القائمة
Calculate technical analysis indicators for stock market analysis
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Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
Get stock prices, quotes, fundamentals, earnings, options, dividends, and analyst ratings using Yahoo Finance. Uses yfinance library - no API key required.
| name | technical-indicators |
| description | Calculate technical analysis indicators for stock market analysis |
Quick guide for calculating technical indicators using Python and pandas-ta.
import yfinance as yf
import pandas_ta as ta
df = yf.download('AAPL', period='1y')
df['RSI'] = ta.rsi(df['Close'], length=14)
print(df[['Close', 'RSI']].tail())
df['RSI'] = ta.rsi(df['Close'], length=14)
# RSI > 70: overbought | RSI < 30: oversold
df['SMA_50'] = ta.sma(df['Close'], length=50)
# Golden Cross: SMA_50 > SMA_200
# Death Cross: SMA_50 < SMA_200
df['EMA_12'] = ta.ema(df['Close'], length=12)
macd = ta.macd(df['Close'])
# MACD > Signal: bullish | MACD < Signal: bearish
bbands = ta.bbands(df['Close'], length=20)
# Price > Upper: overbought | Price < Lower: oversold
adx = ta.adx(df['High'], df['Low'], df['Close'], length=14)
# ADX > 25: strong trend | ADX < 20: weak trend
df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)
stoch = ta.stoch(df['High'], df['Low'], df['Close'], length=14)
df['OBV'] = ta.obv(df['Close'], df['Volume'])
import yfinance as yf
import pandas as pd
import pandas_ta as ta
# Fetch data
ticker = 'AAPL'
df = yf.download(ticker, period='2y')
# Calculate multiple indicators
df['RSI'] = ta.rsi(df['Close'], length=14)
df['SMA_20'] = ta.sma(df['Close'], length=20)
df['SMA_50'] = ta.sma(df['Close'], length=50)
df['EMA_12'] = ta.ema(df['Close'], length=12)
# MACD
macd = ta.macd(df['Close'])
df = pd.concat([df, macd], axis=1)
# Bollinger Bands
bbands = ta.bbands(df['Close'], length=20)
df = pd.concat([df, bbands], axis=1)
# ATR
df['ATR'] = ta.atr(df['High'], df['Low'], df['Close'], length=14)
print(df[['Close', 'RSI', 'SMA_20', 'SMA_50', 'ATR']].tail())