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.

基于200天的OHLCV数据计算7种经典技术指标，并通过多维度投票机制生成综合买入/卖出/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 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"

"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

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 compute all indicators and their individual votes.
Report each indicator's current value and signal, then summarise with the composite vote tally.

将股票代码解析为
```
<CODE>.<MARKET>
```
格式。

获取200日K线数据：

bash

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

运行下方的Python分析代码，计算所有指标及其独立投票结果。
报告每个指标的当前值和信号，然后汇总综合投票结果。

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

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 = {}

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)

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 = 3K - 2D 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 + 2std; lower_bb = mid - 2std 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}")

undefined

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}")

undefined

Output

输出格式

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.

指标 / 指標 / Indicator	当前值 / 當前值 / Value	信号 / 訊號 / Signal
MACD	hist 值	多/空/中性
RSI(14)	数值	超卖/中性/超买
KDJ J	数值	超卖/中性/超买
Bollinger	价格位置	超卖/中性/超买
EMA 50/200	多空排列	多头/空头
ADX	趋势强度	趋势/震荡
OBV	5日趋势	流入/流出

展示指标值、独立投票结果（+1/0/-1）的表格，以及综合汇总行。最后用用户使用的语言给出一句解读。

指标 / 指標 / Indicator	当前值 / 當前值 / Value	信号 / 訊號 / Signal
MACD	hist 值	多/空/中性
RSI(14)	数值	超卖/中性/超买
KDJ J	数值	超卖/中性/超买
Bollinger	价格位置	超卖/中性/超买
EMA 50/200	多空排列	多头/空头
ADX	趋势强度	趋势/震荡
OBV	5日趋势	流入/流出

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

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

MCP fallback

MCP备用方案

If

longbridge

binary is unavailable, call

mcp__longbridge__kline

with equivalent parameters and run the same analysis.

如果

longbridge

二进制文件不可用，请调用

mcp__longbridge__kline

并传入等效参数，然后运行相同的分析。

longbridge-technical

Original

Translation

longbridge-technical

longbridge-technical

When to use

使用场景

Workflow

工作流程

CLI

CLI

Python analysis

Python analysis

EMA helper

EMA helper

--- MACD (12, 26, 9) ---

--- MACD (12, 26, 9) ---

--- RSI (14) ---

--- RSI (14) ---

--- KDJ (9, 3, 3) ---

--- KDJ (9, 3, 3) ---

--- Bollinger Bands (20, 2) ---

--- Bollinger Bands (20, 2) ---

--- EMA cross (50 / 200) ---

--- EMA cross (50 / 200) ---

--- ADX (14) ---

--- ADX (14) ---

ADX > 25 confirms trend; direction from DI cross

ADX > 25 confirms trend; direction from DI cross

--- OBV ---

--- OBV ---

--- Composite ---

--- Composite ---

Output

输出格式

Error handling

错误处理

MCP fallback

MCP备用方案

Related skills

相关技能