{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_uptrends-ai","slug":"uptrends-ai","name":"Uptrends.ai","type":"product","url":"https://app.uptrends.ai","page_url":"https://unfragile.ai/uptrends-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_uptrends-ai__cap_0","uri":"capability://search.retrieval.multi.source.social.media.aggregation.for.stock.mentions","name":"multi-source social media aggregation for stock mentions","description":"Automatically crawls and ingests real-time data from Twitter/X, Reddit, StockTwits, and financial forums using API integrations and web scraping pipelines. The system maintains persistent connections to high-velocity data sources and normalizes heterogeneous post formats into a unified internal representation, enabling downstream NLP analysis on a consolidated dataset rather than requiring manual source-by-source monitoring.","intents":["I want to monitor what retail investors are talking about across all major platforms without manually checking each one","I need to catch emerging stock discussions before they trend on mainstream financial media","I want to see which stocks are generating the most social chatter in real-time"],"best_for":["Active DIY investors trading 2-3 times weekly who lack time for manual social listening","Retail traders wanting early-stage signal detection before institutional adoption"],"limitations":["API rate limits on Twitter/Reddit may cause data gaps during peak market hours","Web scraping-based sources are fragile to platform UI changes and may require frequent maintenance","No access to private Discord/Telegram communities where sophisticated traders often congregate","Latency between post publication and indexing typically 2-5 minutes, missing microsecond-level trading signals"],"requires":["Active internet connection with sufficient bandwidth for continuous streaming ingestion","Valid API credentials for Twitter API v2, Reddit API, or StockTwits API (if using official integrations)","Uptrends.ai account with active subscription tier"],"input_types":["social media posts (text)","forum threads (text with metadata)","ticker symbols (structured)","timestamp metadata"],"output_types":["normalized post records (JSON)","aggregated mention counts per ticker","temporal activity streams"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_1","uri":"capability://data.processing.analysis.ai.driven.sentiment.analysis.and.trend.classification.for.stock.mentions","name":"ai-driven sentiment analysis and trend classification for stock mentions","description":"Applies fine-tuned NLP models (likely transformer-based, possibly BERT or GPT variants) to classify social posts as bullish, bearish, or neutral sentiment, then aggregates sentiment scores at the ticker level to identify emerging trends. The system likely uses attention mechanisms to weight recent posts more heavily and detect sentiment shifts, distinguishing genuine catalysts from noise through pattern matching against historical trend data.","intents":["I want to know if the social chatter around a stock is turning positive or negative","I need to identify which stocks are experiencing sudden sentiment reversals that might signal a move","I want to filter out pump-and-dump hype from legitimate bullish signals"],"best_for":["Swing traders and day traders seeking intraday sentiment momentum indicators","Investors wanting to validate their thesis with crowd sentiment before entering a position"],"limitations":["Sentiment models struggle with sarcasm, irony, and financial jargon (e.g., 'this stock is a dumpster fire' may be misclassified as bearish when it's actually bullish in context)","No distinction between organic sentiment and coordinated pump-and-dump campaigns or bot networks","Sentiment lags price action by 5-30 minutes; by the time sentiment flips positive, institutional buyers may have already moved","Model bias toward majority sentiment in training data may underweight contrarian signals","No explainability layer showing which specific posts or keywords drove a sentiment classification"],"requires":["Aggregated post data from multi-source ingestion pipeline","Sufficient historical labeled data to train or fine-tune sentiment models (unknown if Uptrends uses proprietary labels or public datasets)","Computational resources for real-time inference (likely GPU-accelerated for latency <500ms per batch)"],"input_types":["normalized social media posts (text)","ticker symbols (structured)","temporal metadata (timestamps)"],"output_types":["sentiment scores per post (float, -1.0 to 1.0 range typical)","aggregated sentiment per ticker (float or categorical)","sentiment trend vectors (time-series)","confidence scores per classification"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_10","uri":"capability://text.generation.language.user.education.and.signal.interpretation.guidance","name":"user education and signal interpretation guidance","description":"Provides educational content, tooltips, and contextual guidance to help retail investors understand how to interpret social signals and avoid common pitfalls (false positives, pump-and-dumps, sentiment lag). The system likely includes explainability features showing which posts or keywords drove a sentiment classification, helping users build intuition about signal quality.","intents":["I want to understand what makes a social signal reliable vs. a false positive","I need guidance on how to use social sentiment in my trading strategy","I want to know why a stock is trending and what posts are driving the sentiment"],"best_for":["Novice retail investors new to social signal analysis","Traders wanting to improve their signal interpretation skills"],"limitations":["Educational content is static; cannot adapt to individual user skill level or learning pace","Explainability features may be limited; showing top posts driving sentiment is less informative than showing which linguistic features matter","No A/B testing or feedback loop to measure if education improves user trading outcomes","Risk of over-confidence; users may misinterpret educational content as investment advice"],"requires":["Content management system for educational materials","UI components for tooltips and contextual help","Post-level explainability data (which posts contributed to sentiment score)"],"input_types":["user interactions (clicks, time spent on educational content)","user queries or search terms"],"output_types":["educational articles and guides (text/HTML)","tooltips and contextual help (text)","explainability visualizations (charts showing top posts/keywords)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_2","uri":"capability://planning.reasoning.real.time.trend.emergence.detection.and.ranking","name":"real-time trend emergence detection and ranking","description":"Monitors velocity and acceleration of mention counts, sentiment shifts, and engagement metrics across aggregated posts to identify stocks entering a trend phase. Uses statistical anomaly detection (likely z-score, isolation forest, or LSTM-based approaches) to flag when a ticker's social activity deviates significantly from its baseline, then ranks emerging trends by strength, velocity, and consistency to surface the most actionable signals.","intents":["I want to be alerted when a stock starts trending on social media before it hits the news","I need to rank multiple emerging trends by likelihood of sustained momentum","I want to distinguish between one-off viral posts and genuine sustained trend shifts"],"best_for":["Momentum traders seeking early entry points into emerging moves","Investors with limited time who need a ranked priority list of stocks to research"],"limitations":["Anomaly detection models are prone to false positives during market-wide events (earnings season, Fed announcements) when all stocks see elevated chatter","Ranking algorithms may favor high-volume stocks over high-conviction signals; a single influential trader's post may be drowned out by retail noise","No causal analysis—cannot distinguish between trends driven by fundamental catalysts vs. technical chart patterns vs. meme stock dynamics","Trend persistence is unpredictable; a stock ranked #1 for trend strength may reverse within hours","Requires continuous baseline recalibration as market regimes shift (bull vs. bear markets have different chatter patterns)"],"requires":["Real-time aggregated post stream with timestamps and sentiment scores","Historical baseline data for each ticker (typically 30-90 days minimum for statistical significance)","Computational pipeline for continuous anomaly detection scoring (likely batch-processed every 1-5 minutes)"],"input_types":["aggregated sentiment scores per ticker (float)","mention counts per time window (integer)","engagement metrics (retweets, replies, upvotes)","historical baseline statistics"],"output_types":["trend strength scores per ticker (float, 0-100 scale typical)","trend velocity indicators (mentions/minute, sentiment acceleration)","ranked trend list (ordered array with metadata)","anomaly flags (boolean + z-score or confidence)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_3","uri":"capability://data.processing.analysis.curated.event.and.catalyst.identification.from.social.chatter","name":"curated event and catalyst identification from social chatter","description":"Uses NLP entity extraction and event detection models to identify specific catalysts mentioned in social posts (earnings dates, FDA approvals, product launches, insider trading, litigation, etc.) and correlates them with sentiment and volume spikes. The system likely maintains a knowledge base of known catalyst types and uses pattern matching to extract structured event metadata from unstructured text, then surfaces these events with context to help investors understand the 'why' behind sentiment shifts.","intents":["I want to know what specific news or events are driving social sentiment around a stock","I need to distinguish between sentiment driven by real catalysts vs. pure speculation","I want to catch catalysts that haven't hit mainstream financial news yet"],"best_for":["Fundamental investors wanting to validate thesis with early catalyst detection","Event-driven traders seeking to front-run news before institutional adoption"],"limitations":["Entity extraction struggles with ambiguous references (e.g., 'the CEO' without explicit name mention, or ticker symbols mentioned without context)","No verification layer—cannot distinguish between rumors, speculation, and confirmed facts; a false rumor may be flagged as a legitimate catalyst","Misses catalysts discussed using coded language or insider jargon unfamiliar to the NLP model","Latency in event extraction (typically 5-15 minutes after post publication) may miss fast-moving intraday catalysts","No integration with official company calendars or SEC filings, so relies entirely on social chatter for event discovery"],"requires":["Named entity recognition (NER) models trained on financial text","Event type taxonomy and pattern templates (earnings, FDA, M&A, insider trading, etc.)","Knowledge base of known catalysts and historical event-sentiment correlations","Normalized post data with full text (not just summaries)"],"input_types":["raw social media post text","ticker symbols and company names","temporal metadata"],"output_types":["extracted event entities (structured JSON with event type, date, company, confidence)","event-sentiment correlations (how sentiment changed around event mention)","event timeline per ticker (chronological list of detected catalysts)","confidence scores per extraction"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_4","uri":"capability://automation.workflow.personalized.watchlist.and.alert.configuration","name":"personalized watchlist and alert configuration","description":"Allows users to create custom watchlists of tickers and configure alert thresholds for sentiment changes, trend emergence, mention velocity, and specific catalysts. The system stores user preferences and maintains state to deliver notifications (email, push, in-app) when conditions are met, likely using a rule engine to evaluate conditions against real-time data streams and debounce alerts to avoid notification fatigue.","intents":["I want to monitor only the stocks I care about and ignore the rest of the noise","I need alerts when a stock I'm watching starts trending or sentiment shifts dramatically","I want to customize alert sensitivity so I don't get spammed with false positives"],"best_for":["Active traders managing a focused portfolio of 5-20 stocks","Investors with specific trading strategies (e.g., earnings plays, FDA catalysts) who want targeted alerts"],"limitations":["Alert fatigue is common if thresholds are too sensitive; users often disable alerts after receiving too many false positives","No machine learning personalization—alerts are rule-based and static, not adaptive to user trading patterns or past performance","Notification delivery latency (email typically 30-60 seconds, push notifications 5-10 seconds) may miss fast-moving intraday signals","No integration with trading platforms, so users must manually act on alerts rather than auto-executing trades","Watchlist management is manual; no recommendation engine to suggest new stocks to monitor based on user interests"],"requires":["User account with authentication","Notification delivery infrastructure (email service, push notification service)","Rule engine for condition evaluation (likely cron-based or event-driven)","State persistence for user preferences and watchlist data"],"input_types":["ticker symbols (structured)","alert threshold parameters (numeric: sentiment change %, mention velocity threshold, etc.)","notification preferences (email, push, in-app)"],"output_types":["alert notifications (text, email, push)","watchlist state (JSON with tickers and thresholds)","alert history (audit log of triggered alerts)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_5","uri":"capability://data.processing.analysis.historical.trend.analysis.and.backtesting.against.past.social.signals","name":"historical trend analysis and backtesting against past social signals","description":"Maintains a time-series database of historical sentiment, mention volume, and trend scores for each ticker, allowing users to query past trends and correlate them with price movements. The system likely provides visualization tools (charts, heatmaps) to show how social sentiment preceded or lagged price action, and may include basic backtesting functionality to measure the predictive power of social signals over historical periods.","intents":["I want to see if social sentiment actually predicted price moves in the past","I need to understand the typical lag between social trend emergence and price action","I want to backtest my trading strategy using historical social signal data"],"best_for":["Quantitative traders building models that incorporate social sentiment as a feature","Investors wanting to validate the predictive power of social signals before committing capital"],"limitations":["Backtesting results are prone to survivorship bias (only stocks that survived are in the dataset) and look-ahead bias if not carefully implemented","Historical data quality depends on data retention policies; older data may be incomplete or degraded","No causality analysis—correlation between social sentiment and price movement does not imply causation; both may be driven by external factors","Backtesting assumes you could have acted on signals in real-time, but actual latency and slippage are not modeled","Market regimes change; a strategy that worked in 2021 bull market may fail in 2023 bear market"],"requires":["Historical time-series database with sentiment, mention volume, and price data","Price data integration (likely from external source like Yahoo Finance, Alpha Vantage, or IEX Cloud)","Visualization and charting library (likely D3.js, Plotly, or similar)","Backtesting engine (likely Python-based with libraries like Backtrader or custom implementation)"],"input_types":["ticker symbols (structured)","date range (temporal)","backtesting parameters (entry/exit rules, position sizing)"],"output_types":["historical sentiment charts (time-series visualization)","correlation matrices (sentiment vs. price movement)","backtest results (returns, Sharpe ratio, drawdown, win rate)","lag analysis (how many minutes/hours between sentiment shift and price move)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_6","uri":"capability://data.processing.analysis.cross.ticker.correlation.and.sector.trend.analysis","name":"cross-ticker correlation and sector trend analysis","description":"Analyzes social sentiment and mention patterns across related stocks (same sector, competitors, supply chain) to identify sector-wide trends and identify which stocks are leading vs. lagging sentiment shifts. The system likely uses clustering algorithms to group related stocks and compares their sentiment trajectories to surface relative strength and identify potential rotation opportunities.","intents":["I want to know if a stock is trending because of sector momentum or company-specific catalysts","I need to identify which stocks in a sector are leading sentiment and which are lagging","I want to catch sector rotations before they become obvious"],"best_for":["Sector-focused traders and rotation strategists","Portfolio managers wanting to understand relative strength within holdings"],"limitations":["Sector definitions are arbitrary; stocks may belong to multiple sectors depending on classification scheme used","Correlation analysis assumes linear relationships; non-linear or regime-dependent correlations are missed","Sector trends driven by macro factors (interest rates, commodity prices) may not be visible in social chatter alone","No fundamental analysis layer; a stock may be lagging sentiment due to valuation, not weakness"],"requires":["Sector classification taxonomy (likely based on GICS or similar standard)","Aggregated sentiment data for all stocks in a sector","Correlation and clustering algorithms (likely scikit-learn or similar)"],"input_types":["ticker symbols (structured)","sector classifications","aggregated sentiment scores per ticker"],"output_types":["sector sentiment heatmaps (visualization)","correlation matrices (stock-to-stock sentiment correlation)","relative strength rankings within sector","sector rotation signals (which sectors gaining/losing sentiment)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_7","uri":"capability://safety.moderation.bot.and.spam.detection.filtering.for.social.signal.quality","name":"bot and spam detection filtering for social signal quality","description":"Applies heuristics and machine learning models to identify and downweight posts from bot accounts, coordinated pump-and-dump campaigns, and low-quality sources. The system likely analyzes account age, posting frequency, engagement patterns, and linguistic markers to flag suspicious activity, then either filters these posts from analysis or applies lower confidence weights to their sentiment contributions.","intents":["I want to filter out pump-and-dump hype and bot-driven noise from genuine retail sentiment","I need to know if a trending stock is driven by organic discussion or coordinated manipulation","I want higher confidence in signals that come from legitimate retail investors"],"best_for":["Risk-averse investors wanting to avoid meme stock traps and coordinated manipulation","Traders seeking to distinguish signal from noise in high-volume chatter"],"limitations":["Bot detection is an arms race; sophisticated bot networks can evade detection by mimicking human behavior","False positives are common; legitimate high-volume posters may be flagged as bots","No access to platform-level bot detection data (Twitter, Reddit do their own filtering), so Uptrends must reverse-engineer detection","Coordinated campaigns by legitimate groups (e.g., activist investors, short-sellers) may be flagged as manipulation when they're actually legal advocacy","Detection models require continuous retraining as bot tactics evolve"],"requires":["Account metadata (age, follower count, posting history)","Linguistic analysis models (likely transformer-based for detecting repetitive/templated language)","Network analysis for detecting coordinated posting patterns","Historical labeled data of known bot accounts and pump-and-dump campaigns"],"input_types":["social media posts with account metadata","account age and follower counts","posting frequency and timing patterns"],"output_types":["bot probability scores per post (0-1 range)","quality-adjusted sentiment scores (downweighting low-quality sources)","manipulation flags (boolean + confidence)","source credibility rankings"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_8","uri":"capability://data.processing.analysis.comparative.sentiment.analysis.across.competing.stocks","name":"comparative sentiment analysis across competing stocks","description":"Enables side-by-side comparison of sentiment trajectories, mention volume, and trend strength across competing or related stocks (e.g., NVDA vs. AMD, Tesla vs. traditional automakers). The system likely provides visualization tools and statistical tests to determine if differences in sentiment are statistically significant or just noise, helping investors identify relative strength and potential winners/losers in competitive matchups.","intents":["I want to know if investors are rotating from one competitor to another","I need to identify which stock in a competitive pair is gaining relative sentiment strength","I want to validate my thesis that Stock A is outperforming Stock B in investor sentiment"],"best_for":["Relative value traders comparing competing stocks","Investors making binary choices between alternatives (e.g., which EV maker to invest in)"],"limitations":["Sentiment differences may reflect investor sophistication rather than stock quality (e.g., retail investors may favor meme stocks over fundamentally sound competitors)","No causality analysis; cannot determine if sentiment difference is driving price difference or vice versa","Requires manual selection of comparison pairs; no automated recommendation for which stocks to compare","Statistical significance testing requires sufficient data; comparing small-cap stocks with low mention volume may yield unreliable results"],"requires":["Aggregated sentiment data for multiple tickers","Statistical testing library (likely scipy or similar)","Visualization tools for side-by-side comparison"],"input_types":["two or more ticker symbols (structured)","date range (temporal)","comparison metrics (sentiment, mention volume, trend strength)"],"output_types":["comparative sentiment charts (overlaid time-series)","statistical test results (p-values, confidence intervals)","relative strength rankings","divergence/convergence signals"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_uptrends-ai__cap_9","uri":"capability://tool.use.integration.integration.with.external.data.sources.and.trading.platforms","name":"integration with external data sources and trading platforms","description":"Provides APIs or webhooks to export social sentiment data to external tools (trading platforms, portfolio management software, data analysis environments). The system likely supports standard formats (JSON, CSV) and may offer native integrations with popular platforms like TradingView, Thinkorswim, or Python/R environments for quantitative analysis.","intents":["I want to feed social sentiment data into my trading bot or algorithmic strategy","I need to export historical sentiment data for analysis in Python or R","I want to display Uptrends sentiment scores in my TradingView charts"],"best_for":["Quantitative traders building multi-factor models incorporating social signals","Developers building custom trading applications or bots","Data scientists wanting to analyze social sentiment alongside other market data"],"limitations":["API rate limits may restrict real-time data access for high-frequency strategies","No native integration with most retail trading platforms; custom development required","Data export formats may be limited (e.g., JSON only, no direct database connections)","API documentation quality and stability unknown; breaking changes may require code updates","Webhook delivery is asynchronous and may have latency; not suitable for microsecond-level trading"],"requires":["API key or authentication token","HTTP client library (for REST API) or webhook receiver (for push updates)","Understanding of API rate limits and pagination","Trading platform or analysis environment that supports custom data sources"],"input_types":["API requests (HTTP GET/POST)","query parameters (ticker, date range, metric type)"],"output_types":["JSON responses with sentiment data","CSV exports for bulk analysis","webhook payloads (JSON) for real-time updates","time-series data with timestamps and values"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active internet connection with sufficient bandwidth for continuous streaming ingestion","Valid API credentials for Twitter API v2, Reddit API, or StockTwits API (if using official integrations)","Uptrends.ai account with active subscription tier","Aggregated post data from multi-source ingestion pipeline","Sufficient historical labeled data to train or fine-tune sentiment models (unknown if Uptrends uses proprietary labels or public datasets)","Computational resources for real-time inference (likely GPU-accelerated for latency <500ms per batch)","Content management system for educational materials","UI components for tooltips and contextual help","Post-level explainability data (which posts contributed to sentiment score)","Real-time aggregated post stream with timestamps and sentiment scores"],"failure_modes":["API rate limits on Twitter/Reddit may cause data gaps during peak market hours","Web scraping-based sources are fragile to platform UI changes and may require frequent maintenance","No access to private Discord/Telegram communities where sophisticated traders often congregate","Latency between post publication and indexing typically 2-5 minutes, missing microsecond-level trading signals","Sentiment models struggle with sarcasm, irony, and financial jargon (e.g., 'this stock is a dumpster fire' may be misclassified as bearish when it's actually bullish in context)","No distinction between organic sentiment and coordinated pump-and-dump campaigns or bot networks","Sentiment lags price action by 5-30 minutes; by the time sentiment flips positive, institutional buyers may have already moved","Model bias toward majority sentiment in training data may underweight contrarian signals","No explainability layer showing which specific posts or keywords drove a sentiment classification","Educational content is static; cannot adapt to individual user skill level or learning pace","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":"2026-05-24T12:16:33.649Z","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=uptrends-ai","compare_url":"https://unfragile.ai/compare?artifact=uptrends-ai"}},"signature":"CymBFd2pVfDwsPmbeCkNrD0D7NxDwv5szMPrFR6iUM0xuRiHOyv8tWgUdxtuFkXBYk6fn/DSt1CBX/vXMqlTBQ==","signedAt":"2026-06-21T20:05:15.693Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/uptrends-ai","artifact":"https://unfragile.ai/uptrends-ai","verify":"https://unfragile.ai/api/v1/verify?slug=uptrends-ai","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"}}