BlackHedge

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.