Methodology
How the AnyMarket Algorithm Works
The AnyMarket Algorithm runs automatically after each trading day. The sequence is the same every time:
1. Market data is downloaded Shortly after the close, the model pulls the latest S&P 500 data and adds the new trading day to the dataset.
2. Indicators are recalculated All technical indicators are updated using the new closing price — momentum readings, trend measurements, volatility metrics, and statistical thresholds.
3. Signals are evaluated The model checks whether any buy or sell conditions have been triggered and determines whether the current allocation should change.
4. Allocation is updated If a change is warranted, the model records the trade and updates the portfolio state.
5. Subscribers are notified When the model triggers an allocation change, subscribers receive an automated email alert the same day.
6. The public site is updated Results are published to this site on a 30-day delay for transparency and verification purposes.
Every decision is fully auditable. The same inputs always produce the same output.
Core Market Signals
The model evaluates several types of signals that each capture a different aspect of market behavior — momentum, longer-term trends, volatility, and unusual price events.
No single indicator drives the decision. The model combines these signals within a structured set of rules designed to respond to meaningful shifts in market conditions while filtering out day-to-day noise.
Momentum Signals
The primary momentum indicator is the Relative Strength Index (RSI), a widely used measure of how quickly prices have been rising or falling. High RSI readings suggest markets may be overheated following a strong rally. Low readings often appear during periods of heavy selling pressure.
The model tracks both extreme single-day readings and clusters of repeated signals over time. A sustained run of unusually high readings can indicate that buying pressure has become unsustainable. A sustained run of weak readings may signal panic selling that is beginning to exhaust itself. Depending on the broader context, these patterns can contribute to buy or sell signals.
Trend Signals
Momentum can shift quickly. To capture longer-term direction, the model compares short-term and long-term moving averages of market prices.
When the short-term trend strengthens relative to the long-term trend, it suggests building upward momentum. When the opposite occurs, it may signal the beginning of a broader weakening. These crossovers are widely used by institutional investors to identify major directional shifts.
In this model, trend signals are never acted on in isolation — they must be confirmed by other conditions before triggering a trade.
Volatility Filters
Volatility typically rises before large market declines. The model continuously monitors price stability and uses it as a filter on certain signals.
Some bearish signals are only permitted to trigger when volatility is already elevated. This prevents the model from rotating defensively during otherwise calm market conditions, keeping it focused on periods where the risk of a meaningful decline is genuinely elevated.
Rapid Decline Detection
Markets occasionally experience sharp, sudden selloffs driven by panic rather than fundamentals. These moves can unfold over just a few trading days.
The model includes a rule designed to detect these unusually steep short-term drops. When one is identified and other guardrails allow it, the signal can contribute to a potential buy — recognizing that fear may have pushed prices well below their recent trend.
Additional filters prevent this from treating every sharp decline as an automatic buying opportunity.
Risk Management and Guardrails
Signals alone do not trigger trades. Several guardrails are layered on top to reduce unnecessary activity and limit downside risk.
When the model is in equities, it tracks the highest price reached since the position was entered. If the market falls sharply from that peak, a trailing stop can trigger a defensive rotation to protect accumulated gains.
The model also prevents rapid reversals. Markets frequently produce conflicting signals over short periods, and the system requires meaningful confirmation before reversing a recent decision. This keeps the model focused on sustained shifts rather than noise.
Finally, when the model is in defensive assets, it monitors whether the market has begun recovering. If enough time has passed and prices have climbed sufficiently from the level where the defensive allocation began, the model may re-enter equities even if some indicators remain cautious.
A Structured Decision Process
Each trading day, all signals and guardrails are evaluated together. The output is intentionally binary: remain in equities or rotate to defensive assets.
Because the same rules are applied to the same data every day, the model is fully deterministic — identical inputs always produce identical outputs. There is no discretion, no override, and no subjective adjustment. The strategy responds to what the data says, not to what the news is saying.
