Hi Everyone,
After a lot of research, reading excellent basics at babypips and experimenting with various systems, I have finally implemented a forecasting system based on Neural Networks. Neural networks based systems are proven in financial forecasting and in general in learning patterns of a non-linear systems.
I believe strongly that forex market is a non-linear system which is difficult to model. But one good thing of forex market is that it represents some patterns which when known can be applied in making trading decisions. Proof of this concept is technical analysis and theories that are widely used by traders in identifying these patterns. This makes neural networks a better tool for forex market as neural networks are know their ability of learning unknown processes and forecast the patterns of the process ahead.
Lets get to the main point. In this thread I would describe the working of the system I have developed rather than the system itself as it is a long way to explain. The system is basically a time-series-forecasting system, which means we give as input information about current Bar and the system would give out information about Bars in future. I am sure now you are thinking if its really possible and if so what are the actual inputs and what are the actual outputs. I would strongly suggest the reader to go through the basics of neural networks before reading further.
[B]An Introduction to Neural Networks forecasting[/B]
Note: The system is optimized and targeted to trade on EURUSD 15min chart
INPUTS
EMA(t) - EMA (t-1)
EMA(t-1) - EMA (t-2)
EMA(t-2) - EMA (t-3)
RSI(t)
RSI(t-1)
%R(t)
%R(t-1)
MACD(t)
MACD(t-1)
Stoch(t)
Stoch(t-1)
1H EMA(t)
1H EMA(t-1)
OUTPUTS
EMA(t) - EMA(t+1)
EMA(t+1) - EMA(t+2)
EMA(t+2) - EMA(t+3)
where t means current time or Bar. As you see, we give the difference of the past three EMA of the 15min chart Bars, past two indicator values of RSI, %R, MACD, Stochastic and past two EMA difference of 1H chart Bars. The system will be able to forecast and output the future 3 EMA difference of 15min chart Bars. How is this able to forecast is hard to explain (may be I will start a second thread), but for now you should consider the system is able to do so as it has been trained on several past data (3-6 months). The system for now consists of an indicator and an include file (both attached at the end). I would be showing some examples how it can be used to detect the patterns of forex market and make trading decisions.
Dashed Aqua blue line (bottom most): EMA 5 bar
Yellow line: Forecasted EMA(t+1) / output 1 of neural network
Green Yellow line: Forecasted EMA(t+2) / output 2 of neural network
Gold line (top most): Forecasted EMA(t+3) / output 3 of neural network
In the figure above it is clear from the forecast lines that the trend is going to be Bullish eventhough the current EMA shows Bearish trend. This is a kind of lead indicator.
BUY SIGNAL
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All three forecasts significantly above current EMA
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RSI trending up from below 50 level and about or crossed 50 upwards
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Stochastic Main > Signal and trending up from over sold region
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Optional: MACD going negative to positive
Example
SELL SIGNAL
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All three forecasts significantly below current EMA
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RSI trending down from above50 level and about or crossed 50 downwards
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Stochastic Main < Signal and trending down from overbought region
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Optional: MACD going positive to negative
Example
Installation:
Please read the Instructions.txt file in the zip file attached below. A step by step procedure to install the NeuroTrend is documented in the file. If you find difficulty in installation do not hesitate to ask me…
Finally I would like to say that have fun with the system and if you like it do comment. If you have ideas or suggestions to apply it or adapt it, share it in this thread. My main aim in making it open to this forum is to get experts suggestions and improve it and learn from the experience of its applications…of course also earn a couple of bucks hehehehe
Note: Due to file attachment limitation, the actual files are zipped to NeuroTrendv1.0
Cheers!
Aroon
NeuroTrendv2.0.zip (21.4 KB)