Custom crypto data with extremely low prices like $0.0000000001

The error you're experiencing with handling very low Decimal values in crypto custom data is common when dealing with extremely small or large values that exceed the precision of Decimal during arithmetic operations. Here are some strategies to address this issue:

Strategies:

1. Constrain Precision Globally: Set a consistent decimal precision across your algorithm when dealing with security price and order-related calculations to maintain stability.

2. Rounding of Prices: Ensure your price computations round to a pre-set number of decimal places before performing any arithmetic operations that involve these values.

3. Significant Digits Management: Choose a precision level up to which decimals will be rounded consistently before processing - this helps to avoid overflow exceptions by maintaining significant figures in calculations.

4. Use of Scaling: Implement custom scaling before processing financial calculations and reverse scaling afterward. Multiplying or dividing by a power of ten counteracts difficulties posed by very small values.

5. Numerical Adjustments: Before executing a calculation or passing an order, check for values below the precision threshold and log-transform small values when possible to manage precision indirectly.

6. Custom Buying Power Models: If the built-in buying power checks are leading to errors due to the scale of transactions, consider implementing or extending a custom buying power model:

   • Override HasSufficientBuyingPowerForOrder: Modify the logic to handle precision limits and ensure calculations within your specified precision bounds.

from decimal import Decimal, getcontext

# Example for setting global precision
getcontext().prec = 10  # Adjust the precision as needed

def adjust_price(value: Decimal) -> Decimal:
    """
    Adjusts price calculation by scaling prior to processing.
    """
    scale_factor = Decimal(1e10)  # Example scaling factor
    return (value * scale_factor).quantize(Decimal('1.0000000000')) / scale_factor

Understanding the Framework:

• BuyingPower Models: Ensure your models have robust error-checking mechanisms for significant digits or scale adjustments that could avoid the overflow seen in your log.

• Simulated Orders: Use simulation runs that closely mimic anticipated transaction loads to identify setups where such precision errors manifest prior to live trading.

These strategic adjustments should help you handle extremely low prices in your QuantConnect algorithms while ensuring stable calculations and avoiding decimals that exceed allowed ranges. If you continue to face issues, collaborating with the QuantConnect community or seeking further support might offer additional insights.

Sources: Order Errors - QuantConnect.com, Debugging - QuantConnect.com, Bybit Crypto Futures Regression Algorithm