be read in isolation.
A 30% implied volatility number might look high, but compared to its history it could be cheap. Without the right context, traders risk misjudging whether the market is overpricing or underpricing risk.
The new Volatility Risk Premium Model measures today’s implied volatility versus it’s historical volatility and looks at its historical range to tell you whether the market is currently cheap, expensive, or fairly priced.
This context matters because it directly shapes trading strategy:
Stock Traders can use it to gauge the potential for sharp price swings. If volatility is historically cheap, it may signal complacency — a moment to be cautious about hidden risks. If volatility is expensive, it can hint at heightened uncertainty that might present opportunities to fade extreme sentiment.
Options Traders live and breathe volatility. Cheap volatility can mean attractive entry points for buying calls or puts to capture convex payoffs. Expensive volatility, on the other hand, can create favorable conditions for selling premium — collecting rich option prices in return for taking on risk.
Futures Traders can use volatility signals to size positions and manage risk exposure more intelligently. A cheap volatility environment might encourage running larger positions with tighter stops, while expensive volatility warns that markets may swing wider than usual, requiring risk adjustments.
In short, volatility is the common denominator across all trading styles — but without context, it’s just a number. The Volatility Risk Premium Model gives traders that context, helping them decide not only what to trade but how to trade it, based on whether the market is mispricing risk.
The MenthorQ Volatility Risk Premium Model (VRP)
The chart below is divided into two panels.
The Top Panel shows the asset spot price with daily candlesticks, helping traders visualize how equity prices have moved during the period.
The Bottom Panel focuses on the Volatility Risk Premium (VRP). Each vertical bar represents the daily VRP value — the difference between implied and historical volatility. Bars above zero indicate that implied volatility is trading richer than realized (Overvalued IV), while bars below zero suggest implied volatility is trading cheaper (Undervalued IV).
The red dashed line marks the maximum VRP observed over the past 30 days, acting as a ceiling for when volatility pricing is stretched to the upside.
The green dashed line marks the minimum VRP, signaling when volatility has been historically discounted. Together, these boundaries show the recent range of volatility pricing. At the right-hand side, the model also labels whether implied volatility is currently overvalued or undervalued, along with the percentile rank of today’s reading compared to the past three months.
The Volatility Risk Premium (VRP) is calculated as the difference between implied volatility (IV) — the market’s expectation of future moves — and historical volatility (HV), which measures how much the market has actually moved in the past.
The 3 months percentile tells you where today’s VRP sits relative to its recent history — for example, a 46.77% reading means VRP is higher than roughly 47% of the past three months’ observations, and lower than the rest.
Volatility Risk Premium 46
Interpreting the Volatility Risk Premium isn’t just about knowing if volatility is rich or cheap — it’s about translating that into strategy.
When the VRP is high and IV is overvalued, traders might look to sell volatility through strategies like credit spreads, iron condors, or covered calls, aiming to capture the excess premium.
When the VRP is low or negative and IV is undervalued, it can favor buying volatility, such as long calls, long puts, or debit spreads, where options are priced relatively cheap compared to realized moves.
For futures or stock traders, VRP can act as a risk gauge: high VRP often signals heightened uncertainty, suggesting tighter stops or smaller position sizes, while low VRP can reflect complacency, highlighting moments where adding protective hedges may be wise.
To understand VEX, it is essential to grasp the concept of volatility itself. Volatility represents the degree of variation in the price of a financial instrument over time. It is a critical measure of market uncertainty and is used to gauge the risk associated with an asset or portfolio. Traders and investors rely on volatility to price options, assess risk, and anticipate market movements.
Volatility is categorized into historical volatility (observed past price fluctuations) and implied volatility (market expectations of future fluctuations). Implied volatility, derived from option prices, is particularly significant as it reflects the collective sentiment of market participants about future uncertainty.
The interplay between volatility and options pricing is encapsulated in the Black-Scholes model, which incorporates implied volatility as a key input. This model and its extensions have laid the groundwork for modern options trading and the development of advanced metrics like VEX.
What is VEX?
VEX quantifies the exposure of a portfolio or financial position to changes in volatility. It is derived from the sensitivity of an options portfolio to changes in implied volatility, also known as vega. While vega measures the change in an option’s price for a one-percentage-point change in implied volatility, VEX goes a step further by assessing this sensitivity across an entire portfolio.
The VEX metric enables traders to understand how their portfolio’s value will respond to shifts in market volatility. A positive VEX indicates that a portfolio benefits from rising volatility, while a negative VEX suggests losses in such a scenario. This insight is crucial for risk management, especially during periods of heightened market uncertainty.
The Importance of VEX in Options Trading
Risk Management: Volatility exposure can significantly impact a portfolio’s value, especially during market turbulence. By analyzing VEX, traders can mitigate risk and make informed adjustments to their positions.
Strategic Decision-Making: Traders use VEX to identify opportunities where they can profit from anticipated changes in volatility. For instance, during earnings announcements or geopolitical events, implied volatility tends to spike, creating trading opportunities for those with a positive VEX.
Portfolio Hedging: Hedging against volatility is a common practice among institutional investors. Understanding VEX allows for more precise hedging strategies, reducing exposure to adverse volatility movements.
How VEX is Calculated
Calculating VEX involves aggregating the vegas of all options in a portfolio, weighted by their respective positions. This calculation can be intricate, requiring robust financial modeling tools and accurate market data. For traders managing large portfolios, the use of specialized software or platforms is often necessary to track VEX and related metrics.
VEX in Context: Comparing with Other Volatility Metrics
While VEX focuses on exposure to changes in implied volatility, it is often analyzed alongside other metrics, such as gamma and delta. Gamma measures the rate of change of delta with respect to the underlying asset’s price, while delta represents the sensitivity of an option’s price to changes in the price of the underlying asset. Together, these metrics provide a comprehensive view of an option portfolio’s behavior under various market conditions.
Applications of VEX in Real-World Trading
Event-Driven Strategies: Traders often use VEX to prepare for events that are likely to cause volatility spikes, such as central bank announcements or earnings reports. By aligning their VEX to the expected volatility, they can capitalize on market movements.
Volatility Arbitrage: VEX plays a critical role in volatility arbitrage strategies, where traders seek to exploit discrepancies between implied and realized volatility. By adjusting their VEX, traders can position themselves to benefit from these differences.
Tail Risk Hedging: During extreme market events, such as financial crises, volatility can surge dramatically. Traders with a deep understanding of VEX can design portfolios that protect against these tail risks, preserving capital during turbulent times.
The Evolution of VEX and Volatility Metrics
The development of metrics like VEX has been driven by advancements in financial theory, technology, and market practices. The increasing complexity of financial markets has necessitated more sophisticated tools for analyzing and managing risk. VEX represents the culmination of decades of research and innovation in options trading and risk management.
Challenges and Limitations of Using VEX
Despite its utility, VEX is not without limitations. It assumes a linear relationship between implied volatility and option prices, which may not hold in all market conditions. Additionally, accurately measuring VEX requires high-quality data and robust modeling capabilities, which can be a barrier for smaller traders.
Furthermore, VEX does not account for second-order effects, such as changes in the slope of the implied volatility surface (known as vanna) or the curvature (known as volga). These effects can significantly influence an option’s price, particularly for complex portfolios.
Future Directions for VEX and Volatility Analysis
As financial markets continue to evolve, so too will the tools and metrics used to analyze them. Emerging technologies, such as machine learning and artificial intelligence, have the potential to enhance the accuracy and predictive power of VEX and related metrics. These advancements may also make these tools more accessible to a broader range of market participants.
Conclusion
VEX is an indispensable metric for understanding and managing volatility exposure in options trading. It provides traders with critical insights into how their portfolios will respond to changes in market volatility, enabling them to make informed decisions and manage risk effectively. By integrating VEX with other metrics and leveraging technological advancements, traders and investors can navigate the complexities of modern financial markets with greater confidence and precision.
Managing Risk with the Straddle Strategy allows traders to profit in volatile markets by balancing potential gains and losses.
A straddle involves purchasing both a call and a put option for the same underlying asset, with identical strike prices and expiration dates. This dual position allows investors to profit from substantial price movements in either direction. The essential premise of the straddle is not to predict market direction but to anticipate heightened volatility. By understanding the components and core mechanics of this strategy, traders can appreciate its versatility.
The call option grants the right to purchase the underlying asset at the strike price, while the put option provides the right to sell at the same strike price. If the price of the underlying asset experiences a significant shift, either the call or the put will become profitable enough to offset the loss of the other, potentially leading to substantial net gains.
When to Implement a Straddle Strategy
A straddle is most effective when major events are likely to induce market fluctuations. Examples include corporate earnings reports, economic data releases, geopolitical events, and Federal Reserve interest rate decisions. These scenarios often introduce uncertainty, making directional bets risky but elevating the potential for large price movements.
For instance, an upcoming Federal Reserve announcement can lead to heightened market speculation. In such cases, the straddle strategy positions traders to benefit regardless of whether the announcement is perceived as bullish or bearish.
In terms of market dynamics, straddles also perform well when the market is in a negative gamma regime. In these conditions, market makers—who typically act to stabilize prices—may instead amplify price swings as they adjust their positions in response to large movements.
Understanding Gamma and Volatility in the Context of Straddles
To fully grasp the power of the straddle strategy, it is essential to understand the role of gamma and implied volatility. Gamma measures the rate of change in an option’s delta relative to the underlying asset’s price. When the market is in negative gamma, market makers may exacerbate price swings by adjusting their hedges in the same direction as the price movement.
In this context, straddles become advantageous because they inherently position traders to benefit from increased volatility, regardless of direction. Additionally, implied volatility plays a critical role in pricing options. When implied volatility is low but expected to rise due to an impending market event, purchasing a straddle at a lower premium can yield outsized returns if volatility indeed surges.
Practical Execution: Constructing a Straddle
Constructing a straddle requires precision. The key steps include:
Select the Underlying Asset: Choose an asset that is likely to experience significant price movement.
Identify the Event: Pinpoint the catalyst for volatility, such as earnings announcements or economic reports.
Choose the Strike Price and Expiration: The strike price is typically close to the current market price (at-the-money options). The expiration date should align with the expected timeline of the event.
Purchase the Call and Put Options: Simultaneously buy the call and put options to complete the straddle.
For example, suppose a stock is trading at $100. A trader anticipates significant movement following an earnings report. They buy a call option and a put option, both with a $100 strike price and the same expiration date. If the stock price surges to $120, the call option gains significant value, while the put loses value but is offset by the overall profit. Conversely, if the price drops to $80, the put option becomes highly valuable.
Risk Management and Potential Drawbacks
While the straddle strategy provides a hedge against market directionality, it is not without risks. The primary risk lies in the potential for minimal price movement. If the underlying asset’s price remains near the strike price, both the call and put options may expire worthless, leading to a loss of the premiums paid.
Additionally, straddles can be expensive, particularly when implied volatility is already high. In such cases, the premiums for both options may be prohibitively costly, limiting the potential for profit. Traders must therefore evaluate the cost of the straddle relative to the anticipated price movement.
Another key consideration is time decay, also known as theta. As options approach expiration, their value diminishes if the underlying asset’s price remains stagnant. This time decay can erode the value of the straddle, emphasizing the importance of timing when initiating the strategy.
Advanced Strategies: Adjusting and Monitoring the Straddle
Sophisticated traders may adjust their straddle positions as market conditions evolve. Common adjustments include:
Rolling the Position: Extending the expiration date by closing the current options and opening new positions with a later expiry.
Adding Protective Legs: Converting the straddle into a strangle by adjusting the strike prices to reduce premium costs.
Hedging with Other Assets: Incorporating correlated assets or inverse positions to mitigate potential losses.
By actively managing the straddle, traders can adapt to shifting market dynamics and optimize their outcomes.
Case Study: Earnings Season Straddle
Consider a scenario during an earnings season when a prominent technology company is set to release its quarterly report. Historical data indicates that the company’s stock tends to move significantly post-announcement. A trader anticipates a substantial price swing but remains unsure of the direction.
The trader constructs a straddle by purchasing a call option and a put option, both with a strike price close to the current stock price. Following the earnings report, the stock price surges due to unexpected strong performance. The call option’s value increases dramatically, resulting in a net profit even after accounting for the cost of the put option.
However, had the price remained stable, the trader would have faced a loss equivalent to the premiums paid for both options. This underscores the importance of accurate event analysis and timing.
The Role of Straddles in Portfolio Diversification
Straddles can also serve as a diversification tool within a broader portfolio. By adding positions that benefit from volatility, traders can offset losses in other directional trades. This approach is particularly useful during periods of market uncertainty when traditional asset classes may exhibit correlated movements.
For instance, during times of economic turbulence, equity markets often experience heightened volatility. A straddle on a key market index, such as the S&P 500, can provide a hedge against unexpected market movements, preserving capital and mitigating downside risk.
Conclusion
The straddle strategy offers a powerful means of managing risk and capitalizing on market volatility. By combining call and put options with the same strike price and expiration date, traders can benefit from significant price movements regardless of direction. However, successful implementation requires a thorough understanding of market conditions, implied volatility, and timing.
When used judiciously, straddles can enhance a trader’s ability to navigate uncertain markets, turning volatility from a threat into an opportunity. By continuously monitoring and adjusting positions, traders can maximize their returns while mitigating potential losses. Ultimately, the straddle strategy underscores the importance of adaptability and strategic foresight in options trading.
We have created a dedicated tutorial video on how to use the new screeners below.
We currently provide scores based on 4 Factors: Options, Volatility, Momentum and Seasonality for most of the assets we cover. The Q-Score update daily at the end of the trading day. You can find the Screeners here.
Q-Score Screeners 50
The QScore Screeners are powerful tools designed to help traders quickly identify market sentiment and shifts in asset quality. These screeners enable users to:
Track Positive and Negative QScore Changes: Instantly see which assets are experiencing improvements or deteriorations in their QScore — our proprietary indicator of an asset’s overall strength based on key market metrics.
Spot Extremes in Market Positioning: Identify the assets with the highest QScores, signaling strong institutional interest or bullish setups, and the lowest QScores, which may suggest weakness, risk, or bearish setups.
The Different Q-Score Screeners
The Q-Score screeners are segmented by four key scoring categories: Options, Volatility, Momentum, and Seasonality. Each category offers screeners to identify extreme values (highest/lowest) and 1-day score changes, helping traders pinpoint where the market is shifting.
Options Score
Highest Option Score: Assets with the strongest options-based Q-Score.
Lowest Option Score: Assets with the weakest options-based Q-Score.
In this article you will learn how to set up the MenthorQ Indicators for TrendSpider. You can now access Gamma Levels, Blind Spots Levels and Conversion Levels on the platform. You can now integrate the MenthorQ data into TrendSpider directly via API.
What is TrendSpider?
TrendSpider is a smart, web-based technical analysis platform that uses automation and AI to help traders make more informed, objective decisions. Designed for active traders and investors, TrendSpider streamlines chart analysis, reduces human bias, and helps identify trading setups efficiently.
Special Offer for MenthorQ Users
Want to learn more about TrendSpider and receive a Special Offer?
MenthorQ offers various indicators within the TrendSpider Platform:
Gamma Levels
Blind Spots Levels
Conversions Levels
Levels are updated automatically via API.
MenthorQ Coverage List on Trend Spider: To access our Full Coverage check our our Watchlist on TrendSpider. Access Watchlist.
Check out the Full Video Tutorial.
How to access the Indicators on TrendSpider
To access the Indicators you will need a Premium or Pro Subscription. Once you have an active account follow the steps below:
Login to your MenthorQ Dashboard under the TrendSpider Menu and provide your TrendSpider Email Address. It can take up to 24 hours to get enabled.
You will receive an email or notification from TrendSpider, once enabled from the MenthorQ Team for each of the available indicators.
You need to click on the link you receive in the email or notification to install the indicator in your account.
Once enabled you will find the MenthorQ Indicator under Indicators – Your Custom Indicators.
TrendSpider Integration 61
Connect using the API Key you find within the Dashboard under the TrendSpider Menu.
TrendSpider Integration 62
Now let’s go over the different indicators settings.
Gamma Levels Indicator
Gamma Levels are key price levels where there is significant Positive or Negative Gamma based on market positioning and open interest. These levels act as “sticky” price areas that can influence market movements and liquidity.
They provide insight into how Market Makers may hedge their positions, which impacts market liquidity and volatility. Traders who track these levels can potentially gain a market edge by understanding where price action may slow down or accelerate. Our Gamma Levels are divided into two categories:
Primary Levels: Call Resistance, Put Support, High Vol Level (HVL or Gamma Flip), Call Resistance 0DTE, Put Support 0DTE, HVL 0DTE, 1D Max , 1D Min, Gamma Wall 0DTE
Secondary Levels: GEX 1 to GEX 10
This indicator offers a clean, actionable view of where gamma positioning may influence price movement the following day, giving users a unique institutional perspective not commonly available to retail traders.
TrendSpider Integration 63
Levels Update:
MenthorQ provides End of Day and Intraday Updates with multiple snapshots throughout the trading day. This ensures users have access to regularly refreshed market information. Here is a breakdown on when Gamma Levels updates:
End of Day Levels: Available on Stocks, ETFs, Indices, Crypto, Futures and Forex
Gamma Levels on Indices, Stocks and ETFs: 6pm EST
Gamma Levels on Futures: 11pm EST
Intraday Levels
Gamma Levels on Indices, Stocks and ETFs: Updates 14+ times per day from the session opening
Coverage:
The indicator is available on 1300+ Assets including Stocks, ETFs, Indices, Futures and Crypto.
You can then customize how you want to plot each levels. You can select the color, the opacity, the style, if you want the level to be visible or plotted, and if you want to display the value line and label.
TrendSpider Integration 65
Historical Levels
Within the Gamma Levels and Blind Spots Indicator you can now access historical levels.
TrendSpider Integration 66
You can access the past 30 days of levels and plot them on a chart so you can backtest and see how past data performed.
Blind Spots Levels
Blind Spots Levels is a proprietary indicator designed to uncover hidden areas in the market where price is most likely to react—but which often go unnoticed by traditional technical tools.
Market movements are interconnected. Assets don’t move in isolation, and traders who focus solely on their target asset often miss key signals from other markets. This can lead to late entries, missed opportunities, or taking on unnecessary risk. By identifying blind spots—those hidden market signals that are often overlooked—traders can make more informed decisions and improve their timing, confidence, and risk management.
The indicator highlights zones that may cause sudden reversals, fakeouts, or unexpected acceleration in price. By identifying these “blind spots,” traders can avoid getting caught off guard and instead use these areas for precise entries, exits, or risk management.
Levels Update:
11 pm EST – Monday to Friday
Coverage:
The indicator is available on Futures, Indices and ETFs, and Forex.
The indicator offers the same customization as the Gamma Levels Indicator.
TrendSpider Integration 67
Conversions Levels
Converted Levels is a powerful feature built into all MenthorQ indicators, designed to seamlessly translate spot-level data (from ETFs or indices like SPX, QQQ, NDX) into futures equivalents (like ES, NQ, YM, etc.). Because futures and spot prices rarely match due to spreads, ratios, or fair value differences, this conversion ensures your levels are aligned with real market structure—so you’re never trading blind.
This indicator allows you to convert gamma levels, blind spots levels or swing trading levels automatically. Whether you’re trading SPX to ES, QQQ to NQ, or even NVDA levels on the QQQ chart, this tool ensures your analysis remains accurate, actionable, and aligned with institutional flows.
These are some of the common Levels Conversions:
SPX Gamma Levels to ES
SPY Gamma Levels to ES
QQQ Gamma Levels to NQ
NDX Gamma Levels to NQ
SPX Intraday Gamma Levels to ES
QQQ Intraday Gamma Levels to NQ
SPX Swing Trading Levels to ES
QQQ Swing Trading Levels to NQ
GLD Levels to GC
DIA Levels to YM
USO Levels to CL
NVDA and MAG7 Levels to QQQ
IBIT Levels to BTC or other Crypto assets
How to convert Levels
The idea for Converted Levels came from a simple but powerful insight: if you’re trading futures, you should be able to analyze data from the underlying index or ETF—without having to switch charts. Traders often rely on the SPX or QQQ options chain to identify key reaction zones, but executing on ES or NQ means price levels don’t always match. This disconnect can lead to misaligned entries and missed opportunities.
Converted Levels solves this by letting you overlay important levels from the options chain of correlated assets directly onto your futures chart. Whether you’re using SPX gamma levels while trading ES, or QQQ swing zones on NQ, the indicator ensures your levels are translated with mathematical accuracy—via spread or ratio—so your futures analysis stays precise and aligned with institutional positioning. To learn more about Levels Conversion check out the Guide.
TrendSpider Integration 68
Conversion Settings
You can convert using a Manual or Auto Ratio.
TrendSpider Integration 69
If you choose Auto Ratio, you can then select the Time of Day of the Candle to use for the conversion. For example, if you select 10:00 AM, the price used for the conversion will be based on the closing price of the 10:00 AM candle. This sets the anchor for how your levels are calculated moving forward.
This is especially important for markets like futures, which trade 24 hours a day, 5 days a week. In these markets, choosing a precise time ensures your levels are based on a meaningful session marker—such as the U.S. cash open, European open, or another critical liquidity window.
By aligning to a specific time, you avoid using overnight or low-volume data, and instead build your levels on relevant intraday structure that reflects actual market participation.
TrendSpider Integration 70
Real Time Scanning with MenthorQ Levels in TrendSpider
Markets move fast. By the time you manually check levels or sift through charts, the trade may already be gone. That’s why automation is key.
If you’re serious about catching high-probability setups before they move, you need more than just technical analysis—you need real-time edge. That’s exactly what you get when you combine MenthorQ Levels with TrendSpider’s scanner engine.
With TrendSpider’s scanning tools, you can monitor multiple instruments across MenthorQ’s most actionable levels—from Gamma Levels to Blind Spots to Conversion Levels—and get instant alerts when price interacts with those zones.
The Volatility Surface is a three-dimensional representation of implied volatility (IV) plotted against both strike prices and expiration dates. Unlike the traditional volatility smile, which only shows how IV varies across strikes at a single expiry, the surface gives you the full picture — a landscape of how volatility is priced across the entire options chain.
When visualized, the surface looks like a 3D grid or wave. The X-axis represents the strike price, the Y-axis shows implied volatility, and the Z-axis (depth) reflects the time to expiration. This creates a powerful, multi-layered view of market expectations and risk pricing.
3D Volatility Surface 73
While a traditional 2D volatility smile helps identify sentiment at one point in time, it lacks the ability to show how the market’s pricing of risk evolves across different expiries. That’s where the Volatility Surface stands out.
It allows traders to:
Identify term structure skews (e.g., elevated short-term IV vs. longer-term calm)
Spot event-driven volatility clusters like earnings or macro announcements
Understand how risk is priced over time and across price levels
These insights are critical when building multi-leg or time-based strategies, such as spreads, calendars, and diagonals — or when selling volatility into events.
Using the Volatility Surface in Trading
Let’s look at how this data becomes actionable:
🔹 Options Traders
You notice a steep upward curve on the front-month OTM puts for TSLA, indicating elevated short-term fear. This could be the market pricing in near-term uncertainty — a perfect setup for premium sellers or traders looking to structure short-dated credit spreads at high IV.
🔹 Futures Traders
Analyzing the surface for SPX or QQQ might reveal a flattening term structure, suggesting that implied risk is tapering off. This aligns well with strategies where you reduce size or lean into mean reversion setups — supported by lower expected volatility ahead.
🔹 Swing Traders
You spot IV spikes in 3–4 week expiries, especially on strikes around key support zones. This could hint at institutional hedging, helping you time your swing entries more accurately.
Volatility Surface 2D
This chart offers a flattened, heatmap view of the 3D Volatility Surface, allowing you to quickly scan implied volatility (IV) levels across both strike prices (Y-axis) and expiration dates (X-axis).
How to Read It:
Y-Axis (Vertical): Strike Prices — from low to high
X-Axis (Horizontal): Days to Expiration — from short-term (3 days) to long-term (171 days)
Cell Values (%): Implied Volatility at that specific strike and expiration
The Momentum Strategy is a systematic trading approach designed to select and trade sector ETFs based on momentum shifts. The strategy aims to capture short-term market trends by selecting ETFs with the strongest increasing momentum.
Q-Score Momentum Model
The Q-Score Momentum Model reflects the underlying trend strength of an asset. Our proprietary quant models analyze price action and technical indicators to determine whether an asset exhibits bullish or bearish momentum.
A higher momentum score suggests strong positive price action, while a lower score indicates weakness or potential downside pressure. Traders can use this score to align their positions with prevailing market trends. Our model assigns a score ranging from 0 to 5:
Identify the ETF with the highest difference between today’s momentum score and the momentum score 5 days ago.
Only consider ETFs with a positive difference (i.e., increasing momentum).
If multiple ETFs have the same momentum difference, select the one with the lowest standard deviation of returns over the past 3 months.
Buy at the market open.
Exit Conditions:
Sell at the market open the next day unless the same ETF remains the top pick.
If no ETFs meet the conditions, no position is taken.
Trading Costs:
A commission of $2 per trade is applied (total of $4 per round trip).
Backtesting Period:
January 1, 2014 – January 31, 2025
Initial Capital:
$100,000
Performance Summary
Now let’s look at the historical backtest of this strategy and look at the performance versus the Benchmark (S&P 500 Index).
Momentum Strategy for ETF Trading 82
Now let’s look at some Key Metrics.
Momentum Strategy for ETF Trading 83
We can also look at the return distribution across years comparing this with the SPX.
The Momentum Strategy outperforms the SPX in cumulative return and CAGR, showing strong long-term performance.
It has a higher Sharpe and Sortino ratio, indicating better risk-adjusted returns.
The strategy exhibits stability with lower drawdowns compared to SPX.
Momentum Strategy for ETF Trading 84
And finally let’s look at the distribution of returns by month historically.
Momentum Strategy for ETF Trading 85
Sensitivity to Initial Capital
The strategy was tested with varying capital levels to evaluate its robustness. Larger capital allocations help mitigate the negative effects of commission costs, leading to more stable and consistent performance over time. Performance across different capital allocations is detailed below.
The Seasonality Strategy is a systematic trading approach that selects and trades sector ETFs based on historical seasonality patterns. The strategy aims to capitalize on seasonal trends by investing in the ETF with the highest seasonality score, ensuring the score is non-negative.
Q-Score Seasonality Model
The Q-Score Seasonality Model assesses the historical performance of an asset over a specific time frame. Using 20 years of historical data, our model examines the price behavior of an asset over the next five days and assigns a score ranging from -5 to 5:
-5: Low Seasonality. Indicates a Bearish seasonality
0: No Seasonality. No significant seasonal trend
5: High Seasonality. Indicates a Bullish seasonality
By leveraging the seasonality score, traders can anticipate potential price movements based on past performance and create advanced strategies. Our Seasonality score looks at the trend for the next 5 days. You can read more about our Q-Score in our dedicated Guide.
Seasonality Strategy for ETF Trading 93
Asset Universe
The strategy focuses on the following ETFs:
XLE (Energy)
XLF (Financials)
XLU (Utilities)
XLI (Industrials)
XLK (Technology)
XLV (Healthcare)
XLY (Consumer Discretionary)
XLP (Consumer Staples)
XLB (Materials)
IYR (Real Estate)
IYE (Energy Select)
OIH (Oil Services)
SMH (Semiconductors)
IBB (Biotechnology)
Seasonality Strategy Rules
Entry Conditions:
Identify the ETF with the highest seasonality score (must be ≥0).
If multiple ETFs have the same score, select the one with the highest average return over the past week.
Buy at the market open.
Exit Conditions:
Sell at the market open the next day unless the same ETF remains the top pick (in which case, the position is held).
If no ETFs meet the criteria, no position is taken.
Trading Costs:
A commission of $2 per trade is applied (total of $4 per round trip).
Backtesting Period:
January 1, 2014 – January 31, 2025
Initial Capital:
$100,000
Performance Summary
Now let’s look at the historical backtest of this strategy and look at the performance versus the Benchmark (S&P 500 Index).
Seasonality Strategy for ETF Trading 94
Now let’s look at some Key Metrics.
Seasonality Strategy for ETF Trading 95
We can also look at the return distribution across years comparing this with the SPX.
The Seasonality Strategy significantly outperforms the SPX in terms of cumulative return and CAGR, indicating strong long-term performance.
The strategy exhibits a relatively low beta, suggesting it is not highly correlated with SPX, which can be beneficial for diversification purposes.
The high returns in years like 2019 and 2021 showcase the effectiveness of seasonal trends in certain market environments.
Seasonality Strategy for ETF Trading 96
And finally let’s look at the distribution of returns by month historically.
Seasonality Strategy for ETF Trading 97
Sensitivity to Initial Capital
The strategy was tested with varying capital levels to evaluate its robustness. Larger capital allocations help mitigate the negative effects of commission costs, leading to more stable and consistent performance over time. Performance across different capital allocations is detailed below.
In this article you will learn how to set up the MenthorQ Indicators for MotiveWave. You can now access Gamma Levels, Swing Levels and Blind Spots Levels on the platform. You can now integrate the MenthorQ data into MotiveWave directly via API.
What is MotiveWave?
MotiveWave is a trading and charting platform designed for advanced technical analysis, strategy development, and automated trading. It supports multiple asset classes, including stocks, futures, forex, options, and cryptocurrencies. Learn more about MotiveWave here.
Indicator Features
Within the Indicator you have the following features:
Integration via API
Full Customization of Levels Plots and Labels
Access Multiple Levels Type:
Gamma Levels
Gamma Levels Intraday
Gamma Scalping
Gamma Scalping Intraday
Blind Spots
Swing Levels
Levels Conversion. You can convert all Levels Type from one asset to the other using Manual Ratio
Check out the Video Tutorial.
How to integrate MenthorQ Levels on MotiveWave
Integrating the MenthorQ Levels into MotiveWave is very simple. To do so follow these Steps:
Log into your Account Dashboard and go under Integration. Locate the MotiveWave section and download the indicator zip file.
We have two indicators available: The Windows and the Mac OS Version.
MotiveWave Integration 110
Paste the indicator in the MotiveWave Extensions folder within your computer.
MotiveWave Integration 111
Unzip the File into the folder
MotiveWave Integration 112
This is how the folder should look like after the Zip file has been extracted
MotiveWave Integration 113
Add the Indicator to your chart by looking at the menu: Study – Custom
MotiveWave Integration 114
Configure the Indicator
Once downloaded the indicator go into your MotiveWave Indicator List and look for the MenthorQ Levels Indicator.
Step 1. Add your API Key
Scroll down the Indicator Settings and add your API Key. You can retrieve the API Key within the MenthorQ Account Dashboard.
MotiveWave Integration 115
Step 2. Choose Level Type
You can select what types of Levels you want in your chart and the Gamma Model. You can add Gamma Levels, Swing Levels (when available) and Blind Spots Levels.
For Gamma Levels we have 2 Models:
Gamma Levels. Those are the main MenthorQ Gamma Levels.
Gamma Scalping. This model allows you to access more gamma levels within a smallare range. Tailored for Futures Traders who are looking at tight areas for scalping.
Within the Indicator you have the ability to fully customize the color of the labels, select which labels to plot, choose the style and more.
MotiveWave Integration 118
Step 4. Levels Conversion
Within MotiveWave you can now convert Levels from one asset to the other. For example you can do the following conversions:
Convert End of Day SPX Gamma Levels to ES
Convert Intraday QQQ Gamma Levels to NQ
Convert QQQ Blind Spots Levels to NVDA
Convert GLD Swing Levels to GC
Note: Conversion only works with Manual Ratio. For more information about Levels conversion check out our Tutorial Videos on our Trading Integrations Course.
In today’s fast-paced financial markets, traders and investors seek objective, data-driven insights to enhance their decision-making. The Q-Score, derived from our proprietary Quant Models, offers a comprehensive scoring system that evaluates assets based on four key factors: Momentum, Seasonality, Volatility, and Options.
The Menthor Q-Score brings the power of institutional factor investing directly to retail traders—for the first time. This approach is traditionally used by hedge funds and asset managers; a factor-based approach evaluates securities based on quantifiable characteristics like momentum, volatility, sentiment, positioning, and risk regimes.
The point is — the Q-Score doesn’t replace your strategy. It makes it smarter.
By assigning a numerical score to each factor, the Q-Score provides a structured approach to market analysis. You can find the Q-Score on the Dashboard.
The Menthor Q-Score 126
Take a look at this Tutorial Video on the Menthor Q-Score.
Breaking Down the Q-Score Components
Momentum Score
The Q-Score Momentum Model reflects the underlying trend strength of an asset. Our proprietary quant models analyze price action and technical indicators to determine whether an asset exhibits bullish or bearish momentum.
A higher momentum score suggests strong positive price action, while a lower score indicates weakness or potential downside pressure. Traders can use this score to align their positions with prevailing market trends. Our model assigns a score ranging from 0 to 5:
0: Bearish Momentum
3: Neutral Momentum
5: Bullish Momentum
The Menthor Q-Score 127
Seasonality Score
The Q-Score Seasonality Model assesses the historical performance of an asset over a specific time frame. Using 20 years of historical data, our model examines the price behavior of an asset over the next five days and assigns a score ranging from -5 to 5:
-5:Low Seasonality. Indicates a Bearish seasonality
0: No Seasonality. No significant seasonal trend
5:High Seasonality. Indicates a Bullish seasonality
By leveraging the seasonality score, traders can anticipate potential price movements based on past performance and create advanced strategies. Our Seasonality score looks at the trend for the next 5 days.
The Menthor Q-Score 128
Volatility Score
The Q-Score Volatility Model measures the magnitude of price fluctuations of an asset. Our model assesses realized volatility to determine the likelihood of price swings. The volatility score ranges from 0 to 5:
5: High Volatility Environment, indicating large price fluctuations.
Traders can combine the Volatility Score with IV Rank (Implied Volatility Rank) and Implied Volatility to identify potential volatility arbitrage opportunities. When volatility is low, option premiums tend to be lower, making certain strategies like long straddles less favorable, while high volatility may present opportunities for selling premium.
The Menthor Q-Score 129
Option Score
The Q-Score Options Model ranks an asset based on activity in the options market, providing insight into trader sentiment and expected price direction. The score ranges from 0 (bearish) to 5 (bullish):
0: Strong Bearish Sentiment from the options market.
5: Strong Bullish Sentiment from the options market.
By combining the Options Score with the Momentum Score, traders can gain additional confirmation for potential moves. This forward-looking model integrates key options market indicators to forecast price direction and sentiment shifts.
The Menthor Q-Score 130
How Traders Can Use the Q-Score
The Q-Score provides a structured, quantitative perspective on market conditions. Here are some key ways traders can utilize it:
Trend Confirmation: Use the Momentum Score alongside the Options Score to validate bullish or bearish trends.
Seasonal Patterns: Leverage the Seasonality Score to identify historically strong or weak periods for an asset.
Volatility-Based Strategies: Adjust trading strategies based on the Volatility and Option Scores—favoring trend-following trades in low-volatility environments and mean-reversion trades in high-volatility conditions. These two indicators, together with IV Rank can also be great tools for options buyers or sellers
Options Market: Incorporate the Options Score to gauge sentiment and potential shifts in market positioning.
The Q-Score serves as a dynamic tool for traders, helping them adapt to evolving market conditions. By integrating multiple quantitative factors, it offers a holistic view of asset performance. Traders can refine their entries and exits by aligning strategies with momentum, seasonality, volatility, and options activity, enhancing their decision-making precision.
Who is the Q-Score for?
Q-Score is versatile and can be used by various types of investors and traders.
Swing Traders can use the Q-Score to identify favorable entry points.
Options Traders can use the Q-Score to gauge market sentiment and positioning from the options market, helping identify bullish or bearish setups.
Futures Traders can use Q-Score signals to set tighter stops or avoid trades when scores indicate unfavorable conditions, thus managing risk in volatile markets.
Position Traders can use Q-Scores to identify assets with strong trends and favorable historical performance over medium to longer timeframes.
Trading Examples using the Q-Score
Now let’s look at some examples on how to use the Q-Score for trading and look at the different factors.
Options Score
The Option Q-Score distills options market sentiment into a simple 0–5 scale—0 being strongly bearish, 5 strongly bullish. Watching how the score changes can reveal shifts in trader expectations and positioning.
Rising Q-Score → Growing bullish sentiment, often a sign of confidence in higher prices.
Falling Q-Score → Increasing bearish sentiment or hedging, signaling caution.
A move from neutral (≈3) to strongly bullish (4–5) may suggest options traders are betting on a rally, favoring long setups. A sharp drop from high to low can indicate a defensive turn, prompting reduced exposure. Even when the score stays high, approaching resistance might signal a pause or consolidation before the next move.
Momentum Score
The Momentum Q-Score measures an asset’s trend strength on a 0–5 scale—0 meaning strong bearish momentum, 5 meaning strong bullish momentum. Watching how it changes helps swing traders spot shifts in trend direction and strength.
Rising score → Trend is strengthening, often supporting trades in the same direction.
Falling score → Trend is weakening or turning bearish, signaling caution.
A move from neutral (≈3) to 5 suggests a trend gaining fuel, while a drop from 5 to 1–0 can point to momentum exhaustion or a bearish turn. Used alongside other signals like options sentiment or seasonality, it helps refine trade timing and manage risk.
Seasonality Score
The Seasonality Q-Score measures an asset’s short-term historical price tendencies—typically over the next 5 days—on a scale from -5 (strong bearish seasonality) to +5 (strong bullish seasonality). It helps traders spot recurring patterns based on years of past data.
Rising score → Asset is entering a historically strong period, favoring upside setups.
Falling score → Asset is approaching a historically weak period, signaling caution.
By aligning trades with seasonal strength and avoiding historically weak windows, traders can improve timing, manage risk, and better anticipate market rhythm.
Volatility Score
The Volatility Q-Score measures current and expected market volatility—ranging from calm, low-risk conditions to high-volatility, fast-moving markets. For swing traders, it’s a key tool for adapting strategy and managing risk.
Rising score → Expect wider price swings, greater uncertainty, and potentially more trading opportunities—but also higher risk.
Falling score → Anticipate calmer conditions, smaller price moves, and lower risk of sudden shocks.
Sharp increases can signal a shift to a more volatile market regime, while steady declines may point to stable trends. Used alongside other Q-Scores, it helps traders refine entries, exits, and position sizing to match market conditions.
How to build a Quant Strategy using the Menthor Q-Score
The Menthor Q-Score can also be used to create Quant Trading Strategies. Learn how to use our Q-Score to create Quant Trading Strategies using Momentum, Volatility, Seasonality and Options Models.
Check out our Strategies. Access the Documentation with full backtest:
The Volatility Smile is a term used to describe the relationship between implied volatility (IV) and the strike prices of options within the same expiration date. It visually appears as a U-shaped curve when plotted on a graph with strike price on the X-axis and implied volatility on the Y-axis.
This pattern shows that options with strike prices far from the underlying asset (either higher or lower) tend to have higher implied volatility than options near the spot price. Traders can use this pattern to identify market sentiment, forecast potential price movement, and structure their strategies to capitalize on these insights.
Volatility Smile 133
What Is the Volatility Smile?
In its simplest form, a volatility smile reflects the market’s pricing of risk. Normally, the implied volatility (IV) for at-the-money (ATM) options is lower than that for out-of-the-money (OTM) or in-the-money (ITM) options. This is because investors and traders believe there is more uncertainty about how far an asset’s price could move in either direction. The further the strike price is from the current market price, the more potential for volatility, hence the higher implied volatility.
The U-shaped curve appears because far OTM options (both calls and puts) see a rise in implied volatility due to the tail risk priced in by the market. Traders expect more drastic price movements to be reflected in these options. On the other hand, ITM options show similar behavior with higher implied volatility as well, as they are considered more sensitive to market movements.
How to Use the Volatility Smile in Trading: A Case Study with AAPL
Let’s take the example of Apple (AAPL), a popular stock often used in options strategies. Suppose you are analyzing the volatility smile for AAPL and notice that the implied volatility for strikes at $350, $375, and $400 (OTM calls) is significantly higher than that for the ATM strike at $300, where the implied volatility is lower.
This indicates that the market expects larger movements in AAPL’s stock price but isn’t confident whether these will be to the upside or downside. The higher implied volatility in the OTM calls and puts suggests a concern for large swings in price, perhaps due to an upcoming earnings announcement, major product launch, or broader market events.
Using the Volatility Smile for Option Strategy
In this scenario, a trader can leverage the volatility smile to set up a long straddle or a strangle strategy. Both of these involve buying a call and a put option simultaneously but at different strike prices. The trader can buy the ATM call and put options, which are cheaper than the OTM options due to their lower implied volatility, while also taking advantage of the higher IV in the OTM strikes.
Alternatively, the trader might decide to sell ATM options (where the volatility is lower) while buying the OTM options (where volatility is higher). This strategy is more of a short volatility play, where the expectation is that implied volatility will drop as the expiration date approaches, and the price of AAPL might not move dramatically in either direction. This benefits the trader because the time value of the ATM options would decay faster compared to the OTM options.
Another way to use the volatility smile is to sell volatility when implied volatility is high (in the OTM options), expecting it to revert to the mean. AAPL’s implied volatility might be elevated due to upcoming earnings, for example, and if the earnings announcement passes without major surprises, implied volatility would likely decrease, benefitting the trader who sold options with inflated premiums.
Check out this Video on how to read the Volatility Smile.
Risk and Reward with the Volatility Smile
Trading based on the volatility smile requires a strong understanding of the underlying asset’s price action, as well as the timing of when volatility is expected to change. It’s also important to manage risk carefully, as positioning in options with high implied volatility can be a double-edged sword. While buying options with higher implied volatility can result in substantial profits if the stock makes a significant move, they are also priced higher, meaning a larger move is required to break even or make a profit.
On the flip side, selling volatility works best when the market’s fear (and implied volatility) is at its peak but is expected to subside. The trader’s goal in this case is to sell overpriced options and collect the premium while waiting for implied volatility to drop.
Conclusion
The volatility smile is an essential concept for any options trader. By examining how implied volatility varies with different strike prices, traders can gain insights into the market’s expectations for future price movements. In the case of AAPL, understanding how volatility behaves across strikes can help a trader select the most effective options strategy, such as long straddles or selling volatility, to profit from expected market behavior. Additionally, staying mindful of changes in the volatility smile can help identify when market sentiment is shifting and when adjustments to the trading strategy may be necessary.
In this Guide we will go over our New Intraday Gamma Models. But let’s look at why they are key for any traders.
Market Sentiment Analysis: Gamma models highlight shifts in the options market that can significantly affect underlying asset prices. Metrics like Gamma Flip and Net GEX help traders understand the market’s behavior as it transitions from positive to negative gamma environments, influencing volatility and price movement.
Actionable Insights: The models track key levels such as Primary Levels, 0DTE Levels, and Secondary Gamma Levels, enabling traders to identify areas of likely market reactions or resistance/support zones.
Intraday changes in Top Strikes with Positive GEX Change can pinpoint significant market activity.
Risk Management: Understanding gamma exposure helps traders anticipate potential sharp moves or stability in the market, aiding in position sizing and hedging strategies.
Positive/Negative Gamma. Knowing whether the market is in a Call Dominated Environment or other conditions allows for better alignment with market trends.
Volume and Volatility: By combining gamma analysis with metrics like Volume and Gamma Condition, traders gain a comprehensive view of liquidity and potential pressure points in the market.
Intraday Snapshots
We will provide various intraday snapshots:
8 am EST
9:50 am EST
10:15 am EST
10:45 am EST
11:15 am EST
11:45 am EST
12:15 pm EST
12:45 pm EST
1:15 pm EST
1:45 pm EST
2:15 pm EST
2:45 pm EST
3:15 pm EST
3:45 pm EST
Intraday Gamma Models will be available for Stocks, ETFs and Indices.
Intraday Gamma Models
Now let’s look at the different models and how you can use them.
Net Gamma Exposure
For Gamma Exposure we will provide two different intraday models:
Net GEX All Expirations. This looks at GEX across the full options chain updated intraday
Net GEX 0DTE. Here we calculate the Net Gamma Exposure Chart only on 0DTE or WDTE expirations. In the case of Indices or ETFs like QQQ and SPY? we will provide the 0DTE Net Gamma Exposure Intraday for options expiring the same day. For Stocks that do not have 0DTE we will provide the Net GEX Chart for the next weekly expiration.
You can access the models by using the /netgex_intraday and /netgex_0dte command.
Intraday Gamma Models 140
Volume
For Volume Change you will be able to access the change in Volume for the 0DTE Expirations. In the case of Indices and ETFs you will see the volume for options expiring the same day. For Stocks you will see the volume change for the next weekly expiration.
Intraday Gamma Models 141
GEX Difference
Another key model is the GEX Difference. Here we will provide two models:
GEX Difference vs Last. This command will show you the change in GEX of the current snapshot versus the previous intraday snapshot. In this example we can see the change in GEX from the 10.45 am EST snapshot versus the 9.35 am EST snapshot. Command: /gex_diff_vs_last
Intraday Gamma Models 142
GEX Difference vs EOD. This command will show you the change in GEX of the current snapshot versus the previous end of day snapshot. In this example we can see the change in GEX from the 7.45 am EST pre-market snapshot versus the 9.35 am EST snapshot. Command: /gex_diff_vs_eod
Intraday Gamma Models 143
Intraday Gamma Levels
We will also provide users with Intraday Gamma Levels for TradingView and the other integrations. Command: /levels_tv_intraday and /tv_list_intraday
Intraday Gamma Models 144
Liquidity Summary
We will then provide a clear Summary of the Liquidity Change by looking at GEX, Gamma Levels, Volumes, GEX Change, Gamma Regime and more.
In this Guide we want to share the Swing Model Backtesting Results during one of the busiest earnings release week. We are looking at the week of 10/28/2024 to 11/01/2024. Companies like Google, Apple, Amazon, Meta and more reported this week.
We go over the full backtest during our Live here below:
Backtesting Assumptions
The backtest has the following assumptions:
Data was taken from MenthorQ Swing Trading Model as of Sunday 10/25/2024. The levels from Sunday are calculated after market close on the previous Friday.
We then took the Bias given by the model weather Bullish or Bearish and we downloaded the various levels: Upper Band, Lower Band and Risk Trigger.
We then trade at the Open of Monday 10/28/2024.
The entry price is at the open and the exit price follows various assumptions as per the below strategies.
Here you can find the File with the Data and Results.
We then created various entry and exist strategies. We trade based on two approached: Going Long Short the Stock or by selling Credit Spreads using Options.
Long / Short Stock
Strategy 1:
Long / Short Underlying Stock. We trade the Stock at the open of 10/28/2024.
Entry: Going Long if the Bias was Bullish and Going Short if the Bias was Bearish.
Exit: We close the trade at the open of the day after the earnings report.
Strategy 2:
Long / Short Underlying Stock. We trade the Stock at the open of 10/28/2024.
Entry: Going Long if the Bias was Bullish and Going Short if the Bias was Bearish.
Exit: We close the trade at the close of the day after the earnings report.
Strategy 3:
Long / Short Underlying Stock. We trade the Stock at the open of 10/28/2024.
Entry: Going Long if the Bias was Bullish and Going Short if the Bias was Bearish.
Exit: We close the trade at the open price of Friday 11/01.
Strategy 4:
Long / Short Underlying Stock. We trade the Stock at the open of 10/28/2024.
Entry: Going Long if the Bias was Bullish and Going Short if the Bias was Bearish.
Exit: We close the trade at the close price of Friday 11/08.
Here you can find the Summary Results that are also available on the File. Here are the findings:
3 out of 4 Strategies have a Win Rate of over 50%
All the 4 strategies return a positive return of 2% or more for the Week
Strategy 4 is the best performing with 67% Win Rate and a Portfolio Performance for the Week of 7.22%
Swing Model Backtesting during Earnings 148
Selling Credit Spreads using Options
The second type of strategy leverages the Swing Trading Levels and Bias to define our Credit Spreads. These are the assumptions:
If Bias is Bearish we sell a Call Credit Spread using the Upper Band as the level for our Sold Call
If Bias is Bullish we sell a Put Credit Spread using the Lower Band as the Level for our Sold Put
We have created two strategies using the 5D and 20D Swing Model
Here are the details of the strategies:
Strategy 5: Swing Model 5D
We sell a Put Credit Spread or Call Credit Spread at the Open of Monday 11/28/2024
We use the 5 Days Swing Levels and we choose the Expiration of 11/01/2024
We use the Lower Band for the Strike of our Sold Put if Bias is Bullish and the Upper Band for the Strike or our Sold Call if the Bias is Bearish
Strategy 6: Swing Model 20D
We sell a Put Credit Spread or Call Credit Spread at the Open of Monday 11/28/2024
We use the 20 Days Swing Levels and we choose the Expiration of 11/22/2024
We use the Lower Band for the Strike of our Sold Put if Bias is Bullish and the Upper Band for the Strike or our Sold Call if the Bias is Bearish
Here you can find the Summary Results that are also available on the File. Here are the findings:
Both strategies return a win rate of over 75% with Strategy 5 having a win rate of 87.5%.
Strategy 5 returns a Portfolio Return of 7.26% for the week.
In this guide we will show how to use the MenthorQ Data for your morning preparation. It takes only a few minutes.
1. Liquidity Snapshot
You can access the Liquidity Snapshot by typing the /liq_snapshot command on the Query Bot. Within this screen we particularly monitor the following data points:
Negative Gammaindicates potential for sharp price swings.
Negative GEX: Dealers hedge into trend, regardless of direction = Removes liquidity
Positive GEX: Dealers hedge against trend, regardless of direction = Adds liquidity
Bullish Momentum signals upward price movement.
IV30 vs HV30: Implied volatility is lower than historical volatility, which suggests the market may be calming down after a period of higher actual volatility. This combination can influence both directional and volatility-based trading strategies.
The Put/Call Open Interest Ratio compares the number of open put options to call options. A ratio of 2.56 suggests that there are more put options being traded compared to calls, which might indicate a bearish outlook from option traders, despite the bullish momentum.
Morning Preparation with MenthorQ 157
2. Option Matrix
Next we will look at the Option Matrix. The Matrix simplifies the read of the Option Chain for any assets within our coverage. You can access the Matrix using the command /matrix in the Query Bot.
Morning Preparation with MenthorQ 158
When GEX is positive, expect a more stable market with limited price swings. It’s often a signal that mean reversion trades (buying dips, selling rallies) could be effective.
When GEX is negative, expect more volatile markets with larger price swings. In this case, you might look for momentum trades, riding trends rather than fading them.
When DEX is positive, expect potential upward pressure on the market. If you’re seeing strong support levels and rising prices, it could be a sign to enter long positions, especially if you’re riding the momentum.
When DEX is negative, expect downward pressure. In this case, you might want to be cautious with long positions or look for opportunities to short if the market shows signs of weakening.
High Positive GEX + Positive DEX: Indicates a potentially bullish environment with stable upward pressure. You can look for long setups, especially if the market shows resilience on pullbacks.
Negative GEX + Negative DEX: Indicates a potentially bearish and volatile market. Here, you might look for short setups or be cautious about long trades.
Mixed GEX and DEX (e.g., positive GEX with negative DEX): This could indicate a choppy market, where the price might be stuck in a range or show unexpected volatility. In this scenario, shorter-term trades with tighter stops might be necessary.
Expiry Exp. Move. This column leverages our Option Implied Move Model to forecast how many points up or below the price can move by the expiration date.
3. Net Gamma Exposure (Net GEX)
Next we will look at the Net Gamma Exposure Chartor Net GEX. You can access the chart by using the /netgex command.
Morning Preparation with MenthorQ 159
This is how we can use this chart:
Predicting Volatility: The chart helps traders identify where market makers’ hedging activity may stabilize or destabilize the market. For example, heavy negative GEX at lower strike prices indicates higher volatility if the price starts to drop.
Support and Resistance: The GEX distribution gives clues to important support and resistance levels (e.g., $540 put support and $570 call resistance). Traders can use this information to make decisions about where to enter or exit positions.
Volatility Zones (HVL): The High Vol Level($550) marks a zone where price swings could become more unpredictable, which is critical for risk management.
On top of the Net GEX Chart we can also analyze Net Gamma Exposure across multiple Expirations. This is very important as we can monitor 0DTE Options Flows and Reaction Zones. You can access this chart by using the /netgex_multiexpiry command on the Query Bot.
Morning Preparation with MenthorQ 160
This is how we can use this chart:
Anticipating Price Reactions: By studying GEX across different expirations, traders can anticipate how the asset might react at certain strike prices during different trading sessions. This is especially useful near major expiration dates or Mopex (monthly expiration).
Volatility Management: Knowing where negative GEX clusters are across multiple expirations helps traders avoid-or take advantage of-potential spikes in volatility.
Enhanced Strategy Development: This multi-expiration GEX data enables traders to layer their trades around multiple key levels and expiration dates, improving the precision of their strategies.
5. Swing Trading Model
Then we want to look at our Swing Trading Model. We have two time horizons: 5 days and 20 days. You can access it by using the commands: /swing_5d and /swing_20d. You can also add the Swing Levels to TradingView.
Predicts Key Levels for Entries and Exits: The upper band, lower band, and risk trigger provide clear price targets that day traders can use to set entry, exit, and stop levels.
Upper Band: The upper band gives day traders a target for price resistance. If SPY nears this level, it may encounter selling pressure, and traders might look to take profits or initiate short positions.
Lower Band: (Not visible on this portion of the chart but typically shows as a lower boundary) The lower band is the opposite of the upper band and acts as a support level. Traders could use it as a potential buy signal or target for covering short positions, expecting a bounce.
Risk Trigger: This level indicates a key price point where the model expects an important reaction, either as a support or potential breakdown level. Day traders can use this as a decision point, either to tighten stops or prepare for larger moves.
6. Gamma Levels on VIX
After looking at our asset we want to confirm our analysis by looking at the VIX Index. In particular we can look at the VIX Matrix.
Morning Preparation with MenthorQ 162
Understanding GEX and DEX for VIX options helps traders predict upcoming volatility spikes or calming periods. For example, if GEX is negative and DEX is high, traders can expect heightened volatility, which can influence decisions in both options and futures markets.
The VIX is a direct reflection of market fear and uncertainty. By observing the call resistance and put support levels, traders can get a sense of how much fear (or calm) the market is pricing in at different VIX levels.
Large GEX and DEX values suggest that institutional players are making significant hedging moves, which can influence both VIX options and the broader market. Traders can use this information to manage their positions effectively, particularly during major market-moving events.
The chart gives a granular view of volatility expectations across multiple expirations, helping traders position for both short-term swings and long-term trends in market volatility.
7. CTAs and Systematic Models
The last step is to look at the MenthorQ CTAs Funds Model. Systematic Funds and CTAs are key drivers of liquidity and monitoring their flows is key for any investors. With this model we simplify how you can analyze their liquidity and positioning.
Morning Preparation with MenthorQ 163
This is how we can read the chart:
CTA Position Today, Yesterday, and 1 Month Ago: These columns show how much CTAs are currently positioned in each index, today, how much they were positioned yesterday, and one month ago. This helps traders and analysts track the evolution of CTA positions over time. For example, in the E-Mini S&P 500 Index, there was a slight decrease in the position from yesterday to today, but the position has increased significantly from one month ago. This shows that CAs have been building a long position, potentially influencing upward market moves.
Percentile (1M, 3М, 1Y): These columns indicate how current CTA positions compare to historical positions over 1 month, 3 months, and 1 year. Percentiles show how extreme the current positions are compared to historical data. For example, the E-Mini S&P 500 Index has a 1M percentile of 0.29, meaning the current position is in the 29th percentile over the last month, which suggests it is a relatively moderate position. Higher percentiles indicate more extreme positioning, which can precede large price moves if CTAs start reversing positions.
3M Z Score: The Z score tells us how far the current position is from the mean position over the last three months. A high or low Z score can indicate overbought or oversold conditions. For instance, the E-Mini S&P 500 Index has a Z score of -1.30, indicating that the current CTA position is significantly lower than the 3-month average, suggesting that the index might be oversold and could see buying pressure if CTAs reverse positions.
In this guide we look at 0DTE Options Term Structure. When trading Zero Days to Expiration (0DTE) options, understanding the term structure of options volatility is very important.
The term structure refers to the relationship between the implied volatility (IV) of options with different expiration dates. It provides a snapshot of how the market expects volatility to evolve over time, which is particularly relevant for 0DTE traders who are focused on the immediate price movements of an underlying asset.
In a typical term structure, implied volatility tends to vary depending on the time until expiration. For instance, options with longer expiration dates might have lower implied volatility compared to those that are set to expire soon. This variation occurs because short-term options are more sensitive to upcoming events or news that could affect the underlying asset’s price.
For 0DTE traders, the term structure offers valuable insights into how the market perceives risk on the expiration day. By analyzing this structure, traders can identify opportunities to capitalize on price movements or protect themselves against potential volatility.
0DTE Options Term Structure 165
Term Structure and SKEW
Take a look at our Video Tutorial on how to leverage Term Structure for Options Trading.
How to Analyze Term Structure for 0DTE Options
Understanding the term structure is essential for making informed decisions in 0DTE trading. Here’s how you can leverage it.
1. Identifying Implied Volatility Spikes
One of the key benefits of analyzing the term structure is identifying spikes in implied volatility for 0DTE options compared to longer-dated options.
A spike in IV for 0DTE options suggests that the market is anticipating a significant price movement on the expiration day. This could be due to various factors, such as an impending economic report, earnings release, or geopolitical event.
For traders, a higher IV in 0DTE options presents both opportunities and risks. On one hand, it indicates that there might be substantial price swings, which can be profitable if you’re positioned correctly. On the other hand, it means that options are more expensive, which can increase the cost of entering trades. Understanding these dynamics helps traders decide whether to buy or sell options based on their risk appetite and market expectations.
2. Arbitrage Opportuinities
Differences in IV between 0DTE options and those expiring shortly after can present arbitrage opportunities.
For example, if 0DTE options are priced with unusually high implied volatility compared to options expiring a day or two later, a trader might consider selling the 0DTE options while buying the slightly longer-dated options. This strategy allows traders to take advantage of the temporary mispricing and lock in profits as the market corrects itself.
MenthorQ’s tools can help identify these discrepancies in the term structure, providing traders with real-time data to spot and execute on these opportunities swiftly.
3. Risk Management
The term structure also reveals the risk premiums embedded in 0DTE options. If the term structure shows a sharp rise in IV for 0DTE options, it indicates that the market is demanding a higher risk premium due to anticipated events or uncertainty. Traders can adjust their risk management strategies accordingly, possibly by hedging their positions or reducing exposure.
By monitoring the term structure, traders can better understand the risks they are taking on when trading 0DTE options and adjust their strategies to mitigate potential losses.
In this Guide we will go through 0DTE Options Trading Strategies. The key to success lies in understanding and managing the inherent volatility of these instruments. Unlike traditional options, which might have weeks or months before expiration, 0DTE options are set to expire on the same day they are traded.
This creates a unique set of circumstances where the option’s value is primarily driven by the price movements of the underlying asset, with almost no time value left.
Volatility plays a significant role in 0DTE options trading. As these options have no remaining time value, they become extremely sensitive to changes in the underlying asset’s price.
A small move in the underlying asset can result in a large percentage change in the option’s price, making 0DTE options highly attractive for traders looking to capitalize on short-term market movements. However, this high sensitivity also means that 0DTE options can be quite unpredictable, requiring traders to adopt a structured and disciplined approach.
Key Strategies for Trading 0DTE Options
To navigate the volatility of 0DTE options effectively, traders must employ a combination of strategy, risk management, and the right tools. Here are some key strategies that can help traders maximize their success when trading 0DTE options.
1. Scalping and Day Trading
Given the extremely short timeframe of 0DTE options, scalping and day trading are popular strategies. These approaches involve making quick trades to capture small price movements throughout the day.
Since 0DTE options are highly responsive to changes in the underlying asset’s price, scalping allows traders to take advantage of these rapid shifts. However, this strategy requires a keen eye on the market and the ability to take quick decisions.
2. Selling Credit Spreads
Another effective strategy for 0DTE options is selling credit spreads. This involves selling an option and simultaneously buying another option with the same expiration date but a different strike price. The goal is to collect the premium from the sold option while limiting potential losses with the purchased option.
Selling credit spreads is particularly useful in a high-volatility environment, as it allows traders to benefit from the rapid time decay of the sold option while managing their risk exposure.
3. Iron Condors
Iron Condors are a more advanced strategy that involves selling a put spread and a call spread with the same expiration date, but different strike prices. This strategy is well-suited for 0DTE options because it allows traders to profit from a range-bound market, where the underlying asset’s price stays within the expected range.
The iron condor strategy benefits from time decay, which is accelerated in the final hours of trading, making it an attractive option for traders who anticipate low volatility near expiration.
4. Hedging with Futures or ETFs
Hedging is an essential component of any trading strategy, especially when dealing with the high volatility of 0DTE options. Traders can hedge their positions by using futures contracts or ETFs (Exchange-Traded Funds) that track the underlying asset.
For example, if you’re trading 0DTE options on the S&P 500, you could use the SPY ETF or ES Future to hedge against adverse price movements. This approach helps protect your capital while still allowing you to participate in the potential upside of 0DTE options.
MenthorQ Tools for 0DTE Traders
At MenthorQ, we understand the complexities of trading 0DTE options, which is why we provide a suite of tools designed to help you navigate this volatile environment.
Our platform offers advanced models, allowing you to track key indicators such as gamma levels, skew, and term structure for 0DTEs Options.
1. Tracking Gamma Levels
Gamma is a measure of the rate of change in an option’s delta, which represents the sensitivity of the option’s price to the underlying asset’s price. 0DTE options have high gamma, meaning their price can become very reactive to small changes in the underlying asset’s price.
You can access our Primary and Secondary Levels as well as our 0DTE Levels. You can then monitor our 1D Expected Move Indicator that can provide insights on the daily expected volatility of the asset.
Skew refers to the difference in implied volatility between options at different strike prices. By analyzing the skew, you can gauge market sentiment and identify whether traders are paying a premium for puts or calls as the expiration approaches. This insight can be invaluable for setting up trades that take advantage of market biases or mispricings. Read more about the MenthorQ Skew.
We offer 3 types of Skew: 0DTE Skew, 1 Month Skew and 3 Months Skew.
0DTE Options Trading Strategies 170
3. Understanding Term Structure
The term structure of options volatility provides a view of how implied volatility is distributed across different expiration dates. For 0DTE traders, focusing on the term structure helps in understanding the market’s expectations for volatility on the expiration day. Our tools at MenthorQ allow you to analyze this structure and make informed decisions about whether to hold or sell your 0DTE options. Learn More about Term Structure.
0DTE Options Trading Strategies 171
Combining Strategy, Risk Management, and Tools
Successfully trading 0DTE options requires more than just understanding the underlying mechanics; it demands a disciplined approach that combines strategy, risk management, and the right tools. By integrating these elements, you can better navigate the volatile nature of 0DTE options and increase your chances of achieving consistent, profitable trades.
At MenthorQ, we are committed to provide traders with the resources they need to succeed in this dynamic trading environment. Our platform provides the data, insights, and tools necessary to refine your strategies and improve your trading outcomes.
Understanding the concept of Gamma is crucial, especially when dealing with Zero Days to Expiration (0DTE) options. Gamma is a second-order Greek that measures the rate of change in an option’s delta relative to movements in the underlying asset’s price.
For 0DTE options, gamma levels are particularly significant because these options are highly sensitive to even the smallest changes in the underlying asset’s price. As the expiration approaches, gamma tends to increase, meaning that the delta can change rapidly.
This makes 0DTE options extremely reactive and, therefore, both an opportunity and a risk for traders. A high gamma level indicates that an option’s price can swing dramatically with small price movements in the underlying asset, which can result in substantial gains—or losses—within a very short time frame.
How to use MenthorQ 0DTE Gamma Levels and Net GEX
Tracking gamma levels is crucial for anticipating significant price movements and managing risk effectively.
Net GEX Analysis: Our Net GEX chart provides insight into the short-term sentiment within the options chain. Green indicates a higher presence of call gamma, while red signifies more put gamma. This tool acts as an early warning system, helping you anticipate potential market shifts. Watch our Podcast on Gamma Levels and Net Gamma Exposure
Identifying Reaction Zones: Use our tools to monitor the distance between the current spot price and significant reaction zones. Understanding these zones can help you make more informed trading decisions. Learn About Reaction Zones
0DTE Gamma Levels and Skew 178
Net Gamma Exposure (Net GEX) and Market Sentiment
Net GEX is a proprietary metric that shows the net exposure of gamma in the market.
A positive Net GEX value indicates that call gamma dominates, which often suggests bullish sentiment.
A negative Net GEX value signals that put gamma is more prevalent, pointing to bearish sentiment.
By tracking Net GEX, traders can understand market expectations and adjust their positions accordingly.
We also provide Gamma Exposure Levels on 0DTE, Weekly and Monthly Expirations. You can access this data in one single chart grid.
0DTE Gamma Levels and Skew 179
Bot Commands: To access this charts you can use the /netgex or /netgex_multiexpiry command
Market Reaction Zones
Another crucial aspect of tracking gamma levels is identifying reaction zones—price levels where the underlying asset is likely to experience significant movement due to concentrated options activity.
At MenthorQ, we provide tools that help traders pinpoint these reaction zones, allowing for more precise entry and exit points. Reaction zones can serve as early warning signals for potential price reversals or accelerations, helping traders to position themselves advantageously.
Analyzing Open Interest and Volume
Open Interest and Volume are critical metrics for understanding the dynamics of 0DTE (Zero Days to Expiration) options trading. Open Interest represents the total number of outstanding options contracts that have not been settled, providing insight into the liquidity and activity levels of specific options.
High Open Interest in 0DTE options can indicate significant market interest and potential for substantial price movement as traders adjust positions rapidly throughout the trading day.
Volume, on the other hand, reflects the number of contracts traded within a given period. For 0DTE options, high volume signals active trading and can lead to increased volatility, as these contracts are highly sensitive to market movements.
Together, Open Interest and Volume offer valuable information about market sentiment, potential price action, and the underlying forces driving short-term options trading.
We provide different charts for Open Interest and Volume within the Membership. These are the Bot Commands:
/voloi – provided the Volume and Open Interest data for All Expirations
/voloi_0dte – provides the Volume and Open Interest data for 0DTE Expiration
/voloi_1dte – provides the Volume and Open Interest data for the next Expiration
0DTE Gamma Levels and Skew 180
What Is the 1D Expected Move Indicator?
This tool forecasts the next day’s price movement by analyzing implied volatility, providing a projected trading range that is invaluable for intraday trading and risk management. Learn More.
How to Use the 1D Expected Move Indicator Daily Trading Band: Use the projected price range to identify key support and resistance levels for the trading day, guiding your 0DTE strategies.
0DTE Gamma Levels and Skew 181
Learn How to Use the 1D Expected Move Indicator. Watch Now
Bactesting Results of 1D Expected Move Indicator. Learn More
Bot Commands: /keylevels
Positive and Negative Gamma
It is very important for Traders and Investors to understand the difference between Positive and Negative Gamma when trading any asset, because these gamma conditions can significantly impact their investment strategies and risk exposure.
In Positive Gamma the Market is Long Gamma and we can expect lower volatility
In Negative Gamma the Market is Short Gamma and we can expect higher volatility
We can use the Option Matrix to identify whether the market is in a positive or negative gamma environment, helping you gauge potential price stability or volatility based on the aggregated positioning of options traders.
0DTE Gamma Levels and Skew 182
Watch our use cases videos:
How to prepare for a Market Sell Off and Increased Volatility.Watch Now
Skew refers to the difference in implied volatility (IV) between options at different strike prices. In 0DTE trading, skew analysis helps you gauge market sentiment and identify potential trading opportunities.
In a perfectly balanced market, the Implied Volatility across different strikes would be similar. However, in reality, this is rarely the case. Skew occurs when there is a noticeable difference in Implied Vol, indicating that traders are willing to pay more for options on one side of the market—either puts or calls—based on their expectations of future price movements.
For 0DTE options, skew becomes an even more critical factor. As the expiration date approaches, any existing skew can intensify, leading to significant price disparities between options with different strike prices.
0DTE Gamma Levels and Skew 183
How to Analyze Skew with MenthorQ
Market Sentiment: Use skew analysis to understand whether traders are leaning towards calls or puts as expiration approaches, providing insight into market expectations. Watch Video
Strategy Optimization: Identify and capitalize on overpriced options using strategies like iron condors.
Delta Hedging is a risk management strategy commonly used in options trading to offset or minimize the directional risk associated with an options position.
It involves adjusting the position by buying or selling the underlying asset (usually a stock or an index) to ensure that the net delta of the options and the underlying asset positions is close to zero.
When we look at Delta Hedging we are typically interested in the Delta Hedging Activity of Market Makers in the Options Market.
Market makers hedge their positions to manage and mitigate the risks associated with providing liquidity and facilitating trading. Market making involves constantly quoting bid and ask prices for various financial instruments, such as stocks, options, futures, and other derivatives.
Market makers earn profits from the bid-ask spread and do not take a directional position. However, they face several risks such as price, execution, market direction, volatility, and capital management risks. Delta hedging helps them manage risk.
Why should we care about Delta Hedging?
The Market Makers Delta Hedging activity is key to the financial market for several reasons:
Liquidity. Delta hedging helps market makers to ensure they can fulfill their obligations to buy and sell the stock and continue to provide prices and liquidity to the market.
Price Efficiency. Delta hedging helps ensure that market maker quotes closely reflect the current fair market value of the underlying asset. If market makers didn’t hedge their delta exposures, they might have significant directional risk, which could lead to wider bid-ask spreads or less competitive prices for investors
Market Stability. Delta hedging contributes to overall market stability. Market makers’ ability to provide continuous liquidity helps prevent excessive price volatility and sudden market disruptions.
Before jumping into what delta hedging is, we need to first understand what delta is.
Delta
Delta is a Greek letter used in options to represent the sensitivity of an option’s price to changes in the price of the underlying asset. It ranges from -1 to 1 for put options and 0 to 1 for call options. A delta of 0.5 means the option’s price will move by approximately half of the movement in the underlying asset.
We can visualize Delta as follows:
Calls: we know that for calls delta is positive as it moves from out of the money to in the money.
Puts: the opposite is true for put delta which is negative as it moves from out of the money to in the money.
Delta Hedging 187
In this chart you can see the Delta at different Moneyness.
Let’s go a step forward and let’s take the Delta profile and its payoff, and let’s see what happens to the Delta and Payoff for a call option as the underlying changes.
In this example we have a Call and the underlying going up. As you can see we move up on the Delta curve.
The Delta is particularly useful if we are trying to delta hedge, because it can help us calculate the hedge ratio. The hedge ratio is expressed as a fraction or percentage and helps traders determine the amount of the underlying asset needed to create a delta-neutral position.
For example, we know that an At the Money (ATM) option has a Delta of 0.5. The Delta of being long underlying is always 100 so we multiply the delta by that number. For example if we are long an ATM call, we know that in order to be delta hedged we will have to short 50 shares.
What does all this mean for us?
This below is a very simplistic way of thinking about delta hedging, because it is not taking your full risks into account like volatility, but just the spot moment. But for the purpose of this exercise, this works well.
If we know that the market maker takes the opposite direction from the investor. We can summarize it in this table.
Delta Hedging 189
What we are most interested in is the hedging part. Because as the market maker hedges, he is taking or removing liquidity.
If he goes long he buys the underlying adding liquidity
if he goes short is the opposite
This buying and selling activity has the effect of moving prices. Remember this is a very simplified way. To correctly assess the delta hedging strategy, the market maker would be looking at all the Greeks that have the potential to change its delta such as Gamma, Vega, Vanna, Theta and Charm. If you want to know more about this you can access ourAcademy.
