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Editing File: /home/ubuntu/codegamaai-test/broker_bot/resources/stock_apis/get_stock_info.py

import requests
import yfinance as yf
import datetime
import pandas as pd
import json
import numpy as np

def process_historical_data(historical_data, symbol):
    # Convert Json to DataFrame
    df = pd.DataFrame(historical_data)
    # If 'Date' is not in datetime format, convert it
    df['Date'] = pd.to_datetime(df['Date'])
    # Calculate Daily Returns
    df['Daily_Return'] = df['Close'].pct_change()
    # Calculate Average Daily Returns
    average_daily_return = df['Daily_Return'].mean()
    # Calculate Volatility
    volatility = df['Daily_Return'].std()

# Get the max and min of the 'Close' column
    max_close = df['Close'].max()
    min_close = df['Close'].min()

# Get the date of the max and min close
    max_close_date = df[df['Close'] == max_close]['Date'].values[0]
    min_close_date = df[df['Close'] == min_close]['Date'].values[0]

# Percentage change in the 'Close' column
    percentage_change = ((max_close - min_close) / min_close) * 100

# Highest Stock Price
    highest_stock_price = df['High'].max()
    highest_stock_price_date = df[df['High'] == highest_stock_price]['Date'].values[0]

# Lowest Stock Price
    lowest_stock_price = df['Low'].min()
    lowest_stock_price_date = df[df['Low'] == lowest_stock_price]['Date'].values[0]

# Difference between the highest and lowest stock price
    difference = highest_stock_price - lowest_stock_price

# Average Stock Price
    average_stock_price = df['Close'].mean()

# Average Volume
    average_volume = df['Volume'].mean()

# Average Dividends
    average_dividends = df['Dividends'].mean()

# Average Stock Splits
    average_stock_splits = df['Stock Splits'].mean()

response_string = f"""
    Historical Data for {symbol}:
    Average Daily Return: {average_daily_return}
    Volatility: {volatility}
    Max Close: {max_close} on {max_close_date}
    Min Close: {min_close} on {min_close_date}
    Percentage Change: {percentage_change}
    Highest Stock Price: {highest_stock_price} on {highest_stock_price_date}
    Lowest Stock Price: {lowest_stock_price} on {lowest_stock_price_date}
    Difference: {difference}
    Average Stock Price: {average_stock_price}
    Average Volume: {average_volume}
    Average Dividends: {average_dividends}
    Average Stock Splits: {average_stock_splits}
    """
    return response_string

def historical_stock_data(symbol, interval):
    stock = yf.Ticker(symbol)
    # data = stock.history(period='1mo')
    data = stock.history(period = interval)

# Convert DataFrame to a list of dictionaries
    historical_data = []
    for date, row in data.iterrows():
        daily_data = {
            "Date": date.strftime('%Y-%m-%d'),
            "Open": row['Open'],
            "High": row['High'],
            "Low": row['Low'],
            "Close": row['Close'],
            "Volume": row['Volume'],
            "Dividends": row['Dividends'],
            "Stock Splits": row['Stock Splits']
        }
        historical_data.append(daily_data)

response = process_historical_data(historical_data, symbol)

return response

def stock_info(symbol):
    stock = yf.Ticker(symbol)
    info = stock.info
    response = {
        "Current Price": info.get('currentPrice', 'N/A'),
        "High Price": info.get('dayHigh', 'N/A'),
        "Low Price": info.get('dayLow', 'N/A'),
        "Open Price": info.get('open', 'N/A'),
        "Previous Close": info.get('previousClose', 'N/A'),
        "Fifty Two Week Low": info.get('fiftyTwoWeekLow', 'N/A'),
        "Fifty Two Week High": info.get('fiftyTwoWeekHigh', 'N/A'),
        "Currency": info.get('currency', 'N/A'),
        "Market Cap": info.get('marketCap', 'N/A'),
        "Target Mean Price": info.get('targetMeanPrice', 'N/A')
    }

response_string = f"""Stock Info for {symbol}:\n
    Current Price: {response['Current Price']}\n
    High Price: {response['High Price']}\n
    Low Price: {response['Low Price']}\n
    Open Price: {response['Open Price']}\n
    Previous Close: {response['Previous Close']}\n
    52 Week Low: {response['Fifty Two Week Low']}\n
    52 Week High: {response['Fifty Two Week High']}\n
    Market Cap: {response['Market Cap']}\n
    Currency: {response['Currency']}\n
    Target Mean Price: {response['Target Mean Price']}\n"""

return response_string

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