This dashboard displays advanced portfolio statistics, risk measures, and correlations. Use the tabs to toggle between Technicals, Fundamentals, and Risk analytics.
Student Mode Hover metrics to learn what they mean
IMPORTANT NOTICE: Mindrative Intelligence provides statistical and mathematical analysis only. It is NOT a trading platform and DOES NOT constitute financial or investment advice. The views and analyses presented are for educational purposes only. All investment decisions involve risk, and past performance is not indicative of future results. Users should conduct their own due diligence and consult with a licensed financial professional before making any investment decisions.
Every held name plotted by Forward P/E (x) vs position weight (y). The vertical line is the median P/E (robust to skew from expensive outliers); the horizontal line is the equal-weight threshold. Top-right = expensive & concentrated — the biggest valuation risk in the book.
Valuation signal—
Growth & Margins
Rev Growth—
EPS Growth—
Profit Margin—
Growth-quality matrix: Revenue Growth (x) vs Profit Margin (y) per holding, sized by position weight. Quadrants split at 0% growth and the portfolio's weighted-average margin — the same GARP framework (Growth At a Reasonable Price) used to separate durable compounders from low-quality growth.
Quality signal—
Short Interest & Squeeze Risk
Avg Days-to-Cover—
Avg % Float Short—
Elevated Risk—
Squeeze Risk matrix: Short % of Float (x) vs Days to Cover (y), sized by position weight. Top-right = acute squeeze risk (high short crowdedness and low exit liquidity), a key tail risk.
Squeeze signal—
Exit Liquidity & Capacity Restraints
Liquidation capacity represents how many days of trading volume are required to exit your position. If you require more than 10% of Average Daily Volume (ADV) to liquidate in a single day, it is flagged as an illiquidity risk.
Analyzing portfolio position liquidation capacity...
Max Drawdown Tracker
TAIL RISK
1Y DATAbased on 1-year returns
—
Worst peak-to-trough drop (weighted avg)
Worst Single Symbol—
% Symbols Below 200MA—
Est. Recovery Window ⓘ—
📐 Recovery Math: A — drawdown requires a — gain to break even (compounding works in reverse). The time estimate assumes a 15% annualised recovery rate — actual recovery depends on your portfolio returns.
Systemic Portfolio Beta (β)
VOLATILITY
Weighted portfolio sensitivity vs market
Highest-Beta Symbol—
Lowest-Beta Symbol—
High-Beta Symbols (β>1.5)—
Low-Beta Symbols (β<0.8)—
Avg Beta of Top-5 Weights—
Beta Sensitivity Bands
β < 0.8Defensive
β 0.8 – 1.2Market-Neutral
β 1.2 – 1.8Aggressive
β > 1.8Highly Volatile
📐 What is Beta? β=1 means your portfolio moves in lockstep with the market. β>1 amplifies gains and losses. β<1 is more defensive.
Portfolio Diversification Index
CONCENTRATION
Actual Tickers
—
positions held
Effective Bets (ENB)
—
independent risk drivers
PDI Score
—
ENB ÷ N (100% = equal wt)
Herfindahl Index (HHI)—
Heaviest Single Weight—
Top-3 Combined Weight—
Concentration Grade—
Top-5 Position Weights
Loading…
Implied Risk Share (Top-5)—
Tail Risk: Largest 1 Drop—
📐 ENB Formula: ENB = 1 / Σ(w²). Owning 25 tickers ≠ 25 bets — if 3 names hold 60% of your capital, your effective diversification collapses to ~5 independent positions.
Factor Concentration Warnings
FACTOR RISK
—
sectors with hidden factor concentration
Threshold
>60% factor
of sector wt
Loading sector data…
⚡ What this flags: A sector slice that is heavily loaded toward a single industry sub-group (e.g. Industrials → Aerospace & Defense) behaves as one factor bet — a single macro shock wipes the entire allocation simultaneously.
Multi-Factor Risk Model
FACTOR EXPOSURE
Cross-sectional z-scored factor exposure, weighted by real portfolio weights. Each factor uses a real, held-data proxy (see method note below) and only includes holdings that actually report that field — no imputed values.
Factor Exposure Radar — Portfolio Tilt vs Benchmark (z=0)
Factor Breakdown — Exposure & Weight Coverage
Computing factor exposures from held fundamentals & price history…
📐 Proxies used (real data, no fabrication): Market Beta = regression β of the portfolio's real daily returns vs the S&P 500's real daily returns. Size (SMB) = −log(market cap). Value (HML) = earnings yield (1 / forward P/E). Quality (RMW) = profit margin. Momentum (UMD) = real trailing 6-month return. Low Volatility = −(realized annualized daily volatility). Growth = revenue growth %. Each is standardized (z-score) across the portfolio's own holdings; a positive exposure means the portfolio tilts toward that factor versus its own holding set.
Factor Crowding Matrix
CROWDING
Share of portfolio weight concentrated in names loaded >1 standard deviation onto the same factor — a proxy for whether the portfolio is making one large consensus bet dressed up as several tickers.
Waiting on factor model…
Value at Risk & Expected Shortfall
TAIL RISK
1-day VaR (Parametric / Historical / Cornish-Fisher) and Expected Shortfall (CVaR), computed from each holding's real cached daily price history, combined using actual portfolio weights. Loading…
Return Distribution — Empirical (KDE) vs Normal PDF
Loading real daily price history for held symbols…
CDaR 95% (Conditional Drawdown-at-Risk)—
Sample Size—
📐 Method: All figures use the portfolio's own real historical daily return series (no simulated/random returns). Parametric VaR assumes a normal return distribution; Historical VaR/CVaR use the empirical distribution directly; Cornish-Fisher VaR adjusts the normal quantile for the sample's actual skew & excess kurtosis. Values are 1-day, held-weight based.
Macro Stress Testing
SCENARIO ANALYSIS
Equity shock sensitivity uses the portfolio's real regression beta (CAPM: expected move = β × market shock). Historical crisis rows only appear when real cached daily prices actually overlap that crisis window for a majority of portfolio weight — otherwise that scenario is skipped rather than estimated.
Historical Crisis Simulation (real overlapping price data only)
Checking real cached price history for crisis-window overlap…
📐 Method: Equity shocks apply the real regression β from the Multi-Factor card to hypothetical market moves — a standard CAPM linear sensitivity, not a forecast. Crisis rows replay the portfolio's own real weighted price history over each dated window (using each holding's real actual weight); a symbol/date without real cached data simply isn't included in that day's weighted average, and the whole scenario is hidden if too little real weight is covered. No yield-curve, oil, or FX shock is shown because this dataset has no real per-holding rate/commodity/FX exposure to compute it from.
Performance Attribution (Brinson-Fachler)
ATTRIBUTION
Deconstructs excess return vs. the S&P 500 into Allocation, Selection, and Interaction effects per sector, using real portfolio sector weights/returns and real sector-ETF returns. Sectors without a fetchable real ETF return series are excluded rather than estimated.
Click the Allocation, Selection, or Interaction column to break it down by sector.
Loading real sector-level return data…
📐 Method: Portfolio sector weight/return are real (from actual holdings & weights). Benchmark sector return uses each sector's real SPDR sector-ETF trailing return (XLK, XLF, XLV, etc.). Benchmark sector weight uses a reference approximate S&P 500 GICS sector-weight table (published index composition data, not computed live from this portfolio's own holdings) — treat those weights as directional, not exact-to-the-day. Allocation = (wp−wb)×(rb−Rb); Selection = wb×(rp−rb); Interaction = (wp−wb)×(rp−rb).
Hurst Exponent & Regime Detection H—
Rescaled-range (R/S) analysis classifies whether price action is trending (H > 0.55), random (0.45–0.55), or mean-reverting (H < 0.45).
—
Trending H>0.55
—
Random Walk 0.45–0.55
—
Mean-Reverting H<0.45
Computing Hurst exponents from daily price history…
Relative Rotation Graph
JdK RS-Ratio (x) vs RS-Momentum (y) — rotates clockwise: Improving → Leading → Weakening → Lagging vs benchmark. Comet trails show recent history — click a symbol to pin & isolate it.
● Leading● Weakening● Lagging● Improving
Daily Volume Profile
Volume concentration across prices, split by buy/sell footprint. VPOC is the high-density level. Shaded band is the Value Area (70% of volume).
● VPOC■ Value Area● Buy/Sell Split
Cumulative Volume Delta
Accumulated volume signed by day close-to-close move direction. Gradient fill flips green/red at zero; dashed line is price. Triangles flag price/CVD divergence.
● CVD Line― Price Overlay▲ Divergence
Disclaimer: IMPORTANT DISCLOSURES: This report is provided by the author for strictly informational and educational purposes only and does not constitute an offer, solicitation, or recommendation to buy, sell, or hold any security or financial instrument. This analysis has been prepared without regard to the specific investment objectives, financial situation, or particular needs of any specific recipient. The information contained herein does not constitute investment, tax, legal, or accounting advice, and the author is not acting in a fiduciary or advisory capacity. While the information provided is derived from sources believed to be reliable, the author makes no representation or warranty, express or implied, as to its accuracy, completeness, or timeliness. All expressions of opinion, projections, and "forward-looking statements" are subject to change without notice and involve inherent risks and uncertainties; actual results may differ materially from those expressed or implied. Past performance is not indicative of future results. To the maximum extent permitted by law, the author and publisher expressly disclaim any and all liability for any direct, indirect, or consequential loss or damage arising from the use of, or reliance upon, any information or analysis contained in this report. Readers are strongly encouraged to conduct their own independent due diligence and consult with a licensed financial professional before making any investment decisions. Distribution of this report via social networks does not imply an endorsement of any particular investment strategy or product.
