TradeStation Strategy Testing – Violating These Steps Will Damage Your Account
One of the most rewarding experiences for a TradeStation trader is to pick up a performance report that proves their great strategy idea is indeed a profitable strategy. Strategy testing done properly, as is outlined in this article, can verify the efficacy of your trading strategy and give https://emet-trading-solutions.com/tradestation you confidence to start trading it. But be forewarned, strategy testing done improperly can lead you toward financial destruction.
Strategy testing done incorrectly can result in false hope in a losing strategy.A trader recently shared his experience of getting great results from strategy testing his idea, but after trading it live in the market, he was losing money every day. He was baffled about what he did wrong. Having gotten excellent results on his back-testing performance report, he wondered why his promising strategy was draining his trading account. The problem was he violated several of the proper steps necessary for reliable strategy testing.
With the knowledge of how to get an accurate performance report you will be able to trust your strategy in live trading and protect your trading account. In order to properly test a strategy, there are 5 main steps that are vital to follow; configure TradeStation, “in-sample data” testing, “out-of-sample data” testing, live forward testing on the simulator account, and real live trading execution.
Step 1: Configure TradeStation
Before you begin testing your data, you must configure TradeStation so that the data it pulls onto your performance report will be accurate. Follow these 3 critical items to properly configure TradeStation.
(a) In your TradeStation platform menu, go to “format symbol” and give TradeStation a starting and ending date to test. This historical date range is called the “in-sample data.” Do not include the most recent six months in this “in-sample data.” The most recent six months is called the “out-of-sample data,” and it will be used later during your “out-of-sample data” testing step.
(b) Next, in your TradeStation platform menu, go to “format strategy” and select “properties for all.” Now select the “general” tab and enter the commissions and slippage (be as realistic as possible, or estimate too high if you are not sure). If this step is skipped, then the strategy testing performance report will be meaningless. If this is not done you might have a good looking performance report equity curve, but as soon as you enter the commissions and slippage figures the equity curve can reverse into an underwater equity curve.
(c) The last configuration step is under “properties for all” under the “general” tab. Look in the bottom left section called “strategy testing resolution.” Check the “look-inside-bar back-testing” option and then select the smallest time frame available for your chart style to make the strategy testing more closely resemble live data. When strategy testing, TradeStation uses the open, high, low, and closing data, thus the larger the time frame bar, the more distorted the strategy testing performance report can be. This “look-inside bar back-testing” option will make the computer do a lot more strategy testing calculations. This may really slow down your performance report generation, so please be patient. For an accurate performance report you must use the “look-inside bar back-testing” option.
These configuration steps are critical to getting an accurate performance report, so be sure this is completed precisely before continuing. Once TradeStation has been configured correctly, you can begin testing your strategy.
Step 2: In-Sample Data Testing (also called “Back testing”)
You are now ready to start testing your strategy idea. We will begin with testing the “in-sample data” that you set up for testing during the configuration steps. Begin with bringing up a TradeStation performance report. Right now I have a performance report in front of me that I will refer to, but you will be looking at your own performance report to analyze your own numbers. This is what we will be referring to in the steps below. There are 7 sub-steps to “in-sample data” testing, as follows:
First, look at how many trades the strategy made. To reduce strategy testing errors, where error is defined by [ error = 1 / Square Root (Number Trades In Test) ], you want at least 400 trades to reduce the margin for error to 5% in your strategy testing results. At 100 trades you have a 10% margin for error. The greater the number of inputs in your strategy that you optimize, the greater the number of trades you need to keep from over optimizing your strategy.
Also look at how many times the strategy traded on average per day. The more often a strategy trades the more profit it can generate.
In the performance report that I am looking at, it traded 397 trades in the last 3 1/2 months, averaging 5.3 trades per day.
Second, look at the “Average Trade Amount.” It needs to be large enough that slow order fills and/or larger than normal slippage does not kill the profitability of the strategy.
In my report the “Average Trade Amount” is $162.32. The commissions and slippage amount as defined in the set up steps is already subtracted in this performance report.
65% of the time this strategy trades 1 contract.
35% of the time this strategy trades 3 contracts.
10% of the time this strategy trades 5 contracts.
Third, look to see if the “Profit Factor” and “Ratio Average Win-Average Loss” are both above 1.5 and the percentage of winning trades around 45% or better
This strategy had a “Profit Factor” of 1.83.
This strategy had a “Ratio Average Win-Average Loss” of 2.28 (2.28 means breakeven is around 28% “Percent Winning Trades”)
On this strategy the “Percent Winning Trades” was 44.58%.
Fourth, look at the trade list page and assess the profit run ups and draw downs column. Notice how many trades made money and how much money they made before the trade exit occurred. Looking at what amount of money was made in relation to the profit run up and draw down, you want to know if managing the trades could generate more profits. The example used here shows that a good percentage of trades made much higher profits than where the automated exit points occurred.