The world of algorithmic trading can be a bewildering place. The InvestorsEdge platform gives you access to millions of rows of price, fundamental and analyst estimate data, but it can be difficult to know where to start
Don't worry! To make life easier, we've added 4 successful starter strategies that show you how to use the platform. These strategies have all returned over 20% in the last 10 years and show you how our platform can be used to turbo-charge your investing
Improving the Simple First Model
The simple first model is the model you learnt to create in our Getting Started tutorial, so you'll know that it is made up of a few very simple factors. You can see from the results page that it has returned 20% annual gains over the past 8 years with a Sharpe Ratio (a measure of risk-adjusted returns) of 1.26 and a maximum drawdown of 19.3%.
Not bad, but let's see if we can improve things.
Let's refine our strategy to see if we can reduce its drawdowns - you can often reduce risk and improve risk-adjusted returns by selecting higher quality stocks that have a history of low volatility. So how do we identify if a stock is a quality one or not, and how do we check for volatility?
A simple yet surprisingly effective quality check is to see if the company is paying a dividend - distributing cash to shareholders is often a sign of a strong balance sheet and robust cash flows. We'll add this as a ranking factor along with a method to measure volatility of sales and earnings.
Adding new ranking factors
Let's add some new ranking factors to the model to see how they affect the outcome. Clicking on the Simple First Model on your home page takes you to the results screen:
where you can click on the Strategy Designer screen to make our changes, and then click on the Ranking section to make our changes:
We're going to add 3 new rows to the ranking elements list. Remember that the ranking section defines how our platform sorts its universe of stocks at each rebalance, so while the PE and PriceToFreeCashFlow ratios will remain the main drivers of our returns, we will change the ranking slightly by including the following:
- StdDev(EPS(q), 6)
- StdDev(Sales(q), 6)
Note the weight column - ranking weight controls how much each factor affects the sort order, with higher numbers affecting the sort order to a greater magnitude. We'll set the weights of each of our 3 new factors to 10.
We've also set the direction arrows to specify an ascending order for our StdDev functions and a descending one for yield, as we want to prioritise companies with lower volatility and higher dividends.
Let's backtest this change to see how our changes would have performed - click on the save button and select Choose Dates and Backtest:
So how did we do?
Our overall CAGR (Compound Annual Growth Rate) has improved slightly, but its in other areas that we can see an improvement in our returns:
Our maximum drawdown has reduced from 19.8% to 17.4%, our Sharpe Ratio is up to 1.34 from 1.26, Beta is reduced from 0.79 to 0.7 and annual average dividend yields have increased from 4.05% to have 4.38%. All this data means that our small changes have reduced the risks involved in running this strategy... and as investors that allows us to sleep slightly better at night.