Category Archives: fuzzy logic

From Regime Switching to Fuzzy Logic -SP500

In the previous post I showed how one can implement “regime” switching to create a strategy that switches between a mean-reverting and a momentum sub-strategy.

Can we do something similar (or better) using Fuzzy Logic?

  Here’s the setup: (here for some Fuzzy Logic backround)

We create a basic membership function for the RSI(2) indicator: “Low”, Medium” and “High”
We create a basic membership functions for the Correlation* indicator: “Low”,”High”.

We implement these rules:
1.//mean revert – LOW Autoccorelation
IF “rsi” is  “Low” AND “autocorrel” is “Low”, “Action”, 1 ; //Buy
IF “rsi” is “High” AND  “autocorrel” is “Low”, “Action”, -1 ; //Sell

//MOM – HIGH Autocorrelation
IF “rsi” is “Low” AND “autocorrel” is “High”, “Action”, -1 ; //Sell
IF “rsi” is “High” AND “autocorrel” is “High”, “Action”, 1 ;  //Buy

Here’s the Equity:

As with Regime switching we can use Fuzzy Logic to solve the problem of using one strategy for trading pre- and post-2000 SP500. Furthermore, we have more robust and less specific rules to deal with (buy on “Low” RSI rather than Buy=RSI2<30).

*By “Correlation Indicator” I am referring to the  22-day Correlation (see previous post) between the current return and the previous day’s return. In Amibroker Code: 

Trading with Fuzzy Logic

The case for Fuzzy Logic in Trading

The more I backtest strategies the more I feel the need for robustness in a system. There is no point to optimize return. One should optimize certainty of positive return. Most strategies that do really well in the past are over complicated and over-fitted and tend to loose money.

One way to achieve robust results is to use approximate values.

You can say buy when RSI(3)<25
Or you can say Buy when  RSI(3)  is fairly Low

You can say Buy on Monday of Expiration Week
Or Buy around the middle of the month.

So here’s a system:
Buy when RSI(3) is Low and we are before Expiration week. Let’s test it.
How do we code this in
1. Matlab
2. Amibroker

1. Matlab –
First we construct a Fuzzy Model that will take 2 Inputs
a. RSI(3)
b.DayofMonth     (i.e., 1…31)

Type “fuzzy” in the Terminal and voila…
We then add a second input and name both.

Set the ranges, 0-100 for rsi3 and 1-31 for day of month.
We then move around the MF’s (memebership functions) until we come up with this:

We then come up with the rules:
If rsi is low and month is around the middle then Buy
If rsi is high and month is early Sell
if rsi is High then Sell

Here’s what the rules look like visually:

The Lower the RSI the closer to 1 the output.
The More in the middle of the month the closer to 1 the output.
Keep in mind the Output ranges from 0 to 1. Towards 1 is Buy, towards 0 is Sell.

Here’s how the fuzzy model incorporates the “Common sense” non linearity (for a simple 2 input model)

This may look “scientific” but all it does is say “I like Mid-month and Low RSI”.

So now we have our model. We will save it as “RSI3_ExpWeek_Fis.fis”.

Now on to the script. We need daily prices of SPY to calculate RSI(3) and the day number. We ‘ll get it from Yahoo.


 %Download “SPY” from Yahoo


inputs=[rsi3 dayofmonth];

%open the fis model
b = readfis(‘RSI3_ExpWeek_Fis.fis’);

%Feed the inputs and get result (0…1)

%if result <0.5 Buy, else Sell

[pnl,pnlvector, sh]= backtestlongAmount(ticker_fints,signal,’open’,1,100000);


*This code uses Custom Functions. Read the article and download them  here.
So how did we do?

On the next post I will show how to code this in Amibroker.