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What are the different methods used by multibagger stock traders?

Algorithmic trading

Particle swarm optimization

Multi-swarm optimization

Trading strategy

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Multibagger stock traders employ various sophisticated methods to maximize profits in the stock market. These methods include utilizing deep neural networks for data preprocessing and employing a reward-based classifier as a meta-learner to generate stock signals, which are then fused to form a robust trading strategy . Additionally, the use of multi-frequency continuous-share trading algorithms with GARCH, parallel network layers, and deep reinforcement learning aids in making trading decisions based on different frequencies of data and stock volatilities . Furthermore, approaches like PAA-MS-IDPSO-V utilize multi-swarms and validation sets to discover patterns in financial time series, supporting investment decisions and maximizing profits in stock market operations . Moreover, constructing multi-step future-price-based reward functions and ensemble rewards based on the greedy method and Thompson sampling help agents adapt to different market patterns, enhancing strategy performance significantly .

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Patent Stock trading method Park Kon 13 Feb 2018 1 Citations	The stock trading method in the paper involves setting stock price influencing clauses, selecting investment items based on these clauses, and executing purchases at specified prices for high returns and loss protection.
Journal Article•DOI Multi-step reward ensemble methods for adaptive stock trading 01 Jun 2023-Expert systems with applications	Not addressed in the paper.
Journal Article•DOI Automatic trading method based on piecewise aggregate approximation and multi-swarm of improved self-adaptive particle swarm optimization with validation Rodrigo C. Brasileiro, Victor L. F. Souza, Adriano L. I. Oliveira - Show less +2 more 01 Dec 2017 17 Citations	Not addressed in the paper.
Open access•Posted Content MCTG: Multi-frequency continuous-share trading algorithm with GARCH based on deep reinforcement learning. Zhishun Wang, Wei Lu, Kaixin Zhang, Tianhao Li, Zixi Zhao - Show less +4 more 08 May 2021-arXiv: Trading and Market Microstructure	Not addressed in the paper.
Journal Article•DOI A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning Salvatore Carta, Andrea Corriga, Anselmo Ferreira, Alessandro Sebastian Podda, Diego Reforgiato Recupero - Show less +4 more 01 Feb 2021-Applied Intelligence 63 Citations	Not addressed in the paper.

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