{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_gorillaterminal-ai","slug":"gorillaterminal-ai","name":"GorillaTerminal AI","type":"product","url":"https://gorillaterminal.com","page_url":"https://unfragile.ai/gorillaterminal-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_gorillaterminal-ai__cap_0","uri":"capability://data.processing.analysis.real.time.financial.data.ingestion.and.normalization","name":"real-time financial data ingestion and normalization","description":"Ingests streaming market data from multiple sources (APIs, data feeds, databases) and normalizes heterogeneous formats into a unified schema for downstream analysis. Uses multi-source connectors with automatic schema detection and transformation pipelines to eliminate manual ETL work, enabling analysts to query disparate data sources through a single interface without custom integration code.","intents":["I need to combine price feeds from multiple exchanges without building custom connectors","I want to normalize data from Bloomberg, Reuters, and custom internal databases into one queryable format","I need to ingest real-time market data without writing ETL pipelines"],"best_for":["trading desks with multi-source data requirements but limited engineering resources","financial analysts who need rapid data exploration across heterogeneous sources","mid-market firms avoiding custom data pipeline development"],"limitations":["Freemium tier likely has rate limits on concurrent data source connections (specific limits not documented)","Schema detection is proprietary and not auditable — problematic for regulatory compliance workflows requiring data lineage transparency","Latency for schema transformation and normalization not publicly specified; could introduce delays in ultra-low-latency trading scenarios"],"requires":["API credentials for target data sources (Bloomberg, Reuters, custom APIs, etc.)","Network connectivity to source systems","Freemium or paid GorillaTerminal account"],"input_types":["REST APIs","streaming data feeds","database connections (SQL)","CSV/Excel files"],"output_types":["normalized structured data","queryable data tables","time-series datasets"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_1","uri":"capability://data.processing.analysis.ai.driven.financial.data.analysis.and.pattern.extraction","name":"ai-driven financial data analysis and pattern extraction","description":"Applies machine learning models to normalized financial datasets to automatically identify patterns, anomalies, correlations, and trading signals without manual feature engineering. Uses proprietary algorithms (likely ensemble models combining time-series analysis, statistical methods, and neural networks) to extract insights from multi-dimensional market data, surfacing actionable findings through natural language summaries or structured outputs.","intents":["I want to detect unusual market patterns or anomalies in real-time without writing statistical models","I need to identify correlations between assets or market factors automatically","I want AI to surface trading signals or risk indicators from raw market data"],"best_for":["traders and analysts who lack machine learning expertise but need algorithmic insights","trading desks seeking to augment human decision-making with automated pattern detection","financial analysts exploring large datasets for hypothesis generation"],"limitations":["Model transparency is proprietary — no visibility into which algorithms are used, feature importance, or confidence intervals, creating regulatory and audit trail problems","No documented ability to customize or retrain models on firm-specific data or trading strategies","Freemium tier likely has severe restrictions on analysis frequency or dataset size; heavy users will hit computational limits quickly","Backtesting capabilities not mentioned — cannot validate signal quality against historical data"],"requires":["Normalized financial dataset (from real-time data ingestion capability or uploaded data)","Paid tier for production-grade analysis (freemium tier likely limited to toy datasets)","Understanding of financial metrics and domain context to interpret results"],"input_types":["time-series market data","OHLCV (open, high, low, close, volume) data","multi-asset portfolios","structured financial metrics"],"output_types":["anomaly scores","correlation matrices","trading signals","natural language insights","risk metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_2","uri":"capability://text.generation.language.natural.language.query.interface.for.financial.data.exploration","name":"natural language query interface for financial data exploration","description":"Allows analysts to query financial datasets and trigger analyses using natural language prompts rather than SQL or code, translating English questions into data operations and model invocations. Likely uses a semantic parsing layer (LLM-based or rule-based) to map natural language intent to underlying data queries and analysis pipelines, enabling non-technical users to explore data without SQL knowledge.","intents":["I want to ask 'What stocks are most correlated with oil prices?' without writing SQL","I need to explore market data using conversational queries instead of learning a query language","I want to trigger analyses by describing what I'm looking for in plain English"],"best_for":["non-technical financial analysts and traders who lack SQL/programming skills","business users exploring data for ad-hoc questions without analyst support","teams seeking to democratize data access beyond engineering-heavy workflows"],"limitations":["Natural language parsing is probabilistic — complex or ambiguous queries may be misinterpreted, leading to incorrect analysis results","No documented ability to handle domain-specific jargon or custom financial terminology beyond standard market terms","Unclear how the system handles multi-step reasoning (e.g., 'find stocks that correlate with oil but not with the S&P 500') — may require multiple sequential queries","Freemium tier likely has query rate limits or restricted access to full analysis capabilities"],"requires":["Normalized financial dataset already loaded in GorillaTerminal","Basic understanding of financial concepts (stocks, correlations, etc.)","Freemium or paid account"],"input_types":["natural language text queries","conversational prompts"],"output_types":["query results (data tables)","analysis summaries","visualizations","natural language explanations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_3","uri":"capability://text.generation.language.real.time.market.insights.generation.and.summarization","name":"real-time market insights generation and summarization","description":"Continuously monitors financial datasets and automatically generates natural language summaries of market movements, anomalies, and significant events without user prompting. Uses a combination of statistical thresholds, anomaly detection, and language generation models to identify noteworthy market activity and synthesize human-readable insights, delivering alerts or summaries at configurable intervals.","intents":["I want automated alerts when unusual market activity occurs without manually checking dashboards","I need daily or hourly summaries of what moved in the markets and why","I want AI to flag significant events or anomalies in real-time without manual monitoring"],"best_for":["trading desks needing real-time market monitoring without dedicated analysts","portfolio managers seeking automated market intelligence feeds","traders who need to stay informed on multiple markets simultaneously"],"limitations":["Real-time generation introduces latency — summaries may lag actual market events by seconds to minutes depending on computational load","No documented ability to customize alert thresholds or define what constitutes 'significant' activity for specific trading strategies","Language generation may produce generic or contextually inappropriate summaries without domain-specific tuning","Freemium tier likely has severe restrictions on alert frequency or summary generation frequency"],"requires":["Real-time market data ingestion enabled and active","Paid tier for production-grade alert generation","Configuration of alert thresholds and summary preferences"],"input_types":["continuous streaming market data","time-series financial metrics"],"output_types":["natural language summaries","alert notifications","event logs","structured event data"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_4","uri":"capability://data.processing.analysis.multi.asset.portfolio.analysis.and.risk.assessment","name":"multi-asset portfolio analysis and risk assessment","description":"Analyzes diversified portfolios across multiple asset classes (stocks, bonds, commodities, crypto, etc.) to compute risk metrics, correlations, and portfolio-level insights without manual calculation. Applies statistical methods (likely Value-at-Risk, correlation matrices, volatility analysis) and machine learning to assess portfolio composition, identify concentration risks, and suggest rebalancing opportunities through a unified interface.","intents":["I want to understand correlation and concentration risk across my multi-asset portfolio","I need to compute portfolio-level metrics (Sharpe ratio, VaR, drawdown) without Excel","I want AI to suggest portfolio rebalancing based on current market conditions"],"best_for":["portfolio managers overseeing diversified asset allocations","wealth managers needing rapid portfolio analysis for client reporting","traders managing multi-asset positions across exchanges"],"limitations":["Risk models are proprietary — no transparency into which methodologies are used (VaR, CVaR, Monte Carlo, etc.), limiting auditability for institutional compliance","Rebalancing suggestions lack explainability — no visibility into optimization objectives or constraints applied","Freemium tier likely excludes portfolio analysis or severely limits the number of assets analyzed","No documented support for custom risk models or integration with firm-specific risk frameworks"],"requires":["Portfolio data (holdings, quantities, prices) loaded into GorillaTerminal","Support for multiple asset classes and data sources","Paid tier for institutional-grade portfolio analysis"],"input_types":["portfolio holdings (assets, quantities, prices)","historical price data","multi-asset market data"],"output_types":["risk metrics (VaR, volatility, Sharpe ratio)","correlation matrices","concentration analysis","rebalancing recommendations","portfolio reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_5","uri":"capability://data.processing.analysis.scalable.batch.data.processing.and.analysis","name":"scalable batch data processing and analysis","description":"Processes large financial datasets (millions of records, terabytes of data) through distributed computing infrastructure without requiring users to manage computational resources or write distributed code. Abstracts away parallelization, memory management, and cluster orchestration, allowing analysts to submit batch analysis jobs that scale transparently across cloud infrastructure.","intents":["I need to analyze years of historical market data without my laptop crashing","I want to run backtests across millions of trades without managing Spark clusters","I need to process large datasets faster than my local machine allows"],"best_for":["analysts working with large historical datasets or multi-year backtests","trading firms needing to process massive tick data or order books","teams avoiding infrastructure management overhead"],"limitations":["Batch processing introduces latency — not suitable for real-time trading decisions; typical job execution time not documented","Freemium tier likely has severe restrictions on dataset size or job duration; paid tiers may have unclear pricing for large-scale processing","No documented ability to monitor job progress, resource utilization, or cost in real-time","Unclear how data is persisted and whether intermediate results are cached for iterative analysis"],"requires":["Large dataset uploaded or connected to GorillaTerminal","Paid tier for production-scale batch processing","Patience for job execution time (not real-time)"],"input_types":["large CSV/Parquet files","database exports","streaming data archives"],"output_types":["analysis results","aggregated metrics","processed datasets","job logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_6","uri":"capability://data.processing.analysis.backtesting.and.historical.performance.simulation","name":"backtesting and historical performance simulation","description":"Simulates trading strategies against historical market data to evaluate performance, drawdowns, and risk metrics without live trading. Likely uses event-driven backtesting architecture that replays historical prices and executes strategy logic sequentially, computing returns, Sharpe ratios, maximum drawdown, and other performance metrics to validate strategy viability before deployment.","intents":["I want to test my trading strategy against 5 years of historical data before risking real capital","I need to compute returns, drawdown, and Sharpe ratio for a strategy without manual calculation","I want to validate signal quality by backtesting against past market conditions"],"best_for":["traders developing and validating strategies before live deployment","quantitative analysts evaluating strategy performance across market regimes","portfolio managers stress-testing strategies against historical scenarios"],"limitations":["Backtesting results are inherently optimistic — historical performance does not guarantee future results, and overfitting risk is not addressed","No documented ability to model realistic transaction costs, slippage, or market impact, leading to inflated performance estimates","Unclear whether backtesting supports walk-forward analysis or out-of-sample testing to detect overfitting","Freemium tier likely excludes backtesting or limits historical data range and strategy complexity"],"requires":["Historical market data (OHLCV or tick data)","Strategy definition (likely through natural language or visual interface)","Paid tier for production-grade backtesting"],"input_types":["historical price data","strategy parameters","trade rules or signals"],"output_types":["performance metrics (returns, Sharpe, drawdown)","trade logs","equity curves","performance reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_7","uri":"capability://data.processing.analysis.comparative.market.analysis.and.benchmarking","name":"comparative market analysis and benchmarking","description":"Compares performance, risk, and characteristics of multiple assets, strategies, or portfolios against benchmarks and peer groups to contextualize results. Computes relative metrics (alpha, beta, information ratio, tracking error) and generates comparative visualizations showing how a portfolio or strategy performs relative to indices, competitors, or historical baselines.","intents":["I want to know if my portfolio is outperforming the S&P 500 and by how much","I need to compare my strategy's Sharpe ratio against industry benchmarks","I want to see how my asset allocation compares to peer funds"],"best_for":["portfolio managers reporting performance to stakeholders","traders evaluating strategy quality relative to market benchmarks","wealth managers comparing client portfolios to peer groups"],"limitations":["Benchmark selection is critical but not documented — unclear which indices or peer groups are available or how they're constructed","Relative metrics (alpha, beta) are sensitive to benchmark choice and time period; no documented ability to customize benchmarks","Freemium tier likely excludes comparative analysis or limits benchmark options","No documented support for custom benchmarks or peer group definitions"],"requires":["Portfolio or strategy performance data","Benchmark data (indices, peer groups)","Historical price data for comparison period"],"input_types":["portfolio returns","strategy performance data","benchmark indices"],"output_types":["relative performance metrics (alpha, beta, information ratio)","comparative charts","peer group rankings","performance reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gorillaterminal-ai__cap_8","uri":"capability://image.visual.data.visualization.and.interactive.dashboard.generation","name":"data visualization and interactive dashboard generation","description":"Automatically generates interactive visualizations (charts, heatmaps, time-series plots) from financial data and analysis results, enabling analysts to explore data visually without manual charting. Likely uses a charting library (D3.js, Plotly, or similar) to render interactive dashboards that update in real-time as data changes, supporting drill-down and filtering for exploratory analysis.","intents":["I want to visualize market correlations as an interactive heatmap without using Matplotlib","I need to create dashboards showing real-time portfolio performance and risk metrics","I want to explore time-series data interactively with zoom, pan, and filtering"],"best_for":["analysts and traders who prefer visual exploration over raw data tables","portfolio managers creating client-facing performance dashboards","teams needing rapid visualization without custom development"],"limitations":["Visualization customization likely limited to preset chart types and color schemes; no documented ability to create custom visualizations","Interactive performance may degrade with very large datasets (millions of data points) due to client-side rendering constraints","Freemium tier likely has restrictions on dashboard creation or interactivity features","No documented ability to export dashboards or embed them in external applications"],"requires":["Analyzed financial data or metrics","Web browser for interactive dashboard access","Freemium or paid account"],"input_types":["time-series data","correlation matrices","performance metrics","portfolio data"],"output_types":["interactive charts","dashboards","heatmaps","time-series visualizations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["API credentials for target data sources (Bloomberg, Reuters, custom APIs, etc.)","Network connectivity to source systems","Freemium or paid GorillaTerminal account","Normalized financial dataset (from real-time data ingestion capability or uploaded data)","Paid tier for production-grade analysis (freemium tier likely limited to toy datasets)","Understanding of financial metrics and domain context to interpret results","Normalized financial dataset already loaded in GorillaTerminal","Basic understanding of financial concepts (stocks, correlations, etc.)","Freemium or paid account","Real-time market data ingestion enabled and active"],"failure_modes":["Freemium tier likely has rate limits on concurrent data source connections (specific limits not documented)","Schema detection is proprietary and not auditable — problematic for regulatory compliance workflows requiring data lineage transparency","Latency for schema transformation and normalization not publicly specified; 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