Applying Sports Betting Simulation to Earnings Season: Probability-Based Options Plays

Beat the noise: use 10,000-run simulations to size options trades for earnings

Every earnings season traders face the same friction: loud headlines, swollen implied volatility, and the million-dollar question — how big will the move be and how should I size my options position? If that’s your pain point, this article gives a pragmatic, probability-first playbook that adapts sports-style 10,000-run simulations to options around earnings and event risk. By the end you'll know how to convert simulated outcome distributions into position size, the right strategy (straddle vs iron condor vs strangle), and precise risk controls.

Quick takeaways (read first)

• Simulate 10,000 price paths over your horizon including an earnings jump component to estimate probability of price ranges, expected P&L, and tail risk.
• Buy volatility (straddles/strangles) only when the simulation’s probability of beating the straddle breakeven exceeds the cost-adjusted threshold; otherwise prefer selling defined-risk spreads like iron condors if the simulation shows >x% chance of remaining inside wings.
• Size by risk-to-capital: target a fixed % of your account at risk (e.g., 0.5–1%), compute contracts from max loss, and validate via simulated P&L distribution and expected shortfall (ES).
• Use percentile outputs (5/95, median, expected shortfall) and probability-of-touch to set wings, strike distances, and adjustment triggers.

Why borrow a page from sports models?

Sports handicappers have been using massive Monte Carlo simulations — often 10,000 runs or more — to turn uncertain outcomes into actionable betting edges. The technique works the same for markets: run many stochastic price paths and extract probabilities for outcomes that matter to options sellers or buyers. Sports headlines in late 2025 and early 2026 show broad adoption of 10,000-run models; options traders can copy that precision to quantify the odds of an earnings move beating implied volatility.

"After 10,000 simulations, the model reveals its top picks" — sports model headlines are a useful analogy for probability-first trading.

Set up the simulation: inputs that matter in 2026

To get reliable outputs you must choose inputs that reflect the modern market structure of 2026: higher retail participation, richer options chains (weeklies, micro options), improved implied volatility surfaces, and a post-2025 volatility regime that often features clustered jumps around macro and earnings events.

Core inputs

• Spot price (S0) — current mid-price of the stock.
• Days to expiration (T) — match the options you will trade (weekly, monthly).
• Implied volatility surface (IV) — use the ATM IV and skew for wings; for earnings use IV that includes the earnings premium. For cross-asset hedging ideas and volatility context, read tactical hedging approaches that integrate precious metals and crypto exposure like tactical hedging with precious metals and Bitcoin.
• Expected earnings jump (μjump, σjump) — modeled from historical post-earnings return distribution or analyst surprise consensus; use a distribution (e.g., normal or student-t) rather than a single point estimate.
• Continuous volatility (σcont) — background daily vol pre/post event (annualized).
• Risk-free rate (r) — small effect for short horizons, include for completeness.
• Number of simulations (N) — 10,000 runs is now standard for robust tail estimation; you can scale down in quick scans but use ≥10k for sizing and ES metrics. For storing and querying large simulation outputs efficiently, engineering notes on ClickHouse for scraped data are directly applicable to simulation result pipelines.

Model design choices

• Jump-diffusion — add an earnings jump term at the known event time to capture a discontinuous move instead of pure geometric Brownian motion.
• Fat tails — use student-t or double-exponential jumps if the stock historically exhibits heavy tails post-earnings.
• IV reversion — model the IV path: it is often elevated into earnings and collapses afterward (vol crush). Simulate implied vol changes if you intend to trade vega exposure; tactical hedging notes above cover multi-instrument hedges that account for vol dynamics.
• Correlated market moves — if stock is highly correlated with indices, include correlated market shocks; macro moves often amplify earnings moves.

Practical 10,000-run Monte Carlo recipe

Here is a repeatable workflow you can implement in your backtest environment or run inside a broker-built simulator.

1. Gather inputs: S0, ATM IV, skew, days until earnings, days until option expiry, historical earnings move distribution, and current implied vol term structure.
2. Calibrate jump: estimate μjump and σjump from the company’s last 8–12 earnings reactions or from a cross-sectional sector model. If sparse, use analyst-consensus surprise dispersion and map to price move via historical betas.
3. Simulate background paths: for each run, simulate daily returns using GBM with σcont for days excluding the event day.
4. Inject earnings jump: at the event time, draw a jump from the jump distribution and apply multiplicatively to the path price. - Optionally sample IV shock: draw an IV drop (vol crush) conditional on jump sign/magnitude.
5. Price options per path: Using the simulated post-event spot and simulated IV (or deterministic post-crush IV), compute option payoff at planned expiry for each strategy (straddle, short iron condor, etc.).
6. Aggregate P&L and probabilities: compute the distribution of P&L, probability of expiring ITM for each leg, probability of touching strikes, and percentiles (5/50/95) of final spot.
7. Compute risk metrics: max loss per contract, expected shortfall (e.g., 95% ES), probability-of-ruin for given sizing, and required margin for sellers.

Case study: hypothetical example with decisions

Walkthrough a concise, realistic example so you can see how simulation outputs map to trading decisions.

Scenario

• Stock XYZ current price: $100
• ATM 30-day implied vol: 60% annualized (reflecting earnings)
• Historical median absolute post-earnings move for XYZ: ~8% (σjump = 8%), but tail events up to 25% have occurred in last 24 reports.
• Days to earnings: 3; days to option expiry: 30
• Account size: $200,000; target risk per trade: 0.75% ($1,500)

Simulation outputs (summary from 10,000 runs — hypothetical)

• Median end price (30-day): $101
• 5/95 percentile pricing band: [$86, $118]
• Probability |Δ| > 12%: 18%
• Probability |Δ| > 20%: 4%
• Simulated expected P&L for 1 ATM straddle (buy) paid $8.50: mean P&L = -$1.10, median = -$6.50, P(loss>0) = 28%
• Simulated probability stock stays within ±15% (i.e., inside width): 72%

Interpretation and decision

ATM straddle cost $8.50 implies an expected move of ~8.5% (round-trip annualization aside). The simulation shows an 18% chance of a move beyond ±12% but only 28% chance the straddle is profitable at expiry given the vol crush and time decay. That suggests buying the straddle is not favorable under current pricing.

Alternatively, selling a 15% iron condor (wings at $85 and $115, 15% from spot) shows a 72% chance of expiring fully intact. If the net credit collected for the iron condor is $1.60 (max loss width $15 - credit $1.60 = $13.40), the simulated expected P&L is positive with a reasonable probability and known max loss. For a seller who wants defined risk and higher probability, the condor can be sized accordingly.

Position sizing from simulation

Target account risk = $1,500. For the short iron condor, per-contract max loss = $13.40 * 100 = $1,340. That means one contract aligns with risk limit (~$1,340), so size = 1 contract. For the straddle, per-contract max loss = premium = $850, so a 0.75% risk budget would allow up to 1 contract with leftover capacity; however simulation shows low probability of profit — so you wouldn’t take it.

Rules for choosing strategy from simulation outputs

Turn the simulation outputs into decision rules. Below are practical filters and thresholds used by systematic traders in 2026.

When to buy a straddle/strangle

• If simulation probability(|Δ| > breakeven_move) > breakeven_threshold. Breakeven_move = total_cost / spot. For an $8.50 straddle at $100, breakeven_move ≈ 8.5% (ignoring commissions).
• Set breakeven_threshold > 40–45% for buys (because long options have a negative expected value due to time decay and IV dynamics). If simulation shows >45% chance of exceeding breakeven_move, long straddle may be justified.
• Prefer buying if implied vol is low vs. historical vol rank. Use IV rank and IV percentile (IVR/IVP) metrics standard in 2026 platforms.

When to sell defined-risk spreads (iron condor / put credit spread / call credit spread)

• Sell when simulation shows probability of staying within wings > target probability. For iron condors, many sellers target a 65–80% chance of expiring inside wings based on their edge and tail tolerance.
• Use the simulation to size wings: move wings outward until the simulated probability of touching either wing falls below your target probability-of-loss.
• Ensure risk-reward fits account rules: credit collected vs. max loss and margin required.

When to use directional hedged strategies (ratio spreads, calendars)

• Use when simulation shows skewed outcome distribution (e.g., 60% chance up, 40% down). Build asymmetric positions: buy more upside calls and sell closer puts, or construct a poor-man’s covered call calendar if you expect IV to revert.
• Calendars can exploit elevated IV in near-term options vs longer term if your simulation predicts mean reversion post-earnings.

Refining position sizing with risk metrics

Simulation gives you the P&L distribution. Use these outputs to size positions more rigorously than naive contract counts.

Key metrics to extract

• Max loss per contract — obvious for buys and defined-risk sells.
• Expected Shortfall (ES) at 95% — average loss in the worst 5% of simulated outcomes; useful for tail protection sizing.
• Probability of ruin — probability simulated loss > account risk threshold for given position size.
• Expected P&L — average across sims; helpful but insufficient alone because options have skewed distributions.

Sizing formula (practical)

Use a max-risk per trade approach: choose RiskCap (e.g., 0.75% account). Compute contracts = floor(RiskCap / MaxLossPerContract). Validate with ES: ensure ES >= some fraction of RiskCap (e.g., ES not exceeding 2x RiskCap). If ES is huge, reduce size even if max loss appears small.

Operational notes: execution and live adjustment

Simulations are only as useful as the execution discipline that follows. Here are practical live-trading rules built from simulated triggers.

• Pre-set adjustment triggers: use the simulation to set price or probability triggers (e.g., if real-time probability-of-touch crosses 25% use roll/hedge plan A; if it crosses 40% use plan B). For traders broadcasting or trading from the road, check compact-field gear and streaming rig reviews (compact streaming rigs for traders, compact control surfaces and pocket rigs).
• Use probability-of-touch (PoT): PoT is often a better intraday alert than theoretical delta. If PoT for your sold wing exceeds your calibrated threshold, tighten wings or reduce size.
• Account for IV collapse: for long vol positions assume a post-earnings IV drop in your sims; if your strategy relies on vega, plan monetization before full collapse or buy longer-dated vol to hedge.
• Costs and slippage: include commissions and realistic fills in the simulation P&L (especially for multi-leg iron condors where leg fills matter). Also plan for operational outages and execution risk — read the outage postmortem on recent platform outages to learn incident-response lessons that reduce surprise execution gaps.

Advanced: ensemble and machine learning enhancements in 2026

In 2026, many quant traders layer ensembles and ML to improve jump estimates. Useful techniques:

• Ensemble jump models: combine historical E[R|surprise], volatility of analyst estimates, and short interest to form a composite jump prior.
• Bayesian updating: update your jump distribution in real-time as pre-earnings order flow and options skew change.
• Scenario weighting: weight simulations by macro scenarios (Fed releases, CPI) if earnings coincide with other event risk.

For efficient model training and memory-conscious ensembles see notes on AI training pipelines that minimize memory footprint, and for best practices around large-simulation storage and query use the ClickHouse architecture guide.

Checklist: from simulation to trade in 10 steps

1. Pull S0, IV surface and historical earnings return series.
2. Calibrate jump-diffusion parameters.
3. Run 10,000 Monte Carlo paths including IV path assumptions.
4. Compute percentiles, PoT for candidate strikes, simulated P&L for strategies.
5. Choose strategy by comparing simulated P&L and probability thresholds.
6. Size by RiskCap and validate with ES and probability-of-ruin.
7. Place orders with limit/OTC controls and slippage buffers.
8. Set automated alerts for PoT, price, or P&L thresholds derived from simulations.
9. Post-event, execute exit or roll plan based on realized move vs simulated scenarios.
10. Log the trade and update your jump calibration for the next earnings event.

Common pitfalls and how to avoid them

• Ignoring IV dynamics: failing to simulate vol crush leads to overestimating long-vol wins. Always include a post-earnings IV path.
• Using too few runs: 1,000 runs miss tail probabilities. Use ≥10,000 for sizing.
• Overfitting to one stock’s history: diversify your jump calibration with sector and macro inputs.
• Underestimating liquidity and execution risk: test fills and slippage in the sim P&L; consider which devices and connectivity you'll trade from — see lightweight laptop picks and CES gadget reviews (top lightweight laptops, CES 2026 gadget roundup).

2026 market context that changes the calculus

Two trends in late 2025 and early 2026 matter for event trading:

• Better retail tools and built-in simulators: brokers now offer Monte Carlo and probability-of-touch analytics, so edge comes from better priors and execution, not raw computation. If you rely on partner analytics, reducing onboarding friction for AI-driven feeds helps maintain quality (reducing AI onboarding friction).
• Volatility structure evolution: post-2025 volatility regimes have seen clustered jumps on macro days. That increases the value of conditional simulations that include correlated market shocks.

Final checklist before you trade

• Have you run ≥10,000 simulations with a jump term?
• Do the simulated probabilities justify buy vs sell given the price of vol?
• Is your max risk per trade within account RiskCap and validated by ES?
• Are your adjustment triggers defined and automated where possible?
• Have you accounted for execution costs and liquidity?

Closing: transform uncertainty into disciplined edge

Applying sports-style 10,000-run simulations to earnings season turns fuzzy gut calls into quantified trade hypotheses. The goal isn’t to predict exactly which way the stock will jump — it’s to know the probability profile around which you can size positions, choose strategies (straddle vs iron condor), and set objective adjustment rules. In 2026, with improved retail tools and richer IV surfaces, the differentiator is how you build priors, simulate conditional outcomes, and translate percentiles into contract counts and risk controls.

Start small, validate, and iterate: run the sim on paper or a small-risk allocation, compare realized outcomes to simulated distributions, and refine your jump model and IV assumptions. Over time you’ll learn where your edge lies — and whether it’s in buying skewed tails, selling defined-risk premium, or building asymmetric hedges.

Call to action

If you want a ready-to-run Monte Carlo template tuned for earnings trades (includes jump-diffusion, IV pathing, and P&L output for straddles and iron condors), subscribe to our options signals list or download the free Python/Excel starter kit. Get trade-ready simulations and weekly earnings watchlists optimized for probability-based sizing. When distributing starter kits and signal downloads, follow best practices for secure redirects and live-drop safety (redirect and live-drop safety).

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