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.

When to use

• "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

1. Resolve the symbol to <CODE>.<MARKET> format.
2. Fetch 200 daily candles (need ≥ 52 bars for Senkou Span B):

   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.

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())
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 ---
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]
# Chikou vs price 26 bars ago
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
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
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)
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
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))

# 5. Price vs Tenkan / Kijun
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}")

Output

Report the five line values and signal table, then a composite conclusion. Example structure:

Cite Longbridge Securities / 数据来源:长桥证券 / 數據來源:長橋證券.

Error handling

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 — MACD / RSI / KDJ / Bollinger indicator signals
• longbridge-candlestick — K-line pattern recognition
• longbridge-capital-flow — intraday capital-flow signals