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The purple line in the chart above shows an EMA indicator meaning the last 25 values were taken into account. If the pitch exceeds a certain value, it signals rising prices, and the bot will place a buy order. If the pitch falls below a certain value, the bot will place a sell order.
The pitch will be the main indicator for making decisions about trading. For this tutorial, it will be called the trade factor. For a crypto trading bot to make good decisions, it's essential to get open-high-low-close OHLC data for your asset in a reliable way.
You can use Pythonic's built-in elements and extend them with your own logic. This workflow may be a bit overkill, but it makes this solution very robust against downtime and disconnections.
The output of this element is a Pandas DataFrame. You can access the DataFrame with the input variable in the Basic Operation element. Here, the Basic Operation element is set up to use Vim as the default code editor. First, check whether the input is the DataFrame type. If it is present, then open it, concatenate new rows the code in the try section , and drop overlapping duplicates.
If the file doesn't exist, trigger an exception and execute the code in the except section, creating a new file. As long as the checkbox log output is enabled, you can follow the logging with the command-line tool tail :. For development purposes, skip the synchronization with Binance time and regular scheduling for now.
This will be implemented below. The next step is to handle the evaluation logic in a separate grid; therefore, you have to pass over the DataFrame from Grid 1 to the first element of Grid 2 with the help of the Return element. When you run the whole setup and activate the debug output of the Technical Analysis element, you will realize that the values of the EMA column all seem to be the same.
This is because the EMA values in the debug output include just six decimal places, even though the output retains the full precision of an 8-byte float value.
Developing the evaluation logic inside Juypter Notebook enables you to access the code in a more direct way. To load the DataFrame, you need the following lines:. You can access the latest EMA values by using iloc and the column name. This keeps all of the decimal places. You already know how to get the latest value. The last line of the example above shows only the value. To copy the value to a separate variable, you have to access it with the.
As you can see in the code above, I chose 0. But how do I know if 0. Actually, this factor is really bad, so instead, you can brute-force the best-performing trade factor. So extend the logic to brute-force the best performing values. This has 81 loops to process 9x9 , which takes a couple of minutes on my machine a Core i7 QM.
Sort the list by profit in descending order. When I wrote this in March , the prices were not volatile enough to present more promising results. I got much better results in February, but even then, the best-performing trading factors were also around 0. Start a new grid now to maintain clarity. In Grid 3, add a Basic Operation element to execute the evaluation logic.
Here is the code of that element:. The element outputs a 1 if you should buy or a -1 if you should sell. An output of 0 means there's nothing to do right now. Use a Branch element to control the execution path.
Due to the fact that both 0 and -1 are processed the same way, you need an additional Branch element on the right-most execution path to decide whether or not you should sell. Since you cannot buy twice, you must keep a persistent variable between the cycles that indicates whether you have already bought.
You can do this with a Stack element. The Stack element is, as the name suggests, a representation of a file-based stack that can be filled with any Python data type. You need to define that the stack contains only one Boolean element, which determines if you bought True or not False. As a consequence, you have to preset the stack with one False. You can set this up, for example, in Grid 4 by simply passing a False to the stack. Forward a False variable to the subsequent Stack element.
In the Stack element configuration, set Do this with input to Nothing. Otherwise, the Boolean value will be overwritten by a 1 or 0. This configuration ensures that only one value is ever saved in the stack True or False , and only one value can ever be read for clarity. Right after the Stack element, you need an additional Branch element to evaluate the stack value before you place the Binance Order elements.
Append the Binance Order element to the True path of the Branch element. The workflow on Grid 3 should now look like this:. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 27 commits. Failed to load latest commit information. View code. When a test trade is made an alert is sent to a discord webhook.
Trade logic is left out in this project, but the methods for the api calls, discord calls, key values, and binance exchange data are available to be built off of.
Note: this is set up to communicate with Binance. Setup Instructions to run the application, you will need to use "pip install" to install the following dependancies json requests DateTime time ccxt in the BinanceClient file, update the api key and api secret with your information in the DiscordClient file, update the webhook url with your webhook url Support and Feedback Want to see a new feature or additional functionality?
Feel free to send me an email at : markl89 protonmail. About python based crypto trading bot Topics bitcoin binance cryptocurrency bot python ccxt.