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.

从最新的OHLCV数据中识别15种经典K线蜡烛图形态，并生成包含各形态解释的综合看涨/看跌/中性信号。

回复语言：匹配用户输入语言——简体中文 / 繁体中文 / 英文。

When to use

使用场景

"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"

"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

工作流程

Resolve the symbol to
```
<CODE>.<MARKET>
```
format.

Fetch 200 daily candles:

bash

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

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

将代码解析为
```
<CODE>.<MARKET>
```
格式。

获取200条日线数据：

bash

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

运行下方的Python分析代码以识别形态并计算综合得分。
报告检测到的形态（按最新时间排序），每条包含日期、名称和解读。最后总结综合信号。

CLI

命令行界面（CLI）

bash

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.

bash

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

运行

longbridge kline --help

查看当前参数名称及默认值。

Python analysis

Python分析代码

python

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

python

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] # Doji if b < 0.05 * rg: signals.append((df["time"].iloc[i], "十字星/Doji", 0)) # Hammer (bullish reversal after downtrend) elif lo > 2b and u < 0.1b and not bull.iloc[i]: signals.append((df["time"].iloc[i], "锤子线/Hammer", +1)) elif lo > 2b and u < 0.1b and bull.iloc[i]: signals.append((df["time"].iloc[i], "锤子线/Hammer(bullish)", +1)) # Hanging Man (bearish) elif lo > 2b and u < 0.1b and i > 0 and df["c"].iloc[i-1] < df["c"].iloc[i]: signals.append((df["time"].iloc[i], "吊颈线/Hanging Man", -1)) # Shooting Star (bearish) elif u > 2b and lo < 0.1b and bull.iloc[i-1] if i > 0 else False: signals.append((df["time"].iloc[i], "射击之星/Shooting Star", -1)) # Inverted Hammer (bullish) elif u > 2b and lo < 0.1b: signals.append((df["time"].iloc[i], "倒锤线/Inverted Hammer", +1)) # Marubozu bullish elif b > 0.9rg and bull.iloc[i]: signals.append((df["time"].iloc[i], "光头光脚阳线/Bullish Marubozu", +1)) # Marubozu bearish elif b > 0.9rg and not bull.iloc[i]: signals.append((df["time"].iloc[i], "光头光脚阴线/Bearish Marubozu", -1))

--- two-bar patterns ---

for i in range(max(1, len(df)-5), len(df)): p, c_ = df.iloc[i-1], df.iloc[i] pb, cb = body.iloc[i-1], body.iloc[i] # Bullish engulfing if not bull.iloc[i-1] and bull.iloc[i] and c_["o"] < p["c"] and c_["c"] > p["o"]: signals.append((df["time"].iloc[i], "看涨吞没/Bullish Engulfing", +2)); score += 2 # Bearish engulfing elif bull.iloc[i-1] and not bull.iloc[i] and c_["o"] > p["c"] and c_["c"] < p["o"]: signals.append((df["time"].iloc[i], "看跌吞没/Bearish Engulfing", -2)); score -= 2 # Piercing line elif not bull.iloc[i-1] and bull.iloc[i] and c_["o"] < p["l"] and c_["c"] > (p["o"]+p["c"])/2: signals.append((df["time"].iloc[i], "刺透线/Piercing Line", +1)); score += 1 # Dark cloud cover elif bull.iloc[i-1] and not bull.iloc[i] and c_["o"] > p["h"] and c_["c"] < (p["o"]+p["c"])/2: signals.append((df["time"].iloc[i], "乌云盖顶/Dark Cloud Cover", -1)); score -= 1

--- three-bar patterns ---

for i in range(max(2, len(df)-5), len(df)): a, b_, c_ = df.iloc[i-2], df.iloc[i-1], df.iloc[i] sb = body.iloc[i-1] # Morning star if not bull.iloc[i-2] and sb < 0.3*(body.iloc[i-2]) and bull.iloc[i] and c_["c"] > (a["o"]+a["c"])/2: signals.append((df["time"].iloc[i], "早晨之星/Morning Star", +2)); score += 2 # Evening star elif bull.iloc[i-2] and sb < 0.3*(body.iloc[i-2]) and not bull.iloc[i] and c_["c"] < (a["o"]+a["c"])/2: signals.append((df["time"].iloc[i], "暮色之星/Evening Star", -2)); score -= 2 # Three white soldiers elif bull.iloc[i-2] and bull.iloc[i-1] and bull.iloc[i] and c_["c"]>b_["c"]>a["c"]: signals.append((df["time"].iloc[i], "三白兵/Three White Soldiers", +2)); score += 2 # Three black crows elif not bull.iloc[i-2] and not bull.iloc[i-1] and not bull.iloc[i] and c_["c"]<b_["c"]<a["c"]: signals.append((df["time"].iloc[i], "三黑鸦/Three Black Crows", -2)); score -= 2

add single-bar scores

for _, _, s in signals: score += s

composite = "看多/Bullish" if score >= 2 else ("看空/Bearish" if score <= -2 else "中性/Neutral") print(f"Composite score: {score} → {composite}") for ts, name, s in signals: print(f" {ts} {name} ({'+'if s>=0 else ''}{s})")

undefined

for _, _, s in signals: score += s

composite = "看多/Bullish" if score >= 2 else ("看空/Bearish" if score <= -2 else "中性/Neutral") print(f"Composite score: {score} → {composite}") for ts, name, s in signals: print(f" {ts} {name} ({'+'if s>=0 else ''}{s})")

undefined

Output

输出格式

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.

报告格式（三种语言）：

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

最多展示5种最新形态。最后给出综合信号及一句话解读。

Error handling

错误处理

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

场景	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

MCP fallback方案

If

longbridge

binary is unavailable, call

mcp__longbridge__kline

with equivalent parameters. Surface the same analysis.

如果

longbridge

二进制文件不可用，请使用等效参数调用

mcp__longbridge__kline

，执行相同分析。

longbridge-candlestick

Original

Translation

longbridge-candlestick

longbridge-candlestick

When to use

使用场景

Workflow

工作流程

CLI

命令行界面（CLI）

Python analysis

Python分析代码

--- single-bar patterns (check last 5 bars) ---

--- single-bar patterns (check last 5 bars) ---

--- two-bar patterns ---

--- two-bar patterns ---

--- three-bar patterns ---

--- three-bar patterns ---

add single-bar scores

add single-bar scores

Output

输出格式

Error handling

错误处理

MCP fallback

MCP fallback方案

Related skills

相关技能