Code Editor for analysis.py

import pandas as pd
import numpy as np

def calculate_moving_averages(df, periods):
    """
    Calculate moving averages for given periods
    """
    result = df[['Date', 'Close']].copy()
    
    for period in periods:
        result[f'MA_{period}'] = df['Close'].rolling(window=period).mean()
    
    return result

def calculate_stats(df):
    """
    Calculate basic statistics for a stock
    """
    if df.empty:
        return {}
    
    # Sort by date to ensure proper calculation
    df_sorted = df.sort_values('Date')
    
    # Current metrics
    current_price = df_sorted['Close'].iloc[-1]
    prev_price = df_sorted['Close'].iloc[-2] if len(df_sorted) > 1 else current_price
    daily_return = ((current_price - prev_price) / prev_price) * 100
    
    # Calculate daily returns for volatility
    df_sorted['Daily_Return'] = df_sorted['Close'].pct_change()
    volatility = df_sorted['Daily_Return'].std() * np.sqrt(252) * 100  # Annualized volatility
    
    # 52-week high/low
    high_52w = df_sorted['High'].max()
    low_52w = df_sorted['Low'].min()
    
    # Volume statistics
    avg_volume = df_sorted['Volume'].mean()
    recent_volume = df_sorted['Volume'].tail(5).mean()
    volume_trend = ((recent_volume - avg_volume) / avg_volume) * 100
    
    return {
        'current_price': current_price,
        'daily_return': daily_return,
        'volatility': volatility,
        'high_52w': high_52w,
        'low_52w': low_52w,
        'avg_volume': avg_volume,
        'volume_trend': volume_trend
    }

def calculate_rsi(df, period=14):
    """
    Calculate Relative Strength Index (RSI)
    """
    result = df[['Date', 'Close']].copy()
    
    # Calculate price changes
    delta = result['Close'].diff()
    
    # Separate gains and losses
    gains = delta.where(delta > 0, 0)
    losses = -delta.where(delta < 0, 0)
    
    # Calculate average gains and losses
    avg_gains = gains.rolling(window=period).mean()
    avg_losses = losses.rolling(window=period).mean()
    
    # Calculate RS and RSI
    rs = avg_gains / avg_losses
    rsi = 100 - (100 / (1 + rs))
    
    result['RSI'] = rsi
    
    return result

def calculate_macd(df, fast=12, slow=26, signal=9):
    """
    Calculate MACD (Moving Average Convergence Divergence)
    """
    result = df[['Date', 'Close']].copy()
    
    # Calculate exponential moving averages
    ema_fast = result['Close'].ewm(span=fast).mean()
    ema_slow = result['Close'].ewm(span=slow).mean()
    
    # Calculate MACD line
    macd_line = ema_fast - ema_slow
    
    # Calculate signal line
    signal_line = macd_line.ewm(span=signal).mean()
    
    # Calculate histogram
    histogram = macd_line - signal_line
    
    result['MACD'] = macd_line
    result['Signal'] = signal_line
    result['Histogram'] = histogram
    
    return result