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
    """
    df_copy = df.copy()
    
    for period in periods:
        df_copy[f'MA_{period}'] = df_copy['Close'].rolling(window=period).mean()
    
    return df_copy

def calculate_stats(df):
    """
    Calculate basic statistics for the stock data
    """
    if df.empty:
        return {}
    
    current_price = df['Close'].iloc[-1]
    previous_price = df['Close'].iloc[-2] if len(df) > 1 else current_price
    price_change = current_price - previous_price
    price_change_pct = (price_change / previous_price) * 100 if previous_price != 0 else 0
    
    # Calculate volatility (annualized)
    returns = df['Close'].pct_change().dropna()
    volatility = returns.std() * np.sqrt(252) * 100  # Annualized volatility in %
    
    stats = {
        'current_price': current_price,
        'price_change': price_change,
        'price_change_pct': price_change_pct,
        'mean_price': df['Close'].mean(),
        'volatility': volatility,
        'avg_volume': df['Volume'].mean(),
        'high_52w': df['High'].max(),
        'low_52w': df['Low'].min(),
        'total_return': ((current_price - df['Close'].iloc[0]) / df['Close'].iloc[0]) * 100
    }
    
    return stats

def calculate_rsi(df, period=14):
    """
    Calculate Relative Strength Index (RSI)
    """
    df_copy = df.copy()
    
    # Calculate price changes
    delta = df_copy['Close'].diff()
    
    # Calculate 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))
    
    df_copy['RSI'] = rsi
    
    return df_copy

def calculate_macd(df, fast_period=12, slow_period=26, signal_period=9):
    """
    Calculate MACD (Moving Average Convergence Divergence)
    """
    df_copy = df.copy()
    
    # Calculate EMAs
    ema_fast = df_copy['Close'].ewm(span=fast_period).mean()
    ema_slow = df_copy['Close'].ewm(span=slow_period).mean()
    
    # Calculate MACD line
    macd_line = ema_fast - ema_slow
    
    # Calculate signal line
    signal_line = macd_line.ewm(span=signal_period).mean()
    
    # Calculate histogram
    histogram = macd_line - signal_line
    
    df_copy['MACD'] = macd_line
    df_copy['MACD_Signal'] = signal_line
    df_copy['MACD_Histogram'] = histogram
    
    return df_copy

def calculate_bollinger_bands(df, period=20, std_dev=2):
    """
    Calculate Bollinger Bands
    """
    df_copy = df.copy()
    
    # Calculate moving average and standard deviation
    ma = df_copy['Close'].rolling(window=period).mean()
    std = df_copy['Close'].rolling(window=period).std()
    
    # Calculate bands
    df_copy['BB_Upper'] = ma + (std * std_dev)
    df_copy['BB_Lower'] = ma - (std * std_dev)
    df_copy['BB_Middle'] = ma
    
    return df_copy