This Option Strategy Turned $10k Into $1 Million In One Year

Finance-Specific Summary

The video outlines an options strategy intended to profit from earnings-related volatility mispricing, specifically by selling options around earnings when implied volatility (IV) is often overpriced.

Core Idea / “Edge”

• The presenter claims a repeatable statistical advantage: earnings events can create an imbalance where options are often overpriced due to hedging demand and uncertainty.
• Mechanism
  • IV Crush: IV typically drops rapidly after earnings.
  • Actual move smaller than expected: if the stock moves less than the market-implied earnings move, selling options can profit.
• Why the mispricing persists (claimed)
  • Price-insensitive hedgers (institutions/funds/retail) buy protection regardless of whether IV is truly rich or cheap.
  • Speculators add demand by buying short-dated upside calls, pushing IV higher into earnings.

Instruments / Tickers Mentioned

• Amazon (AMZN)
• No other tickers/assets (ETFs/bonds/crypto/commodities) were mentioned in the subtitles.

Options Structures and Methodology (Framework)

The presenter focuses on two short-volatility earnings structures:

1) Short Straddle (Short Volatility)

• What to do
  • Sell 1 call + 1 put
  • Same strike (typically at-the-money)
  • Same expiration (short-dated, near earnings)
• Profit when
  • The stock moves less than expected
  • IV collapses after earnings
• Key risk
  • High gamma risk near earnings: large adverse underlying moves can overwhelm IV-crush gains.

2) Long Calendar Spread (Debit)

• What to do
  • Sell a near-term option (front month)
  • Buy a longer-dated option (back month)
  • Same strike (typically ATM)
  • Described as a debit strategy (the back option costs more)
• Suggested setup
  • About a 30-day expiration gap between front and back month options
• Risk/return logic
  • Even though it is described as “long vega” overall, the desired outcome is:
    • front-month IV falls more than back-month IV
  • The position is short gamma overall, so smaller underlying moves are preferred
• Tradeoff
  • Calendar: smoother equity curve / better risk control, but lower average returns than straddles
  • Straddle: higher potential returns, but worse tail risk

Backtest / Data Setup (Numbers and Metrics)

Dataset

• 4,500 unique stocks
• 2007 to “today”
• 72,500 earnings events
• Entry timing
  • Positions opened 15 minutes before the close of the trading session prior to earnings

Performance Evaluation / Closure Timing (Referenced)

• “Jump” return measured closed 15 minutes into the session after earnings
• Another timing reference: closed 15 minutes before market close on the day after earnings

Costs

• Includes Interactive Brokers commissions and slippage
• Modeling uses bid-ask spreads and volume (not purely theoretical pricing)

Predictor Variables (Filters) and Rule-Based Entry Model

The model uses predictors from the IV term structure and pre-earnings conditions.

Predictor Variables

• Term structure slope
  • Measures steepness of the IV term structure
  • Uses difference between near-term IV and further-out IV of 45+ days
  • Presenter claims more negative slope → higher returns (backwardation)
• Term structure ratio
  • Similar concept, expressed as a ratio
• 30-day average volume
  • Liquidity / participation measure
• IV/RV ratios
  • Compares implied volatility vs realized volatility over the pre-earnings period

Top Correlations (Described)

• More negative term structure slope (front vs 45+ days) → higher returns (for both straddle and calendar)
• Higher 30-day average volume → better returns (more “price-insensitive” demand)
• Higher IV30/RV30 → better expected return (more implied-overpricing)

Entry Rule (Framework)

Trades are taken only when all three conditions are met:

• Term structure slope is sufficiently negative
• 30-day average volume is above a threshold
• IV30/RV30 is high enough

Filtering Impact

• Straddle: filtered out 88% of events
• Calendar: filtered out 90% of events

Key Performance Results (Backtest Outcomes)

Unfiltered / Distributional Results

Short Straddle (“Straddle Jump”)

• Returns cluster near small profits, but with a long left tail (extreme losses)
• Claims:
  • 1% of the time: lost 130% or more
  • 1% of the time: lost over 41%
  • One outlier: loss over 9,200 on a single trade
  • Mean return near 0% (no edge if trading blindly)

Calendar (“Calendar Jump”)

• More stable distribution; fewer extreme losses
• Worst case is limited to losing the debit paid
• Mean return near 0% unfiltered (no edge if trading blindly)

Filtered Model Mean Returns

• Straddle

  • Mean return: +9% (vs ~0% unfiltered)
  • Standard deviation: 48%

• Calendar

  • Mean return: +7.3% (vs ~0% unfiltered)
  • Standard deviation: 28%

• Max loss (as stated)

  • Straddle max loss: 8,130%
  • Calendar max loss: subtitles were unclear, but the intent is that calendar’s catastrophic loss is far lower (with commission impact mentioned).

Monte Carlo / Sizing and Risk Management (Critical Numbers)

The video emphasizes Kelly criterion sizing and warns against full Kelly due to drawdown/bankruptcy risk.

Monte Carlo Settings

• 10,000 simulated P&L paths
• Starting portfolio: $10,000
• Horizons:
  • 1,000 trading days (~4 years)
  • and 252 trading days (~1 year)

Full Kelly

Straddle

• Kelly fraction: 6.5% per trade
• Claims: no bankruptcies
• But:
  • ~35% of paths had max drawdown > 45%
  • Some paths dropped as low as ~80% capital

Calendar

• Kelly fraction: 60% per trade
• Claims: 485 bankruptcies out of 10,000 (~5%)
• Drawdown distribution described as heavily concentrated around 80–95%
• Conclusion: full Kelly is too aggressive for survival

Fractional Kelly (Presenter Recommendations)

30% Kelly

• Straddle
  • Bet: ≈ 2% per trade
  • Largest drawdown: ≈ 37%
  • Average max drawdown: ≈ 15%
• Calendar
  • Bet: ≈ 18% per trade
  • Max drawdown: ≈ 76%
  • Mean around 40% (still too high)

10% Kelly

• Further reduced sizing for both straddle/calendar
• Presented as the point where drawdowns become more “in line” with sustainable risk
• Presenter states a personal preference for:
  • calendar at ~10% Kelly
• Mentions Sharpe ratio outcomes (subtitles described them as imprecise)

Long-Term Projection (10-Year View)

• Calendar strategy at 10% Kelly
  • Starting: $10,000
  • Mean ending value: ~$6 million
  • Implied CAGR: ~90% (as stated)
  • Mean max drawdown: ~20%
  • Mean longest drawdown duration: ~6 months
  • Win rate: ~66%
  • Expectancy per trade: ~2.65
  • Mean Sharpe: 3.5

Strong emphasis: position sizing / risk management is “number one priority.”

Live Trade Example (AMZN)

The presenter claims using the model on Amazon (AMZN) for upcoming earnings.

• Action: entered a calendar
  • February 7th / March 7th call calendar
  • $3.33 debit
• Outcome timing
  • ~15 minutes into market open after entry: stock moved only ~2.5%
  • Closed for profit: $9,300
• Comparison claim
  • Hypothetical straddle: “made even more” while trading fewer contracts
    • 20 contracts for straddle vs 100 contracts for calendar
  • Reasoning noted:
    • straddle commissions lower and returns potential higher
    • but straddle has worse catastrophic loss risk

Explicit Recommendations / Cautions

• Strongly recommends against trading at full Kelly due to bankruptcy/tail risk
• States they personally only trade the recommended setups flagged by the conditions
• Highlights:
  • edges are noisy
  • systems can fail during drawdowns—avoid abandoning a strategy prematurely

Disclosures / Disclaimers

• No explicit “not financial advice” disclaimer appeared in the provided subtitles.

Presenter / Sources

• Single presenter (name not provided in subtitles)
• Source material described as:
  • “Real historical market data” (no external named provider)
• Tooling mentioned:
  • a Python script
  • a code calculator / trade tracker via a LinkedIn description (link not shown)