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Found 39 Skills
Server-side quantitative indicator runner via Longbridge Securities — execute Pine Script v6 syntax subset against historical K-line data on Longbridge servers without a local Python environment. Supports built-in indicators (MACD, RSI, Bollinger Bands, EMA, SMA, etc.) and custom calculation logic; results returned as JSON. Triggers: "量化指标", "Pine Script", "指标计算", "MACD计算", "RSI计算", "服务端指标", "指标脚本", "量化脚本", "技术指标运行", "量化指標", "指標計算", "MACD計算", "RSI計算", "服務端指標", "指標腳本", "quant indicator", "Pine Script", "indicator calculation", "run indicator", "server-side quant", "MACD script", "RSI calculation", "technical indicator runner", "quant run".
Create agents for financial analysis, investment research, and portfolio management. Covers financial data processing, risk analysis, and recommendation generation. Use when building investment analysis tools, robo-advisors, portfolio trackers, or financial intelligence systems.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Smart CSV importer with format auto-detection. Handles major banks in Canada and US, plus payment platforms (Stripe, PayPal, Wise, WeChat Pay, Alipay) and browser-assisted exports gathered through `/cfo-statement-export`. Use when importing bank or credit card CSV exports. CLEAR step: C (Capture)
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
Evaluate the probability and path of copper prices breaking through key levels or entering a 'back-and-fill' pullback to support levels using cross-asset signals (global stock market resilience + Chinese interest rate environment).
Analyze stock liquidity using bid-ask spreads, volume profiles, order book depth, market impact estimates, and turnover ratios via Yahoo Finance data. Use this skill whenever the user asks about liquidity, trading costs, bid-ask spread, market depth, volume analysis, slippage, market impact, turnover ratio, or how easy/hard it is to trade a stock without moving the price. Triggers: "how liquid is AAPL", "bid-ask spread", "volume analysis", "order book depth", "market impact of a large order", "turnover ratio", "slippage estimate", "can I trade 100k shares without moving the price", "liquidity comparison", "spread analysis", "ADTV", "Amihud illiquidity", "dollar volume", "execution cost estimate", "liquidity score", penny stocks, small caps, or thinly traded securities.
Reconcile general ledger to subledger for a trade date or period — match at the position or transaction level, surface breaks, and classify each break by likely cause. Use for daily or month-end recon runs across asset classes.
Earnings estimate revision analysis for listed companies via Longbridge — tracks analyst consensus revision direction (upgrade / downgrade), earnings surprise (SUE = standardised unexpected earnings), PEAD post-earnings drift signals (consecutive beats + upward revisions = positive momentum), and management guidance revision impact. Builds on raw data from longbridge-consensus. Triggers: "预期修正", "盈利修正", "分析师上调", "分析师下调", "超预期", "低于预期", "PEAD", "财报后漂移", "业绩意外", "管理层指引", "預期修正", "盈利修正", "分析師上調", "分析師下調", "超預期", "低於預期", "財報後漂移", "業績意外", "管理層指引", "earnings revision", "estimate revision", "analyst upgrade", "analyst downgrade", "beat miss surprise", "SUE", "PEAD post-earnings drift", "guidance revision", "estimate cut raise".
C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib
Identify key themes and concerns raised by analysts during earnings calls, including specific analyst attribution and topic categorization.
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.