import matplotlib.pyplot as plt
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import yfinance as yf
from plotly.subplots import make_subplots

def get_stock_price(stockticker: str) -> str:
        ticker = yf.Ticker(stockticker)
        todays_data = ticker.history(period='1d')
        return str(round(todays_data['Close'][0], 2))

def plot_candlestick_stock_price(historical_data):
    """Useful for plotting candlestick plot for stock prices.
    Use historical stock price data from yahoo finance for the week and plot them."""
    df=historical_data[['Close','Open','High','Low']]
    df.index=pd.to_datetime(df.index)
    df.index.names=['Date']
    df=df.reset_index()

    fig = go.Figure(data=[go.Candlestick(x=df['Date'],
                open=df['Open'],
                high=df['High'],
                low=df['Low'],
                close=df['Close'])])
    fig.show()

def historical_stock_prices(stockticker, days_ago):
    """Upload accurate data to accurate dates from yahoo finance."""
    ticker = yf.Ticker(stockticker)
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days_ago)
    start_date = start_date.strftime('%Y-%m-%d')
    end_date = end_date.strftime('%Y-%m-%d')
    historical_data = ticker.history(start=start_date, end=end_date)
    return historical_data

def plot_macd2(df):
    try:
        # Debugging: Print the dataframe columns and a few rows
        print("DataFrame columns:", df.columns)
        print("DataFrame head:\n", df.head())

        # Convert DataFrame index and columns to numpy arrays
        index = df.index.to_numpy()
        close_prices = df['Close'].to_numpy()
        macd = df['MACD'].to_numpy()
        signal_line = df['Signal_Line'].to_numpy()
        macd_histogram = df['MACD_Histogram'].to_numpy()

        fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})

        # Subplot 1: Candlestick chart
        ax1.plot(index, close_prices, label='Close', color='black')
        ax1.set_title("Candlestick Chart")
        ax1.set_ylabel("Price")
        ax1.legend()

        # Subplot 2: MACD
        ax2.plot(index, macd, label='MACD', color='blue')
        ax2.plot(index, signal_line, label='Signal Line', color='red')

        histogram_colors = np.where(macd_histogram >= 0, 'green', 'red')
        ax2.bar(index, macd_histogram, color=histogram_colors, alpha=0.6)

        ax2.set_title("MACD")
        ax2.set_ylabel("MACD Value")
        ax2.legend()

        plt.xlabel("Date")
        plt.tight_layout()

        return fig
    except Exception as e:
        print(f"Error in plot_macd: {e}")
        return None

def plot_macd(df):

    # Create Figure
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.2, 0.1],
                        vertical_spacing=0.15,  # Adjust vertical spacing between subplots
                        subplot_titles=("Candlestick Chart", "MACD"))  # Add subplot titles


    # Subplot 1: Plot candlestick chart
    fig.add_trace(go.Candlestick(
        x=df.index,
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        increasing_line_color='#00cc96',  # Green for increasing
        decreasing_line_color='#ff3e3e',  # Red for decreasing
        showlegend=False
    ), row=1, col=1)  # Specify row and column indices


    # Subplot 2: Plot MACD
    fig.add_trace(
        go.Scatter(
            x=df.index,
            y=df['MACD'],
            mode='lines',
            name='MACD',
            line=dict(color='blue')
        ),
        row=2, col=1
    )

    fig.add_trace(
        go.Scatter(
            x=df.index,
            y=df['Signal_Line'],
            mode='lines',
            name='Signal Line',
            line=dict(color='red')
        ),
        row=2, col=1
    )

    # Plot MACD Histogram with different colors for positive and negative values
    histogram_colors = ['green' if val >= 0 else 'red' for val in df['MACD_Histogram']]

    fig.add_trace(
        go.Bar(
            x=df.index,
            y=df['MACD_Histogram'],
            name='MACD Histogram',
            marker_color=histogram_colors
        ),
        row=2, col=1
    )

    # Update layout with zoom and pan tools enabled
    layout = go.Layout(
        title='MSFT Candlestick Chart and MACD Subplots',
        title_font=dict(size=12),  # Adjust title font size
        plot_bgcolor='#f2f2f2',  # Light gray background
        height=600,
        width=1200,
        xaxis_rangeslider=dict(visible=True, thickness=0.03),
    )

    # Update the layout of the entire figure
    fig.update_layout(layout)
    fig.update_yaxes(fixedrange=False, row=1, col=1)
    fig.update_yaxes(fixedrange=True, row=2, col=1)
    fig.update_xaxes(type='category', row=1, col=1)
    fig.update_xaxes(type='category', nticks=10, row=2, col=1)
    
    fig.show()
    #return fig

def calculate_MACD(df, fast_period=12, slow_period=26, signal_period=9):
    """
    Calculates the MACD (Moving Average Convergence Divergence) and related indicators.

    Parameters:
        df (DataFrame): A pandas DataFrame containing at least a 'Close' column with closing prices.
        fast_period (int): The period for the fast EMA (default is 12).
        slow_period (int): The period for the slow EMA (default is 26).
        signal_period (int): The period for the signal line EMA (default is 9).

    Returns:
        DataFrame: A pandas DataFrame with the original data and added columns for MACD, Signal Line, and MACD Histogram.
    """

    df['EMA_fast'] = df['Close'].ewm(span=fast_period, adjust=False).mean()
    df['EMA_slow'] = df['Close'].ewm(span=slow_period, adjust=False).mean()
    df['MACD'] = df['EMA_fast'] - df['EMA_slow']

    df['Signal_Line'] = df['MACD'].ewm(span=signal_period, adjust=False).mean()
    df['MACD_Histogram'] = df['MACD'] - df['Signal_Line']

    return df