{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_blackhedge","slug":"blackhedge","name":"BlackHedge","type":"product","url":"https://www.blackhedge.io","page_url":"https://unfragile.ai/blackhedge","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_blackhedge__cap_0","uri":"capability://data.processing.analysis.multi.factor.technical.signal.generation.from.price.volume.sentiment.fusion","name":"multi-factor technical signal generation from price-volume-sentiment fusion","description":"Ingests real-time and historical OHLCV data alongside market sentiment indicators (social media, news sentiment scores, options flow) and fuses them through a learned weighting model to generate buy/sell signals. The system likely uses ensemble methods (random forests, gradient boosting, or neural networks) trained on historical price movements to assign confidence scores to each signal. Signals are surfaced with visual chart overlays showing entry/exit zones and probability estimates, making the underlying model decisions interpretable to retail users.","intents":["I want to see AI-generated trading signals overlaid on my charts without manually calculating technical indicators","I need to understand whether a buy signal is based primarily on price action, volume, or sentiment so I can weight my conviction","I want to backtest signal accuracy across different market conditions before risking real capital"],"best_for":["Retail traders learning technical analysis who want algorithmic validation of manual chart reading","Swing traders seeking probabilistic entry/exit confirmation across multiple timeframes","Beginners who lack expertise to weight price, volume, and sentiment signals manually"],"limitations":["Backtested performance metrics are historically unreliable — live trading typically underperforms backtest results by 20-40% due to slippage, execution delays, and regime changes","Model retraining frequency unknown; if signals are stale (>24 hours old), they may not reflect current market microstructure","No explicit handling of black swan events or market regime shifts; models trained on normal distributions fail catastrophically in tail events","Confidence scores may be miscalibrated — a 75% confidence signal does not necessarily mean 75% historical win rate"],"requires":["Real-time market data feed (likely from a broker API or third-party data provider like Alpha Vantage, IEX Cloud, or Polygon.io)","Sentiment data source (Twitter API, news feeds, or proprietary sentiment vendor)","User account with trading broker integration for live signal delivery","Internet connectivity for real-time updates"],"input_types":["OHLCV time series (1m, 5m, 15m, 1h, 4h, 1d candles)","Sentiment scores (numerical, likely normalized -1 to +1)","Volume profile data","Options flow or order book imbalance (if available)"],"output_types":["Signal objects with timestamp, ticker, direction (BUY/SELL/HOLD), confidence score (0-100), and reasoning tags","Chart annotations (arrows, zones, colored regions)","Structured JSON/CSV for backtesting or external tool integration"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_1","uri":"capability://planning.reasoning.predictive.price.movement.forecasting.with.confidence.intervals","name":"predictive price movement forecasting with confidence intervals","description":"Uses supervised learning models (likely LSTM, GRU, or transformer-based architectures) trained on historical price sequences to forecast future price movements over specified horizons (1-hour, 1-day, 1-week ahead). The model outputs point estimates plus confidence intervals or probability distributions, allowing users to quantify uncertainty. Predictions are likely retrained on a rolling window (e.g., daily or weekly) to adapt to recent market behavior. The system may employ ensemble methods (averaging multiple model architectures) to reduce overfitting.","intents":["I want to see AI predictions of where a stock price will move in the next 1-24 hours to plan my entry/exit","I need to understand the uncertainty around predictions — is this a high-confidence forecast or a coin flip?","I want to compare predicted vs. actual prices over time to assess whether the model is actually predictive or just lucky"],"best_for":["Day traders and swing traders seeking probabilistic price targets","Retail investors backtesting algorithmic strategies before deployment","Users with low risk tolerance who want to quantify forecast uncertainty before committing capital"],"limitations":["Financial time series are notoriously non-stationary and exhibit regime changes; models trained on bull markets often fail in bear markets without explicit retraining","Prediction accuracy degrades exponentially with forecast horizon — 1-hour predictions may have 60-70% directional accuracy, but 1-week predictions approach random chance (50%)","No explicit handling of earnings announcements, macroeconomic releases, or geopolitical shocks — these are structural breaks that historical models cannot anticipate","Confidence intervals are often miscalibrated; a 95% confidence interval may contain the true price only 70% of the time in live trading","Look-ahead bias risk if backtesting uses future information (e.g., if signal generation uses data from the prediction horizon)"],"requires":["Minimum 1-2 years of historical OHLCV data per ticker for model training","Computational resources for daily or weekly model retraining (GPU recommended for deep learning models)","Real-time price feed for live prediction updates","Mechanism to log predictions and actual outcomes for performance tracking"],"input_types":["Historical OHLCV sequences (typically 50-200 candles lookback)","Technical indicators (RSI, MACD, Bollinger Bands) as optional features","Volume and volatility metrics","Optionally: macroeconomic indicators (VIX, interest rates, sector rotation)"],"output_types":["Point forecast (predicted price at time T+H)","Confidence interval or prediction interval (e.g., 80% chance price is between $X and $Y)","Probability distribution (if using Bayesian or probabilistic models)","Directional forecast (UP/DOWN/NEUTRAL with probability)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_10","uri":"capability://planning.reasoning.risk.management.and.position.sizing.guidance","name":"risk management and position sizing guidance","description":"Provides recommendations for position sizing based on account size, risk tolerance, and volatility of the stock. The system may use Kelly criterion, fixed fractional sizing, or volatility-adjusted sizing to compute a recommended position size. It also calculates and displays risk metrics (max loss if stop loss is hit, risk-reward ratio) for each potential trade. The system may alert users if they're about to take on excessive risk (e.g., risking >2% of account on a single trade). However, based on the editorial summary, this capability may be limited or missing in the current product.","intents":["I want to know how much of my account to risk on each trade based on my risk tolerance","I want to understand the potential loss if a trade goes against me before I enter","I want to avoid over-leveraging or taking on excessive risk without realizing it"],"best_for":["Risk-averse traders who want to size positions conservatively","Traders using leverage or margin who need to monitor risk exposure","Beginners who lack experience with position sizing and need guidance"],"limitations":["Position sizing recommendations are only as good as the volatility estimates; if volatility is underestimated, recommended sizes may be too large","Kelly criterion can recommend aggressive sizing (e.g., 25% of account) if win rate is high; many traders prefer more conservative sizing (e.g., 2-5% per trade)","No built-in enforcement of position sizing limits; users can ignore recommendations and over-leverage anyway","Risk management tools may be gated behind premium tier, creating a dangerous situation where free users lack risk guidance"],"requires":["Account size input from user (or auto-fetch from broker API)","Risk tolerance specification (e.g., max % of account to risk per trade)","Volatility estimates (ATR, historical volatility, or implied volatility)","Stop loss level (user-specified or calculated from technical levels)"],"input_types":["Account size (USD)","Risk tolerance (% of account per trade, e.g., 2%)","Stock volatility (ATR, historical volatility)","Entry price and stop loss level"],"output_types":["Recommended position size (shares or USD)","Max loss if stop loss is hit (USD and % of account)","Risk-reward ratio (potential profit / potential loss)","Risk alerts (e.g., 'This trade risks 5% of your account — consider reducing size')"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_11","uri":"capability://image.visual.mobile.app.with.push.notifications.and.offline.access","name":"mobile app with push notifications and offline access","description":"Provides a native mobile app (iOS and Android) with a simplified UI optimized for small screens. The app displays watchlists, portfolio P&L, and AI signals with real-time updates via push notifications. The app may support offline access to cached data (last known prices, historical charts) when network connectivity is unavailable. The app likely uses a mobile-specific charting library (TradingView Lightweight Charts Mobile or custom WebGL renderer) for performance. Authentication is handled via biometric (Face ID, Touch ID) or PIN for security.","intents":["I want to check my portfolio and AI signals on the go without opening a web browser","I want to receive push notifications for signal changes so I don't miss trading opportunities","I want to view charts and analysis offline if I'm in an area with poor connectivity"],"best_for":["Active traders who need to monitor positions throughout the day","Mobile-first users who prefer apps over web browsers","Users in areas with intermittent connectivity who need offline access"],"limitations":["Mobile screens are small; displaying complex charts and multiple signals is challenging without scrolling or zooming","Offline access requires local caching of data; if cache is stale (>5 minutes), decisions based on offline data may be incorrect","Push notifications have delivery latency (1-5 seconds typical); users may miss fast-moving markets","Mobile app requires separate development and maintenance from web app; features may lag behind web version"],"requires":["iOS 13+ and Android 8+ for native app development","Mobile charting library (TradingView Lightweight Charts Mobile, Chart.js, or custom WebGL)","Push notification service (Firebase Cloud Messaging for Android, Apple Push Notification service for iOS)","Local database (SQLite, Realm) for offline caching","Biometric authentication library (LocalAuthentication for iOS, BiometricPrompt for Android)"],"input_types":["Real-time price updates (via WebSocket or polling)","Signal change events (via push notifications)","User interactions (taps, swipes, pinches for chart navigation)"],"output_types":["Rendered charts and UI (native iOS/Android views)","Push notifications (to device notification center)","Cached data (stored locally for offline access)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_2","uri":"capability://image.visual.interactive.chart.annotation.and.signal.visualization","name":"interactive chart annotation and signal visualization","description":"Renders candlestick or OHLC charts with overlaid AI-generated signals, support/resistance zones, and confidence heatmaps. The visualization layer likely uses a charting library (TradingView Lightweight Charts, Chart.js, or Plotly) with custom WebGL rendering for performance at high data densities. Signals are drawn as arrows, zones, or colored regions with tooltips showing model reasoning (e.g., 'BUY: 70% confidence from price+volume fusion'). Users can interact with annotations to drill into the underlying data or adjust signal thresholds in real-time.","intents":["I want to see AI signals directly on my price chart without switching between multiple windows or indicators","I need to understand why the AI generated a specific signal — what data drove the decision?","I want to adjust signal sensitivity (e.g., show only high-confidence signals) without retraining the model"],"best_for":["Visual learners who prefer chart-based analysis over numerical dashboards","Traders analyzing multiple timeframes simultaneously (1m, 5m, 1h, 4h, 1d)","Users with limited technical knowledge who benefit from visual cues over raw metrics"],"limitations":["Chart rendering performance degrades with >10,000 data points on a single canvas; may require downsampling or pagination for multi-year datasets","Tooltip/drill-down interactions add latency (100-500ms) if they require server-side data fetching; local caching is essential","Mobile rendering is constrained by screen real estate; signal density may be overwhelming on small screens","Color-blind users may struggle to distinguish signal types if only color-coded (requires shape/icon differentiation)"],"requires":["Web browser with WebGL support (Chrome, Firefox, Safari, Edge 79+)","JavaScript charting library (TradingView Lightweight Charts, Plotly.js, or custom WebGL renderer)","Real-time data streaming capability (WebSocket or Server-Sent Events) for live chart updates","Client-side state management for user preferences (timeframe, signal thresholds, chart type)"],"input_types":["OHLCV candle data (JSON or binary format)","Signal objects with timestamp, type, confidence, and metadata","Support/resistance levels (price points or zones)","Heatmap data (2D grid of confidence scores across price and time)"],"output_types":["Rendered SVG or Canvas chart with interactive overlays","Exportable chart images (PNG, SVG) for sharing or reporting","Drill-down data (raw candle data, signal reasoning, historical accuracy)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_3","uri":"capability://data.processing.analysis.backtesting.engine.with.walk.forward.validation","name":"backtesting engine with walk-forward validation","description":"Allows users to test AI signals against historical price data using a backtesting framework that simulates order execution, slippage, and commissions. The engine likely implements walk-forward validation (training on historical window, testing on subsequent out-of-sample period, rolling forward) to avoid look-ahead bias. Performance metrics include win rate, Sharpe ratio, max drawdown, and profit factor. The system may support Monte Carlo simulations to assess robustness under different market conditions or parameter perturbations.","intents":["I want to test whether the AI signals would have made money if I had followed them historically","I need to understand the risk-adjusted returns (Sharpe ratio, max drawdown) before deploying real capital","I want to optimize signal parameters (e.g., confidence threshold) to maximize historical returns without overfitting"],"best_for":["Quantitative traders validating strategy hypotheses before live deployment","Retail investors assessing whether AI signals are worth paying for (premium tier)","Risk-averse users who want empirical evidence of strategy performance before committing capital"],"limitations":["Backtested results are historically 20-40% optimistic vs. live trading due to slippage, execution delays, and market impact not fully modeled","Walk-forward validation reduces but does not eliminate overfitting; if signal parameters are optimized on the full dataset before walk-forward testing, look-ahead bias persists","Assumes perfect execution (fills at OHLC prices) and ignores liquidity constraints; illiquid stocks may have much worse slippage than modeled","Does not account for regime changes, structural breaks, or black swan events (e.g., circuit breakers, halts) that occurred in historical data","Survivorship bias if backtesting only includes stocks that exist today; delisted stocks are excluded, inflating historical returns"],"requires":["Complete historical OHLCV data for all backtested tickers (minimum 2-5 years)","Assumption of commission rates and slippage (typically 0.1% per trade for retail brokers)","Computational resources for iterative backtests (CPU-intensive for large parameter sweeps)","Clear definition of entry/exit rules and position sizing logic"],"input_types":["Historical OHLCV data (CSV, JSON, or database query)","Signal definitions (entry/exit rules, confidence thresholds)","Position sizing rules (fixed size, Kelly criterion, volatility-adjusted)","Commission and slippage assumptions"],"output_types":["Performance metrics (total return, Sharpe ratio, max drawdown, win rate, profit factor)","Equity curve (cumulative P&L over time)","Trade log (entry/exit prices, P&L per trade, holding period)","Drawdown analysis (peak-to-trough declines, recovery time)","Monte Carlo simulation results (confidence intervals on returns)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_4","uri":"capability://data.processing.analysis.real.time.market.data.ingestion.and.normalization","name":"real-time market data ingestion and normalization","description":"Ingests tick-level or minute-level price data from one or more market data providers (broker APIs, third-party data vendors, or direct exchange feeds) and normalizes it into a unified OHLCV format. The system handles data quality issues (missing candles, duplicate ticks, out-of-order messages) through validation and reconciliation logic. Data is cached locally (in-memory or database) for fast retrieval and backtesting. The ingestion pipeline likely runs asynchronously to avoid blocking the UI or signal generation.","intents":["I want real-time price updates for my watchlist without manually refreshing or switching between broker platforms","I need consistent OHLCV data across multiple timeframes (1m, 5m, 1h, 1d) derived from the same tick stream","I want to ensure data quality — no gaps, duplicates, or stale prices that could corrupt my analysis"],"best_for":["Active traders who need sub-second latency for signal generation and execution","Users analyzing multiple tickers simultaneously (watchlists of 50+ stocks)","Backtesting frameworks that require consistent, high-quality historical data"],"limitations":["Real-time data feeds have latency (100-500ms typical for retail APIs); professional traders use direct exchange feeds with <10ms latency","Data provider outages or API rate limits can cause gaps in the price stream; no built-in failover to secondary providers mentioned","Normalization logic must handle edge cases (pre-market/after-hours trading, stock splits, dividends) which may not be fully automated","Storage costs scale linearly with number of tickers and data retention period; unlimited historical data is expensive"],"requires":["Integration with at least one market data provider (broker API, Alpha Vantage, IEX Cloud, Polygon.io, or direct exchange feed)","API key or authentication credentials for the data provider","Database or in-memory cache (Redis, PostgreSQL, SQLite) for storing OHLCV data","Asynchronous task queue (Celery, Bull, or similar) for non-blocking data ingestion","Network connectivity with sufficient bandwidth for real-time updates"],"input_types":["Tick-level data (timestamp, price, volume, bid/ask spreads)","Minute or hourly candle data (OHLCV)","Corporate actions (stock splits, dividends) for adjustment"],"output_types":["Normalized OHLCV candles (1m, 5m, 15m, 1h, 4h, 1d)","Data quality metrics (gaps, duplicates, staleness)","Adjusted prices (split/dividend adjusted)","Real-time price tickers for UI updates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_5","uri":"capability://tool.use.integration.freemium.access.control.with.feature.gating","name":"freemium access control with feature gating","description":"Implements a subscription tier system where free users have access to basic signals and limited historical data, while premium users unlock advanced models, longer backtesting windows, and higher-frequency signal updates. Access control is enforced at the API level (checking user subscription status before returning data) and UI level (hiding premium features behind paywalls or trial prompts). The system likely tracks feature usage (API calls, backtests run, charts viewed) to enforce rate limits on free tier and upsell premium features when usage approaches limits.","intents":["I want to try the AI signals for free before committing to a paid subscription","I need to understand what premium features unlock and whether they're worth the cost","I want to track my usage and know when I'm approaching rate limits on the free tier"],"best_for":["Freemium SaaS businesses monetizing through feature gating and upsell","Retail trading platforms converting free users to paid subscribers","Teams managing multiple subscription tiers with different feature sets"],"limitations":["Free tier limitations (e.g., 5-minute signal delays, 1-year historical data) may make signals too stale or limited to be useful, creating frustration rather than conversion","Rate limiting on free tier (e.g., 10 backtests/month) can be circumvented by creating multiple accounts, requiring account verification or phone number validation","Premium feature paywalls may appear aggressive or predatory if they gate essential features (e.g., risk management tools) behind paid tier","Churn risk if premium features don't deliver promised value; users may cancel after first month if signals don't improve returns"],"requires":["User authentication system (email/password, OAuth, or social login)","Subscription management backend (Stripe, Paddle, or custom billing system)","API middleware to check subscription status and enforce feature gating","Usage tracking and analytics (to monitor feature adoption and upsell opportunities)","Database to store user subscription tier, expiration date, and feature entitlements"],"input_types":["User ID and subscription tier (from authentication/billing system)","Feature request (API endpoint, UI action)","Usage metrics (API calls, backtests, chart views)"],"output_types":["Access grant/deny decision (boolean or error message)","Feature availability status (available, limited, or locked)","Usage quota remaining (e.g., '3 of 10 backtests remaining this month')","Upsell prompts (e.g., 'Upgrade to unlock advanced models')"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_6","uri":"capability://memory.knowledge.user.watchlist.and.portfolio.tracking","name":"user watchlist and portfolio tracking","description":"Allows users to create custom watchlists of stocks and track their holdings (shares owned, cost basis, current value). The system displays real-time P&L, allocation percentages, and AI signals for each position. Portfolio data is persisted in a user database and synced across devices. The system may integrate with broker APIs to auto-populate holdings from live accounts, or allow manual entry for paper trading. Performance metrics (total return, Sharpe ratio, max drawdown) are calculated at the portfolio level.","intents":["I want to track my stock holdings and see real-time P&L without logging into my broker","I want to see AI signals for all my positions in one place and get alerts when signals change","I want to understand my portfolio allocation and risk exposure across sectors or market caps"],"best_for":["Retail investors managing small to medium portfolios (10-100 positions)","Paper traders backtesting strategies without real capital","Users who want a unified dashboard across multiple brokers"],"limitations":["Manual entry of holdings is error-prone and requires frequent updates (after trades, dividends, splits); auto-sync from brokers is more reliable but requires API integration with each broker","Portfolio-level metrics (Sharpe ratio, max drawdown) are only meaningful if calculated against a benchmark; without benchmark specification, metrics are ambiguous","Real-time P&L requires real-time price updates; if price data is delayed (>5 minutes), P&L is stale","No built-in tax reporting or cost basis tracking for wash sales, which is critical for tax-loss harvesting strategies"],"requires":["User authentication and database to store watchlists and holdings","Real-time price feed for P&L calculation","Optional: broker API integration (Alpaca, Interactive Brokers, Schwab) for auto-sync","Portfolio calculation engine (weighted average cost basis, allocation percentages, Greeks if options are supported)"],"input_types":["Ticker symbols (text input or search)","Holdings data (shares owned, cost basis, entry date)","Real-time prices (from data feed)","Optional: broker account credentials (for auto-sync)"],"output_types":["Watchlist display (ticker, price, % change, AI signal)","Portfolio summary (total value, total return, allocation by sector/cap)","Position-level P&L (unrealized gain/loss, % return, holding period)","Alerts (price targets, signal changes, rebalancing recommendations)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_7","uri":"capability://automation.workflow.alert.and.notification.system.for.signal.changes","name":"alert and notification system for signal changes","description":"Monitors AI signals for user-selected tickers and sends notifications (push, email, SMS, in-app) when signals change (e.g., BUY → SELL) or reach user-defined thresholds (e.g., confidence >80%). Notifications include the signal type, confidence score, and a link to the chart for context. The system likely uses a message queue (RabbitMQ, Kafka) to decouple signal generation from notification delivery, ensuring alerts are sent even if the UI is offline. Users can customize notification preferences (channels, frequency, quiet hours).","intents":["I want to be alerted immediately when the AI generates a new BUY or SELL signal for my watchlist","I want to receive notifications only for high-confidence signals to reduce noise","I want to choose how I'm notified (push, email, SMS) based on urgency and my availability"],"best_for":["Active traders who need to react quickly to signal changes","Users managing large watchlists (50+ tickers) who can't monitor charts constantly","Mobile-first traders who prefer push notifications over desktop alerts"],"limitations":["Notification latency (100-500ms typical) means users may miss fast-moving markets; professional traders use direct broker alerts with <10ms latency","Alert fatigue if confidence thresholds are too low; users may ignore notifications if they're too frequent or inaccurate","SMS and push notifications have delivery guarantees <99.9%; critical alerts may be lost if delivery fails","Customization options (quiet hours, frequency limits) add complexity and may be confusing for non-technical users"],"requires":["Push notification service (Firebase Cloud Messaging, Apple Push Notification service, or custom WebSocket)","Email service (SendGrid, AWS SES, or similar)","SMS service (Twilio, AWS SNS) for SMS alerts","Message queue (RabbitMQ, Kafka, or AWS SQS) for decoupling signal generation from notification delivery","User preferences database (notification channels, frequency, quiet hours, confidence thresholds)"],"input_types":["Signal change events (ticker, old signal, new signal, confidence, timestamp)","User preferences (notification channels, thresholds, quiet hours)","User contact information (email, phone, device tokens)"],"output_types":["Push notifications (to mobile app or browser)","Email notifications (with signal details and chart link)","SMS notifications (brief alert with ticker and signal)","In-app notifications (banner or toast message)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_8","uri":"capability://data.processing.analysis.sentiment.analysis.from.news.and.social.media","name":"sentiment analysis from news and social media","description":"Aggregates news articles and social media posts (Twitter, Reddit, StockTwits) mentioning specific tickers and applies NLP-based sentiment analysis to classify each mention as positive, negative, or neutral. Sentiment scores are aggregated over time windows (e.g., last 24 hours, last week) to produce a ticker-level sentiment metric. The system likely uses pre-trained transformer models (BERT, DistilBERT) fine-tuned on financial text, or third-party sentiment APIs (Intrinio, Benzinga). Sentiment is displayed as a gauge or heatmap and incorporated into the multi-factor signal generation.","intents":["I want to understand market sentiment for a stock beyond just price action — what are people saying on social media and news?","I want to see whether sentiment is bullish or bearish and how it's changed over time","I want to use sentiment as a confirmation signal for AI price predictions"],"best_for":["Retail traders interested in sentiment-driven trading strategies","Contrarian traders who want to identify when sentiment is overextended (e.g., extreme bullish sentiment before a crash)","Users analyzing meme stocks or high-social-media-volume tickers where sentiment is a key driver"],"limitations":["Sentiment analysis on financial text is notoriously inaccurate; sarcasm, irony, and domain-specific language confuse NLP models. Accuracy is typically 70-80%, not 95%+","Social media sentiment is heavily influenced by retail traders and bots; it may not reflect institutional positioning or true market consensus","Sentiment can be manipulated through coordinated social media campaigns (pump-and-dump schemes); no built-in detection of coordinated inauthentic behavior","Sentiment lags price action; by the time sentiment becomes extremely bullish, the price move may already be over","Data sources (Twitter, Reddit) have API rate limits and may not provide complete historical data"],"requires":["News aggregation API (NewsAPI, Finnhub, or Intrinio) or web scraping for news articles","Social media API access (Twitter API, Reddit API) or third-party sentiment provider","NLP model for sentiment classification (pre-trained transformer or custom fine-tuned model)","Text preprocessing pipeline (tokenization, lowercasing, stop word removal)","Time-series aggregation to compute sentiment scores over rolling windows"],"input_types":["News articles (title, body, source, timestamp)","Social media posts (text, author, timestamp, engagement metrics)","Ticker mentions (extracted via NER or keyword matching)"],"output_types":["Sentiment score per mention (positive/negative/neutral, confidence 0-1)","Aggregated sentiment metric (e.g., -1 to +1 scale, or % positive mentions)","Sentiment trend (bullish, bearish, neutral, or changing)","Sentiment heatmap (sentiment over time, by source, by topic)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_blackhedge__cap_9","uri":"capability://planning.reasoning.model.explainability.and.signal.reasoning","name":"model explainability and signal reasoning","description":"Provides interpretable explanations for why the AI generated a specific signal, breaking down the contribution of each factor (price, volume, sentiment, technical indicators) to the final decision. The system likely uses SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to compute feature importance for each signal. Explanations are displayed as text summaries (e.g., 'BUY: 60% from price momentum, 30% from positive sentiment, 10% from volume surge') or visual breakdowns (bar charts, pie charts). This transparency builds user trust and helps users understand when to trust vs. ignore signals.","intents":["I want to understand why the AI generated a BUY signal — which factors drove the decision?","I want to know if a signal is based on factors I trust (e.g., price action) or factors I'm skeptical of (e.g., social media sentiment)","I want to learn from the model's reasoning to improve my own trading intuition"],"best_for":["Traders who want to understand and validate AI decisions before acting on them","Users building trust in AI systems through transparency and explainability","Educators teaching technical analysis who want to show how AI factors combine multiple indicators"],"limitations":["SHAP and LIME explanations are computationally expensive (10-100x slower than forward pass); generating explanations for every signal may add 1-5 second latency","Explanations are only as good as the underlying model; if the model is overfit or biased, explanations will rationalize bad decisions","Feature importance (SHAP values) can be misleading if features are correlated; a feature may appear important only because it's correlated with a truly important feature","Users may over-trust explanations that sound plausible but are actually post-hoc rationalizations of random model outputs"],"requires":["SHAP or LIME library (Python: shap, lime packages)","Access to model weights and architecture (not possible with black-box APIs like OpenAI)","Computational resources for explanation generation (GPU recommended for large models)","UI components to display explanations (text, bar charts, pie charts)"],"input_types":["Model input features (price, volume, sentiment, technical indicators)","Model output (signal, confidence score)","Feature metadata (feature names, units, ranges)"],"output_types":["Feature importance scores (SHAP values or LIME weights)","Text explanation (e.g., 'BUY: 60% from price momentum, 30% from positive sentiment')","Visual breakdown (bar chart, pie chart, or waterfall chart showing feature contributions)","Confidence in explanation (if using uncertainty quantification)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Real-time market data feed (likely from a broker API or third-party data provider like Alpha Vantage, IEX Cloud, or Polygon.io)","Sentiment data source (Twitter API, news feeds, or proprietary sentiment vendor)","User account with trading broker integration for live signal delivery","Internet connectivity for real-time updates","Minimum 1-2 years of historical OHLCV data per ticker for model training","Computational resources for daily or weekly model retraining (GPU recommended for deep learning models)","Real-time price feed for live prediction updates","Mechanism to log predictions and actual outcomes for performance tracking","Account size input from user (or auto-fetch from broker API)","Risk tolerance specification (e.g., max % of account to risk per trade)"],"failure_modes":["Backtested performance metrics are historically unreliable — live trading typically underperforms backtest results by 20-40% due to slippage, execution delays, and regime changes","Model retraining frequency unknown; if signals are stale (>24 hours old), they may not reflect current market microstructure","No explicit handling of black swan events or market regime shifts; models trained on normal distributions fail catastrophically in tail events","Confidence scores may be miscalibrated — a 75% confidence signal does not necessarily mean 75% historical win rate","Financial time series are notoriously non-stationary and exhibit regime changes; models trained on bull markets often fail in bear markets without explicit retraining","Prediction accuracy degrades exponentially with forecast horizon — 1-hour predictions may have 60-70% directional accuracy, but 1-week predictions approach random chance (50%)","No explicit handling of earnings announcements, macroeconomic releases, or geopolitical shocks — these are structural breaks that historical models cannot anticipate","Confidence intervals are often miscalibrated; a 95% confidence interval may contain the true price only 70% of the time in live trading","Look-ahead bias risk if backtesting uses future information (e.g., if signal generation uses data from the prediction horizon)","Position sizing recommendations are only as good as the volatility estimates; 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