Trading Earnings Volatility With Options

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This strategy was inspired by u/mori226. The system trades options spreads (diagonals) around earnings volatility, buying when IV is lowest, selling when it is highest.

Current work-in-progress is in the embedded PDF below and includes backtest results from my prototyping in OptionStack.


Spread Entry

  1. Long: One month after earnings, buy cheap LEAP calls.
    • Entry Criteria (all must be met)
      • It’s been at least 30 days since the last earnings report
        DaysSinceLastEarnings >= 30   .
      • It’s over 30 days until the next earnings report 
        DaysUntilNextEarnings >= 30
      • IV Percentile is between 0 & 10 (per last 100 days)
        0 < IVPercentile100Day <= 10   .
    • Contract Selection
      • 300 – 400 Days till expiration
        300 < DaysTillExpiry < 400  .
      • Strike is OTM by approx 10 x 20-Day ATR
        ClosePrice + (10 * 20D ATR)
    • Allocation
      • 10% of available buying power
  2. Short: Leading up to next earnings, sell calls to match each LEAP.
    • Entry Criteria
      • It’s less than 10 days until the next earnings report 
        DaysUntilNextEarnings <= 10
      • IV Percentile > 90 (per last 100 days)
        IVPercentile100Day > 90   .
    • Contract Selection
      • 20 – 30 Days till expiration
        20 < DaysTillExpiry < 30 
      • Strike is OTM by approx 10 x 20-Day ATR
        ClosePrice + (10 * 20D ATR)

Exits

  1. Long (LEAP) Exit Criteria
    Any of these criteria can be met
    1. LEAP gains 100% return
      LEAPUnrealizedProfitPct >= 100
    2. LEAP takes 50% loss
      LEAPUnrealizedProfitPct <= -50
    3. LEAP expires within 90 days
      DaysTillExpiry <= 90 
  1. Short Exit Criteria
    Any of these criteria can be met
    1. LEAP position was closed
    2. It’s been over 30 days since last earnings report
      DaysSinceLastEarnings >= 30
    3. Price crosses above the short’s strike price
      ClosePrice > ShortStrikePrice
    4. Short leg gains 50% of maximum reward
      PctReturnOfMaxReward >= 50

About the author

quantish
By quantish

quantish

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