longbridge-candlestick

name	longbridge-candlestick
description	K-line candlestick pattern recognition for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Identifies 15 classic patterns (hammer, hanging man, engulfing, doji, morning/evening star, three white soldiers/black crows, shooting star, etc.) from OHLCV data and generates a composite bullish/bearish/neutral signal. Triggers: "K线形态", "蜡烛图形态", "锤子线", "吞没形态", "十字星", "早晨之星", "暮色之星", "三白兵", "三黑鸦", "吊颈线", "射击之星", "K線形態", "蠟燭圖形態", "錘子線", "吞沒形態", "早晨之星", "暮色之星", "candlestick pattern", "hammer", "engulfing", "doji", "morning star", "evening star", "three white soldiers", "shooting star", "K-line pattern".
license	MIT
metadata	{"author":"longbridge","version":"1.0.0","risk_level":"read_only","requires_login":false,"default_install":true,"requires_mcp":false,"tier":"read"}

longbridge-candlestick

Identifies 15 classic K-line candlestick patterns from recent OHLCV data and produces a composite bullish / bearish / neutral signal with per-pattern explanations.

Response language: match the user's input language — Simplified Chinese / Traditional Chinese / English.

When to use

• "NVDA 最近有什么 K 线形态", "700.HK 是否出现锤子线"
• "TSLA candlestick patterns", "看看吞没形态", "有没有早晨之星"
• "600519.SH K線形態分析", "是否出現三白兵"

Workflow

1. Resolve the symbol to <CODE>.<MARKET> format.
2. Fetch 200 daily candles:

   longbridge kline <SYMBOL> --period day --count 200 --format json
   

3. Run the Python analysis below to identify patterns and compute a composite score.
4. Report detected patterns (most recent first), each with date, name, and interpretation. Summarise with a composite signal.

CLI

longbridge kline NVDA.US --period day --count 200 --format json
longbridge kline 700.HK  --period day --count 200 --format json
longbridge kline 600519.SH --period day --count 200 --format json

Run longbridge kline --help to verify current flag names and defaults.

Python analysis

import pandas as pd, json, sys

data = json.loads(sys.stdin.read())  # list of OHLCV dicts
df = pd.DataFrame(data)
df = df.rename(columns={"open": "o", "high": "h", "low": "l", "close": "c", "volume": "v"})
df[["o","h","l","c","v"]] = df[["o","h","l","c","v"]].apply(pd.to_numeric)

body  = (df["c"] - df["o"]).abs()
rng   = df["h"] - df["l"]
upper = df.apply(lambda r: r["h"] - max(r["c"], r["o"]), axis=1)
lower = df.apply(lambda r: min(r["c"], r["o"]) - r["l"], axis=1)
bull  = df["c"] > df["o"]

signals, score = [], 0

# --- single-bar patterns (check last 5 bars) ---
for i in range(max(0, len(df)-5), len(df)):
    r = df.iloc[i]; b = body.iloc[i]; u = upper.iloc[i]; lo = lower.iloc[i]; rg = rng.iloc[i]
    
     b <  * rg:
        signals.append((df[].iloc[i], , ))
    
     lo > *b  u < *b   bull.iloc[i]:
        signals.append((df[].iloc[i], , +))
     lo > *b  u < *b  bull.iloc[i]:
        signals.append((df[].iloc[i], , +))
    
     lo > *b  u < *b  i >   df[].iloc[i-] < df[].iloc[i]:
        signals.append((df[].iloc[i], , -))
    
     u > *b  lo < *b  bull.iloc[i-]  i >   :
        signals.append((df[].iloc[i], , -))
    
     u > *b  lo < *b:
        signals.append((df[].iloc[i], , +))
    
     b > *rg  bull.iloc[i]:
        signals.append((df[].iloc[i], , +))
    
     b > *rg   bull.iloc[i]:
        signals.append((df[].iloc[i], , -))


 i  ((, (df)-), (df)):
    p, c_ = df.iloc[i-], df.iloc[i]
    pb, cb = body.iloc[i-], body.iloc[i]
    
      bull.iloc[i-]  bull.iloc[i]  c_[] < p[]  c_[] > p[]:
        signals.append((df[].iloc[i], , +)); score += 
    
     bull.iloc[i-]   bull.iloc[i]  c_[] > p[]  c_[] < p[]:
        signals.append((df[].iloc[i], , -)); score -= 
    
      bull.iloc[i-]  bull.iloc[i]  c_[] < p[]  c_[] > (p[]+p[])/:
        signals.append((df[].iloc[i], , +)); score += 
    
     bull.iloc[i-]   bull.iloc[i]  c_[] > p[]  c_[] < (p[]+p[])/:
        signals.append((df[].iloc[i], , -)); score -= 


 i  ((, (df)-), (df)):
    a, b_, c_ = df.iloc[i-], df.iloc[i-], df.iloc[i]
    sb = body.iloc[i-]
    
      bull.iloc[i-]  sb < *(body.iloc[i-])  bull.iloc[i]  c_[] > (a[]+a[])/:
        signals.append((df[].iloc[i], , +)); score += 
    
     bull.iloc[i-]  sb < *(body.iloc[i-])   bull.iloc[i]  c_[] < (a[]+a[])/:
        signals.append((df[].iloc[i], , -)); score -= 
    
     bull.iloc[i-]  bull.iloc[i-]  bull.iloc[i]  c_[]>b_[]>a[]:
        signals.append((df[].iloc[i], , +)); score += 
    
      bull.iloc[i-]   bull.iloc[i-]   bull.iloc[i]  c_[]<b_[]<a[]:
        signals.append((df[].iloc[i], , -)); score -= 


 _, _, s  signals:
    score += s

composite =   score >=   (  score <= -  )
()
 ts, name, s  signals:
    ()

Output

Report format (3 languages):

字段 / 欄位 / Field	简体 / 繁體 / English
检测到的形态	检测到的形态 / 檢測到的形態 / Detected patterns
综合信号	看多 / 看空 / 中性
解释	解释 / 解釋 / Explanation

Present at most 5 most-recent patterns. Conclude with the composite signal and a one-sentence interpretation.

Error handling

Situation	简体回复 / 繁體回覆 / English reply
command not found: longbridge	请安装 longbridge-terminal / 請安裝 longbridge-terminal / Install longbridge-terminal first
stderr not logged in / unauthorized	请运行 longbridge auth login / 請執行 longbridge auth login / Run longbridge auth login
Other stderr	直接展示错误信息 / 直接顯示錯誤訊息 / Surface error verbatim

MCP fallback

When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime.

Related skills

• longbridge-kline — raw OHLCV data and charting
• longbridge-technical — indicator-based signals (MACD, RSI, KDJ, etc.)
• longbridge-ichimoku — Ichimoku Cloud system
• longbridge-quote — real-time price and reference data