{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_global-predictions-inc","slug":"global-predictions-inc","name":"Global Predictions Inc","type":"product","url":"https://www.globalpredictions.com","page_url":"https://unfragile.ai/global-predictions-inc","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_global-predictions-inc__cap_0","uri":"capability://data.processing.analysis.time.series.market.trend.forecasting.with.ml.ensemble.models","name":"time-series market trend forecasting with ml ensemble models","description":"Analyzes historical OHLCV (open, high, low, close, volume) data and technical indicators using ensemble machine learning models (likely LSTM, gradient boosting, or hybrid architectures) to generate forward-looking price predictions and trend direction probabilities. The system ingests aggregated market data, applies feature engineering for volatility, momentum, and mean-reversion signals, then outputs probabilistic forecasts with confidence intervals across multiple timeframes (daily, weekly, monthly).","intents":["I want to understand whether a stock or index is likely to trend up or down over the next week/month","I need to identify emerging market opportunities by detecting early trend shifts before they become obvious","I want to supplement my own technical analysis with ML-generated predictions to reduce emotional bias in timing decisions","I need to backtest a trading hypothesis against historical ML predictions to validate my strategy"],"best_for":["Novice to intermediate retail investors using predictions as a secondary research signal","Individual traders wanting to reduce confirmation bias in trend identification","Portfolio managers seeking supplementary quantitative signals to complement fundamental analysis"],"limitations":["Models trained on historical data cannot predict black swan events, geopolitical shocks, or regime changes (e.g., 2008 financial crisis, COVID crash)","Ensemble models introduce latency and computational overhead; real-time predictions likely delayed by hours or days","No transparency on feature importance, model weights, or which underlying algorithms contribute most to predictions","Accuracy degrades significantly during high-volatility periods when historical patterns break down","Cannot account for earnings surprises, regulatory changes, or company-specific catalysts not reflected in price data"],"requires":["Internet connection for real-time or near-real-time market data access","Free Global Predictions account (no API key or authentication complexity disclosed)","Web browser supporting modern JavaScript (Chrome, Firefox, Safari, Edge)","Basic understanding of financial terminology (trend, support/resistance, timeframe)"],"input_types":["ticker symbols (e.g., AAPL, SPY, BTC/USD)","timeframe selection (daily, weekly, monthly)","optional: custom date ranges for historical analysis"],"output_types":["probabilistic price direction forecast (bullish/bearish/neutral with confidence %)","predicted price targets or ranges","trend strength indicators","historical accuracy metrics (if disclosed)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_global-predictions-inc__cap_1","uri":"capability://data.processing.analysis.multi.asset.class.pattern.recognition.and.anomaly.detection","name":"multi-asset class pattern recognition and anomaly detection","description":"Scans historical price and volume data across stocks, indices, commodities, and cryptocurrencies to identify statistical anomalies, unusual correlations, and recurring chart patterns (head-and-shoulders, triangles, breakouts) using unsupervised learning or rule-based pattern matching. The system flags deviations from normal trading behavior (e.g., volume spikes, volatility compression, correlation breakdowns) that may signal emerging opportunities or risks, outputting ranked alerts by statistical significance.","intents":["I want to discover stocks or assets exhibiting unusual technical patterns before they move significantly","I need to detect when correlations between assets break down, signaling potential portfolio risk","I want to identify volume or volatility anomalies that precede major price moves","I need to screen thousands of assets simultaneously to find candidates matching my trading criteria"],"best_for":["Active traders and swing traders seeking early signals for entry/exit timing","Portfolio managers monitoring cross-asset correlations for risk management","Retail investors with limited time who want automated screening of large universes"],"limitations":["Pattern recognition rules are deterministic and well-known; sophisticated traders may front-run these signals","Anomaly detection thresholds not disclosed; unclear whether alerts are statistically significant or prone to false positives","No causal explanation for detected patterns; system cannot distinguish between meaningful signals and random noise","Backtesting results not published; unknown whether detected patterns have predictive power or are curve-fitted to historical data","Requires continuous monitoring; alerts may expire or reverse within hours, creating whipsaw risk for slow-moving traders"],"requires":["Free Global Predictions account with multi-asset data access","Web browser or mobile app for real-time alert notifications","Basic charting literacy to interpret pattern names and anomaly descriptions"],"input_types":["asset universe selection (stocks, indices, commodities, crypto, or all)","pattern type filters (optional: head-and-shoulders, triangles, breakouts, etc.)","anomaly sensitivity threshold (optional: high/medium/low)"],"output_types":["ranked list of assets with detected patterns or anomalies","pattern type and statistical confidence score","historical occurrence frequency of detected pattern","suggested entry/exit price levels (if applicable)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_global-predictions-inc__cap_2","uri":"capability://data.processing.analysis.sentiment.driven.market.insight.synthesis.from.alternative.data","name":"sentiment-driven market insight synthesis from alternative data","description":"Aggregates and analyzes alternative data sources (social media mentions, news sentiment, options flow, insider transactions, or fund flows) to generate market sentiment scores and contrarian signals. The system applies NLP or rule-based scoring to quantify bullish/bearish sentiment, identifies when sentiment diverges from price action (e.g., extreme pessimism at market bottoms), and surfaces contrarian opportunities where crowd positioning may be crowded or extreme.","intents":["I want to gauge whether current market sentiment is overly bullish or bearish relative to historical norms","I need to identify contrarian opportunities where the crowd is positioned too far in one direction","I want to understand what retail or institutional investors are actually doing (not just what they say)","I need to detect sentiment shifts early before they translate into price moves"],"best_for":["Contrarian traders seeking to fade crowded trades and extreme sentiment readings","Macro investors monitoring sentiment as a leading indicator of regime changes","Retail investors wanting to avoid FOMO-driven decisions by understanding crowd psychology"],"limitations":["Sentiment data sources and weighting methodology not disclosed; unclear which signals drive predictions","Social media sentiment easily manipulated by coordinated campaigns, bots, or paid promotion","Sentiment extremes can persist for extended periods without reversing; contrarian signals may trigger too early","Alternative data (options, insider transactions, fund flows) often delayed or aggregated, reducing actionable timing","Sentiment scores lack context; cannot distinguish between informed institutional positioning and retail noise"],"requires":["Free Global Predictions account with sentiment data access","Understanding of contrarian trading principles and sentiment extremes","Tolerance for false signals and whipsaws when sentiment doesn't reverse as expected"],"input_types":["asset or sector selection","sentiment timeframe (daily, weekly, monthly)","optional: sentiment source filters (social, news, options, flows)"],"output_types":["sentiment score (bullish/bearish on -100 to +100 scale or similar)","historical percentile ranking (e.g., 95th percentile bullish = extreme)","contrarian signal strength (if sentiment diverges from price)","sentiment trend direction (accelerating or reversing)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_global-predictions-inc__cap_3","uri":"capability://data.processing.analysis.portfolio.risk.decomposition.and.correlation.analysis","name":"portfolio risk decomposition and correlation analysis","description":"Analyzes a user's portfolio holdings to decompose risk across asset classes, sectors, and geographies, and identifies hidden correlations and concentration risks. The system ingests a portfolio snapshot (holdings, weights, or transaction history), calculates pairwise correlations between assets, performs factor analysis to identify common drivers of returns, and surfaces concentration risks (e.g., overweight to tech, currency exposure, or single-country risk) that may not be obvious from raw holdings.","intents":["I want to understand what's really driving my portfolio returns and risks, not just individual stock performance","I need to identify hidden correlations or concentration risks that could amplify losses in a market downturn","I want to stress-test my portfolio against historical scenarios (e.g., 2008, 2020) to understand tail risks","I need to rebalance my portfolio but don't know which positions to trim or add"],"best_for":["Individual investors with diversified portfolios seeking risk transparency","DIY portfolio managers wanting to avoid concentration risks without hiring an advisor","Investors transitioning from single-asset focus to multi-asset thinking"],"limitations":["Correlation analysis based on historical data; correlations can break down during crises (e.g., all assets sold together in 2008)","Factor analysis assumes linear relationships; nonlinear or regime-dependent risks not captured","No forward-looking risk adjustments; does not account for changing volatility regimes or macro shifts","Portfolio input method unclear; may require manual entry of holdings, limiting real-time updates","Stress-testing scenarios likely historical replays, not forward-looking stress tests based on current market conditions"],"requires":["Free Global Predictions account with portfolio analysis feature","Portfolio data input (holdings, weights, or transaction history)","Basic understanding of correlation, concentration, and factor risk concepts"],"input_types":["portfolio holdings (ticker symbols and weights or share counts)","optional: transaction history or cost basis","optional: custom date range for historical analysis"],"output_types":["correlation matrix between holdings","sector/geography/asset-class concentration breakdown","factor exposure analysis (if available)","historical volatility and drawdown metrics","stress-test results under historical scenarios"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_global-predictions-inc__cap_4","uri":"capability://planning.reasoning.scenario.based.financial.modeling.and.what.if.analysis","name":"scenario-based financial modeling and what-if analysis","description":"Enables users to construct custom scenarios (e.g., interest rate hikes, earnings misses, sector rotation) and simulate their impact on portfolio returns, asset prices, or market indices. The system applies parametric or Monte Carlo simulation methods to model how changes in macro variables (rates, inflation, GDP growth) or micro variables (earnings, margins, valuations) propagate through asset prices, outputting probability distributions of outcomes and sensitivity rankings showing which variables matter most.","intents":["I want to understand how my portfolio would perform if interest rates rise 2% or inflation accelerates","I need to model the impact of a specific earnings miss or guidance cut on a stock's valuation","I want to run multiple scenarios (bull/base/bear cases) to stress-test my investment thesis","I need to identify which macro variables have the biggest impact on my returns"],"best_for":["Fundamental investors building investment theses and wanting to test assumptions","Portfolio managers running scenario analysis for risk management and position sizing","Macro investors modeling regime changes and their portfolio implications"],"limitations":["Scenario construction requires domain expertise; non-expert users may build unrealistic or internally inconsistent scenarios","Model relationships (e.g., how earnings growth translates to valuation) not transparent; unclear whether based on historical regression or expert judgment","Monte Carlo simulations assume normal distributions; tail risks and fat tails not captured","Scenarios are static snapshots; cannot model dynamic feedback loops (e.g., rising rates → slower growth → lower earnings → lower valuations)","Backtesting scenarios against historical outcomes not provided; unknown whether model predictions match actual results"],"requires":["Free Global Predictions account with scenario modeling feature","Portfolio or asset selection for scenario analysis","Basic understanding of macro variables (rates, inflation, growth) and their market impacts"],"input_types":["base case assumptions (current rates, inflation, growth, valuations)","scenario variables to adjust (rates +/- %, inflation +/- %, earnings growth %, etc.)","optional: custom relationships or sensitivities"],"output_types":["probability distribution of portfolio returns under scenario","expected return and volatility under each scenario","sensitivity ranking (which variables impact returns most)","breakeven analysis (at what variable level does outcome change)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_global-predictions-inc__cap_5","uri":"capability://data.processing.analysis.real.time.market.data.aggregation.and.normalization.across.exchanges","name":"real-time market data aggregation and normalization across exchanges","description":"Ingests and normalizes market data (prices, volumes, spreads, order book depth) from multiple exchanges and data providers, handling format differences, latency variations, and data quality issues to present a unified, clean view. The system applies data validation rules to detect stale quotes, crossed markets, or obvious errors, and provides standardized OHLCV data, bid-ask spreads, and volume metrics across stocks, indices, commodities, and crypto in a consistent format.","intents":["I want to access current market prices and volumes without managing multiple exchange APIs","I need to detect data quality issues or stale quotes that could lead to bad trading decisions","I want to compare prices across exchanges to identify arbitrage opportunities","I need historical OHLCV data in a standardized format for backtesting or analysis"],"best_for":["Retail traders and investors wanting simple market data access without API complexity","Developers building trading tools or analysis platforms on top of clean market data","Quant researchers needing standardized data for backtesting across multiple asset classes"],"limitations":["Free tier likely uses delayed or aggregated data (15-20 minute delay common for free market data), not real-time feeds","Data quality and freshness not guaranteed; no SLA or uptime commitment disclosed","Limited historical depth; may only provide recent data (weeks/months) rather than years of history","No tick-level or order-book data; only OHLCV and basic volume metrics provided","Coverage may be limited to major exchanges and liquid assets; emerging markets or illiquid instruments may be missing"],"requires":["Free Global Predictions account with market data access","Internet connection for real-time or near-real-time data streaming","Web browser or API client (if API access provided)"],"input_types":["ticker symbols or asset identifiers","timeframe selection (1-minute, 5-minute, hourly, daily, etc.)","optional: date range for historical data"],"output_types":["OHLCV data (open, high, low, close, volume)","bid-ask spreads and depth (if available)","volume-weighted average price (VWAP)","data quality flags (stale, crossed, or suspicious quotes)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"low","permissions":["Internet connection for real-time or near-real-time market data access","Free Global Predictions account (no API key or authentication complexity disclosed)","Web browser supporting modern JavaScript (Chrome, Firefox, Safari, Edge)","Basic understanding of financial terminology (trend, support/resistance, timeframe)","Free Global Predictions account with multi-asset data access","Web browser or mobile app for real-time alert notifications","Basic charting literacy to interpret pattern names and anomaly descriptions","Free Global Predictions account with sentiment data access","Understanding of contrarian trading principles and sentiment extremes","Tolerance for false signals and whipsaws when sentiment doesn't reverse as expected"],"failure_modes":["Models trained on historical data cannot predict black swan events, geopolitical shocks, or regime changes (e.g., 2008 financial crisis, COVID crash)","Ensemble models introduce latency and computational overhead; real-time predictions likely delayed by hours or days","No transparency on feature importance, model weights, or which underlying algorithms contribute most to predictions","Accuracy degrades significantly during high-volatility periods when historical patterns break down","Cannot account for earnings surprises, regulatory changes, or company-specific catalysts not reflected in price data","Pattern recognition rules are deterministic and well-known; sophisticated traders may front-run these signals","Anomaly detection thresholds not disclosed; unclear whether alerts are statistically significant or prone to false positives","No causal explanation for detected patterns; system cannot distinguish between meaningful signals and random noise","Backtesting results not published; unknown whether detected patterns have predictive power or are curve-fitted to historical data","Requires continuous monitoring; alerts may expire or reverse within hours, creating whipsaw risk for slow-moving traders","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"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":"2026-05-24T12:16:30.892Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=global-predictions-inc","compare_url":"https://unfragile.ai/compare?artifact=global-predictions-inc"}},"signature":"+gQKz7ygeXCy5Pf28hwhd8N5S7CDKVbhD06vlR2WJusub60Og0HDYOTHAAJ36NC9Y7lfPOnKfvMO1F3i3+T9BA==","signedAt":"2026-06-22T02:57:55.286Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/global-predictions-inc","artifact":"https://unfragile.ai/global-predictions-inc","verify":"https://unfragile.ai/api/v1/verify?slug=global-predictions-inc","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"}}