---
title: Hybrid Trading Framework
url: https://www.ml-quant.com/papers/ssrn/5245961/
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: SSRN 5245961
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5245961
featured: 2025-05-21
citations: unknown
topic: Trading, Microstructure & Execution
---


# Hybrid Trading Framework

The article presents a hybrid trading framework that merges deep learning and candlestick pattern recognition to improve trading accuracy and order management.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5245961
- Identifier: SSRN 5245961
- Released: 2025-05-07
- First featured: Quant Letter No. 98 (2025-05-21): https://www.ml-quant.com/issues/2025-05-21/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [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.
- [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.
- [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.
- [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.
- [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.