Data Sources
Axiom Signals combines data from a rotating set of public and licensed providers, including:
- Quotes and reference data — Finnhub and other equity data APIs.
- Macro / economic data — the Federal Reserve Economic Data (FRED) service.
- News — public news feeds surfaced through our providers.
- Analytics infrastructure — Supabase (database, authentication) and Cloud Services.
- AI generation — AI Gateways and underlying large-language-model providers.
The set of providers can change as we improve the platform. We do not currently subscribe to premium, exchange-direct real-time market data feeds; where displayed quotes are delayed, we label them as such.
Freshness & Delay
- Most equity quotes displayed on the Service are delayed by at least the interval permitted by our providers.
- Data may be cached for short intervals to reduce load on providers and to protect against transient failures.
- Timestamps and "as of" labels reflect the best-effort freshness of the underlying data at the moment of display.
- Fundamentals, macro, and earnings data update on their own natural cadences — often daily, weekly, or monthly.
Source Data vs. Derived Analytics vs. AI Explanation
Every value shown on Axiom Signals falls into one of three categories:
- Source data — quotes, fundamentals, macro series, and news pulled from providers.
- Derived analytics — indicators, scores, regime classifications, and evidence tags computed from source data by our own models.
- AI-generated explanation — natural-language commentary generated by a large language model to describe the derived analytics.
Distinguishing the three helps you evaluate what to trust and what to verify.
Signal Methodology
Axiom continuously evaluates a defined universe of stocks and ETFs. A signal is published when a combination of technical structure, momentum, relative strength, volume, volatility, and market-regime alignment meets a threshold. Signals are tiered by the strength of the underlying evidence and are archived when published.
Evidence Architecture
Each signal and market-outlook view is decomposed into:
- Supporting evidence — the modules that agree with the thesis.
- Conflicting evidence — the modules that disagree.
- Missing data — modules that could not evaluate due to unavailable feeds.
- Regime alignment — whether the thesis is consistent with the current market regime.
Confidence
Confidence is a model-derived label ("low," "medium," "medium-high," "high") that summarizes the balance of supporting versus conflicting evidence and the coverage of contributing modules. It is not a probability of profit and is not calibrated as such unless explicitly stated.
Track Record Methodology
When a signal is published, it is written to an append-only archive. Its forward performance is then evaluated at fixed time horizons (for example 15m, 1h, 4h, 1d) using publicly observable price data. The Track Record page reports:
- Number of completed outcomes per horizon.
- Average forward return per horizon.
- Performance relative to SPY over the same window.
- Sample size — always shown alongside every statistic.
Track Record measures the analytical performance of published signals. It does not reflect transaction costs, taxes, spreads, slippage, liquidity, or the returns of a real portfolio.
Limitations
- Methodology evolves as the platform improves; historical outputs may not exactly match today's methodology.
- Provider outages can cause temporarily missing or degraded data.
- Sample sizes for niche regimes or short horizons can be small — read Track Record numbers accordingly.
- All analytical outputs are informational. See Disclaimers & Risk Disclosure.