Agent Skills › staskh/trading_skills › scanner-bullish

scanner-bullish

GitHub

扫描股票并基于SMA、RSI、MACD等指标按综合得分排名，识别看涨趋势。适用于用户请求扫描看涨股、查找趋势股或按动量排序代码时。

.claude/skills/scanner-bullish/SKILL.md staskh/trading_skills

触发场景

扫描看涨股票查找趋势股按动量排序代码

安装

npx skills add staskh/trading_skills --skill scanner-bullish -g -y

更多选项

非标准路径

npx skills add https://github.com/staskh/trading_skills/tree/main/.claude/skills/scanner-bullish -g -y

不安装直接使用

npx skills use staskh/trading_skills@scanner-bullish

指定 Agent (Claude Code)

npx skills add staskh/trading_skills --skill scanner-bullish -a claude-code -g -y

安装 repo 全部 skill

npx skills add staskh/trading_skills --all -g -y

预览 repo 内 skill

npx skills add staskh/trading_skills --list

SKILL.md

Frontmatter

{
    "name": "scanner-bullish",
    "description": "Scan stocks for bullish trends using technical indicators (SMA, RSI, MACD, ADX). Use when user asks to scan for bullish stocks, find trending stocks, or rank symbols by momentum.",
    "dependencies": [
        "trading-skills"
    ]
}

Bullish Scanner

Scans symbols for bullish trends and ranks them by composite score.

Instructions

Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.

uv run python scripts/scan.py SYMBOLS [--top N] [--period PERIOD]

Arguments

SYMBOLS - Comma-separated ticker symbols (e.g., AAPL,MSFT,GOOGL,NVDA)
--top - Number of top results to return (default: 30)
--period - Historical period for analysis: 1mo, 3mo, 6mo (default: 3mo)

Scoring System (max ~9.5 points)

Indicator	Condition	Points
SMA20	Price > SMA20	+1.0
SMA50	Price > SMA50	+1.0
RSI	50-70 (bullish)	+1.0
	30-50 (neutral)	+0.5
	<30 (oversold)	+0.25
MACD	MACD > Signal	+1.0
	Histogram rising	+0.5
EMA9/21	EMA9 > EMA21 (golden cross)	+0.5
	EMA9 < EMA21 (death cross)	-0.25
Dual crossover	Both up, both ≤10 days	+1.0
	Both up, any age	+0.5
	Both down, both ≤10 days	-1.0
	Both down, any age	-0.5
	Directions conflict	-0.5
ADX	>25 with +DI > -DI	+1.5
	+DI > -DI only	+0.5
Momentum	period return / 20	-1 to +2

Output

Returns JSON with:

scan_date - Timestamp of scan
symbols_scanned - Total symbols analyzed
results - Array sorted by score (highest first):
- symbol, score, price
- next_earnings, earnings_timing (BMO/AMC)
- period_return_pct, pct_from_sma20, pct_from_sma50
- rsi, macd, macd_signal, macd_hist, adx, dmp, dmn
- ema9, ema21 — current EMA9 and EMA21 values
- ema_crossover - Most recent EMA9/EMA21 crossover (or null if none found):
  - direction - "up" (EMA9 crossed above EMA21 = bullish) or "down" (crossed below = bearish)
  - days_ago - Trading days since the crossover bar (0 = happened in the most recent bar)
- macd_crossover - Most recent MACD crossover (or null if none found):
  - direction - "up" (MACD crossed above signal = bullish) or "down" (crossed below = bearish)
  - days_ago - Trading days since the crossover bar (0 = happened in the most recent bar)
- signals - List of triggered conditions

EMA Crossover Interpretation

EMA9 > EMA21 with small days_ago (0-5): fresh golden cross — short-term momentum confirmed
EMA9 just crossed below EMA21: death cross — short-term momentum turning negative
EMA9 crossover lagging MACD crossover by days: normal — MACD leads, EMA confirms
null: EMA9/21 relationship unchanged throughout the period

MACD Crossover Interpretation

direction: "up" with small days_ago (0-5): fresh bullish crossover — early entry signal
direction: "up" from a deeply negative signal: recovery from correction
direction: "down": momentum has turned bearish regardless of score
null: no sign change found in the period — trend has been consistently one-directional

Examples

# Scan a few symbols
uv run python scripts/scan.py AAPL,MSFT,GOOGL,NVDA,TSLA

# Get top 10 from larger list
uv run python scripts/scan.py AAPL,MSFT,GOOGL,NVDA,TSLA,AMD,AMZN,META --top 10

# Use 6-month lookback
uv run python scripts/scan.py AAPL,MSFT,GOOGL --period 6mo

Interpretation

Score > 6: Strong bullish trend
Score 4-6: Moderate bullish
Score 2-4: Neutral/weak
Score < 2: Bearish or no trend

Dependencies

pandas
pandas-ta
yfinance

Timezone

All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.