ALTERED

A new series simply titled Altered has emerged, captivating audiences with its uncanny blend of reality and imagination. This series, brought to life by advanced AI algorithms, reinterprets the ordinary through a surrealist lens, challenging viewers to rethink their perception of everyday objects and scenes.Altered seamlessly merges the familiar with the fantastical, presenting images that defy logic yet feel strangely coherent. From a car transformed into a shark-like creature cruising through the ocean, to a watch emerging from the folds of a mysterious landscape, the series explores the juxtaposition of natural and man-made elements in unexpected ways. Each piece captures the essence of surrealist art, evoking the works of Dali and Magritte, yet with a distinctly modern twist.The series dives into themes of isolation and introspection. In one image, a lone golfer stands on a surreal green, dwarfed by an abstract bunker that mimics the shape of an otherworldly sand dune. In another, a sailboat floats eerily above a sea of clouds, creating a dreamlike vision.The AI’s ability to reinterpret reality with a surrealist perspective demonstrates a growing sophistication in understanding and manipulating visual elements, paving the way for new forms of creative expression.Altered is a testament to the boundless possibilities of AI in art, offering a glimpse into a future where machines not only assist but actively contribute to the creation of art that resonates on an emotional and philosophical level. As this series gains attention, it sparks a conversation about the role of AI in the artistic process and its potential to push the boundaries of human creativity.


SUI

Sui is a next-generation blockchain designed to fulfill the potential of decentralized technology by prioritizing security, scalability, and accessibility. Built on the Move programming language, Sui offers developers a secure and efficient environment for creating smart contracts. Its architecture allows for horizontal scalability, which enables the network to handle increased demand without compromising performance. Sui also focuses on improving user experience by integrating features that simplify onboarding and reduce costs, making it accessible for a broader audience. Now the Billion dollar question;GEN 1 = BTCGEN 2 = ETHGEN3 = SOLGEN4 = SUI?

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        1. IntroductionSui Network has gained significant attention recently, with many speculating it could be the next major Layer 1 (L1) blockchain, potentially rivaling established players like Solana. While the "Solana killer" narrative may be overblown, Sui's innovative technology, promising ecosystem, and user-friendly experience merit a deeper analysis to determine its long-term potential as an investment.This thesis will explore Sui's technological strengths, its programming language (Move), the current state of its ecosystem, user experience (UX), and the tokenomics of the SUI token. The goal is to assess whether Sui presents a viable investment opportunity or if it is just another overhyped VC-driven project.2. Technology FeaturesSui's technology is its most compelling aspect. Built on the Move programming language, Sui promises significant scalability and efficiency improvements over existing blockchains. Key features include:High Throughput: Sui claims a potential transaction throughput of 100k+ TPS for single-type transactions. This could make it one of the fastest blockchains available, though this claim is limited to certain transaction types.Fast Finality: Sui's block production time is 0.33 seconds, with a finality time of 500 milliseconds. This low latency could enhance the user experience, particularly for applications requiring quick confirmation times.Parallelization: By enabling parallel transaction processing, Sui reduces bottlenecks and enhances overall network efficiency, a critical factor for scaling decentralized applications (dApps).Optimized Storage Model: Sui uses an object-based transaction model, which allows for more efficient data storage and retrieval. This could reduce costs and improve performance over time.Assessment: Sui's technological foundation is strong, with features that differentiate it from many other L1s. However, scalability claims need to be proven under real-world conditions.3. The Move Programming LanguageMove, the language underlying Sui, is designed with security and ease of use in mind. It offers several advantages over Solidity:Security Enhancements: Move includes protections against common vulnerabilities, such as reentrancy attacks, which have plagued Ethereum-based smart contracts. This could lead to more secure dApps.Ease of Adoption: Move is touted as being easier for developers, particularly those from non-crypto backgrounds, to pick up. While this could broaden the pool of potential developers, the language's adoption remains to be seen.Assessment: Move's security features and potential ease of adoption are positives, but the jury is still out on whether it will attract a significant developer community. Adoption by developers will be a key determinant of Sui's success.4. The Sui EcosystemAs of now, Sui's ecosystem is still in its infancy, with around 30 dApps and only a dozen with a Total Value Locked (TVL) above $10 million. Notable projects:Navi Protocol: Gained some traction.Cetus Protocol and Typus Finance: Interesting DeFi projects.However, despite these initial successes, the ecosystem is relatively small compared to other L1s like Ethereum or Solana. Additionally, there's a lack of unique dApps that offer capabilities unavailable on other chains.Assessment: Sui's ecosystem is growing but still lacks the scale and uniqueness required to become a major player. The presence of native dApps is a positive, but more innovative projects are needed to attract users and liquidity.5. User Experience (UX)Sui's UX is one of its stronger points. Users have reported a smooth and fast experience, with intuitive wallet functions and clear airdrop notifications.Account Abstraction: This allows for more advanced features like social logins using zero-knowledge (ZK) proofs, sponsored transactions, and stable gas fees.Sui Explorer: An advanced blockchain explorer that enhances transparency.Assessment: Sui excels in providing a user-friendly experience, which could be a significant competitive advantage, particularly for attracting non-crypto users. Cross-chain compatibility and bridging are areas that require improvement.6. SUI TokenomicsThe SUI token's valuation and tokenomics present a mixed picture:Valuation: Sui's market cap of $2.6 billion and a fully diluted valuation (FDV) of $10 billion seem reasonable when compared to other L1s. However, the L1 trade is becoming increasingly crowded, which could limit upside potential.Supply Dynamics: A major concern is the 40% increase in circulating supply by October 2025, due to VC unlocks. This could exert significant downward pressure on the price if not managed carefully.VC Influence: The fact that 50% of the supply is controlled by Sui/Mysten Labs raises concerns about the potential for large sell-offs, particularly as VCs may look to realize profits from their early investments.Assessment: While the SUI token's current valuation is not unreasonable, the looming supply increases and VC influence are significant risks. These factors could lead to volatility and potential downward pressure on the token price.7. Conclusion and Investment OutlookBull Case: Sui's strong technology, promising UX, and the potential of the Move language provide a solid foundation for growth. If Sui can expand its ecosystem, attract more developers, and maintain its user-friendly experience, it could carve out a niche in the increasingly crowded L1 space. Long-term, Sui could become a significant player if it can differentiate itself with unique dApps and scale effectively.Bear Case: The primary risks to Sui's success are its relatively small ecosystem, lack of unique applications, and the potential for significant token dilution due to VC unlocks. Additionally, the increasing competition from new and existing L1s could limit Sui's market share and growth potential.


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.

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        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: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.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.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 customizationHere 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: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.Liquidity Pool Analysis:Liquidity: Ensure the token has sufficient liquidity in its pool to allow for significant trades without excessive slippage.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.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: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.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.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: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.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: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.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;Social Metrics: The bot will consider various social metrics like the number of followers, engagement rate, etc.Market Cap: The bot will filter tokens based on their current market cap.Number of Holders: The bot will check the distribution and number of token holders to avoid tokens with highly concentrated ownership.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: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.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 launchAllow 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_clientTesting 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 EnvironmentCreate 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 TokensUse 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 OneCheck 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 DebuggingEnable 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 EnvironmentLimit 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 TestConduct 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 DeploymentFine-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 AlertsSet 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 ServerRecommended 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:Create an Account:Sign up for a Hetzner Cloud account at Hetzner Cloud.Create a New Project:Once logged in, create a new project to organize your resources.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:Sign Up for AWS:Create an account at AWS.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:Sign Up for GCP:Create an account at Google Cloud.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 ServerSSH 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 ScriptTransfer 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 IDEInstall 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 OptimizationMonitor 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 ManagementSet 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.


VoltVoyage

"At VoltVoyage, we offer an unparalleled driving experience with our premium Tesla Model 3 rentals, right from the heart of the Landes. Perfectly positioned between Bordeaux, Biarritz, and the ski resorts of Pau, our central location allows you to explore the best of Southwestern France in style and sustainability. With the largest solar farm in France powering our village, our commitment to green energy runs deep. VoltVoyage isn't just about getting from point A to B; it's about embracing the future of travel—luxurious, eco-friendly, and powered by the sun."Tesla Rental Business PlanBusiness Name: VoltVoyageLegal Structure: Société par Actions Simplifiée Unipersonnelle (SASU)Location: Bordeaux, Biarritz, and Les Landes RegionRental Platform: TuroVehicle: Tesla Model 3 PerformanceMonthly Car Payment: €900Rental Rate: €150 per day1. Executive SummaryBusiness Concept:VoltVoyage will operate a Tesla rental service, offering a premium Tesla Model 3 Performance to customers in Bordeaux, Biarritz, and the Les Landes region. By leveraging the Turo platform, we aim to attract tourists, business travelers, and locals who desire a high-performance, eco-friendly vehicle experience.Goals:Generate revenue by renting the Tesla Model 3 Performance at €150 per day.Cover the monthly car payment of €900 and achieve profitability.Establish a reputable, eco-friendly car rental brand in the region.Target Market:Tourists, business travelers, and locals in Bordeaux, Biarritz, and Les Landes who value luxury, sustainability, and high-performance vehicles.2. Market AnalysisTarget Markets:Bordeaux: A major tourist destination known for its wine, culture, and history, attracting affluent visitors.Biarritz: A coastal city popular with surfers, luxury travelers, and business conferences.Les Landes: A region between Bordeaux and Biarritz known for its natural beauty, drawing outdoor enthusiasts and tourists.Customer Segments:Tourists: High-income visitors looking for a unique and sustainable way to explore the region.Business Travelers: Professionals requiring a premium vehicle for business purposes.Local Residents: Locals interested in experiencing a Tesla without the long-term commitment of ownership.Competitive Landscape:Traditional Rental Companies: Offer a variety of vehicles but may lack electric or high-performance options.Peer-to-Peer Platforms: Other Turo listings and similar services, though Tesla’s brand and the performance of the Model 3 set it apart.3. Business ModelRevenue Streams:Renting the Tesla Model 3 Performance at €150 per day through Turo.Cost Structure:Monthly Car Payment: €900Insurance: €150 - €200 per month (estimate for comprehensive coverage)Turo Platform Fees: Approximately 15% - 25% of the rental price.Maintenance and Charging: €100 - €150 per month for routine maintenance and electricity costs.Marketing and Advertising: €50 - €100 per month for online and local marketing efforts.SASU Administrative Costs: Approximately €1,000 annually (accounting, legal, and registration fees).Break-Even Analysis:Renting the Tesla for 6 days per month covers the €900 monthly payment.Profitability increases with additional rentals, targeting 10-20 days per month for optimal returns.4. Legal Structure: SASUAdvantages of SASU:Limited Liability: Protects personal assets from business liabilities.Flexible Management: You can act as the sole shareholder and director, giving you complete control.Full VAT Recovery: Allows you to reclaim VAT on vehicle purchases, maintenance, and other business expenses.Eligible for Expense Deductions: Business-related expenses, including vehicle payments, insurance, and marketing, can be deducted from your taxable income.Registration Costs:Initial Setup: Approximately €300 - €500 for registration and legal formalities.Annual Administrative Costs: €1,000 - €1,500 (includes accounting, tax filing, and legal compliance).Taxation:Corporate Tax: SASUs are subject to corporate tax (Impôt sur les Sociétés), currently set at 25% on profits.VAT (TVA): You will collect VAT on rentals and can reclaim VAT on expenses. VAT returns must be filed quarterly or monthly, depending on your revenue.5. Marketing and Sales StrategyTuro Listing:Create a compelling listing on Turo with professional photos and a detailed description of the Tesla Model 3 Performance.Highlight unique selling points: high performance, eco-friendliness, and luxury experience.Local Partnerships:Partner with luxury hotels, resorts, and travel agencies in Bordeaux, Biarritz, and Les Landes to offer the Tesla as part of premium travel packages.Collaborate with local businesses for cross-promotional opportunities.Online Presence:Establish social media profiles showcasing the Tesla, customer experiences, and special offers.Utilize targeted advertising on platforms like Instagram and Google Ads to reach potential renters in your region.Promotional Offers:Offer discounts for first-time renters or extended rental periods (e.g., 10% off for 5+ days).Seasonal promotions during peak tourist seasons (summer, holidays).6. Operations PlanBooking and Customer Management:Use Turo’s platform to manage bookings, payments, and customer communications.Respond promptly to inquiries to secure bookings and maintain high customer satisfaction.Maintenance Schedule:Conduct regular maintenance checks to keep the Tesla in top condition, ensuring safety and reliability.Schedule servicing during low-demand periods to minimize rental downtime.Vehicle Delivery and Pickup:Offer delivery and pickup services across Bordeaux, Biarritz, and Les Landes.Charge a delivery fee for remote locations to cover costs and time.Insurance and Risk Management:Obtain comprehensive insurance coverage through Turo or a third-party provider to protect against damages and liability.Consider additional business liability insurance for added protection.7. Financial PlanInitial Costs:Tesla Down Payment (if financed): Approx. €5,000 - €10,000 (depending on financing terms).SASU Registration: €300 - €500 for legal and administrative fees.Insurance: First month’s premium (€150 - €200).Marketing: Initial budget of €200 - €300 for launching your online presence and advertising.Monthly Operating Costs:Car Payment: €900Insurance: €150 - €200Maintenance and Charging: €100 - €150Marketing: €50 - €100Turo Fees: 15% - 25% of rental income.Revenue Projections:10 Days of Rental: €1,50020 Days of Rental: €3,000Net Profit (after expenses):10 days: Approx. €200 - €50020 days: Approx. €1,200 - €2,000Long-Term Financial Goals:Increase rental frequency and profitability.Expand the fleet with additional vehicles if the business proves successful.Explore opportunities to expand into neighboring regions or offer additional services.8. Risk ManagementMarket Risks:Fluctuations in demand due to seasonality or economic factors.Increased competition from other rental services or new market entrants.Operational Risks:Potential vehicle downtime due to maintenance or accidents.Risks associated with renting to inexperienced drivers or high-risk customers.Mitigation Strategies:Maintain a cash reserve to cover lean months and unexpected expenses.Use dynamic pricing on Turo to adjust rates based on demand and competition.Screen renters through Turo’s platform to minimize risk and ensure vehicle safety.9. ConclusionVoltVoyage is poised to capitalize on the growing demand for luxury electric vehicles in the Bordeaux, Biarritz, and Les Landes region. By offering a Tesla Model 3 Performance through Turo, we provide customers with a premium, eco-friendly driving experience that aligns with modern travel trends. With a solid business plan, strategic marketing, and careful financial management, we are confident in achieving profitability and long-term success.


LP

As a DeFi liquidity provider (LP), I’ve developed a strategy that allows me to earn my living through passive income by leveraging concentrated liquidity pools, primarily on the Solana network. Here's a breakdown of how I achieve consistent yields and the factors that make Solana my network of choice.Why Solana?Solana stands out as the only network where you can reliably achieve daily yields of 0.8-1.2% within 7-10% concentrated liquidity ranges over a 30-day period. This level of yield is significantly higher than what’s achievable on other networks like Ethereum, Arbitrum, and Sui, which typically yield only 0.1-0.3% over the same period, unless you take on the risk of extremely tight pools.The key advantage Solana offers is its high volume, which is about 10 times greater than other networks. As a volume trader, this is crucial because my strategy revolves around profiting from the high frequency of trades rather than directional price movements. High trade volumes mean more transaction fees, and thus more yield for LPs like myself.Concentrated Liquidity PoolsI primarily trade $SOL/$USDC pools on platforms like Orca, Raydium, and Meteora. By providing liquidity within specific price ranges, I can maximize the fees I collect from each transaction that occurs within those ranges. As long as the price of $SOL stays within my set range, I continue earning yield. If it moves out of range, I exit the position and rebalance.The Downside of Other NetworksEthereum, despite being a dominant player in DeFi, is not suitable for my strategy due to the high gas fees. Entering and exiting positions on Ethereum requires larger liquidity ranges, leading to much lower yields. This approach only makes sense if you’re working with substantial positions, typically over $100k, which isn’t realistic for most traders.Understanding LP YieldLiquidity provision is a form of passive income where you lend your assets to a blockchain contract in a fully decentralized, permissionless environment. Unlike traditional investments, where you might profit only from price appreciation, in DeFi, you earn small fees for every swap or trade made using your assets within an Automated Market Maker (AMM). This means you can profit whether the price moves up or down, as long as there’s trading volume.Set your LP RangesBy carefully selecting and managing your price ranges, you can maximize your yield as a liquidity provider. The liquidation levels shown in the image help you visualize your set ranges and manage your positions to stay profitable. This is particularly effective on high-volume networks like Solana, where frequent trades within your range can generate substantial fees. As an LP, your liquidity earns trading fees only while the asset's price stays within your chosen range. The top and bottom lines in each chart mark the boundaries of this range—if the price moves beyond them, your position stops earning yield.BTC Liquidation Levels,Liquidation Map | CoinGlassLiquidation levels are popular among users because they can be utilized in a myriad of ways –- from magnetic zones to high risk to reward reversal plays to managing risks like stop loss placement and much more.coinglassThe goal is to set a range that is wide enough to capture significant trading activity but narrow enough to maximize your fee earnings.High trading volumes are ideal for range trading since more trades mean more fees collected within your range. As prices fluctuate, you might exit and rebalance if the price nears the edge of your range to stay within an active zone. For example, with Solana (SOL/USDT), if your liquidity range is set between $147 and $164, you earn fees as long as the price stays within that band. If the price moves outside this range, you need to adjust to remain profitable. The key is profiting from every trade within your range, whether prices go up or down, especially in volatile markets.You need approximately $30K in capital to generate $10K per month at a daily ROI of 1.1%.Earning 1.1% daily equates to an annual return of about 350%, which is significant compared to traditional financial markets.Be Your Own BankIn DeFi, you have the opportunity to be your own bank, profiting from the volume generated by trades rather than relying on price movements dictated by market makers. My customers are high-frequency trading bots, which make up 90% of the trading activity in my pools. These bots generate the consistent volume.


USDT

The influx of USDT (Tether) into the market, particularly an increase of $6 billion, is significant and often correlates with movements in Bitcoin's price. Here's why:1. USDT as a Liquidity Driver:USDT is widely used as a stablecoin for trading across crypto exchanges. When there's a surge in USDT supply, it typically means more liquidity is available in the market.Traders and institutions use this liquidity to buy assets like Bitcoin, driving up its price. This is why an increase in USDT market cap often coincides with Bitcoin price rises.2. Market Cap and Price Correlation:The market cap of USDT reaching an all-time high suggests that a substantial amount of capital is entering the crypto markets. Historically, when the USDT market cap grows, it can signal increased buying power, leading to higher demand for Bitcoin.As Bitcoin is the largest and most traded cryptocurrency, this demand can result in a positive price movement.3. USDT as a Market Sentiment Indicator:The growth in USDT supply can also indicate bullish sentiment, as investors are preparing to deploy capital into crypto assets, with Bitcoin often being the primary target.The correlation between USDT market cap and Bitcoin price is strong because increased USDT supply generally provides more buying power in the market, which can drive up Bitcoin's price.