A Quantum-Encoded Trading Pipeline with CatBoost and LLM Analysis
Summary
The article proposes a market prediction pipeline combining quantum circuit measurements, CatBoost, and a language model. It motivates the design with shortcomings of conventional indicators and reports that an LLM’s analysis changed when prompts or generation settings changed. CatBoost accuracy reportedly rose from 56.2% with standard indicators to 59.3% after feature expansion, which the author considers inadequate for trading after costs.
For the quantum component, eight normalized indicators are encoded with qubit rotations, then linked with controlled-Z gates to represent feature interactions. The article describes the initial independent-qubit output as nearly uniform and says entanglement changed the resulting distribution. The resulting features are passed into a larger system with CatBoost predictions and LLM-generated explanations. It reports backtest and forward-test returns, win rate, and drawdown, but provides no independently verifiable evaluation details in the supplied text. The quantum framing is presented as a modeling analogy; the document does not establish that quantum computation or the reported performance generalizes to live trading.
Key ideas
- The proposed system combines quantum circuit features, CatBoost forecasts, and LLM-generated market explanations.
- The author reports that the LLM’s outputs changed with prompt wording and generation settings, raising reproducibility concerns.
- Adding derived features reportedly improved CatBoost accuracy modestly, while a larger feature set did not overcome stagnation.
- The quantum encoding normalizes indicators into rotation angles and links qubits with controlled-Z gates to model interactions.
- Performance figures are reported for historical and forward evaluations, but the supplied article does not provide enough independent validation to establish robustness.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.