New Trading Methodology for Financial Time Series
Bibliographic record
Abstract
In this work, we have applied state space reconstruction techniques to estimate state space volume and its variation. These values have allowed us to define a trading methodology by considering a sort of acceleration in a high-dimensional state space system as a kind of momentum indicator similar to those used in financial technical analysis. Our interest was to develop a general trading strategy to determine and quantify the amount of predictability in these time series. This trading methodology has been applied to high-frequency currency exchange time series data from the HFDF96 data set provided by Olsen & Associates. The time series studied are the exchange rates between the US Dollar and 18 other foreign currencies from the Euro zone; i.e. Belgium Franc (BEF), Finnish Markka (FIM), German Mark (DEM), Spanish peseta (ESP), French Frank (FRF), Italian Lira (ITL), Dutch Guilder (NLG),\nand finally ECU (XEU); and from outside the Euro zone: Australian Dollar (AUD), Canadian Dollar (CAD), Swiss Frank (CHF), Danish Krone (DKK), British Pound (GBP), Malaysian Ringgit (MYR), Japanese Yen (JPY), Swedish Krona (SEK), Singapore Dollar (SGD), and South African Rand (ZAR)
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".