--- name: longbridge-ichimoku description: | Ichimoku Cloud (一目均衡表) five-line system signal engine for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Computes Tenkan-sen, Kijun-sen, Senkou Span A/B, and Chikou Span from OHLCV data; generates price-vs-cloud position, line-cross signals, and full trend-confirmation scores. Triggers: "一目均衡表", "一目云", "云图", "转折线", "基准线", "先行带", "迟行线", "云上", "云下", "一目均衡表", "一目雲", "雲圖", "轉折線", "基準線", "先行帶", "遲行線", "ichimoku", "ichimoku cloud", "tenkan sen", "kijun sen", "senkou span", "chikou span", "cloud breakout". 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-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 `.` format. 2. Fetch 200 daily candles (need ≥ 52 bars for Senkou Span B): ```bash longbridge kline --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 ```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. ## Python analysis ```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 --- 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: | 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**. ## 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` | | 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 | ## 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