Read Me First — Signal Lab

The Signal Lab studies the publicly described target/value-averaging mechanism associated with the 3% Signal concept. It is intended as a transparent mechanism study, not a claim to reproduce every proprietary feature of any paid implementation.

What this lab is designed to do

The Signal Lab studies the publicly described target/value-averaging mechanism associated with the 3% Signal concept. It is intended as a transparent mechanism study, not a claim to reproduce every proprietary feature of any paid implementation.

Key controls and inputs

• Market regime. Test bull, bear, and rotating-regime environments because target-based rebalancing depends strongly on the sequence of gains and losses.
• Monte Carlo paths. More paths reduce the influence of a few lucky sequences and reveal the distribution of outcomes.
• Seed. A fixed seed makes a run reproducible. Change seeds when checking robustness, not when searching for a better-looking result.
• Quarterly target growth. The public rule’s target is the mechanism under study. Sweep nearby values to understand sensitivity rather than assuming one value is uniquely optimal.
• Safe-asset return / reserve behavior. The non-equity reserve is part of the system and can materially affect both return and drawdown.
• Benchmarks. 100% stock and a simpler stock/bond or 80/20 rebalance control help determine whether the target mechanism adds anything beyond lower average stock exposure.

What the outputs mean

• CAGR / terminal wealth. Shows long-run growth but must be read with risk and reserve depletion.
• Maximum drawdown. Indicates peak-to-trough stress; lower drawdown can come from lower equity exposure.
• Average equity exposure. Essential for a fair benchmark comparison.
• Reserve depletion frequency. A target strategy can demand purchases after declines. If the reserve is exhausted, the mechanism changes exactly when markets are stressed.
• Paired-path benchmark difference. Useful for isolating the rule from path luck.

A good first experiment

1. Use the default public-rule target and a moderate safe-asset return.
2. Run a large paired-path experiment in the Typical or rotating-regime setting.
3. Compare the target rule with 100% stock and the simpler rebalance benchmark.
4. Record CAGR, drawdown, equity exposure, and reserve-depletion frequency together.
5. Repeat in a persistent bull regime and a prolonged bear/sideways regime.
6. Run a small target-growth sweep. Treat a broad plateau as more credible than a single sharp optimum.

How to interpret the result

Do not judge the strategy from one path, one seed, or one favorable market environment. Read return, drawdown, exposure, trade frequency, and benchmark-relative performance together. A result is more credible when it persists across reasonable parameter changes and when the comparison benchmark has similar economic exposure.

Important assumptions and limitations

• The lab implements a public description of the mechanism and may omit proprietary details not publicly specified.
• Monte Carlo market regimes are model assumptions, not forecasts.
• Reserve returns, taxes, trading costs, and implementation timing can materially change real outcomes.
• A value-averaging rule can require buying into severe declines; reserve depletion is therefore a central risk rather than a side metric.
• Optimization over many target rates can overfit the simulation environment.

How this complements backtesting

Backtests reveal how the rule would have interacted with actual historical crashes and bull markets. Simulation adds many alternative sequences and allows explicit regime control. Because target strategies are highly sequence-dependent, using both is particularly important.