Trend-Tracking Trading Strategy Based on Improved RSI: A Case Study of Chinese CSI 300 Stock Index Futures
Bibliographic record
Abstract
In European and American developed countries, quantitative trading is gradually replacing artificial transactions to occupy an important position in the market, and their daily turnover in the market is particularly evident. China securities market and derivatives market started late, and have a relatively obvious difference from abroad, especially in Western countries, in the level of quantitative transactions in mature capital markets. With the improvement of China’s market trading varieties, China’s quantization will develop very rapidly. In this paper, according to the characteristics of China’s CSI 300 Index Futures, we improve trend-tracking trading model based on the improved RSI. Firstly, we apply the wavelet transform for denoising of the price series, then improve RSI, and use the improved RSI and the denoised price series to establish an exit strategy and approach strategy. The strategy is excellent in practical application. In 1 minute K-line data back-test of CSI 300 index futures from 2010 to 2012, the return on invest has reached up to 102 million Yuan, and the ROI risk ratio is 2.61.
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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) | 0.001 | 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".