Using Delta-Neutral Strategies with Machine Learning to Tilt the Odds

Predicting market direction is notoriously difficult. Fortunately, as computing professionals, we have tools that allow us to sidestep directional bias and focus on trades with statistically favorable outcomes. While individual gains may be modest, a consistent mathematical edge—compounded over many trades—can yield substantial returns, thanks to the law of large numbers.

One such approach involves delta-neutral options strategies, specifically straddles and strangles.

Straddle vs. Strangle: Delta-Neutral at Entry

• Straddle: Buy a call and a put at the same strike price and same expiration date.
  • Upside breakeven: Call strike + total premium
  • Downside breakeven: Put strike – total premium
  • Profit occurs when the stock moves beyond either breakeven point.
• Strangle: Buy a call and a put with different strike prices but the same expiration.
  • Upside breakeven: Call strike + total premium
  • Downside breakeven: Put strike – total premium
  • Cheaper than a straddle but requires a larger move to be profitable.

Putting the Odds in Your Favor with Nearest Neighbors

To improve trade selection, we can apply machine learning—specifically the NearestNeighbors algorithm—to historical data. Here's how:

1. Feature Engineering & EDA: Begin with exploratory data analysis to select meaningful input features (e.g., volatility, momentum indicators, volume).
2. Training & Testing: Use 4,000 datapoints to train the model and reserve the last 1,000 for testing. For each test point, find k nearest neighbors (e.g., k = 50).
3. Trade Simulation: Define the required percentage move to breakeven (e.g., 3%). For each neighbor, check whether the stock moved ≥ that percentage up or down within the option’s time horizon (e.g., 15 business days). Count “good trades” vs. “bad trades.”
4. Statistical Evaluation: Record the distance of each neighbor from the current datapoint. Use a t-test to compare distances of good vs. bad trades. If good trades tend to be closer, the current datapoint likely resembles profitable setups.

Example Walkthrough

Suppose a stock trades at $100. You buy a straddle with:

• $101 call for $1.50
• $99 put for $1.50
• Total cost: $3
• Required move: ±3% (to $103 or $97)

You train NearestNeighbors on 4,000 datapoints and test on 1,000. For each test point, you find 50 neighbors, giving you 50,000 simulated trades.

You then check: did the stock move ≥3% up or down within 15 days?

• Good trades: 40,000
• Bad trades: 10,000
• Success rate: 80%

If the t-test confirms that good trades are statistically closer to the current datapoint than bad ones, you’ve got a strong signal to proceed.

For EDA and feature selection Click Here