ARM4
| Overview | ARM2 | ARM3 | ARM4 |
Breakthrough in Artificial
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ARM3 is based on the concept of measuring technical inputs and feeding them into a Neural Network to find profitable relationships. This process has yielded some of our best strategies, including NSP-33 NN. However, one of the challenges in building these strategies has always been, “How do we identify the best inputs to use?” This year, we set about answering this question. The Consensus Block performs two separate jobs, the first of which is called “Data Mining.” For this step, we define a list of indicators and formulas that we think might be predictive. Then we enter them in the Consensus Block and let it measure them across a large symbol list and years of data. The result is a table that indicates the predictability of each input (see example) for different ranges of the input. In the second step, the Consensus Block combines the given inputs using a Genetic Algorithm, resulting in a score from 0 to 100. We only trade those signals with the highest scores. With the Consensus Block, we can now greatly accelerate our development of profitable trading strategies. The fi rst is NSP-35 CB (discussed next). Using the Consensus BlockIn the example to the right, we are using 5 indicators. Values of the GAMS Oscillator range from -4 to +4. The Consensus Block divides this range into 5 equal parts called “Bins.” It then measures the profi tability of GAMS in each of these ranges to determine a score for the corresponding bin. Later, during the Signal Generation process, the Consensus Block combines bin scores to generate a Consensus Score. |
The “Rocket Science” (How it Works)
The following example demonstrates how the various features of the Consensus block can be used to gain knowledge into predictive measurements and create a trading strategy. As previously discussed, several configurations are possible. In order to prove the concept, we will employ the simplest configuration that consists in a strategy with only two blocks.
Simple Consensus Strategy flowchart
Step 1: Data Mining
First, we select a comprehensive number of measurements that - we think - may characterize the behavior of the symbols in our list. For the present example, we are using the SP100 in the daily timeframe. The following dialog box shows some of the variables that were selected. Note how some measurement formulas are repeated with different parameter values, usually the Period parameter in order to see whether a slower or a faster indicator is more predictive. For sake of simplicity, no conditions were specified in this example.
In the Consensus Block, we define the measurements (indicators) we will test.
Measurements can be technical indicators, variations on price or combinations.
A total of 51 variables were specified in this Consensus block. In order to reduce the number of variables to a smaller set of effective measurements, we will start by performing a Data Mining run (with Perform Data Mining Only checked in the dialog box). In addition, because we intend to collect data at every bar for several years (from 2001 to 2006) and over about 100 symbols, we increase the Max Samples setting to 1,000,000, thus ensuring that all collected data samples are represented in the statistical results. The Data Mining run can now be initiated. For a large experiment such as the one described, this run can take several hours, depending on the computer system's specifications.
When the run is completed, we access the Data Mining and Matrix Analysis interface in order to identify the measurements that showed a significant correlation with the recorded target. The next figure, taken from the Consensus Block Analysis screen, depicts the relationship between CCI(9) and the average target value. With the exception of Bin(1), a well-defined decreasing function is clearly visible. After reviewing quartile and standard deviation measurements in the statistics table, we can safely say that – for this particular setup – CCI(9) has predictive value.
Target Sensitivity plot for CCI(9).
However, the next view shows Close / Close ten bars ago (C / C[10]) without significant correlation. This variable can be deleted from the strategy to simplify the configuration in preparation for a full training session.
![C over C[10]](images/ARM4_clip_image009.gif)
Target Sensitivity plot for C / C[10].
After repeating this analysis for all variables, we selected the smallest number that exhibit the best correlation with the targets:
Figure 15. Final Formulas chosen from Consensus Run
Step 2: Evaluating the Results
After we identify the indivators and other formulas we want to use for trading, we train again on ONLY those values, allowing the Consensus Block to combine individual values to produce a Consensus Score after it finishes its Data Mining Step. Consensus Scores vary from 60 to 100. By increasing the Consensus Cutoff and running profitability tests on the resulting signals, we see a DEFINITE RELATIONSHIP between CONSENSUS SCORES and PROFITS.
The following diagrams depict the number of trades and average profit per trade results obtained by varying the Consensus Cutoff (Filter) value, the back-test is the same period used for data mining runs, while the forward-test is the remaining data up to July 7th, 2009. You can see from the graph that the profit per trade increases while the number of trades decreases at higher cutoff values, confirming that the chosen variables can indeed produce valuable predictions for the 5-bar price movement.
Figure 18. Forward-test Profit per Trade vs Consensus Cutoff
This plot not only proves that the Consensus Block “works” – it shows greatly increased profits at high Cutoff Values. The next plot shows the expected relationship between Cutoff Values and Trade Count – the higher the Cutoff, the fewer trades “pass” to the Vote Line. But, the great thing about the Consensus Block is, even at high Cutoff levels (like 96) there are still a very large number of trades.
Number of Trades in the Forward Test vs. Consensus Cutoff Value
Full Statistics for different Consensus Cutoff values.
Step 3: Trading with the Results
The next screen shot shows how the consensus block in this strategy was able to identify the bottom of General Dynamics Corp on 3/9/2009 (in the forward-test period) by combining the predictions obtained by each of the indicators displayed in the chart. Trades like this are common in the output of the new NSP-35 CB Strategy, to be released to the Nirvana Club in the Fall of 2009.
A Consensus Block trade on General Dynamics Corp. The chart
including plots for the indicators used by the Consensus Block.
In 2010, we added Data Mining to our Neural Network process, which dramatically improved performance. In some cases we are seeing the profi ts DOUBLE in our best Strategies!
Millions have been invested in the development of ARM4, which is by far the most powerful Artifi cial Intelligence technology ever created for traders. As a result of recent advances, we are now seeing 80% Hit Rates in many of our Strategies— the fi rst performance goal for The Ultimate Trading Machine.