modules/share_price_trend.py

import io
import matplotlib.pyplot as plt
import FinanceDataReader as fdr
import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed

def get_stock_prices(stock_code, days):
    try:
        df = fdr.DataReader(stock_code)
        end_date = pd.to_datetime('today')
        start_date = pd.date_range(end=end_date, periods=days, freq='B')[0]
        df = df[(df.index >= start_date) & (df.index <= end_date)]
        if df.empty:
            print(f"<p style='color: red;'>No data available for {stock_code}</p>")
            return None
        return df['Close']
    except Exception as e:
        print(f"<p style='color: red;'>Failed to fetch data for {stock_code}: {e}</p>")
        return None

def share_price_trend(stock_codes, days):
    stock_prices = {}
    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = {executor.submit(get_stock_prices, stock_code.strip(), int(days)): stock_code.strip() for stock_code in stock_codes.split(',')}
        for future in as_completed(futures):
            stock_code = futures[future]
            try:
                prices = future.result()
                if prices is not None:
                    stock_prices[stock_code] = prices
            except Exception as e:
                print(f"<p style='color: red;'>Failed to fetch data for {stock_code}: {e}</p>")

    if not stock_prices:
        return "<p style='color: red;'>No data available for the provided stock codes.</p>"

    plt.switch_backend('agg')
    plt.style.use('tableau-colorblind10')

    fig, ax = plt.subplots(figsize=(8, 4.5))
    for stock_code, prices in stock_prices.items():
        relative_prices = prices / prices.iloc[0]
        ax.plot(prices.index, relative_prices, label=stock_code.upper())

    # Remove the axes and ticks
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    
    ax.xaxis.set_visible(False)
    ax.yaxis.set_visible(False)
    
    # Add grid for better readability
    ax.grid(True, which='both', linestyle='--', linewidth=0.5)
    
    ax.legend()
    plt.tight_layout()

    svg_graph = io.StringIO()
    plt.savefig(svg_graph, format='svg')
    svg_graph.seek(0)
    svg_data = svg_graph.getvalue()
    plt.close()

    svg_data = svg_data.replace('<svg ', '<svg width="100%" height="100%" ')
    svg_data = svg_data.replace('</svg>', '''
    <defs>
        <linearGradient id="grad1" x1="0%" y1="0%" x2="100%" y2="0%">
            <stop offset="0%" style="stop-color:rgb(173,216,230);stop-opacity:1" />
            <stop offset="100%" style="stop-color:rgb(0,191,255);stop-opacity:1" />
        </linearGradient>
        <filter id="dropshadow" height="130%">
            <feGaussianBlur in="SourceAlpha" stdDeviation="3"/>
            <feOffset dx="2" dy="2" result="offsetblur"/>
            <feMerge> 
                <feMergeNode/>
                <feMergeNode in="SourceGraphic"/> 
            </feMerge>
        </filter>
    </defs>
    <style>
        @keyframes lineAnimation {
            from {
                stroke-dasharray: 0, 1000;
            }
            to {
                stroke-dasharray: 1000, 0;
            }
        }
        path {
            animation: lineAnimation 1s linear forwards;
        }
    </style>
    </svg>''')

    svg_data = svg_data.replace('stroke="#1f77b4"', 'stroke="url(#grad1)" filter="url(#dropshadow)"')

    html_table = "<h3>Stock Prices Data</h3><div class='table-container'><table>"
    html_table += "<thead><tr><th>Date</th>"
    for stock_code in stock_prices.keys():
        html_table += f"<th>{stock_code.upper()}</th>"
    html_table += "</tr></thead><tbody>"

    # Create a date range with only business days
    all_dates = pd.date_range(start=min(df.index.min() for df in stock_prices.values()), end=pd.to_datetime('today'), freq='B')
    all_dates = all_dates[::-1]  # Reverse the order of dates

    for date in all_dates:
        html_table += f"<tr><td>{date.strftime('%Y-%m-%d')}</td>"
        for stock_code in stock_prices.keys():
            price = stock_prices[stock_code].get(date, None)
            if price is not None:
                html_table += f"<td>{price:,.2f}</td>"
            else:
                html_table += "<td>N/A</td>"
        html_table += "</tr>"

    html_table += "</tbody></table></div>"

    graph_html = f'<h3>Relative Stock Prices Over the Last {days} Days</h3>{svg_data}'
    return graph_html + html_table

# Example usage
# stock_codes = "AAPL,MSFT,GOOGL"
# days = 30
# result = share_price_trend(stock_codes, days)
# print(result)