How to Build a Solana Copy Trading Bot - Tutorial

This project is a Solana copy trading bot that allows users to replicate trades executed by a specified account on the Solana blockchain. The bot fetches trading data using the Bitquery API. However since this is a tutorial project we don't execute a trade but rather store the trade info in an excel document. This is to provide an understanding on how Bitquery APIs could be used to build a full product.

Understanding the Code

You can checkout the entire codebase here. The major logical part is in the main.py file, so lets try to breakdown the code written here.

Imports

This code snippet will cover all the imports needed for running the script.

import requests
import json
import pandas as pd
from constant import token

If any error is encountered due to import statement then try running the pip install ... command.

Get Trades Function

This function provides the trade info for a particular address, HH3BmVQoVsH2c5H3nonkw2ySGogyohBXGGgF7vM7MRdk in this case that could be stored in a doc or copied by adding custom logic. This function hits the Bitquery API with this query to retrieve the latest trades by this account.

def getTrades():

Declaring URL, payload and headers

url = "https://streaming.bitquery.io/eap"

    payload = json.dumps({
        "query": """subscription {
            Solana {
                DEXTrades(
                    where: {
                        Trade: {Buy: {Account: {Address: {is: "HH3BmVQoVsH2c5H3nonkw2ySGogyohBXGGgF7vM7MRdk"}}}},
                        Transaction: {Result: {Success: true}}
                    }
                ) {
                    Trade {
                        Buy {
                            Amount
                            Currency {
                                Name
                                Symbol
                                MintAddress
                            }
                            Price
                        }
                        Dex {
                            ProtocolName
                            ProgramAddress
                            ProtocolFamily
                        }
                        Sell {
                            Currency {
                                MintAddress
                                Name
                                Symbol
                            }
                            Price
                            Amount
                        }
                    }
                    Transaction {
                        Signature
                    }
                }
            }
        }""",
        "variables": "{}"
    })

    headers = {
        'Content-Type': 'application/json',
        'X-API-KEY': 'BQYuTITWanwYGz0YLGdcWSADO74o5RTX',
        'Authorization': token
    }

Response Handling

response = requests.post(url, headers=headers, data=payload)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: {response.status_code}")
        return None

Execute Trades Function

Note that the function doesn't actually execute/replicate the trades retrieved but stores it in an excel document. However, if you wish to build an actual bot or contribute to the project then you can easily do that by adding your own logic to the executeTrades(trades_data) function.

def executeTrades(trades_data):

Error Handling and Variable Declaration

This snippet handles the case where empty trades_data is returned. Also, we define variables such as dex_trades to call data in a more direct manner and trade_records to mould data into the format of our dataframe.

if not trades_data or "data" not in trades_data or "Solana" not in trades_data["data"]:
        print("No trade data found.")
        return

    dex_trades = trades_data["data"]["Solana"]["DEXTrades"]
    
    # Prepare the data for DataFrame
    trade_records = []

Iterating the Dex Trades

In this section we are iterating the dex_trades and updating the trade_records simultaneously.

for trade in dex_trades:
        buy_currency = trade['Trade']['Buy']['Currency']
        sell_currency = trade['Trade']['Sell']['Currency']
        dex_info = trade['Trade']['Dex']
        transaction_info = trade['Transaction']

        trade_records.append({
            'Buy Amount': trade['Trade']['Buy']['Amount'],
            'Buy Currency Name': buy_currency['Name'],
            'Buy Currency Symbol': buy_currency['Symbol'],
            'Buy Mint Address': buy_currency['MintAddress'],
            'Buy Price': trade['Trade']['Buy']['Price'],
            'Sell Amount': trade['Trade']['Sell']['Amount'],
            'Sell Currency Name': sell_currency['Name'],
            'Sell Currency Symbol': sell_currency['Symbol'],
            'Sell Mint Address': sell_currency['MintAddress'],
            'Sell Price': trade['Trade']['Sell']['Price'],
            'Dex Protocol Name': dex_info['ProtocolName'],
            'Dex Program Address': dex_info['ProgramAddress'],
            'Dex Protocol Family': dex_info['ProtocolFamily'],
            'Transaction Signature': transaction_info['Signature']
        })

Creating a DataFrame and Saving Trade Info to an Excel File

df = pd.DataFrame(trade_records)
    excel_file = 'trades_data.xlsx'
    df.to_excel(excel_file, index=False)
    print(f"Data saved to {excel_file}")

Running the Script

This part of code actually runs the script and calls the functions.

trades_data = getTrades()

executeTrades(trades_data)

To run this script enter the following command:

python3 main.py

Video Tutorial