Quantitative Stock Analyzer Pro

Move beyond "this looks good on today's snapshot" with walk-forward backtesting, CVaR risk analysis, constrained optimization, and self-contained HTML reports.

Free vs Pro

The free analyzer runs factor exposure, correlation, and screening on demand. Pro adds the portfolio layer: SLSQP-driven portfolio optimization with constraints, risk decomposition by factor and position, stress scenarios (rate shocks, sector rotations, vol spikes), and backtestable factor-timing signals. Free describes the stocks — Pro optimizes the portfolio.

Upgrade Path

Free for single-ticker analysis; Pro when the question becomes "what weights should I hold" rather than "what is this stock."

The Problem

You've built a momentum-based stock screener using a free factor-scoring toolkit. It looks brilliant on the current snapshot — optimal weights, high Sharpe ratio, clean diversification. But you have no idea if the strategy would have worked historically, what happens in a market crash, or whether the unconstrained optimizer is concentrating 40% of your book in a single sector. You're making allocation decisions based on a single point in time, not validated evidence.

What You Get

• Walk-forward backtesting engine — splits history into in-sample (fit) and out-of-sample (test) windows, reports IS and OOS Sharpe separately so overfitting is visible, not hidden, with 10bps transaction cost modeling
• Full risk suite — Conditional Value at Risk (CVaR) at 95%/99%, scenario stress tests (market -20%, sector shock, vol spike, liquidity haircut), max-drawdown autopsies with recovery time, and Monte Carlo forward path simulation (2,000 paths)
• Constrained portfolio optimization — adds sector caps, asset-class bounds, cardinality constraints (min/max holdings), cash floors, and turnover-aware rebalancing penalty to the standard max-Sharpe/min-variance framework
• Transaction-cost-aware rebalancing — penalizes turnover in the optimizer so it doesn't churn the book every rebalance window, reporting the cost impact side by side with unconstrained weights
• HTML report generator — produces a single self-contained .html file with SVG equity curve, drawdown chart, risk tables, and portfolio weights — openable in any browser, no dependencies
• Factor scoring integration — computes momentum, volatility, value, and quality factors with z-scores, reusing the free version's factor surface alongside Pro's validation tools

Why This Beats Prompting It Yourself

When you ask an agent to "build a backtest," it produces a single-pass backtest that uses full-sample statistics to set parameters — the textbook definition of overfitting. Pro's walk-forward engine enforces out-of-sample separation so the agent cannot cheat. When you ask for "risk analysis," an agent typically reports VaR only, which understates tail risk. Pro requires CVaR (expected shortfall) alongside VaR for every risk assessment, preventing the tail-risk blindness that causes blowups.

Example Session

You: Backtest a momentum tilt on [AAPL, MSFT, NVDA, JPM, XLV] from 2021-01-01 to 2025-12-31.

Agent: Ran walk-forward backtest with 70/30 IS/OOS split, 10bps transaction cost. IS Sharpe: 1.24. OOS Sharpe: 0.98 (79% of IS — signal held up). Max Drawdown: -18.4% (Q3 2022, recovered in 47 trading days). CVaR 95%: -2.9%/day. CVaR 99%: -4.1%/day. Stress Test: Market -20% scenario: portfolio -18.7%. Sector shock (tech -25%): -11.3%. Monte Carlo: P10 terminal wealth 0.82× starting. P(drawdown > 20%) = 23%. Recommendation: Signal is valid OOS. Main risk is sector concentration (68% tech). Consider trimming the highest-correlation pair before the crash scenario.

Use Cases

• Validating a factor-based stock selection strategy before committing real capital
• Stress-testing a multi-asset portfolio against historical crash scenarios (2008, 2020, 2022)
• Producing a shareable HTML report for an investment committee review
• Optimizing a portfolio with hard constraints (max 20% per sector, 5-12 holdings, 40% max turnover)
• Detecting overfitting by comparing in-sample and out-of-sample performance metrics

Known Limitations

Backtests use today's ticker list and are subject to survivorship bias — delisted securities are not included unless explicitly added. Past Sharpe is never future Sharpe; all results are framed as historical evidence, never predictions. yfinance rate limits may cause batch download failures for large universes.

Tags: trading quant finance backtesting risk-management portfolio-optimization monte-carlo

Version: 1.0.0