Feedback on Algo that follow recommendation by Jim Cramer

Based on the provided code and the constraints of the question, it seems that 'Ali' has a well-structured algorithm. However, there are a few areas that could be improved or might cause issues:

1. Handling of Recommendations: In the on_data method, the algorithm processes buy and sell recommendations. However, it doesn't seem to do anything with these recommendations after fetching them. The algorithm should include logic to act on these recommendations, such as placing trades.

2. Universe Selection: The FundamentalSelectionFunction is used to select a universe of stocks based on certain criteria. However, this universe doesn't seem to be used anywhere else in the algorithm. If 'Ali' intends to use this universe for trading, he should include logic to handle these securities.

3. Liquidation Logic: The algorithm keeps track of entry dates for trades and liquidates them after a certain holding period. However, it doesn't seem to account for weekends or holidays when the market is closed. This could lead to holding positions longer than intended.

Here's a suggestion on how to handle the recommendations:

for i, recomm in enumerate([self.buy_recommendations, self.sell_recommendations]):
    if not recomm: continue
    for symbol in recomm:
        if i == 0:  # Buy recommendations
            self.SetHoldings(symbol, 1/len(self.buy_recommendations))
            self.entry_dates[symbol] = self.Time.date()
        else:  # Sell recommendations
            if symbol in self.Portfolio and self.Portfolio[symbol].Invested:
                self.Liquidate(symbol)

This code will equally distribute the portfolio among the buy recommendations and liquidate any securities that are in the sell recommendations. The entry date for each bought security is also tracked for the holding period logic.

Please note that this is just a suggestion and might need to be adjusted based on 'Ali's' specific strategy and risk tolerance.

It looks great, @ali-rouhghalandari! 

Where do you source the data? Are you sure it doesn't have a look-ahead bias? If it's daily data from, say, Quiver Quant, ensure that you add 24 hours to the release timestamp to reflect when the data becomes available. 

I don't think you need the fundamental data - you could add the assets you need from Crammer's reports.

You could simplify the code a lot if you used the algorithm framework to emit insights for the signals, and the portfolio systems will automatically rebalance/execute for you.