Author-Only Appendix · Product Strategy Notes
Book-to-Product Bridge: Features from the Original Manuscript to Consider for EasyCompare
A catalog of features, metrics, and workflows mentioned in the original StockQuanta / CoinQuanta book draft, how we adapted them in Chapters 5 to 9 today, and how we can add them natively into EasyCompare and BetterThanSPY.
While adapting Chapters 5 through 9 from the original manuscript, several sections referenced specific features or planned capabilities from StockQuanta / CoinQuanta. In the live chapters, we grounded every chart and table in what the EasyCompare Go backend (http://localhost:8081) computes today (net_return, cagr, volatility, sharpe_ratio, period_max_drawdown, trend_ath_atl, and spy_correlation). Below are the manuscript features that are not yet in EasyCompare, along with concrete recommendations for how adding them would strengthen both the product and its tie-in with the book.
| Book Chapter & Concept | What the Manuscript Described | Current State in EasyCompare | Product Recommendation for EasyCompare |
|---|---|---|---|
| Ch 5: Extrapolated Crash / Tail Risk Estimate | "Crash risk can be approximated by the previous distribution of performance volatility and extrapolating it to simulate extreme scenarios (see Appendix A)." | Go backend computes exact historical period_max_drawdown and underwater drawdown series, plus annualized volatility. |
Add an optional Estimated Tail Loss (99% CVaR / Cornish-Fisher or 3-Sigma Stress Drop) column in the Go backend (metrics.go). This solves the exact problem noted in Ch 5: when a young stock hasn't lived through a 2008 crash yet, its volatility distribution still warns the user of its potential tail drop. |
| Ch 5: Sortino Ratio (Downside-Only Risk-Adjusted Return) | "Look at risk-adjusted measures of performance that include Sharpe Ratio and Sortino Ratio." | Go backend currently returns sharpe_ratio (using total volatility). |
Compute Sortino Ratio in the Go engine (dividing excess CAGR by downside deviation of negative daily returns only). High-growth winners like NVDA or LLY have big upside spikes that inflate standard volatility; Sortino avoids penalizing upside volatility. |
| Ch 6: Pairwise Correlation Matrix & Portfolio-Level Correlation | "Build a performance profile for your portfolio and compute correlation of candidate assets to your entire portfolio, plus detect asset clones inside your portfolio." | Go backend computes each ticker's correlation against SPY (spy_correlation). |
1. Add a Pairwise Correlation Heatmap / Clone Detector tab in EasyCompare for the currently selected tickers (highlighting pairs with >80% correlation as "Clones" and <20% as "True Diversifiers"). 2. In BetterThanSPY, allow sorting discovery candidates by lowest correlation to the user's current ticker list. |
| Ch 7: Inverse-Volatility Position Sizer ("Risk-Balanced Allocator") | "Allocate your capital in an inversely proportional manner to risk or volatility... e.g. $10,000 across assets." | We built an interactive 2-asset Risk-Balanced Sizing widget inside Chapter 2 (guide.js), but /app itself doesn't yet have an allocation calculator. |
Add a one-click "Risk-Balance My Tickers ($10,000)" popover or modal in EasyCompare's table header: given the N tickers currently on screen, show their Equal-Dollar Risk Share vs. their Inverse-Volatility Dollar Allocation (just like Figure 7.1). |
| Ch 7: Composite Rank (QuantaRank / EasyRank) | "QuantaRank balances Performance, TrendScore, Volatility, Risk-Adjusted Performance, and Correlation into a single composite rank." | Users can sort by any individual column in /app and /betterthanspy, and BetterThanSPY has multi-factor filtering. |
Add a Composite Quant Score (0–100) column in EasyCompare and BetterThanSPY that combines percentile ranks of Sharpe Ratio (35%), TrendScore (25%), Low Max Drawdown (20%), and Low SPY Correlation (20%), with preset weights ("Balanced", "Momentum", "Defensive"). |
| Ch 8: Recency-Weighted, Time-Chunked TrendScore | "TrendScore chunks the price time series to weight recent sub-periods higher while rewarding consistency and low path volatility." | Go backend computes trend_ath_atl (based on distance from ATL/ATH extremes). |
Enhance trend_ath_atl (or add trend_score) in the Go engine by splitting the selected window into 4 to 8 sub-chunks, computing the sign and R² of log-price regression with exponential recency weights, and scaling by inverse volatility. |
Summary of How These Strengthen the Book-to-App Loop
Right now, every figure in Chapters 1 through 9 already links directly into EasyCompare with the exact tickers and date windows pre-loaded. If we add even two or three of the items above (such as the Inverse-Volatility Portfolio Sizer modal, the Pairwise Correlation / Clone Detector, and Sortino Ratio), every single specialized concept from your original manuscript will have a 1-to-1 interactive tool inside EasyCompare.