Score monthly BUY/SELL/HOLD judgment accuracy against realized outcomes.
npx skills add https://github.com/Moyuchiiii/claude-investment-sim --skill learn-moyuchiiii
Please help me install this Agent Skill. Skill: learn Source: https://github.com/Moyuchiiii/claude-investment-sim/tree/main/.claude/skills/learn Command: npx skills add https://github.com/Moyuchiiii/claude-investment-sim --skill learn-moyuchiiii
This Skill provides a structured approach to measure and improve the accuracy of month-by-month trading judgments by comparing predicted actions (BUY/SELL/HOLD) to actual outcomes and tracking performance over time.
Start with the earliest unlearned month and run the full walk-forward loop to generate judgments and compute accuracy.
High-intent search queries and answers about installing and using this skill.
Walk-forward evaluation quantifies trading judgment accuracy by comparing predicted BUY/SELL/HOLD actions against realized outcomes month-by-month across historical data. It tracks performance over time to identify reliable signals and extract generalized lessons.
Walk-forward evaluation is a structured process that scores the accuracy of monthly trading judgments against actual realized outcomes. By stepping through historical data sequentially, it tracks monthly accuracy and records lessons to refine future decisions.
Yes, you can track monthly BUY/SELL/HOLD accuracy across 27 stocks. The evaluation compares your predicted actions to actual outcomes and records lessons learned for future months to guide future decision-making.
To start the walk-forward loop, begin with the earliest unlearned month in your historical datasets. Run the full loop to generate judgments, compute accuracy against realized outcomes, and record lessons for progress tracking.
Yes, computing trading judgment accuracy requires access to learning datasets and scripts that generate judgments. These scripts compare your predicted actions against realized outcomes and record lessons for future months.
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