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Machine learning trading bitcoinMachine learning for Bitcoin trading - Algominr
At the current stage of development, Algominr requires Metatrader 4 a trading client to trade its machine learning strategies. There are several brokers which offer the Metatrader 4 client platform short: MT4 in combination with cryptocurrencies.
Additionally, most of them offer paper trading accounts, so that you can test your strategies without risking any real money. The following list provides a selection of brokers that support MT A more complete list can be found here. Go to one of these brokers and download and install MT4 before continuing with this tutorial.
When starting MT4 for the first time, you will be asked wheter you want to create a demo or a live account. Choose demo account for now. Next, please download and install the current version of Algominr. To import the bitcoin symbol into Algominr, add a new Instrument on the left hand side of the Algominr interface. Next we will want to create a strategy that trades Bitcoin.
In the top left, you have a basic strategy created from a template:. Next, set the order size to 1and hit enter. This will make the strategy trade 1 Bitcoin per trading signal. Leave the rest of the settings as they are. To do this, we will change the Lin reg node to a RForest node.
To build a deeper understanding please take a look at this great resource which delves deeper into the exact machinations of this type of neural network. For debugging and its ability to present code nicely I use Jupyter Notebooks. To train our model we need training data. You can find my data and the complete source code here. First, we start by importing all of the required packages, loading the dataset and removing the rows we are not interested in using. We split the dataset up into a training and test set, and standardise its features.
Standardisation is good practice as it reduces overfitting in cases where variance for some features may be higher than others. The LSTM model requires us to organise the data in blocks. We choose what type of model we want to use; sequential in this case, and we decide our hyper-parameters. We use the mean-squared-error loss function, the Adam optimiser, set the batch size at 32, and go through this network for 10 epochs.
Optimising your hyper-parameters is outside of the scope of this article but some great resources exist online. Now that we have a model that we can use to build predictions we can take a look at how it performs against our test data. As we can see the predictive values match the training data quite well.
So this means I can predict Bitcoin prices now? Not really. If we wanted to forecast the price 20 minutes from now one option would be to run the model over an immediately recent interval of historical data, concatenating the prediction to the end of our array of historical data, and then feeding that array back into the model — continuing this until we have 20 forecasted blocks of price predictions.
The below code does exactly that, and plots both the bid and ask price against the test data. But will this be the case for the remainder of the test data? As the test data consists out of 6, rows we have windows over which we can predict a trade. Interestingly it appears that the trades that provide you with an actual net benefit outweigh the ones that would have led to a loss.
If we were to deploy this agent into the wild, we would likely never run it for more than a couple months at a time. For this reason, we are going to limit the amount of continuous frames in self. One important side effect of traversing the data frame in random slices is our agent will have much more unique data to work with when trained for long periods of time. For example, if we only ever traversed the data frame in a serial fashion i. Our observation space could only even take on a discrete number of states at each time step.
However, by randomly traversing slices of the data frame, we essentially manufacture more unique data points by creating more interesting combinations of account balance, trades taken, and previously seen price action for each time step in our initial data set. Let me explain with an example.
At time step 10 after resetting a serial environment, our agent will always be at the same time within the data frame, and would have had 3 choices to make at each time step: buy, sell, or hold. Now consider our randomly sliced environment. At time step 10, our agent could be at any of len df time steps within the data frame.
While this may add quite a bit of noise to large data sets, I believe it should allow the agent to learn more from our limited amount of data. For example, here is a visualization of our observation space rendered using OpenCV.
The first 4 rows of frequency-like red lines represent the OHCL data, and the spurious orange and yellow dots directly below represent the volume. If you squint, you can just make out a candlestick graph, with volume bars below it and a strange morse-code like interface below that shows trade history.
Whenever self. Finally, in the same method, we will append the trade to self. Our agents can now initiate a new environment, step through that environment, and take actions that affect the environment.
Our render method could be something as simple as calling print self. Instead we are going to plot a simple candlestick chart of the pricing data with volume bars and a separate plot for our net worth. We are going to take the code in StockTradingGraph. You can grab the code from my GitHub.
The first change we are going to make is to update self. Next, in our render method we are going to update our date labels to print human-readable dates, instead of numbers.
Finally, we change self. Back in our BitcoinTradingEnv , we can now write our render method to display the graph. And voila! We can now watch our agents trade Bitcoin. The green ghosted tags represent buys of BTC and the red ghosted tags represent sells. Simple, yet elegant. One of the criticisms I received on my first article was the lack of cross-validation, or splitting the data into a training set and test set.
The purpose of doing this is to test the accuracy of your final model on fresh data it has never seen before. While this was not a concern of that article, it definitely is here. For example, one common form of cross validation is called k-fold validation, in which you split the data into k equal groups and one by one single out a group as the test group and use the rest of the data as the training group.
However time series data is highly time dependent, meaning later data is highly dependent on previous data. This same flaw applies to most other cross-validation strategies when applied to time series data.