{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_pinegap","slug":"pinegap","name":"PineGap","type":"product","url":"https://pinegap.ai","page_url":"https://unfragile.ai/pinegap","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_pinegap__cap_0","uri":"capability://data.processing.analysis.real.time.financial.data.pipeline.processing","name":"real-time financial data pipeline processing","description":"Ingests streaming market data from multiple broker and exchange APIs simultaneously, applies algorithmic transformations (normalization, deduplication, time-series alignment), and materializes processed datasets into queryable tables with sub-second latency. Uses event-driven architecture with buffering and backpressure handling to prevent data loss during market volatility spikes.","intents":["I need to aggregate live price feeds from multiple exchanges without building custom ETL infrastructure","I want to normalize heterogeneous data formats (FIX, REST, WebSocket) into a unified schema automatically","I need to detect and handle data quality issues (missing ticks, out-of-order events) in real-time without manual intervention"],"best_for":["boutique investment firms automating daily portfolio reconciliation","financial advisors building client-facing dashboards with live market data","mid-market trading desks needing sub-second data freshness without Bloomberg infrastructure"],"limitations":["Limited to major brokers (Interactive Brokers, Alpaca, etc.) — proprietary data sources require custom API connectors","No built-in handling for corporate actions (splits, dividends) — requires manual adjustment rules","Latency degrades with >50 concurrent data streams due to single-threaded event loop design"],"requires":["Active API credentials for at least one supported broker","Network connectivity with <100ms latency to exchange gateways","Minimum 2GB RAM for in-memory buffer during market hours"],"input_types":["REST API endpoints (JSON)","WebSocket streams (binary/text protocols)","CSV/Parquet historical data files"],"output_types":["Time-series tables (OHLCV format)","Normalized event streams","Aggregated portfolio snapshots (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_1","uri":"capability://image.visual.drag.and.drop.interactive.dashboard.builder","name":"drag-and-drop interactive dashboard builder","description":"Provides a visual canvas where users compose dashboards by dragging chart, table, and gauge widgets onto a grid layout, binding each widget to data sources via point-and-click configuration. Generates responsive HTML/CSS/JavaScript under the hood, with automatic layout reflow for mobile devices. No code generation required — all configuration stored as declarative JSON that renders client-side.","intents":["I want to build a portfolio performance dashboard in minutes without writing HTML or JavaScript","I need to create client-facing reports that update automatically when underlying data changes","I want to embed interactive charts in client portals without managing separate visualization libraries"],"best_for":["non-technical financial advisors creating client reporting dashboards","operations teams building internal KPI monitoring boards","wealth managers automating monthly performance summaries"],"limitations":["Limited chart type library (~15 types) compared to Plotly/D3.js ecosystems with hundreds of custom visualizations","No programmatic customization of chart styling — only preset color schemes and fonts available","Mobile responsiveness uses fixed breakpoints; doesn't adapt to unusual aspect ratios or embedded contexts"],"requires":["Modern browser with ES6 support (Chrome 60+, Firefox 55+, Safari 11+)","Connected data source (PineGap-hosted or external API)","User account with dashboard creation permissions"],"input_types":["Structured data tables (rows/columns)","Time-series datasets","Hierarchical data (for tree maps)"],"output_types":["Interactive HTML dashboards","Shareable dashboard URLs","Embedded iframe code for client portals"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_10","uri":"capability://data.processing.analysis.custom.metric.definition.and.formula.engine","name":"custom metric definition and formula engine","description":"Provides a formula editor where users define custom metrics by combining built-in metrics, portfolio data, and mathematical operations (sum, average, ratio, etc.). Formulas are evaluated server-side and results are cached for performance. Supports time-series formulas that compute metrics across historical periods. Custom metrics can be used in dashboards, reports, and alerts. Formula syntax is similar to Excel with autocomplete and validation.","intents":["I want to create a custom performance metric that combines multiple standard metrics (e.g., Sharpe ratio adjusted for drawdown)","I need to calculate firm-specific KPIs that aren't available as built-in metrics","I want to define alert thresholds based on custom metrics (e.g., alert when custom risk score exceeds threshold)"],"best_for":["quantitative analysts building custom analytics","firms with proprietary metrics or KPIs","teams needing flexibility beyond standard metric library"],"limitations":["Formula language is limited to mathematical operations; no support for conditional logic (if/then) or loops","Formulas are evaluated server-side; no client-side computation for real-time updates","Performance degrades with complex formulas involving many time-series operations","No version control or formula history; changes overwrite previous definitions"],"requires":["Understanding of available built-in metrics and data fields","Formula syntax knowledge (similar to Excel)"],"input_types":["Formula definitions (text)","Built-in metric names and portfolio data fields"],"output_types":["Custom metric values (numeric)","Custom metric time-series","Validation errors if formula is invalid"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_2","uri":"capability://data.processing.analysis.algorithmic.portfolio.analysis.and.rebalancing.recommendations","name":"algorithmic portfolio analysis and rebalancing recommendations","description":"Analyzes portfolio composition against user-defined risk parameters (volatility targets, sector exposure limits, correlation thresholds) using mean-variance optimization and Monte Carlo simulation. Generates rebalancing recommendations by solving a constrained optimization problem that minimizes transaction costs while achieving target allocations. Backtests recommendations against historical data before surfacing them to users.","intents":["I want to identify portfolio drift and get specific rebalancing trades that minimize tax impact","I need to stress-test my portfolio against historical market scenarios (2008 crisis, COVID crash) to understand downside risk","I want to optimize asset allocation across multiple client accounts while respecting individual constraints"],"best_for":["financial advisors managing 50+ client portfolios needing systematic rebalancing workflows","boutique asset managers building quantitative investment processes","compliance teams automating portfolio monitoring and drift detection"],"limitations":["Optimization assumes normal distribution of returns — doesn't capture tail risk or regime changes","Backtesting uses historical data only; no forward-looking scenario generation or stress testing against hypothetical events","Tax-loss harvesting logic is simplified; doesn't account for wash-sale rules or state-specific tax implications","Requires 2+ years of historical price data for reliable correlation estimates"],"requires":["Complete portfolio holdings data (tickers, quantities, cost basis)","Risk parameters configured (target volatility, sector limits, correlation matrix)","Historical price data for all holdings (minimum 2 years)"],"input_types":["Portfolio holdings lists (CSV/JSON with ticker, quantity, cost basis)","Risk constraint specifications (JSON schema)","Historical OHLCV price data"],"output_types":["Rebalancing trade recommendations (ticker, quantity, direction)","Portfolio statistics (volatility, Sharpe ratio, sector exposure)","Backtest performance reports (returns, drawdown, Sortino ratio)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_3","uri":"capability://tool.use.integration.multi.source.data.connector.framework.with.schema.mapping","name":"multi-source data connector framework with schema mapping","description":"Provides pre-built connectors for major financial data sources (Interactive Brokers, Alpaca, Yahoo Finance, etc.) that abstract away API authentication, pagination, and rate-limiting logic. Users configure connectors via UI forms specifying credentials and data ranges; the framework handles schema mapping by inferring column types and normalizing field names across sources. Custom connectors can be built via REST API templates without writing code.","intents":["I want to pull portfolio data from my broker without managing API keys or writing integration code","I need to combine data from multiple sources (broker, market data provider, accounting system) into a unified schema","I want to add a proprietary data source without waiting for PineGap to build a native connector"],"best_for":["financial advisors integrating multiple broker platforms into a single dashboard","firms with custom data sources needing rapid integration without engineering resources","teams migrating from manual data exports to automated pipelines"],"limitations":["Native connectors available only for major brokers (Interactive Brokers, Alpaca, E*TRADE); regional or specialty brokers require custom API templates","Schema mapping is automatic for standard fields but requires manual configuration for custom or non-standard data","Rate limiting is handled per-connector but not globally — high-frequency polling across multiple sources may hit API quotas","No built-in retry logic for transient failures; failed syncs require manual re-triggering"],"requires":["API credentials for target data source","Network access to source APIs (may require firewall whitelisting)","Data schema definition (field names, types) for custom sources"],"input_types":["API credentials (OAuth tokens, API keys)","Data range specifications (date ranges, account IDs)","Custom API endpoint definitions (REST URL templates)"],"output_types":["Normalized data tables (standardized schema)","Sync logs with error details","Data quality reports (missing fields, type mismatches)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_4","uri":"capability://automation.workflow.automated.client.reporting.and.distribution.workflow","name":"automated client reporting and distribution workflow","description":"Schedules periodic report generation (daily, weekly, monthly) that combines portfolio data, performance metrics, and market commentary into branded PDF or HTML reports. Integrates with email systems to automatically distribute reports to client lists on specified schedules. Tracks delivery status and handles bounces/unsubscribes. Reports are generated server-side from dashboard templates, ensuring consistency across clients.","intents":["I want to send monthly performance reports to 100+ clients automatically without manual PDF creation","I need to customize reports per client (showing only their holdings, their benchmarks) while maintaining a consistent template","I want to track which clients opened their reports and when, for compliance and engagement metrics"],"best_for":["wealth management firms with 50+ clients needing systematic reporting","financial advisors automating monthly client communications","compliance teams maintaining audit trails of client communications"],"limitations":["Report templates are limited to dashboard-based layouts; no support for narrative text blocks or custom sections","Email distribution uses PineGap's SMTP infrastructure; no support for custom email providers or advanced segmentation","PDF generation is server-side only; no client-side rendering, limiting real-time customization","Delivery tracking relies on email open pixels; doesn't work with clients using email privacy tools that block pixel tracking"],"requires":["Active email distribution list with client addresses","Dashboard template configured for report generation","SMTP credentials (if using external email provider)","Minimum 1 report template per client segment"],"input_types":["Dashboard definitions (JSON)","Client email lists (CSV with address, preferences)","Schedule specifications (cron expressions)"],"output_types":["PDF reports (branded, client-specific)","HTML email templates","Delivery logs with open/click tracking","Bounce/unsubscribe notifications"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_5","uri":"capability://data.processing.analysis.time.series.data.aggregation.and.resampling","name":"time-series data aggregation and resampling","description":"Transforms raw tick-level or minute-level market data into OHLCV (open, high, low, close, volume) bars at user-specified intervals (1-minute, hourly, daily, weekly). Handles edge cases like market gaps (weekends, holidays), corporate actions (splits, dividends), and missing data points. Supports multiple aggregation methods (VWAP, TWAP, last-price) for volume-weighted calculations. All transformations are vectorized using columnar operations for performance.","intents":["I want to convert tick data into daily bars for technical analysis without writing custom aggregation code","I need to resample data at multiple timeframes (1-min, 5-min, hourly) for different analysis workflows","I want to handle market gaps and corporate actions automatically so my analysis isn't skewed by data discontinuities"],"best_for":["quantitative analysts building backtesting systems","technical traders analyzing multiple timeframes simultaneously","data engineers building financial data pipelines"],"limitations":["Corporate action adjustments (splits, dividends) require manual configuration; no automatic detection from data","Resampling assumes regular time intervals; irregular data (e.g., after-hours trading) requires pre-filtering","VWAP/TWAP calculations require volume data; will fail silently if volume is missing","Memory usage scales linearly with data volume; >1GB datasets may require chunked processing"],"requires":["Time-series data with timestamp and price columns","Specification of target timeframe (1m, 5m, 1h, 1d, etc.)","Optional: corporate action adjustment rules (split ratios, dividend amounts)"],"input_types":["Tick-level data (timestamp, price, volume)","Minute-level OHLCV bars","CSV/Parquet files with time-series data"],"output_types":["Resampled OHLCV bars at target timeframe","Adjusted price series (post-split/dividend)","Data quality reports (gaps, missing values)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_6","uri":"capability://data.processing.analysis.performance.attribution.and.factor.analysis","name":"performance attribution and factor analysis","description":"Decomposes portfolio returns into components attributable to asset allocation decisions, security selection, and market factor exposure (beta, momentum, value, quality). Uses regression-based factor models (Fama-French, Carhart) to isolate alpha from beta. Supports both Brinson-Fachler attribution (comparing to benchmark) and factor-based attribution. Results are visualized as waterfall charts showing contribution of each decision.","intents":["I want to understand whether my outperformance came from good stock picking or just taking more risk","I need to explain to clients why their portfolio underperformed the benchmark (was it my decisions or market conditions?)","I want to identify which portfolio decisions added/destroyed the most value for performance review"],"best_for":["investment managers justifying performance to clients and boards","portfolio managers analyzing decision quality for continuous improvement","compliance teams documenting investment process and decision rationale"],"limitations":["Factor models assume linear relationships; doesn't capture non-linear factor interactions or regime changes","Brinson-Fachler attribution requires benchmark weights; not applicable for non-benchmarked portfolios","Attribution is backward-looking only; doesn't predict future factor exposure or expected returns","Requires 3+ years of return data for reliable factor regression estimates"],"requires":["Portfolio holdings and returns data (daily or monthly)","Benchmark definition and returns (if using Brinson-Fachler)","Factor data (Fama-French factors, momentum, etc.) — PineGap provides standard factors but custom factors require external data"],"input_types":["Portfolio returns time-series","Benchmark returns time-series","Holdings data with weights over time","Factor return data (daily/monthly)"],"output_types":["Attribution breakdown (allocation effect, selection effect, interaction)","Factor exposure analysis (beta, factor loadings)","Waterfall charts showing return decomposition","Statistical significance tests for alpha"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_7","uri":"capability://data.processing.analysis.risk.metrics.calculation.and.monitoring.dashboard","name":"risk metrics calculation and monitoring dashboard","description":"Computes portfolio risk metrics (volatility, Value-at-Risk, Conditional Value-at-Risk, Sharpe ratio, Sortino ratio, maximum drawdown) using historical returns or Monte Carlo simulation. Updates metrics daily or intraday as prices change. Displays metrics on dedicated dashboard with alerts when thresholds are breached (e.g., volatility exceeds target, drawdown exceeds limit). Supports custom risk metric definitions via formula editor.","intents":["I want to monitor portfolio risk in real-time and get alerts when risk metrics exceed my limits","I need to calculate VaR and CVaR for regulatory reporting (Basel III, Dodd-Frank)","I want to track how portfolio risk has evolved over time and identify periods of elevated risk"],"best_for":["risk managers monitoring institutional portfolios","compliance teams calculating regulatory risk metrics","portfolio managers tracking risk budgets and limits"],"limitations":["VaR calculation assumes normal distribution; underestimates tail risk in crisis scenarios","Monte Carlo simulation is computationally expensive; real-time updates limited to daily frequency for large portfolios","Custom risk metric formulas are limited to built-in functions; no support for complex user-defined logic","Drawdown calculation is path-dependent; requires full historical price series, not just summary statistics"],"requires":["Historical returns data (minimum 1 year for volatility, 3+ years for VaR)","Current portfolio holdings and prices","Risk metric thresholds and alert configuration"],"input_types":["Portfolio returns time-series","Current holdings and market prices","Risk parameter specifications (confidence levels, time horizons)"],"output_types":["Risk metric values (volatility, VaR, Sharpe ratio, etc.)","Risk metric time-series (historical evolution)","Alert notifications (email, SMS, dashboard)","Risk decomposition by asset class or factor"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_8","uri":"capability://data.processing.analysis.comparative.performance.benchmarking.and.peer.analysis","name":"comparative performance benchmarking and peer analysis","description":"Compares portfolio performance against user-selected benchmarks (S&P 500, Russell 2000, custom indices) and peer groups (similar-sized funds, same strategy category). Calculates outperformance/underperformance metrics (alpha, information ratio, tracking error) and visualizes performance in scatter plots (risk vs. return) and time-series charts. Identifies periods when portfolio significantly diverged from benchmark. Supports custom peer group definitions based on AUM, strategy, or other criteria.","intents":["I want to see how my portfolio's risk-adjusted returns compare to relevant benchmarks and peers","I need to identify when my portfolio significantly diverged from its benchmark and understand why","I want to create custom peer groups for comparison (e.g., other mid-cap growth funds with <$500M AUM)"],"best_for":["investment managers demonstrating performance to clients and prospects","fund boards evaluating manager performance","consultants comparing multiple investment options for clients"],"limitations":["Peer group data is limited to PineGap's database; custom peer groups require manual data entry or external data integration","Benchmarking assumes benchmarks are appropriate proxies; doesn't validate benchmark suitability","Information ratio calculation assumes normal distribution; unreliable for strategies with non-normal returns (e.g., options strategies)","Peer comparison is backward-looking; doesn't predict future relative performance"],"requires":["Portfolio returns data","Benchmark returns data (PineGap provides major indices; custom benchmarks require external data)","Peer group definition (if using custom peer groups)"],"input_types":["Portfolio returns time-series","Benchmark returns time-series","Peer fund returns data (optional)"],"output_types":["Outperformance metrics (alpha, information ratio, tracking error)","Scatter plots (risk vs. return with benchmark and peers)","Time-series performance comparison charts","Divergence analysis (periods of significant underperformance)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pinegap__cap_9","uri":"capability://data.processing.analysis.scenario.analysis.and.stress.testing.framework","name":"scenario analysis and stress testing framework","description":"Allows users to define hypothetical market scenarios (e.g., 'S&P 500 down 20%, bonds up 5%') and simulate portfolio impact under those scenarios. Supports historical scenarios (replay 2008 crisis, COVID crash) and user-defined scenarios. Calculates portfolio returns, risk metrics, and position-level P&L under each scenario. Generates sensitivity tables showing how portfolio value changes with different market moves. Uses Monte Carlo simulation for probabilistic scenario analysis.","intents":["I want to understand how my portfolio would perform in a market crash or recession","I need to stress-test my portfolio against extreme but plausible scenarios for risk management","I want to see which positions would be most affected by different market moves (interest rate shock, currency crisis)"],"best_for":["risk managers stress-testing portfolios for regulatory requirements","portfolio managers understanding downside scenarios before major market moves","advisors explaining portfolio resilience to clients during market volatility"],"limitations":["Scenario analysis assumes linear relationships between market moves and portfolio returns; doesn't capture convexity or option-like payoffs","Historical scenarios are backward-looking; may not capture future tail risks","Monte Carlo simulation is computationally expensive; limited to daily frequency for large portfolios","Requires assumptions about correlations and volatilities; results are sensitive to these assumptions"],"requires":["Portfolio holdings and current prices","Market data for scenario definition (historical prices or user-specified moves)","Risk model (correlations, volatilities) for Monte Carlo simulation"],"input_types":["Portfolio holdings","Scenario definitions (market moves, historical dates)","Risk parameters (correlations, volatilities)"],"output_types":["Portfolio P&L under each scenario","Risk metrics under scenarios (volatility, VaR, drawdown)","Sensitivity tables (portfolio value vs. market moves)","Position-level impact analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active API credentials for at least one supported broker","Network connectivity with <100ms latency to exchange gateways","Minimum 2GB RAM for in-memory buffer during market hours","Modern browser with ES6 support (Chrome 60+, Firefox 55+, Safari 11+)","Connected data source (PineGap-hosted or external API)","User account with dashboard creation permissions","Understanding of available built-in metrics and data fields","Formula syntax knowledge (similar to Excel)","Complete portfolio holdings data (tickers, quantities, cost basis)","Risk parameters configured (target volatility, sector limits, correlation matrix)"],"failure_modes":["Limited to major brokers (Interactive Brokers, Alpaca, etc.) — proprietary data sources require custom API connectors","No built-in handling for corporate actions (splits, dividends) — requires manual adjustment rules","Latency degrades with >50 concurrent data streams due to single-threaded event loop design","Limited chart type library (~15 types) compared to Plotly/D3.js ecosystems with hundreds of custom visualizations","No programmatic customization of chart styling — only preset color schemes and fonts available","Mobile responsiveness uses fixed breakpoints; doesn't adapt to unusual aspect ratios or embedded contexts","Formula language is limited to mathematical operations; no support for conditional logic (if/then) or loops","Formulas are evaluated server-side; no client-side computation for real-time updates","Performance degrades with complex formulas involving many time-series operations","No version control or formula history; changes overwrite previous definitions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pinegap","compare_url":"https://unfragile.ai/compare?artifact=pinegap"}},"signature":"bNDb/7oCNmuVkgkJc3HTp1o+c8iGxfu/SMQApmaHfQj7diWh46BH4dORsGB9gOfNAUmnHGp1kqLNDu4rSpZRCA==","signedAt":"2026-06-21T03:07:18.996Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pinegap","artifact":"https://unfragile.ai/pinegap","verify":"https://unfragile.ai/api/v1/verify?slug=pinegap","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}