{"id":231656,"date":"2025-10-10T08:56:57","date_gmt":"2025-10-10T12:56:57","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=231656"},"modified":"2025-10-13T08:58:03","modified_gmt":"2025-10-13T12:58:03","slug":"options-indicators","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/options-indicators\/","title":{"rendered":"Options Indicators"},"content":{"rendered":"\n<p><strong>Executive summary<\/strong> \u2014 Options activity embeds information and pressure that can forecast equities at horizons from one day to several months. In this article we explore four recurring mechanisms: (i) asymmetric information (informed traders choose options when shorting stock is costly), (ii) embedded leverage (OTM contracts amplify informed views), (iii) hedging demand (delta\/gamma hedgers push underlying prices temporarily), and (iv) crowd\u2011level imbalance (ratios of option to stock trading, skew, and moneyness mix). These mechanisms translate into robust, implementable stock\u2011level predictors.<\/p>\n\n\n\n<p>We then uncover how Visual Sectors have constructed Options Indicators, the step by step process and backtest results<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-options-trading-vs-options-data\">Options trading vs Options data<\/h2>\n\n\n\n<p>Options are risky to trade, but the footprints they leave\u2014volume, moneyness, order type, and hedging pressure\u2014are a gold mine for signals about where the underlying stock is headed next. This idea isn\u2019t new; it\u2019s been explored for decades, from classic asymmetric\u2011information models to modern microstructure measures that use real\u2011time Greeks. What\u2019s changed is data quality and scale: options activity is now large enough that its own flows and hedges can move stocks\u2014creating short\u2011lived, tradable predictability.<\/p>\n\n\n\n<p>After exchange\u2011traded equity options launched in 1973, researchers asked a simple question: do informed traders migrate to options to express views more precisely or with leverage? Theory says yes, especially when options allow better alignment with the sign\/magnitude of private information. That thesis matured into testable signals:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Relative option activity vs. stock activity (O\/S): When shorting stock is costly, negative information is more likely to show up in options trading first, making high O\/S bearish in the cross\u2011section. Empirically, going long low\u2011O\/S and short high\u2011O\/S names earns about 1.47% next\u2011month alpha in U.S. equities, and the effect is stronger where short\u2011sale frictions are higher. (\u201cprovide an average risk\u2011adjusted hedge return of 1.47% in the month following the formation date.\u201d) [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=1624062\">1<\/a>]<\/li>\n\n\n\n<li>Where in the options chain do informed traders trade? Evidence shows they favor higher\u2011leverage contracts (OTM calls for bullish news; OTM puts for bearish), and that weighted average moneyness has forecasting power for next\u2011day and next\u2011month returns\u2014especially in high\u2011IV names. (\u201cStock returns increase with this measure.\u201d) [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3507507\">2<\/a>]<\/li>\n\n\n\n<li>Who\u2019s trading? Using order\u2011type\/trader\u2011type data, customer \u201copening buy\u201d flow dominates option volume and is particularly predictive, consistent with earlier work showing that customer (full\u2011service) order flow leads stock moves. (\u201cspecific market participant\u2019s options trading volume is a predecessor to asset price movements.\u201d) [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2363475\">3<\/a>]<\/li>\n\n\n\n<li>Hedging pressure as a driver: With options volume and O\/S ratios much larger than in the 1990s, market\u2011maker delta\u2011hedging itself has become a short\u2011horizon return driver. A new measure\u2014Expected Hedging Demand (EHD) built from Greeks\u2014predicts higher 1\u20135 day returns, followed by reversal, consistent with price pressure, not long\u2011horizon information. (\u201cthe positive impact \u2026 lasts up to five trading days, and then a reversal follows.\u201d) [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4729672\">4<\/a>]<\/li>\n<\/ul>\n\n\n\n<p>The upshot: options data are not a novelty. They have been scrutinized theoretically and empirically, with practical, repeatable signals emerging across four mechanisms that matter most.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-factor-1-asymmetric-information\">Factor 1 &#8211; Asymmetric information<\/h2>\n\n\n\n<p>Concept. When some traders know more, they prefer venues that let them size bets to information. Options offer sign\u2011targeted exposure (calls\/puts) and embedded leverage. If shorting stock is costly, negative information migrates to options markets.<\/p>\n\n\n\n<p>How it works. Contrast total options volume with stock volume for the same firm (the O\/S ratio). In a multimarket microstructure model with short\u2011sale costs and capital constraints, informed traders disproportionately use options to act on bad news. That makes O\/S a negative cross\u2011sectional signal for next\u2011period stock returns. Empirically, sorting monthly on O\/S and going long the bottom decile vs. short the top decile delivers ~1.47% next\u2011month 5\u2011factor alpha, concentrated in the first few trading days; the effect fades by month two.<\/p>\n\n\n\n<p><em>\u201cPortfolio alphas associated with O\/S are increasing in the cost of shorting.\u201d (limits\u2011to\u2011shorting sharpen the signal). [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=1624062\">5<\/a>]<\/em><\/p>\n\n\n\n<p>Why it\u2019s legit. This is about where informed traders route orders given frictions. The same paper also shows the O\/S signal weakens when options\u2019 effective leverage (and spreads) are very high\u2014because wider spreads deter switching from equities to options for negative signals.<\/p>\n\n\n\n<p>Limitations. It\u2019s a short\u2011horizon signal (days to ~1 month). And because it relies on public, unsigned totals, it\u2019s strongest where shorting is hard; in easy\u2011to\u2011short names, the edge compresses.<\/p>\n\n\n\n<p>How to use it. Compute each stock\u2019s options volume (5\u201335 trading\u2011day maturities) divided by equity volume; rebalance monthly; emphasize stocks with high short\u2011sale fees\/low lendable supply.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-factor-2-hedging-demand\">Factor 2 \u2014 Hedging demand<\/h2>\n\n\n\n<p>Concept. Today\u2019s options market is big enough that delta\u2011hedging flows can push the underlying. When option makers collectively need to buy (sell) stock to hedge, that creates mechanical, short\u2011lived price pressure.<\/p>\n\n\n\n<p>How it works. Expected Hedging Demand (EHD) uses real\u2011time Greeks to estimate how much hedging the options book will need (via elasticity of delta, ED = \u0393\u00b7S\/\u0394, aggregated across open interest). High EHD today predicts higher stock returns over ~1\u20135 days, which then reverse\u2014a signature of transitory demand.<\/p>\n\n\n\n<p><em>\u201cEHD significantly predicts future stock returns in the cross section \u2026 [and] the positive impact \u2026 lasts up to five trading days, and then a reversal follows.\u201d[<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4729672\">4<\/a>]<\/em><\/p>\n\n\n\n<p>This effect remains after controls and is economically meaningful (roughly double\u2011digit annualized when scaled by realistic turnover in deciles), and it is distinct from classic characteristics (size, spreads, volume) per cross\u2011sectional regressions and double\u2011sorts.<\/p>\n\n\n\n<p>Limitations. It\u2019s very short\u2011term and can invert across stocks with extremely high ED (elastic demand curves dampen net price impact). Implementation is data\u2011 and compute\u2011heavy (intra\u2011day Greeks, open interest).<\/p>\n\n\n\n<p>How to use it. Build a daily EHD from OptionMetrics\/Greeks by contract, open\u2011interest\u2011weighted to the firm day; buy high\u2011EHD names for hours\u2013days; expect mean reversion beyond a week.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-factor-3-embedded-leverage-signal-in-where-activity-concentrates-on-the-chain\">Factor 3 \u2014 Embedded leverage (signal in where activity concentrates on the chain)<\/h2>\n\n\n\n<p>Concept. If informed traders prefer higher\u2011leverage options, the distribution of trading across moneyness is itself informative.<\/p>\n\n\n\n<p>How it works. Construct AveMoney: a dollar\u2011volume\u2011weighted average of K\/S across a stock\u2019s traded options. Higher AveMoney (more activity in higher\u2011strike calls, i.e., further OTM calls) predicts higher next\u2011day (and, for high\u2011IV names, next\u2011month) returns. The effect is strongest when implied volatility is high and when open interest is growing, consistent with informed risk\u2011taking rather than pure hedging.<\/p>\n\n\n\n<p><em>\u201cStock returns increase with this measure.\u201d (AveMoney). A long\u2011high vs. short\u2011low AveMoney (calls) portfolio delivers \u201ca five\u2011factor alpha of 12% per year\u201d\u2014and ~33% per year in high\u2011IV stocks. [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3507507\">2<\/a>]<\/em><\/p>\n\n\n\n<p>Limitations. Put\u2011side patterns are noisier (puts are often hedges); the cleanest edge is on call\u2011side AveMoney and in high\u2011IV cohorts. Turnover and transaction costs matter for daily rebalancing.<\/p>\n\n\n\n<p>How to use it. Each day:<\/p>\n\n\n\n<p>(1) compute AveMoney=\u03a3(K\/S)\u00d7(mid\u00d7volume)\/\u03a3(mid\u00d7volume) \\text{AveMoney}=\\sum (K\/S)\\times \\text{(mid\u00d7volume)} \/ \\sum (\\text{mid\u00d7volume})AveMoney=\u03a3(K\/S)\u00d7(mid\u00d7volume)\/\u03a3(mid\u00d7volume);<br><br>(2) sort on call\u2011only AveMoney;<br><br>(3) long high\u2011AveMoney, short low\u2011AveMoney;<br><\/p>\n\n\n\n<p>(4) focus on high\u2011IV buckets and on days with rising open interest.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-factor-4-crowd-wisdom-options-based-sentiment-from-who-is-buying-what\">Factor 4 \u2014 Crowd wisdom (options\u2011based sentiment from who is buying what)<\/h2>\n\n\n\n<p>Concept. \u201cWisdom of crowds\u201d in options is measurable via put\u2011call ratios\u2014if you filter by order type and trader type to capture investor sentiment rather than market\u2011maker inventory.<\/p>\n\n\n\n<p>How it works. Using ISE\u2019s trader\u2011segmented data, customer \u201copening buy\u201d flow dominates and is predictive; strategies that rank stocks by customer call\u2011to\u2011put ratios and go long the most bullish\/short the most bearish deliver risk\u2011adjusted excess returns. The authors put it plainly: \u201cspecific market participant\u2019s options trading volume is a predecessor to asset price movements.\u201d [<a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2363475\">3<\/a>]<\/p>\n\n\n\n<p>The paper also documents who drives activity\u2014customer traders account for ~67% of opening\u2011buy call volume and ~63% for puts\u2014and notes prior evidence that such \u201cfull\u2011service\u201d (customer) flow was the most reliable predictor of future gains. The diagram on page 4 (\u201cBreakdown of ISE Option Data\u201d) shows the pipeline from order type \u2192 trader type \u2192 put\/call \u2192 ratio, making the sentiment construction transparent.<\/p>\n\n\n\n<p>Limitations. Aggregate PCRs can be contrarian at extremes and blend stock\u2011specific with market\u2011wide fear. Trader\u2011segmented PCRs are stronger but require specialized feeds.<\/p>\n\n\n\n<p>How to use it. Prefer customer \u201copening buy\u201d PCRs at the single\u2011name level; weight by minimum volume (e.g., \u226550 contracts) to avoid micro\u2011prints; rebalance daily.<\/p>\n\n\n\n<p>Pulling it together: when options data works best<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Near catalysts or earnings: O\/S and moneyness activity tend to anticipate the sign and magnitude of near\u2011term news.<\/li>\n\n\n\n<li>Where shorting is hard: O\/S is strongest when short\u2011sale costs and loan fees are high.<\/li>\n\n\n\n<li>High implied\u2011volatility cohorts: Call\u2011side moneyness concentration has its biggest edge here.<\/li>\n\n\n\n<li>High hedging intensity: Large, same\u2011day EHD spikes create 1\u20135 day drifts (then reversal).<\/li>\n<\/ol>\n\n\n\n<p>Bottom line: Trading options may be risky, but options data\u2014O\/S, hedging demand, moneyness concentration, and trader\u2011segmented sentiment\u2014consistently improves prediction of short\u2011horizon stock moves. The edges are real, researched, and replicable with clean data and careful cost control.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-visual-sectors-case-study\">Visual Sectors case study<\/h2>\n\n\n\n<p>At Visual Sectors our mission is to help retail investors busy with their day to day chores find edge using new approaches and strictly data-based research.<\/p>\n\n\n\n<p>So having learned all the above about options information, we decided to create indicators that would have very clear use cases, backtested performance metrics and wouldn\u2019t take much time to implement in strategies.<\/p>\n\n\n\n<p>Let\u2019s explore the process of creating one of the indicators &#8211; Accumulation change indicator (ACI)<\/p>\n\n\n\n<p>The logic behind this indicator is to grasp the general sentiment for equity. We wanted to use delta-adjusted open interest to define whether the sentiment is bullish or bearish.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-feature-engineering-factors-in-play\">Feature engineering: factors in play<\/h3>\n\n\n\n<p>First of all, we looked at our sentiment map to actually try and spot recurring patterns<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"528\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1-1100x528.jpeg\" alt=\"Options Indicator\" class=\"wp-image-232214 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1-1100x528.jpeg 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1-700x336.jpeg 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1-300x144.jpeg 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1-768x368.jpeg 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-1.jpeg 1136w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/528;\" \/><\/figure>\n\n\n\n<p>Source: Visual Sectors<\/p>\n\n\n\n<p>Delta-adjusted OI is displayed within 2 buckets &#8211; bullish and bearish<br>We quantify daily, 5 day, 30 day change by expiration, strike and total<\/p>\n\n\n\n<p>So to engineer features of the new indicator we had the following options to explore:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Days till expiration (DTE): if grouped by time, which group has the best predictive power? We ignored anything less than 5 DTE because theta made options prices change too rapidly. This wouldn\u2019t work for swing traders placing directional trades <br><br>The buckets for testing we want with:\n<ul class=\"wp-block-list\">\n<li>5-15 DTE<\/li>\n\n\n\n<li>16-30 DTE<\/li>\n\n\n\n<li>31 &#8211; 45 DTE<\/li>\n\n\n\n<li>46 &#8211; 60 DTE<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Moneyness: how \u201cfar\u201d in the money or out of the money open positions have predictive power? If a stock has bets with 500% upside &#8211; do we actually think it\u2019s a valid price target? Probably not. So we can choose to ignore it.<br><br>The straightforward way to calculate moneyness is to apply 100% to current stock price and just measure how far the strike is from current price. <br><br>However, not all stocks have a similar range of trades. Some would only move by 10% a month, other &#8211; more than 50%. Also, we noticed the dispersion of open interest wasn\u2019t similar too. Some very popular stocks could have a massive bulk of open interest 200-300% out of the money (for calls), smaller stocks with less open interest could have all of it accumulated within several per cent of the current price. <br><br>So we decided to test traditional moneyness calculation, but also, OI weighted. <br><br>The idea is to focus on the large chunks of open interest and define strikes that fall within a bucket of 60%, 70%, 80% or 90% of open interest. So we can actually pay attention to flexible ranges, like 80% through 150% of the current price for calls for 1 stock compared to 90-110% for another stock.<\/li>\n\n\n\n<li>Holding and analysis period: how many days of Open interest change should we track? When should we sell? <br><br>It\u2019s actually pretty straightforward. Delta-adjusted open interest change over 1 trading session, 2, 3, 4, 5 (aka 1 calendar week), 2 and 3 weeks. Same for holding periods. If you buy the equity using this indicator &#8211; how long should you hold it before selling? <br><br>It gives us 49 possible combinations to backtest<\/li>\n\n\n\n<li>Weekday: is there a difference between buying on Tuesdays or on Fridays? Funny enough, there is. So we decided to add 6 more variants: \u201cany day\u201d + 5 weekdays<br><\/li>\n<\/ol>\n\n\n\n<p>All those factors and the variants of the indicator to backtest summed up to 9,408 backtests that needed to be run for each stock. Our data covers a universe of 3,455 stocks.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-backtesting-challenges\">Backtesting challenges<\/h2>\n\n\n\n<p>We needed to make several other decisions like: which period does the backtest have to cover? What performance metrics are the priority? Should we add stop-loss\/take-profit rules?<\/p>\n\n\n\n<p>1. Defining the period was pretty straightforward. The idea that the market has changed post-COVID (but really &#8211; post-2018 panic rate cuts) pretty much defined the depth of the tests. But we also didn\u2019t want the \u201chelicopter money\u201d of post-COVID bounce back distorting results. So we chose January 1st, 2022 at the starting point and April 30th 2025 as the end date. This period has 2 very distinct bear markets and an extended bull market with 2 different regimes (low and high volatility market expansion)<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"613\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2-1100x613.jpeg\" alt=\"Options Indicator\" class=\"wp-image-232220 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2-1100x613.jpeg 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2-700x390.jpeg 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2-300x167.jpeg 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2-768x428.jpeg 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-2.jpeg 1474w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/613;\" \/><\/figure>\n\n\n\n<p>Source: Tradingview<\/p>\n\n\n\n<p>2. Take-profit and stop-loss<br><br>One of the big questions for a \u201ccalendar\u201d defined system is obviously: what if the price makes a rapid move? Either in your favor or not, do you keep holding?<\/p>\n\n\n\n<p>So we took all the positive records (price going the predicted direction) and all the negative records (price going the wrong way) and ran a quick backtest to see how often it reverses, and from what levels. Empirically, we identified 60th percentile for take profit and 40th percentile for stop loss.<\/p>\n\n\n\n<p>Basically, we ignore the top 40% of the best moves for the sake of keeping what 60% of price moves can lock in profit. Same way: if we see a price move that\u2019s within 40% smallest losses, we cut and don\u2019t sit out larger drawdowns.<\/p>\n\n\n\n<p>Perhaps, we can further explore these rules but for now &#8211; it\u2019s a switch on\/off mode to double the amount of backtests mentioned above: with take profit and stop loss or just according to the calendar.<\/p>\n\n\n\n<p>3. Performance metrics<\/p>\n\n\n\n<p>We focused on indicator variants that returned at least 40 observations across the 40 month backtest period. The argument for the priority metric internally was heated so we decided to let everyone decide for themselves and created simple filters and sorted columns of:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>$1,000 invested (only invest $1,000 per trade)<\/li>\n\n\n\n<li>Annualised cumulative return (reinvest profit)<\/li>\n\n\n\n<li>Win rate by trade (% of profitable trades)<\/li>\n\n\n\n<li>Win rate by month (% of profitable months)<\/li>\n\n\n\n<li>Alpha compared to broad market index<\/li>\n\n\n\n<li>Reward:Risk: median losing trade loss vs average winning trade win<\/li>\n\n\n\n<li>Sharpe ratio<\/li>\n\n\n\n<li>Sortino ratio<\/li>\n\n\n\n<li>Latency to profit (how many trading sessions would it take to get into positive returns)<\/li>\n\n\n\n<li>Hit and Loss streaks (how many losing\/profitable trades in a row have been recorded at its maximum)<\/li>\n\n\n\n<li>Win 3 out of 4 probability &#8211; something to keep your chin up, out of any random 4 trades in a row, what\u2019s the chance to see 3 profitable?<\/li>\n\n\n\n<li>Maximum drawdown<\/li>\n<\/ol>\n\n\n\n<p>All of those are calculated for buy and hold for set days and for take profit\/stop loss<\/p>\n\n\n\n<p>4. Overfitting<\/p>\n\n\n\n<p>To avoid overfitting we decided to return results not just for the whole period of 40 months but also separately for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bull markets<\/li>\n\n\n\n<li>Bear markets<\/li>\n\n\n\n<li>Full calendar years of 2022, 2023,2024 and 4 months of 2025<\/li>\n<\/ul>\n\n\n\n<p>Winning combinations for each stock would display the full period results, but filter out variants where even one of the mentioned above periods would underperform significantly. The borderline requirement was to return 10% annually, have a 55% month win rate, max drawdown less than 10% per trade.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-results-overview\">Results overview<\/h3>\n\n\n\n<p>The amount of results was overwhelming, so we used Google\u2019s LookerStudio to display them<\/p>\n\n\n\n<p>The variables would act as filters:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"203\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3-1100x203.jpeg\" alt=\"Options Indicator\" class=\"wp-image-232244 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3-1100x203.jpeg 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3-700x129.jpeg 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3-300x55.jpeg 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3-768x142.jpeg 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-3.jpeg 1449w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/203;\" \/><\/figure>\n\n\n\n<p>Performance metrics would be displayed as columns:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"722\" height=\"562\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-4.jpeg\" alt=\"Options Indicators\" class=\"wp-image-232250 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-4.jpeg 722w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-4-700x545.jpeg 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-4-300x234.jpeg 300w\" data-sizes=\"(max-width: 722px) 100vw, 722px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 722px; aspect-ratio: 722\/562;\" \/><\/figure>\n\n\n\n<p>Source: Visual Sectors<\/p>\n\n\n\n<p>And each stock would open a breakdown by periods:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"245\" height=\"225\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-5.jpeg\" alt=\"Options Indicators\" class=\"wp-image-232269 lazyload\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 245px; aspect-ratio: 245\/225;\" \/><\/figure>\n\n\n\n<p>Source: Visual Sectors<\/p>\n\n\n\n<p>So here are our winners:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CLF for winning months %<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"621\" height=\"230\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-6.jpeg\" alt=\"Options Indicator\" class=\"wp-image-232272 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-6.jpeg 621w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-6-300x111.jpeg 300w\" data-sizes=\"(max-width: 621px) 100vw, 621px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 621px; aspect-ratio: 621\/230;\" \/><\/figure>\n\n\n\n<p>Source: Visual Sectors<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>NVAX for return on $1,000 invested<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"809\" height=\"222\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-7.jpeg\" alt=\"Options Indicator\" class=\"wp-image-232274 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-7.jpeg 809w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-7-700x192.jpeg 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-7-300x82.jpeg 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2025\/10\/Options-Indicators-Visual-Sector-7-768x211.jpeg 768w\" data-sizes=\"(max-width: 809px) 100vw, 809px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 809px; aspect-ratio: 809\/222;\" \/><\/figure>\n\n\n\n<p>Source: Visual Sectors<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Options offer sign\u2011targeted exposure (calls\/puts) and embedded leverage.<\/p>\n","protected":false},"author":1690,"featured_media":199514,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":true,"footnotes":""},"categories":[339,338,341,9563],"tags":[20626,7150,20625,20627],"contributors-categories":[20596],"class_list":{"0":"post-231656","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-ibkr-quant-news","9":"category-quant-development","10":"category-options-quant","11":"tag-delta-gamma-hedging","12":"tag-implied-volatility","13":"tag-option-expected-hedging-demand","14":"tag-options-indicators","15":"contributors-categories-visual-sectors"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.3) - 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