Sure, I can provide you with a Python-based simple algorithmic trading bot using the mean reversion technique. This bot will use the Coinbase API to gather data and implement the buying and selling logic. In this example, I'll use a package named coinbasepro-python as the Coinbase Pro API client. You can install it with pip: bash Copy code pip install cbpro Here is the code: python Copy code import pandas as pd import cbpro import time # Coinbase API keys public_key = '<Your_Public_Key>' secret_key = '<Your_Secret_Key>' passphrase = '<Your_Passphrase>' # Coinbase Pro client client = cbpro.AuthenticatedClient(public_key, secret_key, passphrase) # Buy/Sell logic def buy_sell_logic(df): buy_sell = [] flag = -1 for i in range(0, len(df)): if df['Mean'][i] < df['Close'][i]: if flag != 1: buy_sell.append(True) flag = 1 else: buy_sell.append(False) elif df['Mean'][i] > df['Close'][i]: if flag != 0: buy_sell.append(True) flag = 0 else: buy_sell.append(False) else: buy_sell.append(False) return buy_sell # Fetching historical data def get_data(start_date, end_date): data = [] start = pd.Timestamp(start_date, tz='UTC') end = pd.Timestamp(end_date, tz='UTC') while start < end: results = client.get_product_historic_rates('BTC-USD', granularity=86400, start=start.isoformat(), end=(start + pd.DateOffset(days=300)).isoformat()) data.extend(results) start += pd.DateOffset(days=300) time.sleep(1) # To avoid rate limits df = pd.DataFrame(data, columns=['Time', 'Low', 'High', 'Open', 'Close', 'Volume']) df['Time'] = pd.to_datetime(df['Time'], unit='s') df.set_index('Time', inplace=True) df.sort_index(ascending=True, inplace=True) # Calculating the mean df['Mean'] = df['Close'].rolling(window=50).mean() # Buy/Sell logic df['Buy_Signal_Flag'] = buy_sell_logic(df) return df # Executing orders def execute_orders(df): for i in range(len(df)): if df['Buy_Signal_Flag'][i]: if df['Mean'][i] > df['Close'][i]: # Buy order order = client.buy(price=str(df['Close'][i]), size='1', order_type='limit', product_id='BTC-USD') print('Buy order executed at $' + str(df['Close'][i]) + ', TXN ID: ' + order['id']) elif df['Mean'][i] < df['Close'][i]: # Sell order order = client.sell(price=str(df['Close'][i]), size='1', order_type='limit', product_id='BTC-USD') print('Sell order executed at $' + str(df['Close'][i]) + ', TXN ID: ' + order['id']) # Main if __name__ == "__main__": df = get_data('2022-01-01', '2022-12-31') execute_orders(df)