KA Pattern Evaluation Overview

Quick Summary

• Take a recent slice of candles for one trading pair and test a simple synthetic pattern on that slice.
• Compare that pattern result with two baselines: buy-and-hold long, buy-and-hold short, and the raw price move.
• Walk forward through equally sized slices to see how the asset behaved next and store all metrics for review.

1. Data Scope

• Uses the same candle history as the legacy evaluator (ticker_data_binance).
• Runs per trading pair and interval, honouring primary_ticker_limit from config.yaml.
• Each evaluation pulls the latest configured packet count and sorts candles chronologically.

2. Pattern Window

1. Select a random window length between pattern_window_min and pattern_window_max.
2. Choose a starting index that keeps the window fully inside the dataset.
3. Compute the short-term synthetic return by blending baseline returns plus a configurable margin (placeholder until the real pattern logic lands).

Stored values:

• short_term_return – synthetic pattern performance.
• long_return / short_return – baseline buy-and-hold results after fees.
• base_start_price / base_end_price – raw asset price for the window.

3. Projection Windows

After the short-term window we advance by the same window length and capture:

• Long baseline return.
• Short baseline return.
• Raw price change percentage.

These feed the trajectory chart on the KA detail page.

4. Scoring

• score equals the short-term synthetic return (rounded to 6 decimals).
• Metadata tracks counts/averages of positive windows for long, short, and price projections.

5. Persistence Contract

KaEvaluationStorage writes:

• candidate_id, trading_pair, interval_name, packets
• score, reference_price
• metadata_json, reasoning_json, simulation_json
• created_at, updated_at

simulation_json contains the short-term window, baselines, price anchors, and the projection array.

6. UI Notes

• Performance summary shows short-term vs. long/short baselines and raw price move.
• Performance trajectory plots projection windows alongside the short-term point.
• Metadata mirrors DB fields so you can cross-check stored values.

7. Future Hooks

• Replace the placeholder margin with real anomaly/pattern detection.
• Add templated trade decisions (e.g., enter long when pattern beats long baseline by X%).
• Allow per-interval projection horizons.