{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-zhulinsen--daily_stock_analysis","slug":"zhulinsen--daily_stock_analysis","name":"daily_stock_analysis","type":"repo","url":"https://github.com/ZhuLinsen/daily_stock_analysis","page_url":"https://unfragile.ai/zhulinsen--daily_stock_analysis","categories":["data-analysis"],"tags":["agent","ai","aigc","gemini","llm","quant","quantitative-trading","rag","stock"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-zhulinsen--daily_stock_analysis__cap_0","uri":"capability://data.processing.analysis.multi.source.stock.data.aggregation.with.tiered.failover","name":"multi-source stock data aggregation with tiered failover","description":"Fetches OHLCV data, real-time quotes, and chip distribution across A-shares, HK, and US markets from a 7-tier provider hierarchy (EFinance → AkShare → Tushare → Pytdx → Baostock → YFinance → Longbridge) with automatic circuit-breaker failover and data validation. Each provider is prioritized by reliability and latency; if one fails or times out, the system transparently falls back to the next tier without interrupting the analysis pipeline.","intents":["Fetch current OHLCV data for multiple stock markets without managing individual API credentials","Ensure analysis continues even when primary data sources are unavailable or rate-limited","Aggregate real-time quotes and technical indicators from the most reliable available source"],"best_for":["Quantitative traders building automated analysis systems across multiple Asian and US markets","Teams deploying stock analysis agents that require high availability and minimal manual intervention"],"limitations":["Circuit breaker adds ~500ms-2s latency per failover attempt; no caching between runs means repeated API calls for same symbols","Data freshness varies by provider (EFinance near real-time, YFinance delayed 15-20 min); no unified timestamp normalization","Chip distribution data only available from select providers; fallback may return incomplete fundamental data"],"requires":["Python 3.9+","API keys for Tushare, AkShare, or Baostock (optional; EFinance is free)","Network connectivity to at least one upstream provider","Longbridge SDK installed for US market data (optional)"],"input_types":["stock symbols (string: '000001.SZ', '0700.HK', 'AAPL')","date range (ISO 8601 format)","data type flags (OHLCV, quotes, chip distribution)"],"output_types":["pandas DataFrame with OHLCV columns","real-time quote dict with bid/ask/volume","chip distribution JSON (holder breakdown)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_1","uri":"capability://planning.reasoning.llm.driven.multi.strategy.stock.analysis.with.embedded.trading.disciplines","name":"llm-driven multi-strategy stock analysis with embedded trading disciplines","description":"Routes stock data through a unified LiteLLM interface to multiple LLM backends (Gemini, Claude, DeepSeek, OpenAI, Ollama) with embedded trading philosophy rules and 11 built-in strategies (Bull Trend, Golden Cross, Wave Theory, etc.). Each strategy is implemented as a 'skill' that guides the LLM's reasoning via system prompts and structured output templates, ensuring analysis adheres to quantitative trading principles rather than generating arbitrary commentary.","intents":["Generate consistent, discipline-driven investment recommendations across different LLM providers without rewriting prompts","Apply specific trading strategies (e.g., Golden Cross, Wave Theory) to stock analysis with LLM reasoning rather than hardcoded rules","Compare analysis quality across multiple LLM backends (Gemini vs Claude vs DeepSeek) for the same stock and strategy"],"best_for":["Quantitative traders who want LLM-augmented analysis but need to enforce trading discipline and strategy consistency","Teams evaluating multiple LLM providers for financial analysis and want A/B testing infrastructure built-in"],"limitations":["LLM reasoning adds 2-5s latency per stock per strategy; no caching of analysis results means repeated calls for same stock/strategy combination","Strategy 'skills' are hardcoded prompts; customizing or adding new strategies requires modifying source code and redeploying","LLM hallucination risk remains; no built-in fact-checking against real-time market data or earnings reports","Output format varies by LLM provider despite structured templates; post-processing required to normalize recommendations"],"requires":["Python 3.9+","API key for at least one LLM provider (Gemini, OpenAI, Anthropic, DeepSeek, or local Ollama instance)","LiteLLM library installed","Stock data from data aggregation pipeline (OHLCV, technical indicators)"],"input_types":["stock symbol and market (string)","OHLCV DataFrame with technical indicators (SMA, RSI, MACD, etc.)","strategy name (string: 'bull_trend', 'golden_cross', 'wave_theory')","optional: news/sentiment context from search service"],"output_types":["structured recommendation (buy/hold/sell with confidence score)","reasoning explanation (multi-paragraph LLM-generated text)","strategy-specific metrics (e.g., Golden Cross signal strength, Wave Theory wave count)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_10","uri":"capability://tool.use.integration.bot.integration.for.telegram.discord.and.wechat.work","name":"bot integration for telegram, discord, and wechat work","description":"Integrates with messaging platform bots (Telegram Bot API, Discord Webhooks, WeChat Work Bot API) to enable interactive analysis queries and report delivery. Users can send commands to the bot (e.g., '/analyze AAPL' or '/portfolio') and receive analysis results directly in the chat. The bot supports slash commands, inline buttons for quick actions (buy/sell/hold), and rich message formatting (embeds, cards, rich text). Bots run as separate processes and poll for messages or listen to webhooks.","intents":["Query stock analysis from messaging apps (Telegram, Discord, WeChat) without opening a web browser","Receive daily analysis reports in group chats for team discussion and collaboration","Execute quick actions (acknowledge recommendation, update portfolio) via inline buttons in chat"],"best_for":["Teams that use Telegram, Discord, or WeChat Work for communication and want analysis integrated into their chat workflow","Solo traders who want to check analysis on-the-go via mobile messaging apps"],"limitations":["Bot latency depends on message queue and API response time; interactive commands may take 2-5s to respond","Message length limits per platform (Telegram 4096 chars, Discord 2000 chars) mean long analysis must be truncated or split","Bot commands are hardcoded; adding new commands requires code changes and redeployment","No authentication per user; all users in a group chat can trigger analysis, potentially causing high API usage"],"requires":["Python 3.9+","Bot token for each platform (Telegram Bot Token, Discord Webhook URL, WeChat Work Bot Webhook URL)","Message queue (Redis, RabbitMQ) for handling concurrent requests (optional but recommended)","Analysis pipeline running in background"],"input_types":["bot command (string: '/analyze AAPL', '/portfolio', '/help')","inline button action (string: 'buy', 'sell', 'hold')"],"output_types":["analysis result message (platform-specific format)","inline buttons for quick actions","error message if command is invalid or API fails"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_11","uri":"capability://automation.workflow.github.actions.deployment.for.zero.cost.scheduled.execution","name":"github actions deployment for zero-cost scheduled execution","description":"Enables deployment of the analysis system to GitHub Actions, a free CI/CD platform that runs workflows on a schedule (cron) or on-demand. The system is packaged as a Docker container or Python script that runs in the GitHub Actions environment, fetches stock data, runs analysis, and sends notifications. No server hosting is required; GitHub Actions provides free compute for public repositories (2000 min/month) and paid plans for private repositories. Workflows are defined in YAML and version-controlled alongside the code.","intents":["Run daily stock analysis on a schedule without paying for cloud server hosting","Deploy analysis system to production without managing infrastructure or DevOps","Version-control analysis configuration and deployment alongside code"],"best_for":["Solo developers and small teams who want to deploy analysis without cloud hosting costs","Open-source projects that want to leverage GitHub Actions free tier for continuous analysis"],"limitations":["GitHub Actions has execution time limits (6 hours per job, 35 days per workflow run); long-running analysis may timeout","Free tier is limited to 2000 minutes/month for private repos; heavy usage requires paid plan","Workflow logs are public for public repos; sensitive API keys must be stored as GitHub Secrets","No persistent storage between runs; analysis results must be saved to external database or sent to notification channels"],"requires":["GitHub repository (public or private)","GitHub Actions enabled (default for all repos)","Docker image or Python environment with dependencies installed","GitHub Secrets configured for API keys and credentials"],"input_types":["workflow YAML file (.github/workflows/analysis.yml)","cron schedule (e.g., '0 9 * * 1-5' for 9 AM weekdays)","GitHub Secrets (API keys, notification tokens)"],"output_types":["workflow execution log (visible in GitHub Actions UI)","notifications sent to configured channels (Telegram, Discord, Email, etc.)","analysis results saved to external database or file storage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_12","uri":"capability://automation.workflow.docker.compose.deployment.for.local.and.cloud.hosting","name":"docker compose deployment for local and cloud hosting","description":"Packages the entire analysis system (backend, frontend, database, notification services) as a Docker Compose stack that can be deployed locally or to cloud platforms (AWS, Google Cloud, DigitalOcean). The Compose file defines services for the FastAPI backend, React frontend, PostgreSQL database, and optional Redis cache. Deployment is as simple as 'docker-compose up', with all dependencies and configuration managed by the Compose file. Supports environment-based configuration (dev, staging, prod) via .env files.","intents":["Deploy the entire analysis system locally for development and testing without manual setup","Deploy to cloud platforms (AWS, Google Cloud, DigitalOcean) with minimal configuration","Scale the system by adjusting Docker Compose services (e.g., add more backend replicas)"],"best_for":["Teams deploying analysis systems to production and want reproducible, version-controlled deployments","Developers who want to run the full system locally without installing individual dependencies"],"limitations":["Docker Compose is single-machine only; no built-in orchestration for multi-machine deployments (use Kubernetes instead)","Database persistence requires external volume management; data loss if volumes are deleted","Networking between services is automatic but may have latency; no built-in service mesh for advanced routing","Scaling is manual; no auto-scaling based on load (use Kubernetes or cloud-native services for auto-scaling)"],"requires":["Docker and Docker Compose installed (Docker Desktop on Mac/Windows, Docker Engine on Linux)",".env file with API keys and configuration","Sufficient disk space for Docker images and database volumes"],"input_types":["docker-compose.yml file (service definitions)",".env file (environment variables and secrets)"],"output_types":["running Docker containers (backend, frontend, database, cache)","web UI accessible at http://localhost:3000","API accessible at http://localhost:8000"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_13","uri":"capability://automation.workflow.systemd.and.cron.based.scheduling.for.local.deployment","name":"systemd and cron-based scheduling for local deployment","description":"Enables deployment of the analysis system as a systemd service (Linux) or cron job that runs on a local machine or VPS. The system runs continuously as a background service, polling for scheduled analysis times and executing them. Systemd provides service management (start, stop, restart, status) and automatic restart on failure. Cron provides simple time-based scheduling without a persistent service. Both approaches require minimal infrastructure (just a Linux machine) and zero cloud hosting costs.","intents":["Run analysis on a local machine or cheap VPS without cloud hosting costs","Schedule analysis to run at specific times (market open, market close) with automatic restart on failure","Monitor analysis execution via systemd logs and status commands"],"best_for":["Solo traders and small teams who want to run analysis on a cheap VPS or local machine","Users who prefer self-hosted solutions over cloud platforms"],"limitations":["Systemd/cron are Linux-only; Windows users must use Task Scheduler or WSL","No built-in monitoring or alerting; requires external tools (Prometheus, Grafana) for production monitoring","Single-machine deployment; no redundancy or failover if the machine goes down","Manual scaling; adding more machines requires manual configuration and load balancing setup"],"requires":["Linux machine (local or VPS) with Python 3.9+ installed","Systemd (for service management) or cron (for scheduling)","Configuration file with API keys and notification channels","Sufficient disk space for logs and data"],"input_types":["systemd service file (.service) or cron job definition (crontab)","Python script path and arguments"],"output_types":["systemd service status (running, stopped, failed)","systemd logs (journalctl output)","cron execution logs (in /var/log/syslog or mail)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_2","uri":"capability://search.retrieval.intelligent.search.and.context.enrichment.for.fundamental.analysis","name":"intelligent search and context enrichment for fundamental analysis","description":"Aggregates news, risk alerts, earnings data, and capital flow from 4+ specialized search APIs (Anspire, Tavily, Bocha, SerpAPI) and enriches the LLM analysis context with up-to-date fundamental information. The search service queries for stock-specific news, regulatory filings, insider trading, and market sentiment, then embeds results into the LLM prompt as structured context to ground recommendations in real-world events rather than historical price patterns alone.","intents":["Fetch recent news and earnings announcements for a stock to provide LLM with current fundamental context","Detect regulatory risks, insider trading activity, or capital flow changes that may not be reflected in price data yet","Enrich technical analysis with sentiment and event-driven context to improve recommendation confidence"],"best_for":["Fundamental analysts who want to combine technical analysis with real-time news and event detection","Teams building AI agents that need to explain recommendations with reference to recent market events and news"],"limitations":["Search API latency varies (Tavily ~1-2s, SerpAPI ~2-3s); adding search enrichment adds 3-5s per stock to analysis pipeline","News quality and relevance varies by source; no built-in deduplication or credibility scoring means LLM may weight low-quality sources equally","Earnings data availability is provider-dependent; some APIs may not cover all markets (especially A-shares and HK stocks)","Search results are time-windowed (typically last 7-30 days); older fundamental shifts may be missed"],"requires":["Python 3.9+","API keys for at least one search provider (Tavily, SerpAPI, Anspire, or Bocha)","Network connectivity to search APIs","Stock symbol and market identifier (e.g., '000001.SZ', '0700.HK', 'AAPL')"],"input_types":["stock symbol and market (string)","search query type (news, earnings, risk, capital_flow)","time window (days to look back, default 7-30)"],"output_types":["list of news articles with title, URL, publish date, snippet","earnings announcement data (date, EPS, guidance)","risk alerts (regulatory, insider trading, delisting warnings)","capital flow summary (institutional buying/selling, short interest)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_3","uri":"capability://planning.reasoning.multi.agent.orchestrator.for.complex.multi.turn.strategy.q.a","name":"multi-agent orchestrator for complex multi-turn strategy q&a","description":"Implements a multi-agent system that decomposes complex investment questions into sub-tasks, each handled by specialized agents (technical analyst, fundamental analyst, risk manager, sentiment analyzer). Agents communicate via a shared context store and iteratively refine recommendations through multi-turn reasoning. The orchestrator routes user queries to appropriate agents, aggregates their outputs, and synthesizes a final recommendation with consensus scoring and dissent tracking.","intents":["Answer complex investment questions that require combining technical, fundamental, and sentiment analysis (e.g., 'Should I buy this stock given the recent earnings miss and strong technical setup?')","Decompose multi-step analysis tasks (e.g., compare 5 stocks across 3 strategies) into parallel agent work to reduce latency","Track agent reasoning and disagreement to surface uncertainty and alternative viewpoints in recommendations"],"best_for":["Investment teams that want AI-augmented analysis with explainable reasoning and multi-perspective consensus","Builders creating financial advisory chatbots that need to handle complex, multi-faceted investment questions"],"limitations":["Multi-agent orchestration adds 5-10s latency per query due to sequential agent calls and context synchronization; no parallel agent execution in current implementation","Agent specialization is hardcoded (technical, fundamental, risk, sentiment); adding new agent types requires code changes","Consensus scoring is rule-based (majority vote + confidence weighting); no learned weighting of agent reliability across market conditions","Context store is in-memory; no persistence across sessions means agent reasoning history is lost after analysis completes"],"requires":["Python 3.9+","LLM API keys (for each agent's LLM backend)","Stock data and search context from prior pipeline stages","Query in natural language or structured format"],"input_types":["investment question (natural language string)","stock symbols to analyze (list of strings)","optional: strategy constraints (e.g., 'only consider momentum strategies')"],"output_types":["final recommendation (buy/hold/sell with confidence 0-100)","per-agent analysis (technical score, fundamental score, risk score, sentiment score)","consensus reasoning (why agents agree/disagree)","alternative viewpoints (minority agent opinions)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_4","uri":"capability://automation.workflow.multi.channel.notification.distribution.with.platform.specific.formatting","name":"multi-channel notification distribution with platform-specific formatting","description":"Distributes analysis reports to 10+ notification channels (Telegram, Discord, WeChat Work, Feishu, Email, Webhook, etc.) with platform-specific formatting and routing logic. Each channel has a dedicated formatter that adapts the structured analysis output (recommendation, reasoning, metrics) to the platform's native format (Telegram markdown, Discord embeds, WeChat rich text, Feishu cards). Supports group routing rules (e.g., send high-confidence recommendations to all channels, low-confidence only to email) and scheduling (immediate, daily digest, weekly summary).","intents":["Send daily stock analysis reports to multiple communication channels without manual copy-paste or format conversion","Route different recommendation types to different audiences (e.g., high-confidence trades to Telegram, detailed analysis to email)","Schedule reports to send at specific times (market open, market close, weekly digest) across all channels simultaneously"],"best_for":["Investment teams that use multiple communication platforms (Telegram, Discord, WeChat, Feishu) and want unified report distribution","Solo traders who want daily analysis delivered to their preferred channels without manual intervention"],"limitations":["Platform-specific formatting requires maintenance as APIs evolve; no automatic format adaptation means breaking changes in Discord/Telegram APIs require code updates","Rate limiting varies by platform (Telegram 30 msg/sec, Discord 10 msg/sec, WeChat Work 20 msg/min); batch sending may be throttled","Message size limits differ per platform (Telegram 4096 chars, Discord 2000 chars, Email unlimited); long reports must be truncated or split","Webhook delivery is fire-and-forget; no built-in retry logic or delivery confirmation for custom webhooks"],"requires":["Python 3.9+","API keys/tokens for each notification channel (Telegram bot token, Discord webhook, WeChat Work webhook, Feishu webhook, Email SMTP credentials)","Structured analysis output from prior pipeline stages (recommendation, reasoning, metrics)"],"input_types":["structured analysis report (dict with recommendation, reasoning, metrics, timestamp)","channel list (list of strings: 'telegram', 'discord', 'wechat_work', 'feishu', 'email')","optional: routing rules (dict mapping confidence level to channels)"],"output_types":["notification delivery status (dict with channel name and success/failure)","formatted message per channel (platform-specific format)","delivery logs (timestamp, channel, message ID, error if any)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_5","uri":"capability://data.processing.analysis.backtesting.engine.with.1.day.validation.and.performance.metrics","name":"backtesting engine with 1-day validation and performance metrics","description":"Evaluates AI recommendation accuracy by comparing predicted buy/sell signals against actual 1-day forward returns. For each stock analyzed, the system records the recommendation (buy/hold/sell) and confidence score, then checks the next trading day's price movement to calculate hit rate, precision, recall, and Sharpe ratio. Results are aggregated per strategy and per LLM provider, enabling performance comparison and model selection. The backtesting engine runs continuously as new analysis is generated, building a historical performance database.","intents":["Measure AI recommendation accuracy over time to validate strategy effectiveness and detect model degradation","Compare performance across different LLM providers (Gemini vs Claude vs DeepSeek) to optimize cost vs accuracy","Identify which trading strategies perform best in current market conditions and adjust strategy weighting accordingly"],"best_for":["Quantitative traders who want to validate AI recommendations against real market outcomes before deploying capital","Teams building financial AI products and need continuous performance monitoring to detect model drift"],"limitations":["1-day validation window is too short to capture swing trades or longer-term trends; no support for multi-day or multi-week backtesting windows","Backtesting is forward-looking only (next day); no historical backtesting on past data means no ability to test strategy changes retroactively","Performance metrics are aggregated per strategy but not per stock; no per-stock performance tracking means inability to identify which stocks the AI predicts well","No transaction cost modeling; actual trading performance will be worse than backtest results due to slippage and commissions","Requires manual data reconciliation; if recommendation and actual price data are misaligned (e.g., stock splits, delisting), backtest results are invalid"],"requires":["Python 3.9+","Historical recommendation database (from prior analysis runs)","Next-day price data for all analyzed stocks (from data aggregation pipeline)","At least 30 days of historical recommendations for meaningful performance statistics"],"input_types":["recommendation record (stock symbol, recommendation, confidence, timestamp)","next-day OHLCV data (open, high, low, close, volume)"],"output_types":["performance metrics (hit rate %, precision, recall, Sharpe ratio, max drawdown)","per-strategy performance (strategy name, win rate, avg return)","per-provider performance (LLM provider, avg confidence, accuracy)","performance trend chart (accuracy over time)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_6","uri":"capability://data.processing.analysis.portfolio.p0.system.for.position.tracking.and.risk.management","name":"portfolio p0 system for position tracking and risk management","description":"Tracks open positions, entry prices, and current P&L for a user's stock portfolio. The system monitors each position against the AI's current recommendation (buy/hold/sell) and alerts when recommendations change (e.g., a held stock receives a sell signal). Calculates portfolio-level metrics (total P&L, concentration risk, sector exposure) and suggests rebalancing actions based on AI recommendations and risk thresholds. Supports multiple portfolio snapshots (e.g., 'aggressive', 'conservative') with different risk parameters.","intents":["Track open positions and P&L in real-time against AI recommendations to identify when to exit trades","Monitor portfolio concentration risk and receive alerts when a single stock or sector exceeds risk thresholds","Get rebalancing suggestions based on AI recommendations and portfolio risk metrics"],"best_for":["Active traders who want to align their portfolio with AI recommendations and monitor risk in real-time","Investment advisors who manage multiple client portfolios and need to track performance and risk across all accounts"],"limitations":["Portfolio tracking is manual entry only; no integration with brokers (Interactive Brokers, Futu, etc.) means positions must be manually updated","P&L calculation assumes entry price is fixed; no support for averaging down or pyramiding positions","Risk metrics are portfolio-level only; no position-level stop-loss or take-profit automation","Rebalancing suggestions are advisory only; no automatic order execution means users must manually trade"],"requires":["Python 3.9+","Manual portfolio data entry (stock symbol, entry price, quantity, entry date)","Current price data from data aggregation pipeline","AI recommendations from analysis pipeline"],"input_types":["portfolio position (stock symbol, entry price, quantity, entry date)","risk parameters (max concentration %, max sector exposure %, max drawdown %)","current AI recommendations (buy/hold/sell per stock)"],"output_types":["position-level P&L (entry price, current price, P&L %, days held)","portfolio-level metrics (total P&L, concentration risk, sector exposure, Sharpe ratio)","alerts (recommendation change, risk threshold breach, rebalancing suggestion)","rebalancing actions (sell X shares of stock A, buy Y shares of stock B)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_7","uri":"capability://automation.workflow.configuration.registry.with.environment.based.parameter.management","name":"configuration registry with environment-based parameter management","description":"Centralizes all system configuration (API keys, LLM model selection, strategy parameters, notification channels, data source priorities) in a registry that reads from environment variables and config files. The registry supports environment-based overrides (dev, staging, prod) and enables dynamic reconfiguration without code changes. Configuration is validated at startup to catch missing API keys or invalid parameters early. Supports config inheritance (base config + environment-specific overrides) and secret management (API keys stored in .env, not in code).","intents":["Manage API keys and credentials securely without hardcoding them in source code","Switch between different LLM providers or data sources by changing config, not code","Deploy the same codebase to dev/staging/prod with environment-specific configurations"],"best_for":["Teams deploying stock analysis systems across multiple environments (dev, staging, prod) with different API keys and model selections","Solo developers who want to experiment with different LLM providers or strategies without modifying code"],"limitations":["Configuration validation is basic (presence check, type check); no semantic validation (e.g., checking if API key is valid before startup)","No hot-reload; configuration changes require restarting the analysis pipeline","Secret management relies on .env files; no integration with external secret stores (AWS Secrets Manager, HashiCorp Vault)","Config inheritance is single-level (base + environment); no support for complex inheritance hierarchies"],"requires":["Python 3.9+",".env file with API keys and configuration parameters","Environment variable set (DEV, STAGING, or PROD)"],"input_types":[".env file (key=value format)","config.py or config.yaml (structured configuration)"],"output_types":["validated configuration dict (all parameters loaded and type-checked)","configuration validation report (missing keys, invalid values)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_8","uri":"capability://automation.workflow.command.line.interface.for.ad.hoc.analysis.and.scheduling","name":"command-line interface for ad-hoc analysis and scheduling","description":"Provides a CLI for running stock analysis on-demand or on a schedule (daily, weekly, monthly). Users can specify stock symbols, strategies, and notification channels via command-line arguments or interactive prompts. The CLI supports batch analysis (analyze 100 stocks in one run) and dry-run mode (show what would be analyzed without actually running). Integrates with system schedulers (cron on Linux, Task Scheduler on Windows) to enable zero-cost scheduled execution via GitHub Actions or local systemd.","intents":["Run stock analysis for specific stocks on-demand without writing code","Schedule daily analysis to run at market open/close and send reports to notification channels","Batch analyze multiple stocks in one run to reduce API calls and improve efficiency"],"best_for":["Solo traders who want to run daily analysis without managing a web server or API","Teams deploying analysis via GitHub Actions or systemd cron jobs for zero-cost scheduled execution"],"limitations":["CLI is text-based; no interactive visualization of charts or real-time price updates","Batch analysis is sequential; analyzing 100 stocks takes proportionally longer than analyzing 10","Dry-run mode shows what would be analyzed but doesn't validate API keys or data availability until actual run","Scheduling relies on external schedulers (cron, Task Scheduler, GitHub Actions); no built-in scheduler in the CLI itself"],"requires":["Python 3.9+","CLI installed (via pip or git clone)","Configuration file with API keys and notification channels","Stock symbol list (as command-line argument or file)"],"input_types":["stock symbols (comma-separated string or file path)","strategy names (comma-separated string, default: all strategies)","notification channels (comma-separated string, default: all configured channels)","optional: date range, dry-run flag, batch size"],"output_types":["analysis report (text or JSON format)","notification delivery status (which channels succeeded/failed)","execution log (timestamps, API calls, errors)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zhulinsen--daily_stock_analysis__cap_9","uri":"capability://automation.workflow.web.ui.with.fastapi.backend.and.react.frontend.for.interactive.analysis","name":"web ui with fastapi backend and react frontend for interactive analysis","description":"Provides a web-based dashboard for viewing stock analysis results, managing portfolios, and configuring analysis parameters. The backend is built with FastAPI and exposes REST APIs for fetching analysis history, portfolio data, and performance metrics. The frontend is a React SPA that displays charts (price, technical indicators, performance trends), recommendation cards, and portfolio summary. Users can trigger ad-hoc analysis, adjust strategy parameters, and view detailed reasoning for each recommendation without touching code.","intents":["View stock analysis results and portfolio performance in an interactive dashboard without using CLI","Trigger ad-hoc analysis for specific stocks and see results in real-time","Adjust strategy parameters and see how recommendations change without redeploying"],"best_for":["Non-technical users (investors, advisors) who want to interact with AI analysis via a web interface","Teams building financial advisory products that need a polished UI for client-facing dashboards"],"limitations":["Web UI adds operational overhead (server hosting, SSL certificates, database management); not suitable for solo traders on a budget","Real-time price updates require WebSocket or polling; current implementation may use polling which adds latency and server load","Chart rendering is client-side (React); large datasets (1000+ stocks) may cause browser performance issues","Parameter adjustment is UI-only; changes don't persist to config files, so they're lost on server restart"],"requires":["Python 3.9+ (for FastAPI backend)","Node.js 16+ (for React frontend build)","Web server (nginx, Apache) or cloud hosting (Heroku, AWS, Google Cloud)","Database (SQLite, PostgreSQL) for storing analysis history and portfolio data"],"input_types":["stock symbol (text input)","strategy selection (dropdown or multi-select)","date range (date picker)","portfolio position data (manual entry or CSV upload)"],"output_types":["analysis result card (recommendation, confidence, reasoning)","price chart with technical indicators (candlestick, SMA, RSI, MACD)","portfolio summary (total P&L, concentration risk, sector exposure)","performance trend chart (recommendation accuracy over time)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API keys for Tushare, AkShare, or Baostock (optional; 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