πŸ“Š Retirement + Portfolio Preview πŸ“ˆ

Free Preview
Enter a few key assumptions to see a quick retirement projection, Monte Carlo risk view, and portfolio-linked return estimate. For deeper planning, open the full planner after the preview.
Dollars:
Portfolio β†’ Auto Return & Volatility
Uses /api/portfolio_stats
ΞΌ (CAGR): β€” Οƒ (Vol): β€”
Portfolio Lab
Estimate return (CAGR) + volatility (Οƒ), then apply into planner.
CAGR (annual)
β€”
Volatility Οƒ (annual)
β€”
Allocation (will populate after Calculate in Step 2)
...
Ticker Weight Value Max Drawdown
Return (indexed growth)
Total Return
β€”
Start β†’ End
β€”
CAGR (annual)
β€”
Indexed growth (start = 1.0)
Drawdown (portfolio + components)
Max Drawdown
β€”
Peak β†’ Trough
β€”
Recovery
β€”
Underwater (portfolio drawdown % over time)
Components (using normalized weights)
Ticker Weight Max DD Trough
Run Calculate to populate.
Cash Deployment Cockpit
Status: not loaded
Deployment Posture
β€”
Open this tab to load the current cash deployment reading.
Deployment Risk Score ⓘ
β€”
β€”
Entry Opportunity Score ⓘ
β€”
β€”
Signal Alignment Score ⓘ
β€”
β€”
ML Confirmation Layer ⓘ
Waiting for ML winner configuration...
No ML winner configuration loaded yet.
This layer shows the currently validated ML challenger winners by horizon. CDC rules still drive the cockpit; ML acts as a historical validation and ranking layer.
Recent Daily Trend ⓘ
Select 1W, 1M, 3M, 6M, 12M, 3Y, or 5Y to view the market mood proxy and deployment-score history.
Waiting for backend history...
Lines show Market Mood (Raw Regime), Deployment Risk Score, Entry Opportunity Score, and Signal Alignment Score across the selected trading-day history.
Benchmark Price Validation ⓘ
Waiting for benchmark price history...
Synced to the same period toggle above.
Benchmark price history is shown separately for after-the-fact validation of historical deployment zones.
Dots mark ranked candidate entry windows selected from a combination of factors β€” including Market Mood, Deployment Risk, Entry Opportunity, Signal Alignment, and posture logic β€” rather than simply the deepest price lows.
Candidate Agreement Table ⓘ
Waiting for CDC / ML candidate agreement data...
Rank Entry Date Source CDC Hist. ML Live ML Simple Agreement Read Return TD 63d Return 126d Return 252d Return
No CDC / ML candidate agreement rows loaded yet.
Historical rows show mature CDC / ML / Simple validation. Live rows use the same unified candidate universe and show current CDC rank, Live ML rank, return-to-date, and whether 63d/126d/252d outcomes are still maturing.
Market Mood (Raw Regime) ⓘ
Waiting for backend data...
Fear & Greed Proxy
β€”
Regime Band
β€”
Regime Trend
β€”
Price Context ⓘ
Waiting for backend data...
Drawdown
β€”
Drawdown Band
β€”
Trend vs 125MA
β€”
Price Action
β€”
Distance vs 125MA
β€”
Market Mood (Raw Regime) is the broad fear/greed backdrop. Deployment Risk Score is the caution level for putting cash to work right now. They can differ when the market mood is fearful but the environment is still deteriorating or not yet attractive enough for deployment.
ML Risk Overlay (QQQ)
Status
β€”
Regime probability (model uncertainty)
Most likely: β€”
Alternate: β€”
Think of it like a risk warning level:
  • If DEFENSIVE is ~5% β†’ basically ignore; deploy normally.
  • If DEFENSIVE is ~33% β†’ deploy carefully (defensive is a real possibility).
  • If DEFENSIVE is ~70% β†’ likely stay defensive.
Open this tab to load ML state + signals.
As of
β€”
Confidence β“˜
β€”
Deployment band β“˜
β€”
β€”
How this works (training β†’ live):
β€’ Training (history): For each past day T, we saved the signals at T (using only data up to T), then assigned a state label based on what happened after T (6-month reward vs 3-month drawdown β€œpain”).
β€’ Live (today): We compute today’s signals and the model outputs a Status % (e.g., DEFENSIVE 88%) meaning: in history, days with similar signals most often ended up labeled DEFENSIVE β€” so the Status % is the model’s confidence for that state (not β€œtoday vs 6 months ago”).
Signals (what the model β€œsaw” today)
Signal Today Percentile Read β“˜
1m return β€” β€” β€”
3m return β€” β€” β€”
QQQ vs GLD (3m) β€” β€” β€”
QQQ vs TLT (3m) β€” β€” β€”
QQQ vs HYG (3m) β€” β€” β€”
3m vol β€” β€” β€”
Vol trend β€” β€” β€”
Dist to 200d SMA β€” β€” β€”
1y drawdown β€” β€” β€”
Percentile is β€œwhere today ranks vs history” for that signal (0% = very low vs history, 100% = very high vs history). The Read column summarizes what it implies (leans deploy vs leans defensive).
Signals β†’ State (why)
β€”
Top drivers today
  • Open this tab to load drivers.
What that usually maps to
β€”
Action hint
β€”
10-day trend
β€”
Defensive probability (bars). Higher = more β€œdefensive” regime risk.
Important (horizon): Not a next-day price call. Calibrated to ~6-month forward reward with a ~3-month drawdown β€œpain check.”
So RISK_ON can still appear even if QQQ is down recently β€” short-term drawdowns can still happen.
How to use the band: scale in/out gradually (not β€œall-in timing”).
Open this tab to load the 10-day series.
ML Guide
Set style weights (must sum to 100%), or click a point on the frontier slice. Then click Load to fill Portfolio Lab inputs. (Per your rule: Load = hydrate only β†’ you still click Calculate.)
Step 1: style weights
Style weights (sum must be 100%)
β€”
β€”
Efficient frontier slice (preview) β“˜
Οƒ (risk) vs CAGR
Click a point to generate the nearest 6 presets (2 safer, 2 riskier, 2 neighbors).
Target: β€”
Note: this is a UI preview slice. Later you’ll swap these preview points for true stats from your DB (RUN ETL) universe + ranking logic.
Presets (6)
My Portfolio Presets
Note: Enter weights as percentages (for example: 40%, 35%, 25%). This sets your CAGR inputs. Your Monte Carlo uses ΞΌ = CAGR + ½·σ² internally (your existing architecture).
Future (ML-ready): correlation matrix, rolling volatility, regime tag, forecast confidence, etc.
Reality Check Pack
ⓘ
Adds 3 additional β€œstress test” runs on top of the main Monte Carlo (more compute, better reality check).
Stress window is fixed at first 5 years after retirement (same as your current stress logic).
More inputs

This tool doesn’t predict markets. It stress-tests whether your plan survives different futures β€” it tells you how much risk you can afford, not which ticker will win.

Preview Summary
Enter your assumptions and click Calculate to generate your preview.
The preview will summarize your projected final assets, Monte Carlo success rate, main watch area, and suggested next scenario to test.
πŸ“‰ Asset vs. Retirement Expense
πŸ“‰ Monte Carlo Simulation: Retirement Asset Range
Benchmark (fixed): S&P 500 SPY β€’ 20y β€’ CAGR 10.8% β€’ Οƒ 19.4%
βœ… Probability of plan success ⓘ (no depletion through age 90): 100.0%
Strong
Success = assets never hit $0 before age 90.
Band guide: <65% = caution, 66–88% = okay, 89%+ = strong
If Caution: adjust one lever β€” retire later β€’ spend less β€’ save more β€’ reduce volatility.
πŸ“Œ Executive dashboard
How safe is this plan?
Baseline success: 100.0% Strong
How fragile is it under stress?
Enable Reality Check Pack to see stress fragility.
What should I change first?
Run Sensitivity to show the top lever.
⚠️ Depletion Risk Summary (Based on 300 Monte Carlo Simulations)
  • 0.0% chance of running out of money by age 75
  • 0.0% chance of running out of money by age 85
  • 0.0% chance of running out of money by age 90
πŸ“ MC notes (end labels)
  • End age: β€”
  • Median assets: β€” (plan) vs β€” (benchmark)
  • Depletion: β€” (plan) vs β€” (benchmark)
Monte Carlo results include volatility drag and sequence risk. Return Std Dev (Οƒ) ⓘ controls how β€œbumpy” yearly market returns are around your average return. Note: this version uses a single Οƒ across pre- and post-retirement.
Assumes current savings, asset, CPP, expenses, and retirement age remain fixed. Market returns and inflation are randomly varied in each simulation.
⚑ Live What-If
Restore sliders from the current inputs above.
More controls
🧭 Strategy Coach (beta)
Lever (choose one)
🎯 Goals (beta) β€” extra expenses or inflows
Enabled Name Type When Amount Infl.

Note: goals are merged client-side into asset liquidations (post-tax) before the live update call.

πŸ”„ Compare Saved Scenarios
Scenario snapshot (saved in DB)
Select Scenario A / B above; this shows the key plan inputs saved on each scenario.
Scenario Plan snapshot Assumptions
Waiting for selection…
πŸ”„ Compare
Monte Carlo Simulation

Sensitivity – Dollar Impact (Core Inputs)
Retirement Age – Dollar Impact (Own Scale)
Tax-Aware Retirement Plan
These inputs mirror the top form; they’re tucked away to keep the page clean while reviewing Tax-Aware Visuals.
Applied each retirement year (unless RRIF minimum is explicitly enabled).
Tax Inputs (Tax-lite)
Common inputs (example): OAS Monthly β‰ˆ $800 starting at 65; Clawback threshold β‰ˆ $90,000/yr; Clawback rate 15%; RRIF start age 71. Check Index OAS to grow OAS with inflation. Adjust to your year/province.
Example only: ~$57,375 β†’ ~14.5%; ~$114,750 β†’ ~20.5%; income above that typically ~26-32% depending on province/territory. See simple bracket table (CRA) .
Note: With brackets ON the flat Income Tax Rate (%) is ignored. Use Taxable Drag (%) to include ongoing tax on taxable investment income. RRIF-min taxes still show under β€œTax paid from Taxable (overlay)”.
Optional friction on Taxable growth only. Enter manually, or use the helper below and tick Auto-apply to fill this for you. If you want to overwrite the helper, simply uncheck Auto-apply and type your own value here.
Taxable Drag Helper
Stream Allocation (% of taxable) Yield (% on that slice) Effective Tax Rate (%) Contribution to Absolute Drag (%)
Eligible dividends 0.000
Interest (incl. foreign withholding) 0.000
Realized capital gains
Use effective rate (after inclusion), e.g. 50%Γ—30% = 15%.		 0.000
Absolute Drag (Ξ£ contributions) 0.000%
Implied Taxable Drag d = Absolute Drag Γ· Return Rate 0.00%
Allocation is the share of your Taxable balance in that stream (total ≀ 100%). Yield is the annual % paid/realized on that slice (e.g., 5% dividend yield; for realized capital gains, enter the % of that slice you expect to realize as gains this year β€” for example, if you hold $300k of growth stocks and realize $15k of gains in the year, the Yield for that row is 15k Γ· 300k = 5%, even if you sold more than $15k worth of shares). For each row we compute Allocation Γ— Yield Γ— Effective Rate to get that row’s contribution to Absolute Drag (% of the taxable balance). Summing rows gives total Absolute Drag. We then divide Absolute Drag by your Return Rate to get the model’s Taxable Drag (%), which is applied to Taxable growth only.
Example (common inputs): 60% equities @ 2% yield taxed 10% β†’ 0.12%; 40% bonds @ 4% yield taxed 30% β†’ 0.48%. Absolute Drag = 0.60%. With an 8% return, Taxable Drag = 0.60 Γ· 8 = 7.5%.
Allocate your current assets across these three accounts. The total here should match your current portfolio balance. ⓘ
Total allocated: $0
Pre-retirement contribution split: Each pre-retirement year we deposit savings in this order TFSA β†’ RRSP β†’ Taxable. Amounts below are annual targets; Taxable is auto-computed as Monthly Savings Γ— 12 βˆ’ TFSA βˆ’ RRSP. ⓘ
Tip: If TFSA + RRSP exceeds Monthly Savings Γ— 12, we’ll use TFSA first, then RRSP, and set Taxable to $0 for that year.
Savings grow each pre-retirement year by your Annual Saving Increase Rate (%) and are then split in this order TFSA β†’ RRSP β†’ Taxable. ⓘ
TFSA cap grows automatically from $7,000 in 2025 by β‰ˆ2.1%/yr for 2026+. We apply that cap before RRSP and Taxable in the yearly split. ⓘ
Tax-Aware Visuals
Parity mode β€” charts never block the table

Part A β€” Six-withdrawal ledger: Year-by-year retirement flows by source using your chosen withdrawal order (e.g., Taxable β†’ TFSA β†’ RRSP), with OAS and RRIF-minimum overlays, tax on taxable and RRSP, and ending balances by account. Use the CSV to view all rows.

Age Baseline Assets (100% Taxable) From Taxable From TFSA From RRSP (Gross) RRIF Min % CPP / Support OAS (Gross) OAS Clawback OAS (Net) Tax on Taxable (overlay) Tax on RRSP (overlay) Taxable Drag End Taxable End TFSA End RRSP End Total

Optimize withdrawals (beta)
Next-$1 Analyzer (first retirement year)
RRSP marginal on next $1: β€”
Taxable marginal on next $1: β€”
OAS headroom / status: β€”
Optimizer result (summary)
β€”
Turn this preview into a clearer decision report

Want a cleaner summary of what your plan is telling you?

This free preview shows the calculation engine, charts, Monte Carlo risk view, and projection table. A future personalized report could organize your live inputs and results into a more readable decision summary with key risks, watch areas, and suggested scenarios to test next.

What a future personalized report could include
Retirement readiness, Monte Carlo success range, spending pressure, portfolio risk, and the main assumptions driving your result.
What the Advanced Planner helps you test
Later retirement, lower spending, different return assumptions, portfolio volatility, and cash deployment context.

Educational decision-support only. Not individualized financial advice.

Next step
View Sample Report
The sample report shows the format a future personalized report could follow.