Guided demo
The walkthrough follows a fictional client portfolio called Northstar Compounders. It starts with attractive winners and clean performance, then reveals crowding, factor dependence, stress sensitivity, and the journaled decision path that follows.
Why this demo works
The portfolio is not broken. The story just gets more honest as the analytics stack builds tension.
Portfolio value
$12.4M
Quarter return
+18.7%
Style tilt
Growth-heavy
Cash weight
6.2%
At first glance
Strong returns, recognizable winners, and a clean top-holdings view give the book instant credibility before any deeper analytics begin.
Quarter return
+18.7%
Top positions are names most clients already understand.
Concentration still feels intentional rather than dangerous.
The overview module earns trust before the harder conversation starts.
Then tension appears
The crowding lens translates hidden overlap into something explainable: several independent-looking names are effectively one growth expression under pressure.
Effective bets
14.2
NVDA, AVGO, TSM, MSFT, and AMZN cluster around the same demand narrative.
Position count says 47, but the risk story says much less.
This is the first moment in the tour that changes the user's mental model.
What is really driving it
Rolling factor attribution sharpens the narrative by separating stock-picking confidence from the style tailwinds that have been doing most of the lifting lately.
Dominant style
Growth + momentum
Growth and momentum explain most of the last leg of outperformance.
Quality still helps, but it is no longer the main stabilizer.
The portfolio story shifts from names alone to names plus environment.
Synthetic dossier
Quarter-end review, April 2026
Accounts merged
2
Fidelity taxable + Merrill IRA
Portfolio value
$12.4M
47 normalized positions after cleanup
Top 10 weight
68%
Enough concentration to matter
Stress case
-8.6%
Fast growth-factor unwind
Top exposures
Looks healthy
NVDA
AI leader and main performance engine
11.4%
+34%
MSFT
Quality ballast with cloud overlap
9.6%
+21%
AMZN
Cloud and consumer growth linkage
8.1%
+16%
META
Ad rebound and AI beneficiary
7.2%
+24%
AVGO
Infrastructure and chip correlation
6.4%
+19%
TSM
Supply-chain reinforcement of the same theme
5.8%
+15%
Crowding lens
14.2 effective bets
Factor regime
Growth leading
Nov
Dec
Jan
Feb
Mar
Apr
Stress board
Actionable tension
Growth unwind
-8.6%
The portfolio is not just growth-tilted; its best winners reinforce the same regime exposure.
Rates up shock
-5.1%
Long-duration winners and crowded quality-growth names compress together under a valuation reset.
Semiconductor drawdown
-6.4%
Direct chip exposure and second-order hyperscaler linkage create more overlap than the client expects.
Risk made concrete
The stress module makes the risk communicable in dollars and top contributors, which is where the product stops feeling academic and starts feeling operational.
Worst board case
-8.6%
A fast growth unwind becomes an immediate portfolio-level discussion.
The losses concentrate through the same winners that drove recent success.
The scenario board creates urgency without resorting to alarmism.
Decision payoff
Trade Actions and the Trading Journal close the loop: rebalance ideas appear under constraints, then the human rationale is captured while the decision is still fresh.
Planned moves
3 trims / 2 adds
The product feels useful because it finishes on decision support, not diagnosis.
The journal turns a recommendation into a process artifact that can be reviewed later.
This is the emotional finish of the tour because it feels accountable and human.
Before / after
Constraint-aware
AI infra cluster
31.3%
Effective bets
14.2
Worst stress case
-8.6%
NVDA
-120 bps
Reduce crowding while keeping the core winner intact.
AVGO
-70 bps
Lower duplicate infrastructure exposure.
ICE
+65 bps
Introduce steadier cash-flow ballast with lower thematic overlap.
UNH
+55 bps
Improve scenario resilience without abandoning growth identity.
Journal finish
Post-review rebalance note
The book still deserves a growth bias, but the same winners are starting to represent the same risk factor more than the client story admits.
Lesson carried forward
Document concentration relief as a process choice, not as a signal that the winners are no longer high quality.
Rule followed: Reduce overlap before the next stress event, not after it.
Evidence attached: Scenario board screenshot and before/after trade plan.
Next review: Recheck effective number of bets after execution.
What the demo proves
Reduce overlapping semiconductor and AI infrastructure exposure while preserving the core names still carrying the best fundamental narrative.
Add names or sleeves that improve scenario resilience without collapsing the portfolio's long-term growth identity.
Record the process rule behind the trade so the next review can judge discipline, not just outcome.
Module pulse
The tour works when every screen earns the next question: what is the portfolio, what is hiding inside it, what drives it, how does it break, what should change, and how was that choice documented?
Builds trust quickly with a clean baseline before introducing deeper risk tension.
47 positions
Sector stack
Clean baseline
Tech
34%
Consumer
21%
Health
16%
Financials
12%
Top positions, sector weights, and recent winners are immediately legible.
The client sees a polished portfolio surface instead of a raw broker export.
This is where the guided demo earns permission to go deeper.
Turns a subtle correlation problem into a concrete conversation worth having.
14.2 effective bets
Position count vs risk
Hidden overlap
Headline
47
Effective bets
14.2
Several AI and hyperscaler positions collapse into one common risk expression.
The module creates the first genuine 'I did not realize that' moment in the demo.
This is the sharpest wedge for advisory storytelling.
Explains why the portfolio has worked and why that may not be stable.
Growth + momentum
Rolling drivers
Regime shift
Rolling attribution shows factor leadership shifts over time.
It separates stock narrative from regime narrative.
Useful for clients who want to know if returns are repeatable or style-dependent.
Converts abstract macro scenarios into dollar and position-level exposure.
-8.6% growth shock
Shock board
P/L impact
Growth
Rates
Semi
Named shocks map to estimated portfolio drawdown.
Top contributors make scenario communication concrete.
Good bridge from analytics into action prioritization.
Makes the system feel decision-oriented without pretending there is one perfect answer.
3 trims, 2 adds
Before / after mix
Constraint aware
Before
After
Recommendations are framed under constraints and tradeoffs.
Before/after portfolio logic is easier to defend than a ranked list alone.
This is where the demo feels like a decision-support product, not just reporting.
Preserves why the trade or rebalance happened so the learning loop stays attached.
Capture + reflect
Reflection capture
Process trace
Reason
100%
Rules
92%
Emotion
74%
Lesson
88%
Synthetic notes, tags, and screenshots show the reflection workflow clearly.
The journal shifts the product from analysis delivery into repeatable process.
This is the emotional finish because it feels human, not purely quantitative.