StockGPT vs TrendRadar
Side-by-side comparison to help you choose.
| Feature | StockGPT | TrendRadar |
|---|---|---|
| Type | Product | MCP Server |
| UnfragileRank | 26/100 | 51/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem | 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Accepts free-form natural language questions about stocks, market trends, and financial metrics, then routes them through an LLM-based query parser that translates user intent into structured data requests. The system interprets colloquial financial terminology (e.g., 'Is Apple overvalued?', 'What's the tech sector doing?') and maps these to underlying market data APIs, returning conversational responses rather than raw database results.
Unique: Uses LLM-based intent parsing to translate colloquial financial questions directly into market data API calls, eliminating the need for users to learn ticker symbols, financial metrics terminology, or database query syntax. Most competitors require structured input (ticker + metric selection) or charge for natural language access.
vs alternatives: More accessible than Bloomberg Terminal or FactSet for casual users because it removes the learning curve of financial databases, but less reliable than professional tools because LLM parsing can hallucinate or misinterpret financial intent.
Integrates with multiple real-time market data providers (likely Yahoo Finance, Alpha Vantage, or similar free/freemium APIs) to fetch current stock prices, volume, intraday movements, and sector performance. Implements a caching layer to reduce API call frequency and costs, with TTL-based invalidation to balance freshness against rate limits. The system normalizes data from heterogeneous sources into a unified schema before serving to the LLM context.
Unique: Abstracts away the complexity of integrating multiple free market data APIs by normalizing heterogeneous schemas and implementing intelligent caching with TTL-based invalidation. Most competitors either lock data behind paywalls or require users to manage API integrations themselves.
vs alternatives: Cheaper than professional data terminals (Bloomberg, FactSet) because it leverages free APIs, but less reliable and slower because free providers have rate limits and delayed updates compared to institutional-grade feeds.
Takes aggregated market data and user queries, then uses an LLM (likely GPT-3.5 or similar) to generate contextual financial analysis, trend interpretation, and investment thesis summaries. The system constructs prompts that inject current market data, historical context, and financial metrics into the LLM's context window, then post-processes outputs to extract key insights. No human financial analyst reviews outputs before serving to users.
Unique: Combines real-time market data injection with LLM-based analysis to generate contextual financial narratives without human analyst review. Unlike professional research firms, it prioritizes speed and accessibility over accuracy and accountability, making it fundamentally a supplementary tool rather than a primary research layer.
vs alternatives: Faster and cheaper than hiring a financial analyst or subscribing to research platforms, but unreliable for critical investment decisions because LLMs hallucinate financial facts and lack accountability standards of licensed advisors.
Enables users to query multiple stocks simultaneously and receive comparative metrics (valuation ratios, growth rates, sector positioning, relative performance). The system batches ticker lookups to minimize API calls, aggregates results into a unified comparison table, and uses the LLM to generate narrative comparisons (e.g., 'Stock A is cheaper than Stock B on a P/E basis but has slower growth'). Supports sector-level aggregation to identify relative strength across industries.
Unique: Automates multi-stock comparison by batching API calls and using LLM-generated narratives to explain relative positioning, eliminating manual spreadsheet work. Most free tools require users to manually pull data for each stock; professional tools charge for this capability.
vs alternatives: More accessible than FactSet or Bloomberg for casual comparison, but less reliable because LLM-generated comparisons can miss accounting nuances and statistical significance that professional analysts would catch.
Maintains conversation history within a user session, allowing follow-up questions that reference previous queries without re-stating context (e.g., 'How does that compare to its 52-week high?' after asking about current price). The system stores recent queries and responses in session state, injects relevant context into subsequent LLM prompts, and manages context window size to avoid exceeding token limits. No persistent storage across sessions; history is cleared when user closes the browser.
Unique: Implements lightweight session-based context management that allows multi-turn financial conversations without requiring users to repeat context, while avoiding the complexity and cost of persistent storage. Most free financial tools are single-query interfaces; professional platforms charge for conversation history.
vs alternatives: More conversational than traditional financial databases or search engines, but less persistent than professional research platforms because session memory is ephemeral and not cross-device.
Aggregates market data across multiple stocks within a sector to compute sector-level metrics (average P/E, median growth rate, sector momentum, relative strength vs. S&P 500). Uses LLM to interpret these aggregates and identify sector rotation patterns, leadership changes, and macroeconomic drivers. Supports hierarchical sector classification (e.g., Technology > Software > SaaS) to enable drill-down analysis.
Unique: Automates sector-level analysis by aggregating constituent stock data and using LLM to interpret macro trends, eliminating manual spreadsheet work. Most free tools focus on individual stocks; sector analysis is typically locked behind professional platforms.
vs alternatives: More accessible than professional sector research tools, but less reliable because aggregation logic is opaque and LLM narratives can overfit to recent price movements rather than fundamental drivers.
Extracts key financial metrics (P/E ratio, dividend yield, debt-to-equity, ROE, free cash flow, earnings growth) from market data APIs and normalizes them into a consistent schema. Handles missing data gracefully (e.g., dividend yield is N/A for non-dividend stocks) and computes derived metrics (e.g., PEG ratio from P/E and growth rate). Provides both raw metrics and LLM-generated interpretations (e.g., 'P/E of 15 is below historical average, suggesting undervaluation').
Unique: Normalizes heterogeneous fundamental data from free APIs into a consistent schema and provides LLM-generated interpretations, making financial metrics accessible to non-technical users. Most free tools either show raw metrics without context or charge for interpreted analysis.
vs alternatives: More accessible than financial databases for casual users because it explains metrics in plain English, but less reliable than professional research because metrics are stale and lack accounting adjustments.
Allows users to create watchlists of stocks and set price-based alerts (e.g., 'notify me if Apple drops below $150'). Stores watchlist state in browser session or optional user account, periodically polls market data APIs to check alert conditions, and triggers notifications when thresholds are breached. Supports multiple alert types (price level, percentage change, volume spike) and notification channels (in-app, email if account is linked).
Unique: Provides lightweight watchlist and alert management without requiring paid subscriptions or complex setup, leveraging free market data APIs and browser-based state management. Most free tools lack alert functionality; professional platforms charge for this feature.
vs alternatives: More accessible than paid alert services because it's free and requires no setup, but less reliable because polling frequency is limited by API rate limits and alerts may trigger with significant delays.
+1 more capabilities
Crawls 11+ Chinese social platforms (Zhihu, Weibo, Bilibili, Douyin, etc.) and RSS feeds simultaneously, normalizing heterogeneous data schemas into a unified NewsItem model with platform-agnostic metadata. Uses platform-specific adapters that extract title, URL, hotness rank, and engagement metrics, then merges results into a single deduplicated feed ordered by composite hotness score (rank × 0.6 + frequency × 0.3 + platform_hot_value × 0.1).
Unique: Implements platform-specific adapter pattern with 11+ crawlers (Zhihu, Weibo, Bilibili, Douyin, etc.) plus RSS support, normalizing heterogeneous schemas into unified NewsItem model with composite hotness scoring (rank × 0.6 + frequency × 0.3 + platform_hot_value × 0.1) rather than simple ranking
vs alternatives: Covers more Chinese platforms than generic news aggregators (Feedly, Inoreader) and uses weighted composite scoring instead of single-metric ranking, making it superior for investors tracking multi-platform sentiment
Filters aggregated news against user-defined keyword lists (frequency_words.txt) using regex pattern matching and boolean logic (required keywords AND, excluded keywords NOT). Implements a scoring engine that weights matches by keyword frequency tier and calculates relevance scores. Supports regex patterns, case-insensitive matching, and multi-language keyword sets. Articles matching filter criteria are retained; non-matching articles are discarded before analysis and notification stages.
Unique: Implements multi-tier keyword frequency weighting (high/medium/low priority keywords) with regex pattern support and boolean AND/NOT logic, scoring articles by keyword match density rather than simple presence/absence checks
vs alternatives: More flexible than simple keyword whitelisting (supports regex and exclusion rules) but simpler than ML-based relevance ranking, making it suitable for rule-driven curation without ML infrastructure
TrendRadar scores higher at 51/100 vs StockGPT at 26/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Detects newly trending topics by comparing current aggregated feed against historical baseline (previous execution results). Marks new topics with 🆕 emoji and calculates trend velocity (rate of rank change) to identify rapidly rising topics. Implements configurable sensitivity thresholds to distinguish genuine new trends from noise. Stores historical snapshots to enable trend trajectory analysis and prediction.
Unique: Implements new topic detection by comparing current feed against historical baseline with configurable sensitivity thresholds. Calculates trend velocity (rank change rate) to identify rapidly rising topics and marks new trends with 🆕 emoji. Stores historical snapshots for trend trajectory analysis.
vs alternatives: More sophisticated than simple rank-based detection because it considers trend velocity and historical context; more practical than ML-based anomaly detection because it uses simple thresholding without model training; enables early-stage trend detection vs. mainstream coverage
Supports region-specific content filtering and display preferences (e.g., show only Mainland China trends, exclude Hong Kong/Taiwan content, or vice versa). Implements per-region keyword lists and notification channel routing (e.g., send Mainland China trends to WeChat, international trends to Telegram). Allows users to configure multiple region profiles and switch between them based on monitoring focus.
Unique: Implements region-specific content filtering with per-region keyword lists and channel routing. Supports multiple region profiles (Mainland China, Hong Kong, Taiwan, international) with independent keyword configurations and notification channel assignments.
vs alternatives: More flexible than single-region solutions because it supports multiple geographic markets simultaneously; more practical than manual region filtering because it automates routing based on platform metadata; enables region-specific monitoring vs. global aggregation
Abstracts deployment environment differences through unified execution mode interface. Detects runtime environment (GitHub Actions, Docker container, local Python) and applies mode-specific configuration (storage backend, notification channels, scheduling mechanism). Supports seamless migration between deployment modes without code changes. Implements environment-specific error handling and logging (e.g., GitHub Actions annotations for CI/CD visibility).
Unique: Implements execution mode abstraction detecting GitHub Actions, Docker, and local Python environments with automatic configuration switching. Applies mode-specific optimizations (storage backend, scheduling, logging) without code changes.
vs alternatives: More flexible than single-mode solutions because it supports multiple deployment options; more maintainable than separate codebases because it uses unified codebase with mode-specific configuration; more user-friendly than manual mode configuration because it auto-detects environment
Sends filtered news articles to LiteLLM, which abstracts over multiple LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) to generate structured analysis including sentiment classification, key entity extraction, trend prediction, and executive summaries. Uses configurable system prompts and temperature settings per provider. Results are cached to avoid redundant API calls and formatted as structured JSON for downstream processing and notification delivery.
Unique: Uses LiteLLM abstraction layer to support 50+ LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) with unified interface, allowing provider switching via config without code changes. Implements in-memory result caching and structured JSON output parsing with fallback to raw text.
vs alternatives: More flexible than single-provider solutions (e.g., direct OpenAI API) because it supports cost-effective provider switching and local model fallback; more robust than custom provider integration because LiteLLM handles retries and error handling
Translates article titles and summaries from Chinese to English (or other target languages) using LiteLLM-abstracted LLM providers with automatic fallback to alternative providers if primary provider fails. Maintains translation cache to avoid redundant API calls for identical content. Supports batch translation of multiple articles in single API call to reduce latency and cost. Integrates with notification system to deliver translated content to non-Chinese-speaking users.
Unique: Implements LiteLLM-based translation with automatic provider fallback and in-memory caching, supporting batch translation of multiple articles per API call to optimize latency and cost. Integrates seamlessly with multi-channel notification system for language-specific delivery.
vs alternatives: More cost-effective than dedicated translation APIs (Google Translate, DeepL) when using cheaper LLM providers; supports automatic fallback unlike single-provider solutions; batch processing reduces per-article cost vs. sequential translation
Distributes filtered and analyzed news to 9+ notification channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.) using channel-specific adapters. Implements atomic message batching to group multiple articles into single notification payloads, respecting per-channel rate limits and message size constraints. Supports channel-specific formatting (Markdown for Slack, card format for WeWork, plain text for Email). Includes retry logic with exponential backoff for failed deliveries and delivery status tracking.
Unique: Implements channel-specific adapter pattern for 9+ notification platforms with atomic message batching that respects per-channel rate limits and message size constraints. Supports heterogeneous formatting (Markdown for Slack, card format for WeWork, plain text for Email) from single article payload.
vs alternatives: More comprehensive than single-channel solutions (e.g., email-only) and more flexible than generic webhook systems because it handles platform-specific formatting and rate limiting automatically; atomic batching reduces notification fatigue vs. per-article delivery
+5 more capabilities