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longbridge-ichimoku

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Original

English
🇨🇳

Translation

Chinese

longbridge-ichimoku

longbridge-ichimoku

Computes the full Ichimoku Cloud five-line system from 200 days of OHLCV data and produces bullish / bearish / neutral signals with per-component interpretation.
Response language: match the user's input language — Simplified Chinese / Traditional Chinese / English.
基于200天的OHLCV数据计算完整的Ichimoku Cloud（一目均衡表）五线系统，并生成包含各组件解读的看多/看空/中性信号。
响应语言：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 一目云位置", "700.HK 是否在云上方", "转折线有没有上穿基准线"
  • "TSLA ichimoku signal", "is AAPL above the cloud", "cloud breakout"
  • "600519.SH 雲圖分析", "先行帶是否扩张", "遲行線確認"
  • "NVDA 一目云位置", "700.HK 是否在云上方", "转折线有没有上穿基准线"
  • "TSLA ichimoku signal", "is AAPL above the cloud", "cloud breakout"
  • "600519.SH 雲圖分析", "先行帶是否扩张", "遲行線確認"

Workflow

工作流程

  1. Resolve the symbol to 
    <CODE>.<MARKET>
    format.
  2. Fetch 200 daily candles (need ≥ 52 bars for Senkou Span B):
    bash
    longbridge kline <SYMBOL> --period day --count 200 --format json
  3. Run the Python analysis below to compute all five lines and derive signals.
  4. Report each component's value and signal, then summarise with a composite conclusion.
  1. 将股票代码解析为 
    <CODE>.<MARKET>
    格式。
  2. 获取200日K线数据（计算Senkou Span B需要至少52根K线）：
    bash
    longbridge kline <SYMBOL> --period day --count 200 --format json
  3. 运行下方的Python分析代码，计算所有五条线并生成信号。
  4. 报告各组件的数值和信号，然后给出综合结论。

CLI

命令行工具（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
查看当前参数名称和默认值。

Python analysis

Python分析代码

python
import pandas as pd, json, sys

data = json.loads(sys.stdin.read())
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)
df = df.reset_index(drop=True)

def midpoint(h, l, n):
    return (h.rolling(n).max() + l.rolling(n).min()) / 2
python
import pandas as pd, json, sys

data = json.loads(sys.stdin.read())
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)
df = df.reset_index(drop=True)

def midpoint(h, l, n):
    return (h.rolling(n).max() + l.rolling(n).min()) / 2

--- Five lines ---

--- 五条线计算 ---

tenkan = midpoint(df["h"], df["l"], 9) # 转折线 / 轉折線 / Tenkan-sen kijun = midpoint(df["h"], df["l"], 26) # 基准线 / 基準線 / Kijun-sen span_a = ((tenkan + kijun) / 2).shift(26) # 先行带A (shifted forward 26) span_b = midpoint(df["h"], df["l"], 52).shift(26) # 先行带B (shifted forward 26) chikou = df["c"].shift(-26) # 迟行线 (shifted back 26)
i = len(df) - 1 # latest bar index c_now = df["c"].iloc[i] t_now = tenkan.iloc[i] k_now = kijun.iloc[i] sa_now = span_a.iloc[i] sb_now = span_b.iloc[i]
tenkan = midpoint(df["h"], df["l"], 9) # 转折线 / 轉折線 / Tenkan-sen kijun = midpoint(df["h"], df["l"], 26) # 基准线 / 基準線 / Kijun-sen span_a = ((tenkan + kijun) / 2).shift(26) # 先行带A (前移26期) span_b = midpoint(df["h"], df["l"], 52).shift(26) # 先行带B (前移26期) chikou = df["c"].shift(-26) # 迟行线 (后移26期)
i = len(df) - 1 # 最新K线索引 c_now = df["c"].iloc[i] t_now = tenkan.iloc[i] k_now = kijun.iloc[i] sa_now = span_a.iloc[i] sb_now = span_b.iloc[i]

Chikou vs price 26 bars ago

迟行线与26期前价格对比

chikou_ref = df["c"].iloc[i - 26] if i >= 26 else None
cloud_top = max(sa_now, sb_now) if pd.notna(sa_now) and pd.notna(sb_now) else None cloud_bottom = min(sa_now, sb_now) if pd.notna(sa_now) and pd.notna(sb_now) else None
signals = []
chikou_ref = df["c"].iloc[i - 26] if i >= 26 else None
cloud_top = max(sa_now, sb_now) if pd.notna(sa_now) and pd.notna(sb_now) else None cloud_bottom = min(sa_now, sb_now) if pd.notna(sa_now) and pd.notna(sb_now) else None
signals = []

1. Price vs Cloud

1. 价格与云带位置关系

if cloud_top and c_now > cloud_top: signals.append(("价格在云上 / 價格在雲上 / Price above cloud", +2)) elif cloud_bottom and c_now < cloud_bottom: signals.append(("价格在云下 / 價格在雲下 / Price below cloud", -2)) else: signals.append(("价格在云内 / 價格在雲內 / Price inside cloud", 0))
if cloud_top and c_now > cloud_top: signals.append(("价格在云上 / 價格在雲上 / Price above cloud", +2)) elif cloud_bottom and c_now < cloud_bottom: signals.append(("价格在云下 / 價格在雲下 / Price below cloud", -2)) else: signals.append(("价格在云内 / 價格在雲內 / Price inside cloud", 0))

2. Tenkan / Kijun cross

2. 转折线与基准线交叉

if pd.notna(t_now) and pd.notna(k_now): t_prev = tenkan.iloc[i-1]; k_prev = kijun.iloc[i-1] if t_now > k_now and t_prev <= k_prev: signals.append(("转折线上穿基准线(买入) / 轉折線上穿基準線 / Tenkan crosses above Kijun (buy)", +2)) elif t_now < k_now and t_prev >= k_prev: signals.append(("转折线下穿基准线(卖出) / 轉折線下穿基準線 / Tenkan crosses below Kijun (sell)", -2)) elif t_now > k_now: signals.append(("转折线 > 基准线(多头排列) / 轉折線>基準線 / Tenkan > Kijun (bullish)", +1)) else: signals.append(("转折线 < 基准线(空头排列) / 轉折線<基準線 / Tenkan < Kijun (bearish)", -1))
if pd.notna(t_now) and pd.notna(k_now): t_prev = tenkan.iloc[i-1]; k_prev = kijun.iloc[i-1] if t_now > k_now and t_prev <= k_prev: signals.append(("转折线上穿基准线(买入) / 轉折線上穿基準線 / Tenkan crosses above Kijun (buy)", +2)) elif t_now < k_now and t_prev >= k_prev: signals.append(("转折线下穿基准线(卖出) / 轉折線下穿基準線 / Tenkan crosses below Kijun (sell)", -2)) elif t_now > k_now: signals.append(("转折线 > 基准线(多头排列) / 轉折線>基準線 / Tenkan > Kijun (bullish)", +1)) else: signals.append(("转折线 < 基准线(空头排列) / 轉折線<基準線 / Tenkan < Kijun (bearish)", -1))

3. Cloud color (span_a vs span_b)

3. 云带颜色 (先行带A vs 先行带B)

if pd.notna(sa_now) and pd.notna(sb_now): if sa_now > sb_now: signals.append(("云为阳色(看多) / 雲為陽色 / Green cloud (bullish)", +1)) else: signals.append(("云为阴色(看空) / 雲為陰色 / Red cloud (bearish)", -1))
if pd.notna(sa_now) and pd.notna(sb_now): if sa_now > sb_now: signals.append(("云为阳色(看多) / 雲為陽色 / Green cloud (bullish)", +1)) else: signals.append(("云为阴色(看空) / 雲為陰色 / Red cloud (bearish)", -1))

4. Chikou confirmation

4. 迟行线确认

if chikou_ref is not None and pd.notna(chikou_ref): chikou_now = df["c"].iloc[i] # chikou = current close plotted 26 back if chikou_now > chikou_ref: signals.append(("迟行线确认多头 / 遲行線確認多頭 / Chikou confirms bullish", +1)) else: signals.append(("迟行线确认空头 / 遲行線確認空頭 / Chikou confirms bearish", -1))
if chikou_ref is not None and pd.notna(chikou_ref): chikou_now = df["c"].iloc[i] # 迟行线 = 当前收盘价向后偏移26期 if chikou_now > chikou_ref: signals.append(("迟行线确认多头 / 遲行線確認多頭 / Chikou confirms bullish", +1)) else: signals.append(("迟行线确认空头 / 遲行線確認空頭 / Chikou confirms bearish", -1))

5. Price vs Tenkan / Kijun

5. 价格与转折线/基准线关系

if pd.notna(t_now) and c_now > t_now: signals.append(("价格 > 转折线(短期支撑) / Price > Tenkan / short-term support", +1)) if pd.notna(k_now) and c_now > k_now: signals.append(("价格 > 基准线(中期支撑) / Price > Kijun / medium-term support", +1))
total = sum(s for _, s in signals) composite = "强烈看多/Strong Bullish" if total >= 5 else ( "看多/Bullish" if total >= 2 else ( "看空/Bearish" if total <= -2 else ( "强烈看空/Strong Bearish" if total <= -5 else "中性/Neutral")))
print(f"Ichimoku composite: {total:+d} → {composite}") print(f" Tenkan-sen (转折线): {t_now:.2f}") print(f" Kijun-sen (基准线): {k_now:.2f}") print(f" Senkou A (先行带A): {sa_now:.2f}" if pd.notna(sa_now) else " Senkou A: N/A") print(f" Senkou B (先行带B): {sb_now:.2f}" if pd.notna(sb_now) else " Senkou B: N/A") print(f" Cloud top: {cloud_top:.2f} bottom: {cloud_bottom:.2f}" if cloud_top else " Cloud: N/A") print(f" Current price: {c_now:.2f}") for label, s in signals: print(f" [{'+' if s>0 else ('-' if s<0 else ' ')}{abs(s)}] {label}")
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if pd.notna(t_now) and c_now > t_now: signals.append(("价格 > 转折线(短期支撑) / Price > Tenkan / short-term support", +1)) if pd.notna(k_now) and c_now > k_now: signals.append(("价格 > 基准线(中期支撑) / Price > Kijun / medium-term support", +1))
total = sum(s for _, s in signals) composite = "强烈看多/Strong Bullish" if total >= 5 else ( "看多/Bullish" if total >= 2 else ( "看空/Bearish" if total <= -2 else ( "强烈看空/Strong Bearish" if total <= -5 else "中性/Neutral")))
print(f"Ichimoku composite: {total:+d} → {composite}") print(f" Tenkan-sen (转折线): {t_now:.2f}") print(f" Kijun-sen (基准线): {k_now:.2f}") print(f" Senkou A (先行带A): {sa_now:.2f}" if pd.notna(sa_now) else " Senkou A: N/A") print(f" Senkou B (先行带B): {sb_now:.2f}" if pd.notna(sb_now) else " Senkou B: N/A") print(f" Cloud top: {cloud_top:.2f} bottom: {cloud_bottom:.2f}" if cloud_top else " Cloud: N/A") print(f" Current price: {c_now:.2f}") for label, s in signals: print(f" [{'+' if s>0 else ('-' if s<0 else ' ')}{abs(s)}] {label}")
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Output

输出格式

Report the five line values and signal table, then a composite conclusion. Example structure:
指标 / 指標 / Component值 / 值 / Value信号 / 訊號 / Signal
转折线 Tenkan-sen数值—
基准线 Kijun-sen数值—
先行带 A Senkou A数值—
先行带 B Senkou B数值云色
迟行线 Chikou Span当前收盘确认多/空
价格 vs 云高于/低于/在内+2 / -2 / 0
综合信号—看多/看空/中性
Cite Longbridge Securities / 数据来源:长桥证券 / 數據來源:長橋證券.
报告五条线的数值和信号表，然后给出综合结论。示例结构：
指标 / 指標 / Component值 / 值 / Value信号 / 訊號 / Signal
转折线 Tenkan-sen数值—
基准线 Kijun-sen数值—
先行带 A Senkou A数值—
先行带 B Senkou B数值云色
迟行线 Chikou Span当前收盘确认多/空
价格 vs 云高于/低于/在内+2 / -2 / 0
综合信号—看多/看空/中性
标注 Longbridge Securities / 数据来源:长桥证券 / 數據來源:長橋證券。

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
Fewer than 52 bars returned告知数据不足，需要至少 52 根 K 线 / 需至少 52 根 K 線 / Need at least 52 bars for Senkou B
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
返回K线数量不足52根告知数据不足，需要至少 52 根 K 线 / 需至少 52 根 K 線 / Need at least 52 bars for Senkou B
其他stderr错误直接展示错误信息 / 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 and run the same Python analysis.
如果
longbridge
二进制文件不可用，调用
mcp__longbridge__kline
并传入等效参数，然后运行相同的Python分析代码。

Related skills

相关技能

  • longbridge-kline
    — raw OHLCV data and charting
  • longbridge-technical
    — MACD / RSI / KDJ / Bollinger indicator signals
  • longbridge-candlestick
    — K-line pattern recognition
  • longbridge-capital-flow
    — intraday capital-flow signals
  • longbridge-kline
    — 原始OHLCV数据及绘图
  • longbridge-technical
    — MACD / RSI / KDJ / 布林带指标信号
  • longbridge-candlestick
    — K线形态识别
  • longbridge-capital-flow
    — 日内资金流信号