TOC-FOLIO

Portfolio Construction & Optimization Platform

Investment Analysis & Proposal Engine

Comprehensive portfolio optimization powered by Kelly Criterion with Higher Moments, utilizing TOC-23's proprietary Capital Market Assumptions. Import existing portfolios, classify holdings, optimize allocations, and export client-ready proposals.

Active Asset Classes
Approved Funds
9
Business Cycle
Mid Cycle
Portfolio Value
$50M

Tax Configuration

Federal income character per asset class with dynamic state overlay. All cells are editable — adjust to match your client's specific situation. Effective rate auto-calculates.

Fed. Ordinary + NIIT
%
Fed. LTCG + NIIT
%
State
Wtd Avg Eff. Rate
AI-Powered Tax Research (Secured via Server Proxy)
Enter your team access code to run AI-powered tax analysis. API key is secured server-side and never exposed to the browser.
Addepar API Credentials (per-user, session-cached)
Per-user credentials for pulling live client portfolios from Addepar. Cached in session storage (cleared when you close the browser). Requests go through proxy.php so the secret never appears in the URL.
Asset Class Ordinary % LTCG/QD % Tax-Exempt % Deferred % State Applies? Eff. Rate

Capital Market Assumptions

These assumptions drive the entire analysis — efficient frontier optimization, Monte Carlo simulation, and portfolio comparison. Adjust the business cycle, time horizon, include/exclude asset classes, set allocation constraints, and add alpha overlays. Changes propagate immediately to all calculations.

Business Cycle
Forecast Horizon
Asset Classes
Source
Fiducient 2026

Asset Class Configuration

Toggle include/exclude, set min/max allocation constraints (%), and add alpha overlays. Cycle adjustments are applied automatically based on the business cycle selection above.

Use Asset Class Geo Return Std Dev Cycle Adj Adj Return Min % Max % Tax α Diligence α Option α Skewness Kurtosis

Return vs. Risk

Adjusted Returns by Asset Class

Correlation Matrix

Pairwise correlations for included asset classes. Darker = higher positive, red = negative.

Import Portfolio Positions

Upload custodian statements or previously scrubbed files. Supports Excel (.xlsx/.xls/.csv), PDF, and JSON. Files are parsed in-browser, displayed in an editable spreadsheet for QC, then exported to Excel or JSON for archival. Re-upload scrubbed files to restore your work.

📄
Drop file here or click to upload
Excel, CSV, PDF, or JSON — multiple files supported

Add Positions Manually

PDF AI-Assist
If PDF parsing misses positions, enter your API key on the Dashboard tab, then upload the PDF. Claude will attempt to extract structured holdings data.

Current Portfolio Analysis

Portfolio allocation across CMA asset classes. Populated from Import tab or enter manually below.

Current Allocation (%)

Kelly Criterion Efficient Frontier

g(w) ≈ μp − (λ·σp²)/2 + (S3·σp³)/(6·λ) − (K4·σp⁴)/24

How Kelly Optimization with Higher Moments Works ↓

The Kelly Criterion was originally developed to determine optimal bet sizing in information theory. Applied to portfolio management, it maximizes the expected logarithmic growth rate of wealth — the allocation that makes your portfolio grow fastest over time while accounting for the compounding drag of volatility.

The standard Kelly formula is simple: g(w) = μ − σ²/2, where the optimal portfolio maximizes expected return minus half the variance. This captures the key insight that volatility destroys compound growth — a portfolio that gains 20% then loses 20% doesn't break even, it loses 4%.

Higher Moments extend this framework to account for real-world return distributions that aren't normally distributed:

Skewness (S⊂3) — Measures asymmetry. Negative skewness (most equity and credit asset classes) means large losses are more common than large gains. The formula adds S⊂3·σ³/(6·λ), which penalizes negatively-skewed assets and rewards positively-skewed ones like managed futures.

Excess Kurtosis (K⊂4) — Measures tail thickness. Higher kurtosis means more extreme events. The formula subtracts K⊂4·σ⁴/24, penalizing fat-tailed distributions where catastrophic losses (or gains) occur more frequently than a normal distribution predicts.

The λ (lambda) parameter controls risk appetite. Higher λ values scale down risk — λ=8 produces conservative allocations suitable for capital preservation, while λ=0.6 approaches full Kelly and maximizes long-run growth rate at the cost of significant volatility.

After-tax optimization applies asset-class-specific effective tax rates before computing the growth rate, ensuring the frontier reflects what investors actually keep rather than gross returns. This naturally favors tax-efficient structures like municipal bonds, qualified dividends, and deferred-gain strategies.

The five portfolios on the frontier (Conservative λ=8, Mod. Conservative λ=5, Moderate λ=3, Growth λ=1.8, Aggressive λ=0.6) each contain ≥10 asset classes with ≥7 overlapping between adjacent portfolios for implementation stability.

Portfolio Comparison Matrix

Side-by-side comparison across key risk, return, and tax metrics.

Return Comparison

Risk Metrics

Monte Carlo Simulation

25-year forward projection, 5,000 simulations at P25, P50, P75 confidence intervals.

TOC-23 Approved Investment Platform

Fund-level implementation sourced through TOC-23 and vetted through Fiducient Advisors.

All Funds Private Credit Real Estate Private Equity Real Assets Public Equity Hedge Funds Venture Capital

Asset Class to Fund Mapping

Portfolio Construction & Manager Selection

Implementation portfolio built from the TOC-23 approved manager lineup. Select a Kelly-optimized portfolio from the dropdown to populate CMA-driven sub-class weights. Managers with fixed allocations (% of parent group) hold steady; Kelly-driven positions are proportioned by the optimizer. Upload a new manager spreadsheet to override defaults.

When I edit Alloc % or $:
Bogey Income Solver:
Major Asset Class Sub Asset Class Manager Strategy Ticker/SMA Status Alloc Mode Alloc % $ Amount Exp Ret Mgmt Fee Perf Fee Yield Tax α
Portfolio Total 0.0% $0

Implementation Allocation

Implementation vs. CMA Weights

Asset Location

Place each asset class into taxable or tax-protected entities to maximize after-tax return. Tax-inefficient assets (ordinary income, HFs, REITs) belong in tax-protected entities; tax-efficient assets (munis, SMAs with tax alpha, PE) belong in taxable accounts. Private-investment-eligible entities are required for PE/PC/Private RE allocations.

Step 1: Define Entities

Add the accounts / trusts / entities the client uses. Sum of sizes should approximate the portfolio value.

Entity Name Size ($) Tax Status Private-Eligible Income Target ($/yr) Actions
Entity Total $0 $0

Step 2: Generate Placement

Use the deterministic heuristic (tax-inefficiency × tax-shelter value) or call Claude for a narrative-driven allocation. You can override any cell in the matrix below.

Step 3: Placement Matrix (Editable)

Rows = asset classes from the selected proposed portfolio. Columns = your entities. Values are dollars. Color coding: green cells are in-range, amber near limit, red over-capacity or misaligned with private-eligibility.

Step 4: Flowchart

Sankey-style visual of the placement. Left = asset classes (sized by dollars), right = entities. Click a flow to edit its allocation in the matrix above.