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bitcoin python trading

I'm in the process of developing a crypto trading bot in Python. I've been writing a series of articles documenting the progress. The Github repo can be. Different bot trading strategies Crypto trading bots rely on algorithmic trading in order to run and process complex mathematical formulas and automate and. In this article, go through the basics of creating a fully working trading algorithm for cryptocurrencies and connect it to Alpaca. TECHNICAL ANALYSIS CRYPTO

Commissions are very competitive and you may be hard-pressed to find another exchange with lower fees. Lastly, Binance has a good track record of security. There have only been a few instances of Binance getting hacked which is something that all exchanges are susceptible to due to the nature of the business.

While security is a big plus point for Binance when it comes to exchanges, there are better alternatives, depending on your needs. If your primary interest is trading Bitcoin, you might be better off trading Bitcoin futures with a regulated broker like Interactive Brokers for example. The benefit of a regulated broker is that they have insurance schemes in place to protect the consumer.

If trading futures is not your thing, there are several regulated brokers that offer CFD trading on cryptocurrencies which can be a good alternative. And in most cases, the trading fees are significantly higher. API changes are generally a good thing. It means the exchange is trying to update and improve their infrastructure most of the time. Not all changes will lead to our code breaking, but some of them certainly can.

There are also outages. In late , Binance took its API offline roughly once a month for a few hours each time for planned updates. This requires manual intervention and additional position management for algo traders. In early , amidst a surge in crypto prices, Binance experienced outages during volatile times due to an influx of new users signing up and a sharp rise in trading volume. Binance CEO Changpeng Zhao, better known as CZ, has commented several times that outages are normal in a growing industry such as crypto, and that users should expect it could happen again in the future.

What are the alternatives to using the Binance API? Coinmarketcap has more than exchanges listed in its database, and many of them offer an API. Some of the names that stand out from their list as well-established exchanges include Coinbase, Kraken, Bitfinex, Bitstamp, and Bittrex. Futures traders may also want to take a look at Bitmex which is a popular exchange with competitive commissions. They even pay you for certain order types rather than charging a trading fee. Quandl provides mostly free data that can be useful.

There are several third-party libraries available, the most popular is python-binance. There is an interesting story on how this library came to be the most popular Python library for Binance. When Binance launched in , they held a contest to see who can come up with the best library for various different programming languages. The winners were each rewarded Binance coins BNB. There were several good submissions for the Python category but python-binance ultimately won.

This was a clever approach to ensure the best developers worked hard to create a good library. Another popular library is CCXT. This library supports over a hundred different exchanges and is available in many different programming languages.

There is also a recommendation and link to it in the Binance API documentation. CCXT is a good option if you plan to trade on multiple exchanges, or want the flexibility of switching to another exchange down the road without having to fully relearn another library.

Lastly, Binance connector seems like it is often updated and a great library all around. Sign up with Binance The first step is to create an account with Binance. We highly recommend that you do. There are tutorials on getting started on things like your first deposit and much more on Binance Academy.

Obtaining an API key After logging in to your Binance account, click on the profile icon on the top right-hand side of the screen. From there, select API Management. Next, you will be asked to create a label for the API key. If you have two-factor authentication enabled, you will be asked to authenticate once again at this point. Binance will send you an email to confirm the API key creation. Click on the confirmation link from your confirmation email to proceed. Your API key should be created at this point.

Note that this is the only time your API secret will be revealed. If you navigate away from this screen, you will not be able to see it again. In such a scenario, you will need to delete your API key and start over again by creating a new one. Guard your API details as you would with any password as anyone can get access to your account if they were able to get a hold of this information.

Before moving forward, there are a few settings here that we need to take a look at. This permits the API to read your account details and is enabled by default. Perhaps it is a bit misleading since it includes the term Only, but you can still perform other operations with this option checked. That means the account is not set up for trading futures. Note: if the Enable Futures box is shown, you should still set up your futures account first before attempting to create an API key if you plan to trade futures.

Otherwise, it may lead to API authentication errors. It is easy to setup Futures trading on your account if it is not already enabled. From the main Binance login page, click on the Derivatives option in the top menu bar. From there, click on Futures which should take you to the Futures trading screen. There should be an option on that page to open a Futures account.

If you plan to make withdrawals from within your Python trading script, you will need to enable it. This is for security purposes. However, if you are running the script from your local machine, you could run into issues as most internet service providers do not offer static IP addresses.

There are third-party solutions, such as noip. Installing the python-binance library The easiest way to install the python-binance library is to use pip. This can be done via the command prompt. Here is the syntax for Windows users.

Here is an example: And this is how you can store environment variables on a Mac or in a Linux environment using the terminal. Does Binance offer a demo account? We only saw an option to log on with a GitHub account. These will be separate from the keys created in the previous step and will be used only to access your demo account. Choose the option to generate a HMAC key. In the next step, enter in descriptive name for your key. After clicking generate, you will be taken to a screen that displays your key.

The only difference is that you have to use a different URL to access Testnet. However, we will discuss a workaround in the next step. Please note, Test Network accounts get deleted typically at the start of each month. Any open trades will be wiped out. More details about the Spot Test Network can be found on the same page where you go to create your key.

We will start up a client and check out four functions that detail different account balances. We will need the Client class from the python-binance library as well as the os library to retrieve the API keys that we stored as environment variables. We are all set to start accessing the API at t his point. Unfortunately, the python-binance library does not have support for the demo environment previously discussed.

It will also provide some other info such as the current commission rate and if your account is enabled for margin trading. This might be a bit too much information. If we are only interested in our BTC balance, we can use another endpoint. Similarly, margin account details are separated as well. Here is how you can access your margin account. Note that an exception will be raised if you have not activated a margin account.

How can I retrieve the latest price for Bitcoin? There are several methods to get the latest price for Bitcoin. Fortunately, there is a better solution than constantly making requests to the API. It involves using Binance WebSocket. Using the Binance WebSocket for the latest Bitcoin price The Binance WebSocket requires us to only send a command once to open up a stream, and then data will automatically stream over as prices get updated.

From the Bitfinex documentation, we saw that one of the public endpoints is called candles, which returns the data behind the candlestick charts on all the exchanges. This kind of data contains a timestamp, open, close, high and low price, and usually the trade volume. At first, we will try the simplest way to interact with this endpoint through the client by just calling it with its default settings.

That's a nice achievement, but we usually are interested in a period long ago or different currency pairs. To achieve specifically what we want, we should specify the following parameters: symbol: currency pair,default: BTCUSD; interval: temporal resolution, e.

Now we can write and run our first query. So if we were to increase the time interval of interest to the entire year, for example, we would not be able to get it at a 1-hour resolution. So to get past this limitation, we need to write a function that would split our big query into multiple minor queries. Also, I must mention that we need to keep in mind that there is a limit to how many requests we can make to the Bitfinex API. Currently, this limit is 60 calls per minute, which means that after each request, we should wait for a minimum of 1 second before we start the next call.

The function below remains 1. The only extra thing we need to calculate and provide is the step size in milliseconds. We recalculate the step size every new while loop to know how many data points we should ask in every new minor query. This is very similar to the limit we defined before. To reduce the number of requests we do to the API, it would be best to go for the maximum step size. Then we can remove potential duplicates, make sure that everything is in the correct order, and restore the numerical timestamp into a human-readable format.

In the final step, we can save our downloaded historical data and save it to a. So, it's pretty evident that, in some kind, we should speed up our learning process. One of the best ways that come to my head is that we should use multiprocessing. If you were following my past tutorials, you should already be familiar with my BipedalWalker-v3 Reinforcement learning tutorial, where I used multiple environments to train my agent.

I tested that we were not winning a lot of speed while running multiple environments in the background and detecting states for each environment. So, we created 16 running environments, and we detected their next state in a batch - this is how to speed it up.

In this part, I decided that I needed to do similar stuff. We'll reconstruct our custom environment so that we can start multiple of them in the background. This way, our bot will have the opportunity to try more different trade combinations and learn price action features faster. Multiprocessing example While we talk about python multiprocessing, I am not a pro, but I have successfully implemented it in several past projects.

First, I'll show you a simple example where we'll use pipe communication between multiple processes. Then I'll show you how I similarly implemented our custom environment. First of all, I am not going to explain step-by-step how python multiprocessing works. I'll discuss the concept of data sharing and message passing between custom environment processes while using the multiprocessing module.

In our case, any newly created processed environment will do the following: run independently; have their own memory space. Below you can see my example code to understand the basics of multiprocessing. So I created a class called class Environment Process that will start an independent process running in the background.

For simplicity, my processes, as a result, will return the received number multiplied by 2. For simplicity, I create only two custom processes within this custom environment. For communication between the main program and environment, processes will use a pipe communication method. Pipe returns two connection objects which represent the two ends of the pipe. Each connection object has send and recv methods. In the main loop, I generate a random number for each process between 0 and 10 and send it.

Not to spam my console, I use 1-second sleep, and then I run another loop to read pipe for answers and print it to our screen. Multiprocessing environment In a similar principle works our multiprocessing environment with a custom Bitcoin trading bot. Instead of giving us back a simple number, we'll run our custom trading environment to make a predicted action and return a state, reward, and other parameters of a particular environment.

Of course, it's much easier to tell than to convert my words to code because while programming a script to work with multiple processes, sometimes it's pretty complicated to debug. Multiprocessing training agents This part is quite complicated, and I think I'm not committed to explaining its code. So I'll give you a function that we'll use every time we train our custom Bitcoin Reinforcement Learning trading agent.

All the network parameters are defined in the same way as before. You can run more workers if you have GPU because training will be done on a graphical processing unit, and environments will run on a process. Otherway, if you don't have GPU, you won't get significant speed improvement because the hardest part training will be done on the CPU. Suppose you think this is a lot of parameters; here, I give only the most critical parameters.

In that case, much more might be changed when we start talking about optimizing our model, but from training and testing results, you'll see that it's enough parameters to create a primary view of a model. So I trained all 3 of them, and I can show you my training results and parameters for each model in the following table: Training results table As you can see, I mostly tried to change model type, windows size, and batch size.

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