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Python crypto exchanges bitcoin or btc

Python crypto exchanges

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Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. Im currently trying to build an implementation of the Ephemeral Diffie-Hellman algorithm using the python cryptography module. It's important for our purposes that the users are authenticated. Both Alice and Bob have a public-private key pair and a certificate signed by a certificate authority to be able to verify their public key and link it to their identity.

Using Authenticated DH means that the sent messages see image will be signed using the above private key. However, I can not seem to understand what the described exchange function actually does. Is anyone able to explain to me where to situate it in the DH-algorithm?

Preferably using the analogy of the following image:. That means of course that you need to do this twice, once for Bob, and once for Alice. Stack Overflow for Teams � Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Learn more about Collectives. Learn more about Teams. Asked 5 years, 9 months ago. Modified 5 years, 9 months ago. Viewed 5k times. Preferably using the analogy of the following image: Thanks in advance!

Improve this question. Add a comment. Sorted by: Reset to default. Highest score default Trending recent votes count more Date modified newest first Date created oldest first. Example code in python2: from cryptography. Improve this answer. The requests are performed with requests , passing a per-application key in an authentication Apikey header, e. Requests to APIs take time to complete. If we make the request directly in the GUI thread it will block the rest of the application executing � including responding to user input.

The application would become unresponsive spinning wheel of death, faded window. For a complete overview this QWorker approach see the PyQt5 threads tutorial. First we define a signals QObject which contains the signals we want to emit from our worker thread. This includes signals to emit finished , error , progress how much is complete and data the returned data. We also add a cancel signal which allows the parent app to signal to an active worker thread when a new request has been queued before the first one completes.

If True it will return without emitting the finished signal. Once all requests are finished the resulting data is emitted using the earlier defined signals. However, it's still polite not to waste other people's bandwidth if you can avoid it.

This uses a simple SQLite file database to store the results of previous requests. You can put the cache wherever you want on disk, the only requirement is that it is user-writeable so it continues to work after the app is packaged.

The API calls return high , low , open and close values for each day and for each cryptocurrency in addition to a separate market volume value. These are plotted as a series of lines, with each cryptocurrency close value plotted in a different colour, with high and low values drawed as dotted lines either side. The open value is not plotted. PyQtGraph plot with multiple currencies and volume data. The currency values are all plotted on the same scale, using the same axis.

We only plot the currency lines only once we have the data back from the API in case any currencies are not activated so the initial setup is just of the axis and grid. We also set the axis names, and add an infinite vertical line, which is just to track through the plot to get per-day currency conversion rates.

The volume axis is plotted on a separate scale, as a dotted black line. This can be zoomed vertically independently of the currencies. This is a bit tricky to achieve in PyQtGraph , requiring you to manually create a ViewBox object and connect it up to the main axis. Unlike for the currencies we do add the curve here, since we know it will always be present. The initial state is a diagonal line for no reason. The plot is updated in response to the data being returned by the API request worker.

This triggers the. References to plotted lines are kept in a dictionary self. This allows us to check on each update whether we already have a line defined, and update it rather than recreating it.

We can also remove lines for currencies that we no longer want to draw if they've been deselected in the currency list. The market activity volume plot however is always there, so we can just perform a simple update to the existing line. In addition to the plotted lines, we also show a list with the currency conversion rates for all cryptocurrencies at the currently position in the graph. As you move your pointer back and forward these rates update automatically.

Goodforbitcoin rates table. We define a QStandardItemModel model which we can use to update the data in the table, and set the headers for the colums. Finally, the. If the item is checked, and the currency is not currently displayed the currency identifier is not in our. Likewise, if the item is unchecked but the currency is displayed, we remove it and trigger a redraw. We always download data for all currencies, even if they are not currently displayed, so we can update the plot immediately.

You might want to have a go at changing this behaviour. This slot receives a pos value which is a QPoint position relative to the axis. We first use the. This next method checks if the position i is within the range of our data the number of days of data we have.

Then for each currency it gets the corresponding value the close value and then sets this onto the second QStandardItems � the column with the currency exchange rates � as a 4dp number. This means we don't need to define the rows explicitly, they are created automatically based on the data returned by the API. Now we have the working app, the last step is to bundle it up for distribution.

Installers for Windows and Mac are available at the top of this article. To do this we're using PyInstaller the current standard for bundling Python applications. Because this app has no external data files building an installer for it is pretty straightforward.

Install PyInstaller with pip3 install pyinstaller then run �. This produces a spec file which contains the information needed by PyInstaller to build the distribution installers.

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