Quality STOCKS Score Dashboard

Historical Quality Score Dashboard — independent, fixed-threshold 1–100 quality scoring for up to 38 symbols at once. The Historical Quality Score Dashboard is a multi-symbol scoring tool for TradingView. It runs in its own pane below the chart and, on the final bar, renders a full dashboard that scores every ticker you list against a fixed historical rulebook. You enter all of your symbols in a single comma-separated (or new-line-separated) box — up to 38 are used and any extras are ignored. Full TradingView symbols such as NASDAQ:AAPL or NYSE:BRK.B are accepted, and the dashboard strips the exchange prefix for a clean display name. For each symbol it makes one historical data request and computes the entire metric set inside that symbol’s own context, so there is no cross-contamination between tickers. The single most important design principle is that every score is absolute, not relative. Each symbol receives its 1–100 Quality Score by being measured against fixed numeric thresholds that never move based on the rest of the list. This is the opposite of a ranking screener: re-ordering or swapping symbols in the list changes nothing about any individual score. A symbol scores what it scores on its own historical record. The Quality Score is built from seven components, each with its own point cap. CAGR contributes up to 20 points on a rising scale (negative compound growth scores zero; growth above roughly 15% earns the full 20). The Sharpe ratio adds up to 20 points using the same banded approach. Maximum drawdown contributes up to 15 points and rewards shallower historical declines, while annualized volatility adds up to 10 points and rewards a calmer return stream. Momentum contributes up to 15 points by checking whether 12-, 6-, and 3-month returns are positive, with a bonus for strong 12-month momentum. Consistency adds up to 10 points from the share of periods that closed positive. Trend contributes a final 10 points from a moving-average and 52-week-high checklist. The seven caps add to 100, and the total is clamped between 1 and 100. The trend component is itself a small checklist scored out of 10: two points each for price trading above its 50-, 100-, and 200-period moving averages, two points when the 50-period average is above the 200-period average, and two points when price sits within 10% of its 52-week high. This gives a quick, transparent read on whether a symbol is in a healthy uptrend or struggling beneath its key averages. Sitting next to the Quality Score is the Historical Probability Score, also on a 1–100 scale. It is a weighted blend of the same building blocks — consistency and trend at 25% each, momentum at 20%, and Sharpe and drawdown at 15% each — designed to express the overall quality of a symbol’s historical setup in one number. It is explicitly a historical setup score, never a prediction of future returns. Every symbol then receives a plain-language label. Scores of 85 and above are rated Excellent, 70–84 Strong, 55–69 Moderate, 40–54 Weak, and anything below 40 Poor. A matching watchlist decision spells out the takeaway: Top Quality Watchlist, Strong Watchlist, Neutral / Review, Weak Setup, or Avoid Based on History. Symbols that fail validation — an invalid ticker, missing data, or fewer than the minimum required bars — are clearly flagged as Not Rated with an Insufficient Data note rather than being scored on thin history. All metrics share three calculation controls. The calculation timeframe can be Daily, Weekly, or Monthly, with Monthly being the lightest on TradingView and the recommended choice for a full list of 38 symbols. The start mode determines the window: Since inception uses each symbol’s first available price, Custom start year begins at a chosen year, and Lookback years uses a rolling window of N years. A risk-free rate input (default 4%) feeds the Sharpe and Sortino calculations. A minimum-bars requirement — 252 daily, 104 weekly, or 36 monthly by default — prevents a symbol from being scored on too little history. The dashboard is rendered in the ToolTack black-and-green theme and shows a rich column set in Full mode: price, CAGR, total return, volatility, Sharpe, Sortino, drawdown, Calmar, 12-month momentum, positive-period percentage, trend score, the Quality Score, the Historical Probability Score, the rating, and the decision. A Compact mode trims this to the essentials, and a Lightweight dashboard mode forces compact columns for a faster render. To keep a long list readable, rows are split across a left and a right table that sit side by side, each carrying the same title and header. An optional benchmark comparison adds a “Vs Bench” column (Full display only, default SPY) that summarizes whether a symbol historically outperformed, underperformed, or behaved similarly to the benchmark on return, risk-adjusted return, and drawdown. A display-only sort lets you re-order rows by Quality Score, CAGR, Sharpe, lowest drawdown, 12-month momentum, or Historical Probability Score — purely for convenience, since sorting never changes any underlying score. Because every metric is computed on confirmed historical bars with no lookahead and no future data, the dashboard is stable and reproducible. It is, however, a backward-looking research instrument by design. A high score describes a strong historical profile in the regime that produced it — it is never a guarantee that the profile will continue. The tool is built to be combined with fundamentals, valuation, and macro context, and it carries a clear on-screen disclaimer that it is for educational and research use only.

The Historical Quality Score Dashboard is designed for investors and analysts who want to compare a watchlist of symbols on a single, consistent historical rulebook instead of eyeballing dozens of charts. It suits long-term and swing investors building or pruning a watchlist, portfolio managers who want a quick quality read across stocks, ETFs, sectors, and indices, and systematic researchers who prefer fixed, transparent thresholds over opaque rankings. It is equally useful for crypto and multi-asset traders comparing instruments across asset classes, for anyone screening for low-drawdown, high-consistency profiles, and for educators and students who want a clear, well-documented worked example of return and risk metrics computed side by side.

Comparing the historical quality of many symbols by hand is slow and inconsistent — the metrics live in different tools, the thresholds drift from one symbol to the next, and it is easy to let a strong-looking peer group flatter a mediocre name. The Historical Quality Score Dashboard removes that friction by computing return, risk, drawdown, momentum, consistency, and trend for every symbol on the same fixed scale and condensing them into a single 1–100 Quality Score plus a Historical Probability Score. Because the thresholds are absolute, the scores stay meaningful no matter what else is in the list, so you can add or remove symbols freely without distorting any individual result. The plain-language ratings and decisions translate the numbers into an immediate takeaway, the optional benchmark column puts each symbol in context, and the split-table layout keeps a full 38-symbol list glanceable. Transparent, editable thresholds, a no-lookahead engine, and an honest “not a forecast” framing make it a dependable research starting point rather than a black box.

• • Watchlist quality screening across up to 38 symbols at once
• • Ranking-free, absolute scoring of individual symbols
• • Comparing stocks, ETFs, sectors, and indices on one scale
• • Multi-asset comparison across stocks, crypto, forex, and futures
• • Identifying low-drawdown, high-consistency profiles
• • Risk-adjusted return assessment via Sharpe and Sortino
• • Drawdown and Calmar-based resilience analysis
• • Momentum screening using 3-, 6-, and 12-month returns
• • Trend-health checks against the 50/100/200 moving averages
• • Long-term CAGR and total-return comparison
• • Benchmark-relative review against SPY or a custom index
• • Since-inception versus rolling-window historical analysis
• • Custom start-year regime studies
• • Portfolio candidate shortlisting and pruning
• • Sector and thematic basket evaluation
• • Filtering out symbols with insufficient history
• • Education on return and risk metrics computed side by side
• • Quick monthly or weekly portfolio health reviews
• • Pre-screening before deeper fundamental research
• • Building a repeatable, transparent quality-rating workflow