worst setup pattern miner

The problem

Traders often struggle to identify why they give back profits or where their execution quality collapses. Finding the specific environmental and behavioral variables that cause high-damage losses is difficult to isolate manually from messy journal data.

What it does

• Analyzes execution logs, trade journals, and broker statements to isolate negative expectancy patterns.
• Segments performance by market regime, volatility, time of day, session, and day of the week.
• Identifies behavioral correlations such as revenge trading, FOMO, and rule adherence failures.
• Calculates damage in R-multiples to rank patterns by their actual impact on the bottom line.
• Generates a concrete "Stop Trading" playbook with specific rules to avoid high-loss contexts.

Why this beats prompting it yourself

This skill goes beyond simple win/loss ratios by normalizing data into R-multiples and combining dimensions into compound pattern cards. It systematically audits behavior against environment, preventing you from missing the hidden correlations that generic prompts overlook.

Use cases

• Mining a quarterly trade log to find the specific hour that drains the most capital.
• Auditing execution quality to distinguish between good losses and bad behavior.
• Creating a "Friday Lunch" or "Post-Loss" restriction rule based on historical data.
• Isolating which setup variations consistently fail during low-volatility regimes.

Known limitations

Requires a minimum of 20 trades for statistical relevance. Findings are descriptive of past performance and may not predict future regime shifts.