## Monday, 28 September 2015

### Runge-Kutta Example and Code

Following on from my last post I thought I would, as a first step, code up a "straightforward" Runge-Kutta function and show how to deal with the fact that there is no "magic mathematical formula" to calculate the slopes that are an integral part of Runge-Kutta.

My approach is to fit a quadratic function to the last n_bars of price and take the slope of this via my Savitzky-Golay filter convolution code, and in doing so the Runge-Kutta value k1 can easily be obtained. The extrapolation beyond the k1 point to the points k2 and k3 is, in effect, trying to fit to points that have a half bar lead over the last available price. To accommodate this I use the last n_bar - 1 points of a 2 bar simple moving average plus the "position" of points k2  and k3 to calculate the slopes at k2 and k3. A 2 bar simple moving average is used because this has a half bar lag and is effectively an interpolation of the known prices at the "half bar intervals," and therefore points k2 and k3 are one h step ahead of the last half bar interval. The k4 point is again simply calculated directly from prices plus the half bar interval projection from point k3. If all this seems confusing, hopefully the Octave code below will clear things up.
clear all

%  create the raw price series
period = input( 'Period? ' ) ;
sideways = zeros( 1 , 2*period ) ;
uwr = ( 1 : 1 : 2*period ) .* 1.333 / period ; uwr_end = uwr(end) ;
unr = ( 1 : 1 : 2*period ) .* 4 / period ; unr_end = unr(end) ;
dwr = ( 1 : 1 : 2*period ) .* -1.333 / period ; dwr_end = dwr(end) ;
dnr = ( 1 : 1 : 2*period ) .* -4 / period ; dnr_end = dnr(end) ;

trends = [ sideways , uwr , unr.+uwr_end , sideways.+uwr_end.+unr_end , dnr.+uwr_end.+unr_end , dwr.+uwr_end.+unr_end.+dnr_end , sideways ] .+ 2 ;
noise = randn( 1 , length(trends) ) .* 0.0 ;
price = sinewave( length( trends ) , period ) .+ trends .+ noise ;
ma_2 = sma( price , 2 ) ;

%  regress over 'n_bar' bars
n_bar = 9 ;
%  and a 'p' order fit
p = 2 ;

%  get the relevant coefficients
slope_coeffs = generalised_sgolay_filter_coeffs( n_bar , p , 1 ) ;

%  container for 1 bar ahead projection
projection_1_bar = zeros( 1 , length( price ) ) ;

for ii = n_bar : length( price )

%  calculate k1 value i.e. values at price(ii), the most recent price
k1 = price( ii-(n_bar-1) : ii ) * slope_coeffs( : , end ) ;
projection_of_point_k2 = price(ii) + k1 / 2 ;

%  calculate k2 value
k2 = [ ma_2( ii-(n_bar-2) : ii ) ; projection_of_point_k2 ]' * slope_coeffs( : , end ) ;
projection_of_point_k3 = price(ii) + k2 / 2 ;

%  calculate k3 value
k3 = [ ma_2( ii-(n_bar-2) : ii ) ; projection_of_point_k3 ]' * slope_coeffs( : , end ) ;
projection_of_point_k4 = price(ii) + k3 / 2 ;

%  calculate k4 value
k4 = [ price( ii-(n_bar-2) : ii ) , projection_of_point_k4 ] * slope_coeffs( : , end ) ;

%  the runge-kutta weighted moving average
projection_1_bar(ii) = price(ii) + ( k1 + 2 * ( k2 + k3 ) + k4 ) / 6 ;

end

%  shift for plotting
projection_1_bar = shift( projection_1_bar , 1 ) ;
projection_1_bar( : , 1:n_bar ) = price( : , 1:n_bar ) ;

plot( price , 'c' , projection_1_bar , 'r' ) ;
This code produces a plot like this, without noise,

and this with noise ( line 12 of the code ).

The cyan line is the underlying price and the red line is the Runge-Kutta 1 bar ahead projection. As can be seen, when the price is moving in rather straight lines the projection is quite accurate, however, at turnings there is some overshoot, which is to be expected. I'm not unduly concerned about this overshoot as my intention is simply to get the various k values as features, but this overshoot does have some implications which I will discuss in a future post.

## Friday, 25 September 2015

### Runge-Kutta Methods

As stated in my previous post I have been focusing on getting some meaningful features as possible inputs to my machine learning based trading system, and one of the possible ideas that has caught my attention is using Runge-Kutta methods to project ( otherwise known as "guessing" ) future price evolution. I have used this sort of approach before in the construction of my perfect oscillator ( links here, here, here and here ) but I deem this approach unsuitable for the type of "guessing" I want to do for my mfe-mae indicator as I need to "guess" rolling maximum highs and minimum lows, which quite often are actually flat for consecutive price bars. In fact, what I want to do is predict/guess upper and lower bounds for future price evolution, which might actually turn out to be a more tractable problem than predicting price itself.

The above wiki link to Runge-Kutta methods is a pretty dense mathematical read and readers may be wondering how approximation of solutions to ordinary differential equations can possibly relate to my stated aim, however the following links visualise Runge-Kutta in an accessible way:
Readers should hopefully see that what Runge-Kutta basically does is compute a "future" position given a starting value and a function to calculate slopes. When it comes to prices our starting value can be taken to be the most recent price/OHLC/bid/offer/tick in the time series, and whilst I don't possess a mathematical function to exactly calculate future evolution of price slopes, I can use my Savitzky Golay filter convolution code to approximate these slopes. The final icing on this cake is the weighting scheme for the Runge-Kutta method, as shown in the buttersblog link above, i.e. which is just linear regression on the k slope values with the most recent price set as the intercept term! Hopefully this will be a useful way to generate features for my conditional restricted boltzmann machine, and if I use regularized linear regression I might finally be able to use my particle swarm optimisation code.

More in due course.

## Thursday, 10 September 2015

### Recent reading

In my last post I mentioned that I was going away for the summer, but now I'm back. During the summer I didn't get to do as much reading etc. as I had hoped, but I did manage to play around with the Rssa package for singular spectrum analysis and this is still an ongoing investigation. I also briefly looked at independent component analysis and the FastICA package.

The common theme of the above is the extraction of meaningful time series features, and this general area is what I will be looking into for my next set of posts.