Backtesting the "Cramer Effect" & Recommendations from Cramer

Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which he recommends to buy. We will use this data to build a portfolio

The Cramer-effect/Cramer-bounce: After the show Mad Money the recommended stocks are bought by viewers almost immediately (afterhours trading) or on the next day at market open, increasing the price for a short period of time.

You can read about the setup and results in my Blog Post

You can also access the data easily with the Flat Data Viewer

How to use this repo

• Automatic data scraping (with Github Actions): Every day at 00:00 the scrape_mad_money.py tool runs and commits the data (if there was a change) to this repo. Feel free to use the created .csv file for your own projects
  • (Why do we scrape the whole data range every day?): This way we can see the changes from commit to commit. If anything happens which would alter the historical data, we would be aware.
• ("manual") Data scraping: Use the scrape_mad_money.py to get the buy and sell recommendations Cramer made over the years
  • Result is a .csv file which you can use
• Backtesting the buy calls: Use the notebook mad_money_backtesting.ipynb
  • To add your backtesting strategy, go to the backtesting_strategies.py file and implement yours based on the existing ones

Warning: code quality is just "mehh", I did not pay much attention here, this is just a quick experiment

Backtesting

In the notebook there are notes how the experiment(s) were conducted and facts, limitations about the approach. You can also add your own approaches.

Available Strategies:

• BuyAndHold (and repeat)
• AfterShowBuyNextDayCloseSell
• AfterShowBuyNextDayOpenSell
• NextDayOpenBuyNextDayCloseSell

Buy and Hold (and repeat) Results

How is this different from the real-life scenario?

We backtest each mentioned stock individually, then aggregate the results. We define a cash amount for each symbol separately (e.g. $1k) and not an overall budget. This change should not alter the expected returns (in %) much if we assume you have infinite money, so you can put your money in each of the mentioned stocks every day.

As we don't have (free) complete after-hours trading data, the scenario when we "buy at the end of the Mad Money Show" is approximated with the value of the stock value at market close. This obviously alters the end result for the short term experiments if a stock has high daily volatility and it changes a lot afterhours. (Of course the "buy at next trading day open" is not effected by this, only if we count on the after hours data).