Ev Calculator Expected Value Stock Analyzer

Quantitative stock position analysis using probability-weighted bull, base, and bear scenarios.

Install
cmdop skills install agensi-ev-calculator-expected-value-stock-analyzer

The EV Calculator Expected Value Stock Analyzer is a skill that applies scenario-based modeling to quantify the expected value of stock positions. Rather than relying on price action or intuition, it structures analysis across three scenarios — bull, base, and bear — and assigns probability weights to each to produce a final weighted expected return and dollar-value EV for a given position.

The skill generates scenario probabilities by drawing on catalyst-driven upside factors and macro downside risks, then refines those weights using options sentiment, historical earnings surprise patterns, and short interest data. Each calculation also includes a meta-analysis layer that reports on the quality and confidence level of the underlying data inputs, so the agent can surface uncertainty alongside the output.

Output takes the form of structured per-ticker reports. Each report covers the current position value, a breakdown of scenario probabilities against return percentages, and a final weighted expectancy score with an attached confidence rating. This structured format is designed to support hold, trim, or exit decisions grounded in statistical expectancy.

This skill is appropriate when an agent needs to automate probability-weighted risk analysis across a portfolio of individual equity positions. It is not suited to fixed-income instruments, derivatives pricing, portfolio-level optimization across asset classes, or any use case requiring real-time market data feeds, as no environment variables or data connections are defined in this skill.

Use cases

  • Calculate probability-weighted expected value for an individual stock position before an earnings event
  • Combine options sentiment and short interest data to refine bull and bear scenario weights
  • Generate structured EV reports for multiple tickers in a portfolio review workflow
  • Assess data quality and confidence levels attached to each scenario calculation
  • Automate hold, trim, or exit decision inputs based on statistical expectancy rather than price action

When to use it

  • Analyzing individual equity positions where catalyst-driven upside and macro downside risks can be identified
  • Workflow requires a structured, repeatable EV framework across multiple tickers
  • Agent needs confidence ratings alongside scenario outputs to flag low-data-quality situations
  • Replacing ad-hoc manual balancing of options Greeks, short interest, and earnings volatility data

When not to use it

  • Analysis targets fixed-income instruments, options contracts, or non-equity asset classes
  • Use case requires live or streaming market data, as no data connections are configured
  • Portfolio-level optimization across multiple asset classes is needed rather than per-position EV
  • A specific package registry or versioned dependency is required, as none is defined for this skill