quant-scanner

name	quant-scanner
description	Quantitative stock scanner with model-driven adaptive factor scoring. Scans ~100 US large/mid-cap stocks via Finnhub API, detects market regime (volatility, momentum, breadth), dynamically adjusts factor weights, and produces ranked selection/anomaly lists with HTML reports. Use for US stock screening, quantitative stock picking, factor analysis, market regime detection, stock scoring, building a model portfolio, or finding momentum/value/quality plays.
version	1.0.0
homepage	https://finnhub.io
commands	["/quant_scan - Run full US stock quantitative scan with HTML report","/quant_top - Quick top-10 stock picks","/quant_regime - Show current market regime analysis","/quant_analyze - Deep factor analysis on specific tickers"]
metadata	{"clawdbot":{"emoji":"📊","requires":{"bins":"[Truncated]","env":"[Truncated]"},"install":[]}}

Quant Scanner v1.0

Model-driven quantitative stock scanner for US equities. Scans ~100 large/mid-cap stocks, detects the current market regime, adaptively adjusts 5-factor scoring weights, and outputs ranked picks with an HTML report.

Setup

Required: A free Finnhub API key.

1. Go to https://finnhub.io and sign up (free)
2. Copy your API key from the dashboard
3. Set it as an environment variable:

export FINNHUB_API_KEY="your_key_here"

Or pass it directly via --api-key:

python3 {baseDir}/scripts/quant_scan.py --api-key YOUR_KEY

Quick Commands

Full Scan (HTML + JSON report)

python3 {baseDir}/scripts/quant_scan.py

This will:

1. Fetch quote, metrics, profile, and analyst recommendations for ~100 stocks
2. Detect market regime (volatility, momentum, breadth)
3. Score each stock on 5 adaptive factors
4. Output a ranked list + HTML report

Quick Top 10

python3 {baseDir}/scripts/quant_scan.py --top 10

Specific Tickers Only

python3 {baseDir}/scripts/quant_scan.py --tickers AAPL,NVDA,TSLA,GOOGL,META

Custom Universe File

python3 {baseDir}/scripts/quant_scan.py --universe my_watchlist.txt

Where my_watchlist.txt has one ticker per line.

JSON Output Only (for piping)

python3 {baseDir}/scripts/quant_scan.py --format json

How It Works

5-Factor Adaptive Scoring

Each stock is scored on 5 factors. The weights adapt to the detected market regime:

Factor	Base Weight	Bullish Adj	Bearish Adj	High-Vol Adj
Momentum (5D/13W/MTD return + relative strength vs S&P 500)	30%	x1.3 boost	x0.7 dampen	—
Value (P/E, forward P/E, PEG, dividend yield)	20%	x0.8 reduce	x1.3 boost	—
Quality (ROE, net margin, revenue growth, EPS growth)	25%	—	—	x1.2 boost
Analyst Sentiment (buy/hold/sell consensus ratio)	10%	—	—	—
Risk-Adjusted (beta, volatility, leverage, liquidity)	15%	—	—	x1.4 penalty

Market Regime Detection

The scanner analyzes the full universe to determine:

• Volatility Level — HIGH / MEDIUM / LOW (3-month average annualized std dev)
• Momentum Regime — BULLISH / NEUTRAL / BEARISH (13-week return distribution)
• Market Breadth — BROAD RALLY / MIXED / BROAD DECLINE (5-day positive percentage)

Data Sources (all free-tier Finnhub)

Endpoint	Data
quote	Current price, daily change, high/low
stock/metric	5D/13W/26W/52W/YTD returns, P/E, ROE, margins, beta, volatility, growth rates, relative to S&P 500
stock/profile2	Company name, sector, market cap
stock/recommendation	Analyst buy/hold/sell consensus

Default Universe (~100 stocks)

Mega Tech: AAPL, MSFT, GOOGL, AMZN, NVDA, META, TSLA Semiconductors: AVGO, AMD, INTC, QCOM, MU, MRVL, AMAT, LRCX, KLAC, ON, ARM Software/Cloud: CRM, ORCL, ADBE, NOW, SNOW, PLTR, PANW, CRWD, DDOG, NET, ZS, SHOP Financials: JPM, V, MA, BAC, GS, MS, BLK, SCHW, AXP, C Healthcare: UNH, LLY, JNJ, ABBV, PFE, MRK, TMO, ABT, BMY, GILD, AMGN, ISRG, VRTX Consumer: WMT, COST, HD, MCD, NKE, SBUX, TGT, LOW, PG, KO, PEP Energy: XOM, CVX, COP, SLB, EOG, OXY Industrials: CAT, GE, HON, UNP, BA, RTX, LMT, DE Others: NFLX, DIS, ABNB, UBER, COIN, SOFI, SMCI, MSTR China ADR: BABA, PDD, JD, BIDU, NIO, LI, XPEV

Output

HTML Report (default)

A styled, responsive HTML file with:

• KPI dashboard (top pick, market regime, key metrics)
• Market regime detection panel
• Model portfolio (top 5 with factor decomposition)
• Full selection ranking (top 30)
• Anomaly ranking (top 20)
• Sector distribution
• Regime-adaptive weight explanation table

JSON Report

Machine-readable output with all scored stocks, regime detection, and portfolio.

Limitations

• Finnhub free tier: 60 API calls/minute — scanning 100 stocks takes ~2 minutes
• No intraday candle data on free tier — uses metric endpoint for returns
• Returns are point-in-time snapshots, not a real backtest
• Not investment advice — research and monitoring only

Examples

User: scan the market and find me the best momentum plays
→ /quant_scan

User: what's the market regime right now?
→ /quant_regime

User: analyze NVDA AVGO AMD in detail
→ /quant_analyze NVDA AVGO AMD

User: give me a quick top 10 stock ranking
→ /quant_top

Use with Lynn (Zero Config)

Part of Lynn — a personal AI agent with memory and soul. Lynn has this built-in by default.

With Lynn desktop agent, quant-scanner works seamlessly:

• Natural language — just say "scan US stocks" or "what should I buy", Lynn auto-triggers the skill
• Memory integration — Lynn remembers your preferred tickers, past scan results, and investment style
• Multi-model routing — Lynn uses the best model for analysis (reasoning model for factor interpretation, fast model for data fetching)
• Scheduled scans — set up daily pre-market scans that run automatically
• Combined with A-share scanner — Lynn also has a Tushare-powered A-share quantitative scanner for Chinese stocks

# Install Lynn
git clone https://github.com/LynnMerkyor/Lynn.git
cd Lynn && npm install

# Then just chat:
# "scan the US market for me"
# "compare NVDA and AMD"
# "set up a daily pre-market scan"