Pinpoint and synergistic trading strategies of candlesticks
Bibliographic record
Abstract
The candlestick trading strategy is a very popular technical method to convey the growth and decline of the demand and supply in the financial market. In this paper, we aim to investigate the predictive power of the candlestick two-day patterns, and to determine the key factors to improve performance. The data set of this study includes daily opening, high, low, and closing prices, and daily volumes of all electronic securities in the Taiwan Stock Exchange between 1998 and 2007.The result of this paper indicates that the harami pattern can obtain information about short-term price movements derived from the demand and supply in Taiwan stock market, because the performances from the harami signals are significantly positive overwhelmingly. The main contribution of this study is that it improves these trading strategies with three confirmation factors, that is, the open of the day after a reversal pattern, the changes of real bodies between two days, and the changes in volume. In addition, this is the first time that candlesticks research has employed the Quantile Regression Model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".