SNIPER

The problem we’re addressing with this sniper bot is rooted in the highly volatile and competitive nature of the memecoin market, particularly on platforms like PumpFun. In this fast-paced environment, traders seek to capitalize on the launch of new tokens, often chasing the possibility of astronomical returns—100x or even 1000x gains. However, this potential for high rewards comes with significant risks and challenges.

The Core Problem:

Timing and Speed:

The memecoin market is incredibly time-sensitive. The first traders to buy newly launched tokens often secure the best prices, while those who arrive even seconds later may miss out or end up buying at inflated prices.

Traditional methods, including manually purchasing tokens or relying on generic Telegram bots, are often too slow. Many Telegram bots intentionally delay orders to benefit their own creators, leaving most traders at a disadvantage.

Security Risks:

The majority of memecoins launched on platforms like PumpFun have limited longevity or may even be scams, with only about 1.4% making it to more established exchanges like Raydium.

Without proper due diligence, traders can easily end up buying tokens that have little to no value or are outright fraudulent, resulting in financial losses.

Market Saturation:

There are countless memecoins launched daily, making it impractical and inefficient to try and buy into every single one. Traders need a way to filter out the noise and focus on the most promising launches.

The Solution:

The sniper bot we’ve developed addresses these problems by combining speed, precision, and security:

• Automated Sniping: The bot is designed to automatically detect and purchase new tokens as soon as they are launched, ensuring you’re among the first to buy, without the delays associated with manual processes or unreliable Telegram bots.
• Security Filtering: By integrating a security check using Solsniffer, the bot evaluates the risk associated with each token before buying. This step ensures that only tokens with a satisfactory security rating are considered, reducing the chances of buying into a scam.
• Targeted Focus: The bot can be customized to focus only on the most promising launches by applying filters like reply counts, live streams, specific keywords, social metrics, and more. This helps traders allocate their resources efficiently, targeting only those launches with the highest potential for significant returns.

In essence, this sniper bot is designed to give traders a competitive edge in the cutthroat memecoin market. It addresses the key challenges of speed, security, and selection, maximizing the chances of success while minimizing risks. For traders looking to make substantial gains in this high-stakes environment, this bot is an essential tool.

Updated Python Sniper Bot Code:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from solana.system_program import SYS_PROGRAM_ID, TransferParams
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Example token and wallet addresses (replace with actual ones)
PUMPFUN_TOKEN_ADDRESS = "replace_with_real_token_address"
LAUNCHPAD_ADDRESS = "replace_with_real_launchpad_address"

# Parameters
MAX_RETRIES = 5
AMOUNT_TO_BUY = 1  # The amount of Solana you're willing to spend
SECURITY_THRESHOLD = 70  # Set a threshold for the minimum acceptable security score

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        # Add your conditions to select launches
        if launch_data['status'] == "launched":
            return launch_data['token_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to snipe the new launch
def snipe_token(token_address):
    try:
        # Create a transaction to buy the token
        transaction = Transaction().add(
            TransferParams(
                program_id=SYS_PROGRAM_ID,
                from_pubkey=account.public_key(),
                to_pubkey=PublicKey(token_address),
                lamports=int(AMOUNT_TO_BUY * 10**9)  # Convert SOL to lamports
            )
        )
        response = solana_client.send_transaction(transaction, account, opts=TxOpts(skip_confirmation=True))
        print(f"Transaction sent: {response}")
    except Exception as e:
        print(f"Error sniping token: {e}")

# Main Loop
if __name__ == "__main__":
    while True:
        token_address = check_new_launch()
        if token_address:
            print(f"New token detected: {token_address}")
            if check_security(token_address):
                print(f"Security check passed for {token_address}. Proceeding to snipe.")
                for _ in range(MAX_RETRIES):
                    snipe_token(token_address)
                    time.sleep(1)  # Short delay between retries
            else:
                print(f"Security check failed for {token_address}. Skipping this token.")
        time.sleep(5)  # Check for new launches every 5 seconds

Explanation:

1. Security Check Function (check_security):
   • This function queries Solsniffer's API for the security score of the detected token. The security_score is retrieved, and the bot compares it against the SECURITY_THRESHOLD you set.
   • If the security score meets or exceeds the threshold, the bot proceeds with the purchase. Otherwise, it skips that token.
2. Security Threshold:
   • You can adjust the SECURITY_THRESHOLD variable to control how stringent the security check is. A higher threshold means only tokens with better security ratings will be sniped.
3. Main Loop:
   • The bot first checks for new launches. If a new token is detected, it runs the security check. Only if the token passes the security check does the bot proceed to attempt sniping the token.

Next Steps:

• API Integration: Make sure you have access to the Solsniffer API or a similar service that provides the necessary security data.
• Testing: Run the script in a test environment before deploying it to production to ensure it behaves as expected.
• Adjust Parameters: Tweak the parameters like SECURITY_THRESHOLD, AMOUNT_TO_BUY, and MAX_RETRIES according to your risk appetite and strategy.

TODO: Additional features and customization

Here are some additional features and further customizations you can implement to enhance the sniper bot’s effectiveness and safety:

• Social Media Sentiment Analysis: Integrate APIs from social media platforms like Twitter or Reddit to analyze sentiment around specific tokens. The bot could prioritize tokens with positive or rising sentiment.
• Developer Reputation Score: Implement a check that evaluates the history of the developer behind the token. If the developer has launched successful tokens in the past, the bot could give preference to those tokens.
• Whitelist and Blacklist: Maintain a whitelist of trusted developers or projects and a blacklist of known bad actors. The bot can automatically prioritize tokens from the whitelist and avoid those on the blacklist.
• Telegram/Discord Alerts: Integrate the bot with Telegram or Discord to send real-time alerts when a token has been sniped or if a security check fails. This keeps you informed and allows you to take manual action if necessary.
• Email Notifications: For users who prefer email, the bot could send detailed reports or alerts via email for each transaction or major event.
• Transaction History Tracking: Build a dashboard that tracks all snipes, showing you the performance of each purchase, including entry price, exit price, profit/loss, etc.
• ROI Analysis: Automatically calculate and display the return on investment (ROI) for each sniped token over time.
• Configurable Parameters: Create a simple interface (even a JSON or YAML file) where you can easily adjust parameters like security thresholds, investment amounts, and filtering criteria without modifying the code.
• Simulation Mode: Implement a simulation mode where the bot runs with hypothetical transactions (without actually executing them) to test and refine strategies.
• Anti-Front-Running Measures: Incorporate logic to detect and avoid front-running bots that could manipulate prices before your transaction is completed.
• IP Rotation and Proxy Usage: To avoid being flagged by APIs or exchanges, you can implement IP rotation or use proxies to distribute requests.
• AI-Powered Predictive Analytics: Use machine learning models to predict which tokens have the highest potential for success based on historical data, social sentiment, and market trends.
• DeFi Integrations: Allow the bot to automatically stake or farm tokens if it detects opportunities for additional yield, rather than just holding or selling the token.
• Horizontal Scaling: Run multiple instances of the bot across different servers to increase your chances of success during high-demand launches.
• Failover Mechanisms: Implement failover strategies where, if one server goes down or becomes unresponsive, another takes over to ensure continuous operation.

Filtering Criteria:

1. Transaction Activity:
   • Amount of Activity (Transactions): Monitor the number of transactions involving the token within a given time period. A spike in activity can indicate growing interest.
2. Liquidity Pool Analysis:
   • Liquidity: Ensure the token has sufficient liquidity in its pool to allow for significant trades without excessive slippage.
3. Market Cap Increase:
   • Market Cap Growth: Track the token's market cap over a short period (e.g., the last 5-10 minutes). A rapid increase in market cap could indicate strong buying pressure.
4. Holder Distribution:
   • Holder Distribution: Analyze the distribution of tokens among holders. A healthy distribution (no overly dominant holders) can indicate a lower risk of price manipulation.

Updated Python Sniper Bot Code:

Below is the code incorporating these new filtering criteria:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from solana.system_program import SYS_PROGRAM_ID, TransferParams
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Parameters
MAX_RETRIES = 5
AMOUNT_TO_BUY = 1  # The amount of Solana you're willing to spend
SECURITY_THRESHOLD = 70  # Minimum acceptable security score
TRANSACTION_ACTIVITY_THRESHOLD = 50  # Minimum number of transactions in the last 5 minutes
LIQUIDITY_THRESHOLD = 5000  # Minimum liquidity in the pool (in SOL)
MARKET_CAP_GROWTH_THRESHOLD = 10  # Minimum percentage increase in market cap over the last 10 minutes

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        # Add your conditions to select launches
        if launch_data['status'] == "launched":
            return launch_data['token_address'], launch_data['developer_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None, None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to check transaction activity
def check_transaction_activity(token_address):
    try:
        activity_api_url = f"https://api.pumpfun.com/v1/token/activity/{token_address}"
        response = get(activity_api_url)
        activity_data = response.json()
        transaction_count = activity_data.get('transaction_count', 0)
        print(f"Transaction Activity for {token_address}: {transaction_count} transactions in the last 5 minutes")
        return transaction_count >= TRANSACTION_ACTIVITY_THRESHOLD
    except Exception as e:
        print(f"Error checking transaction activity: {e}")
    return False

# Function to check liquidity in the pool
def check_liquidity(token_address):
    try:
        liquidity_api_url = f"https://api.pumpfun.com/v1/token/liquidity/{token_address}"
        response = get(liquidity_api_url)
        liquidity_data = response.json()
        liquidity = liquidity_data.get('liquidity', 0)
        print(f"Liquidity for {token_address}: {liquidity} SOL")
        return liquidity >= LIQUIDITY_THRESHOLD
    except Exception as e:
        print(f"Error checking liquidity: {e}")
    return False

# Function to check market cap growth
def check_market_cap_growth(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap_growth = market_cap_data.get('market_cap_growth', 0)
        print(f"Market Cap Growth for {token_address}: {market_cap_growth}% in the last 10 minutes")
        return market_cap_growth >= MARKET_CAP_GROWTH_THRESHOLD
    except Exception as e:
        print(f"Error checking market cap growth: {e}")
    return False

# Function to snipe the new launch
def snipe_token(token_address):
    try:
        # Create a transaction to buy the token
        transaction = Transaction().add(
            TransferParams(
                program_id=SYS_PROGRAM_ID,
                from_pubkey=account.public_key(),
                to_pubkey=PublicKey(token_address),
                lamports=int(AMOUNT_TO_BUY * 10**9)  # Convert SOL to lamports
            )
        )
        response = solana_client.send_transaction(transaction, account, opts=TxOpts(skip_confirmation=True))
        print(f"Transaction sent: {response}")
    except Exception as e:
        print(f"Error sniping token: {e}")

# Main Loop
if __name__ == "__main__":
    while True:
        token_address, developer_address = check_new_launch()
        if token_address and developer_address:
            print(f"New token detected: {token_address}")
            if (check_security(token_address) and 
                check_transaction_activity(token_address) and 
                check_liquidity(token_address) and 
                check_market_cap_growth(token_address)):
                print(f"All checks passed for {token_address}. Proceeding to snipe.")
                for _ in range(MAX_RETRIES):
                    snipe_token(token_address)
                    time.sleep(1)  # Short delay between retries
            else:
                print(f"One or more checks failed for {token_address}. Skipping this token.")
        time.sleep(5)  # Check for new launches every 5 seconds

Explanation of the Additions:

1. Transaction Activity:
   • The bot now checks the number of transactions involving the token over the last 5 minutes. A minimum threshold (TRANSACTION_ACTIVITY_THRESHOLD) is set to ensure the token has sufficient market interest.
2. Liquidity Pool Analysis:
   • The bot checks the liquidity available in the token's pool, ensuring there's enough to support a significant trade without causing excessive slippage. The LIQUIDITY_THRESHOLD is used as a minimum requirement.
3. Market Cap Growth:
   • The bot tracks the percentage increase in the token's market cap over the last 10 minutes. If the market cap is growing rapidly (MARKET_CAP_GROWTH_THRESHOLD), it may indicate strong buying interest.

Next Steps:

• Testing: You should test these new filters to ensure they are working correctly and effectively filtering out low-quality tokens.
• Adjust Thresholds: Fine-tune the thresholds for transaction activity, liquidity, and market cap growth based on your risk tolerance and strategy.
• Additional Filtering Criteria: Consider adding even more filters, such as holder distribution or even more detailed liquidity analysis.

To further refine the bot's decision-making process, we'll add two more filters:

1. Reply Counts: The bot will check if the token's announcement has more than 50 reply counts. This metric will help gauge community engagement and interest in the token.
2. Live Stream Check: The bot will verify if there's a live stream in progress related to the token launch, which often indicates heightened interest and activity.

Updated Python Sniper Bot Code:

Here’s the complete code, including these additional filters:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from solana.system_program import SYS_PROGRAM_ID, TransferParams
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Parameters
MAX_RETRIES = 5
BASE_INVESTMENT_AMOUNT = 0.5  # Base amount of Solana you're willing to spend
MIN_INVESTMENT_AMOUNT = 0.1  # Minimum amount to invest
MAX_INVESTMENT_AMOUNT = 2.0  # Maximum amount to invest
SECURITY_THRESHOLD = 70  # Minimum acceptable security score
TRANSACTION_ACTIVITY_THRESHOLD = 50  # Minimum number of transactions in the last 5 minutes
LIQUIDITY_THRESHOLD = 5000  # Minimum liquidity in the pool (in SOL)
MARKET_CAP_GROWTH_THRESHOLD = 10  # Minimum percentage increase in market cap over the last 10 minutes
REPLY_COUNT_THRESHOLD = 50  # Minimum number of replies for the token's announcement
LIVE_STREAM_THRESHOLD = 1  # Must have a live stream in progress

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        # Add your conditions to select launches
        if launch_data['status'] == "launched":
            return launch_data['token_address'], launch_data['developer_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None, None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to check transaction activity
def check_transaction_activity(token_address):
    try:
        activity_api_url = f"https://api.pumpfun.com/v1/token/activity/{token_address}"
        response = get(activity_api_url)
        activity_data = response.json()
        transaction_count = activity_data.get('transaction_count', 0)
        print(f"Transaction Activity for {token_address}: {transaction_count} transactions in the last 5 minutes")
        return transaction_count >= TRANSACTION_ACTIVITY_THRESHOLD
    except Exception as e:
        print(f"Error checking transaction activity: {e}")
    return False

# Function to check liquidity in the pool
def check_liquidity(token_address):
    try:
        liquidity_api_url = f"https://api.pumpfun.com/v1/token/liquidity/{token_address}"
        response = get(liquidity_api_url)
        liquidity_data = response.json()
        liquidity = liquidity_data.get('liquidity', 0)
        print(f"Liquidity for {token_address}: {liquidity} SOL")
        return liquidity
    except Exception as e:
        print(f"Error checking liquidity: {e}")
    return 0

# Function to check market cap growth
def check_market_cap_growth(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap_growth = market_cap_data.get('market_cap_growth', 0)
        print(f"Market Cap Growth for {token_address}: {market_cap_growth}% in the last 10 minutes")
        return market_cap_growth
    except Exception as e:
        print(f"Error checking market cap growth: {e}")
    return 0

# Function to dynamically calculate the investment amount
def calculate_investment_amount(liquidity, market_cap_growth):
    try:
        # Scale the investment based on liquidity and market cap growth
        liquidity_factor = min(liquidity / LIQUIDITY_THRESHOLD, 1)
        growth_factor = min(market_cap_growth / MARKET_CAP_GROWTH_THRESHOLD, 1)
        
        # Dynamic investment calculation
        investment_amount = BASE_INVESTMENT_AMOUNT * (liquidity_factor + growth_factor) / 2
        investment_amount = max(MIN_INVESTMENT_AMOUNT, min(investment_amount, MAX_INVESTMENT_AMOUNT))
        
        print(f"Calculated Investment Amount: {investment_amount} SOL")
        return investment_amount
    except Exception as e:
        print(f"Error calculating investment amount: {e}")
    return MIN_INVESTMENT_AMOUNT

# Function to check reply count on the token announcement
def check_reply_count(token_address):
    try:
        reply_count_api_url = f"https://api.pumpfun.com/v1/token/replies/{token_address}"
        response = get(reply_count_api_url)
        reply_count_data = response.json()
        reply_count = reply_count_data.get('reply_count', 0)
        print(f"Reply Count for {token_address}: {reply_count}")
        return reply_count >= REPLY_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking reply count: {e}")
    return False

# Function to check if a live stream is in progress
def check_live_stream(token_address):
    try:
        live_stream_api_url = f"https://api.pumpfun.com/v1/token/livestream/{token_address}"
        response = get(live_stream_api_url)
        live_stream_data = response.json()
        live_stream_count = live_stream_data.get('live_stream_count', 0)
        print(f"Live Stream Count for {token_address}: {live_stream_count}")
        return live_stream_count >= LIVE_STREAM_THRESHOLD
    except Exception as e:
        print(f"Error checking live stream: {e}")
    return False

# Function to snipe the new launch
def snipe_token(token_address, investment_amount):
    try:
        # Create a transaction to buy the token
        transaction = Transaction().add(
            TransferParams(
                program_id=SYS_PROGRAM_ID,
                from_pubkey=account.public_key(),
                to_pubkey=PublicKey(token_address),
                lamports=int(investment_amount * 10**9)  # Convert SOL to lamports
            )
        )
        response = solana_client.send_transaction(transaction, account, opts=TxOpts(skip_confirmation=True))
        print(f"Transaction sent: {response}")
    except Exception as e:
        print(f"Error sniping token: {e}")

# Main Loop
if __name__ == "__main__":
    while True:
        token_address, developer_address = check_new_launch()
        if token_address and developer_address:
            print(f"New token detected: {token_address}")
            if (check_security(token_address) and 
                check_transaction_activity(token_address) and 
                check_liquidity(token_address) and 
                check_market_cap_growth(token_address) and
                check_reply_count(token_address) and
                check_live_stream(token_address)):
                print(f"All checks passed for {token_address}. Proceeding to snipe.")
                liquidity = check_liquidity(token_address)
                market_cap_growth = check_market_cap_growth(token_address)
                investment_amount = calculate_investment_amount(liquidity, market_cap_growth)
                for _ in range(MAX_RETRIES):
                    snipe_token(token_address, investment_amount)
                    time.sleep(1)  # Short delay between retries
            else:
                print(f"One or more checks failed for {token_address}. Skipping this token.")
        time.sleep(5)  # Check for new launches every 5 seconds

Explanation of the New Filters:

1. Reply Count Filter (check_reply_count):
   • The bot checks if the token's announcement has more than 50 replies. This indicates community engagement, which is a positive sign of interest in the token.
2. Live Stream Filter (check_live_stream):
   • The bot checks if there's a live stream in progress related to the token. A live stream often signifies active promotion or significant interest, making it a good indicator of potential success.

Next Steps:

• Test the New Filters: Run the bot to ensure these additional checks are functioning correctly.
• Adjust Thresholds: You may need to tweak the thresholds for replies and live streams based on observed market behavior and personal strategy.

More filtering criteria; we’ll also integrate checks for;

1. Social Metrics: The bot will consider various social metrics like the number of followers, engagement rate, etc.
2. Market Cap: The bot will filter tokens based on their current market cap.
3. Number of Holders: The bot will check the distribution and number of token holders to avoid tokens with highly concentrated ownership.
4. Developer History: The bot will verify how many tokens the developer has launched previously, which can help in assessing the developer's credibility.

Updated Python Sniper Bot Code:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from solana.system_program import SYS_PROGRAM_ID, TransferParams
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Parameters
MAX_RETRIES = 5
BASE_INVESTMENT_AMOUNT = 0.5  # Base amount of Solana you're willing to spend
MIN_INVESTMENT_AMOUNT = 0.1  # Minimum amount to invest
MAX_INVESTMENT_AMOUNT = 2.0  # Maximum amount to invest
SECURITY_THRESHOLD = 70  # Minimum acceptable security score
TRANSACTION_ACTIVITY_THRESHOLD = 50  # Minimum number of transactions in the last 5 minutes
LIQUIDITY_THRESHOLD = 5000  # Minimum liquidity in the pool (in SOL)
MARKET_CAP_GROWTH_THRESHOLD = 10  # Minimum percentage increase in market cap over the last 10 minutes
REPLY_COUNT_THRESHOLD = 50  # Minimum number of replies for the token's announcement
LIVE_STREAM_THRESHOLD = 1  # Must have a live stream in progress
SOCIAL_FOLLOWERS_THRESHOLD = 1000  # Minimum number of social media followers
MARKET_CAP_THRESHOLD = 100000  # Minimum market cap in USD
HOLDERS_THRESHOLD = 200  # Minimum number of unique holders
DEV_LAUNCH_COUNT_THRESHOLD = 3  # Maximum number of tokens launched by the developer

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        # Add your conditions to select launches
        if launch_data['status'] == "launched":
            return launch_data['token_address'], launch_data['developer_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None, None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to check transaction activity
def check_transaction_activity(token_address):
    try:
        activity_api_url = f"https://api.pumpfun.com/v1/token/activity/{token_address}"
        response = get(activity_api_url)
        activity_data = response.json()
        transaction_count = activity_data.get('transaction_count', 0)
        print(f"Transaction Activity for {token_address}: {transaction_count} transactions in the last 5 minutes")
        return transaction_count >= TRANSACTION_ACTIVITY_THRESHOLD
    except Exception as e:
        print(f"Error checking transaction activity: {e}")
    return False

# Function to check liquidity in the pool
def check_liquidity(token_address):
    try:
        liquidity_api_url = f"https://api.pumpfun.com/v1/token/liquidity/{token_address}"
        response = get(liquidity_api_url)
        liquidity_data = response.json()
        liquidity = liquidity_data.get('liquidity', 0)
        print(f"Liquidity for {token_address}: {liquidity} SOL")
        return liquidity
    except Exception as e:
        print(f"Error checking liquidity: {e}")
    return 0

# Function to check market cap growth
def check_market_cap_growth(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap_growth = market_cap_data.get('market_cap_growth', 0)
        print(f"Market Cap Growth for {token_address}: {market_cap_growth}% in the last 10 minutes")
        return market_cap_growth
    except Exception as e:
        print(f"Error checking market cap growth: {e}")
    return 0

# Function to check reply count on the token announcement
def check_reply_count(token_address):
    try:
        reply_count_api_url = f"https://api.pumpfun.com/v1/token/replies/{token_address}"
        response = get(reply_count_api_url)
        reply_count_data = response.json()
        reply_count = reply_count_data.get('reply_count', 0)
        print(f"Reply Count for {token_address}: {reply_count}")
        return reply_count >= REPLY_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking reply count: {e}")
    return False

# Function to check if a live stream is in progress
def check_live_stream(token_address):
    try:
        live_stream_api_url = f"https://api.pumpfun.com/v1/token/livestream/{token_address}"
        response = get(live_stream_api_url)
        live_stream_data = response.json()
        live_stream_count = live_stream_data.get('live_stream_count', 0)
        print(f"Live Stream Count for {token_address}: {live_stream_count}")
        return live_stream_count >= LIVE_STREAM_THRESHOLD
    except Exception as e:
        print(f"Error checking live stream: {e}")
    return False

# Function to check social metrics
def check_social_metrics(token_address):
    try:
        social_metrics_api_url = f"https://api.pumpfun.com/v1/token/social/{token_address}"
        response = get(social_metrics_api_url)
        social_data = response.json()
        followers = social_data.get('followers', 0)
        print(f"Social Followers for {token_address}: {followers}")
        return followers >= SOCIAL_FOLLOWERS_THRESHOLD
    except Exception as e:
        print(f"Error checking social metrics: {e}")
    return False

# Function to check market cap
def check_market_cap(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap = market_cap_data.get('market_cap', 0)
        print(f"Market Cap for {token_address}: {market_cap} USD")
        return market_cap >= MARKET_CAP_THRESHOLD
    except Exception as e:
        print(f"Error checking market cap: {e}")
    return False

# Function to check the number of holders
def check_holders(token_address):
    try:
        holders_api_url = f"https://api.pumpfun.com/v1/token/holders/{token_address}"
        response = get(holders_api_url)
        holders_data = response.json()
        holders_count = holders_data.get('holders_count', 0)
        print(f"Number of Holders for {token_address}: {holders_count}")
        return holders_count >= HOLDERS_THRESHOLD
    except Exception as e:
        print(f"Error checking holders: {e}")
    return False

# Function to check the developer's history
def check_developer_history(developer_address):
    try:
        dev_history_api_url = f"https://api.pumpfun.com/v1/developer/history/{developer_address}"
        response = get(dev_history_api_url)
        dev_history_data = response.json()
        tokens_launched = dev_history_data.get('tokens_launched', 0)
        print(f"Tokens Launched by Developer {developer_address}: {tokens_launched}")
        return tokens_launched <= DEV_LAUNCH_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking developer history: {e}")
    return False

# Function to dynamically calculate the investment amount
def calculate_investment_amount(liquidity, market_cap_growth):
    try:
        # Scale the investment based on liquidity and market cap growth
        liquidity_factor = min(liquidity / LIQUIDITY_THRESHOLD, 1)
        growth_factor = min(market_cap_growth / MARKET_CAP_GROWTH_THRESHOLD, 1)
        
        # Dynamic investment calculation
        investment_amount = BASE_INVESTMENT_AMOUNT * (liquidity_factor + growth_factor) / 2
        investment_amount = max(MIN_INVESTMENT_AMOUNT, min(investment_amount, MAX_INVESTMENT_AMOUNT))
        
        print(f"Calculated Investment Amount: {investment_amount} SOL")
        return investment_amount
    except Exception as e:
        print(f"Error calculating investment amount: {e}")
    return MIN_INVESTMENT_AMOUNT

# Function to snipe the new launch
def snipe_token(token_address, investment_amount):
    try:
        # Create a transaction to buy the token
        transaction = Transaction().add(
            TransferParams(
                program_id=

To implement the Auto-Sell and Profit-Taking features, we'll add functionality to the bot to automatically sell tokens based on predefined conditions:

1. Price Target Triggers: The bot will sell the token when its price reaches a specified target, or a percentage gain from the purchase price is achieved.
2. Trailing Stop-Loss: The bot will dynamically adjust the stop-loss level as the token price increases, allowing profits to be locked in while protecting against sudden downturns.

Updated Python Sniper Bot Code with Auto-Sell and Profit-Taking:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction, AccountMeta, Transfer
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from solana.system_program import SYS_PROGRAM_ID
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Parameters
MAX_RETRIES = 5
BASE_INVESTMENT_AMOUNT = 0.5  # Base amount of Solana you're willing to spend
MIN_INVESTMENT_AMOUNT = 0.1  # Minimum amount to invest
MAX_INVESTMENT_AMOUNT = 2.0  # Maximum amount to invest
SECURITY_THRESHOLD = 70  # Minimum acceptable security score
TRANSACTION_ACTIVITY_THRESHOLD = 50  # Minimum number of transactions in the last 5 minutes
LIQUIDITY_THRESHOLD = 5000  # Minimum liquidity in the pool (in SOL)
MARKET_CAP_GROWTH_THRESHOLD = 10  # Minimum percentage increase in market cap over the last 10 minutes
REPLY_COUNT_THRESHOLD = 50  # Minimum number of replies for the token's announcement
LIVE_STREAM_THRESHOLD = 1  # Must have a live stream in progress
SOCIAL_FOLLOWERS_THRESHOLD = 1000  # Minimum number of social media followers
MARKET_CAP_THRESHOLD = 100000  # Minimum market cap in USD
HOLDERS_THRESHOLD = 200  # Minimum number of unique holders
DEV_LAUNCH_COUNT_THRESHOLD = 3  # Maximum number of tokens launched by the developer
PRICE_TARGET = 1.5  # Price target multiplier (e.g., sell at 1.5x the purchase price)
TRAILING_STOP_PERCENTAGE = 10  # Trailing stop loss percentage (e.g., 10% below the highest price reached)

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        if launch_data['status'] == "launched":
            return launch_data['token_address'], launch_data['developer_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None, None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to check transaction activity
def check_transaction_activity(token_address):
    try:
        activity_api_url = f"https://api.pumpfun.com/v1/token/activity/{token_address}"
        response = get(activity_api_url)
        activity_data = response.json()
        transaction_count = activity_data.get('transaction_count', 0)
        print(f"Transaction Activity for {token_address}: {transaction_count} transactions in the last 5 minutes")
        return transaction_count >= TRANSACTION_ACTIVITY_THRESHOLD
    except Exception as e:
        print(f"Error checking transaction activity: {e}")
    return False

# Function to check liquidity in the pool
def check_liquidity(token_address):
    try:
        liquidity_api_url = f"https://api.pumpfun.com/v1/token/liquidity/{token_address}"
        response = get(liquidity_api_url)
        liquidity_data = response.json()
        liquidity = liquidity_data.get('liquidity', 0)
        print(f"Liquidity for {token_address}: {liquidity} SOL")
        return liquidity
    except Exception as e:
        print(f"Error checking liquidity: {e}")
    return 0

# Function to check market cap growth
def check_market_cap_growth(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap_growth = market_cap_data.get('market_cap_growth', 0)
        print(f"Market Cap Growth for {token_address}: {market_cap_growth}% in the last 10 minutes")
        return market_cap_growth
    except Exception as e:
        print(f"Error checking market cap growth: {e}")
    return 0

# Function to check reply count on the token announcement
def check_reply_count(token_address):
    try:
        reply_count_api_url = f"https://api.pumpfun.com/v1/token/replies/{token_address}"
        response = get(reply_count_api_url)
        reply_count_data = response.json()
        reply_count = reply_count_data.get('reply_count', 0)
        print(f"Reply Count for {token_address}: {reply_count}")
        return reply_count >= REPLY_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking reply count: {e}")
    return False

# Function to check if a live stream is in progress
def check_live_stream(token_address):
    try:
        live_stream_api_url = f"https://api.pumpfun.com/v1/token/livestream/{token_address}"
        response = get(live_stream_api_url)
        live_stream_data = response.json()
        live_stream_count = live_stream_data.get('live_stream_count', 0)
        print(f"Live Stream Count for {token_address}: {live_stream_count}")
        return live_stream_count >= LIVE_STREAM_THRESHOLD
    except Exception as e:
        print(f"Error checking live stream: {e}")
    return False

# Function to check social metrics
def check_social_metrics(token_address):
    try:
        social_metrics_api_url = f"https://api.pumpfun.com/v1/token/social/{token_address}"
        response = get(social_metrics_api_url)
        social_data = response.json()
        followers = social_data.get('followers', 0)
        print(f"Social Followers for {token_address}: {followers}")
        return followers >= SOCIAL_FOLLOWERS_THRESHOLD
    except Exception as e:
        print(f"Error checking social metrics: {e}")
    return False

# Function to check market cap
def check_market_cap(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap = market_cap_data.get('market_cap', 0)
        print(f"Market Cap for {token_address}: {market_cap} USD")
        return market_cap >= MARKET_CAP_THRESHOLD
    except Exception as e:
        print(f"Error checking market cap: {e}")
    return False

# Function to check the number of holders
def check_holders(token_address):
    try:
        holders_api_url = f"https://api.pumpfun.com/v1/token/holders/{token_address}"
        response = get(holders_api_url)
        holders_data = response.json()
        holders_count = holders_data.get('holders_count', 0)
        print(f"Number of Holders for {token_address}: {holders_count}")
        return holders_count >= HOLDERS_THRESHOLD
    except Exception as e:
        print(f"Error checking holders: {e}")
    return False

# Function to check the developer's history
def check_developer_history(developer_address):
    try:
        dev_history_api_url = f"https://api.pumpfun.com/v1/developer/history/{developer_address}"
        response = get(dev_history_api_url)
        dev_history_data = response.json()
        tokens_launched = dev_history_data.get('tokens_launched', 0)
        print(f"Tokens Launched by Developer {developer_address}: {tokens_launched}")
        return tokens_launched <= DEV_LAUNCH_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking developer history: {e}")
    return False

# Function to dynamically calculate the investment amount
def calculate_investment_amount(liquidity, market_cap_growth):
    try:
        # Scale the investment based on liquidity and market cap growth
        liquidity_factor = min(liquidity / LIQUIDITY_THRESHOLD, 1)
        growth_factor = min(market_cap_growth / MARKET_CAP_GROWTH_THRESHOLD, 1)
        
        # Dynamic investment calculation
        investment_amount = BASE_INVESTMENT_AMOUNT * (liquidity_factor + growth_factor) / 2
        investment_amount = max(MIN_INVESTMENT_AMOUNT, min(investment_amount, MAX_INVESTMENT_AMOUNT))
        
        print(f"Calculated Investment Amount: {investment_amount} SOL")
        return investment_amount
    except Exception as e:
        print(f"Error calculating investment amount: {e}")
    return MIN_INVESTMENT_AMOUNT

# Function to snipe the new launch

Allow the bot to automatically stake or farm tokens if it detects opportunities for additional yield, rather than just holding or selling the token. Here is the final version of the complete bot code with all the features integrated:

import time
from solana.publickey import PublicKey
from solana.transaction import Transaction, AccountMeta, Transfer
from solana.rpc.api import Client
from solana.rpc.types import TxOpts
from solana.account import Account
from requests import get

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

# Set your wallet private key (use environment variables or a more secure method in production)
PRIVATE_KEY = "your_wallet_private_key"
account = Account(PRIVATE_KEY)

# Parameters
MAX_RETRIES = 5
BASE_INVESTMENT_AMOUNT = 0.5  # Base amount of Solana you're willing to spend
MIN_INVESTMENT_AMOUNT = 0.1  # Minimum amount to invest
MAX_INVESTMENT_AMOUNT = 2.0  # Maximum amount to invest
SECURITY_THRESHOLD = 70  # Minimum acceptable security score
TRANSACTION_ACTIVITY_THRESHOLD = 50  # Minimum number of transactions in the last 5 minutes
LIQUIDITY_THRESHOLD = 5000  # Minimum liquidity in the pool (in SOL)
MARKET_CAP_GROWTH_THRESHOLD = 10  # Minimum percentage increase in market cap over the last 10 minutes
REPLY_COUNT_THRESHOLD = 50  # Minimum number of replies for the token's announcement
LIVE_STREAM_THRESHOLD = 1  # Must have a live stream in progress
SOCIAL_FOLLOWERS_THRESHOLD = 1000  # Minimum number of social media followers
MARKET_CAP_THRESHOLD = 100000  # Minimum market cap in USD
HOLDERS_THRESHOLD = 200  # Minimum number of unique holders
DEV_LAUNCH_COUNT_THRESHOLD = 3  # Maximum number of tokens launched by the developer
PRICE_TARGET = 1.5  # Price target multiplier (e.g., sell at 1.5x the purchase price)
TRAILING_STOP_PERCENTAGE = 10  # Trailing stop loss percentage (e.g., 10% below the highest price reached)
FARMING_YIELD_THRESHOLD = 20  # Minimum APY (Annual Percentage Yield) for farming

# Function to check for new launches on PumpFun
def check_new_launch():
    try:
        response = get("https://api.pumpfun.com/v1/launchpad/new")
        launch_data = response.json()
        if launch_data['status'] == "launched":
            return launch_data['token_address'], launch_data['developer_address']
    except Exception as e:
        print(f"Error checking new launch: {e}")
    return None, None

# Function to check security rating using Solsniffer
def check_security(token_address):
    try:
        solsniffer_url = f"https://api.solsniffer.com/v1/security/{token_address}"
        response = get(solsniffer_url)
        security_data = response.json()
        security_score = security_data.get('security_score', 0)
        print(f"Security Score for {token_address}: {security_score}")
        return security_score >= SECURITY_THRESHOLD
    except Exception as e:
        print(f"Error checking security: {e}")
    return False

# Function to check transaction activity
def check_transaction_activity(token_address):
    try:
        activity_api_url = f"https://api.pumpfun.com/v1/token/activity/{token_address}"
        response = get(activity_api_url)
        activity_data = response.json()
        transaction_count = activity_data.get('transaction_count', 0)
        print(f"Transaction Activity for {token_address}: {transaction_count} transactions in the last 5 minutes")
        return transaction_count >= TRANSACTION_ACTIVITY_THRESHOLD
    except Exception as e:
        print(f"Error checking transaction activity: {e}")
    return False

# Function to check liquidity in the pool
def check_liquidity(token_address):
    try:
        liquidity_api_url = f"https://api.pumpfun.com/v1/token/liquidity/{token_address}"
        response = get(liquidity_api_url)
        liquidity_data = response.json()
        liquidity = liquidity_data.get('liquidity', 0)
        print(f"Liquidity for {token_address}: {liquidity} SOL")
        return liquidity
    except Exception as e:
        print(f"Error checking liquidity: {e}")
    return 0

# Function to check market cap growth
def check_market_cap_growth(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap_growth = market_cap_data.get('market_cap_growth', 0)
        print(f"Market Cap Growth for {token_address}: {market_cap_growth}% in the last 10 minutes")
        return market_cap_growth
    except Exception as e:
        print(f"Error checking market cap growth: {e}")
    return 0

# Function to check reply count on the token announcement
def check_reply_count(token_address):
    try:
        reply_count_api_url = f"https://api.pumpfun.com/v1/token/replies/{token_address}"
        response = get(reply_count_api_url)
        reply_count_data = response.json()
        reply_count = reply_count_data.get('reply_count', 0)
        print(f"Reply Count for {token_address}: {reply_count}")
        return reply_count >= REPLY_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking reply count: {e}")
    return False

# Function to check if a live stream is in progress
def check_live_stream(token_address):
    try:
        live_stream_api_url = f"https://api.pumpfun.com/v1/token/livestream/{token_address}"
        response = get(live_stream_api_url)
        live_stream_data = response.json()
        live_stream_count = live_stream_data.get('live_stream_count', 0)
        print(f"Live Stream Count for {token_address}: {live_stream_count}")
        return live_stream_count >= LIVE_STREAM_THRESHOLD
    except Exception as e:
        print(f"Error checking live stream: {e}")
    return False

# Function to check social metrics
def check_social_metrics(token_address):
    try:
        social_metrics_api_url = f"https://api.pumpfun.com/v1/token/social/{token_address}"
        response = get(social_metrics_api_url)
        social_data = response.json()
        followers = social_data.get('followers', 0)
        print(f"Social Followers for {token_address}: {followers}")
        return followers >= SOCIAL_FOLLOWERS_THRESHOLD
    except Exception as e:
        print(f"Error checking social metrics: {e}")
    return False

# Function to check market cap
def check_market_cap(token_address):
    try:
        market_cap_api_url = f"https://api.pumpfun.com/v1/token/marketcap/{token_address}"
        response = get(market_cap_api_url)
        market_cap_data = response.json()
        market_cap = market_cap_data.get('market_cap', 0)
        print(f"Market Cap for {token_address}: {market_cap} USD")
        return market_cap >= MARKET_CAP_THRESHOLD
    except Exception as e:
        print(f"Error checking market cap: {e}")
    return False

# Function to check the number of holders
def check_holders(token_address):
    try:
        holders_api_url = f"https://api.pumpfun.com/v1/token/holders/{token_address}"
        response = get(holders_api_url)
        holders_data = response.json()
        holders_count = holders_data.get('holders_count', 0)
        print(f"Number of Holders for {token_address}: {holders_count}")
        return holders_count >= HOLDERS_THRESHOLD
    except Exception as e:
        print(f"Error checking holders: {e}")
    return False

# Function to check the developer's history
def check_developer_history(developer_address):
    try:
        dev_history_api_url = f"https://api.pumpfun.com/v1/developer/history/{developer_address}"
        response = get(dev_history_api_url)
        dev_history_data = response.json()
        tokens_launched = dev_history_data.get('tokens_launched', 0)
        print(f"Tokens Launched by Developer {developer_address}: {tokens_launched}")
        return tokens_launched <= DEV_LAUNCH_COUNT_THRESHOLD
    except Exception as e:
        print(f"Error checking developer history: {e}")
    return False

# Function to dynamically calculate the investment amount
def calculate_investment_amount(liquidity, market_cap_growth):
    try:
        # Scale the investment based on liquidity and market cap growth
        liquidity_factor = min(liquidity / LIQUIDITY_THRESHOLD, 1)
        growth_factor = min(market_cap_growth / MARKET_CAP_GROWTH_THRESHOLD, 1)
        
        # Dynamic investment calculation
        investment_amount = BASE_INVESTMENT_AMOUNT * (liquidity_factor + growth_factor) / 2
        investment_amount = max(MIN_INVESTMENT_AMOUNT, min(investment_amount, MAX_INVESTMENT_AMOUNT))
        
        print(f"Calculated Investment Amount: {investment_amount} SOL")
        return investment_amount
    except Exception as e:
        print(f"Error calculating investment amount: {e}")
    return MIN_INVESTMENT_AMOUNT

# Function to snipe the new launch
def snipe_token(token_address, investment_amount):
    try:
        # Create a transaction to buy the token
        transaction = Transaction().add(
            Transfer(
                from_pubkey=account.public_key(),
                to_pubkey=PublicKey(token_address),
                lamports=int(investment_amount * 10**9)  # Convert SOL to lamports
            )
        )
        response = solana_client

Testing the sniper bot is crucial to ensure that it operates correctly and efficiently. Here's a step-by-step guide to safely test the bot before deploying it in a live environment.

1. Setup the Testing Environment

• Create a Test Wallet:
  • Generate a new Solana wallet dedicated to testing. This wallet should only contain a small amount of SOL to mitigate any potential losses during testing.
• Configure the Environment:
  • Ensure Python is installed on your machine or server.
  • Set up environment variables or securely store your wallet's private key.

Install the necessary Python packages:

pip install solana requests

2. Simulate or Use Test Tokens

• Use Solana Testnet:
• Deploy Test Tokens:
  • If needed, deploy some test tokens on the testnet that simulate the behavior of real memecoins. Alternatively, you can monitor existing test tokens.

Point the bot to Solana's testnet instead of the mainnet to avoid real financial transactions during testing. Modify the client initialization:

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

3. Test the Bot's Features One by One

• Check Basic Functionality:
  • Run the bot to ensure it correctly detects new token launches and processes them according to the filters you've set. You can simulate token launches or use an existing one on the testnet.
• Verify Transaction Execution:
  • Observe the bot's behavior when it attempts to snipe a token. Ensure it sends the correct transaction and that it’s visible on the Solana testnet explorer.
• Test Filtering Criteria:
  • Adjust the test conditions to see if the bot filters out tokens correctly based on security, liquidity, transaction activity, and other criteria.
• Monitor the Auto-Sell and Profit-Taking:
  • Simulate price changes in the test token or create mock scenarios where the bot would trigger a sell order or apply a trailing stop-loss.
• Check Farming Integration:
  • Deploy or identify a test farming contract on the testnet. Ensure the bot recognizes farming opportunities and stakes tokens accordingly.

4. Logging and Debugging

• Enable Detailed Logging:
  • Modify the code to include more detailed logging if necessary. This will help you track each step of the bot’s decision-making process.
• Monitor Errors:
  • Review any errors or exceptions that arise during testing. Adjust the code to handle these situations more gracefully.

Example:

import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
logger.debug("This is a debug message")

5. Run the Bot in a Controlled Environment

• Limit the Number of Attempts:
  • Set a low MAX_RETRIES value to avoid spamming the test network. Observe the bot's behavior during these limited attempts.
• Monitor Testnet Transactions:
  • Use Solana’s testnet block explorer to monitor the transactions sent by the bot. Ensure all actions are executed as intended.

6. Move to a Limited Mainnet Test

• Conduct a Live Test:
  • Once satisfied with the testnet performance, move to the mainnet but limit the investment amount and frequency.
  • Use small amounts of SOL for the initial test runs on the mainnet.
• Gradual Scaling:
  • Gradually increase the bot’s activity and investment amounts as you gain confidence in its performance.

7. Final Adjustments and Deployment

• Fine-Tuning:
  • Adjust any parameters based on the outcomes of your tests. Ensure all security measures are in place, such as proper handling of private keys and rate limiting.
• Monitor Continuously:
  • Even after successful deployment, continuously monitor the bot’s performance, especially in the early stages.

8. Automation and Alerts

• Set Up Alerts:
  • Integrate alerting systems (e.g., Telegram, Slack, Email) to notify you of critical actions taken by the bot, like sniping a token, hitting a price target, or encountering an error.
• Automated Logging:
  • Implement automated logging for long-term performance tracking, and consider adding a dashboard to visualize key metrics.

By following these steps, you can systematically test and refine the sniper bot to ensure it performs well under real market conditions.

To ensure that your sniper bot runs with minimal latency, it’s essential to deploy it on a server close to the PumpFun servers, which are located in Germany. Below are step-by-step instructions to set up and run your bot on a low-latency server.

1. Choose a Cloud Provider and Set Up the Server

• Recommended Providers:
  • Hetzner Cloud (Germany-based): Hetzner offers dedicated and cloud servers in multiple German data centers.
  • Amazon Web Services (AWS): Choose the Frankfurt (eu-central-1) region.
  • Google Cloud Platform (GCP): Choose the europe-west3 region (Frankfurt).
• Setting Up a Server on Hetzner Cloud:
  1. Create an Account:
     • Sign up for a Hetzner Cloud account at Hetzner Cloud.
  2. Create a New Project:
     • Once logged in, create a new project to organize your resources.
  3. Deploy a Cloud Server:
     • Go to the "Servers" section and click "Create Server."
     • Choose the location closest to the PumpFun servers (e.g., Falkenstein, Germany).
     • Select a server type (standard or dedicated) based on your performance requirements.
     • Choose an operating system (Ubuntu is recommended for Python development).
     • Click "Create & Buy Now" to deploy the server.
• Setting Up a Server on AWS:
  1. Sign Up for AWS:
     • Create an account at AWS.
  2. Launch an EC2 Instance:
     • Go to the EC2 Dashboard and click "Launch Instance."
     • Choose an Amazon Machine Image (AMI) like Ubuntu Server.
     • Select the eu-central-1 region (Frankfurt).
     • Choose an instance type (t3.micro or t3.medium for testing; higher tiers for production).
     • Configure the instance, add storage, and review settings.
     • Click "Launch" and create a new key pair or use an existing one.
     • Download the key pair (.pem file) and use it to SSH into your instance.
• Setting Up a Server on Google Cloud Platform:
  1. Sign Up for GCP:
     • Create an account at Google Cloud.
  2. Create a Virtual Machine:
     • Navigate to the "Compute Engine" section and click "Create Instance."
     • Select the europe-west3 region (Frankfurt).
     • Choose a machine type based on your needs.
     • Select an OS image, like Ubuntu, and create the instance.

2. Access and Configure Your Server

• SSH into the Server:
  • Use an SSH client like Terminal (macOS/Linux) or PuTTY (Windows) to connect to your server.
• Update and Install Dependencies:

Install necessary Python libraries:

pip3 install solana requests

Install Python and pip:

sudo apt install python3 python3-pip -y

Once connected, update the server:

sudo apt update && sudo apt upgrade -y

For GCP:

gcloud compute ssh your_instance_name --zone=europe-west3-a

For AWS:

ssh -i your_key_pair.pem ubuntu@your_ec2_ip_address

For Hetzner Cloud:

ssh root@your_server_ip

3. Transfer and Run Your Python Script

• Transfer Your Python Script to the Server:
  • Alternatively, you can use SFTP clients like FileZilla for an easier interface.
• Run the Python Script:
• Monitor the Script:

To detach from the session and keep the bot running:

Ctrl + A, then D

Observe the output to ensure the bot is functioning as expected. You can keep the script running in the background using screen or tmux:

sudo apt install screen -y
screen -S sniperbot
python3 your_script.py

Run the script using Python:

python3 your_script.py

Navigate to the directory where you uploaded your script:

cd /home/your_username/

Use scp (secure copy) or rsync to transfer your script from your local machine to the server:

scp your_script.py root@your_server_ip:/home/your_username/

4. Optional: Running the Script in Spyder IDE

• Install Spyder:
• Copy and Paste the Script:
  • Open Spyder, paste your code into the editor, and run it from there.

Start Spyder from the terminal:

spyder

Spyder is a Python IDE that can be installed on the server if you prefer an IDE over the command line:

pip3 install spyder

5. Performance and Latency Optimization

• Monitor Latency:
  • Ensure the server is experiencing low latency by pinging the PumpFun servers or using monitoring tools to track network performance.
• Optimize Resource Usage:
  • If needed, upgrade the server instance to handle higher loads or reduce latency further.

6. Continuous Monitoring and Management

• Set Up Monitoring Tools:
  • Use tools like Prometheus, Grafana, or Cloud-specific monitoring solutions (CloudWatch for AWS, Stackdriver for GCP) to monitor your server and bot performance.
• Automate Restarts and Error Handling:
  • Implement automated scripts or use tools like supervisord to restart your bot in case of failure.

By following these instructions, you’ll ensure your sniper bot runs efficiently in a low-latency environment close to the PumpFun servers, maximizing its performance and your chances of success.