FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
author Borwein, J
5 Pith papers cite this work. Polarity classification is still indexing.
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Introduces a paired one-switch benchmark that quantifies protocol-induced inflation from decision-time leakage in financial ML backtests on equity panels from 2016-2024.
Cost-aware execution filters enable selected machine learning strategies, particularly long-only XGBoost, to achieve over 65% annualized returns and Sharpe ratios above 1 in hourly BTC trading despite 10bp costs.
citing papers explorer
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Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
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When Alpha Disappears: A One-Switch Benchmark for Decision-Time Leakage in Financial Backtests
Introduces a paired one-switch benchmark that quantifies protocol-induced inflation from decision-time leakage in financial ML backtests on equity panels from 2016-2024.
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Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting
Cost-aware execution filters enable selected machine learning strategies, particularly long-only XGBoost, to achieve over 65% annualized returns and Sharpe ratios above 1 in hourly BTC trading despite 10bp costs.
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