A Cryptocurrency Multiple Trading Strategy with Kalman Filter Innovation Volatility Interval Forecasts
Bibliographic record
Abstract
Pairs trading and multiple trading strategies are types of market-neutral strategies to use a pair or a combination of stocks and other financial instruments with co-integration or co-movements to generate potential profits, which may not be affected by the direction of the overall market. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) to utilize mean reversion in nonstationary but co-integrated asset prices. In this paper, we propose novel resilient pairs trading and multiple trading strategies using the combination of the KF algorithm and the KF innovation volatility interval forecasts using neural networks. The proposed trading strategies are implemented and investigated using the hourly prices of Bitcoin, Ethereum and Bitcoin Cash in the bear market. Those crypto assets are selected because they move in the same direction in the long term and have high trading volumes. The experimental results reveal performance for the proposed trading strategies with the upper and lower trading intervals using KF innovation volatility interval forecasts superior to that of the trading strategies with upper and lower trading bands using KF innovation volatility point forecasts. The performance and robustness of the proposed trading strategies using a proper assumption of transaction costs have also been examined. The strategies using innovation volatility interval forecasts consistently generate higher profits and a more robust number of transactions with or without transaction costs than those using innovation volatility point forecasts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".