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COPY TRADING

To refine the filtering criteria for identifying potential insider wallets, we'll focus on specific aspects of the parsed data:

  1. Early-Stage Purchases: We'll filter for trades that occur during the early stages of a token's trading history.
  2. High Profitability: We'll focus on wallets with a high PNL (Profit and Loss).
  3. Trade Volume and Frequency: We'll prioritize wallets with significant trading volume and a high number of trades.

Step 3: Implementing Filtering Criteria

3.1 Early-Stage Purchases

We'll filter trades based on their timestamp, comparing it to the token's launch date. Trades made within a certain window (e.g., the first few days) can be considered early-stage.

3.2 High Profitability

We'll filter wallets that have a high cumulative PNL, which can indicate successful insider trading.

3.3 Trade Volume and Frequency

Wallets with high trade volume and frequency might indicate active and potentially informed traders.

Example Code for Filtering

import requests
from datetime import datetime, timedelta

# Step 1: Fetch and parse data (using the functions from previous steps)

# Step 2: Filtering Criteria

# 3.1: Filter for Early-Stage Purchases
def filter_early_stage_purchases(traders, token_launch_date, days_threshold=3):
    early_stage_traders = []
    for trader in traders:
        trade_time = datetime.utcfromtimestamp(trader['timestamp'])
        if trade_time <= token_launch_date + timedelta(days=days_threshold):
            early_stage_traders.append(trader)
    return early_stage_traders

# 3.2: Filter for High Profitability
def filter_high_profitability(wallets, min_pnl=100000):
    profitable_wallets = []
    for wallet in wallets:
        if wallet['pnl'] >= min_pnl:
            profitable_wallets.append(wallet)
    return profitable_wallets

# 3.3: Filter for High Volume and Frequency
def filter_high_volume_frequency(wallets, min_volume=50000, min_trades=100):
    active_wallets = []
    for wallet in wallets:
        if wallet['volume'] >= min_volume and wallet['number_of_trades'] >= min_trades:
            active_wallets.append(wallet)
    return active_wallets

# Example usage:
if __name__ == "__main__":
    # Fetch and parse data from previous steps
    top_traders_data = get_top_traders(network="solana", token="SOL")
    if top_traders_data:
        parsed_traders = parse_dexscreener_data(top_traders_data)
        
        # Assuming we have the token launch date
        token_launch_date = datetime(2024, 8, 1)  # Example date
        
        # Step 3.1: Filter early-stage purchases
        early_stage_traders = filter_early_stage_purchases(parsed_traders, token_launch_date)
        print("Early-Stage Traders:")
        for trader in early_stage_traders:
            print(trader)
        
        # Fetch and parse wallet analysis from Birdeye
        wallet_address = "39PcuE9MBnW4FnqcDJYiy4ppRApdDq4XN4bfksG2nEuP"
        wallet_analysis = get_wallet_analysis(wallet_address)
        if wallet_analysis:
            parsed_wallet = parse_birdeye_data(wallet_analysis)
            wallets = [parsed_wallet]  # In a real scenario, this could be multiple wallets
            
            # Step 3.2: Filter high profitability
            profitable_wallets = filter_high_profitability(wallets)
            print("Profitable Wallets:")
            for wallet in profitable_wallets:
                print(wallet)
            
            # Step 3.3: Filter high volume and frequency
            active_wallets = filter_high_volume_frequency(wallets)
            print("Active Wallets:")
            for wallet in active_wallets:
                print(wallet)

Explanation:

  1. Filter for Early-Stage Purchases:
    • Function: filter_early_stage_purchases
    • Input: List of traders and the token's launch date.
    • Logic: Checks if the trade's timestamp is within a specified threshold (e.g., the first 3 days after launch).
  2. Filter for High Profitability:
    • Function: filter_high_profitability
    • Input: List of wallets and a minimum PNL value.
    • Logic: Only includes wallets where the cumulative PNL exceeds the threshold.
  3. Filter for High Volume and Frequency:
    • Function: filter_high_volume_frequency
    • Input: List of wallets and minimum thresholds for volume and number of trades.
    • Logic: Filters wallets that meet both criteria.

Next Steps:

  1. Combine Filters: You can combine these filters to create a list of wallets that meet all criteria.
  2. Automate Monitoring: Set up periodic checks to keep your filtered list updated.
  3. Copytrading Integration: Implement logic to copy trades automatically for wallets that pass these filters.

Let's combine the filtering criteria to create a unified process that identifies potential insider wallets. This process will filter wallets based on early-stage purchases, high profitability, and high trade volume/frequency.

Step 4: Combining Filters

We'll create a function that applies all the filters in sequence and returns a list of wallets that meet all the criteria.

Combined Filtering Code

import requests
from datetime import datetime, timedelta

# Step 1: Fetch and parse data (using the functions from previous steps)

# 3.1: Filter for Early-Stage Purchases
def filter_early_stage_purchases(traders, token_launch_date, days_threshold=3):
    early_stage_traders = []
    for trader in traders:
        trade_time = datetime.utcfromtimestamp(trader['timestamp'])
        if trade_time <= token_launch_date + timedelta(days=days_threshold):
            early_stage_traders.append(trader)
    return early_stage_traders

# 3.2: Filter for High Profitability
def filter_high_profitability(wallets, min_pnl=100000):
    profitable_wallets = []
    for wallet in wallets:
        if wallet['pnl'] >= min_pnl:
            profitable_wallets.append(wallet)
    return profitable_wallets

# 3.3: Filter for High Volume and Frequency
def filter_high_volume_frequency(wallets, min_volume=50000, min_trades=100):
    active_wallets = []
    for wallet in wallets:
        if wallet['volume'] >= min_volume and wallet['number_of_trades'] >= min_trades:
            active_wallets.append(wallet)
    return active_wallets

# 4.1: Combine All Filters
def combine_filters(traders, token_launch_date, days_threshold, wallets, min_pnl, min_volume, min_trades):
    # Step 1: Filter early-stage purchases
    early_stage_traders = filter_early_stage_purchases(traders, token_launch_date, days_threshold)
    
    # Step 2: Filter wallets based on profitability
    profitable_wallets = filter_high_profitability(wallets, min_pnl)
    
    # Step 3: Filter wallets based on volume and frequency
    active_wallets = filter_high_volume_frequency(profitable_wallets, min_volume, min_trades)
    
    # Step 4: Cross-reference the wallets from early-stage traders with the filtered list
    insider_wallets = []
    for trader in early_stage_traders:
        for wallet in active_wallets:
            if trader['wallet_address'] == wallet['wallet_address']:
                insider_wallets.append(wallet)
    
    return insider_wallets

# Example usage:
if __name__ == "__main__":
    # Fetch and parse top traders for a specific token on the Solana network
    top_traders_data = get_top_traders(network="solana", token="SOL")
    if top_traders_data:
        parsed_traders = parse_dexscreener_data(top_traders_data)
        
        # Assuming we have the token launch date
        token_launch_date = datetime(2024, 8, 1)  # Example date
        
        # Fetch and parse wallet analysis from Birdeye
        wallet_address = "39PcuE9MBnW4FnqcDJYiy4ppRApdDq4XN4bfksG2nEuP"
        wallet_analysis = get_wallet_analysis(wallet_address)
        if wallet_analysis:
            parsed_wallet = parse_birdeye_data(wallet_analysis)
            wallets = [parsed_wallet]  # In a real scenario, this could be multiple wallets
            
            # Step 4.1: Combine filters
            insider_wallets = combine_filters(
                traders=parsed_traders,
                token_launch_date=token_launch_date,
                days_threshold=3,
                wallets=wallets,
                min_pnl=100000,
                min_volume=50000,
                min_trades=100
            )
            
            print("Insider Wallets:")
            for wallet in insider_wallets:
                print(wallet)

Explanation:

  1. Combine All Filters:
    • Function: combine_filters
    • Inputs:
      • traders: List of parsed trader data.
      • token_launch_date: The date the token was launched.
      • days_threshold: Number of days after launch considered "early-stage."
      • wallets: List of parsed wallet performance data.
      • min_pnl: Minimum Profit and Loss (PNL) to filter for profitable wallets.
      • min_volume: Minimum trade volume.
      • min_trades: Minimum number of trades.
    • Logic:
      • First, it filters for early-stage purchases.
      • Then, it filters wallets based on profitability.
      • Next, it filters for high volume and frequency.
      • Finally, it cross-references the traders with the filtered wallets to identify potential insiders.
  2. Cross-Referencing:
    • The function checks if any of the early-stage traders' wallet addresses match the wallets that passed the profitability and volume/frequency filters. This step ensures that only those traders who made early trades and are highly profitable are identified as potential insiders.
  3. Example Usage:
    • The example usage demonstrates how to fetch and parse data, apply the combined filters, and print out the potential insider wallets.

Next Steps:

  1. Run the Combined Filters: Test the script with real data to see how it performs in identifying potential insider wallets.
  2. Automate the Process: Set up a schedule to run this analysis periodically, updating the list of insider wallets.
  3. Copytrading Integration: Start implementing the logic to copy trades automatically from the identified insider wallets.

Let's proceed with automating the monitoring and copytrading process. This involves setting up a schedule to periodically check for new insider wallets and automatically execute trades based on their activity.

Step 5: Automating Monitoring and Copytrading

5.1 Automation Setup

We'll use the schedule library to run the script at regular intervals. This will ensure that the wallet analysis and copytrading actions are performed automatically.

5.2 Copytrading Logic

We'll implement the logic to mirror trades from identified insider wallets. This involves interacting with the Solana blockchain to execute trades via a wallet.

Step 5.1: Setting Up Automation

First, we'll set up a scheduler that runs the script at a regular interval, such as every hour.

import schedule
import time

# Step 5.1: Define the main task to run periodically
def monitor_and_copytrade():
    # Fetch and parse top traders for a specific token on the Solana network
    top_traders_data = get_top_traders(network="solana", token="SOL")
    if top_traders_data:
        parsed_traders = parse_dexscreener_data(top_traders_data)
        
        # Assuming we have the token launch date
        token_launch_date = datetime(2024, 8, 1)  # Example date
        
        # Fetch and parse wallet analysis from Birdeye
        wallet_address = "39PcuE9MBnW4FnqcDJYiy4ppRApdDq4XN4bfksG2nEuP"
        wallet_analysis = get_wallet_analysis(wallet_address)
        if wallet_analysis:
            parsed_wallet = parse_birdeye_data(wallet_analysis)
            wallets = [parsed_wallet]  # In a real scenario, this could be multiple wallets
            
            # Combine filters to identify insider wallets
            insider_wallets = combine_filters(
                traders=parsed_traders,
                token_launch_date=token_launch_date,
                days_threshold=3,
                wallets=wallets,
                min_pnl=100000,
                min_volume=50000,
                min_trades=100
            )
            
            print("Insider Wallets Identified:")
            for wallet in insider_wallets:
                print(wallet)
                # Proceed with copytrading for this wallet
                copytrade(wallet['wallet_address'])

# Schedule the task to run every hour
schedule.every().hour.do(monitor_and_copytrade)

# Keep the script running
while True:
    schedule.run_pending()
    time.sleep(1)

Step 5.2: Implementing Copytrading Logic

Now, let's create the copytrade function that will execute trades based on the actions of identified insider wallets.

from web3 import Web3

# Initialize Web3 (assuming Solana compatibility through a suitable provider)
w3 = Web3(Web3.HTTPProvider("https://solana-mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID"))

# Step 5.2: Define the copytrade function
def copytrade(wallet_address):
    # Fetch recent trades from the insider wallet (this could involve additional API calls)
    recent_trades = get_recent_trades(wallet_address)
    
    # Loop through trades and execute a mirrored trade
    for trade in recent_trades:
        if trade['type'] == 'buy':
            execute_buy(trade)
        elif trade['type'] == 'sell':
            execute_sell(trade)

def get_recent_trades(wallet_address):
    # Example function to fetch recent trades
    # In reality, this could involve complex logic to interact with a Solana-specific API
    return [
        {"type": "buy", "token": "SOL", "amount": 10, "price": 20},
        {"type": "sell", "token": "SOL", "amount": 5, "price": 25}
    ]

def execute_buy(trade):
    # Placeholder function for buying tokens
    print(f"Executing buy: {trade['amount']} {trade['token']} at {trade['price']}")

def execute_sell(trade):
    # Placeholder function for selling tokens
    print(f"Executing sell: {trade['amount']} {trade['token']} at {trade['price']}")

Explanation:

  1. Automation with Schedule:
    • The script is scheduled to run the monitor_and_copytrade function every hour. This function fetches, analyzes, and filters data, then identifies insider wallets.
  2. Copytrading Logic:
    • copytrade Function: This function fetches recent trades from an identified insider wallet and mirrors them. It uses placeholder functions execute_buy and execute_sell to simulate the trades. In a real-world scenario, these would interact with the Solana blockchain via Web3 to execute the trades.
  3. Web3 Setup:
    • The Web3 instance is initialized to interact with the Solana blockchain. You'll need to ensure that you have the correct provider URL (e.g., from Infura or another provider) and that Web3 is compatible with Solana or another library is used if Web3 doesn't directly support Solana.
  4. Example Trades:
    • The get_recent_trades function is a placeholder that simulates fetching recent trades. In practice, this would involve querying a Solana-specific API or on-chain data to get real trades executed by the insider wallet.

Next Steps:

  1. Replace Placeholders: Replace the placeholder functions with real implementations to interact with the Solana blockchain and execute trades.
  2. Test the System: Test the entire system end-to-end with a small amount of capital to ensure that it correctly identifies insider wallets and mirrors their trades.
  3. Optimize and Scale: Once tested, you can optimize the system for performance and scale it to handle more wallets and tokens.

Let's proceed with implementing the real trade execution logic. To do this, we need to integrate with the Solana blockchain for executing buy and sell orders. Here's how we'll approach it:

Step 6: Implementing Real Trade Execution Logic

6.1 Solana Blockchain Integration

We'll need to interact with the Solana blockchain to execute trades. This will involve using a Solana-specific library like solana-py instead of Web3, which is typically used for Ethereum.

6.2 Setting Up a Solana Wallet

You'll need to have a Solana wallet set up with the necessary private keys for signing transactions. We'll use the solana-py library to handle this.

6.3 Executing Buy and Sell Orders

We'll write functions to execute buy and sell orders on the Solana network, using the data fetched from the identified insider wallets.

Step 6.1: Setting Up the Solana Environment

First, you'll need to install the solana-py library:

pip install solana

Step 6.2: Setting Up a Solana Wallet

Here's how to set up your Solana wallet in Python:

from solana.publickey import PublicKey
from solana.keypair import Keypair
from solana.rpc.api import Client
from solana.transaction import Transaction, AccountMeta
from solana.system_program import transfer, TransferParams

# Initialize the Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Load your wallet keypair (You need to have a JSON file with your private key)
def load_wallet(path_to_keypair_json):
    with open(path_to_keypair_json, 'r') as f:
        keypair_data = json.load(f)
    return Keypair.from_secret_key(bytes(keypair_data))

# Example usage
wallet = load_wallet("path_to_your_wallet_keypair.json")

Step 6.3: Executing Buy and Sell Orders

Now, let's implement the functions to execute buy and sell orders on the Solana network:

from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from spl.token.instructions import transfer_checked, TransferCheckedParams

# Initialize the Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Example function to execute a buy order
def execute_buy(wallet, token_address, amount):
    try:
        # Transaction logic for buying a token
        transaction = Transaction()
        
        # Assume the transaction is a simple transfer of SOL
        transfer_instruction = transfer(
            TransferParams(
                from_pubkey=wallet.public_key,
                to_pubkey=PublicKey(token_address),
                lamports=int(amount * 1e9)  # Assuming amount is in SOL
            )
        )
        transaction.add(transfer_instruction)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Buy executed: {response}")
    except Exception as e:
        print(f"Failed to execute buy: {e}")

# Example function to execute a sell order
def execute_sell(wallet, token_address, amount):
    try:
        # Transaction logic for selling a token
        transaction = Transaction()
        
        # Add your custom logic for selling a token on Solana
        # For simplicity, we're using the same transfer example

        transfer_instruction = transfer(
            TransferParams(
                from_pubkey=wallet.public_key,
                to_pubkey=PublicKey(token_address),
                lamports=int(amount * 1e9)  # Assuming amount is in SOL
            )
        )
        transaction.add(transfer_instruction)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Sell executed: {response}")
    except Exception as e:
        print(f"Failed to execute sell: {e}")

# Example usage
if __name__ == "__main__":
    # Load your wallet
    wallet = load_wallet("path_to_your_wallet_keypair.json")

    # Execute a buy order
    execute_buy(wallet, "target_wallet_or_token_address", amount=1.5)  # 1.5 SOL
    
    # Execute a sell order
    execute_sell(wallet, "target_wallet_or_token_address", amount=1.0)  # 1.0 SOL

Explanation:

  1. Loading the Wallet:
    • The load_wallet function loads your Solana wallet from a JSON keypair file. This keypair will be used to sign and send transactions on the Solana network.
  2. Executing a Buy Order:
    • The execute_buy function creates a transaction to buy a token or transfer SOL. You can modify this to interact with specific tokens or smart contracts on Solana.
  3. Executing a Sell Order:
    • The execute_sell function handles selling tokens or transferring SOL back to another wallet. Like the buy function, this can be customized based on your specific needs.
  4. Error Handling:
    • Both functions include basic error handling to ensure that failures are logged and can be debugged.

Next Steps:

  1. Customize Trading Logic: Modify the buy and sell functions to interact with specific tokens or DEXs on Solana, depending on your trading strategy.
  2. Test Trades: Run small test trades to ensure that the logic works as expected before scaling up.
  3. Integrate with Monitoring: Combine these functions with the automated monitoring system to create a fully automated copytrading bot.

Let's customize the trading logic specifically for handling different tokens on the Solana blockchain. The key aspects we'll focus on include:

  1. Handling Different Tokens: Ensuring the logic is flexible enough to handle various SPL tokens.
  2. Adjusting for Token Decimals: Different tokens have different decimal places, which need to be accounted for during transfers.
  3. Integrating with Decentralized Exchanges (DEXs): If you want to buy or sell tokens through a DEX on Solana, we’ll integrate with platforms like Serum.

Step 1: Handling Different Tokens

To handle different SPL tokens, you need to dynamically manage token addresses and their associated details (like decimals). You can create a configuration or database to store token-specific details.

# Example token configuration
TOKEN_CONFIG = {
    "USDC": {
        "address": "5R3Z4ZzkJXgdgMdjb6e62FjX4txLEWTojmDTHHhM69W8",  # Example USDC mint address
        "decimals": 6
    },
    "SOL": {
        "address": "So11111111111111111111111111111111111111112",  # SOL address (often used for native SOL transactions)
        "decimals": 9
    },
    # Add more tokens as needed
}

def get_token_details(token_symbol):
    return TOKEN_CONFIG.get(token_symbol)

# Example usage
token_details = get_token_details("USDC")
print(token_details)

Step 2: Adjusting for Token Decimals

When transferring tokens, you need to consider the number of decimal places each token uses. For example, USDC on Solana has 6 decimal places, while native SOL has 9. This affects how you calculate the amount to transfer.

def calculate_amount(amount, decimals):
    return int(amount * (10 ** decimals))

# Example usage
usdc_amount = calculate_amount(10.5, 6)  # 10.5 USDC
sol_amount = calculate_amount(0.25, 9)  # 0.25 SOL
print(f"USDC amount in smallest units: {usdc_amount}")
print(f"SOL amount in smallest units: {sol_amount}")

Step 3: Integrating with Decentralized Exchanges (DEXs)

If you want to trade tokens via a DEX like Serum, you need to interact with their smart contracts. Here’s a simplified example of placing a trade on Serum. Note that interacting with Serum involves more complexity, such as order placement, matching, and settlement.

Installing the Required Libraries:

pip install pyserum

Example Serum Trade Execution:

from solana.rpc.api import Client
from pyserum.connection import conn
from pyserum.market import Market
from solana.keypair import Keypair
from solana.transaction import Transaction

# Initialize Solana and Serum clients
solana_client = Client("https://api.mainnet-beta.solana.com")
serum_connection = conn("https://api.mainnet-beta.solana.com")

# Load your wallet
wallet = load_wallet("path_to_your_wallet_keypair.json")

# Fetch a Serum market (e.g., USDC/SOL)
market_address = PublicKey("9wFFe7owYyA2BNCZ3Z9BNEgktzM7ZKCUYZymm55FqZwR")  # Example Serum market address
market = Market.load(serum_connection, market_address)

# Example function to place an order on Serum
def place_serum_order(market, side, price, size, owner):
    transaction = Transaction()
    order = market.place_order(
        payer=owner.public_key,
        owner=owner,
        side=side,  # "buy" or "sell"
        limit_price=price,
        max_quantity=size,
    )
    transaction.add(order)
    response = solana_client.send_transaction(transaction, owner)
    return response

# Example usage: Place a buy order for 0.5 SOL at 50 USDC per SOL
order_response = place_serum_order(
    market,
    side="buy",
    price=50,  # 50 USDC per SOL
    size=0.5,  # 0.5 SOL
    owner=wallet
)
print(f"Order response: {order_response}")

Explanation:

  1. Token Configuration:
    • The TOKEN_CONFIG dictionary holds details about various tokens, including their mint addresses and decimals. The get_token_details function retrieves these details when needed.
  2. Adjusting for Decimals:
    • The calculate_amount function adjusts the amount based on the token's decimals, ensuring you handle transfers correctly according to the token’s smallest unit.
  3. Serum Integration:
    • The Serum DEX integration uses the pyserum library. The place_serum_order function creates and sends a transaction to place an order on a Serum market. This example covers a basic limit order, but more complex orders are possible.

Next Steps:

  1. Customize for Specific Tokens: Expand the TOKEN_CONFIG with more tokens you want to trade, and ensure your trading logic can handle them.
  2. Test DEX Integration: Test the Serum integration with small trades to ensure it works as expected.
  3. Combine with Monitoring: Integrate this trading logic with your automated monitoring system, so trades are automatically executed based on insider wallet activity.

Trading on Raydium and Jupiter instead of Serum is a great choice, as these platforms are popular decentralized exchanges (DEXs) on the Solana network. I'll guide you through customizing the trading logic to interact with both Raydium and Jupiter.

Step 1: Raydium Integration

Raydium operates similarly to Serum, but it has its own set of smart contracts and liquidity pools. We’ll use the Raydium SDK or directly interact with its smart contracts.

Using Raydium SDK

Raydium doesn’t have an official Python SDK like Serum, but you can interact with its smart contracts via the Solana spl-token program.

Here's how to execute trades on Raydium using a combination of solana-py and raydium smart contracts:

from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.keypair import Keypair
from solana.rpc.types import TxOpts
from spl.token.instructions import transfer_checked, TransferCheckedParams

# Initialize the Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Load your wallet
wallet = load_wallet("path_to_your_wallet_keypair.json")

# Raydium-specific configuration
RAYDIUM_POOL_ADDRESS = PublicKey("specific_pool_address")  # Raydium liquidity pool address
RAYDIUM_PROGRAM_ID = PublicKey("Raydium program ID")  # Raydium AMM program ID

# Function to interact with Raydium AMM
def trade_on_raydium(wallet, input_token, output_token, amount_in, amount_out_min):
    transaction = Transaction()
    
    # Create the instruction to interact with Raydium
    raydium_instruction = raydium_trade_instruction(
        wallet.public_key, input_token, output_token, amount_in, amount_out_min
    )
    transaction.add(raydium_instruction)

    # Sign and send the transaction
    response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
    print(f"Raydium trade executed: {response}")

def raydium_trade_instruction(owner, input_token, output_token, amount_in, amount_out_min):
    # This function would create the specific instruction to trade on Raydium
    # You need to build this instruction based on Raydium's smart contract specifications
    # This is a placeholder function; you need to replace this with the actual Raydium trading logic.
    pass

# Example usage
if __name__ == "__main__":
    # Example trade on Raydium
    trade_on_raydium(wallet, "input_token_address", "output_token_address", amount_in=1.0, amount_out_min=0.9)

Step 2: Jupiter Aggregator Integration

Jupiter is an aggregator for Solana DEXs, finding the best routes for token swaps. They offer a comprehensive API that makes trading simpler.

Using Jupiter API

You can interact with the Jupiter API directly to get quotes and execute swaps:

import requests
from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.rpc.types import TxOpts

# Initialize Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Load your wallet
wallet = load_wallet("path_to_your_wallet_keypair.json")

# Function to fetch swap routes from Jupiter
def get_jupiter_swap_quote(input_token, output_token, amount):
    url = f"https://quote-api.jup.ag/v1/quote?inputMint={input_token}&outputMint={output_token}&amount={amount}&slippageBps=50"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['data'][0]  # Assume the first route is optimal
    else:
        print(f"Failed to fetch quote from Jupiter: {response.status_code}")
        return None

# Function to execute a swap on Jupiter
def execute_jupiter_swap(wallet, swap_route):
    try:
        transaction = Transaction()
        
        # Create the swap instruction from Jupiter's route
        swap_instruction = PublicKey(swap_route['swapTransaction'])  # You need to decode or use the actual transaction data
        transaction.add(swap_instruction)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Jupiter swap executed: {response}")
    except Exception as e:
        print(f"Failed to execute Jupiter swap: {e}")

# Example usage
if __name__ == "__main__":
    # Fetch quote for swap
    quote = get_jupiter_swap_quote("input_token_address", "output_token_address", amount=1000000)
    if quote:
        print("Best quote from Jupiter:", quote)
        # Execute the swap
        execute_jupiter_swap(wallet, quote)

Explanation:

  1. Raydium Integration:
    • The trade_on_raydium function is a placeholder for interacting with Raydium’s smart contracts. To fully implement this, you’ll need to work with Raydium’s specific program instructions and interact with its AMM pools.
  2. Jupiter Integration:
    • The Jupiter API simplifies trading by providing optimal routes across multiple DEXs. The get_jupiter_swap_quote function fetches the best trade route, while execute_jupiter_swap executes the trade.
    • Jupiter’s API provides a straightforward way to handle complex swap operations without directly interacting with each DEX's contract.

Step 3: Testing the Integration

Just like before, test these integrations on the Solana Devnet:

  1. Raydium Testing: Modify the RAYDIUM_POOL_ADDRESS and RAYDIUM_PROGRAM_ID with testnet values, then test the trade_on_raydium function.
  2. Jupiter Testing: The Jupiter API can be tested directly on the mainnet, but it's safer to use small amounts first to ensure everything is functioning correctly.

Next Steps:

  1. Complete Raydium Integration: Implement the actual instruction creation logic for Raydium trading.
  2. Test on Devnet: Thoroughly test the integration on Solana’s Devnet to avoid any unexpected issues on Mainnet.
  3. Scale and Automate: Once testing is successful, integrate this logic with your monitoring and copytrading system.

To start with Raydium, we'll need to focus on interacting directly with Raydium's Automated Market Maker (AMM) and liquidity pools on Solana. I'll guide you through creating the actual trade instructions for Raydium.

Step 1: Understanding Raydium's AMM

Raydium uses liquidity pools where you can swap between tokens. Each pool is associated with a specific trading pair, and trades are conducted via Raydium's smart contracts.

Step 2: Setting Up Raydium Trade Instructions

To trade on Raydium, you’ll interact with its AMM program. The steps involve:

  1. Finding the Pool: Identify the liquidity pool associated with the trading pair.
  2. Creating the Trade Instruction: This involves creating a transaction that interacts with Raydium’s AMM to perform a swap.

Raydium's AMM Program IDs:

  • Raydium AMM: EhhTKdU5d5ogTjvAB6z8szvvLRUvHTUURAdUhjiGQXiK
  • Raydium Serum: ALR5WgHo3xLZGzST9o49PY88by7r87hfGzj5Br7Ka4rc

Step 3: Implementing the Raydium Trading Logic

Let’s implement the Raydium trading logic in Python:

from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.publickey import PublicKey
from solana.rpc.types import TxOpts
from solana.keypair import Keypair
from spl.token.instructions import TransferCheckedParams, transfer_checked
from solana.system_program import SYS_PROGRAM_ID

# Initialize the Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Raydium-specific configuration
RAYDIUM_AMM_PROGRAM_ID = PublicKey("EhhTKdU5d5ogTjvAB6z8szvvLRUvHTUURAdUhjiGQXi")  # Raydium AMM Program ID

# Function to find the liquidity pool address
def find_liquidity_pool(input_token, output_token):
    # Implement logic to find the correct liquidity pool based on the input and output tokens
    # For example, this could involve querying Raydium’s on-chain data
    return PublicKey("liquidity_pool_address")  # Placeholder, replace with real pool address

# Function to create a Raydium trade instruction
def create_raydium_trade_instruction(wallet, pool_address, input_token, output_token, amount_in, min_amount_out):
    # Construct the trade instruction for Raydium's AMM
    # The actual instruction would depend on the specific smart contract requirements of Raydium's AMM
    transaction = Transaction()

    # You would need to add the specific instructions here, such as:
    # - Initialize swap
    # - Transfer tokens to pool
    # - Execute swap
    # This is a placeholder and must be replaced with the actual transaction logic

    return transaction

# Function to execute the trade on Raydium
def trade_on_raydium(wallet, input_token, output_token, amount_in, min_amount_out):
    # Find the liquidity pool
    pool_address = find_liquidity_pool(input_token, output_token)

    # Create the trade instruction
    transaction = create_raydium_trade_instruction(wallet, pool_address, input_token, output_token, amount_in, min_amount_out)

    # Sign and send the transaction
    response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
    print(f"Raydium trade executed: {response}")

# Example usage
if __name__ == "__main__":
    # Load your wallet
    wallet = load_wallet("path_to_your_wallet_keypair.json")

    # Example trade on Raydium
    trade_on_raydium(wallet, "input_token_address", "output_token_address", amount_in=1.0, min_amount_out=0.9)

Explanation:

  1. Find the Liquidity Pool:
    • The find_liquidity_pool function should be implemented to identify the correct liquidity pool for the trading pair. This might involve querying on-chain data or using an API.
  2. Create the Raydium Trade Instruction:
    • The create_raydium_trade_instruction function builds a transaction that interacts with Raydium’s AMM. This transaction must include steps like transferring tokens to the liquidity pool and executing the swap.
    • This is a placeholder function, and you need to implement the actual logic based on Raydium's AMM contract.
  3. Execute the Trade:
    • The trade_on_raydium function ties everything together: it finds the correct liquidity pool, creates the trade instruction, and sends the transaction.

Step 4: Testing on Devnet

Before deploying this on the mainnet, test the setup on Solana’s Devnet:

  1. Switch to Devnet: Change the RPC endpoint to https://api.devnet.solana.com to avoid using real funds during testing.
  2. Use Test Tokens: Obtain test tokens on Devnet to simulate trades.
  3. Deploy and Test: Run the script and verify that trades are executed correctly by monitoring the transaction outcomes on the Solana Explorer.

Next Steps:

  1. Implement Real Raydium Logic: Replace the placeholder instructions with the actual Raydium AMM trading logic.
  2. Test and Verify: Thoroughly test the logic on Devnet and ensure all components work as expected.
  3. Move to Mainnet: Once testing is successful, move to the mainnet and begin live trading.

To integrate with Jupiter, a decentralized exchange aggregator on the Solana blockchain, you'll interact with their API to get the best swap routes and then execute those swaps. Jupiter aggregates liquidity across many DEXs on Solana, including Raydium, Serum, and others, providing optimal swap routes.

Here’s how to customize the trading logic for Jupiter.

Step 1: Understanding Jupiter's API

Jupiter provides a REST API that allows you to:

  1. Get Swap Quotes: Fetch the best trade routes for a given token pair.
  2. Execute Swaps: Use the fetched route to execute the trade.

Step 2: Setting Up Jupiter API Integration

We'll use Python's requests library to interact with the Jupiter API.

    • inputMint and outputMint are the mint addresses of the input and output tokens, respectively.
    • amount is the amount of the input token in its smallest units (e.g., lamports for SOL).
    • slippageBps is the allowed slippage in basis points (1 bps = 0.01%).

Fetching Swap Quotes:You can fetch the best swap route for a token pair using Jupiter's API.

import requests

# Function to fetch swap routes from Jupiter
def get_jupiter_swap_quote(input_token, output_token, amount):
    url = f"https://quote-api.jup.ag/v1/quote?inputMint={input_token}&outputMint={output_token}&amount={amount}&slippageBps=50"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['data'][0]  # Assume the first route is optimal
    else:
        print(f"Failed to fetch quote from Jupiter: {response.status_code}")
        return None

# Example usage
input_token = "So11111111111111111111111111111111111111112"  # SOL mint address
output_token = "Es9vMFrzrJKB2UexsBgoz2LDitwRZyhpN1f6fNkpK1p1"  # USDC mint address
amount = 1000000  # Amount in the smallest unit of the input token (e.g., 1 SOL = 1,000,000 lamports)

quote = get_jupiter_swap_quote(input_token, output_token, amount)
if quote:
    print("Best quote from Jupiter:", quote)

Explanation:

Install Required Libraries:If you haven't already installed requests, do so now:

pip install requests

Step 3: Executing the Swap on Jupiter

Once you have the quote, you can use it to execute the swap. Jupiter's API returns a pre-signed transaction in base64 format, which you can directly send to the Solana network.

from solana.rpc.api import Client
from solana.transaction import Transaction
from solana.rpc.types import TxOpts
import base64

# Initialize the Solana client
solana_client = Client("https://api.mainnet-beta.solana.com")

# Function to execute a swap on Jupiter
def execute_jupiter_swap(wallet, swap_route):
    try:
        # Decode the transaction from base64
        transaction_data = base64.b64decode(swap_route['swapTransaction'])
        
        # Deserialize the transaction
        transaction = Transaction.deserialize(transaction_data)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Jupiter swap executed: {response}")
    except Exception as e:
        print(f"Failed to execute Jupiter swap: {e}")

# Example usage
if quote:
    execute_jupiter_swap(wallet, quote)

Explanation:

  • Transaction Deserialization: Jupiter’s API provides a base64-encoded transaction. You decode this and then deserialize it into a Transaction object.
  • Sending the Transaction: The transaction is then signed using your wallet and sent to the Solana network.

Step 4: Testing on Devnet

As with Raydium, it's best to test this logic on the Solana Devnet before using it on the mainnet.

  1. Use Test Tokens: Ensure you have test tokens on Devnet to perform swaps.
  2. Test the Complete Flow: Fetch a swap quote and execute it on Devnet, verifying the transaction on Solana Explorer.

Switch to Devnet:

solana_client = Client("https://api.devnet.solana.com")

Full Example Test Script

Here’s a complete script that fetches a quote from Jupiter and executes the swap:

import requests
from solana.rpc.api import Client
from solana.transaction import Transaction
from solana.rpc.types import TxOpts
import base64

# Initialize the Solana client for Devnet
solana_client = Client("https://api.devnet.solana.com")

# Load your wallet
wallet = load_wallet("path_to_your_test_wallet_keypair.json")

# Function to fetch swap routes from Jupiter
def get_jupiter_swap_quote(input_token, output_token, amount):
    url = f"https://quote-api.jup.ag/v1/quote?inputMint={input_token}&outputMint={output_token}&amount={amount}&slippageBps=50"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['data'][0]  # Assume the first route is optimal
    else:
        print(f"Failed to fetch quote from Jupiter: {response.status_code}")
        return None

# Function to execute a swap on Jupiter
def execute_jupiter_swap(wallet, swap_route):
    try:
        # Decode the transaction from base64
        transaction_data = base64.b64decode(swap_route['swapTransaction'])
        
        # Deserialize the transaction
        transaction = Transaction.deserialize(transaction_data)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Jupiter swap executed: {response}")
    except Exception as e:
        print(f"Failed to execute Jupiter swap: {e}")

# Example usage
if __name__ == "__main__":
    input_token = "So11111111111111111111111111111111111111112"  # SOL mint address
    output_token = "Es9vMFrzrJKB2UexsBgoz2LDitwRZyhpN1f6fNkpK1p1"  # USDC mint address
    amount = 1000000  # 1 SOL in lamports
    
    # Fetch quote
    quote = get_jupiter_swap_quote(input_token, output_token, amount)
    
    if quote:
        print("Best quote from Jupiter:", quote)
        # Execute the swap
        execute_jupiter_swap(wallet, quote)

Next Steps:

  1. Refine and Test: Test the entire process on Solana Devnet. Verify the output and ensure that swaps are executed correctly.
  2. Move to Mainnet: Once confirmed, move your setup to Solana’s mainnet and perform real trades.
  3. Integrate with Monitoring: Combine this trading logic with your automated monitoring system for copytrading based on insider activity.

Sure, let's create a final Python script that will:

  1. Track the specified insider wallets.
  2. Monitor their trades using the Jupiter API for swaps.
  3. Automatically execute trades on Solana based on their activities.

This script will:

  1. Fetch the recent trades for each wallet.
  2. Identify the tokens involved in those trades.
  3. Fetch swap routes using the Jupiter API.
  4. Execute the swaps on Solana if the conditions are met.

Final Python Script

import requests
from solana.rpc.api import Client
from solana.transaction import Transaction
from solana.rpc.types import TxOpts
import base64
import time

# Initialize the Solana client for Devnet (or switch to Mainnet as needed)
solana_client = Client("https://api.devnet.solana.com")

# Load your wallet
def load_wallet(path_to_keypair_json):
    with open(path_to_keypair_json, 'r') as f:
        keypair_data = json.load(f)
    return Keypair.from_secret_key(bytes(keypair_data))

# Insider wallets to track
INSIDER_WALLETS = [
    "39PcuE9MBnW4FnqcDJYiy4ppRApdDq4XN4bfksG2nEuP",
    "FQd7Csjt3zWP8pgTo3YntVAwYo4crW3JWfqiBgztSj3s",
    "Grx451o9TBJp9dfVbMgzictW2TUbMFmaNhTu62DnMCMC",
    "9PrmF8sfoD2YShtWUghrGEYgxwabpnzog8gT1V6LEtdq",
    "F1DRDBNW1CX3BPoZPQafidvMeaepg6LS19J4vHqn7xMt"
]

# Function to fetch recent trades from an insider wallet
def get_recent_trades(wallet_address):
    url = f"https://api.birdeye.so/v1/wallet/{wallet_address}/trades"
    response = requests.get(url)
    if response.status_code == 200:
        trades = response.json().get("trades", [])
        return trades
    else:
        print(f"Failed to fetch recent trades for wallet {wallet_address}: {response.status_code}")
        return []

# Function to fetch swap routes from Jupiter
def get_jupiter_swap_quote(input_token, output_token, amount):
    url = f"https://quote-api.jup.ag/v1/quote?inputMint={input_token}&outputMint={output_token}&amount={amount}&slippageBps=50"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        return data['data'][0]  # Assume the first route is optimal
    else:
        print(f"Failed to fetch quote from Jupiter: {response.status_code}")
        return None

# Function to execute a swap on Jupiter
def execute_jupiter_swap(wallet, swap_route):
    try:
        # Decode the transaction from base64
        transaction_data = base64.b64decode(swap_route['swapTransaction'])
        
        # Deserialize the transaction
        transaction = Transaction.deserialize(transaction_data)

        # Sign and send the transaction
        response = solana_client.send_transaction(transaction, wallet, opts=TxOpts(skip_confirmation=False))
        print(f"Jupiter swap executed: {response}")
    except Exception as e:
        print(f"Failed to execute Jupiter swap: {e}")

# Function to track and execute trades for each insider wallet
def track_and_trade(wallet):
    for insider_wallet in INSIDER_WALLETS:
        print(f"Tracking wallet: {insider_wallet}")
        
        # Fetch recent trades
        recent_trades = get_recent_trades(insider_wallet)
        
        for trade in recent_trades:
            input_token = trade['input_token']
            output_token = trade['output_token']
            amount = trade['amount']  # Assuming amount is in smallest unit (lamports)

            print(f"Found trade: {amount} of {input_token} to {output_token}")
            
            # Fetch the swap route from Jupiter
            swap_route = get_jupiter_swap_quote(input_token, output_token, amount)
            
            if swap_route:
                print(f"Executing trade from {input_token} to {output_token}")
                # Execute the swap on Jupiter
                execute_jupiter_swap(wallet, swap_route)
            else:
                print("No valid swap route found.")

# Example usage
if __name__ == "__main__":
    # Load your wallet
    wallet = load_wallet("path_to_your_wallet_keypair.json")

    # Track and trade for the listed insider wallets
    while True:
        track_and_trade(wallet)
        time.sleep(300)  # Sleep for 5 minutes before checking again

Explanation:

  1. Wallet Tracking:
    • The script tracks the provided insider wallets using their Solana addresses.
  2. Fetching Recent Trades:
    • The get_recent_trades function fetches recent trades for each insider wallet using the BirdEye API.
  3. Fetching and Executing Trades:
    • For each trade, the script fetches the optimal swap route using the Jupiter API and then executes the swap using Solana's network.
  4. Automated Execution:
    • The track_and_trade function automates the monitoring of trades and execution of swaps. It’s set to run indefinitely, checking every 5 minutes.

Testing on Devnet

  1. Switch to Devnet: Ensure you are testing on Solana's Devnet by adjusting the RPC endpoint.
  2. Run the Script: Execute the script, monitor the output, and verify transactions on the Solana Explorer for Devnet.

Moving to Mainnet

Once you've confirmed everything works as expected on Devnet:

  1. Switch the RPC Endpoint: Change to the Mainnet endpoint (https://api.mainnet-beta.solana.com).
  2. Run the Script on Mainnet: Deploy the script on Mainnet, monitoring trades in real-time.

Before we start testing, here are a few steps to make sure the setup is ready:

Pre-Testing Checklist

  1. Wallet Setup:
    • Ensure you have a Solana wallet set up with test funds. If you're testing on Devnet, you can use Solana's faucet to get test SOL.
  2. Environment Configuration:
    • Make sure the RPC endpoint is set to https://api.devnet.solana.com for testing on Devnet.
  3. API Key Management:
    • Ensure that you have the correct API keys (if required) for BirdEye and Jupiter APIs. This script assumes no API keys are needed, but some setups might require them.

Step 1: Set Up Your Python Environment

Ensure that you have all the required Python libraries installed:

pip install requests solana base64

Step 2: Running the Test Script

Now, run the test script. Make sure to replace "path_to_your_wallet_keypair.json" with the path to your Solana wallet keypair JSON file.

Here’s how you do it:

  1. Save the Script:
    • Copy the final code provided into a Python script file, say insider_trade_tracker.py.
    • Open your terminal or command prompt.
    • Navigate to the directory where insider_trade_tracker.py is saved.
    • Run the script:

Run the Script:

python insider_trade_tracker.py

Monitoring the Output

  • Tracking Logs: The script will print logs as it tracks each insider wallet and their trades.
  • Transaction Confirmation: After each swap execution, you should see transaction confirmation logs.

Step 3: Verifying Transactions on Solana Explorer

For each transaction executed by the script:

  1. Copy the Transaction Signature from the output logs.
  2. Visit the Solana Explorer.
  3. Search the Transaction using the transaction signature.
  4. Check the Status: Ensure that the transaction was successfully processed.

Debugging and Adjustments

If you encounter any issues:

  1. Check the Logs: The script’s output will include error messages that can help you diagnose problems.
  2. Adjust Parameters: Modify parameters like the sleep interval (time.sleep(300)) to adjust how frequently the script checks for trades.
  3. Validate API Responses: Ensure that the BirdEye and Jupiter APIs are returning valid data.

Moving to Mainnet

Once you're confident that the script works correctly on Devnet:

  1. Change the RPC Endpoint: Switch from Devnet to Mainnet by updating the RPC endpoint to https://api.mainnet-beta.solana.com.
  2. Run the Script on Mainnet: Use the script with real funds, carefully monitoring the transactions.

Ready to Test?

If everything is set, go ahead and run the script. If you need any further assistance during the testing process, feel free to ask!