---
title: Enhancing Short-term Futures Trading with Deep Learning Indicator Model
url: https://www.ml-quant.com/papers/repec/spt-admaec-v-13-y-2023-i-6-f-13-6-6/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spt:admaec:v:13:y:2023:i:6:f:13_6_6
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.scienpress.com%2FUpload%2FAMAE%252fVol%252013_6_6.pdf%3Bh%3Drepec%3Aspt%3Aadmaec%3Av%3A13%3Ay%3A2023%3Ai%3A6%3Af%3A13_6_6
featured: 2023-09-21
citations: unknown
topic: Trading, Microstructure & Execution
---


# Enhancing Short-term Futures Trading with Deep Learning Indicator Model

The study finds that using artificial intelligence to determine exact RSI indicators can help day traders achieve higher profits in short-term stock index prediction.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.scienpress.com%2FUpload%2FAMAE%252fVol%252013_6_6.pdf%3Bh%3Drepec%3Aspt%3Aadmaec%3Av%3A13%3Ay%3A2023%3Ai%3A6%3Af%3A13_6_6
- Identifier: RePEc:spt:admaec:v:13:y:2023:i:6:f:13_6_6
- Released: 2023-09-21
- First featured: Quant Letter No. 16 (2023-09-21): https://www.ml-quant.com/issues/2023-09-21/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [On parametric optimal execution and machine learning surrogates](https://www.ml-quant.com/papers/arxiv/2204.08581/): A study introduces a numerical algorithm using dynamic programming and deep learning for optimal order execution, highlighting the convenience of using neural-network substitutes in stochastic control issues.
- [Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers](https://www.ml-quant.com/papers/arxiv/2306.05479/): A deep learning method outperforms other approaches in estimating filltimes of limit orders.
- [Deep limit order book forecasting: a microstructural guide](https://www.ml-quant.com/papers/arxiv/2403.09267/): The research applies deep learning techniques to predict mid-price changes for NASDAQ-traded stocks, providing a framework to evaluate the feasibility of these predictions.
- [Improving Deep Learning of Alpha Term Structures from the Order Book](https://www.ml-quant.com/papers/ssrn/4770476/): The article evaluates the efficiency of four deep learning models in predicting high-frequency returns in equities, emphasizing the role of network structure, input choice, and time inclusion.
- [HLOB - Information Persistence and Structure in Limit Order Books](https://www.ml-quant.com/papers/arxiv/2405.18938/): A new deep learning model, HLOB, has been developed for predicting Limit Order Book mid-price changes, outperforming nine other models and offering new insights into information distribution in Limit Order Books.
- [Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation](https://www.ml-quant.com/papers/arxiv/2501.08822/): The MDQR model, an advanced Queue-Reactive model, uses neural networks to understand complex market dependencies, making it useful for practical applications like strategy creation.