Are you interested in using artificial intelligence to inform your trading decisions? Do you have a breakout trading bot written in Python that uses technical analysis to identify potential breakout stocks? In this article, we’ll walk you through how to set up your bot to provide Alexa with stock tips when you ask, “Hey, Alexa, give me a stock tip for today.”

First, let’s assume that you already have a breakout trading bot written in Python that uses technical analysis to identify potential breakout stocks.

You can copy one of the trading bots I have defined in this series.

Step 1: Create an Alexa Skill

To create a custom Alexa skill for your trading bot, you’ll need to define a voice user interface (VUI) that specifies the set of commands that your skill will support and the responses that Alexa will provide when the user interacts with your skill.

To get started, log in to the Alexa developer console and create a new skill. Give your skill a name and choose the “Custom” model type. Then, define an intent called “GetStockTip” with sample utterances like “Give me a stock tip for today” or “What stock should I buy?”.

Here’s an example of what your intent schema might look like:

{
  "intents": [
    {
      "name": "GetStockTip",
      "samples": [
        "Give me a stock tip for today",
        "What stock should I buy",
        "Tell me about a good stock to invest in"
      ]
    }
  ]
}

Step 2: Create a Lambda Function

Once you’ve defined your intent and sample utterances, you can create a Lambda function that will handle the incoming requests from Alexa and generate the appropriate response.

To get started, create a new Lambda function in the AWS Management Console and choose “Author from scratch”. Give your function a name and choose “Python 3.8” as the runtime.

Here’s an example of what your Lambda function might look like:

import trading_bot

def lambda_handler(event, context):
    session = event["session"]
    intent = event["request"]["intent"]
    intent_name = intent["name"]
    if intent_name == "GetStockTip":
        stock_tip = trading_bot.get_stock_tip()
        response = "Invest in {} with a target price of {} and stoploss of {}".format(
            stock_tip["ticker"], stock_tip["target_price"], stock_tip["stoploss"]
        )
    else:
        response = "Sorry, I don't understand that command."
    return {
        "version": "1.0",
        "response": {
            "outputSpeech": {
                "type": "PlainText",
                "text": response
            },
            "shouldEndSession": True
        }
    }

In this example, we’re calling a function called get_stock_tip() from our trading_bot module to generate a stock tip. This function might use technical analysis to identify potential breakout stocks and return a dictionary with the ticker symbol, target price, and stoploss.

We then format the response string with the stock tip information and return it to Alexa in a JSON format that includes the outputSpeech field with the text response and the shouldEndSession field to indicate that the session should end after providing the response.

Step 3: Deploy Your Skill (this is little tricky)

Finally, you can test your custom Alexa skill by deploying it to a private endpoint and authorizing Alexa to use it. To do this, you’ll need to create an endpoint in the Alexa developer console that points to your Lambda function. You can then use the Alexa app to link your account with your custom skill and test it out.

To define a private endpoint for your Alexa skill, you’ll need to create an AWS Lambda function that serves as the backend for your skill. This function will handle the requests that Alexa sends to your skill, generate the appropriate response based on your breakout trading bot, and then send that response back to Alexa.

To create a private endpoint, you’ll first need to create an Amazon Virtual Private Cloud (VPC) in which your Lambda function will run. This VPC provides an isolated network environment in which you can securely run your Lambda function and prevent unauthorized access.

To create a VPC, you’ll need to log in to the AWS Management Console and navigate to the VPC Dashboard. From there, you can create a new VPC by specifying a CIDR block, subnet, and other settings. Once your VPC is created, you can then create a new subnet within the VPC to host your Lambda function.

Next, you’ll need to create a new Lambda function and configure it to run within your VPC. To do this, you can use the AWS Lambda console to create a new function and specify the VPC settings. You’ll also need to configure the function’s security group to allow inbound traffic from the IP address ranges used by the Alexa service.

Once your Lambda function is set up and running within your VPC, you can then define a private endpoint for your Alexa skill. This endpoint is essentially a URL that points to your Lambda function and specifies the type of request that your skill can handle.

To create a private endpoint, you’ll need to log in to the Alexa developer console and navigate to the Endpoint section of your skill’s configuration. From there, you can specify the URL of your Lambda function, along with the appropriate security settings to ensure that only authorized requests are allowed.

Finally, to authorize Alexa to use your private endpoint, you’ll need to generate an authorization token and provide it to the Alexa service. This token is essentially a unique identifier that tells Alexa that your skill is authorized to access your Lambda function and generate responses.

To generate an authorization token, you can use the AWS Identity and Access Management (IAM) console to create a new role that grants your Lambda function the appropriate permissions to access your VPC and other resources. You can then generate a token for this role and provide it to the Alexa service when setting up your skill’s endpoint.

With these steps complete, you’ll have a fully functional Alexa skill that can access your breakout trading bot and generate stock tips on demand. By setting up a private endpoint and authorizing Alexa to use it, you can ensure that your skill is secure and only accessible to authorized users.

Conclusion

By following these steps, you can easily set up your Python breakout trading bot to provide stock tips to Alexa when asked. This can help you stay on top of the market and make informed trading decisions based on the latest technical analysis.

Of course, this is just a simple example. In reality, you’ll need to do a lot more work to create a truly effective breakout trading bot that can reliably identify potential breakout stocks and generate accurate stock tips. You may also need to consider additional factors, such as market trends, news events, and company financials, when making your investment decisions.

But by combining the power of artificial intelligence with the convenience of Alexa, you can create a powerful tool that can help you stay ahead of the curve and make smarter investments in the stock market. So why not give it a try today and see what kind of results you can achieve?