Two problems that often result from system optimization are one (1) a reduction in the number of trades, a system that generates too few trades can both be more difficult to trade and a low number of trades does not inspire confidence, and two (2) there is a selectivity or specialization risk that, by sheer bad luck, the specific chosen parameter values will under perform in the future even while the basic system continues to work across most other parameters. It is thought that hedge funds often run classes of similar systems to avoid the risk of simply choosing a set of unlucky parameters but individual futures traders may not have the capital to deploy a class of similar systems for essentially a single trading concept.
We present the concept of signal multiplexing as a method to increase the number of trades which offers the possibility of greater confidence and profits– while also providing the added bonus of reducing the specialization risk of trading a single parameter set.
To demonstrate the concept, we build a RSI swing trading system for the ES but instead of optimizing for a single specific optimum: we optimize across three signals any of which can get us into or out of a trade. We chain the signals using conditional “OR statements”.
RsiLength1(1), RsiLength2(2), RsiLength3(3),
Value1 = Rsi(Close,RsiLength1);
Value2 = Rsi(Close,RsiLength2);
Value3 = Rsi(Close,RsiLength3);
If value1 cross under RsiBuyThreshold1 or
value2 cross under RsiBuyThreshold2 or
value3 cross under RsiBuyThreshold3
Then buy next bar at market;
If value1 cross over RsiSellThreshold1 or
value2 cross over RsiSellThreshold2 or
value3 cross over RsiSellThreshold3
Then sell next bar at market;
For comparison purposes, we compare the RSI 2 using standard settings against our computer optimized RSI Multiplex system.
|Total Net Profit||89,600||133,925|
|Avg Trade Net Profit||227||260|
We generate significantly greater total net profit, greater profit factor, more trades, increased the largest winning trade, reduced the largest losing trade, and smoothed the equity curve. We held out the last 30% of data out-of-sample, and we can compare how the systems did during the out-of-sample period:
Our multiplexed system did significantly better during the out of sample period. As a final sanity check, we also optimized a single entry and exit and the net profits were only $98,475 while the profit factor was 1.58: our Multiplexed system outperformed. While in this case, the results from signal multiplexing were superior in every way, it is worth pointing out that even if the results weren’t the best that a multiplexed system could still do better in reality because of the benefits of diversification. In this case, we optimized across three entries and exits which was chosen somewhat arbitrarily but one could certainly optimize across even more signals. One last check is because this is a long biased system, it is possible that simply being long more could have enhanced the results but even when looking at the short only results: we seen greater short profits confirming the potential value for multiplexing signals.
Note: Because we used genetic optimization, your results may differ slightly from ours.
Curtis is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact him at firstname.lastname@example.org.
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