Longbridge Technical

longbridge-technical

Computes seven classic technical indicators from 200 days of OHLCV data and produces a composite buy / sell / neutral signal via a multi-dimensional voting mechanism.

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

When to use

• "NVDA MACD 金叉了吗", "TSLA RSI 超买了吗", "700.HK 技术指标怎么看"
• "600519.SH 布林带收口", "AAPL 技術分析信號", "ADX 趋势强吗"
• "technical analysis for NVDA", "is TSLA overbought on RSI", "MACD signal"

Workflow

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

   longbridge kline <SYMBOL> --period day --format json   # run --help for available flags
   

3. Run the Python analysis below to compute all indicators and their individual votes.
4. Report each indicator's current value and signal, then summarise with the composite vote tally.

CLI

longbridge kline NVDA.US   --period day --format json   # run --help for available flags
longbridge kline 700.HK    --period day --format json
longbridge kline 600519.SH --period day --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)

votes = {}

# EMA helper
def ema(s, n): return s.ewm(span=n, adjust=False).mean()

# --- MACD (12, 26, 9) ---
ema12 = ema(df["c"], 12); ema26 = ema(df["c"], 26)
macd = ema12 - ema26; signal = ema(macd, 9); hist = macd - signal
votes["MACD"] = +1 if hist.iloc[-1] > 0 and hist.iloc[-1] > hist.iloc[-2] else (
                -1 if hist.iloc[-1] < 0 and hist.iloc[-1] < hist.iloc[-2] else 0)

# --- RSI (14) ---
delta = df["c"].diff(); gain = delta.clip(lower=0); loss = (-delta).clip(lower=0)
avg_g = gain.ewm(alpha=1/14, adjust=False).mean()
avg_l = loss.ewm(alpha=1/14, adjust=False).mean()
rsi = 100 - 100 / (1 + avg_g / avg_l.replace(0, 1e-9))
rsi_last = rsi.iloc[-1]
votes["RSI"] = -1 if rsi_last > 70 else (+1 if rsi_last < 30 else 0)

# --- KDJ (9, 3, 3) ---
low9  = df["l"].rolling(9).min(); high9 = df["h"].rolling(9).max()
rsv   = (df["c"] - low9) / (high9 - low9 + 1e-9) * 100
K = rsv.ewm(alpha=1/3, adjust=False).mean()
D = K.ewm(alpha=1/3, adjust=False).mean()
J = 3*K - 2*D
votes["KDJ"] = +1 if J.iloc[-1] < 20 else (-1 if J.iloc[-1] > 80 else 0)

# --- Bollinger Bands (20, 2) ---
mid = df["c"].rolling(20).mean(); std = df["c"].rolling(20).std()
upper_bb = mid + 2*std; lower_bb = mid - 2*std
c_last = df["c"].iloc[-1]
votes["Bollinger"] = +1 if c_last < lower_bb.iloc[-1] else (
                     -1 if c_last > upper_bb.iloc[-1] else 0)

# --- EMA cross (50 / 200) ---
e50 = ema(df["c"], 50); e200 = ema(df["c"], 200)
votes["EMA_cross"] = +1 if e50.iloc[-1] > e200.iloc[-1] else -1

# --- ADX (14) ---
tr = pd.concat([df["h"]-df["l"], (df["h"]-df["c"].shift()).abs(),
                (df["l"]-df["c"].shift()).abs()], axis=1).max(axis=1)
dm_plus  = (df["h"]-df["h"].shift()).clip(lower=0)
dm_minus = (df["l"].shift()-df["l"]).clip(lower=0)
atr14 = tr.ewm(alpha=1/14, adjust=False).mean()
di_plus  = 100 * dm_plus.ewm(alpha=1/14, adjust=False).mean() / atr14
di_minus = 100 * dm_minus.ewm(alpha=1/14, adjust=False).mean() / atr14
dx = (di_plus - di_minus).abs() / (di_plus + di_minus + 1e-9) * 100
adx = dx.ewm(alpha=1/14, adjust=False).mean()
# ADX > 25 confirms trend; direction from DI cross
votes["ADX"] = +1 if adx.iloc[-1] > 25 and di_plus.iloc[-1] > di_minus.iloc[-1] else (
               -1 if adx.iloc[-1] > 25 and di_minus.iloc[-1] > di_plus.iloc[-1] else 0)

# --- OBV ---
obv = (df["v"] * df["c"].diff().apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))).cumsum()
votes["OBV"] = +1 if obv.iloc[-1] > obv.iloc[-5] else (-1 if obv.iloc[-1] < obv.iloc[-5] else 0)

# --- Composite ---
total = sum(votes.values())
composite = "买入/Buy" if total >= 3 else ("卖出/Sell" if total <= -3 else "持观望/Neutral")

print(f"Composite vote: {total:+d}  →  {composite}")
print(f"  MACD hist={hist.iloc[-1]:.4f}  vote={votes['MACD']:+d}")
print(f"  RSI(14)={rsi_last:.1f}  vote={votes['RSI']:+d}")
print(f"  KDJ J={J.iloc[-1]:.1f}  vote={votes['KDJ']:+d}")
print(f"  Bollinger  vote={votes['Bollinger']:+d}  (price vs bands)")
print(f"  EMA50/200  vote={votes['EMA_cross']:+d}  (50={'above' if e50.iloc[-1]>e200.iloc[-1] else 'below'} 200)")
print(f"  ADX={adx.iloc[-1]:.1f}  DI+={di_plus.iloc[-1]:.1f} DI-={di_minus.iloc[-1]:.1f}  vote={votes['ADX']:+d}")
print(f"  OBV trend  vote={votes['OBV']:+d}")

Output

Present a table of indicator values, individual votes (+1 / 0 / -1), and a composite summary row. End with a one-sentence interpretation in the user's language.

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-candlestick — K-line pattern recognition
• longbridge-ichimoku — Ichimoku Cloud system
• longbridge-capital-flow — intraday capital-flow signals