Bibliographic record
Abstract
This article build a prediction model based on the historical trading volume of gold and Bitcoin, then establish an investment return maximization model to maximize the return amount, and finally propose a feasible trading strategy to traders based on the model results. First, this article will process the data visualization after missing values to analyze the trend of bitcoin and gold trading volume, yield, risk and volatility respectively. Second, This article combine the ARIMA model and the LSTM model to establish an ensemble learning model, and use the critic method to combine the two series of predictions with appropriate weights to predict the asset price of gold and bitcoin for each trading day during the period from 2016 to 2021, again, According to the ensemble learning prediction results, the optimal average cost method (DCA) (DCA) is used to solve the prediction curve and verify the optimality, and it is concluded that there is a maximum benefit when k=19% and p=54%. Finally, based on the above predictive analysis results, this article explain to traders the model building process and the results it presents, and propose feasible investment decisions about gold and bitcoin from the aspects of trading behavior and trading psychology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".