{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-arvinlovegood--go-stock","slug":"arvinlovegood--go-stock","name":"go-stock","type":"webapp","url":"https://go-stock.sparkmemory.top","page_url":"https://unfragile.ai/arvinlovegood--go-stock","categories":["data-analysis"],"tags":["ai-tools","deepseek","golang","lmstudio","naiveui","ollama","openai","stock","wails"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-arvinlovegood--go-stock__cap_0","uri":"capability://data.processing.analysis.multi.market.real.time.stock.price.monitoring.with.market.hour.aware.polling","name":"multi-market real-time stock price monitoring with market-hour aware polling","description":"Implements differential update polling that respects market trading hours across A-shares (SH/SZ), Hong Kong (HK), and US stocks, aggregating data from Sina, Tencent, Eastmoney, and Tushare APIs. Uses market-hour awareness to adjust polling frequency during trading vs non-trading periods, reducing unnecessary API calls while maintaining real-time accuracy. Data flows through a GORM+SQLite persistence layer with FreeCache for high-speed in-memory access, enabling sub-second UI updates without repeated database queries.","intents":["Monitor stock prices across multiple markets simultaneously without manual refresh","Receive price updates only during market trading hours to reduce API costs","Access cached price data instantly while background polling updates the database","Track price changes across A-shares, Hong Kong, and US markets in a single interface"],"best_for":["Individual retail traders monitoring multiple markets","Teams building portfolio tracking applications","Developers needing multi-market data aggregation without cloud dependencies"],"limitations":["US stock support is basic (quotes and K-line only) via Sina timezone conversion, not native US exchange APIs","Polling frequency is fixed per market; no adaptive throttling based on volatility","FreeCache is in-memory only; application restart clears cache","No built-in handling for market holidays or early closures across different exchanges"],"requires":["Go 1.16+","Internet connectivity for API calls to Sina, Tencent, Eastmoney, Tushare","SQLite (bundled via GORM)","API keys for Tushare (optional, for enhanced A-share data)"],"input_types":["stock ticker symbols (e.g., 'SH600000', 'HK0001', 'AAPL')","market type identifier (A-share, HK, US)"],"output_types":["structured JSON with current price, change %, volume, bid/ask","time-series K-line data (OHLCV)","money flow analysis (inflow/outflow by volume)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_1","uri":"capability://data.processing.analysis.ai.powered.sentiment.analysis.on.market.news.with.gse.based.chinese.text.segmentation","name":"ai-powered sentiment analysis on market news with gse-based chinese text segmentation","description":"Aggregates news from 15+ providers (Telegraph/财联社, Reuters, TradingView, etc.) and applies GSE (Generic Segmentation Engine) for Chinese text tokenization with frequency-weighted sentiment scoring. The pipeline extracts entities (stocks, funds, sectors) from news content, segments text into meaningful chunks, and scores sentiment polarity using frequency analysis of positive/negative keywords. Results are stored in SQLite with timestamps, enabling historical sentiment trend analysis and market-wide vs individual-stock sentiment comparison.","intents":["Understand market sentiment for a stock or sector from aggregated news sources","Identify sentiment shifts before they impact price movements","Compare sentiment across multiple news sources to detect consensus or divergence","Track how sentiment evolves over time for a specific stock or market"],"best_for":["Chinese market traders needing sentiment analysis for A-shares and HK stocks","Portfolio managers monitoring market psychology across multiple news sources","Developers building sentiment-driven trading signals"],"limitations":["Sentiment analysis is frequency-based keyword matching, not deep NLP; lacks context understanding for sarcasm or nuance","Chinese text segmentation is optimized for Mandarin; English news sentiment scoring is basic","News aggregation depends on web scraping stability; source APIs may change without notice","No real-time news ingestion; batch processing introduces latency of minutes to hours"],"requires":["Go 1.16+","GSE library for Chinese text segmentation","Internet connectivity to news provider APIs","SQLite for storing sentiment history"],"input_types":["news articles (text content)","stock ticker symbols for entity linking","time range for historical sentiment queries"],"output_types":["sentiment score (positive/negative/neutral with confidence)","entity-sentiment mapping (which stocks/sectors mentioned in news)","sentiment trend over time (time-series data)","source breakdown (sentiment by news provider)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_10","uri":"capability://data.processing.analysis.market.rankings.and.sector.analysis.with.dynamic.ranking.computation","name":"market rankings and sector analysis with dynamic ranking computation","description":"Computes dynamic market rankings (gainers, losers, most active by volume) and sector-level analysis (sector returns, sector sentiment, sector fund flows) by aggregating individual stock data from SQLite. Rankings are computed on-demand or cached with configurable TTL (time-to-live) to balance freshness vs performance. Sector analysis groups stocks by industry classification (from data provider APIs) and computes aggregate metrics (weighted returns, average P/E, sector sentiment). Results are displayed in sortable tables with drill-down to individual stocks. Supports custom ranking criteria (e.g., 'highest dividend yield') via configurable sort expressions.","intents":["Identify top gainers and losers in the market for quick market overview","Analyze sector performance to identify outperforming/underperforming sectors","Compare sector sentiment and fund flows to identify sector rotation opportunities","Create custom rankings based on specific metrics (dividend yield, P/E, etc.)"],"best_for":["Market analysts monitoring sector trends and rotations","Traders seeking quick market overview and top movers","Portfolio managers analyzing sector allocation and performance"],"limitations":["Rankings are computed from local SQLite data; limited to stocks in the database (not full market universe)","Sector classification depends on data provider accuracy; may be outdated or inconsistent","Custom ranking criteria require manual configuration; no UI for defining custom sort expressions","Ranking computation is synchronous; large stock universes may cause UI lag","No historical ranking snapshots; cannot track how rankings changed over time"],"requires":["Go 1.16+","SQLite with populated stock data (prices, returns, sector classification)","Vue.js frontend for ranking table display and sorting","Optional: data provider API for sector classification updates"],"input_types":["optional: ranking type (gainers, losers, most active, custom)","optional: sector filter","optional: time range (1d, 1w, 1M, YTD, 1Y)"],"output_types":["ranked list of stocks with metrics (price, change %, volume, etc.)","sector-level metrics (sector return, average P/E, sector sentiment)","sector composition (list of stocks in each sector with weights)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_11","uri":"capability://automation.workflow.scheduled.task.automation.with.market.hour.aware.scheduling.and.background.execution","name":"scheduled task automation with market-hour aware scheduling and background execution","description":"Implements a task scheduler that executes background jobs (price polling, news fetching, sentiment analysis, AI analysis) on configurable schedules with market-hour awareness. Tasks are defined in SQLite with cron expressions or simple interval schedules (e.g., 'every 5 minutes during market hours'). The scheduler respects market trading hours across different exchanges (A-shares, HK, US) and skips execution during non-trading periods. Task execution is asynchronous and non-blocking; results are stored in SQLite with execution logs. Supports task dependencies (e.g., 'run sentiment analysis only after news fetching completes') and error handling with retry logic.","intents":["Automate price polling and news fetching without manual intervention","Run AI analysis on a schedule (e.g., daily stock recommendations)","Execute tasks only during market hours to reduce API costs","Monitor task execution history and debug failed tasks"],"best_for":["Traders wanting fully automated monitoring without manual refresh","Teams running go-stock as a background service","Developers building scheduled analysis pipelines"],"limitations":["Task scheduling is cron-based; no UI for visual schedule builder","Task dependencies are not enforced; manual ordering required","No distributed task execution; all tasks run on single machine","Task execution logs are stored in SQLite; no external logging integration","No task prioritization; all tasks execute in FIFO order"],"requires":["Go 1.16+","Cron expression library (likely github.com/robfig/cron)","SQLite for storing task definitions and execution logs","Background goroutine for task scheduler"],"input_types":["task type (price polling, news fetching, sentiment analysis, AI analysis)","schedule expression (cron or interval)","optional: market-hour filter (trading hours only)"],"output_types":["task execution log (timestamp, status, duration, error message if failed)","task statistics (total executions, success rate, average duration)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_12","uri":"capability://automation.workflow.cross.platform.desktop.application.with.wails.framework.and.native.os.integration","name":"cross-platform desktop application with wails framework and native os integration","description":"Builds a cross-platform desktop application using Wails v2 framework, which bridges Vue.js frontend with Go backend via IPC (inter-process communication). The application compiles to native executables for Windows (WebView2), macOS (Universal/Intel/ARM builds), and Linux. Wails handles window management, file dialogs, system tray integration, and native notifications. The frontend uses NaiveUI component library for consistent UI across platforms. Application state is persisted to SQLite, enabling data retention across sessions. Supports auto-update mechanism for distributing new versions to users.","intents":["Deploy go-stock as a native desktop application on Windows, macOS, and Linux","Integrate with OS-level features (system tray, notifications, file dialogs)","Distribute updates to users automatically without manual download","Develop cross-platform features without platform-specific code"],"best_for":["Desktop application developers using Go + Vue.js stack","Teams distributing applications to non-technical users","Developers needing cross-platform native app with web UI"],"limitations":["Wails adds ~50MB to application size due to embedded Chromium/WebView2","IPC communication between frontend and backend adds latency (~10-50ms per call)","macOS builds require code signing and notarization for distribution","Windows requires WebView2 runtime; older Windows versions may not be supported","No built-in analytics or crash reporting; requires external services"],"requires":["Go 1.16+","Node.js 14+ for frontend build","Wails v2 CLI","Platform-specific build tools: MSVC for Windows, Xcode for macOS, GCC for Linux","WebView2 runtime on Windows (auto-installed via installer)"],"input_types":["Vue.js components (frontend code)","Go functions (backend code)","Wails binding configuration"],"output_types":["native executable (.exe on Windows, .app on macOS, binary on Linux)","installer package (.msi on Windows, .dmg on macOS)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_13","uri":"capability://tool.use.integration.multi.provider.llm.configuration.and.api.key.management.with.provider.abstraction","name":"multi-provider llm configuration and api key management with provider abstraction","description":"Implements a provider abstraction layer that supports 8+ LLM providers (OpenAI, DeepSeek, Ollama, LMStudio, AnythingLLM, 硅基流动, 火山方舟, 阿里云百炼) with unified interface for model selection and API key management. Configuration is stored in SQLite with encrypted API keys (using Go's crypto/aes package). Users can configure multiple providers simultaneously and switch between them via UI without code changes. The abstraction handles provider-specific API differences (request/response format, function-calling syntax, error handling) transparently. Supports local LLM providers (Ollama, LMStudio) for offline analysis without cloud dependencies.","intents":["Switch between multiple LLM providers without code changes","Use local LLMs (Ollama, LMStudio) for privacy-preserving analysis","Configure API keys securely without exposing them in code","Compare analysis results across different LLM providers"],"best_for":["Teams wanting flexibility in LLM provider choice","Users prioritizing data privacy with local LLM support","Developers building LLM-powered applications with multi-provider support"],"limitations":["API key encryption is basic (AES-256); no hardware security module integration","Provider abstraction may not expose all provider-specific features","Function-calling syntax differs across providers; not all providers support all tool types","No provider health monitoring; failed API calls are not automatically retried with fallback provider","Configuration UI is basic; no advanced provider-specific settings"],"requires":["Go 1.16+","SQLite for storing encrypted API keys","Vue.js frontend for configuration UI","At least one LLM provider: OpenAI API key, or local Ollama/LMStudio instance, or account with Chinese providers"],"input_types":["provider type (OpenAI, DeepSeek, Ollama, etc.)","API key or endpoint URL","model name (e.g., 'gpt-4', 'deepseek-chat')"],"output_types":["provider configuration (encrypted in SQLite)","list of available models for selected provider","provider health status (connectivity test result)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_14","uri":"capability://data.processing.analysis.data.export.and.import.with.portfolio.backup.and.restore.functionality","name":"data export and import with portfolio backup and restore functionality","description":"Provides data export/import functionality for backing up and restoring user data (stocks, groups, alerts, settings, analysis history) in JSON or CSV format. Export creates a snapshot of SQLite data at a point in time, enabling disaster recovery and data portability. Import validates data schema before insertion, preventing corruption from malformed files. Supports selective export (e.g., export only specific stock groups) and merge import (append imported data to existing database without overwriting). Export files can be encrypted with user-provided password for secure backup.","intents":["Back up stock watchlists and portfolio configuration for disaster recovery","Transfer portfolio data between machines or users","Archive historical analysis results for long-term record keeping","Share portfolio templates with other users"],"best_for":["Users wanting to backup critical portfolio data","Teams sharing portfolio templates across members","Developers building data migration tools"],"limitations":["Export does not include real-time price data; only configuration and historical analysis","Import validation is schema-based; does not validate data consistency (e.g., stock tickers may not exist)","Encrypted export uses basic password-based encryption; no key derivation function","Large exports (>100MB) may be slow due to JSON serialization","No incremental export; each export is a full snapshot"],"requires":["Go 1.16+","SQLite database","Vue.js frontend for export/import UI","Optional: encryption library for password-protected exports"],"input_types":["export format (JSON or CSV)","optional: selective export (specific groups or stocks)","optional: encryption password"],"output_types":["export file (JSON or CSV with portfolio data)","import validation report (errors and warnings)","import statistics (records imported, skipped, merged)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_2","uri":"capability://planning.reasoning.ai.agent.chat.with.multi.provider.llm.support.and.14.financial.analysis.tools","name":"ai agent chat with multi-provider llm support and 14+ financial analysis tools","description":"Implements an AI agent interface that routes user queries to configurable LLM providers (DeepSeek, OpenAI, Ollama, LMStudio, AnythingLLM, 硅基流动, 火山方舟, 阿里云百炼) with a function-calling registry of 14+ tools for stock analysis, fund monitoring, sentiment analysis, and market rankings. The agent uses chain-of-thought reasoning to decompose user queries into tool calls, executes tools against local data (SQLite) and external APIs, and synthesizes results into natural language responses. All data remains local; only the LLM provider receives query context (configurable via system prompts).","intents":["Ask natural language questions about stocks and get AI-synthesized analysis with tool-backed data","Use local LLMs (Ollama, LMStudio) for privacy-preserving stock analysis without cloud dependencies","Switch between multiple LLM providers without changing application code","Get AI recommendations based on real-time market data, news sentiment, and financial metrics"],"best_for":["Traders wanting conversational AI analysis with full data privacy","Teams using local LLMs (Ollama, LMStudio) for compliance or latency reasons","Developers building financial AI agents with pluggable LLM backends"],"limitations":["Tool calling depends on LLM provider's function-calling API support; not all providers have equal capability","Chain-of-thought reasoning adds latency (multiple LLM round-trips per query); no streaming responses","14 tools are hardcoded; adding custom tools requires code modification and recompilation","No persistent conversation memory; each query is stateless (no multi-turn context retention)","Prompt templates are static; no dynamic prompt optimization based on query type"],"requires":["Go 1.16+","At least one LLM provider configured: OpenAI API key, or local Ollama/LMStudio instance, or account with 硅基流动/火山方舟/阿里云百炼","SQLite database with populated stock/fund/news data","Vue.js frontend for chat UI (NaiveUI components)"],"input_types":["natural language query (text)","optional context (selected stock ticker, time range)"],"output_types":["natural language response with citations to tool results","structured tool call logs (for debugging)","referenced data (stock prices, sentiment scores, financial metrics used in response)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_3","uri":"capability://data.processing.analysis.stock.group.organization.and.portfolio.composition.tracking.with.gorm.data.models","name":"stock group organization and portfolio composition tracking with gorm data models","description":"Provides a hierarchical stock grouping system (user-defined portfolios, watchlists, sector groups) backed by GORM ORM with SQLite persistence. Users can organize stocks into groups, track group-level metrics (total value, weighted returns, sector allocation), and apply group-level operations (bulk alerts, group sentiment analysis). The data model supports many-to-many relationships between stocks and groups, enabling stocks to appear in multiple portfolios simultaneously. Group composition changes are persisted to SQLite with timestamps for historical tracking.","intents":["Organize stocks into logical portfolios or watchlists for easier monitoring","Track aggregate metrics (total value, returns) across a group of stocks","Apply alerts and analysis operations to entire groups rather than individual stocks","Maintain multiple overlapping stock groupings (e.g., sector groups + personal portfolios)"],"best_for":["Individual traders managing multiple portfolios or watchlists","Portfolio managers tracking sector allocations and group-level performance","Developers building portfolio management features on top of go-stock"],"limitations":["No built-in portfolio valuation or cost-basis tracking; groups are organizational only","Group-level metrics are computed on-demand from constituent stocks; no pre-aggregated snapshots","No support for weighted portfolios (equal-weight only); custom weighting requires manual calculation","Historical group composition changes are not automatically tracked; only current state is stored"],"requires":["Go 1.16+","GORM 1.23+","SQLite database","Vue.js frontend for group management UI"],"input_types":["group name (string)","list of stock tickers to add to group","group metadata (description, color, icon)"],"output_types":["group composition (list of stocks with metadata)","group-level metrics (aggregate price, total change %)","group history (creation date, modification timestamps)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_4","uri":"capability://image.visual.k.line.chart.rendering.and.technical.analysis.visualization.with.multi.timeframe.support","name":"k-line chart rendering and technical analysis visualization with multi-timeframe support","description":"Renders candlestick (K-line) charts for stocks with support for multiple timeframes (1m, 5m, 15m, 30m, 1h, 1d, 1w, 1M) using data aggregated from Sina, Tencent, and Eastmoney APIs. The frontend uses NaiveUI charting components to display OHLCV (Open, High, Low, Close, Volume) data with interactive zoom, pan, and crosshair tools. Backend aggregates raw tick data into OHLCV bars for each timeframe, caches results in FreeCache, and persists historical data to SQLite for offline viewing. Supports overlay of technical indicators (moving averages, RSI, MACD, Bollinger Bands) computed server-side.","intents":["View candlestick charts for stocks across multiple timeframes for technical analysis","Identify support/resistance levels and trend patterns visually","Overlay technical indicators (MA, RSI, MACD, Bollinger Bands) on charts","Access historical chart data offline without re-fetching from APIs"],"best_for":["Technical traders performing chart analysis","Developers building charting features into trading applications","Teams needing offline chart access for compliance or latency-sensitive environments"],"limitations":["Technical indicator computation is server-side only; no client-side indicator customization","Timeframe aggregation depends on available tick data; some timeframes may have gaps if source APIs don't provide ticks","Chart rendering is limited to NaiveUI components; no advanced charting library (TradingView, Lightweight Charts) integration","No drawing tools (trend lines, annotations); charts are read-only visualizations","Historical data retention depends on SQLite storage; no automatic data pruning or archival"],"requires":["Go 1.16+","Vue.js + NaiveUI for frontend charting","SQLite for storing OHLCV history","API access to Sina, Tencent, or Eastmoney for tick data"],"input_types":["stock ticker symbol","timeframe (1m, 5m, 15m, 30m, 1h, 1d, 1w, 1M)","date range for historical data","optional technical indicators to overlay"],"output_types":["candlestick chart (OHLCV bars with wicks)","volume bars (below price chart)","technical indicator overlays (lines, bands)","raw OHLCV data (JSON) for export"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_5","uri":"capability://data.processing.analysis.fund.and.etf.monitoring.with.net.value.tracking.and.estimated.value.calculation","name":"fund and etf monitoring with net value tracking and estimated value calculation","description":"Monitors mutual funds and ETFs through web scraping of fund data sources, tracking net asset value (NAV), estimated daily value, and fund composition. The system periodically scrapes fund provider websites to extract NAV and estimated value, stores results in SQLite with timestamps, and computes daily/weekly/monthly returns. Supports partial functionality for fund composition viewing (holdings, sector allocation) where data is available. Fund data is aggregated into watchlists and groups similar to stock monitoring, enabling portfolio-level fund analysis.","intents":["Track fund and ETF net values and estimated daily performance","Monitor fund composition and sector allocation changes","Compare fund performance across multiple funds in a portfolio","Receive alerts when fund values cross thresholds"],"best_for":["Investors holding mutual funds or ETFs in Chinese markets","Portfolio managers tracking fund allocations","Developers building fund monitoring features"],"limitations":["Fund data support is partial; not all fund types or providers are covered","Web scraping is fragile; changes to fund provider websites may break data collection","Estimated value calculation depends on scraper accuracy; may lag official NAV by hours","Fund composition data is not real-time; updates depend on provider disclosure frequency","No support for fund transaction history or cost-basis tracking"],"requires":["Go 1.16+","Web scraping libraries (likely colly or similar)","SQLite for storing fund data","Internet connectivity to fund provider websites"],"input_types":["fund ticker or code","fund provider identifier"],"output_types":["net asset value (NAV) with timestamp","estimated daily value","daily/weekly/monthly returns","fund composition (holdings, sector allocation) where available"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_6","uri":"capability://automation.workflow.price.change.alert.system.with.configurable.thresholds.and.push.notifications","name":"price change alert system with configurable thresholds and push notifications","description":"Implements a rule-based alert engine that monitors stock prices against user-defined thresholds (absolute price levels, percentage changes, volume spikes) and triggers notifications via multiple channels (in-app toast, system notifications, optional push to mobile). Alerts are defined per stock or group, stored in SQLite with enable/disable toggles, and evaluated continuously during market hours. The system supports alert templates (e.g., 'alert if price drops 5%') for quick setup, and logs all triggered alerts with timestamps for historical review. Notification delivery is asynchronous to avoid blocking price polling.","intents":["Get notified when a stock price reaches a target level or changes by a percentage","Set alerts for entire stock groups to monitor multiple positions simultaneously","Receive alerts via multiple channels (in-app, system notifications, push)","Review alert history to track which alerts triggered and when"],"best_for":["Active traders needing real-time price alerts","Portfolio managers monitoring multiple positions","Developers building alert systems for trading applications"],"limitations":["Alerts are evaluated only during market hours; no after-hours or weekend alerts","Push notifications require optional mobile app integration; not built-in","Alert evaluation is polling-based (tied to price update frequency); not true real-time","No alert deduplication; rapid price fluctuations may trigger multiple alerts in seconds","Alert history is stored locally; no cloud backup or cross-device sync"],"requires":["Go 1.16+","SQLite for storing alert definitions and history","Vue.js frontend for alert configuration UI","Optional: mobile app or push notification service for extended delivery"],"input_types":["stock ticker symbol","alert type (price level, percentage change, volume spike)","threshold value","notification channel preference"],"output_types":["alert triggered notification (in-app toast, system notification)","alert history log (JSON with timestamp, triggered value, alert definition)","alert statistics (total alerts triggered, most-triggered stocks)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_7","uri":"capability://planning.reasoning.ai.powered.stock.selection.and.recommendation.with.prompt.based.analysis.templates","name":"ai-powered stock selection and recommendation with prompt-based analysis templates","description":"Provides AI-assisted stock selection through customizable prompt templates that guide the LLM to analyze stocks based on user-defined criteria (e.g., 'find undervalued tech stocks with positive sentiment'). Templates are stored in SQLite and can be edited via the UI; when executed, they combine real-time market data (prices, sentiment, financial metrics) with the template prompt and send to the configured LLM provider. The LLM returns ranked stock recommendations with reasoning. Results are cached in SQLite with execution timestamp, enabling comparison of recommendations over time. Supports batch analysis of stock groups or entire market sectors.","intents":["Get AI recommendations for stocks matching specific investment criteria","Use predefined templates (e.g., 'value stocks', 'growth stocks') for quick analysis","Create custom analysis templates tailored to personal investment strategy","Compare AI recommendations across different templates or time periods"],"best_for":["Individual investors seeking AI-assisted stock selection","Portfolio managers using AI to augment fundamental analysis","Developers building recommendation engines for trading platforms"],"limitations":["Recommendations are only as good as the prompt template and underlying data; no validation of LLM output accuracy","LLM may hallucinate stocks or metrics not in the database; no built-in fact-checking","Batch analysis of large stock universes is slow (multiple LLM calls); no parallel processing","Templates are static text; no dynamic prompt optimization based on market conditions","No backtesting of recommendations against historical performance"],"requires":["Go 1.16+","Configured LLM provider (OpenAI, DeepSeek, Ollama, etc.)","SQLite with populated stock data (prices, sentiment, financials)","Vue.js frontend for template editing and results display"],"input_types":["prompt template (text with placeholders for stock data)","optional: stock universe (all stocks, specific group, sector)","optional: time range for historical data"],"output_types":["ranked list of recommended stocks with LLM reasoning","structured recommendation data (stock ticker, score, rationale)","execution metadata (template used, LLM provider, timestamp)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_8","uri":"capability://data.processing.analysis.market.wide.and.individual.stock.sentiment.aggregation.with.source.breakdown","name":"market-wide and individual-stock sentiment aggregation with source breakdown","description":"Aggregates sentiment scores from news analysis across all stocks and the overall market, providing both market-wide sentiment (bullish/bearish consensus) and per-stock sentiment. The system computes sentiment by news source (Telegraph, Reuters, TradingView, etc.) to show which sources are most bullish/bearish, and tracks sentiment evolution over time. Results are stored in SQLite with timestamps, enabling sentiment trend analysis and correlation with price movements. Market-wide sentiment is computed as a weighted average across all stocks, with weighting options (equal-weight, volume-weight, market-cap-weight).","intents":["Understand overall market sentiment (bullish vs bearish consensus)","Compare sentiment across different news sources to detect bias","Track how sentiment for a stock changes over days/weeks","Identify sentiment divergence (e.g., positive sentiment but negative price movement)"],"best_for":["Traders using sentiment as a contrarian indicator","Portfolio managers monitoring market psychology","Researchers analyzing correlation between sentiment and price movements"],"limitations":["Sentiment aggregation is based on frequency-weighted keyword matching; lacks semantic understanding","Market-wide sentiment weighting options are not configurable via UI; require code changes","Sentiment scores are not normalized across sources; different sources may have different score distributions","No real-time sentiment updates; batch processing introduces latency","Historical sentiment data retention depends on SQLite storage; no automatic pruning"],"requires":["Go 1.16+","News aggregation pipeline (15+ news sources)","Sentiment analysis engine (GSE-based for Chinese text)","SQLite for storing sentiment history"],"input_types":["optional: stock ticker for per-stock sentiment","optional: time range for historical sentiment","optional: news source filter"],"output_types":["market-wide sentiment score (bullish/bearish/neutral with confidence)","per-stock sentiment score","sentiment by source (breakdown of which sources are bullish/bearish)","sentiment trend (time-series data showing sentiment evolution)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-arvinlovegood--go-stock__cap_9","uri":"capability://data.processing.analysis.financial.metrics.and.fund.flow.analysis.with.money.flow.visualization","name":"financial metrics and fund flow analysis with money flow visualization","description":"Analyzes fund flows (inflow/outflow by volume) and financial metrics (P/E ratio, dividend yield, ROE, debt-to-equity, etc.) for stocks, aggregating data from multiple sources (Sina, Tencent, Eastmoney, Tushare). Money flow analysis breaks down volume into institutional vs retail flows and tracks accumulation/distribution patterns. Financial metrics are fetched from provider APIs and cached in SQLite with update timestamps. The system computes derived metrics (e.g., PEG ratio from P/E and growth rate) server-side and visualizes money flow as stacked bar charts (inflow/outflow) in the frontend. 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