NN median target predictions and how M5 charts react to them

To me, the target predictions (in blue), which were taken from a NARX neural network predicting the median price of the H4 bars of this USDCAD data, do not look like targets but more like support/resistance lines. Every 48 M5 closing points (one H4 closing point) the target price is updated with the network’s prediction.

I can’t make out what to possibly use this for, but they could be used as support/resistance guides. Any suggestions?


Part #2 – a decision support system

Error rate with the latest NARX simulations was moderately low. It was measured by dividing the difference between y-actual and y-predicted for the median price by the average length of the candle.

with H4 and H8 constructed bars, the lowest errors with respect to candle size was 18-21% in the case of some USDCAD data, and EURSEK (a very impressive 15%) which means that the average error between predicted and actual median was around only 15% of the average length of a bar.

Part #2 starts now:

Using that target prediction to create a profitable decision support system.

The main algorithm that I’m about to try is:

1. use NN to predict target for either H4 or H8

2. when target prediction is obtained, monitor the progression of the price:

if price moves away from target median by x pips (and a confirmation from indicator is obtained, if any), enter market.

3. exit market if either:

TP is hit

SL is hit

Target price candle is closed.

A PNN-based idea for a research

Probabilistic neural networks:

First of all, an idea originated from the work found in C.T.Lee and Y.P. Chen in Da-Yeh university in Taiwan (2007). They have used a back-propagation network to test the usefulness of stochastic indicators %K and %D.

Their mechanism of defining uptrend/neutral/downtrend regions certainly deserves more interest. Defining regions is always one of the hardest tasks one could accomplish, and it is always important to teach the network before it can generalize on other data.

This model can be used with a PNN, (or a self-organizing map) that is better at classification than a usual FFNN with backprop.

Another paper that might contribute to this idea is the work by Schumann and Lohrbach. It was a next-day price forecasting paper, yet they use a similarly useful classification system, for up days and down days, and what would be called a neutral day. It is similar to the threshold function used in my paper.

Left to decide, should the region classification be done manually or automatically? The first has the idea that we are using human cognition to divide regions. The disadvantage is that this kind of classification might slightly differ between one person or another, or might not have the greatest examples for a network to learn from.

Two NARX Daily results (during financial crisis time)

Note: window size here decreased to 3. It suited the Daily predicions better. Inputs used are median, close, macd and SMA5.

3 in-develoment strategies

So I haven’t posted in a few days, things have been completely hectic for me with all the new possibilities of data optimization and the new types of Neural Networks I’ve been using. To summarize what’s been going on with my research so far:

1. TP optimization with FFNN’s

Using 4 inputs (type of cross, MACD histogram value, MACD 9EMA value, and RSI 14) and one output (TP) in a Feed-Forward Neural Network has yielded an overall profit around 50% better than the regional optimal fixed TP, but less than the overall optimal fixed TP. This can be considered a good result given that a strategy using the overall optimal fixed TP relies on not more than 10% heavy hits that are not guaranteed to be sustained in every time span.

However, this result is still obtained with the MSE as a performance function, and without really smoothing the TP data. Next is to edit the MSE function to place more error weight on output higher than target, since those outputs result in a total miss even 1 point above target. Lowering the number of outputs above target will significantly lower the number of misses and therefore lead to a considerably greater performance that will be well above the overall optimal fixed TP. I will also attempt to map the set of outputs into a min-max region that is centered around either the overall optimal fixed TP or the lower local fixed TP, with a range of around 25% in each direction.

2. Hourly median price approximation with a NARX network

Performing one step ahead with a NARX network, with 4 inputs (Hourly median price, MACD histogram value, MACD 9EMA value, and RSI 14) and one output (Hourly median price) will approximate the next median price with an average goal error of 50%, but when plotted against a chart of high-low bars of the same prices, we find out that the 90-95% of the predicted prices fall within the next bar. This is phenomenal given that even with such random inputs that do not tell much about the circumstances of the trend, we can still give an answer that will have a 90-95% chance of being hit in the next hour. Shaping this method up could lead us to a result very close to a 100% hit, especially with a goal not targeted for over-fitting. Also, missed hits are pretty obvious to spot if you consider how outrageous it appears.

3. Clustering output with a Radial Based Probabilistic Neural Network

With a Radial Based Probabilistic Neural Network, one can classify the type of the next bar: an up, down, or flat bar, or type of candle (hammer, rising star, doji, etc..). I have not tried this yet, but it is very promising given the strength of PNN’s in classification, and the relatively high error tolerance (No need for 90% , 70% will still be highly profitable and well beyond any known trading system). I will try clustering soon with PNN’s and SOM’s (Self-Organizing Maps).

If any of the earlier 3 points seem extremely vague or not understandable, it’s perfectly OK. It will be much clearer when I get around to uploading charts and diagrams that will hopefully clear out the above-mentioned mumbo-jumbo.