Vibe-Trading

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.

Paid capability marketplace for global multi-asset data; use it when free Vibe-Trading sources lack coverage, depth, or provider quality, and keep free sources as the default for routine OHLCV.

金融监管知识库：A股涨跌停/ST退市新规/融券、港股T+0/做空机制、美股PDT/熔断、加密监管政策、跨境税务基础

Professional financial research report generation — standard structure (summary / views / main body / risks / recommendation), Markdown formatting standards, rating system, and terminology guide.

A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

Risk measurement and stress testing — VaR/CVaR/max drawdown calculation, Monte Carlo simulation, extreme-value tail-risk analysis, and historical scenario stress testing.

Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV data.

U.S. SEC EDGAR fetch interface — resolve a ticker to its CIK, list recent filings (10-K / 10-Q / 8-K and friends) with primary-document URLs, and pull XBRL companyfacts financial series. Free, no API key; rate-limited by IP so every request is throttled and carries a contact User-Agent. United States only.

行业轮动分析——申万行业景气度评分、行业动量排名、产业链传导、估值/盈利/资金流多维比较框架

市场情绪分析——恐贪指数/Put-Call Ratio/融资融券/北向资金信号解读、社交媒体舆情量化框架

Shadow Account — 从用户交割单提炼盈利模式（3-5 条人话规则）→ 跨 A股/港股/美股/crypto 多市场回测 → 差值归因 → 8-section PDF 报告。叙事：你的影子，没有情绪噪音。

Smart Money Concepts (ICT) signal engine. Uses the smartmoneyconcepts library to implement institutional-trading-school analysis of BOS, ChoCH, FVG, and order blocks (OB).

Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.

Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.

Strategy Development Manager: convert academic papers and research reports into validated factors and strategies with automated backtesting, persistent storage, and decay monitoring.

Strategy Discovery: evidence-gated facade over Alpha Zoo + the SDM strategy store — answers what strategies exist and what state they are in, with per-regime evidence instead of scenario tags; reports evidence freshness on every returned row and rebuilds the disposable evidence cache from local backtest runs.

Create, modify, and optimize quantitative trading strategies, then backtest and evaluate them.

Core technical indicator collection (trend EMA/ADX + mean-reversion BB/RSI + volume-price OBV/volume ratio), generates a composite signal via three-dimensional voting. Pure pandas implementation for any OHLCV data.

Buy-side discipline system. For each holding, maintain a written investment thesis — core thesis in 5 sentences, falsifiable assumptions, red lines, valuation anchors — and re-check it each quarter against new earnings/events. Scores thesis health 1-10 from assumption breakage and red-line triggers, and recommends hold / add / trim / exit. Use it right after buying a stock (to write the thesis) and every earnings season (to check it). Bridges the research→buy→monitor gap that most investors skip.