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Enriching stock market data using Open AI API

The Nasdaq-100 is a stock market index made up of 101 equity securities issued by 100 of the largest non-financial companies listed on the Nasdaq stock exchange. It helps investors compare stock prices with previous prices to determine market performance.

In this project you are provided with two CSV files containing Nasdaq-100 stock information:

  • nasdaq100_CA.csv: contains information about companies in the index such as symbol, name, etc. For this analysis, only companies headquartered in California have been selected.
  • nasdaq100_price_change.csv: contains price changes per stock across periods including (but not limited to) one day, five days, one month, six months, one year, etc.

As an AI developer, you will leverage the OpenAI API to classify companies into sectors and produce a summary of sector and company performance for this year, for the companies in the index that are headquartered in California.

CSV with Nasdaq-100 stock data

In this project, you have available two CSV files nasdaq100_CA.csv and nasdaq100_price_change.csv.

nasdaq100_CA.csv

symbol,name,headQuarter,dateFirstAdded,cik,founded
AAPL,Apple Inc.,"Cupertino, CA",,0000320193,1976-04-01
ABNB,Airbnb,"San Francisco, CA",,0001559720,2008-08-01
ADBE,Adobe Inc.,"San Jose, CA",,0000796343,1982-12-01
...

nasdaq100_price_change.csv

symbol,1D,5D,1M,3M,6M,ytd,1Y,3Y,5Y,10Y,max
AAPL,-1.7254,-8.30086,-6.20411,3.042,15.64824,42.99992,8.47941,60.96299,245.42031,976.99441,139245.53954
ABNB,2.1617,-2.21919,9.88336,19.43286,19.64241,68.66902,23.64013,-1.04347,-1.04347,-1.04347,-1.04347
ADBE,0.5409,-1.77817,9.16191,52.0465,38.01522,57.22723,21.96206,17.83037,109.05718,1024.69214,251030.66399
ADI,0.9291,-4.03352,2.58486,3.65887,5.01602,17.02062,8.09735,63.42847,92.81874,286.77518,26012.63736
...

# Start your code here!
import os
import pandas as pd
from openai import OpenAI

# Instantiate an API client
client = OpenAI()

# Import & Transofrm the two datasets
nasdaq100_CA = pd.read_csv('nasdaq100_CA.csv')
nasdaq_100_price_change = pd.read_csv('nasdaq100_price_change.csv')
ytd = nasdaq_100_price_change[["symbol", "ytd"]] 
nasdaq100_ca = nasdaq100_CA.merge(ytd, on='symbol')

# Use the OpenAI API to classify
for company in nasdaq100_ca["symbol"]:
    prompt = f'''Classify company {company} into one of the following sectors. Answer only with the sector name: Technology, Consumer Cyclical, Industrials, Utilities, Healthcare, Communication, Energy, Consumer Defensive, Real Estate, Financial.'''
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{ "role": "user", "content": prompt}],
        temperature=0.0
        )
    sector = response.choices[0].message.content
    nasdaq100_ca.loc[nasdaq100_ca["symbol"] == company, "Sector"] = sector
nasdaq100_ca.value_counts("Sector")

# Use the OpenAI API to summarize 
prompt = f"""Provide summary information about Nasdaq-100 stock performance year to date (YTD) of companies headquartered in CA, recommending the two best sectors and two or more companies per sector. Company data: {nasdaq100_ca}"""
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{ "role": "user", "content": prompt}],
    temperature=0.0
    )
stock_recommendations = response.choices[0].message.content
print(stock_recommendations)