Web pages (and other public data) worth to save daily

Quanto said:
For traders, which web pages (and other data like CSV data) are worth to save/archive/collect on a reqular basis (like daily)?
For example the following data is IMO worth to collect daily:
https://finance.yahoo.com/world-indices
ftp://ftp.nasdaqtrader.com/SymbolDirectory/options.txt
...
More...
Why would you do that? Put tickers and dates in variables/list and:


import yfinance as yf

# Define ticker symbol and date range
ticker_symbol = "SPY"
start_date = "2000-01-01"
end_date = "2050-01-01"

# Fetch data
data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Save data to CSV file on desktop
desktop_path = "~/Desktop/spy_price_data.csv" # Update this path as per your desktop location
data.to_csv(desktop_path)

print("Data saved to:", desktop_path)





BTW option data, eod , I found more challenging using a python library, it doesn’t always is correct. But for a few hundred dolllars you can buy eod historical for al nyse tickers through various vendors (CBO datashop is more expensive)


Edit , now I see you want other data :) With the libraries you can collect a lot for free, usefulness is another story
 
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Quanto said:
For traders, which web pages (and other data like CSV data) are worth to save/archive/collect on a reqular basis (like daily)?
For example the following data is IMO worth to collect daily:
https://finance.yahoo.com/world-indices
ftp://ftp.nasdaqtrader.com/SymbolDirectory/options.txt
...
More...
%%
Congrats, you like to read a lot;
me 2, except i hand record stuff like SPY+ other stuff i trade in my trading notebook, 200dma+ such .............................
The most reliable computer, not in the same class as the weakest ink-paper .
And Quanto, you probably noticed it;
that intraday Hi-low column SPY [S&P500]......... is wrong .Its backwards, same for NasdaQ.
I print some charts also, but mostly things + i buy +sell + related.
 
2rosy said:
if your time is worth less than the cost of buying the data from others then I guess collect it and store it.
More...
It's about data (mostly lists) that is not easily recreateable from historic data.
Ie. you would need to write your own version of the program that created the data (by using other underlying data)...
One can't write that many programs for reproducing the same result, and with some cases it's even impossible...

So, saving (archiving) such data periodically (usually daily) makes sense, for later looking up to see what was on a specific date.

Ie. like collecting historic fundamental data of a company, like such data:
https://finance.yahoo.com/quote/AMD/key-statistics
It then allows you to answer for example this question:
What was the "Short % of Shares Outstanding" of the AMD stock on date x?

B/c normally such data is usually just updated (ie. overwriting the old data), not archived...
 
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