# Alternative Data > A Claude skill from brainbytes-dev/everything-claude-trading. - **Type:** Skill - **Install:** `agentstack add skill-brainbytes-dev-everything-claude-trading-alternative-data` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [brainbytes-dev](https://agentstack.voostack.com/s/brainbytes-dev) - **Installs:** 0 - **Category:** [Finance & Payments](https://agentstack.voostack.com/c/finance-and-payments) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [brainbytes-dev](https://github.com/brainbytes-dev) - **Source:** https://github.com/brainbytes-dev/everything-claude-trading/tree/main/skills/data/alternative-data ## Install ```sh agentstack add skill-brainbytes-dev-everything-claude-trading-alternative-data ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Alternative Data name: alternative-data description: Alternative data for trading — satellite, credit card, web traffic, app usage. origin: ECT ## When to Activate - User wants to incorporate alternative data into trading strategies - Evaluating alternative data vendors or datasets - Building signals from satellite imagery, credit card data, or web traffic - Assessing alpha potential, coverage, and cost of alternative data sources - Designing an alternative data evaluation framework ## First Questions 1. What is the trading strategy the data will support (earnings prediction, nowcasting, sentiment)? 2. What asset class and universe (US equities, global macro, commodities)? 3. What is the data budget (alt data ranges from free to $500K+/year)? 4. What is the required history depth (backtesting needs years, live needs current)? 5. What is the technical infrastructure for ingesting and processing the data? ## Core Concepts ### Alternative Data Taxonomy ``` Data by source: Individuals (exhaust data from human activity): - Credit/debit card transactions (consumer spending) - App usage and downloads (mobile engagement) - Social media posts and sentiment (Twitter/X, Reddit, StockTwits) - Web browsing and search trends (Google Trends, clickstream) - Geolocation / foot traffic (store visits, mall traffic) - Job postings and employee reviews (Glassdoor, LinkedIn) Business processes (corporate exhaust): - SEC filings (13F, 10-K, 8-K, insider transactions) - Patent filings and citations - Supply chain data (shipping manifests, bill of lading) - Government contracts and procurement data - Corporate jet tracking (executive travel patterns) Sensors (physical world observation): - Satellite imagery (parking lots, oil storage, crop health) - IoT sensor data (industrial activity, energy grid) - Weather data (agricultural commodities, energy demand) - AIS shipping data (tanker tracking, trade flows) - Environmental monitoring (emissions, water usage) Derived / processed: - News NLP sentiment scores - Earnings transcript analysis - ESG scores from multiple data points - Nowcasting models (real-time GDP/inflation estimates) ``` ### Satellite Imagery ``` Applications in trading: Retail foot traffic: Count cars in retailer parking lots (Walmart, Target, Home Depot) Correlate with quarterly revenue before earnings announcement Providers: Orbital Insight, RS Metrics, SpaceKnow Alpha: 2-5 day edge before earnings release Limitations: weather, seasonal patterns, small sample (not all stores visible) Oil storage: Measure floating-roof oil tank levels from satellite shadows Estimate crude oil inventory changes in real-time Providers: Kayrros, Orbital Insight, Ursa Space Alpha: estimate EIA inventory report before release (1-3 day edge) Coverage: Cushing OK, major global storage hubs Agricultural: Normalized Difference Vegetation Index (NDVI) from multispectral imagery Estimate crop yields before USDA reports Providers: Descartes Labs, Gro Intelligence, Planet Labs Alpha: commodity futures positioning ahead of WASDE reports Construction and infrastructure: Track construction progress on factories, data centers, mines Estimate capex execution and project timelines Used for: mining companies, real estate, infrastructure REITS Processing pipeline: Raw imagery -> Cloud/shadow removal -> Feature extraction -> Time series ML models: CNNs for object detection, change detection algorithms Frequency: daily revisit for most commercial providers Resolution: 30cm-3m (sufficient for car counting, tank measurement) ``` ### Credit Card Data ``` What it provides: Aggregated, anonymized consumer spending data by merchant Panel-based: sample of cardholders extrapolated to population Granularity: daily or weekly, by merchant, category, geography Key providers: Earnest Research (now part of Morningstar) Second Measure (now part of Bloomberg) Facteus (formerly 1010data) Mastercard SpendingPulse Bloomberg Second Measure Signal construction: 1. Aggregate daily spend by company (e.g., all Starbucks transactions) 2. Compute YoY growth rate (seasonally adjusted) 3. Compare to consensus revenue estimate 4. Generate surprise signal: data_implied_revenue - consensus 5. Position: long if positive surprise, short if negative Alpha characteristics: - IC: 0.03-0.06 for revenue surprise prediction - Horizon: 1-30 days before earnings - Coverage: consumer-facing companies (retail, restaurants, ecommerce) - Decay: alpha peaks at earnings announcement, decays within 5 days post - Limitations: sample bias, panel changes, only captures card spend (not cash, B2B) Data quality issues: - Panel representativeness: does the sample match the population? - Merchant mapping: correctly attributing transactions to public companies - Seasonal adjustment: holiday patterns, store openings/closings - Backfill bias: vendors may revise historical data ``` ### App Usage and Download Data ``` What it provides: - App downloads (daily installs by app, country, device) - Daily/monthly active users (DAU/MAU) - Session duration, engagement metrics - In-app purchase revenue estimates Providers: Sensor Tower, App Annie (now data.ai), Apptopia, SimilarWeb Applications: Gaming companies: predict DAU trends for EA, Take-Two, Activision Social media: track engagement trends for Meta, Snap, Pinterest Fintech: measure adoption of Cash App, Robinhood, Coinbase Streaming: estimate subscriber growth for Netflix, Spotify, Disney+ Ride-sharing: track Uber/Lyft usage patterns Signal construction: Download momentum: 30-day change in daily downloads vs prior period Engagement trend: DAU/MAU ratio change (stickiness metric) Revenue nowcast: in-app purchase estimates vs consensus Limitations: - Panel-based estimates (not census data) - iOS data quality declined after App Tracking Transparency (ATT) - Android data more available but iOS users spend more - Not all revenue comes from app (web, enterprise, hardware) ``` ### Evaluation Framework ``` The ALPHA framework for evaluating alternative data: A - Alpha potential: Does the data predict future returns or fundamental outcomes? Test: IC (information coefficient) between data signal and forward returns Minimum viable IC: > 0.02 for large-cap equities Test on out-of-sample period (data vendor backtest is not sufficient) L - Latency: How quickly is the data available after the underlying event? Real-time: satellite imagery, app data (minutes to hours) Delayed: credit card (T+2 to T+7), SEC filings (same day) Historical only: some academic datasets (no live feed) P - Point-in-time: Is the data available as-of each historical date (no backfill)? Critical for backtesting: vendor may revise history Demand: point-in-time database with timestamps for each data delivery H - History: How many years of historical data exist? Minimum for strategy development: 5+ years (ideally 10+) Many alt datasets start in 2015-2018 (limited history) Short history = overfitting risk in strategy development A - Accessibility: API availability, data format, delivery frequency Integration effort: days (clean API) vs months (raw unstructured) Exclusivity: widely available data has less alpha (already priced in) Cost considerations: Tier 1 (free/cheap): Google Trends, SEC filings, open satellite — $0-$5K/year Tier 2 (moderate): news sentiment, basic credit card — $50-200K/year Tier 3 (premium): detailed credit card, satellite analytics — $200-500K/year Tier 4 (enterprise): exclusive datasets, custom analytics — $500K+/year Rule of thumb: alt data cost must be < 10% of expected alpha generation ``` ### Vendor Landscape Overview ``` Major alt data aggregators: Bloomberg Enterprise Data: broad catalog, integrated with terminal Refinitiv DataScope: alternative datasets via API Nasdaq Data Link (formerly Quandl): marketplace model, many free datasets Eagle Alpha: alt data sourcing and evaluation advisory Specialized providers by category: Sentiment: RavenPack, Alexandria Technology, Refinitiv MarketPsych Credit card: Second Measure, Earnest, Facteus Satellite: Orbital Insight, SpaceKnow, RS Metrics, Kayrros Web/app: SimilarWeb, Sensor Tower, data.ai Employment: Revelio Labs, LinkUp, Glassdoor Geolocation: SafeGraph, Placer.ai, Advan Research Supply chain: Panjiva (S&P Global), ImportGenius ESG: MSCI ESG, Sustainalytics, Trucost ``` ## Detailed Methodology ### Building an Alt Data Signal ``` Step 1: Data ingestion and cleaning - Ingest via API or flat file delivery - Handle missing data (interpolation, forward-fill, or exclude) - Map to securities (ticker mapping is error-prone — validate carefully) - Store with timestamps (point-in-time) Step 2: Feature construction - Compute growth rates (YoY, MoM, WoW) - Normalize across companies (z-score within sector) - Lag appropriately (account for data delivery delay) - Combine with traditional data (earnings estimates, price momentum) Step 3: Alpha testing - Compute IC and ICIR against forward returns (1d, 5d, 20d, 60d) - Decile analysis: monotonic return spread across signal deciles? - Factor-adjusted alpha: regress on Fama-French + momentum - Out-of-sample: split data temporally, never use future data Step 4: Strategy integration - Combine with existing alpha signals (ensemble or linear combination) - Weight by IC, ICIR, or Bayesian shrinkage - Backtest full strategy with alt data signal included - Measure marginal Sharpe improvement from adding alt data ``` ## Quality Gate Before integrating alternative data into a strategy: - [ ] Alpha tested on true out-of-sample period (not vendor-provided backtest) - [ ] Point-in-time data delivery confirmed (no look-ahead bias) - [ ] Coverage is sufficient for the target universe (avoid selection bias) - [ ] Data quality validated: missing data rate, mapping errors, outliers - [ ] History is long enough for meaningful statistical testing (5+ years preferred) - [ ] Cost justified relative to expected alpha improvement - [ ] Legal and compliance review completed (data sourcing, PII, MNPI concerns) - [ ] Data delivery reliability assessed (SLA, failover, backup sources) - [ ] Exclusivity evaluated: is this data widely available? (crowded alpha decays fast) - [ ] Integration pipeline built and tested (ingestion, cleaning, signal generation, monitoring) ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [brainbytes-dev](https://github.com/brainbytes-dev) - **Source:** [brainbytes-dev/everything-claude-trading](https://github.com/brainbytes-dev/everything-claude-trading) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-brainbytes-dev-everything-claude-trading-alternative-data - Seller: https://agentstack.voostack.com/s/brainbytes-dev - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.