Search a ticker. Go past the price.
One search opens the filing-level profile: operating KPIs pulled from 8-Ks, forensic quality scores, a reverse-DCF time machine, earnings-call tone, and peer valuation comps.
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MARKET OVERVIEW
PORTFOLIO HOLDINGS
SORT:
TOP MOVERS (by σ)
SENTIMENT
BREADTH
SHORT INTEREST
UNIVERSE: CHECKING...
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⚠
PORTFOLIO CHANGED
Results below are from the previous portfolio. Click RE-ANALYZE to update.
FACTOR EXPOSURES
z-score vs universe
ACTIVE EXPOSURE VS BENCHMARK
Computing active exposure…
portfolio z − benchmark z · positive = over-exposed
SECTOR ALLOCATION
INDUSTRY EXPOSURE
COUNTRY EXPOSURE
RISK DECOMPOSITION
Portfolio variance split into 5 sources:
Market beta,
Industry & sector tilts,
Style factor tilts,
Factor Interaction (cross-block; negative = factors hedging each other), and
Specific (idiosyncratic stock moves).
TOTAL ACTIVE RISK
vs benchmark
Tracking error (TE) vs benchmark, split into Factor tilts (your residual loading differences vs benchmark) and Idiosyncratic stock-specific bets. Bloomberg PORT-style decomposition. Benchmark from the Active Exposure selector above.
Pick a benchmark in the Active Exposure section above to compute.
TOP CTEV BY POSITION
contribution to portfolio vol
CTEV = Contribution to Total Ex-Ante Volatility. Top positions by their % contribution to portfolio risk (w · MCR / σ_p). Sums to 100% across all holdings.
DOWNSIDE RISK & LOSS SCENARIOS
1-in-20 day loss = on 95% of days, you won't lose more.
1-in-100 = extreme bad day (~2x/year).
Avg tail loss = avg damage on worst-5% days.
FACTOR VARIANCE CONTRIBUTIONS
Marginal: each factor's share of your portfolio's total risk.
Positive = that factor is making your portfolio riskier. Negative = that factor is
offsetting other risks (acting like a built-in hedge). Add them all up and
you get your total factor risk.
STRESS TESTING
historical crisis scenarios × full factor model
Projected portfolio P&L under historical crisis scenarios.
stock return = Xi · fscenario + ε —
each stock's exposures to market, style, and industry factors are hit with that scenario's calibrated factor returns, plus a portfolio-level specific shock. Click any card for the full factor + sector attribution.
PERFORMANCE ATTRIBUTION
Breaks your portfolio's recent return into factor contributions:
how much came from market,
style factors (momentum, value, etc.),
sectors,
country factors, and
stock selection (idiosyncratic).
A small residual row catches what the model can't explain.
POSITION RISK
| Ticker | Name | Side | Size | Weight | Beta | Beta Ctrb | Vol | Spec Risk | MCR | Risk Ctrb | Risk % | Country | Sector |
|---|
FACTOR EXPOSURES BY POSITION (Z-SCORE)
Detecting cross-asset regime...
REGIME MODEL
LEADING INDICATORS
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CREDIT CONDITIONS
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LI INDEX
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COMBINED
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LEADING INDICATORS ⓘ
Requires FRED API key
CREDIT CONDITIONS ⓘ
Requires FRED API key
LEADING INDICATORS
A proprietary composite of forward-looking economic indicators spanning manufacturing, services, labor, housing, consumer sentiment, financial conditions and interest rates.
Each indicator is evaluated on two dimensions: its absolute level relative to historically meaningful thresholds and its recent directional momentum. The combination determines a cycle phase for each indicator: Recovery, Expansion, Slowdown, or Contraction.
Individual scores are aggregated using proprietary weights calibrated across multiple economic cycles. A positive composite signals RISK ON, a negative composite signals RISK OFF.
The model is designed to identify regime shifts early, before they become market consensus.
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CREDIT CONDITIONS
Monitors the health of credit markets by tracking spreads across multiple tiers of the bond market — from high quality to high yield.
Each tier is scored using a proprietary trend-following methodology that evaluates both the position and direction of credit spreads. Scores are aggregated across all tiers.
When credit conditions are favorable (spreads narrowing), the model signals RISK ON. When conditions deteriorate (spreads widening), it signals RISK OFF.
Credit spreads often lead equity markets — widening spreads signal stress before it shows up in stock prices.
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REGIME
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AVG EQUITY CORR
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63-day rolling
STOCK / BOND
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SPY vs TLT (neg = normal)
AVG EQUITY CORRELATION — ROLLING
CROSS-ASSET CORRELATION MATRIX (63D)
AVG PAIRWISE CORRELATION
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63-day rolling
HISTORICAL AVERAGE
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vs current
EFFECTIVE # OF STOCKS
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out of -- positions
PORTFOLIO CORRELATION — ROLLING
EFFECTIVE STOCKS (HOW DIVERSIFIED YOU REALLY ARE)
PORTFOLIO CORRELATION MATRIX (63D)
FRED API KEY REQUIRED
The Macro Dashboard uses the FRED API (Federal Reserve Economic Data).
Get a free key at fred.stlouisfed.org/docs/api/api_key.html
PORTFOLIO VS MARKET
Your portfolio's gross-weighted average on each metric, vs the US market and the global universe (both cap-weighted). ETFs decomposed; shorts count with absolute weight.
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EVENT CALENDAR
UPCOMING EVENTS
PORTFOLIO HOLDINGS
MARKET HOURS
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Analysing dividend income...
PROJECTED MONTHLY INCOME
UPCOMING EX-DIVIDEND DATES
DIVIDEND DETAIL
Growth rates are annualised (CAGR) from historical dividend payments. Short positions show negative income (you pay the dividend). Yield = annual DPS ÷ current price.
NO POSITIONS
Add positions in the PORTFOLIO tab first.
MONTE CARLO PROJECTOR
Simulates thousands of possible futures for your portfolio based on its historical behaviour.
Shows the range of outcomes you can realistically expect.
Running simulation...
PROJECTED PORTFOLIO VALUE
The shaded areas show where your portfolio is likely to end up. Darker = more likely. The orange line is the most likely path.
CHANCES
How likely each outcome is across all simulations.
ENDING PORTFOLIO VALUE
Where your portfolio could end up at the end of the period.
MODEL DETAILS
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SHORT INTEREST
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AVG SI % OF FLOAT
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STOCKS TRACKED
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DATA POINTS
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LOADING SHORT INTEREST DATA
Fetching current short interest data for ~2000 stocks. This runs automatically and takes approximately 5–10 minutes.
Starting...
0%
TOTAL SHARES SHORTED
SI % OF FLOAT
DOLLAR SHORT INTEREST
MOST SHORTED STOCKS
% of Float
BIGGEST MoM CHANGES
vs prior report
BUILDING SECTOR TIME SERIES
Re-fetching Finviz data to compute per-sector SI% history. This takes approximately 5–10 minutes.
Starting...
0%
INSIDER BUYING
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INSIDER SELLING
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BUY/SELL RATIO
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higher = more bullish
NET BUYERS
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FETCHING INSIDER DATA
Starting...0%
AGGREGATE INSIDER BUYING ($ VALUE / WEEK)
LARGEST INSIDER BUYS
| DATE | TICKER | INSIDER | VALUE |
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Industry Finder
Every GICS industry ranked by a blended attractiveness score — forward growth, business quality and cheap valuation combined into one 0–100 number. Sort by any column, then click an industry to screen the stocks inside it.
INDUSTRY SCREENER
Screen and compare valuation, growth, and risk metrics across every stock in an industry
or
SMART SORT PRESETS
Pick up to 3 metrics. For each: choose direction (LOWEST or HIGHEST) and weight (more dots = heavier weight in the composite score).
FILTERS (click to expand)
INDUSTRY
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MEDIAN P/E
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MEDIAN FWD P/E
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MEDIAN EPS GROWTH
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MEDIAN REV GROWTH
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| TICKER | NAME | CTY | MC | P/E | FWD P/E | P/B | P/S | EV/EBITDA | EPS GR | REV GR | FWD EPS GR | FWD REV GR | BETA | 12M MOM | VOL | DEBT |
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STOCK PROFILE
📊
Look up a ticker to begin
Pulls revenue segmentation directly from the company's most recent 10-K filing on SEC EDGAR, plus market valuation context. Best with US-listed common stocks (segment data lives in 10-Ks).
NO POSITIONS
Add positions in the PORTFOLIO tab first.
LIQUIDITY COST ESTIMATOR
Estimates how many days it would take to exit each position at a given percentage of average daily volume (ADV).
Portfolio liquidation time assumes all positions unwind in parallel. Positions taking >3 days carry meaningful market-impact risk — VaR numbers are unreliable for those names.
Fetching volume data...
PORTFOLIO LIQUIDATION
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days to fully exit (parallel)
WEIGHTED AVG DAYS
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NAV-weighted average
EXIT COST (SLIPPAGE)
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estimated price impact
ILLIQUID POSITIONS
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>3 days to exit
NAV IN ILLIQUID
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% of portfolio
DAYS TO LIQUIDATE BY POSITION
PORTFOLIO LIQUIDATION SCHEDULE
REAL-WORLD EXIT RISK
Standard risk models assume you can sell everything instantly. In reality, selling large positions moves the price against you and takes multiple days. This section shows how much worse your actual losses could be when accounting for those exit costs and the extra time you're exposed to the market while unwinding.
WORST-CASE LOSS BREAKDOWN
COST OF SELLING OVER TIME
POSITION LIQUIDITY DETAIL
@ 10% ADV
NO POSITIONS
Add positions in the PORTFOLIO tab first.
SCENARIO ENGINE
Regressing portfolio against macro factors...
Fitting orthogonalised factor sensitivities for each position
ESTIMATED PORTFOLIO IMPACT
0.00%
DOLLAR P&L
$0
Drag the sliders to simulate market moves. Sensitivities are estimated from the last 252 trading days using exponentially-weighted regression.
WHAT-IF SCENARIO
PRESETS
POSITION IMPACT
Sensitivity = how much a stock moves per unit change in each factor, based on historical data. Faded values are statistically weak (p > 0.10). Hover any value for details.
EXTREME RISK
Add positions in the PORTFOLIO tab first.
EXTREME RISK ENGINE
Fitting fat-tailed distribution to portfolio returns...
Running Cornish-Fisher expansion + Generalised Pareto tail fit
WHAT THIS PAGE TELLS YOU
Standard risk models (the ones that report "1-in-100 day loss = 2%") assume
your portfolio's daily moves follow a bell curve.
Real portfolios don't. They have fat tails — extreme losses
happen 5×–50× more often than the bell curve says they should.
This page compares three estimates of what your portfolio could lose on a very-bad day, ranging from "once a month" to "once a generation":
This page compares three estimates of what your portfolio could lose on a very-bad day, ranging from "once a month" to "once a generation":
- Bell-Curve estimate — naive, what most retail tools show. Tends to understate extreme losses.
- Skew + Fat-Tail Adjusted (Cornish-Fisher) — same math but corrected for your portfolio's actual asymmetry and tail-heaviness.
- Tail-Fit estimate (Extreme Value Theory) — a separate model fit only to your worst-day data. Best for 1-in-1000-day or rarer events.
KEY TAKEAWAY
HOW BAD CAN A SINGLE DAY GET?
three estimates per scenario — same rare event, different distribution assumption
Each row is a different "how rare" scenario.
"1-in-100 day" = the kind of bad day you'd see about 2-3 times a year on average.
"1-in-1000 day" = roughly once every 4 years.
"1-in-10000 day" = once-in-a-generation events (think Black Monday 1987 or COVID March 2020).
The columns show what each model thinks you'd lose that day, as a % of NAV and in dollars.
The two right-hand columns show how much the better estimates differ from the naive Bell-Curve estimate.
YOUR DAILY RETURN HISTORY vs THE BELL CURVE
where reality breaks from the textbook
Orange bars = actual count of trading days at each return level (your portfolio's history).
Grey line = what a bell-curve model would predict.
If orange bars stick out beyond the grey line in the far-left tail,
you have more big-loss days than the bell curve admits — that's fat-tail risk.
▬ bell-curve prediction
·
▬ your portfolio's actual returns
DOLLAR-LOSS BY HOW RARE THE DAY IS
Each bar = expected $-loss in a once-in-X-day event.
Orange = skew/fat-tail-adjusted estimate.
Red = extreme-tail model estimate.
Read this as: "if my portfolio has a 1-in-1000 day, I should expect a loss of about $X."
WHEN THINGS GO BAD, HOW BAD ON AVERAGE?
The previous table answers "how bad could it get?". This table answers
"OK but if it actually does go that bad, how bad is the AVERAGE bad day among the worst ones?"
Technical name: Expected Shortfall (ES) or CVaR. Required by
Basel III bank regulation because plain VaR only tells you the threshold — it doesn't
say how DEEP the loss could be once you cross it.
HOW HEAVY IS YOUR TAIL?
We fit a special "extreme tail" curve to your worst-day data. The most important number is the
Tail Heaviness Index (Greek letter ξ, "xi"):
● below 0 = thin tail (good — bounded losses) · ● around 0 = normal tail (typical of bonds, cash) · ● above 0 = heavy tail (typical of equities; the higher, the heavier)
Most equity portfolios land around +0.1 to +0.3. Concentrated, leveraged, or crisis-era portfolios can climb above +0.3.
● below 0 = thin tail (good — bounded losses) · ● around 0 = normal tail (typical of bonds, cash) · ● above 0 = heavy tail (typical of equities; the higher, the heavier)
Most equity portfolios land around +0.1 to +0.3. Concentrated, leveraged, or crisis-era portfolios can climb above +0.3.
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VOL REGIME
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VIX
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VIX/VIX3M
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VVIX
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SKEW
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VOL PREMIUM
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implied - realized
VIX TERM STRUCTURE
VIX (30-day), VIX3M (90-day), VIX6M (180-day) implied volatility. Normal: VIX < VIX3M < VIX6M (contango). Danger: when VIX rises above VIX3M the curve inverts, signaling near-term panic.
VIX / VIX3M RATIO
Below 0.85 = deep contango — complacent if VIX is low (e.g. VIX < 15), but forward fear if VIX is elevated (e.g. VIX > 25, meaning markets expect things to get worse). 0.85–1.0 = normal. Above 1.0 = inverted, near-term fear exceeds longer-term — one of the most reliable stress signals. Signal is strongest when combined with absolute VIX level.
VVIX — VOLATILITY OF VIX
How expensive are options on the VIX itself. Below 100 = calm. Above 120 = market expects a big VIX move. Key signal: when VVIX spikes while VIX is still low, options traders are buying crash protection before it happens. Note: VVIX data coverage can be spotty — flat periods at start of chart indicate missing data.
CBOE SKEW INDEX
Measures cost of tail-risk hedging (deep OTM puts vs ATM options). 120–130 = low tail hedging demand. 130–145 = normal. Above 145 = elevated tail hedging. Above 155 = institutions aggressively buying crash protection. Watch for: VIX low + SKEW high = surface calm but smart money hedging. CBOE redesigned SKEW methodology in 2023 — levels shifted higher structurally.
REALIZED VOL vs IMPLIED VOL (SPY)
Blue = actual 30-day realized vol from SPY returns. Orange dashed = VIX (implied vol / what the market expects). VIX above realized (positive premium) = options are expensive, good time to sell premium. Realized above VIX (negative premium) = options are cheap, unusual — the market is underpricing risk.
SECTOR REALIZED VOL (30D)
Median 30-day realized volatility per sector vs. 30 days ago. Red bars = vol expanding (market repricing risk higher). Green bars = vol compressing (risk calming). Shows where uncertainty is concentrating.
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CROSS-ASSET REALIZED VOL (30D)
Current 30-day vol for each asset class ranked against its own history. High percentile = unusually volatile. Low percentile = unusually calm. Key signal: when all assets are low-vol simultaneously it signals complacency. When bond vol spikes while equity vol stays low, a dislocation may not yet be priced.
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S&P 500 FORWARD RETURNS — VOLATILITY REGIME EXTREMES
Historical average S&P 500 returns 10 and 20 trading days after each vol signal fired. NOW = condition is active today. Higher N = more data points = more reliable.
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US BREADTH
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> 50D
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> 200D
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US — % ABOVE 50-DAY EMA
50D FORWARD RETURNS
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US — % ABOVE 200-DAY EMA
200D FORWARD RETURNS
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ZWEIG BREADTH THRUST / COLLAPSE TRACKER
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NET NEW HIGHS (52-WEEK HIGHS − LOWS)
S&P 500 FORWARD RETURNS — NET NEW HIGHS
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HIGH-LOW INDEX (10-DAY MA)
S&P 500 FORWARD RETURNS — HIGH-LOW INDEX
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McCLELLAN OSCILLATOR
S&P 500 FORWARD RETURNS — McCLELLAN EXTREMES
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INDICATOR GUIDE
Multi-MA Alignment: When all 4 timeframes (20D/50D/100D/200D) agree (>70% or <30%), it's a strong breadth signal for forward returns.
Net New Highs: 52-week highs minus lows. Negative readings while market is near highs = major divergence warning.
High-Low Index: NH/(NH+NL) as 10d MA. Above 70 = broad strength. Below 30 = broad weakness.
McClellan Osc: 19d − 39d EMA of advances minus declines. Above +100 = overbought thrust. Below −100 = oversold extreme.
Zweig Thrust: % above 50D EMA goes from <40 to >61.5 within 10 days. One of the most reliable bull signals historically.
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AAII BULL-BEAR SPREAD
S&P 500 FORWARD RETURNS — AAII SPREAD EXTREMES
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NAAIM EXPOSURE INDEX
S&P 500 FORWARD RETURNS — NAAIM EXPOSURE EXTREMES
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Contrarian interpretation:
When retail investors (AAII), fund managers (NAAIM), or large speculators (COT) reach extreme positioning,
markets tend to reverse. Extreme bearishness → contrarian bullish.
Extreme bullishness → contrarian bearish.
Percentile ranks are computed against all available history.
CFTC COMMITMENTS OF TRADERS
S&P 500 NET SPEC POSITIONING
VIX NET SPEC POSITIONING
GOLD NET SPEC POSITIONING
CRUDE OIL NET SPEC POSITIONING
CFTC Commitments of Traders:
The COT report shows weekly futures positioning broken into three groups:
Non-Commercial (large speculators/hedge funds),
Commercial (hedgers/producers), and
Non-Reportable (small traders).
Net Speculative = large spec longs minus shorts. Extreme net long or short readings often precede reversals.
Institutional Flow
What the most-watched managers hold, what they just bought and sold, and where their conviction clusters. Parsed directly from each fund's SEC Form 13F. Positions are as of quarter end and filed up to 45 days later, so read this as positioning, not real-time trades.
Who moved a stock last quarter
Conviction managers
Concentrated discretionary books where position sizes express real views. These are the funds behind the consensus above.
Quant and multi-strategy books
Tracked for completeness, excluded from the consensus: these books hold thousands of hedged positions, so a single line rarely expresses conviction. Read them for gross positioning, not stock picks.
Insider Transactions
Open-market insider buys & sells across the US universe, straight from SEC Form 4.
What this is
Every open-market insider buy and sell across our US universe, straight from SEC Form 4 filings. An insider's multiple same-day trades are combined into one row (summed shares, weighted-average price). IPO-window trades and options/tax transactions are excluded, so what's left is discretionary open-market activity.
How the colours work
Green = buy, red = sell (shown on Type, Value and % of Stake).
Row shading = conviction. The greener the row, the larger the buy was as a share of the stake the insider already held; the redder, the larger the sell was of what they held. A faint wash is a small nibble; a strong wash means they moved most of their position.
A green NEW tag (and full green shading) = the insider opened a brand-new position.
Amber % Off High = the stock is 30%+ below its 52-week high (insiders buying into weakness).
Relationship
Sector
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PORTFOLIO TRACKING
Holdings, P&L, allocation & dividends from your transactions · values in USD
Trade Log
Log every trade and see your real performance: win rate, profit factor, expectancy and R-multiples. Open trades are marked to the latest close.
ADD A TRADE
| ENTRY | SYMBOL | SIDE | SHARES | ENTRY | EXIT | P&L $ | P&L % | R | TAG |
|---|
No trades yet. Log your first one above.
PRIVATE REVERSE DCF
Reverse-DCF for a private company from fully manual inputs. Enter the enterprise value (and/or equity value), the trailing-twelve-month baseline, and up to 5 years of history. Same two-stage math as the public Reverse DCF: it solves for the high-stage growth (years 1–5) that makes today’s value fair, fading to the terminal rate by the horizon. Nothing is fetched or stored, this runs entirely on what you type.
Assumptions
WACC / discount8.0%
Terminal growth2.5%
Horizon15y
High-growth years5y
Valuation & TTM baseline (all $ in millions)
Enterprise Value ($M)
FCF — TTM ($M)
Equity value / mkt cap ($M)
Net Income — TTM ($M)
FCF basis solves implied growth vs Enterprise Value; Net-income (owner-earnings) basis solves vs Equity value. Fill either pair (or both).
History (oldest → newest, $M, optional)
| Year | FCF ($M) | Net Income ($M) |
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Trading Journal
Log every trading day: how it went, what you did well or badly, and the lessons. Click any day to write it up.
MON
TUE
WED
THU
FRI
SAT
SUN
HOW DID THE DAY GO?
TAGS — what went well / badly
NOTES
News
Every headline we can find for a stock, aggregated across sources (EODHD + Google News), newest first.
Screener
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FILTERS
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