Pricing Model & Performance

Pricing Model: QuantumExecute is currently free to use during the trial period. Users can sign up and execute trades with no platform fees in the test environment. For production use (outside of trial), QE’s pricing model will be communicated to users before any charges apply. Typically, algorithmic execution services may charge fees in various ways, such as:

• A small commission on the traded volume (e.g., X bps per executed trade volume).

• A percentage of the savings achieved versus a benchmark (performance-based fee).

• A subscription or licensing fee for institutional clients on a monthly/annual plan. The exact model for QE post-trial is to be determined, but the guiding principle is that the cost will be a fraction of the value provided. Our goal is that the execution cost savings achieved by our algorithms far outweigh any fees, ensuring a net benefit to users. For now, users can enjoy the trial and evaluate the impact. Any introduction of fees will be transparent and likely accompanied by additional features or support services.

Algorithm Performance: QuantumExecute’s algorithms have demonstrated significant performance improvements in real trading scenarios. For example, in recent client cases, the Smart TWAP strategy achieved around 70% maker order share (meaning the majority of the executed volume received maker fees or rebates) on large ETH/BTC orders, with an average slippage of under 1 basis point (0.01%). This represented an estimated 70–90% cost saving compared to using the exchange’s standard TWAP algorithm for the same trades. Such savings come from both lower direct fees (thanks to more maker fills) and better execution prices (lower market impact).

Different algorithms show varying levels of cost reduction depending on the scenario. Based on historical simulations and internal backtests:

• Smart TWAP/VWAP: ~70%–100% estimated cost reduction vs. simple execution (almost halving or eliminating impact costs for time-sliced trades).

• POV: ~70%–100% cost savings, especially effective in thin markets by staying under the radar and not moving prices much.

• Arrival Price: ~50%–200% cost impact improvement. In some cases, this strategy not only saved cost but actually achieved net gains (negative slippage, denoted by >100% improvement) relative to the arrival price.

• Alpha-Enhanced: ~50%–400% cost improvement potential. When strong predictive signals are present, this algorithm can sometimes turn what would have been execution cost into additional alpha (profit), hence the >100% scenario. (Note: “>100% cost saving” indicates the execution outperformed the benchmark so much that it effectively generated profit relative to that benchmark, not that trading had zero cost.)

Disclaimer: The above figures are indicative estimates derived from backtests and certain client use cases. Actual results can vary due to market conditions and parameter choices. They also do not account for any exchange rebates (for maker trades) which, if included, would further increase savings in many cases. Users are encouraged to review the TCA reports after their own trades to gauge actual performance in their specific context.