How the models work,
and why.

Research Philosophy

This is a self-directed research workspace. It models portfolio allocation across multiple asset classes and runs quantitative analysis on defined-risk options strategies. It does not provide personalised investment advice, manage assets on your behalf, or execute live broker orders in cloud mode.

The distinction matters. A model gives you numbers to reason with. What you decide to do with those numbers — and whether the underlying assumptions reflect your situation — is a judgement only you can make. This tool surfaces the assumptions explicitly so you can challenge them.

The frameworks documented here are drawn from publicly available academic and practitioner research. Where a methodology has a named source — Browne, Dalio, Black-Scholes — that attribution is given. Nothing on this page, and nothing the tool produces, constitutes a recommendation to buy, sell, or hold any security.

The Allocation Model

The portfolio tool organises assets into five risk tiers arranged as a pyramid — a broader, more stable base tapering to a narrower, higher-risk apex. The default tier weights sum to 100%:

Why this shape?

The weights are risk-proportional. Each tier up carries substantially higher expected volatility and drawdown depth, so the position size decreases accordingly. A 4% allocation to a highly volatile asset can contribute meaningfully to long-run return while limiting the damage any single bad year does to the overall portfolio — because the mass is in the stable base, not the apex.

This is not a novel idea. Risk-proportional weighting appears across multiple frameworks — most explicitly in risk-parity construction and in the layered portfolio structures used in behavioural finance research on goal-based investing (Shefrin & Statman, 2000). The specific 4/12/20/28/36 weights are the tool's default; they can be overridden in your profile.

What the tool calculates

Enter a total portfolio value and current holdings per tier. The tool computes each tier's percentage of total, the drift from target weight, and the trade amounts needed to restore the target allocation. The rebalance output is a model calculation — the decision to act on it, at what price, and through which account, is entirely yours.

Layers & Instrument Examples

The table below lists commonly studied instruments for each tier — US-listed for global access, and Indian-listed equivalents for investors in that market. These are illustrative examples drawn from publicly available ETF research, not curated picks. Verify suitability, costs, and tax treatment for your situation.

Rebalancing

Rebalancing is the process of periodically selling assets that have grown above their target weight and buying those that have fallen below it. Over time, this mechanically sells what has become relatively expensive and buys what has become relatively cheap — without any attempt to predict markets.

The mathematical effect is well-documented: a rebalanced portfolio of uncorrelated volatile assets has been shown to produce a higher long-run compound return than a buy-and-hold portfolio with identical starting weights, assuming the assets revert toward their mean relationships over time. This is sometimes called the "rebalancing bonus" (Fernholz & Shay, 1982). The size of the bonus depends heavily on asset correlation and volatility — it is not guaranteed, and it can be negative in strongly trending markets where asset divergence persists.

What the tool models

Given current holdings and target weights, the tool calculates the minimum set of trades needed to return every tier to its target percentage. It does not model tax implications, transaction costs, or bid-ask spread — those are inputs only you can supply. The output is a research starting point, not an execution plan.

Rebalancing frequency

Academic literature on rebalancing frequency (Perold & Sharpe, 1988; Bernstein, 1996) suggests that the marginal benefit of more frequent rebalancing is small beyond a quarterly cadence for most multi-asset portfolios, and can be negative once transaction costs and tax drag are accounted for. The optimal frequency depends on your specific cost structure — the tool does not prescribe one.

Reference Frameworks

The base tier (Tier 9) of the allocation model is designed to hold a diversified, all-weather core. Two publicly documented frameworks inform the structure of that layer.

Harry Browne — Permanent Portfolio (1981)

Browne's framework allocates equally — 25% each — across stocks, long-term government bonds, cash, and gold. The rationale is that each quadrant performs well in one of four economic environments: prosperity (stocks), deflation (long bonds), recession (cash), and inflation (gold). By holding all four, the portfolio stays approximately stable across economic regimes rather than depending on a correct macro forecast.

Published historical analysis places the 30-year CAGR at approximately 6.5–7%, with standard deviation around 6% and meaningfully lower maximum drawdowns than equity-only portfolios. These are historical observations, not forecasts.

Ray Dalio — All Weather Portfolio

Dalio's framework, documented publicly in his 2011 Bridgewater research and in subsequent interviews, allocates by risk contribution rather than nominal dollar weight. Because bonds have lower volatility than stocks, they require a larger nominal allocation to contribute equal risk. The result is a portfolio that is roughly balanced between growth assets and deflation/inflation hedges.

Published historical backtests place the 30-year CAGR at approximately 7.5–8%, with a documented maximum drawdown of around 12% in 2008 — substantially below equity indices in the same period.

How these inform this tool

The Tier 9 base layer is a modelling space where you can hold an All-Weather-style allocation. The tool does not replicate either framework precisely — it provides a tracking and rebalancing calculator that works with whatever assets you place in each tier. The frameworks above are the conceptual reference for why a diversified multi-asset base layer tends to produce more stable long-run outcomes than equity concentration.

The Options Strategy

The options layer of this tool covers two complementary research modes. The Strategy tab configures and prices live setups for Iron Butterfly, Double Calendar, and Iron Condor based on today's market data. The Research tab backtests all three strategies across multiple timeframes and ranks them by annualised return for your holding period, so you can evaluate which structure has historically fit your cadence before committing to a setup.

The three strategies

All three structures are defined-risk: the maximum possible loss is known at entry. None require ownership of the underlying.

Profit zones at a glance

Strategy Matrix — how ranking works

The Research tab backtests all three matrix strategies (Iron Condor, Iron Butterfly, Double Calendar) across timeframes relevant to your holding period: daily (0–2 DTE), weekly, bi-weekly, and monthly. Each combination is run against historical SPX closing prices and scored by annualised return — total P&L divided by required capital, scaled to a year. Results are ranked from highest to lowest so you can see which structure and timeframe produced the best risk-adjusted outcome over the selected lookback period.

The matrix also surfaces a Mode Winner: the single best-performing strategy and timeframe combination for your configured holding period. This is a historical backtest result — it reflects what would have worked in the past under the model's assumptions, not a forecast of future performance.

Why SPX options specifically

The choice of SPX as the underlying is structural, not speculative. SPX options are European-style — they can only be exercised at expiration, eliminating early assignment risk. They are cash-settled — no shares change hands on expiry. Under US tax law (Section 1256 of the Internal Revenue Code), index options held at year-end receive 60/40 tax treatment: 60% of gains are taxed as long-term capital gains regardless of holding period. SPX options market depth is among the highest of any options market, producing tight bid-ask spreads.

None of these structural properties constitute a reason to trade any strategy. They explain why, if someone is already researching defined-risk options income strategies, SPX is a commonly studied instrument.

What Black-Scholes models

The P&L analysis uses the Black-Scholes-Merton pricing model to estimate theoretical option premiums. The inputs are: spot price, strike, time to expiration, implied volatility (defaulting to VIX as a proxy), and risk-free rate. The model produces an estimated credit received, a payoff curve across a range of spot prices at expiration, break-even strikes, and a probability estimate for the underlying staying within the short strikes based on historical move distributions.

Black-Scholes assumes constant volatility, log-normal returns, no dividends, and frictionless markets. None of these hold precisely in practice. The tool's outputs are theoretical estimates — actual fills depend on bid-ask spread, skew, liquidity, and the specific options chain available on the day.

Strike selection methodology

The tool selects strikes based on current spot price, a regime assessment derived from SMA trend score and VIX tier, and a configurable bias offset. The short strikes are placed at a fixed distance (the spread width) from a regime-adjusted centre point. The long strikes are placed further out by the wing width. All of these parameters are visible and adjustable — the methodology is the model, not a recommendation.

Using This Tool

AltSal has two modes. Basic is a guided starter view with four screens: Home (market overview and allocation layers), Portfolio (layer-by-layer planning), Simulate (paper scenarios), and Journal (research notes). No broker connection, no campaign builder. Pro is the full research workspace described below, with live market data, strategy backtesting, a campaign builder, and optional IBKR order routing. Switch modes from the navigation bar at any time.

Portfolio tab (Pro)

Enter your total capital. The tool calculates the target dollar amount for each tier based on the 5-layer allocation model and your investing style. The performance chart tracks a modelled portfolio value over time using historical return assumptions. Use the allocation calculator to model different total amounts or investing styles before making any changes to real positions.

Strategy tab (Pro)

The Market Data bar at the top of the tab displays live price, Bollinger Bands, RSI, VIX level, and SMA trend signals for the selected underlying. These are reference data points for contextualising current market conditions — not signals to act on directly.

The Regime Context panel translates those signals into a directional bias and suggested put-wing width. The built-in Bias Guide walks through each step of the calculation — trend score, VIX tier, regime lookup, and mode normalisation — so the output is fully auditable. The result is a research starting point, not a directional call.

The Trade Setup section models Iron Butterfly, Double Calendar, and Iron Condor structures across modes from 1DTE to Monthly. Each preset card shows configurable wing widths or strike distances alongside an estimated credit derived from Black-Scholes. The Strike Visualiser shows the exact strike ladder with hover details for each leg, letting you inspect the structure before any decision is made. The P&L Analysis panel lets you override premium assumptions with actual broker quotes to produce a more precise payoff curve.

The Trade Backtest runs the active preset configuration against historical price data — from 1 to 10 years — and returns annualised return, win rate, max drawdown, and a trade-by-trade log. A "skipped trades" toggle surfaces what-if entries that were excluded, which is useful for auditing the selection logic.

The Paper Campaign panel lets you log a simulated entry at today's modelled strikes without using real capital. This keeps a structured research record across multiple market cycles, separate from any live positions.

Research tab (Pro)

The ETF Screener (US only) ranks ETFs by annualised return since launch relative to the SPX and NDX benchmarks. Results are sortable by ticker, return, CAGR, and category, and can be filtered by tier fit — making it easier to cross-reference ETF candidates against each allocation layer of the framework. Data is sourced from Yahoo Finance and cached for six hours.

The Strategy Matrix backtests every combination of trade mode, FO style, frequency, contracts, strike-adjust, and stand-aside setting against the selected historical period. The parameter grid (period, mode, style, frequency, adjust, stand-aside) lets you narrow the search space before running. Results surface annualised return, win rate, max drawdown, and capital required for each configuration, ranked so the highest-scoring rows appear first. Use the filter row to isolate specific modes, P&L outcomes, or strategy types within the results.