🧠 Trade with AI Engine

Create custom trading strategies and multi-level orders to combat emotional trading and lock in profits by pre-defining your orders, limits and targets and let the engine do the rest.

What is AI Engine and why should I care?

Using advanced AI and predefined values, the Chainpal Bot allows maximum customization. The TradingBot class manages the balance and holdings, allowing for buying and selling in a novel manner. The check_conditions method checks the current price against predefined conditions to decide whether to sell based on the buy price. This allows for the execution of entire trading strategies with a single prompt, making it easy to automate complex trading.

Example (Token Buy & Set Take Profit)

You can use the AI Engine to buy tokens with pre-defined values, slippage, settings values and even set stop losses and take profit targets all in one prompt, just by text input. As the Engine evolves we will add more functionality like checking balances, closing trades, performing token functions Audits, token sentiment analysis, ta and wallet analysis portfolio statistics, and much more.

Under The Hood

AI Engine Snippet

class TradingBot:
    def __init__(self, balance):
        self.balance = balance
        self.holdings = 0
        self.buy_price = None

    def buy(self, price, amount):
        if self.balance >= amount:
            self.balance -= amount
            self.holdings += amount / price
            self.buy_price = price
            print(f"Bought {amount / price} coins at ${price} each.")

    def sell(self, price, percentage):
        amount_to_sell = self.holdings * (percentage / 100)
        self.balance += amount_to_sell * price
        self.holdings -= amount_to_sell
        print(f"Sold {amount_to_sell} coins at ${price} each.")

    def check_conditions(self, current_price):
        if self.buy_price:
            if current_price >= self.buy_price * 1.5:
                self.sell(current_price, 50)  # Sell 50% at 50% gain
            elif current_price <= self.buy_price * 0.7:
                self.sell(current_price, 100)  # Sell all at 30% loss

# Example usage
bot = TradingBot(balance=1000)
bot.buy(price=0.003, amount=300)  # Buying with $300 at $0.003 per coin
bot.check_conditions(current_price=0.0045)  # Checking conditions at $0.0045 per coin
bot.check_conditions(current_price=0.0021)  # Checking conditions at $0.0021 per coin

How does it work for Trading?

• The TradingBot class manages the balance and holdings.

• The buy and sell methods deal with the purchase and sell of the tokens.

• The check_conditions method checks the current price against predefined conditions to decide whether to buy/sell based on the current price.

The above allows for creating multiple loops from a single prompt that run in parallel with each other and allow for crazy profit-taking and entry executions that no other bots are currently are capable of. For a more practical example of exactly how this can be used, please visit ...

Multi-Order Execution

Smart Transaction Simulator

The AI Engine is able to use multiple sources, scanners and transaction simulators to return the safety of a token and keep up with malicious codes forever changing functions rewritten by scammers to be able to stay up to date with the latest honeypots, delayed honeypots, TX limits, gas limits and general code with high launch-to-rug ratios by utilizing multiple data points from independent audit companies and TG scanners to return the safety of the tokens for informational user DYOR purposes. For more information, please head over to ...

🧬 Simulator Audits

Examples Of Use

• Create multi-level orders with different buy/sell conditions

• Place orders with text

• Manage Settings by simply talking to the bot

• Ask for Technical analysis of a token

• Switch wallets by prompt

• Monitor price for entry into tokens

• Get wallet balances by asking

• Get token information and safety with simple request

• Simulate a rug-free environment with multiple scanner sources

• Ask how many trades you have open

Although primarily made for trading strategy execution, we aim to apply the AI Engine across the entire bot to access its functionalities, such as safety and general management down the line.