| Ecosystem |
| 0 |
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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Combines price-volume-sentiment in a single ensemble model rather than treating them as separate indicators; likely uses learned feature importance weighting rather than fixed technical indicator formulas, making it adaptive to market regime changes. The visual overlay approach (signals directly on charts) reduces cognitive load vs. separate indicator windows.
vs alternatives: More interpretable than black-box neural networks (shows which factors drove each signal) and faster to execute than manual multi-indicator analysis, but less transparent than traditional technical analysis rules and unvalidated against live trading performance.
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.
Unique: Outputs explicit confidence intervals or probability distributions rather than point estimates alone, allowing users to quantify forecast uncertainty. Likely uses ensemble methods (multiple architectures averaged) to reduce overfitting and improve generalization. The rolling retraining approach adapts to recent market regimes rather than using static models.
vs alternatives: More transparent about uncertainty than simple point forecasts, and adaptive retraining is better than static models, but still subject to fundamental limits of financial forecasting — no model can reliably predict prices beyond noise levels without structural market knowledge or insider information.
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.
Unique: Integrates position sizing guidance with AI signals, allowing users to see recommended position sizes for each signal without manual calculation. Volatility-adjusted sizing adapts to market conditions (high volatility → smaller positions). Risk alerts provide guardrails to prevent over-leveraging.
vs alternatives: More integrated than standalone position sizing calculators, and volatility-adjusted sizing is more sophisticated than fixed fractional sizing. However, still relies on user discipline to follow recommendations; no hard enforcement of position limits.
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.
Unique: Optimizes UI for mobile screens with simplified layouts and touch-friendly controls. Offline caching allows users to view cached data and charts without network connectivity. Biometric authentication provides security without requiring password entry on mobile.
vs alternatives: More convenient than web app for on-the-go monitoring, and push notifications are more timely than email alerts. However, smaller screen real estate limits the amount of information displayed, and offline data may be stale.
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.
Unique: Integrates AI signal overlays directly into the charting layer rather than as separate indicator windows, reducing context switching. Likely uses WebGL or Canvas for high-performance rendering of dense signal annotations. Tooltips and drill-down interactions provide model transparency without cluttering the main chart.
vs alternatives: More integrated and visually coherent than TradingView's separate indicator panes, and faster to render than server-side chart generation. Less customizable than professional trading platforms (Bloomberg, Refinitiv) but more accessible to retail users.
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.
Unique: Implements walk-forward validation (out-of-sample testing) rather than simple historical backtesting, reducing look-ahead bias. Likely includes Monte Carlo simulations to assess robustness under parameter perturbations. Transparent reporting of slippage and commission assumptions makes results more realistic than naive backtests.
vs alternatives: More rigorous than simple buy-and-hold comparisons, and walk-forward validation is more honest than in-sample optimization. However, still subject to fundamental backtesting limitations (execution assumptions, regime changes, survivorship bias) that make live results typically worse than backtest results.
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.
Unique: Normalizes data from multiple sources into a unified OHLCV format, allowing users to switch providers without rewriting analysis code. Asynchronous ingestion prevents data fetching from blocking signal generation or UI rendering. Data quality validation (gap detection, duplicate removal) is likely automated rather than manual.
vs alternatives: More robust than single-provider solutions because it can failover or aggregate data from multiple sources. Faster than synchronous REST APIs because it uses streaming (WebSocket or Server-Sent Events). More accessible than direct exchange feeds because it abstracts away exchange-specific protocols.
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.
Unique: Combines API-level and UI-level access control to prevent free users from accessing premium data through API calls or browser dev tools. Usage tracking and rate limiting are enforced server-side rather than client-side, making them tamper-proof. Upsell prompts are contextual (triggered when users approach rate limits) rather than aggressive.
vs alternatives: More transparent than hidden paywalls (users know what's free vs. paid upfront), and server-side enforcement is more secure than client-side gating. However, aggressive feature gating can harm conversion if free tier is too limited to demonstrate value.
+4 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
BlackHedge scores higher at 43/100 vs Supabase at 42/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities