An InvestorsEdge strategy is made up of 4 building blocks:
Step 1 - Build a universe of stocks
Your first task is to select a universe of stocks that you want to include in your strategy.
The universe below selects all Common Shares and Depository Receipts in the US with Market Capitalizations over $150m, and Yields and Net Current Asset Values greater than 0:
Step 2 - Define Rebalancing Rules
Next you set your strategy to rebalance at a frequency of your choosing and select the maximum number of positions it will open. At each rebalance point your strategy will buy the top positions in your universe, sorted by your ranking factors..
The rules below instruct the system to start with a cash balance of $10,000, to rebalance on the first day of each month and to open a maximum of 20 positions.
Step 3 - Rank your Universe
The secret behind our platform's success is our ranking engine - use it to sort your universe to end up with the stocks most likely to outperform their peers at the top of your list.
For each factor the platform sorts your universe of securities from best to worst and then assigns each a percentile score from 100 for the best to 0 for the worst. All the scores for a company are then combined into an overall rank based on weights you supply.
You have control over the sort order of each factor, which is controlled by the arrow symbols on each line.
The ranking system below sorts your universe of stocks by 7 factors, with PriceToFreeCashFlow having the highest weight and therefore the most influence on the order of your stocks. Notice that ROA is ranked from 100 to 0 within each sector.
Step 4 - Apply Entry & Exit Rules
Optionally, you can apply specific stop loss, entry and exit rules together with telling the system how to account for slippage and commissions.
Now you have learned about the four basic building blocks of an InvestorsEdge strategy you're ready to roll your sleeves up and get to work - the next section of tutorials shows you how to turbo-charge your trading ideas.
Putting It All Together
The strategy we have been using as an example is the Dividends model that you received as part of your starter pack - when you run a backtest against the last 18 years of data you get the following simulated returns: