| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts natural language questions about trading activity and portfolio performance, processing them through an LLM-based conversational interface that interprets trader intent and generates contextual responses. The system maintains conversation state across multiple turns, allowing follow-up questions and drill-downs into specific trades or time periods without requiring users to re-upload or re-specify their data context. This differs from traditional dashboard analytics by treating the portfolio as a conversational subject rather than a static visualization.
Unique: Uses multi-turn conversational LLM with persistent portfolio context rather than stateless query-response pattern; maintains trader intent across follow-up questions without requiring data re-submission or context re-specification
vs alternatives: More accessible than traditional portfolio analytics dashboards (no SQL/charting literacy required) and more behavioral-focused than algorithmic trading platforms that optimize for alpha prediction
Analyzes sequences of trades to identify recurring behavioral patterns — such as revenge trading after losses, overtrading in specific market conditions, or systematic bias toward certain asset classes. The system likely uses statistical aggregation and LLM-based narrative synthesis to surface patterns that would require manual review across hundreds of trades. This capability bridges quantitative metrics (win rate, drawdown) with qualitative behavioral insights (emotional decision-making, discipline lapses).
Unique: Combines quantitative trade sequence analysis with LLM-driven narrative interpretation to surface behavioral patterns that pure statistical dashboards miss; focuses on trader psychology rather than market prediction
vs alternatives: Addresses the emotional/behavioral component of trading performance that algorithmic platforms ignore, positioning itself as a coach rather than a signal generator
Accepts trading data uploads in multiple formats (CSV, JSON, broker statements) and normalizes them into a standardized internal schema for analysis. The system likely performs format detection, field mapping, and data validation to handle variations in how different brokers export trade records. This is a critical integration point that avoids the friction of direct broker API connections but requires users to manually export and upload their data.
Unique: Supports multi-format ingestion with automatic normalization rather than requiring broker API connections; trades convenience of real-time data for accessibility to users across all brokers
vs alternatives: Lower barrier to entry than platforms requiring broker API keys, but introduces data staleness and manual workflow friction compared to direct API integrations used by competitors
Computes standard trading performance metrics (win rate, profit factor, Sharpe ratio, maximum drawdown, average trade duration) from uploaded trade data and contextualizes them through conversational explanation. Rather than displaying raw numbers, the system explains what each metric means, how the trader's performance compares to benchmarks, and what the metrics reveal about trading style. This bridges the gap between quantitative rigor and accessibility for non-technical traders.
Unique: Pairs quantitative metric calculation with LLM-generated narrative explanations and benchmark contextualization, making financial metrics accessible to non-technical traders rather than presenting raw numbers
vs alternatives: More educational and accessible than pure analytics dashboards; more rigorous and transparent than algorithmic platforms that hide performance attribution in black-box models
Enables users to ask questions about specific individual trades or trade sequences, receiving detailed analysis of entry/exit decisions, timing, position sizing, and outcomes. The system retrieves relevant trade data from the portfolio context and generates explanations of what happened, why it happened, and what could have been done differently. This capability supports iterative learning by allowing traders to drill down from high-level patterns to specific trade decisions.
Unique: Supports iterative drill-down from portfolio patterns to individual trade decisions through conversational queries, enabling traders to connect high-level insights to specific execution decisions
vs alternatives: More focused on behavioral learning than algorithmic platforms; more detailed and conversational than static trade journals or spreadsheet reviews
Allows users to ask questions that implicitly or explicitly filter trades by time period, market condition, or asset class (e.g., 'How did I trade during the March 2023 rally?' or 'Compare my performance in bull vs. bear markets'). The system interprets these natural language filters, applies them to the portfolio data, and generates comparative analysis. This capability enables traders to understand how their behavior and performance vary across different market regimes without requiring manual data slicing.
Unique: Interprets natural language time/condition filters and applies them dynamically to portfolio data without requiring users to manually specify date ranges or market definitions
vs alternatives: More flexible and conversational than dashboard filters that require users to manually select date ranges; more accessible than quantitative platforms requiring explicit regime definitions
Analyzes position sizing decisions across the portfolio and identifies patterns in risk management — such as oversized positions, inconsistent stop-loss placement, or risk-per-trade variance. The system calculates metrics like risk-per-trade percentage, position size relative to account, and maximum exposure, then generates coaching feedback on whether sizing is appropriate for the trader's stated risk tolerance. This addresses a critical gap in trader education where position sizing discipline directly impacts long-term survival.
Unique: Combines quantitative position sizing metrics with behavioral coaching feedback, addressing both the technical calculation and the discipline/consistency aspects of risk management
vs alternatives: More focused on behavioral risk management than algorithmic platforms; more rigorous than trader journals that lack systematic position sizing analysis
Maintains conversation state and portfolio context across multiple user sessions, allowing traders to return to previous analyses and continue drilling down into patterns without re-uploading data or re-specifying context. The system stores conversation history, portfolio snapshots, and analysis state in a user-specific knowledge base, enabling continuity and reference to previous insights. This differs from stateless chatbots by treating the portfolio as persistent context that accumulates insights over time.
Unique: Maintains persistent portfolio context and conversation history across sessions rather than treating each query as stateless; enables traders to build on previous insights over time
vs alternatives: More sophisticated than stateless chatbots; more user-centric than analytics dashboards that require manual navigation to previous analyses
+1 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.
Supabase scores higher at 42/100 vs Trading Literacy at 40/100. Supabase also has a free tier, making it more accessible.
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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