# Import necessary libraries
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

# Ensure plots show in notebook
%matplotlib inline

# Define a function to calculate Bollinger Bands
def bollinger_bands(data, window=20, num_sd=2):
    rolling_mean = data['Close'].rolling(window=window).mean()
    rolling_std = data['Close'].rolling(window=window).std()

    data['Upper Band'] = rolling_mean + (rolling_std * num_sd)
    data['Lower Band'] = rolling_mean - (rolling_std * num_sd)
    data['Moving Average'] = rolling_mean

    return data

# Plot the Bollinger Bands
def plot_bollinger(data, symbol):
    plt.figure(figsize=(12, 6))
    plt.plot(data['Close'], label='Close Price', color='blue')
    plt.plot(data['Upper Band'], label='Upper Band', color='red', linestyle='--')
    plt.plot(data['Lower Band'], label='Lower Band', color='green', linestyle='--')
    plt.plot(data['Moving Average'], label='Moving Average', color='orange')
    plt.fill_between(data.index, data['Lower Band'], data['Upper Band'], color='gray', alpha=0.2)
    plt.title(f'Bollinger Bands for {symbol}')
    plt.legend(loc='best')
    plt.grid(True)
    plt.show()

# Complete function to show Bollinger Bands for a given stock symbol
def show_bollinger_bands(symbol, start="1976-01-01", end="2013-12-31", window=20, num_sd=2):
    # Download historical stock data with adjusted prices
    data = yf.download(symbol, start=start, end=end, auto_adjust=True)

    # Calculate Bollinger Bands
    data = bollinger_bands(data, window=window, num_sd=num_sd)

    # Plot the result
    plot_bollinger(data, symbol)

# Example: Show Bollinger Bands for Autodesk (ADSK)
show_bollinger_bands("ADSK")

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def show_bollinger_bands(symbol):

    # Step 1: Download data
    start = "1976-01-01"
    end = "2013-12-31"

    # Download historical data
    data = yf.download(symbol, start=start, end=end)

    # Step 2: Define a function to calculate Bollinger Bands
    def bollinger_bands(price, window=20, num_sd=2):
        rolling_mean = price['Close'].rolling(window=window).mean()
        rolling_std = price['Close'].rolling(window=window).std()

        price['Upper Band'] = rolling_mean + (rolling_std * num_sd)
        price['Lower Band'] = rolling_mean - (rolling_std * num_sd)
        price['Moving Average'] = rolling_mean

        return price

    # Apply the function
    data = bollinger_bands(data)

    # Step 3: Plot the data with Bollinger Bands
    plt.figure(figsize=(12, 6))
    plt.plot(data['Close'], label='Close Price', color='blue')
    plt.plot(data['Upper Band'], label='Upper Band', color='red', linestyle='--')
    plt.plot(data['Lower Band'], label='Lower Band', color='green', linestyle='--')
    plt.plot(data['Moving Average'], label='Moving Average', color='orange')
    plt.fill_between(data.index, data['Lower Band'], data['Upper Band'], color='gray', alpha=0.2)
    plt.title(f'Bollinger Bands for {symbol}')
    plt.legend(loc='best')
    plt.show()

show_bollinger_bands("ALLE")

C:\Users\Afia Jahan\AppData\Local\Temp\ipykernel_68908\3736596034.py:8: FutureWarning: YF.download() has changed argument auto_adjust default to True
  data = yf.download(symbol, start=start, end=end)
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show_bollinger_bands("AIZ")

C:\Users\Afia Jahan\AppData\Local\Temp\ipykernel_68908\3736596034.py:8: FutureWarning: YF.download() has changed argument auto_adjust default to True
  data = yf.download(symbol, start=start, end=end)
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show_bollinger_bands("ADSK")

C:\Users\Afia Jahan\AppData\Local\Temp\ipykernel_68908\3736596034.py:8: FutureWarning: YF.download() has changed argument auto_adjust default to True
  data = yf.download(symbol, start=start, end=end)
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show_bollinger_bands("AMZN")

C:\Users\Afia Jahan\AppData\Local\Temp\ipykernel_68908\3736596034.py:8: FutureWarning: YF.download() has changed argument auto_adjust default to True
  data = yf.download(symbol, start=start, end=end)
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