The Alpha Score is a single number from 0 to 100 — but that single number is the weighted output of multiple distinct analytical dimensions, each contributing a different slice of evidence. The score breakdown chart in the expanded signal row shows exactly how much each dimension contributed and whether its evidence was positive, neutral, or negative. Two signals can have the same Alpha Score of 74 while being fundamentally different in quality — one built on broad agreement across all dimensions, the other built on one exceptionally strong dimension surrounded by a sea of neutral readings.
Learning to read the breakdown chart is how you distinguish the highest-quality signals from the ones that technically cleared compliance but deserve more scrutiny. This article explains what each position on the chart means, how to spot the patterns that indicate genuine convergence versus one-dimensional signals, and what the red flag pattern looks like when you are about to see it.
Understanding the analysis dimensions
The AI evaluates each opportunity across multiple independent analytical dimensions — including technical patterns, market sentiment, institutional positioning, and macro conditions. Each dimension provides a different lens on the same opportunity.
Each dimension's raw output is normalized to a 0–100 scale before it enters the composite. The relative emphasis on each dimension adapts to the current market environment, based on what the learning system has identified as most predictive in similar conditions.
What low vs. high scores mean
A score of 0 from a dimension means one of two things: either the dimension had no data available for this ticker (e.g., a stock with no listed options means the Options Flow contribution is 0 because there is nothing to analyze), or the dimension's data was explicitly negative — a meaningful adverse signal actively pulling the Alpha Score down. The chart distinguishes between these with different visual indicators: a gray bar means no data, a red bar means active negative signal.
A score of 50 is neutral — the dimension found nothing particularly bullish or bearish about this ticker. This is the most common value for dimensions that deal with low-volume data (e.g., SEC filings only update quarterly, so Fundamental often reads 50 during non-reporting periods). A score of 95 means the dimension found an exceptionally strong positive signal — for example, a stock where multiple technical indicators all fired simultaneously, or where a very positive earnings call transcript significantly lifted the sentiment reading.
Broad agreement vs. one strong signal
The highest-quality signals in the feed are characterized by broad agreement: most or all of the analytical dimensions are reading between 65 and 85, with no single dimension dominating and no dimension reading below 30. When the majority of dimensions are in the 65–85 range, you are seeing genuine multi-dimensional convergence — the technical setup, the forecast, the sentiment, the macro environment, and the options positioning are all pointing the same direction. These signals have higher compliance approval rates and better historical outcomes than signals of equivalent Alpha Score built on one dominant dimension.
One-dominant-dimension signals are where the AI found something technically very compelling (Technical = 92) but the surrounding evidence was mostly neutral (everything else 45–60). The Alpha Score may still be 70+ if the strong dimension carries enough influence in the current market conditions. These signals execute sometimes, but with smaller position sizes — lower conviction translates directly to smaller allocation on execution. The breakdown chart lets you see this pattern before deciding how much attention to pay to a given signal.
Patterns to watch for
The specific pattern to watch for is one dimension at 90 or above while the remaining dimensions are clustered in the 45–55 range with one or two dipping below 30. This is the red flag pattern. It means the AI found one genuinely strong signal in a neutral-to-slightly-negative environment. The Alpha Score may look acceptable because the dominant dimension carries significant weight, but the Conviction score will be noticeably lower — reflecting the lack of agreement across dimensions.
The compliance engine will still approve some of these signals if they meet the minimum quality threshold and all checks pass. But position sizes will be smaller than for equivalent-Alpha broad-agreement signals. Lower conviction means lower certainty, which means lower allocation. If you want only the cleanest signals in your feed, the breakdown chart gives you everything you need to apply that filter manually before the AI's automatic sizing kicks in.
How market conditions influence the analysis
When the AI detects a regime transition, the dimension weights in the Alpha Score composite shift — sometimes significantly. The change is visible in the score breakdown if you compare a signal generated just before a regime change with one generated just after. When market conditions shift, you will see the emphasis on different analytical dimensions change — some gain influence while others recede. The same underlying data produces a different Alpha Score under the new weights.
This is intentional and correct. Different market conditions favor different types of analysis. The AI adjusts its emphasis accordingly, which you can observe in the changing dimension scores across signals generated before and after a market shift. The regime chip on each signal row tells you which weight set was active at generation time — a useful reference when comparing signals generated during different market conditions in the same session.