Backend AI Financial Analyst

backend.py

import os
import requests
import json
from apiKey import apikey
from apiKey import serpapi
import yfinance as yf
from yahooquery import Ticker
import openai

os.environ["SERPAPI_API_KEY"] = serpapi
openai.api_key = apikey

def get_company_news(company_name):
    params = {
        "engine": "google",
        "tbm": "nws",
        "q": company_name,
        "api_key": os.environ["SERPAPI_API_KEY"],
    }

    response = requests.get('https://serpapi.com/search', params=params)
    data = response.json()

    return data.get('news_results')


def write_news_to_file(news, filename):
    with open(filename, 'w') as file:
        for news_item in news:
            if news_item is not None:
                title = news_item.get('title', 'No title')
                link = news_item.get('link', 'No link')
                date = news_item.get('date', 'No date')
                file.write(f"Title: {title}\n")
                file.write(f"Link: {link}\n")
                file.write(f"Date: {date}\n\n")



def get_stock_evolution(company_name, period="1y"):
    # Get the stock information
    stock = yf.Ticker(company_name)

    # Get historical market data
    hist = stock.history(period=period)

    # Convert the DataFrame to a string with a specific format
    data_string = hist.to_string()

    # Append the string to the "investment.txt" file
    with open("investment.txt", "a") as file:
        file.write(f"\nStock Evolution for {company_name}:\n")
        file.write(data_string)
        file.write("\n")

    # Return the DataFrame
    return hist


def get_financial_statements(ticker):
    # Create a Ticker object
    company = Ticker(ticker)

    # Get financial data
    balance_sheet = company.balance_sheet().to_string()
    cash_flow = company.cash_flow(trailing=False).to_string()
    income_statement = company.income_statement().to_string()
    valuation_measures = str(company.valuation_measures)  # This one might already be a dictionary or string

    # Write data to file
    with open("investment.txt", "a") as file:
        file.write("\nBalance Sheet\n")
        file.write(balance_sheet)
        file.write("\nCash Flow\n")
        file.write(cash_flow)
        file.write("\nIncome Statement\n")
        file.write(income_statement)
        file.write("\nValuation Measures\n")
        file.write(valuation_measures)




def get_data(company_name, company_ticker, period="1y", filename="investment.txt"):
    news = get_company_news(company_name)
    if news:
        write_news_to_file(news, filename)
    else:
        print("No news found.")

    hist = get_stock_evolution(company_ticker)

    get_financial_statements(company_ticker)

    return hist


def financial_analyst(request):
    print(f"Received request: {request}")
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-0613",
        messages=[{
            "role":
            "user",
            "content":
            f"Given the user request, what is the comapany name and the company stock ticker ?: {request}?"
        }],
        functions=[{
            "name": "get_data",
            "description":
            "Get financial data on a specific company for investment purposes",
            "parameters": {
                "type": "object",
                "properties": {
                    "company_name": {
                        "type":
                        "string",
                        "description":
                        "The name of the company",
                    },
                    "company_ticker": {
                        "type":
                        "string",
                        "description":
                        "the ticker of the stock of the company"
                    },
                    "period": {
                        "type": "string",
                        "description": "The period of analysis"
                    },
                    "filename": {
                        "type": "string",
                        "description": "the filename to store data"
                    }
                },
                "required": ["company_name", "company_ticker"],
            },
        }],
        function_call={"name": "get_data"},
    )

    message = response["choices"][0]["message"]

    if message.get("function_call"):
        # Parse the arguments from a JSON string to a Python dictionary
        arguments = json.loads(message["function_call"]["arguments"])
        print(arguments)
        company_name = arguments["company_name"]
        company_ticker = arguments["company_ticker"]

        # Parse the return value from a JSON string to a Python dictionary
        hist = get_data(company_name, company_ticker)
        print(hist)

        with open("investment.txt", "r") as file:
            content = file.read()[:14000]

        second_response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo-16k",
            messages=[
                {
                    "role": "user",
                    "content": request
                },
                message,
                {
                    "role": "system",
                    "content": """write a detailled investment thesis to answer
                      the user request as a html document. Provide numbers to justify
                      your assertions, a lot ideally. Always provide
                     a recommendation to buy the stock of the company
                     or not given the information available."""
                },
                {
                    "role": "assistant",
                    "content": content,
                },
            ],
        )

        return (second_response["choices"][0]["message"]["content"], hist)