eiten | Statistical and Algorithmic Investing Strategies | Portfolio library

Let us see how we can use all the strategies given in the toolkit to build our portfolios. The first thing you need to do is modify the stocks.txt file in the stocks folder and add the stocks of your choice. It is recommended to keep the list small i.e anywhere between 5 to 50 stocks should be fine. We have already put a small stocks list containing a bunch of tech stocks like AAPL, MSFT, TSLA etc. Let us build our portfolios now. This is the main command that you need to run. This command will use last 5 years of daily data excluding the last 90 days and build several portfolios for you. Based on those portfolios, it will then test them on the out of sample data of 90 days and show you the performance of each portfolio. Finally, it will also compare the performance with your choice of market index which is QQQ here. Let's dive into each of the parameters in detail.
is_test: The value determined if the program is going to keep some separate data for future testing. When this is enabled, the value of future_bars should be larger than 5.
future_bars: These are the bars that the tool will exclude during portfolio building and will forward test the portfolios on the excluded set. This is also called out of sample data.
data_granularity_minutes: How much granular data do you want to use to build your portfolios. For long term portfolios, you should use daily data but for short term, you can use hourly or minute level data. The possible values here are 3600, 60, 30, 15, 5, 1. 3600 means daily.
history_to_use: Whether to use a specific number of historical bars or use everything that we receive from yahoo finance. For minute level data, we only receive up to one month of historical data. For daily, we receive 5 years worth of historical data. If you want to use all available data, the value should be all but if you want to use smaller history, you can set it to an integer value e.g 100 which will only use the last 100 bars to build the portfolios.
apply_noise_filtering: This uses random matrix theory to filter out the covariance matrix from randomness thus yielding better portfolios. A value of 1 will enable it and 0 will disable it.
market_index: Which index do you want to use to compare your portfolios. This should mostly be SPY but since we analyzed tech stocks, we used QQQ.
only_long: Whether to use long only portfolio or enable short selling as well. Long only portfolios have shown to have better performance using algorithmic techniques.
eigen_portfolio_number: Which eigen portfolio to use. Any value between 1-5 should work. The first eigen portfolio (1) represents the market portfolio and should act just like the underlying index such as SPY or QQQ. The second one is orthogonal and uncorrelated to the market and poses the greatest risk and reward. The following ones have reduced risk and reward. Read more on eigen-portfolios.
stocks_file_path: File that contains the list of stocks that you want to use to build your portfolio.