PT Reddit Mentions | ChartExchange

i asked ChatGPT if it can gather all 500 stocks in SPY and gather all price targets and create an average percent for all stocks to hit their PT so i can see the upside of SPY. apparently ChatGPT ran into rate limits so it told me to run it but im not at my cpu. can one of you regards run it and let me know what percentage it comes up with?
# sp500_price_targets.py
# Requires: requests, pandas, tqdm
# pip install requests pandas tqdm
import requests
import pandas as pd
import time
import json
import re
from tqdm import tqdm
from urllib.parse import quote_plus
WIKI_URL = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
HEADERS = {
"User-Agent": "Mozilla/5.0 (compatible; PriceTargetBot/1.0; +https://example.com/bot)"
}
OUT_CSV = "sp500_price_targets.csv"
SLEEP_BETWEEN = 0.9 # pace requests to avoid blocking; increase if you get blocked
RETRY_DELAY = 5
MAX_RETRIES = 3
def get_sp500_list():
r = requests.get(WIKI_URL, headers=HEADERS, timeout=20)
r.raise_for_status()
# simple parsing: find table rows with tickers (works for Wikipedia page layout)
df_list = pd.read_html(r.text)
# the first table on the page is the constituents
df = df_list[0]
# expected column 'Symbol' or 'Ticker'
if 'Symbol' in df.columns:
sym_col = 'Symbol'
elif 'Ticker' in df.columns:
sym_col = 'Ticker'
else:
raise RuntimeError("Couldn't find ticker column in Wikipedia table.")
df = df[[sym_col, 'Security']].rename(columns={sym_col: 'Ticker', 'Security': 'Name'})
# normalize tickers for Yahoo (BRK.B -> BRK-B, BF.B -> BF-B)
df['Ticker'] = df['Ticker'].astype(str).str.replace('\\.', '-', regex=True)
return df
def fetch_yahoo_price_and_target(ticker):
"""
Fetch the Yahoo Finance quote page and pull current price and analyst targetMean.
Returns a dict with keys: currentPrice, targetMeanPrice, numberOfAnalysts (if present)
"""
base = f"https://finance.yahoo.com/quote/{quote_plus(ticker)}"
for attempt in range(MAX_RETRIES):
try:
r = requests.get(base, headers=HEADERS, timeout=20)
if r.status_code == 200 and r.text:
# Yahoo embeds a JSON blob in the page under 'root.App.main'
m = re.search(r'root\.App\.main\s*=\s*({.*?})\s*;\n', r.text, re.S)
if not m:
# fallback: try to fetch the /analysis page which sometimes has target info
r2 = requests.get(base + "/analysis", headers=HEADERS, timeout=20)
r2.raise_for_status()
# attempt find 'Target Mean Price' text on analysis page
m2 = re.search(r'"targetMeanPrice":\s*([\d\.]+)', r2.text)
current_price = None
cp = re.search(r'"regularMarketPrice":\s*{[^}]*"raw":\s*([\d\.]+)', r.text)
if cp:
current_price = float(cp.group(1))
target = float(m2.group(1)) if m2 else None
return {"currentPrice": current_price, "targetMeanPrice": target, "raw": r2.text}
j = json.loads(m.group(1))
# navigate JSON for price and target fields (Yahoo's structure may change)
try:
price = j['context']['dispatcher']['stores']['QuoteSummaryStore']['price']['regularMarketPrice']['raw']
except Exception:
# fallback
price = None
target = None
num_analysts = None
try:
target = j['context']['dispatcher']['stores']['QuoteSummaryStore']['financialData']['targetMeanPrice']['raw']
except Exception:
# sometimes under 'recommendationTrend' or 'analysis' store
try:
target = j['context']['dispatcher']['stores']['QuoteSummaryStore']['earningsTrend']['trend'][0]['max']
except Exception:
target = None
# often number of analysts available under 'upgradeDowngradeHistory' or 'summaryDetail'
try:
# some pages include 'numberOfAnalystOpinions'
num_analysts = j['context']['dispatcher']['stores']['QuoteSummaryStore']['recommendationTrend']['trend'][0].get('numberOfAnalysts')
except Exception:
num_analysts = None
return {"currentPrice": price, "targetMeanPrice": target, "numAnalysts": num_analysts}
else:
time.sleep(RETRY_DELAY)
except Exception as e:
time.sleep(RETRY_DELAY)
# if we reach here, failed
return {"currentPrice": None, "targetMeanPrice": None, "numAnalysts": None}
def main(limit=None):
df = get_sp500_list()
if limit:
df = df.head(limit)
rows = []
for _, row in tqdm(df.iterrows(), total=len(df), desc="tickers"):
ticker = row['Ticker']
name = row['Name']
data = fetch_yahoo_price_and_target(ticker)
cp = data.get('currentPrice')
tgt = data.get('targetMeanPrice')
n_analysts = data.get('numAnalysts')
pct_to_target = None
if cp and tgt:
try:
pct_to_target = (float(tgt) / float(cp) - 1.0) * 100.0
except Exception:
pct_to_target = None
rows.append({
"Ticker": ticker,
"Name": name,
"CurrentPrice": cp,
"TargetMeanPrice": tgt,
"NumAnalysts": n_analysts,
"PctToTarget": pct_to_target
})
time.sleep(SLEEP_BETWEEN)
out = pd.DataFrame(rows)
out.to_csv(OUT_CSV, index=False)
print(f"Saved results to {OUT_CSV}")
return out
if __name__ == "__main__":
# optionally set a limit for a quick sample, e.g. main(limit=20)
df_out = main(limit=None)
print(df_out.head(10))

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