entry-signals

Trading entry signals are evaluated by loading historical pattern data and ranking 30 active patterns using their recorded success rates, sample sizes, and confidence scores against your predefined confidence threshold to guide position entries.

Use confidence scores and success rates for backtesting by loading the .pattern_history.json dataset, filtering the 30 active patterns by a predefined confidence threshold, and interpreting their historical success rates and sample sizes to make risk-aware trade decisions.

Rank market entry signals across multiple assets by using a dataset-driven approach that evaluates 30 active historical patterns, filtering them by a configured confidence threshold to support risk-aware entry decisions.

Yes, historical pattern data can guide entries for volatile assets by comparing top-performing patterns and their confidence scores against your predefined threshold to determine if opening a position is statistically supported.

Yes, trading decision support signal analysis requires loading the .pattern_history.json dataset and honoring the confidence threshold configured in .skill_meta.json to correctly interpret the 30 active patterns for entry decisions.

Trading entry signals become unreliable when the configured confidence threshold is set too low or pattern sample sizes are insufficient, resulting in noisy historical data that fails to support accurate risk-aware entry decisions across assets.