Agents

Every cents research call fans out to seven agents. Each one returns an AgentResult containing an evidence list, a single conviction delta (clamped to ±10), per-dimension scores, and a human-readable summary. The orchestrator aggregates them with confidence and age weighting.

Sample report

The static report below is rendered by cents research --export html and embedded verbatim — same data, same dimension scores, same evidence rows.

Per-agent breakdown

Fundamentals

• Source: Financial Modeling Prep (/api/v3/ratios, /api/v3/key-metrics).
• Evidence: P/E ratio, gross/operating/net margins, ROE, debt-to-equity, current ratio, analyst price-target spread.
• Dimension scores: primarily valuation and quality; risk-adjacent metrics (debt, liquidity) feed risk.
• Logic: compares each ratio to the thesis’s stated valuation (undervalued / fair / overvalued) and emits supporting, contradicting, or neutral evidence accordingly.

Technical

• Source: Alpaca market data (price bars, daily aggregates).
• Evidence: moving-average crossovers, momentum slope, 52-week range position, recent volume vs. trailing average.
• Dimension scores: technical, with a small risk contribution when volatility is elevated.
• Logic: classic trend-following heuristics anchored to the symbol’s own history; returns supporting evidence when the trend aligns with the thesis time horizon.

Macro

• Source: FRED (Fed funds rate, 10y-2y spread, VIX, unemployment).
• Evidence: rate regime, yield-curve shape, VIX percentile, employment trend.
• Dimension scores: macro (and risk when stress signals fire).
• Logic: evidence is contextual rather than directional — a steepening yield curve nudges conviction up for cyclicals, down for long-duration assets, framed by the thesis’s time horizon.

Sentiment

• Source: NewsAPI headlines + Anthropic Claude for classification.
• Evidence: recent headline volume and per-headline sentiment scores.
• Dimension scores: sentiment.
• Logic: Claude classifies each headline as positive / negative / neutral; the agent aggregates with a recency weighting. Falls back to a keyword heuristic if ANTHROPIC_API_KEY is missing, and to a no-op if NEWS_API_KEY is missing.

Moat

• Source: FMP financials (multi-year ratios + segment data).
• Evidence: margin stability over 3-5 years, ROIC trend, gross-margin durability.
• Dimension scores: moat, secondary quality.
• Logic: stable margins + rising ROIC = supporting moat evidence; compressing margins = contradicting. Avoids one-period snapshots.

Insider

• Source: FMP insider-transaction endpoint.
• Evidence: net insider buying/selling, cluster activity (multiple insiders trading in the same direction within a window).
• Dimension scores: small contributions to quality and risk.
• Logic: cluster buying is treated as a stronger supporting signal than isolated transactions; cluster selling registers as contradicting.

Orchestrator

• Source: the six agents above.
• Output: a single AgentResult whose evidence is the union of every child agent’s evidence and whose conviction delta is the weighted sum.
• Weighting:
  • Confidence weighting — each evidence item carries a confidence in [0, 1]; the agent’s average evidence confidence scales its conviction delta before aggregation.
  • Age decay — evidence weight decays linearly from 1.0 to 0.1 over a per-dimension TTL (technical/sentiment 7 days, macro/valuation/ risk 30 days, quality/moat 90 days). Old evidence is never fully discarded — it floors at 0.1 so historical context survives.
  • Clamping — every individual agent’s conviction delta is clamped to ±10 so a single agent can’t dominate the synthesis.
• Synthesis summary: human-readable string of the form NVDA synthesis: fundamentals: +3.1 | technical: +1.8 | sentiment: +0.4 = +5.3 total (weighted).

Reading the dimension scores

The five dimensions — valuation, quality, moat, technical, risk, plus secondary macro and sentiment — are independent axes. The orchestrator sums per-agent dimension contributions, so a high overall conviction delta with one badly negative dimension is a flag worth reading, not just a number to act on.