NEWR
New Relic, Inc.
stock NYSE

I have recently made the decision that I wanted to pour countless hours into attempting to make simple predictions using AI into the stock market. I'll paste my Python code below, and as of right now everything outputs. I would appreciate some recommendations for what else I could scrape to make the results more accurate, and a better AI to use. Thanks all!
import subprocess
import time
from bs4 import BeautifulSoup
import requests
import yfinance as yf
import ollama
import textwrap

print("innit done")

# Global variables
FindPriceRunning = True
PERunning = True
MKTCapRunning = True
StockPrice = " "
StockPrices = "Jack is cool"
FindPriceRT = 1
StockPE = " "
StockPEs = "Jack is really cool"
PERT = 1
MKTCap = " "
MKTCaps = "Jack is super super cool"
MKTCapRT = 1
Stock_Histories = 'Jack is cool, and the dataset begins here'
def find_price(stock):
global StockPrices
global StockPrice
global FindPriceRT
print(PERT)
print(f"Finding attributes for {stock}...")
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')

# Find stock price
price_element = soup.find('span', {'data-testid': 'qsp-price'})
if price_element:
stock_price = price_element.text
StockPrice = f"{stock} Stock Price: ${stock_price}"
print(StockPrice)
StockPrices += f", and {StockPrice}"
FindPriceRT += 1
except (requests.exceptions.RequestException, Exception) as e:
pass
def find_PE(stock):
global StockPEs
global StockPE
global PERT
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')
pe_element = soup.find('fin-streamer', {'data-field': 'trailingPE'})
if pe_element:
stock_pe = pe_element.text
StockPE = (f"{stock} P/E Ratio: {stock_pe}")
print(f"{stock} P/E Ratio: {stock_pe}")
StockPEs = (f"{StockPEs}, and {StockPE}")
PERT += 1
except (requests.exceptions.RequestException, Exception):
pass
def find_MKTCap(stock):
global MKTCaps
global MKTCap
global MKTCapRT
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')

# Find Market Cap using the correct tag
MKTCap_element = soup.find('fin-streamer', {'data-field': 'marketCap'})
if MKTCap_element:
MKTCap = MKTCap_element.text # Corrected this line
MKTCap = f"{stock} Market Cap: {MKTCap}"
print(MKTCap)
MKTCaps = f"{MKTCaps}, and {MKTCap}"
MKTCapRT += 1
except (requests.exceptions.RequestException, Exception):
pass
def find_PHistory(stock, period="30d"):
global Stock_Histories
# Download historical data for the given stock symbol
data = yf.download(stock, period=period, interval="1d")

# Initialize the stock_history variabler
stock_history = ""
# Loop through each row to format the output and build the history string
for date, row in data.iterrows():
# Ensure the price is a float
close_price = float(row['Close'].iloc[0])
stock_history += f"{date.date()} {close_price:.6f}, "
stock_history = stock_history.rstrip(", ")
print('Stock history recorded over the past 1k days ')
Stock_Histories += (f"{stock}: {stock_history}")
print('Stock histories updated')

return stock_history


# List of S&P 500 stocks
SandP500 = ["FHI", "MMM", "AOS", "ABT", "ABBV", "ACN", "ADBE", "AMD", "AES", "AFL", "A", "APD", "ABNB", "AKAM", "ALB",
"ARE", "ALGN", "ALLE", "LNT", "ALL", "GOOGL", "GOOG", "MO", "AMZN", "AMCR", "AEE", "AEP", "AXP", "AIG",
"AMT", "AWK", "AMP", "AME", "AMGN", "APH", "ADI", "ANSS", "AON", "APA", "APO", "AAPL", "AMAT", "APTV",
"ACGL", "ADM", "ANET", "AJG", "AIZ", "T", "ATO", "ADSK", "ADP", "AZO", "AVB", "AVY", "AXON", "BKR", "BALL",
"BAC", "BAX", "BDX", "BRK.B", "BBY", "TECH", "BIIB", "BLK", "BX", "BK", "BA", "BKNG", "BWA", "BSX", "BMY",
"AVGO", "BR", "BRO", "BF.B", "BLDR", "BG", "BXP", "CHRW", "CDNS", "CZR", "CPT", "CPB", "COF", "CAH", "KMX",
"CCL", "CARR", "CAT", "CBOE", "CBRE", "CDW", "CE", "COR", "CNC", "CNP", "CF", "CRL", "SCHW", "CHTR", "CVX",
"CMG", "CB", "CHD", "CI", "CINF", "CTAS", "CSCO", "C", "CFG", "CLX", "CME", "CMS", "KO", "CTSH", "CL",
"CMCSA", "CAG", "COP", "ED", "STZ", "CEG", "COO", "CPRT", "GLW", "CPAY", "CTVA", "CSGP", "COST", "CTRA",
"CRWD", "CCI", "CSX", "CMI", "CVS", "DHR", "DRI", "DVA", "DAY", "DECK", "DE", "DELL", "DAL", "DVN", "DXCM",
"FANG", "DLR", "DFS", "DG", "DLTR", "D", "DPZ", "DOV", "DOW", "DHI", "DTE", "DUK", "DD", "EMN", "ETN",
"EBAY", "ECL", "EIX", "EW", "EA", "ELV", "EMR", "ENPH", "ETR", "EOG", "EPAM", "EQT", "EFX", "EQIX", "EQR",
"ERIE", "ESS", "EL", "EG", "EVRG", "ES", "EXC", "EXPE", "EXPD", "EXR", "XOM", "FFIV", "FDS", "FICO", "FAST",
"FRT", "FDX", "FIS", "FITB", "FSLR", "FE", "FI", "FMC", "F", "FTNT", "FTV", "FOXA", "FOX", "BEN", "FCX",
"GRMN", "IT", "GE", "GEHC", "GEV", "GEN", "GNRC", "GD", "GIS", "GM", "GPC", "GILD", "GPN", "GL", "GDDY",
"GS", "HAL", "HIG", "HAS", "HCA", "DOC", "HSIC", "HSY", "HES", "HPE", "HLT", "HOLX", "HD", "HON", "HRL",
"HST", "HWM", "HPQ", "HUBB", "HUM", "HBAN", "HII", "IBM", "IEX", "IDXX", "ITW", "INCY", "IR", "PODD",
"INTC", "ICE", "IFF", "IP", "IPG", "INTU", "ISRG", "IVZ", "INVH", "IQV", "IRM", "JBHT", "JBL", "JKHY", "J",
"JNJ", "JCI", "JPM", "JNPR", "K", "KVUE", "KDP", "KEY", "KEYS", "KMB", "KIM", "KMI", "KKR", "KLAC", "KHC",
"KR", "LHX", "LH", "LRCX", "LW", "LVS", "LDOS", "LEN", "LII", "LLY", "LIN", "LYV", "LKQ", "LMT", "L", "LOW",
"LULU", "LYB", "MTB", "MPC", "MKTX", "MAR", "MMC", "MLM", "MAS", "MA", "MTCH", "MKC", "MCD", "MS", "MSFT",
"MAA", "MCO", "MCK", "MDT", "MRK", "MMC", "MOS", "NAVI", "NEE", "NKE", "NOC", "NLSN", "NTAP", "NVR", "NFLX",
"NVDA", "NEWR", "ORLY", "O", "ODFL", "OMC", "ON", "PGR", "PEP", "PFE", "PM", "PNC", "PSA", "PEG", "PYPL",
"PNR", "PPL", "PXD", "QRTEA", "QCOM", "REGN", "ROST", "RMD", "RSG", "RTX", "OZK", "SPGI", "SBUX", "SLB",
"SNA", "STT", "SYK", "SPG", "SWKS", "SIVB", "TMO", "TSLA", "TXN", "TGT", "TRV", "UNP", "UNH", "UPS", "USB",
"VZ", "V", "VRSK", "WMT", "WBA", "WM", "WELL", "WBD", "WDC", "WEC", "WRB", "WU", "XEL", "XLNX", "XYL",
"YUM"]


def format_full_prompt(stock_data):
full_prompt = f"""
Based on the following stock data, predict **which stock is most likely to increase the most relative to its current value in 90 days**:
1 **Stock Prices** (Current Price of each stock in the S&P 500):
{StockPrices}
2️ **P/E Ratios** (Valuation of each stock in the S&P 500):
{StockPEs}
3️ **Market Caps** (Total value of each stock in the S&P 500):
{MKTCaps}
4️ **Stock Price History (Last 1000 days)**:
{Stock_Histories}
**Using this data, analyze trends and return the top 5 stocks that are most likely to increase in price over the next 90 days.**
Rank them by expected percentage growth and explain why they were chosen.
"""
return full_prompt


def get_prediction(full_prompt):
response = ollama.chat(model="llama2", messages=[{"role": "user", "content": full_prompt}])
print(response)
return response.get('text', 'No text key in response')


def main():
start_time = time.time()
for stock in SandP500:
find_price(stock)
find_PE(stock)
find_MKTCap(stock)
find_PHistory(stock, "90d")

# Format the prompt and get AI prediction
full_prompt = format_full_prompt(StockPrices)
prediction = get_prediction(full_prompt)

print("Prediction:", prediction)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Time elapsed to complete : {elapsed_time:.2f} seconds")



# Run the script
if __name__ == "__main__":
main()

I have recently made the decision that I wanted to pour countless hours into attempting to make simple predictions using AI into the stock market. I'll paste my Python code below, and as of right now everything outputs. I would appreciate some recommendations for what else I could scrape to make the results more accurate, and a better AI to use. Thanks all!
import subprocess
import time
from bs4 import BeautifulSoup
import requests
import yfinance as yf
import ollama
import textwrap

print("innit done")

# Global variables
FindPriceRunning = True
PERunning = True
MKTCapRunning = True
StockPrice = " "
StockPrices = "Jack is cool"
FindPriceRT = 1
StockPE = " "
StockPEs = "Jack is really cool"
PERT = 1
MKTCap = " "
MKTCaps = "Jack is super super cool"
MKTCapRT = 1
Stock_Histories = 'Jack is cool, and the dataset begins here'
def find_price(stock):
global StockPrices
global StockPrice
global FindPriceRT
print(PERT)
print(f"Finding attributes for {stock}...")
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')

# Find stock price
price_element = soup.find('span', {'data-testid': 'qsp-price'})
if price_element:
stock_price = price_element.text
StockPrice = f"{stock} Stock Price: ${stock_price}"
print(StockPrice)
StockPrices += f", and {StockPrice}"
FindPriceRT += 1
except (requests.exceptions.RequestException, Exception) as e:
pass
def find_PE(stock):
global StockPEs
global StockPE
global PERT
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')
pe_element = soup.find('fin-streamer', {'data-field': 'trailingPE'})
if pe_element:
stock_pe = pe_element.text
StockPE = (f"{stock} P/E Ratio: {stock_pe}")
print(f"{stock} P/E Ratio: {stock_pe}")
StockPEs = (f"{StockPEs}, and {StockPE}")
PERT += 1
except (requests.exceptions.RequestException, Exception):
pass
def find_MKTCap(stock):
global MKTCaps
global MKTCap
global MKTCapRT
try:
source = requests.get(f"https://finance.yahoo.com/quote/{stock}/")
source.raise_for_status()
soup = BeautifulSoup(source.text, 'html.parser')

# Find Market Cap using the correct tag
MKTCap_element = soup.find('fin-streamer', {'data-field': 'marketCap'})
if MKTCap_element:
MKTCap = MKTCap_element.text # Corrected this line
MKTCap = f"{stock} Market Cap: {MKTCap}"
print(MKTCap)
MKTCaps = f"{MKTCaps}, and {MKTCap}"
MKTCapRT += 1
except (requests.exceptions.RequestException, Exception):
pass
def find_PHistory(stock, period="30d"):
global Stock_Histories
# Download historical data for the given stock symbol
data = yf.download(stock, period=period, interval="1d")

# Initialize the stock_history variabler
stock_history = ""
# Loop through each row to format the output and build the history string
for date, row in data.iterrows():
# Ensure the price is a float
close_price = float(row['Close'].iloc[0])
stock_history += f"{date.date()} {close_price:.6f}, "
stock_history = stock_history.rstrip(", ")
print('Stock history recorded over the past 1k days ')
Stock_Histories += (f"{stock}: {stock_history}")
print('Stock histories updated')

return stock_history


# List of S&P 500 stocks
SandP500 = ["FHI", "MMM", "AOS", "ABT", "ABBV", "ACN", "ADBE", "AMD", "AES", "AFL", "A", "APD", "ABNB", "AKAM", "ALB",
"ARE", "ALGN", "ALLE", "LNT", "ALL", "GOOGL", "GOOG", "MO", "AMZN", "AMCR", "AEE", "AEP", "AXP", "AIG",
"AMT", "AWK", "AMP", "AME", "AMGN", "APH", "ADI", "ANSS", "AON", "APA", "APO", "AAPL", "AMAT", "APTV",
"ACGL", "ADM", "ANET", "AJG", "AIZ", "T", "ATO", "ADSK", "ADP", "AZO", "AVB", "AVY", "AXON", "BKR", "BALL",
"BAC", "BAX", "BDX", "BRK.B", "BBY", "TECH", "BIIB", "BLK", "BX", "BK", "BA", "BKNG", "BWA", "BSX", "BMY",
"AVGO", "BR", "BRO", "BF.B", "BLDR", "BG", "BXP", "CHRW", "CDNS", "CZR", "CPT", "CPB", "COF", "CAH", "KMX",
"CCL", "CARR", "CAT", "CBOE", "CBRE", "CDW", "CE", "COR", "CNC", "CNP", "CF", "CRL", "SCHW", "CHTR", "CVX",
"CMG", "CB", "CHD", "CI", "CINF", "CTAS", "CSCO", "C", "CFG", "CLX", "CME", "CMS", "KO", "CTSH", "CL",
"CMCSA", "CAG", "COP", "ED", "STZ", "CEG", "COO", "CPRT", "GLW", "CPAY", "CTVA", "CSGP", "COST", "CTRA",
"CRWD", "CCI", "CSX", "CMI", "CVS", "DHR", "DRI", "DVA", "DAY", "DECK", "DE", "DELL", "DAL", "DVN", "DXCM",
"FANG", "DLR", "DFS", "DG", "DLTR", "D", "DPZ", "DOV", "DOW", "DHI", "DTE", "DUK", "DD", "EMN", "ETN",
"EBAY", "ECL", "EIX", "EW", "EA", "ELV", "EMR", "ENPH", "ETR", "EOG", "EPAM", "EQT", "EFX", "EQIX", "EQR",
"ERIE", "ESS", "EL", "EG", "EVRG", "ES", "EXC", "EXPE", "EXPD", "EXR", "XOM", "FFIV", "FDS", "FICO", "FAST",
"FRT", "FDX", "FIS", "FITB", "FSLR", "FE", "FI", "FMC", "F", "FTNT", "FTV", "FOXA", "FOX", "BEN", "FCX",
"GRMN", "IT", "GE", "GEHC", "GEV", "GEN", "GNRC", "GD", "GIS", "GM", "GPC", "GILD", "GPN", "GL", "GDDY",
"GS", "HAL", "HIG", "HAS", "HCA", "DOC", "HSIC", "HSY", "HES", "HPE", "HLT", "HOLX", "HD", "HON", "HRL",
"HST", "HWM", "HPQ", "HUBB", "HUM", "HBAN", "HII", "IBM", "IEX", "IDXX", "ITW", "INCY", "IR", "PODD",
"INTC", "ICE", "IFF", "IP", "IPG", "INTU", "ISRG", "IVZ", "INVH", "IQV", "IRM", "JBHT", "JBL", "JKHY", "J",
"JNJ", "JCI", "JPM", "JNPR", "K", "KVUE", "KDP", "KEY", "KEYS", "KMB", "KIM", "KMI", "KKR", "KLAC", "KHC",
"KR", "LHX", "LH", "LRCX", "LW", "LVS", "LDOS", "LEN", "LII", "LLY", "LIN", "LYV", "LKQ", "LMT", "L", "LOW",
"LULU", "LYB", "MTB", "MPC", "MKTX", "MAR", "MMC", "MLM", "MAS", "MA", "MTCH", "MKC", "MCD", "MS", "MSFT",
"MAA", "MCO", "MCK", "MDT", "MRK", "MMC", "MOS", "NAVI", "NEE", "NKE", "NOC", "NLSN", "NTAP", "NVR", "NFLX",
"NVDA", "NEWR", "ORLY", "O", "ODFL", "OMC", "ON", "PGR", "PEP", "PFE", "PM", "PNC", "PSA", "PEG", "PYPL",
"PNR", "PPL", "PXD", "QRTEA", "QCOM", "REGN", "ROST", "RMD", "RSG", "RTX", "OZK", "SPGI", "SBUX", "SLB",
"SNA", "STT", "SYK", "SPG", "SWKS", "SIVB", "TMO", "TSLA", "TXN", "TGT", "TRV", "UNP", "UNH", "UPS", "USB",
"VZ", "V", "VRSK", "WMT", "WBA", "WM", "WELL", "WBD", "WDC", "WEC", "WRB", "WU", "XEL", "XLNX", "XYL",
"YUM"]


def format_full_prompt(stock_data):
full_prompt = f"""
Based on the following stock data, predict **which stock is most likely to increase the most relative to its current value in 90 days**:
1 **Stock Prices** (Current Price of each stock in the S&P 500):
{StockPrices}
2️ **P/E Ratios** (Valuation of each stock in the S&P 500):
{StockPEs}
3️ **Market Caps** (Total value of each stock in the S&P 500):
{MKTCaps}
4️ **Stock Price History (Last 1000 days)**:
{Stock_Histories}
**Using this data, analyze trends and return the top 5 stocks that are most likely to increase in price over the next 90 days.**
Rank them by expected percentage growth and explain why they were chosen.
"""
return full_prompt


def get_prediction(full_prompt):
response = ollama.chat(model="llama2", messages=[{"role": "user", "content": full_prompt}])
print(response)
return response.get('text', 'No text key in response')


def main():
start_time = time.time()
for stock in SandP500:
find_price(stock)
find_PE(stock)
find_MKTCap(stock)
find_PHistory(stock, "90d")

# Format the prompt and get AI prediction
full_prompt = format_full_prompt(StockPrices)
prediction = get_prediction(full_prompt)

print("Prediction:", prediction)
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Time elapsed to complete : {elapsed_time:.2f} seconds")



# Run the script
if __name__ == "__main__":
main()