AI & Wall Street
We’re taking RL to Wall Street using our library BLOX.
BLOX was designed to allow people of all experience levels to quickly experiment, test and deploy machine learning models. There are a ton of machine learning libraries out there and even more tools that wrap around them to make these libraries easy to use. How is this any different?
Let’s look at some details about the library
- BLOX relies on one of the most familiar data-interchange formats — JSON. JSON is easy to read, incredibly well supported and regardless of your programming background, you’re probably familiar with the syntax.
- It can be interfaced easily through the commandline
- You can easily create your own extensions and integrate them into the library with a single line of code
- It automatically distributes and scales across all compute nodes
- Most importantly, it makes data science and engineering easy
Throughout the rest of this tutorial we’ll be demonstrating how easy it is to use BLOX, but for more information on how great it is check out overview tutorial.
Building a stock trading app
Like I mentioned, we’ll be using BLOX to build, train and deploy a machine learning model that is able to not only decide if you should buy, sell or hold stock, but how many shares. We’ll break this procedure into 4 steps.
me and the boys drowning in anticipation for our gainz
1. Installing BLOX
In order to install BLOX navigate to our github page and download the source directly. Once the package is unpacked you can simply run
$ cd BLOX && sudo python setup.py install
Or if you prefer to do even less work….
$ pip install git+https://github.com/linearlabstech/blox
from here you’ll be able to import the
BLOX module in any python +3.6 script.
2. Downloading The Data
In our repository you’ll find a folder titled
Examples here you’ll find two examples of how to implement BLOX, for this article we only care about the one conveniently named “Day Trader”. In this folder you’ll find a script named
data_downloader.py which will be used for pre-processing the backtesting data and downloading our test data.
In our example, we provide the back testing data for Microsoft ($MSFT) in CSV format from 2013–2018. If you’re looking to train on a different ticker you can check out this Kaggle repo and download your own data if you’d like.
Your data should look something like this, depending on your ticker
we’ll use the middle 5 columns for our model
We’ll need to convert this CSV to a BLOX readable dataset. To do this run the code below.
$ python data_downloader.py --csv <your_ticker_data_file>.csv --output_file backtest.ds *
What the script is doing is taking the features of each historical trading day (Open,Close,High,Low and Volume) as input and use the next trading day’s average price (Average Price = Open Price – Close Price).
Last, you’ll need to run the same script in order to get the test data which is the stock data from the last 13 weeks
$ python data_downloader.py -t <YOUR_TICKER> **
<your_ticker_data_file> is a variable, for example I used
<YOUR_TICKER> is also a variable. For this I use
3. Training our model
Now it’s time to build your model! But before we do that, let’s talk about the agent’s environment (the rules and parameters of the training environment). In the agent’s environment we’ve established a few rules ahead of time like.
- Our reward is the net value of making a sale, so making money is good, losing money is bad
- We’re starting off with $10,000 straight cash money
- We initially purchase 1 share of stock
- We’re only allowed one transaction a day and we cannot buy or sell more than 55 shares at a time
- We’ll periodically record things like our wallet (cash on hand), number of shares, account value and of course our net gain
Alright, let’s begin. Using the provided config and network files, you’ll first want to train your model like so
$ blox -c train.cfg.json --train
This will start training your model on the backtesting data you just converted. After a few epochs (a full cycle through the training data), our net value looked a little something like this.
screenshot from tensorboard
252.3% ROI after 5 mins of training!
Everyone seems happy with the amount of G A I N Z we’re getting
Pretty sick, right? But let’s say you’re not totally blown away. Maybe you’re asking questions like
The stock market has grown quite a bit over the last half decade. How much would you have gained if you just bought in and did nothing?
That’s a really fair question, especially since you can purchase things like index funds that are tied to the market and usually slightly outperform mutual funds according to this article. So let’s take a look at MSFT’s performance over this time period and compare.
MSFT’s performance from 2010–2019
For the period of training data that we had, Microsoft’s stock value raised ~172%! That’s insane, but not 253.2% insane! So that puts us at a 81% improvement.
When I was discussing RL in the section above, I briefly mentioned how Open AI trained agents to play DOTA 2 and were actually really good at it. In fact, they were so good at the game after they trained their agent, they noticed it had developed new strategies for winning. So I think we should ask, is there anything that we can learn from our agent in order to get better at trading?
To do this, let’s first examine our agent’s trading skills (our net value) while it was learning.
The firsts 6 training epochs
Hmmm, it seems like it wasn’t really returning a good ROI until after the fourth training epoch. Since we recorded various statistics like cash on hand and number of shares in our account, we can investigate those to help explain what changed.
Our agent’s trading pattern
Here we have the number of shares in our account while training. The dashed vertical lines are roughly the end of an epoch. Which means we can start to identify the pattern it’s learning for trading. In the first three epochs we see the agent is making a large trades frequently, which aren’t providing much value in the end. Afterwards and specifically the last epoch we notice that it initially buys as many shares as it can, making only small trades to gain value. We then see an abrupt sell of almost all of it’s shares until a later time where it makes a very large purchase. But why? Let’s look at the Microsoft’s historical data to see what inspired that type of reaction.
Looking at the period when the agent takes it’s trading break, we see higher volatility in the stock price (2015–2016). It’s likely the agent starts to identify indicators of volatility and loss and can predict the best times to invest or sit it out.
Ok, so we showed how our model can outperform the market and recognize patterns of volatility and loss, but neural nets are subject to overfitting — especially for small data sets. So how do we know we can generalize outside of our training data?
Let’s test our model on some other data that it hasn’t seen before. Let’s make sure we’re actually able to deliver on our gainz.
We’ll use BLOX again, but this time, we’ll use the
--test flag and our test data we already downloaded.
$ blox -c test.cfg.json --test
This command will run a single pass over the dataset we’ve selected. below are our test results.
Our test data results
The results are in, using our test data from the last 65 days, we see about 6.6% ROI! That’s awesome!
read original article at https://medium.com/@ttroxell/beating-the-market-b4998bc033d3?source=rss——artificial_intelligence-5