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Can anybody be brief about how to make a bitcoin trading bot python Malaysia it? Important links. We will default the commission per trade to 0. Of course, the hold action will ignore the amount and do nothing.
Next, we need to write our reset method to initialize the environment. Here we use both self. An important piece of our environment is the concept of a trading session.
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. So we are left with simply taking a slice of the full data frame to use as the training set from the beginning of the frame up to some arbitrary index, and using the rest of the data as the test set.
Next, since our environment is only set up to handle a single data frame, we will create two environments, one for the training data and one for the test data. Now, training our model is as simple as creating an agent with our environment and calling model. Here, we are using tensorboard so we can easily visualize our tensorflow graph and view some quantitative metrics about our agents.
For example, here is a graph of the discounted rewards of many agents over , time steps:. Wow, it looks like our agents are extremely profitable! It was at this point that I realized there was a bug in the environment… Here is the new rewards graph, after fixing that bug:. As you can see, a couple of our agents did well, and the rest traded themselves into bankruptcy. However, the agents that did well were able to 10x and even 60x their initial balance, at best. However, we can do much better.
In order for us to improve these results, we are going to need to optimize our hyper-parameters and train our agents for much longer. Time to break out the GPU and get to work!