improving requirements

app.py

@@ -1,6 +1,5 @@
 from openai import OpenAI
 import streamlit as st
-import utils as u
 from langchain_openai import ChatOpenAI
 from tools import sentiment_analysis_util
 import numpy as np

utils.py

@@ -1,177 +0,0 @@
-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