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Key takeaways

• AI tools like ChatGPT can help both experienced and new crypto investors track portfolios with ease, freeing up time for other investment activities and making the process more accessible.

• Defining specific requirements, such as which cryptocurrencies to track and the desired data points, is essential for building an effective portfolio tracker tailored to your investment goals.

• By combining ChatGPT with real-time crypto data from APIs like CoinMarketCap, you can generate valuable market commentary and analysis, providing deeper insights into your portfolio performance.Developing additional features like price alerts, performance analysis and a user-friendly interface can make your tracker more functional, helping you stay ahead of market trends and manage your crypto investments more effectively.

If you’re a cryptocurrency investor, you’ve clearly got a strong appetite for risk! Cryptocurrency portfolios involve many immersive stages, from desktop research on the profitability of cryptocurrencies to actively trading crypto to monitoring regulations. Managing a portfolio of cryptocurrencies can be complex and time-consuming, even for savvy investors. 

Conversely, if you’re a newbie in the world of cryptocurrencies and want to set yourself up for success, you may be put off by the complexity of it all. 

The good news is that artificial intelligence (AI) offers valuable tools for the crypto industry, helping you simplify portfolio tracking and analysis when applied effectively. 

As an experienced crypto investor, this can help free up your valuable time to focus on other activities in your investment lifecycle. If you’re a new investor, AI can help you take that all-important first step. Read on to see how AI, and specifically, ChatGPT, can help you build a customized portfolio tracker.

To begin with, what is it? 

Let’s find out.

What is ChatGPT?

ChatGPT is a conversational AI model that can deliver various tasks using user-defined prompts — including data retrieval, analysis and visualizations. 

The GPT stands for “Generative Pre-trained Transformer,” which references the fact that it is a large language model extensively trained on copious amounts of text from diverse sources across the internet and designed to understand context and deliver actionable results for end-users. 

The intelligence of ChatGPT makes it a powerful resource for building a crypto portfolio tracker specifically geared toward your investment profile and objectives.

Let’s learn how to build a custom portfolio tracker with ChatGPT.

Step 1: Define your requirements

Technical specifics notwithstanding, it is crucial to first define what you expect from your crypto portfolio tracker. For example, consider the following questions:

• What cryptocurrencies will you track? 

• What is your investment approach? Are you looking to actively day trade cryptocurrencies or are you looking to “buy and hold” them for the long term?

• What are the data points you need to compile for the tracker? These may include but are not limited to price, market cap, volume and even news summaries from the web that could materially alter your investment decisions.

• What exactly do you need the tracker to deliver for you? Real-time updates? Periodic summaries? Perhaps a combination of both?

• What do you want the output to look like? Alerts, performance analysis, historical data or something else?

Once you have a clear understanding of your requirements, you can move on to the next steps. It is best practice to write down your requirements in a consolidated specifications document so you can keep refining them later if required.

Step 2: Set up a ChatGPT instance

This is the fun bit! Well, it is if you enjoy geeking out on code. Remember that ChatGPT is a large language model with a vast amount of intelligence sitting underneath it. 

Using ChatGPT effectively therefore requires you to be able to access the underlying model, which you can do via an Application Program Interface, or API. 

The company that owns ChatGPT — OpenAI — provides API access to the tool you can utilize to build your tracker. It’s simpler than you might think. You can use a basic three-step process to set up your own ChatGPT instance:

1. Navigate to OpenAI and sign up for an API key.

2. Set up an environment to make API calls. Python is an ideal choice for this, but there are alternatives, such as Node.js.

3. Write a basic script to communicate with ChatGPT using the API key. Here’s a Pythonic script that you may find useful for incorporating OpenAI capabilities into Python. (Note that this is only intended as a representative example to explain OpenAI integration and not to be viewed as financial advice.)

Step 3: Integrate a cryptocurrency data source

With your ChatGPT instance set up, it is time to complete the other part of the puzzle, namely, your cryptocurrency data source. There are many places to look, and several APIs can help with the information required for this step. 

Examples include CoinGecko, CoinMarketCap and CryptoCompare. Do your research on these options and choose one that fits your requirements. Once you’ve made your choice, choose one that fits your requirements and integrate it with the ChatGPT instance you spun up as part of Step 2. 

For example, if you decide to use the CoinMarketCap API, the following code will get you the latest price of Bitcoin, which you may be trading as part of your crypto portfolio. 

Step 4: Combine ChatGPT and crypto data

You’ve done the hard bit, and given that you now have both an AI capability (ChatGPT) and a cryptocurrency data source (CoinMarketCap in this example), you are ready to build a crypto portfolio tracker. To do this, you can leverage prompt engineering to tap into ChatGPT’s intelligence to request data and generate insights.

For example, if you want your tracker to return a summary of cryptocurrency prices at a desired time, summarized in a data frame for visualization, consider writing the following code:

====================================================================

```python

    # Set your OpenAI API key

    client = OpenAI(api_key=openai_api_key)

    messages = [

        {"role": "system", "content": "You are an expert market analyst with expertise in cryptocurrency trends."},

        {"role": "user", "content": f"Given that the current price of {symbol} is ${price:.2f} as of {date}, provide a concise commentary on the market status, including a recommendation."}

    ]

    try:

        response = client.chat.completions.create(

            model="gpt-4o-mini",

            messages=messages,

            max_tokens=100,

            temperature=0.7

        )

        commentary = response.choices[0].message.content

        return commentary

    except Exception as e:

        print(f"Error obtaining commentary for {symbol}: {e}")

        return "No commentary available."

def build_crypto_dataframe(cmc_api_key: str, openai_api_key: str, symbols: list, convert: str = "USD") -> pd.DataFrame:

    records = []

    # Capture the current datetime once for consistency across all queries.

    current_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    for symbol in symbols:

        price = get_crypto_price(cmc_api_key, symbol, convert)

        if price is None:

            commentary = "No commentary available due to error retrieving price."

        else:

            commentary = get_openai_commentary(openai_api_key, symbol, price, current_timestamp)

        records.append({

            "Symbol": symbol,

            "Price": price,

            "Date": current_timestamp,

            "Market Commentary": commentary

        })

    df = pd.DataFrame(records)

    return df

# Example usage:

if __name__ == '__main__':

    # Replace with your actual API keys.

    cmc_api_key = ‘YOUR_API_KEY’

    openai_api_key = ‘YOUR_API_KEY’

    # Specify the cryptocurrencies of interest.

    crypto_symbols = ["BTC", "ETH", "XRP"]

    # Build the data frame containing price and commentary.

    crypto_df = build_crypto_dataframe(cmc_api_key, openai_api_key, crypto_symbols)

    # Print the resulting dataframe.

    print(crypto_df)

```

====================================================================

The above piece of code takes three cryptocurrencies in your portfolio — Bitcoin (BTC), Ether (ETH) and XRP (XRP), and uses the ChatGPT API to get the current price in the market as seen in the CoinMarketCap data source. It organizes the results in a table with AI-generated market commentary, providing a straightforward way to monitor your portfolio and assess market conditions.

Step 5: Develop additional features

You can now enhance your tracker by adding more functionality or including appealing visualizations. For example, consider:

• Alerts: Set up email or SMS alerts for significant price changes.

• Performance analysis: Track portfolio performance over time and provide insights. 

• Visualizations: Integrate historical data to visualize trends in prices. For the savvy investor, this can help identify the next major market shift.

Step 6: Create a user interface

To make your crypto portfolio tracker user-friendly, it’s advisable to develop a web or mobile interface. Again, Python frameworks like Flask, Streamlit or Django can help spin up simple but intuitive web applications, with alternatives such as React Native or Flutter helping with mobile apps. Regardless of choice, simplicity is key.

Did you know? Flask offers lightweight flexibility, Streamlit simplifies data visualization and Django provides robust, secure backends. All are handy for building tools to track prices and market trends!

Step 7: Test and deploy

Make sure that you thoroughly test your tracker to ensure accuracy and reliability. Once tested, deploy it to a server or cloud platform like AWS or Heroku. Monitor the usefulness of the tracker over time and tweak the features as desired. 

The integration of AI with cryptocurrencies can help track your portfolio. It lets you build a customized tracker with market insights to manage your crypto holdings. However, consider risks: AI predictions may be inaccurate, API data can lag and over-reliance might skew decisions. Proceed cautiously.

Happy AI-powered trading! 

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