![]() ![]() In the package, we have included code for simple visual analytics with the data. Note, we pass on an argument ‘profile’, ‘metrics’ or ‘ration’, and get back the list of corresponding data items. We can easily examine the kind of data that has been pulled from the API service. Let’s say we want to download all the financial data for the following companies,Ī nicely formatted DataFrame is ready for your use! Building a DataFrame is as easy as passing on a list of ticker symbols and the code does all the data scraping and structuring job for you. We provide a built-in method to do just that. Working with Python dictionaries is fine but for large-scale data analytics, we should think of building a Pandas DataFrame. A partial screenshot is provided below.īuild a DataFrame with multiple companies’ data If we examine this dictionary, we will note that a huge amount of data has been pulled from the API endpoint. We can try it but we won’t be successful because we have not registered the secret key with the class object yet.įor all the methods in this class, we have to pass on the ticker symbol of the company (on the US financial market). Let’s say we want to build a data dictionary for the company Apple (with the ticker symbol ‘AAPL’). We definitely want to start pulling the data now. We cannot access the data without registering the key Note that you need to have a file called Secret_Key.txt in the same directory as the code files. ![]() Read the secret API key from a file and register it We start by importing regular libraries and the class object. If the author has any questions, just reach out to me! Happy to share my experiences.To keep the code clean, in this article, we show the use of the class in a test Jupyter notebook. I'm constantly adding new indexes and sources (see ) but it's tough! Lots of scraping, downloading and parsing CSVs, Excel sheets, some puppeteer magic. I only focus on index data so it's a bit easier but it's still hard. In my experience from developing Backtest ( ), a backtesting tool for EU index investors, the hardest part has been finding good and reliable data that goes as far back in time as possible. I suspect no retail investors will buy into rallies and sell into slumps, exactly the behavior Bullish's tagline mocks. If SP500 futures moving but markets are closed, what action could you possibly undertake profitably? Market open is a special auction you can't really arbitrage like that, so you'd be hoping for intraday moves? Are these the behaviors you _want_ to be inducing? Should retail investors be making trades during times of high volatility? Gather more data more frequently (reload WSJ / Y! finance? front page?)Ģ. Looks like the behaviors this information is intended to induce is:ġ. > The idea is for the mailing list to go out every weekday before the market opens so you can be informed enough to decide if it’s worth paying close attention to the market on any given day and make some moves. my framework for data collection and reporting: what behavior, if any, do I expect to happen as a result of data? If the answer is none, there is no point in the email / report / pagerduty alert. I appreciate the effort, especially the free tier only approach, but. Tried Robinhood Snacks but can't get over the feeling it's written by low paid interns and doesn't offer any actual insight, just condensed mainstream narratives. (value/quant stuff from a billionaire hedge fund manager).(somewhat annoying but also interesting at times).Matt Levine's Money Stuff (couldn't find the sign up link but it's always great).I did some research on the finance newsletter space myself a while back, here's some of my favorites I ran across: Might also be cool if the email had other features like Tax Loss Harvest notifications when an ETF you're watching drops more than X%. I find myself often googling to check S&P futures so this email would remove that step for me. I think that has a lot of overlooked behavioral value, why not also feature 20 & 30y data, considering this is the time series many young investors will need to consider for retirement? The number one issue I have with a lot of finance newsletters is the lack of historical context so I think it's extremely valuable you featured historical data right next to the daily data to give context on how small daily moves actually are (minus this latest bout of volatility). ![]()
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