Code Editor for analytics.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_volatility(df, window=30):
    """Calculate rolling volatility (annualized)"""
    returns = df['Close'].pct_change().dropna()
    volatility = returns.rolling(window=window).std().iloc[-1] * np.sqrt(252) * 100
    return volatility

def calculate_basic_stats(df):
    """Calculate basic statistical measures"""
    close_prices = df['Close']
    
    # Calculate returns
    total_return = ((close_prices.iloc[-1] - close_prices.iloc[0]) / close_prices.iloc[0]) * 100
    
    # Annualized return (assuming 252 trading days)
    days = len(df)
    years = days / 252
    annualized_return = ((close_prices.iloc[-1] / close_prices.iloc[0]) ** (1/years) - 1) * 100
    
    stats = {
        'mean': close_prices.mean(),
        'median': close_prices.median(),
        'std': close_prices.std(),
        'min': close_prices.min(),
        'max': close_prices.max(),
        'total_return': total_return,
        'annualized_return': annualized_return
    }
    
    return stats

def calculate_rsi(df, window=14):
    """Calculate Relative Strength Index"""
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    
    return rsi

def calculate_bollinger_bands(df, window=20, num_std=2):
    """Calculate Bollinger Bands"""
    rolling_mean = df['Close'].rolling(window=window).mean()
    rolling_std = df['Close'].rolling(window=window).std()
    
    upper_band = rolling_mean + (rolling_std * num_std)
    lower_band = rolling_mean - (rolling_std * num_std)
    
    return upper_band, lower_band, rolling_mean

def calculate_macd(df, fast=12, slow=26, signal=9):
    """Calculate MACD (Moving Average Convergence Divergence)"""
    ema_fast = df['Close'].ewm(span=fast).mean()
    ema_slow = df['Close'].ewm(span=slow).mean()
    
    macd = ema_fast - ema_slow
    signal_line = macd.ewm(span=signal).mean()
    histogram = macd - signal_line
    
    return macd, signal_line, histogram