Code Editor for analysis.py

import pandas as pd
import numpy as np

def calculate_moving_averages(df, show_sma=True, show_ema=True):
    """
    Calculate Simple Moving Average (SMA) and Exponential Moving Average (EMA)
    """
    df = df.copy()
    
    if show_sma:
        df['SMA_20'] = df['Close'].rolling(window=20).mean()
    
    if show_ema:
        df['EMA_20'] = df['Close'].ewm(span=20).mean()
    
    return df

def calculate_volatility(prices, window=30):
    """
    Calculate rolling volatility (standard deviation of returns)
    """
    returns = prices.pct_change().dropna()
    volatility = returns.rolling(window=window).std() * np.sqrt(252) * 100  # Annualized volatility
    return volatility.iloc[-1] if len(volatility) > 0 else 0

def calculate_basic_stats(df):
    """
    Calculate basic statistics for a stock
    """
    if len(df) == 0:
        return {
            'current_price': 0,
            'daily_change': 0,
            'daily_change_pct': 0,
            'high_52w': 0,
            'low_52w': 0,
            'avg_volume': 0,
            'volatility_30d': 0
        }
    
    df_sorted = df.sort_values('Date')
    
    current_price = df_sorted['Close'].iloc[-1]
    prev_price = df_sorted['Close'].iloc[-2] if len(df_sorted) > 1 else current_price
    
    daily_change = current_price - prev_price
    daily_change_pct = (daily_change / prev_price * 100) if prev_price > 0 else 0
    
    high_52w = df_sorted['High'].max()
    low_52w = df_sorted['Low'].min()
    avg_volume = df_sorted['Volume'].mean()
    
    volatility_30d = calculate_volatility(df_sorted['Close'], window=min(30, len(df_sorted)))
    
    return {
        'current_price': current_price,
        'daily_change': daily_change,
        'daily_change_pct': daily_change_pct,
        'high_52w': high_52w,
        'low_52w': low_52w,
        'avg_volume': avg_volume,
        'volatility_30d': volatility_30d
    }