If you’re just dipping your toes into the world of automated crypto trading, I’ve put together a simple script to help you get started, especially if you’re scratching your head on how to hook up your trading ideas with the Coinbase API. This little script is all about trading Cardano (ADA) against the Euro (EUR) and uses a pretty straightforward linear progression algorithm to make trades based on what it thinks the market will do next.

But, here’s a heads-up: trading fees can be a real game-changer. When I was testing this script, I found that the fees from each trade started eating into my profits, and before I knew it, I was actually losing money instead of making it. It was a bit of an “aha” moment for me about just how crucial it is to factor in those fees when you’re planning your trading strategy.

When I was testing this script, I found that the fees from each trade started eating into my profits, and before I knew it, I was actually losing money instead of making it.

That “aha” moment really hits you like a splash of cold water, doesn’t it? You’ve set up your trading bot, all excited about the wonders of automation. It’s churning away, buying low and selling at that sweet 3% higher price point the linear regression algorithm has determined. You’re practically rubbing your hands together, waiting for the profits to roll in.

But then, reality strikes. Coinbase, with its trading fees, steps in and takes a hefty percent off your transactions. Suddenly, your 3% gain doesn’t just shrink; it vanishes into thin air, leaving you with less than you started. It’s like running a lap and realizing you’re further from where you began. It’s in these moments you truly understand the critical importance of accounting for trading fees in your strategy. It’s not just about the gains; it’s about what’s left after the fees have had their bite.

So, as you play around with this starter script, just keep in mind it’s really just the first step. It’s meant to help you get the hang of connecting your algo-trading ideas to Coinbase, but don’t stop there. The crypto market is super dynamic, and you’ll need to keep tweaking and adjusting your strategy, especially considering those pesky trading fees, to stay ahead of the game.

Errors I overcame

Diving into the world of automated trading has been quite the adventure, full of ups, downs, and a few facepalm moments. One of the quirky things I had to figure out the hard way was dealing with the Coinbase API and its not-so-friendly attitude towards decimal points in orders. Picture this: you’ve got your trading bot all set up, ready to make some slick trades, and then bam, the API throws a tantrum because your order amounts have decimals. Talk about being finicky, right?

So, I had to get a bit creative and start rounding down my balances to whole numbers before even thinking about making a purchase. Who knew a little rounding could be the peacekeeper between my bot and the API?

But wait, there’s more. The API also seems to get a bit overwhelmed if your buy or sell orders are for more than what’s available at that price point. It’s like trying to buy ten apples when there are only five on the shelf — it’s just not happening. To work around this, I had to implement loops in the script, constantly tweaking the amount in the orders until the API finally gave the green light. It was a bit of trial and error, but hey, that’s all part of the fun in getting this stuff to work.

So, if you’re venturing into setting up your own trading bot, keep these little nuggets in mind. A bit of rounding here and some persistent looping there could be the difference between a smooth-sailing trading bot and one that’s constantly hitting a wall. Happy trading, and may the odds be ever in your favor (or at least may the API be a little less grumpy)!

Getting your Coinbase API keys

First, you’ll need to create your API keys from settings.

Necessary PIP installs

pip install coinbase-advancedtrade-python
pip3 install coinbase-advanced-py
pip install coinbase
pip install numpy
pip install pandas
pip install ccxt

This script is pretty much a little digital trader that lives to execute trades on your behalf, based on some smart logic and market predictions. It’s like having a mini broker inside your computer, except this one doesn’t sleep, eat, or take coffee breaks.

import time
from coinbase.wallet.client import Client
from coinbase_advanced_trader.config import set_api_credentials
from coinbase_advanced_trader.strategies.market_order_strategies import fiat_market_sell
from coinbase_advanced_trader.strategies.market_order_strategies import fiat_market_buy
import numpy as np
import pandas as pd
from datetime import datetime
from getpass import getpass  # Use getpass for secure input
from ccxt import coinbasepro
from decimal import Decimal
import math


# Set your Coinbase API key and secret
API_KEY = "YOUR COINBASE API KEY"
API_SECRET = "YOUR COINBASE SECRET API KEY"

# Set the API credentials once
set_api_credentials(API_KEY, API_SECRET)


# Fetch historical data and make predictions
def fetch_data_and_predict():
    data_prep = []
    exchange = coinbasepro()  # Renamed variable to avoid conflict
    for trade in exchange.fetch_trades('ADA/EUR', limit=1000):  # Fetching the latest 1000 trades
        time_of_trade = trade['datetime']
        trade_price = trade['price']
        convert_time_of_trade_to_datetime = datetime.strptime(time_of_trade, "%Y-%m-%dT%H:%M:%S.%fZ")
        data_prep.append((convert_time_of_trade_to_datetime, trade_price))

    dfa = pd.DataFrame(data_prep, columns=['date', 'Price'])
    dfa['date'] = pd.to_datetime(dfa['date']).astype(int) / 10**9  # Convert datetime to Unix timestamp

    # Perform linear regression on min and max prices by hour
    dfa['hour'] = dfa['date'].apply(lambda x: datetime.fromtimestamp(x).hour)
    min_prices = dfa.groupby('hour')['Price'].min().reset_index()
    max_prices = dfa.groupby('hour')['Price'].max().reset_index()

    min_slope, min_intercept = np.polyfit(min_prices['hour'], min_prices['Price'], 1)
    max_slope, max_intercept = np.polyfit(max_prices['hour'], max_prices['Price'], 1)

    # Predict next hour's min and max prices
    next_hour = max(min_prices['hour'].max(), max_prices['hour'].max()) + 1
    predicted_min_price = min_slope * next_hour + min_intercept
    predicted_max_price = max_slope * next_hour + max_intercept

    # Calculate sell scenario line (top 20% between predicted min and max prices)
    top_20_percent_threshold = (predicted_max_price - predicted_min_price) * 0.8
    sell_scenario_line = predicted_min_price + top_20_percent_threshold

    return predicted_min_price, predicted_max_price, sell_scenario_line



def sell_ada_for_euros(ada_amount):
    int(ada_amount)
    while ada_amount > 3:
        try:
            # Define the trading parameters
            product_id = "ADA-EUR"  # Replace with the desired trading pair
            ada_size = int(ada_amount)  # Specify the amount of ADA you want to sell
            print(ada_size)
    
            # Perform a market sell
            market_sell_order = fiat_market_sell(product_id, ada_size)
    
            # Print the order details
            print(f"Successfully sold {market_sell_order['filled_size']} ADA at the current market price.")
            print(f"Order ID: {market_sell_order['order_id']}")
            break  # Exit the loop if purchase is successful
    
        except Exception as e:
            print(f"Error: {e}")
            ada_amount -= 1  # Reduce the euro amount by 1 and try again


def buy_ada_with_euros(euro_amount):
    int(euro_amount)
    while euro_amount > 3:  # Ensure there's at least 1 euro to try buying ADA
        try:
            # Define the trading parameters
            product_id = "ADA-EUR"  # Replace with the desired trading pair
            ada_size = euro_amount  # Attempt to use the current euro amount

            # Perform a market buy
            market_buy_order = fiat_market_buy(product_id, ada_size)

            # Print the order details
            print(f"Successfully bought ADA with {euro_amount} euros at the current market price.")
            print(f"Order ID: {market_buy_order['order_id']}")
            break  # Exit the loop if purchase is successful

        except Exception as e:
            print(f"Error with {euro_amount} euros: {e}")
            euro_amount -= 1  # Reduce the euro amount by 1 and try again



def execute_trades(client, predicted_min_price, predicted_max_price, sell_scenario_line):
    # Fetch the current price of ADA/EUR
    current_price = float(client.get_spot_price(currency_pair='ADA-EUR')['amount'])

    # Fetch account balances
    eur_account = client.get_account('EUR')
    ada_account = client.get_account('ADA')

    # Check and print balances
    eur_balance = float(eur_account.balance.amount) if eur_account else 0
    ada_balance = float(ada_account.balance.amount) if ada_account else 0
    print(f"EUR Balance: {eur_balance}, ADA Balance: {ada_balance}")

    # Proceed with trading logic only if accounts are found
    if eur_account and ada_account:
        if current_price <= predicted_min_price and eur_balance > 3:
            eur_balance = math.floor(eur_balance * 100) / 100.0
            print(type(eur_balance))
            print("Buy order would be placed using entire EUR balance:", eur_balance)
            # Place buy order
            try:
                # Call the function to buy ADA with Euros
                Decimal(eur_balance)
                buy_ada_with_euros(eur_balance)
                print("Buy order placed:", eur_balance)
            except Exception as e:
                print(f"An error occurred: {e}")
        elif current_price >= sell_scenario_line and ada_balance > 3:
            ada_balance = math.floor(ada_balance * 100) / 100.0
            print(type(ada_balance))
            print("Sell order would be placed using entire ADA balance:", ada_balance)
            # Place sell order
            Decimal(ada_balance)
            sell_ada_for_euros(ada_balance)
            print("Sell order placed:", ada_balance)
        else:
            print("Hold. Current price is:", current_price)
            print(predicted_min_price, predicted_max_price, sell_scenario_line)
            # Hold logic here
    else:
        print("Missing EUR or ADA account. Unable to execute trades.")

# Set your Coinbase API credentials
api_key = 'YOUR COINBASE API KEY'
api_secret = 'YOUR COINBASE SECRET API KEY'
client = Client(api_key, api_secret)



def job():
    # Your existing code to fetch predictions and execute trades
    predicted_min_price, predicted_max_price, sell_scenario_line = fetch_data_and_predict()
    execute_trades(client, predicted_min_price, predicted_max_price, sell_scenario_line)

# Keep the script running
while True:
    time.sleep(60)
    job()

Here’s a rundown of what’s happening under the hood:

1. API Connections: First off, the script cozies up with the Coinbase API, getting all the access it needs to fetch real-time data, check out your balances, and, most importantly, place those buy and sell orders. It’s like the script’s VIP pass into the world of Coinbase trading.
2. Market Analysis: Next, it’s all about getting the lay of the land. The script pulls in the latest trade data for ADA/EUR, crunching numbers, and looking for patterns. It’s on a constant lookout for the perfect time to make a move, whether it’s buying low or selling high.
3. Making Moves: Here’s where the magic happens. Based on its analysis, the script decides when to buy ADA with your Euros or sell ADA to stack up more Euros. But remember those quirks I mentioned earlier? The script is smart enough to round down those pesky decimals and keep tweaking its orders to make sure they go through without a hitch.
4. Looping Logic: The market never sleeps, and neither does this script. It’s always looping, watching the market like a hawk, ready to adjust its strategies on the fly. Whether the prices are soaring or taking a dive, the script is ready to make the most out of the situation.

It’s important to note that while this script is a great starting point for anyone looking to get their feet wet in automated trading on Coinbase, it’s just the beginning. The world of crypto trading is vast and ever-changing, so continuous learning, tweaking, and adapting are part of the game. Happy trading, and may your digital broker bring you plenty of successful trades!

Considerations and Best Practices

• Security: The script uses hard-coded API credentials, which is not recommended for production code. A more secure approach would involve storing these credentials in environment variables or a secure vault.
• Error Handling: The script includes basic error handling to retry orders in case of failure, which is crucial for a trading bot to handle the inherent unreliability of network requests and API responses.
• Data Analysis: Linear regression is a simple prediction model that may not always provide accurate forecasts for volatile markets like cryptocurrencies. Traders might consider more sophisticated models or a combination of indicators for better predictions.