StonksGPT vs TrendRadar
Side-by-side comparison to help you choose.
| Feature | StonksGPT | TrendRadar |
|---|---|---|
| Type | Product | MCP Server |
| UnfragileRank | 25/100 | 51/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Accepts free-form natural language queries about companies and returns structured company intelligence by translating user intent into database lookups and aggregated data sources. The system likely uses semantic understanding to map conversational queries (e.g., 'What's Apple's revenue trend?') to specific financial metrics and company attributes, then retrieves and synthesizes results from multiple underlying data sources without requiring users to learn terminal syntax or specific query languages.
Unique: Eliminates terminal-style query syntax by using conversational NLP to map free-form questions directly to financial data lookups, lowering the barrier to entry compared to Bloomberg terminals or SEC Edgar's structured search interface
vs alternatives: Faster onboarding than traditional financial terminals because users ask questions in natural language rather than learning proprietary query syntax or database schemas
Integrates company data from multiple sources (likely SEC filings, company websites, financial databases) into a unified query interface, abstracting away the need for users to manually visit separate platforms. The system maintains connectors or ETL pipelines to ingest and normalize data from heterogeneous sources, then serves unified responses that cite or blend information from multiple origins.
Unique: Abstracts away manual source-switching by maintaining ETL pipelines to ingest and normalize SEC filings, company websites, and financial databases into a unified query layer, whereas competitors like Yahoo Finance or Seeking Alpha require users to navigate separate sections for each data type
vs alternatives: Reduces research friction compared to manually cross-referencing SEC Edgar, company investor relations pages, and financial databases because all data is accessible through a single conversational interface
Retrieves and presents company financial metrics (revenue, market cap, P/E ratio, debt levels, employee count, etc.) with historical snapshots to show trends over time. The system stores or accesses time-series financial data, likely from quarterly/annual SEC filings or financial data providers, and can surface how metrics have evolved across multiple reporting periods.
Unique: Surfaces historical financial trends through conversational queries rather than requiring users to manually pull and compare multiple SEC filings or use spreadsheet-based analysis, making trend analysis accessible to non-technical investors
vs alternatives: More accessible than SEC Edgar for trend analysis because users ask 'How has Apple's revenue grown?' in natural language rather than manually downloading and comparing 10-Q filings across years
Generates concise, human-readable company overviews by synthesizing business descriptions, industry classification, key products/services, and leadership information from multiple sources. The system likely uses text generation or template-based synthesis to create coherent company profiles that combine structured data (industry, employee count) with narrative content (business model, competitive positioning).
Unique: Generates natural-language company overviews through synthesis rather than serving static company descriptions, allowing dynamic profile generation tailored to user queries, whereas competitors like Crunchbase serve pre-written profiles
vs alternatives: Faster company research than reading SEC filings or company websites because synthesized summaries distill key information into conversational responses without requiring users to navigate dense documents
Maintains conversation context across multiple turns, allowing users to ask follow-up questions about a company without re-specifying the company name or context. The system likely stores the current conversation state (company in focus, previously retrieved metrics) and uses it to interpret subsequent queries, enabling natural dialogue flow.
Unique: Maintains multi-turn conversation context to enable natural follow-up questions without re-specifying company names, whereas stateless financial lookup tools require users to re-enter company identifiers with each query
vs alternatives: More natural research flow than stateless tools like Yahoo Finance search because users can ask 'What about their debt levels?' after asking about revenue, without re-specifying the company
Provides free access to core company lookup and summarization features with usage quotas or rate limits, while premium tiers unlock higher query volumes, advanced filtering, or additional data sources. The system implements quota tracking and tier enforcement at the API or session level to differentiate free vs. paid users.
Unique: Removes financial barriers to entry by offering free access to core company research features, whereas Bloomberg terminals and institutional data providers require expensive subscriptions upfront, making financial research accessible to retail investors
vs alternatives: Lower barrier to entry than Bloomberg or FactSet because free tier allows casual users to explore company data without commitment, though premium features and pricing are not clearly communicated
Resolves company names or tickers to specific entities, handling ambiguity when multiple companies share similar names or when users provide partial/misspelled identifiers. The system likely uses fuzzy matching, ticker resolution, or entity disambiguation to map user input to canonical company records in the underlying database.
Unique: Handles company name ambiguity and partial matches through fuzzy matching rather than requiring exact ticker input, making company lookup more forgiving for non-expert users compared to terminal-style tools that require precise tickers
vs alternatives: More user-friendly than ticker-only lookup because users can search by company name and the system resolves to the correct entity, whereas Bloomberg terminals require users to know exact ticker symbols
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 StonksGPT at 25/100.
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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