Quantitative Research vs Vendor Evaluation — Which AI Skill Should You Use?

The short version

By ClawHub Community

Backtest trading strategies and validate investment ideas with statistical rigor.

By Mohit Agarwal (PM Skills)

Compare suppliers with weighted scorecards and get a recommendation on who to buy from.

Backtests trading strategies with proper methodology to avoid common pitfalls like look-ahead bias
Validates alpha signals and investment hypotheses using statistical measures like Sharpe ratios and t-statistics
Analyzes factor models and portfolio construction approaches
Tests statistical arbitrage strategies and pairs trading setups
Detects market regimes and evaluates strategy robustness across different market conditions

Creates a weighted scorecard with criteria like functional fit, pricing, implementation difficulty, and vendor stability
Generates specific questions to ask each vendor about their product, pricing, support, and security
Scores each vendor against your criteria and calculates a total weighted score
Delivers a recommendation with the winner, runner-up, key risks, and contract terms to negotiate
Includes reference check questions to ask existing customers of each vendor

You're testing a trading idea and need to know if it actually works or just looks good on paper
You need to validate an investment strategy before putting real money behind it
You're building a portfolio and want to understand the statistical edge of different approaches
You're analyzing market patterns or signals and need rigorous statistical validation

You're comparing software tools or service providers and need a systematic way to decide
You're running an RFP process and need to score multiple vendors fairly
You need to justify a vendor choice to your team or leadership with data
You're evaluating suppliers and want to avoid missing important criteria or risks