Scam protection

Basic

A trading bot can protect itself from scam projects by using the following strategies:

1. Project Analysis

• The bot analyzes projects before investing in them, examining factors such as team, technology, roadmap, and community.

2. Liquidity check

• The bot checks the liquidity of project tokens to make sure they can be easily bought and sold.

3. Social Media Monitoring

• The bot monitors social media for negative reviews or warnings about projects.

4. Use of Stop Losses

• The bot sets stop losses on its positions to limit losses if token prices fall.

Example

Suppose a bot is analyzing a new project called "XYZ". The bot studies the project team, which has a lot of experience in the industry, and the roadmap, which looks promising.

However, the bot also discovers that the XYZ token has low liquidity and there are some negative reviews about the project on social media. In light of this information, the bot decides not to invest in XYZ.

In addition, the bot sets stop losses on all its positions to protect itself from potential scam projects. If the token price falls below a certain level, the bot will automatically sell tokens to limit losses.

By integrating these strategies into its trading system, the bot can reduce the risk of investing in scam projects and protect its funds.

Basic Code

Disclamer: We provide basic codes to avoid information leaks

Scam Protection Strategy Code

// import requests from bs4 import BeautifulSoup

def analyze_project(project_name): """Analyzes the project for signs of scam.

  Args: project_name: Project name.

  Returns: Dictionary with analysis results. """ # Get information about the project from online sources results = {} results["website"] = get_website_info(project_name) results["social_media"] = get_social_media_info(project_name) results["team"] = get_team_info(project_name) results["whitepaper"] = get_whitepaper_info(project_name)

  # Analyze the collected information to identify signs of scam results["scam_indicators"] = [] if results["website"]["trust_score"] < 50: results["scam_indicators"].append("Low website trust score") if len(results["social_media"]["followers"]) < 1000: results["scam_indicators"].append("Low number of social media followers") if not results["team"]["has_linkedin"]: results["scam_indicators"].append("No LinkedIn profiles of team members") if not results["whitepaper"]["has_technical_details"]: results["scam_indicators"].append("No technical details in technical document")
  return results


# Functions to get information about the project def get_website_info(project_name): # ...

def get_social_media_info(project_name): # ...

def get_team_info(project_name): # ...

def get_whitepaper_info(project_name): # ...


# Example usage project_name = "XYZ" results = analyze_project(project_name)

# Print the analysis results print("Project analysis results:") for key, value in results.items(): print(f"{key}: {value}")

This feature gathers information about a project from various online sources and analyzes it to identify signs of scam. It uses factors such as the website's trust rating, number of social media followers, LinkedIn profiles of team members, and the presence of technical details in the white paper. The function returns a dictionary with the results of the analysis, including any identified signs of scam. You can customize the function according to your specific criteria for identifying scam projects.

Stop Loss Strategy Code

// import web3

def set_stop_loss(w3, pair, amount, price, stop_loss_price): """Sets a stop loss for a position.

  Args: w3: Web3 object connected to the blockchain. pair: Trading pair (e.g., "XYZ/ETH"). amount: Amount of tokens to sell when the stop loss is reached. price: Stop loss price.
    stop_loss_price: The price at which the stop loss will be executed. """ # Create an exchange contract exchange_contract = w3.eth.contract( address="YOUR_EXCHANGE_CONTRACT_ADDRESS", abi="YOUR_EXCHANGE_CONTRACT_ABI" )

  # Create stop_loss order stop_loss_order = exchange_contract.functions.createStopLossOrder( pair, amount, stop_loss_price ).buildTransaction()

  # Send order transaction_hash = w3.eth.sendTransaction(stop_loss_order)

  # Wait for the order to be executed receipt = w3.eth.wait_for_transaction_receipt(transaction_hash)

  # Check if the order has been executed if receipt["status"] == 1: print("Stop loss order set.") else: print("Failed to set stop loss order.")


# Example usage w3 = connect_to_wallet(private_key, network) pair = "XYZ/ETH" amount = 100 price = 0.1 stop_loss_price = 0.05

set_stop_loss(w3, pair, amount, price, stop_loss_price)

This function connects to the blockchain, creates an exchange contract, and sends a transaction to create a stop loss order. It uses the createStopLossOrder function to create an order that will be executed when the token price reaches or falls below the specified stop loss price. To use this function, you need to replace YOUR_EXCHANGE_CONTRACT_ADDRESS and YOUR_EXCHANGE_CONTRACT_ABI with actual values.