Finding Optimal Parameter Values for the MACD Indicator: Evidence From the Japanese Nikkei 225 Futures Market Using a New Methodology
Bibliographic record
Abstract
This paper explores: (1) what parameter values are most often used to optimize the Moving Average Convergence Divergence (MACD) trading system for the Japanese Nikkei 225 futures market; and, (2) the characteristics of good-performing models with the optimized parameter values. To accomplish this purpose, this paper presents a new methodology to find the three optimal parameter values of the MACD trading system; this approach systematically examines specific ranges of optimal parameter values. Evidence from the Japanese futures market demonstrates the validity of this new methodological approach. From this, we find that for the Japanese market the technical trading system is most often optimized by three parameter values within three specific ranges over the last 11 years (2011–2021). These optimal value combinations have a unique characteristic form. These findings give insightful and broader perspectives about the market. This issue, methodology and the results have not been discussed in the existing literature. This paper also considers how the models with optimal parameter values performed during the pandemic period (2020–2021).
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".