Boot Camp: (#7) 200-50 EMA Momentum Universe - no results?

Here is the corrected version of the provided content:

Here are some steps to troubleshoot and potentially resolve the issue with the backtest not producing results:

1. Correct Code Formatting: Ensure the code follows PEP 8 standards. Here is the corrected code snippet:

    # region imports
    from AlgorithmImports import *
    # endregion
    from AlgorithmImports import *
   
    class EMAMomentumUniverse(QCAlgorithm):
   
        def initialize(self):
            self.set_start_date(2019, 1, 7)
            self.set_end_date(2019, 4, 1)
            self.set_cash(100000)
            self.universe_settings.resolution = Resolution.DAILY
            self.add_universe(self.coarse_selection_function) 
            self.averages = {}
   
        def coarse_selection_function(self, universe):
            selected = []
            universe = sorted(universe, key=lambda c: c.dollar_volume, reverse=True)
            universe = [c for c in universe if c.price > 10][:100]
   
            for coarse in universe:
                symbol = coarse.symbol
   
                if symbol not in self.averages:
                    # 1. Call history to get an array of 200 days of history data
                    history = self.history(symbol, 200, Resolution.DAILY)
   
                    # 2. Adjust SelectionData to pass in the history result
                    self.averages[symbol] = SelectionData(history)
   
                self.averages[symbol].update(self.time, coarse.adjusted_price)
   
                if self.averages[symbol].is_ready() and self.averages[symbol].fast > self.averages[symbol].slow:
                    selected.append(symbol)
   
            return selected[:10]
   
        def on_securities_changed(self, changes):
            for security in changes.removed_securities:
                self.liquidate(security.symbol)
   
            for security in changes.added_securities:
                self.set_holdings(security.symbol, 0.10)
   
    class SelectionData:
        # 3. Update the constructor to accept a history array
        def __init__(self, history):
            self.slow = ExponentialMovingAverage(200)
            self.fast = ExponentialMovingAverage(50)
   
            # 4. Loop over the history data and update the indicators
            for bar in history.itertuples():
                self.update(bar.index[1], bar.close)
   
        def is_ready(self):
            return self.slow.is_ready and self.fast.is_ready
   
        def update(self, time, price):
            self.fast.update(time, price)
            self.slow.update(time, price)
   

2. Check Universe Selection: Ensure that the coarse_selection_function is correctly filtering and selecting symbols. Make sure the universe is not empty after filtering.

3. History Data Retrieval: Confirm that the history method is correctly fetching historical data for the symbols. Check if the data is being retrieved and processed correctly in the SelectionData class.

4. Indicator Initialization: Ensure that the SelectionData class is correctly initializing and updating the Exponential Moving Averages with the historical data.

5. Debugging: Add debug statements to print out key variables and states, such as the number of symbols selected, the state of the indicators, and any errors or warnings during execution.

6. Review Documentation: Refer to the QuantConnect documentation for the history method and universe selection to ensure correct usage and implementation. Here are some relevant examples:

   • History Requests: You can request historical data using the history method. For example:

     trade_bars_df = self.history(TradeBar, [symbol], 200, Resolution.DAILY)
     

   • Universe Selection: Ensure that the universe selection function is correctly implemented and returning a list of symbols.

   • On Securities Changed: The on_securities_changed method should handle changes in the universe correctly by liquidating removed securities and setting holdings for added securities.

By following these steps, you should be able to identify and resolve the issue preventing the backtest from producing results.

Sources: Momentum And State Of Market Filters, Momentum Effect In Stocks, Ema Cross Universe Selection Algorithm, Constituents Universe Regression Algorithm, Futures Fast Trend Following, with Trend Strength