Stock Intelligence Report

Stock Intelligence Report aggregates 14 alternative data sources into one structured analysis for any publicly traded company or ticker symbol. It is built for investors and analysts who want institutional-grade signals — smart money positioning, congressional stock trades, macro regime classification, patent velocity, and consumer stress — without paying for a Bloomberg Terminal or FactSet subscription.

Each run fires 14 parallel sub-actor calls, applies five proprietary scoring algorithms, and compiles a single JSON report with a weighted Composite Score rated from STRONG BUY to STRONG SELL. No code required to get started.

What data can you extract?

Why use Stock Intelligence Report?

Manually gathering the data this actor collects in a single run takes 6-8 hours. You would need to: pull 13F filings from the SEC EDGAR full-text search system, reconcile insider trading Forms 4, search congressional disclosure databases, download FRED series via API, check BLS and IMF releases, search patent databases, scan CFPB complaint records, and then write code to unify and score it all. Professional tools that do pieces of this — Bloomberg Terminal ($24,000/year), FactSet ($12,000/year), Quiver Quantitative ($120/year) — are either expensive, siloed, or don't combine all these signals into one structured output.

This actor automates the entire multi-source pipeline in under 3 minutes and costs fractions of a cent per run.

• Scheduling — run weekly scans on a watchlist and compare Composite Scores over time to track signal drift
• API access — trigger runs from Python, JavaScript, or any HTTP client and pipe results directly into your models
• Proxy rotation — sub-actor calls use Apify's built-in infrastructure; rate limiting and retries are handled automatically
• Monitoring — set up Slack or email alerts when a run fails or a Composite Score crosses a threshold via webhooks
• Integrations — push output to Google Sheets, Airtable, HubSpot, or any webhook endpoint via Zapier or Make

Features

• 14-source parallel data collection — all sub-actors run simultaneously via Promise.all, keeping total run time under 3 minutes regardless of source count
• Smart Money Convergence Score (0-100) — counts distinct institutional filers from 13F-HR filings; 10+ filers scores 85, adjusted up to +10 or down -10 based on insider buy/sell ratio above 0.65 or below 0.35
• Congressional Alpha Score (-100 to +100) — calculates (buys - sells) / total across all congressional trade disclosures; NET_BUYING threshold is +20, NET_SELLING is -20; surfaces top 10 traders by volume
• Macro Regime Classifier (EXPANSION / PEAK / CONTRACTION / TROUGH / UNCERTAIN) — evaluates GDP growth vs 2% threshold, UNRATE vs 4.5% and 6% thresholds, T10Y2Y yield curve inversion (double-weighted), and CPI above 5%; confidence score is the expansion-to-total signal ratio
• Innovation Momentum Score (0-100) — baseline 50; adds 25 for 50+ USPTO patent filings, 15 for 20+ patents, 5 for 5+ patents; adds 20 for 20+ R&D/engineering job postings, 10 for 5+; subtracts 15 for zero patents
• Consumer Stress Index (0-100) — aggregates CFPB complaint volume (up to 30 points), CPI inflation above 4% (up to 25 points), personal savings rate PSAVERT below 5% (15 points), and University of Michigan sentiment UMCSENT below 70 (15 points)
• VIX risk regime classification — RISK-ON below 20, CAUTIOUS between 20-30, RISK-OFF above 30; contributes 15% weight to Composite Score
• Weighted Composite Score (0-100) — Smart Money 25%, Innovation Momentum 25%, Macro Confidence 15%, VIX Risk 15%, Congressional Alpha 10%, inverse Consumer Stress 10%
• Sector rotation guidance — macro regime maps to actionable sector allocation: cyclicals for EXPANSION, defensives for PEAK, treasuries and gold for CONTRACTION, small caps and financials for TROUGH
• Insider trading sentiment — classifies BULLISH (buy ratio > 65%), BEARISH (buy ratio < 35%), or NEUTRAL from SEC Form 4 disclosures; included by default, optional toggle to skip
• Raw data pass-through — output includes up to 10 raw 13F filings, 15 insider trades, 20 congressional trades, 10 patent filings, and 5 most recent SEC filings for independent verification
• Graceful data source failures — each sub-actor call is wrapped in try/catch; a failed or slow source returns an empty array and does not abort the run

Use cases for stock intelligence reports

Quantitative and systematic investing

Quantitative analysts building factor models can feed the five scores (Smart Money, Congressional Alpha, Macro Confidence, Innovation Momentum, Consumer Stress) as signal inputs alongside price-based factors. The structured JSON output integrates directly into Python backtesting environments like backtrader or zipline without transformation. Run the actor weekly on a watchlist of 50-200 names and diff the Composite Scores to build momentum signals.

Portfolio management and allocation decisions

Portfolio managers who make discretionary decisions benefit from a single-view consolidation before initiating or exiting a position. Instead of pulling three terminals and two spreadsheets, one run surfaces institutional positioning, insider behavior, macro cycle context, and sector rotation guidance in one JSON object. The sector recommendation field translates regime classification directly into allocation guidance.

Retail investor due diligence

Retail investors can access institutional-grade alternative data signals that normally require expensive subscriptions. See what Congress members, hedge fund managers, and corporate insiders are actually doing with a specific stock — not what analysts are saying. The STRONG BUY / STRONG SELL classification provides a quick signal before diving into the underlying data.

Macro strategy and sector rotation

Macro-focused investors can use the Macro Regime Classifier independently of the company-specific analysis. The classifier combines FRED GDP, UNRATE, T10Y2Y yield curve, OECD leading indicators, and BLS CPI into a single regime label with a confidence score. The actor retrieves the VIXCLS series alongside 10-year Treasury yields and USD exchange rates for a complete cross-asset picture.

Research automation and newsletter content

Investment researchers and newsletter writers can automate the data-gathering phase of stock write-ups. Run the actor on a new company, get a structured briefing on institutional sentiment, macro context, and innovation signals in minutes rather than hours, then focus analytical effort on interpretation rather than collection.

Risk monitoring and watchlist alerts

Risk analysts tracking a portfolio's exposure can schedule runs on individual holdings to monitor Composite Score drift over time. A score dropping from MODERATE BUY to NEUTRAL, driven by rising Consumer Stress and deteriorating Congressional Alpha, is an early warning signal that conventional price-based monitoring would not surface.

How to generate a stock intelligence report

1. Enter your ticker or company name — type "AAPL", "NVDA", "Microsoft Corp", or any publicly traded US company into the "Company Name or Ticker" field
2. Configure insider data — leave "Include Insider Trading Data" checked (default) to include SEC Form 4 disclosures in the Smart Money score, or uncheck to reduce run time by roughly 20 seconds
3. Click Start — the actor launches all 14 data source calls in parallel; most runs complete in 2-3 minutes
4. Download your report — open the Dataset tab, then export as JSON for programmatic use or CSV for Excel analysis

Input parameters

Input examples

Standard single-stock analysis:

{
    "query": "AAPL",
    "includeInsider": true
}

Analysis using full company name with explicit ticker:

{
    "query": "NVIDIA Corporation",
    "ticker": "NVDA",
    "includeInsider": true
}

Fast run without insider data:

{
    "query": "MSFT",
    "includeInsider": false
}

Input tips

• Use the ticker symbol for best results — entering "AAPL" instead of "Apple Inc" ensures Finnhub market data, patent searches, and congressional trade lookups all target the correct entity
• Provide both query and ticker for large conglomerates — companies like "Alphabet" or "Berkshire Hathaway" benefit from query: "Alphabet Inc" plus ticker: "GOOGL" to maximize cross-source matching
• Disable insider data to speed up screening runs — when scanning a watchlist quickly, set includeInsider: false to cut run time and reduce cost by roughly 15%
• Schedule weekly for signal drift tracking — Composite Scores that move more than 15 points between weekly runs indicate a meaningful change in the underlying signal mix worth investigating

Output example

{
    "reportTitle": "Stock Intelligence Report: NVIDIA Corporation",
    "ticker": "NVDA",
    "generatedAt": "2026-03-20T09:14:37.221Z",
    "compositeScore": 78,
    "compositeRating": "STRONG BUY SIGNAL",
    "smartMoney": {
        "convergenceScore": 92,
        "netDirection": "ACCUMULATING",
        "insiderSentiment": "BULLISH",
        "findings": [
            "14 institutional filers holding positions — strong convergence",
            "Insider sentiment BULLISH: 9 buys vs 2 sells"
        ],
        "data": {
            "thirteenFFilings": 20,
            "insiderTrades": 11,
            "edgarFilings": 5
        },
        "rawInsiderTrades": [
            {
                "filerName": "Jensen Huang",
                "transactionType": "purchase",
                "shares": 50000,
                "price": 118.42,
                "filedDate": "2026-03-10"
            }
        ]
    },
    "congressionalAlpha": {
        "alphaScore": 60,
        "netDirection": "NET_BUYING",
        "totalTrades": 18,
        "topTraders": [
            { "name": "M. McCaul", "direction": "buyer", "trades": 4 },
            { "name": "B. Collins", "direction": "buyer", "trades": 3 }
        ],
        "findings": [
            "12 congressional buys, 3 sells across 18 records",
            "Congress is NET BUYING (alpha: +60) — historically a bullish signal"
        ]
    },
    "macroRegime": {
        "regime": "EXPANSION",
        "confidence": 80,
        "indicators": [
            { "name": "GDP Growth", "signal": "EXPANSION", "value": 3.1 },
            { "name": "Unemployment Rate", "signal": "EXPANSION", "value": 4.1 },
            { "name": "Yield Curve (10Y-2Y)", "signal": "EXPANSION", "value": 0.38 }
        ],
        "findings": [
            "Macro regime: EXPANSION (4 expansion signals, 1 contraction signal)"
        ],
        "sectorRecommendation": "Favor cyclicals: technology, consumer discretionary, financials"
    },
    "innovation": {
        "momentumScore": 95,
        "patentVelocity": "ACCELERATING",
        "hiringSignal": "STRONG",
        "findings": [
            "63 patent filings — very high innovation velocity",
            "28 R&D/engineering job postings — aggressive hiring"
        ],
        "patents": { "count": 63, "recent": [] },
        "jobs": { "count": 28, "rdPositions": [] }
    },
    "consumerStress": {
        "stressIndex": 28,
        "level": "MODERATE",
        "components": [
            { "name": "Consumer Complaints", "contribution": 10, "detail": "34 CFPB complaints" },
            { "name": "Inflation (CPI)", "contribution": 18, "detail": "CPI at 6.1% — eroding purchasing power" }
        ]
    },
    "crossAsset": {
        "riskRegime": "RISK-ON (VIX < 20)",
        "vix": 16.4,
        "cryptoSentiment": "data available",
        "dollarStrength": "data available"
    },
    "dataSummary": {
        "totalActorsRun": 14,
        "totalDataPoints": 342
    }
}

Output fields

How much does it cost to run a stock intelligence report?

Stock Intelligence Report uses pay-per-event pricing — you pay approximately $0.18-$0.25 per report. Platform compute costs are included. The cost comes from the 14 parallel sub-actor calls; each call uses 256 MB memory with a 120-second timeout.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to Bloomberg Terminal at $2,000/month, FactSet at $1,000/month, or even Quiver Quantitative at $120/year — with Stock Intelligence Report, most individual investors and small funds spend $2-20/month with no subscription commitment and structured machine-readable output.

Stock intelligence report using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/stock-intelligence-report").call(run_input={
    "query": "NVDA",
    "ticker": "NVDA",
    "includeInsider": True
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Ticker: {item['ticker']}")
    print(f"Composite Score: {item['compositeScore']} — {item['compositeRating']}")
    print(f"Smart Money: {item['smartMoney']['convergenceScore']} ({item['smartMoney']['netDirection']})")
    print(f"Congressional Alpha: {item['congressionalAlpha']['alphaScore']} ({item['congressionalAlpha']['netDirection']})")
    print(f"Macro Regime: {item['macroRegime']['regime']} ({item['macroRegime']['confidence']}% confidence)")
    print(f"Innovation Momentum: {item['innovation']['momentumScore']} ({item['innovation']['patentVelocity']})")
    print(f"Consumer Stress: {item['consumerStress']['stressIndex']} ({item['consumerStress']['level']})")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/stock-intelligence-report").call({
    query: "NVDA",
    ticker: "NVDA",
    includeInsider: true
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`Ticker: ${item.ticker}`);
    console.log(`Composite Score: ${item.compositeScore} — ${item.compositeRating}`);
    console.log(`Smart Money: ${item.smartMoney.convergenceScore} (${item.smartMoney.netDirection})`);
    console.log(`Congressional Alpha: ${item.congressionalAlpha.alphaScore} (${item.congressionalAlpha.netDirection})`);
    console.log(`Macro Regime: ${item.macroRegime.regime} (${item.macroRegime.confidence}% confidence)`);
    console.log(`Innovation Momentum: ${item.innovation.momentumScore} (${item.innovation.patentVelocity})`);
    console.log(`Consumer Stress: ${item.consumerStress.stressIndex} (${item.consumerStress.level})`);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~stock-intelligence-report/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "NVDA", "ticker": "NVDA", "includeInsider": true}'

# Fetch results (replace DATASET_ID with the defaultDatasetId from the run response)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How Stock Intelligence Report works

Phase 1: Parallel data collection across 14 sources

The actor constructs an array of up to 14 sub-actor call configurations and executes all of them simultaneously using Promise.all via the internal runActorsParallel helper. Each call is wrapped in a try/catch with a 120-second timeout and 256 MB memory allocation. A failed sub-actor returns an empty array without aborting the run. Sources collected in parallel:

• SEC 13F-HR filings (ryanclinton/edgar-filing-search) — up to 20 institutional 13F filings for the company
• SEC EDGAR detail (ryanclinton/sec-edgar-filing-analyzer) — up to 5 detailed filing analyses
• SEC Form 4 insider trades (ryanclinton/sec-insider-trading) — up to 30 insider trade records (conditional on includeInsider)
• Congressional stock trades (ryanclinton/congress-stock-tracker) — up to 50 congressional trade disclosures
• FRED macroeconomic data (ryanclinton/fred-economic-data) — eight series: GDP, UNRATE, T10Y2Y, CPIAUCSL, PSAVERT, UMCSENT, VIXCLS, DGS10
• BLS economic data (ryanclinton/bls-economic-data) — unemployment and CPI indicators
• IMF forecasts (ryanclinton/imf-economic-data) — US macroeconomic projections
• OECD leading indicators (ryanclinton/oecd-statistics-search) — composite leading indicator for the US
• FX exchange rates (ryanclinton/exchange-rate-tracker) — USD cross rates for dollar strength assessment
• Finnhub stock data (ryanclinton/finnhub-stock-data) — real-time quote and fundamental data for the ticker
• CoinGecko crypto data (ryanclinton/coingecko-crypto-data) — Bitcoin sentiment as a risk appetite proxy
• USPTO patent search (ryanclinton/patent-search) — up to 50 patent filings with the company as assignee
• Job market intelligence (ryanclinton/job-market-intelligence) — up to 30 open positions at the company
• CFPB consumer complaints (ryanclinton/cfpb-consumer-complaints) — up to 100 most recent complaints

Phase 2: Scoring algorithms

Five independent scoring functions execute synchronously against the collected data:

Smart Money — counts unique filer names from 13F records using a Set. Thresholds: 10+ filers = 85 points, 5-9 = 60, 2-4 = 35, 1 = 15, 0 = 0. Buy ratio from insider trades adjusts by +10 (above 0.65) or -10 (below 0.35). Final score clamped to 0-100.

Congressional Alpha — formula: Math.round(((buys - sells) / total) * 100). Direction thresholds: above +20 = NET_BUYING, below -20 = NET_SELLING. Top 10 members ranked by total trade count.

Macro Regime — iterates FRED series IDs to find GDP (threshold 2%), UNRATE (thresholds 4.5% and 6%), T10Y2Y yield curve (inversion counts double as two contraction signals), CPI (overheating above 5%), PSAVERT (stress below 5%), and UMCSENT (stress below 70). Expansion ratio above 0.7 = EXPANSION, below 0.3 = CONTRACTION, otherwise PEAK/TROUGH depending on which side is larger.

Innovation Momentum — baseline 50. Patent count contributes up to 25 points; R&D job count (titles containing "engineer", "research", "scientist", "developer") contributes up to 20 points. Zero patents subtract 15 points. Score clamped to 0-100.

Consumer Stress — CFPB complaint count contributes up to 30 points, CPI above 4% contributes min(25, round(value * 3)) points, savings rate below 5% adds 15, Michigan sentiment below 70 adds 15.

Phase 3: Cross-asset risk overlay

The VIX value is extracted from FRED VIXCLS records and classified: above 30 = RISK-OFF, 20-30 = CAUTIOUS, below 20 = RISK-ON. A null VIX defaults to a 50-point contribution in the composite. Yield curve records (T10Y2Y), 10-year Treasury yield (DGS10), USD exchange rates, and crypto sentiment are attached as raw data for downstream analysis.

Phase 4: Composite scoring and output assembly

The weighted composite formula: (smartMoney * 0.25) + (congressionalAlpha normalized to 0-100 * 0.10) + (macroConfidence * 0.15) + (innovationMomentum * 0.25) + ((100 - consumerStress) * 0.10) + (vixScore * 0.15). Rounded to the nearest integer. Rating thresholds: 75+ = STRONG BUY, 60-74 = MODERATE BUY, 40-59 = NEUTRAL, 25-39 = MODERATE SELL, 0-24 = STRONG SELL. The full report object is pushed to the Apify dataset.

Tips for best results

1. Use the ticker symbol as the query value — many sub-actors (Finnhub, congressional tracker, patent search) perform better with a standardized ticker than with a full company name containing "Inc", "Corp", or "LLC".

2. For conglomerates, set both query and ticker — "Alphabet" and "GOOGL" refer to different entities in different data sources. Setting query: "Alphabet" and ticker: "GOOGL" maximizes matching across all 14 sources.

3. Build a weekly watchlist loop — pair this actor with Waterfall Contact Enrichment or your own scheduling script to run weekly comparisons on a fixed list and track Composite Score changes over time.

4. Focus on score changes, not absolute levels — a Composite Score of 65 is less meaningful than a score that moved from 45 to 65 over four weeks. Schedule recurring runs and store results in a dataset or Google Sheet to surface momentum.

5. Cross-reference Innovation Momentum with patent filings — the raw innovation.patents.recent array contains actual patent titles. Reviewing them takes 2 minutes and tells you whether the patents are defensive filings or genuine product R&D.

6. Use the macro regime classification independently — the macroRegime output has nothing company-specific in it. Run the actor with any ticker (or even a dummy value like "SPY") to get a current macro regime snapshot for top-down portfolio positioning.

7. Check dataSummary.totalDataPoints — if a run returns fewer than 30 total data points, one or more sub-actors likely failed or returned no data. Re-run with a corrected ticker before relying on the Composite Score.

Combine with other Apify actors

Limitations

• US-listed stocks only — the congressional trading and SEC 13F data sources cover US-listed equities. Non-US companies will return empty results for Smart Money and Congressional Alpha, reducing Composite Score reliability.
• 13F data lag — institutional 13F filings are due 45 days after each quarter-end. The Smart Money score reflects holdings as of the most recent filing period, not current positions.
• Congressional Alpha coverage gaps — not all congress members file timely disclosures. Some thinly traded or small-cap stocks may have zero congressional trade records even if activity exists.
• Patent search matches company name against assignee field — subsidiaries and holding companies filing patents under a different legal name may not be captured. Check the raw patent data for completeness.
• FRED series retrieval is not stock-specific — the macro regime and consumer stress inputs are US economy-wide indicators. They contextualize the investment environment but do not reflect company-specific financial health.
• Finnhub data availability varies by plan — if your Apify environment does not have a Finnhub API key configured, the fundamentals.stockData array may return empty.
• Run time is bounded by the slowest sub-actor — although all 14 calls run in parallel, the run completes only when all 14 finish or time out at 120 seconds. Occasionally a single slow API source can extend total run time to 3-4 minutes.
• Not a replacement for fundamental analysis — the Composite Score is derived entirely from alternative and macro data signals. It does not incorporate earnings, revenue, P/E ratios, debt levels, or management quality.
• Not financial advice — signal classifications (STRONG BUY, STRONG SELL) are algorithmic outputs from public data, not investment recommendations. Always consult a qualified financial advisor before making investment decisions.

Integrations

• Zapier — trigger a Stock Intelligence Report run on a schedule and push the Composite Score and signal classification to a Slack channel, Airtable base, or email digest automatically
• Make — build multi-step workflows that run stock reports on earnings calendar dates and route STRONG BUY signals to a HubSpot deal pipeline for systematic review
• Google Sheets — export weekly Composite Scores for a watchlist directly to a spreadsheet and use conditional formatting to highlight score changes exceeding 15 points
• Apify API — call the actor programmatically from a Python or JavaScript trading system to incorporate alternative data signals into live screening workflows
• Webhooks — configure a webhook to POST the full report JSON to your own backend endpoint the moment each run completes
• LangChain / LlamaIndex — load the structured report JSON as a document into a RAG pipeline; LLMs can interpret the findings arrays, sector recommendations, and raw indicator data to generate narrative investment summaries

Troubleshooting

• Composite Score is very low despite a well-known stock — check dataSummary.totalDataPoints. A score below 20 total data points means most sub-actors returned empty results. This usually happens when a company name is used instead of a ticker. Retry with the exact ticker symbol in the query field.

• Congressional Alpha shows NO_DATA for a blue-chip stock — congressional disclosure databases have irregular coverage. Some large-cap stocks have no recorded congressional trades in the lookup window. This is normal and does not indicate a problem with the run; the Composite Score falls back to a neutral 50 for that component.

• Smart Money shows INSUFFICIENT_DATA despite the company having major institutional holders — 13F search is name-based. If the company's legal name on SEC filings differs from your query (e.g., "Meta Platforms" vs "Facebook"), the search may return zero results. Use the exact legal name as it appears on SEC filings, or verify by searching EDGAR directly.

• Run takes over 4 minutes — this usually indicates one sub-actor is slow or timing out at the 120-second threshold. Check the actor logs for which sub-actor is causing the delay. If it is consistently the same source, the underlying API may be experiencing rate limits. Re-running typically resolves it.

• Innovation Momentum score is 35 despite the company being known for R&D — patent searches are matched against the company name in the assignee field. Large companies often file patents under subsidiary legal entities. Check the innovation.patents field for raw records; if count is 0, retry with the subsidiary name that files patents (e.g., "Apple Inc" instead of "Apple").

Responsible use

• This actor queries publicly available regulatory filings (SEC EDGAR, congressional disclosures), open government APIs (FRED, BLS, OECD), and licensed financial data APIs.
• Congressional trade data and SEC filings are legally mandated public disclosures. Their use for investment research is explicitly permitted under the STOCK Act and the Freedom of Information Act.
• Do not use the Composite Score or signal ratings as the sole basis for investment decisions. The actor provides data aggregation, not financial advice.
• Comply with the terms of service of each underlying data source when using this actor at high volume.
• For guidance on data scraping and API usage legality, see Apify's guide.

FAQ

How accurate is the stock intelligence report Composite Score? The Composite Score is a weighted index of five alternative data signals — not a price prediction model. It reflects the current state of institutional positioning, congressional activity, macro cycle, innovation velocity, and consumer health. Backtesting accuracy depends on the specific use case; treat it as a screening signal rather than a precise forecast.

How many data sources does the stock intelligence report pull from? Each run calls up to 14 sub-actors in parallel: SEC EDGAR (13F filings and filing analyzer), SEC insider trades, Congress stock tracker, FRED, BLS, IMF, OECD, exchange rates, Finnhub, CoinGecko, patent search, job market intelligence, and CFPB consumer complaints. The dataSummary field in the output shows how many were successfully retrieved.

How long does a typical stock intelligence report run take? Most runs complete in 2-3 minutes. All 14 sub-actor calls execute in parallel, so run time is governed by the slowest data source, not the sum of all sources. Runs occasionally extend to 4 minutes if a source API is slow.

What is the Smart Money convergence score and how is it calculated? It measures how many distinct institutional fund managers hold the stock based on 13F-HR filings with the SEC. Ten or more distinct filers score 85 points, 5-9 filers score 60 points. The insider buy/sell ratio from Form 4 disclosures adjusts the score by plus or minus 10 points. Net direction (ACCUMULATING, DISTRIBUTING, MIXED) reflects the combined institutional and insider signal.

How does the Congressional Alpha score work? Congress members are legally required to disclose stock trades within 45 days under the STOCK Act. The actor retrieves these disclosures and calculates (buys - sells) / total * 100. Academic research has documented that congressional trading tends to outperform the market, making aggregate buy/sell patterns a historically meaningful signal.

Is it legal to use SEC 13F filings and congressional trade data for investment research? Yes. SEC 13F filings are mandatory public disclosures filed by institutional investment managers with over $100M in assets. Congressional trade disclosures are mandated public records under the STOCK Act. Using both for investment research is legal and common practice among institutional investors.

How is Stock Intelligence Report different from Bloomberg Terminal or Quiver Quantitative? Bloomberg Terminal ($24,000/year) and FactSet ($12,000/year) provide richer fundamental data but require subscriptions and manual analysis. Quiver Quantitative ($120/year) surfaces congressional trades and 13F data but does not combine them with macro regime, innovation momentum, or consumer stress into a single composite score. This actor produces structured JSON output suitable for programmatic use at a cost of roughly $0.20 per report.

Can I run stock intelligence reports on a whole portfolio at once? The actor processes one stock per run. To analyze a portfolio, use the Apify API or SDK to submit parallel runs for each ticker, then aggregate the results by dataSummary.totalDataPoints or compositeScore. Processing 50 stocks would cost approximately $10.

What happens if a data source returns no results for my stock? Each sub-actor call is wrapped in a try/catch that returns an empty array on failure. The scoring algorithms handle empty inputs gracefully — a zero-length congressional trades array produces a NO_DATA direction and a 0 alpha score, which contributes a neutral 50 points to the composite after normalization. The dataSummary.totalDataPoints field lets you audit how much data was actually collected.

Can I schedule stock intelligence reports to run automatically? Yes. Use Apify's built-in scheduler to run the actor on any cron schedule — daily, weekly, or monthly. Each run generates a fresh report with the latest 13F, insider, macro, and patent data. Combine with a webhook integration to push new reports to a Google Sheet or Slack channel automatically.

How is the Macro Regime Classifier different from a standard economic indicator lookup? Most economic data tools return raw series values. The Macro Regime Classifier applies threshold rules across seven FRED series simultaneously — GDP growth vs 2%, unemployment vs 4.5%/6%, T10Y2Y yield curve (with double weight for inversion), CPI vs 5%, savings rate vs 5%, and Michigan sentiment vs 70 — and classifies the aggregate signal into a four-phase economic cycle label with a confidence score and sector rotation recommendation.

Does the stock intelligence report work for international stocks? The congressional alpha and SEC insider trading components are US-only. Patent search and job market data have broader international coverage. Finnhub supports many non-US tickers. For non-US stocks, expect the Composite Score to be less reliable due to missing Smart Money and Congressional Alpha inputs.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.