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How to Create a Cryptocurrency Trading Bot with ChatGPT

In this article, we are going to create a cryptocurrency trading bot using ChatGPT, an advanced AI developed by OpenAI. The bot will utilize Alpaca API for paper trading, CoinGecko API for getting cryptocurrency prices, and SNScrape for social media data analysis. To interact with our bot, we'll use the OpenAI API. Also, we'll use Pinecone for efficient similarity search.

Let's get started.


To follow along with this guide, you'll need:

- A development environment capable of running Python (like Jupyter Notebook or Google Colab)

- An account with Alpaca, CoinGecko, and OpenAI

- An understanding of Python, APIs, and cryptocurrency trading basics


First, we need to install the necessary Python packages. You can do this by running the following commands:

!pip install alpaca-trade-api
!pip install pycoingecko
!pip install requests
!pip install openai
!pip3 install git+
!pip install pinecone-client

Setting Up Alpaca API

Alpaca is a stock and cryptocurrency trading API that allows developers to manage accounts, orders, and positions, get real-time price and historical data, and more.

import alpaca_trade_api as tradeapi

API_KEY = 'Your API Key'
API_SECRET = 'Your Secret Key'

api = tradeapi.REST(API_KEY, API_SECRET, base_url=BASE_URL)
account = api.get_account()

Setting Up CoinGecko API

CoinGecko is a cryptocurrency price and information data platform. We will use it to get the real-time prices of cryptocurrencies.

from pycoingecko import CoinGeckoAPI

cg = CoinGeckoAPI()
bitcoin_price = cg.get_price(ids='bitcoin', vs_currencies='usd')

Setting Up ChatGPT

Next, we will integrate ChatGPT using OpenAI's API. Here we will make a simple function to chat with the model. We'll use this to help make decisions.

import openai

openai.api_key = 'Your OpenAI Key'

def chat_with_gpt3(prompt):
    response = openai.Completion.create(
    return response.choices[0].text.strip()

Integrating SNScrape for Social Media Analysis

We will use SNScrape to track the mentions of certain cryptocurrencies on Twitter, a kind of sentiment analysis.

import snscrape.modules.twitter as sntwitter
def get_tweets(keyword, limit):
    tweets_list = []

    # Using TwitterSearchScraper to scrape data and append tweets to list
    for i,tweet in enumerate(sntwitter.TwitterSearchScraper(keyword + ' since:2020-06-01 until:2020-06-30').get_items()):
        if i>limit:
    return tweets_list

Making Trading Decisions

We can now start to create the function that will be making our trading decisions. It will get the current price of a cryptocurrency, retrieve the latest tweets about it, and then ask GPT-3 what action it should take.

def make_decision(crypto_name):
    # Get the current price of the cryptocurrency from CoinGecko
    crypto_price = cg.get_price(ids=crypto_name, vs_currencies='usd')[crypto_name]['usd']
    # Get the latest tweets about the cryptocurrency
    tweets = get_tweets(crypto_name, 10)
    # Ask GPT-3 to make a decision
    prompt = f"The current price of {crypto_name} is {crypto_price}. Here are the latest tweets about it:\n{tweets}\nShould I buy, sell, or hold {crypto_name}?"
    decision = chat_with_gpt3(prompt)
    return decision

Execute Trades

We can create a function to execute trades based on GPT-3 decisions.

def execute_trade(crypto_name, decision):
    if decision.lower() == 'buy':
    elif decision.lower() == 'sell':

This basic setup gives us a starting point for a more sophisticated trading bot. From here, you could add more AI capabilities, such as using Pinecone to find similar cryptocurrencies to trade, or improving the sentiment analysis of the social media scraping.

Remember, always backtest your strategies before deploying any trading bot, and be aware of the risks involved in trading.

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