A Prediction Model for Short Price Jump in Cryptocurrency Market
Bibliographic record
Abstract
In recent years, the global cryptocurrency market has attracted a diverse range of traders, from seasoned professionals to newcomers, resulting in a highly volatile environment. This volatility presents numerous opportunities for traders to capitalize on rapid price fluctuations. In this context, we introduce a Random Forest model designed to predict whether a coin will experience growth in the next trading candle, using several input features. We used Binance historical daily data from 1 Jan 2018 to 31 Dec 2021 to train our models and evaluated them using different time spans (varied between Jan 2022 to Oct 2023) as testing datasets. Moreover, we also used an over sampled training dataset to enhance the training process. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. To be more specific, regarding the testing datasets of 1 to 31 Oct 2023, 1 Jul 2023 to 30 Sep 2023, and LSK/USDT from 1 Jan 2022 to 31 OCT 2023, using a growth rate of 1%, we achieved 18%, 30%, and 68% profits, respectively. This study underscores the potential for leveraging well-designed machine learning models to achieve significant profits, even in bearish market conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".