Candle Pipelines

Candle pipelines determine how market data is modified to create different test scenarios for the candles-based Monte Carlo analysis. Jesse includes two built-in pipelines.

Moving Block Bootstrap

Uses moving block bootstrap to resample price movements while keeping short-term patterns. This is the recommended pipeline for most cases.

What it does:

• Resamples blocks of connected price movements
• Keeps short-term patterns in price changes
• Automatically finds the right block size from the batch size
• Keeps realistic price behavior

When to use this:

• Highly recommended for most cases because it preserves local structure without needing to hand-pick sigma values.
• The batch size (in one-minute candles) controls the typical block length that is resampled. For example, 7 * 24 * 60 corresponds to one week. Increase it to retain more short-term trend/direction; decrease it to encourage more frequent regime mixing.

Gaussian Noise

Adds random noise to candle prices while keeping realistic price relationships.

What it does:

• Uses the specified sigma values to add gaussian noise to price data
• Keeps proper OHLC relationships (High >= max(Open, Close), Low <= min(Open, Close))
• Makes sure all prices stay positive
• Keeps volume and timestamp data unchanged

Choosing sigma values:

• Set close_sigma to roughly match the average absolute price change per one-minute candle in your dataset. For example, if the average 1m change is about $10, set close_sigma = 10.0.
• Set high_sigma and low_sigma smaller than close, for example 2.0, to create realistic high/low wicks without overwhelming the candle body.
• These are not one-size-fits-all. You should experiment with different sigma values to best approximate the level of noise you want to inject for your asset and timeframe.

About batch size:

• The batch size is the number of one-minute candles processed per batch by the pipeline. For example, 7 * 24 * 60 equals one week of one-minute candles.
• You can tune this value depending on how much short-term market direction you want to preserve within each transformation step. Larger batches can better retain short-horizon structure; smaller batches increase mixing.

Which pipeline should I use?

If you're unsure which pipeline to choose, go with Moving Block Bootstrap. It produces realistic market scenarios without requiring you to manually tune noise parameters, making it the better default choice.

Use Gaussian Noise when you want fine-grained control over exactly how much randomness is injected into the price data, or when you want to test your strategy against specific levels of market noise.