The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained. In this post, you will discover how to develop LSTM networks in Python using the Keras deep learning library to address a demonstration time-series prediction problem 27/08/ · Prediction of Forex Rate (USD/INR) Using LSTM & GRU. The foreign exchange rate (Forex) market is the largest and most crucial trading market in the world followed by the credit blogger.comted Reading Time: 9 mins 30/03/ · Duel DQN RL-Forex-trader-LSTM using keras-rl Prerequisites Getting Started Create Environment & Agent Train and Validate Configuring Agent Running Inital Result Trade History: Buy (green) Sell (red) Cumulative Return, Max Drawdown Period (red) Authors License
Multidimensional LSTM Networks to Predict Bitcoin Price | Jakob Aungiers
This article builds on the work from my last one on LSTM Neural Network for Time Series Prediction. If you haven't read that, I would highly recommend checking it out to get to grips with the basics of LSTM neural networks from a simple non-mathematical angle. As anyone who's been on a date with me knows; I find small talk boring, so let's just jump right into it! The first thing we will need is the data, keras lstm forex.
Luckily, Kaggle have a fun dataset of minute-by-minute historical data set from Bitcoin which includes 7 factors. We will however need to normalise this dataset before feeding it into our network of LSTMs. We will do this as per the previous article where we take a sliding window of size N across the data and re-base to data to be returns from 0 where.
Now this being a multidimensional approach, we are going to be doing this sliding window approach across all of our dimensions. Normally, this would be a pain in the ass. Luckily the Python Pandas library comes to the rescue! We can represent each window as a Pandas dataframe and we can then perform the normalisation operation across the whole dataframe i. across all columns. The other thing you will notice with this dataset is that especially at the beginning, the data is not very clean.
If it is, we will swipe left throw away the window and move on to the next one. Whilst we're here, let's make these functions into a self contained class called ETL extract, transform, load and save it as etl. py that way we can call this whole data loader as a library. See the first time I tried to do this, my machine shuddered to a halt and gave me a memory error. The issue, you see, comes from keras lstm forex fact that the Bitcoin dataset, being a minute-by-minute dataset, is quite large.
When normalised it is around 1 million data windows. And loading all of these 1 million windows into Keras to train on at once makes for a pretty bad time, keras lstm forex. So how would I train on this data without adding an extra Gb of RAM to my machine?
In a nutshell; a generator iterates over data of unknown and potentially infinite length, only passing out the next piece every time keras lstm forex is called.
This trains the model with low memory utilisation. Technically speaking, keras lstm forex, if you made the windows small enough you could even train this model on your IoT toaster machine if you really wanted to! On its own this was acceptable as it took around mins to get through the training data batches, keras lstm forex.
However, if I wanted to tweak the model and re-run it, it would take an awful long time to re-train it again. What can we do? Well, how about pre-normalising it then saving the normalised numpy arrays of windows to a file, hopefully one that preserves the structure and is super-fast to access? HDF5 to the rescue! Through the use of the h5py library we can easily save the clean and normalised data windows as a list of numpy arrays that takes a fraction of a second IO time to access.
The only extra thing we need to add in when predicting our test set is a generator function that iterates the generator and splits out the x and y outputs.
However we still want the y values true dataso we store them in a separate list as we want to use them for plotting against to be able to visualize our results compared to the keras lstm forex data. We then do the same but rather than predict on a a step-by-step basis we initialise a window of size 50 with the first prediction, keras lstm forex, and then keep sliding the window along the new predictions taking them as true data, keras lstm forex, so we slowly start predicting on the predictions and hence are forecasting the next 50 steps forward.
Finally, keras lstm forex, we save the test set predictions and test set true y values in a HDF5 file again so we can easily access them in the future without re-running everything, should the model turn out to be useful. We then plot the results on 2 matplotlib charts. One showing the daily 1-step-ahead predictions, the other showing steps ahead predictions.
We then go for the forecasting of Bitcon price! As per my last article, we will try and do keras lstm forex types of forecasts:.
Here is the results of point-by-point predictions:. The results of this look like:. What can we see? Well, keras lstm forex can see that when keras lstm forex 1 step ahead it's doing a very reasonable job, keras lstm forex. Occasionally it's out, but in general it follows the true data quite well. However, the predictions do appear far more volatile than the true data.
Without doing keras lstm forex tests it's hard to ascertain why this might be and if a model re-parameterisation would fix this. When predicting the trend however this model starts to fall a bit on its face. The trend doesn't seem particularly keras lstm forex to model and is inconsistent at times.
What is interesting is the size of the predicted trend line does seem to correlate with the size of the price moves volatility.
I am going to use this section to take off my AI hat and put on my investment manager hat to explain a few key truths The main thing one should realise is that predicting returns is a pretty futile exercise. I mean sure, it's the holy grail of forecasting to be able to predict returns, and whilst some top end hedge funds do try to do just that by finding new alpha indicators in truth it's a pretty hard thing to do due to the huge swaths of external influences that push an asset price.
In real terms it's comparable to trying to predict the next step of a random walk. However, all is not lost and our exercise isn't completely pointless. See, whilst with limited time series data, even with multiple dimensions it's hard to predict returns, what we can see, especially from the second chart, is that there is an avenue there to predicting volatility. And not just volatility, but we could also expand that to predict market environments in a way allowing us to know what keras lstm forex of market environment we are currently in.
Why would this be useful? Well a lot of different strategies which I won't go into here work well in different market environments respectively, keras lstm forex. A momentum strategy might work well in a low vol, keras lstm forex, strongly trending environment whilst an arbitrage strategy might be more successful in producing high returns in a high vol environment.
We can see that by knowing our current market environment and predicting the future market environments are key to allocating the correct strategy to the market at any given time, keras lstm forex. Whilst this is more of a general investment approach for traditional markets, the same would apply to the Bitcoin market. So as you can see, predicting longer-term Bitcoin prices is currently as with all types of stock markets pretty hard and nobody can claim to do so from just the technical time-series data because there are a lot more factors that go into the price changes.
Another issue which keras lstm forex worth touching keras lstm forex with the use of LSTM neural networks across a dataset like this is the fact that we are taking the whole time series data set as a stationary time series. That is to say, the properties of the time series are assumed unchanged throughout time.
There is work that can be done help with this non-stationarity issue, the leading edge research of this currently focuses on using Bayesian methods alongside of LSTMs to overcome the issue of time series non-stationarity.
But that's far out of scope for this short article. I may make another post in the future detailing the implementation of this. Check back soon! In the meantime, feel free to browse the full code for this project on my GitHub page: Multidimensional-LSTM-BitCoin-Time-Series. ABOUT ARTICLES CONTACT. Multidimensional LSTM Networks to Predict Bitcoin Price Sat 15th Jul Dataset Time The first thing we will need is the data.
shape[1], x1. Hello Sweet Bitcoin Profit We then go for the forecasting of Bitcon price! The results of this look like: What can we see? Conclusion I am going to use this section to take off my AI hat and put on my investment manager hat to explain a few key truths
Beginer Time Series with forex trading market USD JPY using LSTM
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03/03/ · We just saw that there is a big difference in the architecture of a typical RNN and a LSTM. In LSTM, our model learns what information to store in long term memory and what to get rid of. Quick implementation of LSTM for Sentimental Analysis. Here, I used LSTM on the reviews data from Yelp open dataset for sentiment analysis using blogger.comted Reading Time: 7 mins 27/08/ · Prediction of Forex Rate (USD/INR) Using LSTM & GRU. The foreign exchange rate (Forex) market is the largest and most crucial trading market in the world followed by the credit blogger.comted Reading Time: 9 mins 25/06/ · The LSTM model will be trained to learn the series of previous observations and predict the next observation in the sequence. We will apply this model in predicting the foreign exchange rate of India. The data set in the experiment is taken from Kaggle that is publicly available as Foreign Exchange Rates Estimated Reading Time: 4 mins
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