indicator-expert

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

OpenAlgo Indicator Expert Skill

OpenAlgo 指标专家技能

Environment

环境

Python with openalgo, pandas, numpy, plotly, dash, streamlit, numba
Data sources: OpenAlgo (Indian markets via
```
client.history()
```
,
```
client.quotes()
```
,
```
client.depth()
```
), yfinance (US/Global)

Real-time: OpenAlgo WebSocket (

client.connect()

subscribe_ltp

subscribe_quote

subscribe_depth

)

Indicators: openalgo.ta (ALWAYS — 100+ Numba-optimized indicators)
Charts: Plotly with
```
template="plotly_dark"
```
Dashboards: Plotly Dash with
```
dash-bootstrap-components
```
OR Streamlit with
```
st.plotly_chart()
```
Custom indicators: Numba
```
@njit(cache=True, nogil=True)
```
+ NumPy
API keys loaded from single root
```
.env
```
via
```
python-dotenv
```
+
```
find_dotenv()
```
— never hardcode keys
Scripts go in appropriate directories (charts/, dashboards/, custom_indicators/, scanners/) created on-demand
Never use icons/emojis in code or logger output

已安装 openalgo、pandas、numpy、plotly、dash、streamlit、numba 库的 Python 环境
数据源：OpenAlgo（通过
```
client.history()
```
、
```
client.quotes()
```
、
```
client.depth()
```
获取印度市场数据）、yfinance（获取美国/全球市场数据）

实时数据：OpenAlgo WebSocket（

client.connect()

、

subscribe_ltp

、

subscribe_quote

、

subscribe_depth

）

指标库：openalgo.ta（必须使用，包含100+经 Numba 优化的指标）
图表：使用 Plotly，默认配置
```
template="plotly_dark"
```
仪表盘：搭配
```
dash-bootstrap-components
```
的 Plotly Dash，或是使用
```
st.plotly_chart()
```
的 Streamlit
自定义指标：使用 Numba
```
@njit(cache=True, nogil=True)
```
+ NumPy 实现
API 密钥通过
```
python-dotenv
```
+
```
find_dotenv()
```
从根目录的
```
.env
```
文件加载，绝对不要硬编码密钥
脚本按需放置在对应目录（charts/、dashboards/、custom_indicators/、scanners/），目录不存在时自动创建
代码和日志输出中禁止使用图标/emoji

Critical Rules

关键规则

ALWAYS use openalgo.ta for ALL technical indicators. Never reimplement what already exists in the library.
Data normalization: Always convert DataFrame index to datetime, sort, and strip timezone after fetching.
Signal cleaning: Always use
```
ta.exrem()
```
after generating raw buy/sell signals. Always
```
.fillna(False)
```
before exrem.
Plotly dark theme: All charts use
```
template="plotly_dark"
```
with
```
xaxis type="category"
```
for candlesticks.
Numba for custom indicators: Use
```
@njit(cache=True, nogil=True)
```
— never
```
fastmath=True
```
(breaks NaN handling).
Input flexibility: openalgo.ta accepts numpy arrays, pandas Series, or lists. Output matches input type.

WebSocket feeds: Use

client.connect()

client.subscribe_ltp()

subscribe_quote()

subscribe_depth()

for real-time data.

Environment: Load
```
.env
```
from project root via
```
find_dotenv()
```
— never hardcode API keys.
Market detection: If symbol looks Indian (SBIN, RELIANCE, NIFTY), use OpenAlgo. If US (AAPL, MSFT), use yfinance.
Always explain chart outputs in plain language so traders understand what the indicator shows.

所有技术指标必须使用 openalgo.ta 实现，绝对不要重复开发库中已有的功能。
数据标准化：获取数据后必须将 DataFrame 索引转换为 datetime 格式、排序，并移除时区信息。
信号清洗：生成原始买卖信号后必须使用
```
ta.exrem()
```
处理，调用 exrem 前必须先执行
```
.fillna(False)
```
。
Plotly 深色主题：所有图表使用
```
template="plotly_dark"
```
，K线图的x轴类型设置为
```
xaxis type="category"
```
。
自定义指标使用 Numba 优化：使用
```
@njit(cache=True, nogil=True)
```
装饰器，禁止使用
```
fastmath=True
```
（会破坏 NaN 处理逻辑）。
输入灵活性：openalgo.ta 支持 numpy 数组、pandas Series 或列表作为输入，输出类型与输入保持一致。

WebSocket 数据推送：使用

client.connect()

、

client.subscribe_ltp()

subscribe_quote()

subscribe_depth()

获取实时数据。

环境配置：通过
```
find_dotenv()
```
加载项目根目录的
```
.env
```
文件，绝对不要硬编码 API 密钥。
市场自动识别：如果标的代码是印度市场格式（SBIN、RELIANCE、NIFTY），使用 OpenAlgo 数据源；如果是美国市场（AAPL、MSFT），使用 yfinance 数据源。
输出解释：始终用通俗易懂的语言解释图表输出，方便交易者理解指标含义。

Data Source Priority

数据源优先级

Market	Data Source	Method	Example Symbols
India (equity)	OpenAlgo	`client.history()`	SBIN, RELIANCE, INFY
India (index)	OpenAlgo	`client.history(exchange="NSE_INDEX")`	NIFTY, BANKNIFTY
India (F&O)	OpenAlgo	`client.history(exchange="NFO")`	NIFTY30DEC25FUT
US/Global	yfinance	`yf.download()`	AAPL, MSFT, SPY

市场	数据源	方法	示例标的
印度（股票）	OpenAlgo	`client.history()`	SBIN, RELIANCE, INFY
印度（指数）	OpenAlgo	`client.history(exchange="NSE_INDEX")`	NIFTY, BANKNIFTY
印度（F&O）	OpenAlgo	`client.history(exchange="NFO")`	NIFTY30DEC25FUT
美国/全球	yfinance	`yf.download()`	AAPL, MSFT, SPY

OpenAlgo API Methods for Data

OpenAlgo 数据API方法

Method	Purpose	Returns
`client.history(symbol, exchange, interval, start_date, end_date)`	OHLCV candles	DataFrame (timestamp, open, high, low, close, volume)
`client.quotes(symbol, exchange)`	Real-time snapshot	Dict (open, high, low, ltp, bid, ask, prev_close, volume)
`client.multiquotes(symbols=[...])`	Multi-symbol quotes	List of quote dicts
`client.depth(symbol, exchange)`	Market depth (L5)	Dict (bids, asks, ohlc, volume, oi)
`client.intervals()`	Available intervals	Dict (minutes, hours, days, weeks, months)
`client.connect()`	WebSocket connect	None (sets up WS connection)
`client.subscribe_ltp(instruments, callback)`	Live LTP stream	Callback with `{symbol, exchange, ltp}`
`client.subscribe_quote(instruments, callback)`	Live quote stream	Callback with `{symbol, exchange, ohlc, ltp, volume}`
`client.subscribe_depth(instruments, callback)`	Live depth stream	Callback with `{symbol, exchange, bids, asks}`

方法	用途	返回值
`client.history(symbol, exchange, interval, start_date, end_date)`	获取 OHLCV K线数据	DataFrame（包含 timestamp、open、high、low、close、volume 字段）
`client.quotes(symbol, exchange)`	获取实时快照数据	Dict（包含 open、high、low、ltp、bid、ask、prev_close、volume 字段）
`client.multiquotes(symbols=[...])`	获取多标的快照数据	行情 Dict 组成的列表
`client.depth(symbol, exchange)`	获取5档市场深度数据	Dict（包含 bids、asks、ohlc、volume、oi 字段）
`client.intervals()`	获取可用的时间周期	Dict（包含 minutes、hours、days、weeks、months 字段）
`client.connect()`	建立 WebSocket 连接	无返回值（完成WS连接初始化）
`client.subscribe_ltp(instruments, callback)`	订阅实时最新成交价推送	回调返回 `{symbol, exchange, ltp}`
`client.subscribe_quote(instruments, callback)`	订阅实时行情推送	回调返回 `{symbol, exchange, ohlc, ltp, volume}`
`client.subscribe_depth(instruments, callback)`	订阅实时深度数据推送	回调返回 `{symbol, exchange, bids, asks}`

Indicator Library Reference

指标库参考

All indicators accessed via

from openalgo import ta

所有指标通过

from openalgo import ta

导入使用：

Trend (20)

趋势类（20个）

ta.sma

ta.ema

ta.wma

ta.dema

ta.tema

ta.hma

ta.vwma

ta.alma

ta.kama

ta.zlema

ta.t3

ta.frama

ta.supertrend

ta.ichimoku

ta.chande_kroll_stop

ta.trima

ta.mcginley

ta.vidya

ta.alligator

ta.ma_envelopes

ta.sma

ta.ema

ta.wma

ta.dema

ta.tema

ta.hma

ta.vwma

ta.alma

ta.kama

ta.zlema

ta.t3

ta.frama

ta.supertrend

ta.ichimoku

ta.chande_kroll_stop

ta.trima

ta.mcginley

ta.vidya

ta.alligator

ta.ma_envelopes

Momentum (9)

动量类（9个）

ta.rsi

ta.macd

ta.stochastic

ta.cci

ta.williams_r

ta.bop

ta.elder_ray

ta.fisher

ta.crsi

ta.rsi

ta.macd

ta.stochastic

ta.cci

ta.williams_r

ta.bop

ta.elder_ray

ta.fisher

ta.crsi

Volatility (16)

波动率类（16个）

ta.atr

ta.bbands

ta.keltner

ta.donchian

ta.chaikin_volatility

ta.natr

ta.rvi

ta.ultimate_oscillator

ta.true_range

ta.massindex

ta.bb_percent

ta.bb_width

ta.chandelier_exit

ta.historical_volatility

ta.ulcer_index

ta.starc

ta.atr

ta.bbands

ta.keltner

ta.donchian

ta.chaikin_volatility

ta.natr

ta.rvi

ta.ultimate_oscillator

ta.true_range

ta.massindex

ta.bb_percent

ta.bb_width

ta.chandelier_exit

ta.historical_volatility

ta.ulcer_index

ta.starc

Volume (14)

成交量类（14个）

ta.obv

ta.obv_smoothed

ta.vwap

ta.mfi

ta.adl

ta.cmf

ta.emv

ta.force_index

ta.nvi

ta.pvi

ta.volosc

ta.vroc

ta.kvo

ta.pvt

ta.obv

ta.obv_smoothed

ta.vwap

ta.mfi

ta.adl

ta.cmf

ta.emv

ta.force_index

ta.nvi

ta.pvi

ta.volosc

ta.vroc

ta.kvo

ta.pvt

Oscillators (20+)

震荡指标类（20+个）

ta.cmo

ta.trix

ta.uo_oscillator

ta.awesome_oscillator

ta.accelerator_oscillator

ta.ppo

ta.po

ta.dpo

ta.aroon_oscillator

ta.stoch_rsi

ta.rvi_oscillator

ta.cho

ta.chop

ta.kst

ta.tsi

ta.vortex

ta.gator_oscillator

ta.stc

ta.coppock

ta.roc

ta.cmo

ta.trix

ta.uo_oscillator

ta.awesome_oscillator

ta.accelerator_oscillator

ta.ppo

ta.po

ta.dpo

ta.aroon_oscillator

ta.stoch_rsi

ta.rvi_oscillator

ta.cho

ta.chop

ta.kst

ta.tsi

ta.vortex

ta.gator_oscillator

ta.stc

ta.coppock

ta.roc

Statistical (9)

统计类（9个）

ta.linreg

ta.lrslope

ta.correlation

ta.beta

ta.variance

ta.tsf

ta.median

ta.mode

ta.median_bands

ta.linreg

ta.lrslope

ta.correlation

ta.beta

ta.variance

ta.tsf

ta.median

ta.mode

ta.median_bands

Hybrid (6+)

混合类（6+个）

ta.adx

ta.dmi

ta.aroon

ta.pivot_points

ta.sar

ta.williams_fractals

ta.rwi

ta.adx

ta.dmi

ta.aroon

ta.pivot_points

ta.sar

ta.williams_fractals

ta.rwi

Utilities

工具函数

ta.crossover

ta.crossunder

ta.cross

ta.highest

ta.lowest

ta.change

ta.roc

ta.stdev

ta.exrem

ta.flip

ta.valuewhen

ta.rising

ta.falling

ta.crossover

ta.crossunder

ta.cross

ta.highest

ta.lowest

ta.change

ta.roc

ta.stdev

ta.exrem

ta.flip

ta.valuewhen

ta.rising

ta.falling

Modular Rule Files

模块化规则文件

Detailed reference for each topic is in

rules/

Rule File	Topic
indicator-catalog	Complete 100+ indicator reference with signatures and parameters
data-fetching	OpenAlgo history/quotes/depth, yfinance, data normalization
plotting	Plotly candlestick, overlay, subplot, multi-panel charts
custom-indicators	Building custom indicators with Numba + NumPy
websocket-feeds	Real-time LTP/Quote/Depth streaming via WebSocket
numba-optimization	Numba JIT patterns, cache, nogil, NaN handling
dashboard-patterns	Plotly Dash web applications with callbacks
streamlit-patterns	Streamlit web applications with sidebar, metrics, plotly charts
multi-timeframe	Multi-timeframe indicator analysis
signal-generation	Signal generation, cleaning, crossover/crossunder
indicator-combinations	Combining indicators for confluence analysis
symbol-format	OpenAlgo symbol format, exchange codes, index symbols

各主题的详细参考文档存放于

rules/

规则文件	主题
indicator-catalog	完整的100+指标参考，包含函数签名和参数说明
data-fetching	OpenAlgo 行情/快照/深度数据获取、yfinance 使用、数据标准化
plotting	Plotly K线、指标叠加、子图、多面板图表绘制
custom-indicators	基于 Numba + NumPy 开发自定义指标
websocket-feeds	通过 WebSocket 实现实时 LTP/行情/深度数据推送
numba-optimization	Numba JIT 最佳实践、缓存、nogil、NaN 处理
dashboard-patterns	带回调功能的 Plotly Dash 网页应用开发
streamlit-patterns	带侧边栏、指标卡片、Plotly 图表的 Streamlit 网页应用开发
multi-timeframe	多时间周期指标分析
signal-generation	信号生成、清洗、金叉/死叉判断
indicator-combinations	多指标组合共振分析
symbol-format	OpenAlgo 标的代码格式、交易所代码、指数标的说明

Chart Templates (in rules/assets/)

图表模板（位于 rules/assets/ 目录）

Template	Path	Description
EMA Chart	`assets/ema_chart/chart.py`	EMA overlay on candlestick
RSI Chart	`assets/rsi_chart/chart.py`	RSI with overbought/oversold zones
MACD Chart	`assets/macd_chart/chart.py`	MACD line, signal, histogram
Supertrend	`assets/supertrend_chart/chart.py`	Supertrend overlay with direction coloring
Bollinger	`assets/bollinger_chart/chart.py`	Bollinger Bands with squeeze detection
Multi-Indicator	`assets/multi_indicator/chart.py`	Candlestick + EMA + RSI + MACD + Volume
Basic Dashboard	`assets/dashboard_basic/app.py`	Single-symbol Plotly Dash app
Multi Dashboard	`assets/dashboard_multi/app.py`	Multi-symbol multi-timeframe dashboard
Streamlit Basic	`assets/streamlit_basic/app.py`	Single-symbol Streamlit app
Streamlit Multi	`assets/streamlit_multi/app.py`	Multi-timeframe Streamlit app
Custom Indicator	`assets/custom_indicator/template.py`	Numba custom indicator template
Live Feed	`assets/live_feed/template.py`	WebSocket real-time indicator
Scanner	`assets/scanner/template.py`	Multi-symbol indicator scanner

模板	路径	描述
EMA 图表	`assets/ema_chart/chart.py`	K线图叠加 EMA 指标
RSI 图表	`assets/rsi_chart/chart.py`	带超买超卖区域标注的 RSI 指标图
MACD 图表	`assets/macd_chart/chart.py`	包含 MACD 线、信号线、直方图的 MACD 指标图
Supertrend	`assets/supertrend_chart/chart.py`	带方向颜色标注的 Supertrend 指标叠加图
布林带	`assets/bollinger_chart/chart.py`	带挤压检测功能的布林带指标图
多指标组合	`assets/multi_indicator/chart.py`	K线 + EMA + RSI + MACD + 成交量组合图
基础仪表盘	`assets/dashboard_basic/app.py`	单标的 Plotly Dash 应用
多标的仪表盘	`assets/dashboard_multi/app.py`	多标的多时间周期仪表盘
基础 Streamlit 应用	`assets/streamlit_basic/app.py`	单标的 Streamlit 应用
多周期 Streamlit 应用	`assets/streamlit_multi/app.py`	多时间周期 Streamlit 应用
自定义指标模板	`assets/custom_indicator/template.py`	Numba 自定义指标开发模板
实时数据模板	`assets/live_feed/template.py`	WebSocket 实时指标计算模板
扫描器模板	`assets/scanner/template.py`	多标的指标扫描器模板

Quick Template: Standard Indicator Chart Script

快速模板：标准指标图表脚本

python

import os
from datetime import datetime, timedelta
from pathlib import Path

import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from dotenv import find_dotenv, load_dotenv
from openalgo import api, ta

python

import os
from datetime import datetime, timedelta
from pathlib import Path

import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from dotenv import find_dotenv, load_dotenv
from openalgo import api, ta

--- Config ---

script_dir = Path(file).resolve().parent load_dotenv(find_dotenv(), override=False)

SYMBOL = "SBIN" EXCHANGE = "NSE" INTERVAL = "D"

script_dir = Path(file).resolve().parent load_dotenv(find_dotenv(), override=False)

SYMBOL = "SBIN" EXCHANGE = "NSE" INTERVAL = "D"

--- Fetch Data ---

client = api( api_key=os.getenv("OPENALGO_API_KEY"), host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"), )

end_date = datetime.now().date() start_date = end_date - timedelta(days=365)

df = client.history( symbol=SYMBOL, exchange=EXCHANGE, interval=INTERVAL, start_date=start_date.strftime("%Y-%m-%d"), end_date=end_date.strftime("%Y-%m-%d"), ) if "timestamp" in df.columns: df["timestamp"] = pd.to_datetime(df["timestamp"]) df = df.set_index("timestamp") else: df.index = pd.to_datetime(df.index) df = df.sort_index() if df.index.tz is not None: df.index = df.index.tz_convert(None)

close = df["close"] high = df["high"] low = df["low"] volume = df["volume"]

client = api( api_key=os.getenv("OPENALGO_API_KEY"), host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"), )

end_date = datetime.now().date() start_date = end_date - timedelta(days=365)

close = df["close"] high = df["high"] low = df["low"] volume = df["volume"]

--- Compute Indicators ---

ema_20 = ta.ema(close, 20) rsi_14 = ta.rsi(close, 14)

--- Chart ---

fig = make_subplots( rows=2, cols=1, shared_xaxes=True, row_heights=[0.7, 0.3], vertical_spacing=0.03, subplot_titles=[f"{SYMBOL} Price + EMA(20)", "RSI(14)"], )

Candlestick

x_labels = df.index.strftime("%Y-%m-%d") fig.add_trace(go.Candlestick( x=x_labels, open=df["open"], high=high, low=low, close=close, name="Price", ), row=1, col=1)

EMA overlay

fig.add_trace(go.Scatter( x=x_labels, y=ema_20, mode="lines", name="EMA(20)", line=dict(color="cyan", width=1.5), ), row=1, col=1)

RSI subplot

fig.add_trace(go.Scatter( x=x_labels, y=rsi_14, mode="lines", name="RSI(14)", line=dict(color="yellow", width=1.5), ), row=2, col=1) fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1) fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)

fig.update_layout( template="plotly_dark", title=f"{SYMBOL} Technical Analysis", xaxis_rangeslider_visible=False, xaxis_type="category", xaxis2_type="category", height=700, ) fig.show()

undefined

fig.update_layout( template="plotly_dark", title=f"{SYMBOL} Technical Analysis", xaxis_rangeslider_visible=False, xaxis_type="category", xaxis2_type="category", height=700, ) fig.show()

undefined