| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Orchestrates a five-phase sequential workflow (Analyst Team → Research Team → Trader Agent → Risk Management Team → Portfolio Manager) using LangGraph state machines, where each phase processes market data and prior outputs to generate progressively refined trading decisions. Implements state propagation across agent boundaries with explicit message passing and reflection loops, enabling structured reasoning chains where later agents build on earlier analysis.
Unique: Implements explicit five-phase sequential pipeline with state propagation and reflection loops built into LangGraph graph structure, rather than ad-hoc agent chaining. Uses dual-model strategy (deep_think_llm for complex reasoning, quick_think_llm for rapid tasks) to balance reasoning depth with latency, and includes structured debate system (bull/bear researchers) that generates opposing viewpoints before synthesis.
vs alternatives: More structured than generic multi-agent frameworks (AutoGen, LangChain agents) because it enforces a domain-specific trading pipeline with explicit phase boundaries and state contracts, reducing hallucination and improving auditability for financial decisions.
Provides a unified client factory that abstracts six LLM providers (OpenAI, Anthropic, Google, xAI, OpenRouter, Ollama) behind a single interface, enabling runtime provider switching without code changes. Implements provider detection via configuration, model instantiation with provider-specific parameters, and fallback logic for API failures, allowing agents to use different models for different reasoning tasks (deep vs quick thinking).
Unique: Implements a unified client factory pattern that instantiates provider-specific LLM clients (OpenAI ChatOpenAI, Anthropic ChatAnthropic, etc.) from a single configuration object, enabling runtime provider selection. Supports dual-model strategy where different agents use different providers based on reasoning complexity (deep_think_llm vs quick_think_llm), not just cost optimization.
vs alternatives: More flexible than LangChain's built-in provider support because it allows per-agent provider assignment and explicit deep/quick thinking model selection, rather than global model configuration. Reduces vendor lock-in compared to frameworks hardcoded to single providers.
Implements a trader agent that synthesizes analyst reports and debate outcomes into a unified trading decision with specific execution parameters: action (buy/sell/hold), confidence score (0-1), position size (percentage of portfolio), entry price, stop-loss, and take-profit levels. Uses deep thinking LLM to reason about position sizing based on confidence, volatility, and portfolio constraints. Outputs are structured for downstream execution systems.
Unique: Implements trader agent that synthesizes analyst reports and debate outcomes into structured trading decision with specific execution parameters (entry, stop-loss, take-profit, position size), rather than just buy/sell signals. Uses deep thinking LLM to reason about position sizing based on confidence and volatility, producing outputs ready for downstream execution systems.
vs alternatives: More actionable than analyst reports alone because it produces specific execution parameters (entry, stop-loss, take-profit). More structured than generic synthesis because it outputs domain-specific trading decision format that execution systems can consume directly.
Provides a framework for creating custom agents by extending base agent classes and implementing agent-specific logic (data gathering, reasoning, output formatting). Agents are registered in the LangGraph graph and receive state as input, producing outputs that are added to shared state. Supports agent tools (data fetching, calculations) that agents can invoke during reasoning. Enables teams to add domain-specific agents (e.g., ESG analyst, options analyst) without modifying core framework.
Unique: Provides extensible agent architecture where custom agents can be created by extending base classes and implementing agent-specific logic, then registered in LangGraph graph. Agents receive state as input and produce outputs added to shared state, enabling seamless integration without modifying core framework.
vs alternatives: More extensible than fixed-agent systems because it allows adding custom agents without framework changes. More flexible than generic agent frameworks because it provides trading-specific base classes and patterns that reduce boilerplate for financial agents.
Implements a dual-model strategy where complex reasoning tasks (analyst reports, research debate, risk assessment) use deep_think_llm (expensive, high-quality models like Claude 3 Opus), while rapid synthesis tasks use quick_think_llm (fast, cost-effective models like GPT-4o mini). Configuration allows per-task model assignment without code changes. Reduces overall latency and cost compared to using expensive models for all tasks, while maintaining reasoning quality where it matters most.
Unique: Implements explicit dual-model strategy where complex reasoning tasks use deep_think_llm and rapid synthesis uses quick_think_llm, with per-task model assignment configurable without code changes. Reduces overall latency and cost compared to using expensive models for all tasks, while maintaining reasoning quality where it matters most.
vs alternatives: More cost-effective than single-model systems because it uses expensive models only for critical reasoning tasks. More flexible than fixed model assignments because configuration allows experimenting with different model combinations without code changes.
Implements a vendor router (route_to_vendor) that abstracts market data acquisition across multiple sources (Yahoo Finance, Alpha Vantage, local cache) with automatic fallback logic. When primary vendor fails or rate-limits, the system transparently retries with secondary vendors, and caches results locally to reduce API calls and improve latency. Technical indicators (RSI, MACD, Bollinger Bands) are computed on-demand and cached per ticker.
Unique: Implements a vendor router with explicit fallback chain (yfinance → Alpha Vantage → local cache) and automatic retry logic, rather than requiring caller to handle vendor failures. Caches both raw OHLCV data and computed technical indicators, reducing redundant calculations across agent analyses. Supports local cache-only mode for offline backtesting.
vs alternatives: More resilient than single-vendor data layers (e.g., yfinance-only) because it transparently handles API outages and rate limits. More efficient than recalculating indicators per agent because it caches computed values, reducing latency and API calls compared to frameworks that fetch fresh data for each analysis.
Implements a two-researcher debate phase where one researcher generates bullish arguments and another generates bearish arguments for a given ticker, using structured prompts that enforce opposing viewpoints. A trader agent then synthesizes both perspectives into a unified trading decision (buy/sell/hold with confidence score and position sizing), ensuring the final decision accounts for both upside and downside risks rather than relying on single-perspective analysis.
Unique: Implements explicit bull/bear researcher agents with opposing system prompts that enforce contrarian viewpoints, followed by a trader agent that synthesizes both perspectives into a single decision. Unlike generic multi-agent systems, the debate structure is domain-specific to trading (bull/bear is a natural financial dichotomy) and includes synthesis logic that accounts for both upside and downside scenarios.
vs alternatives: More balanced than single-perspective LLM analysis because it forces generation of counterarguments before decision-making, reducing confirmation bias. More structured than generic debate frameworks because it uses domain-specific prompts (bull/bear) and includes explicit synthesis step that produces actionable trading decisions, not just debate transcripts.
Implements a three-agent risk management team (Value-at-Risk agent, Correlation agent, Liquidity agent) that independently evaluates proposed trades against portfolio-level constraints, followed by a Portfolio Manager agent that approves or rejects trades based on aggregated risk assessments. Each risk agent uses deep thinking to analyze different risk dimensions, and the Portfolio Manager synthesizes their outputs with portfolio state to make final approval decisions.
Unique: Implements a three-agent risk assessment team (VaR, Correlation, Liquidity) that independently evaluates trades, with a Portfolio Manager agent that synthesizes their outputs and has final veto authority. Each risk agent uses deep thinking LLM to reason about risk dimensions, rather than using simple rule-based checks, enabling nuanced risk assessment that accounts for market context.
vs alternatives: More comprehensive than single-metric risk checks (e.g., VaR-only) because it evaluates multiple risk dimensions independently and synthesizes them. More explainable than black-box risk models because each agent produces reasoning traces that justify approval/rejection decisions, useful for compliance and audit trails.
+5 more capabilities
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs TradingAgents at 46/100.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities