Long Short-Term Memory Pattern Recognition in Currency Trading
- URL: http://arxiv.org/abs/2403.18839v1
- Date: Fri, 23 Feb 2024 12:59:49 GMT
- Title: Long Short-Term Memory Pattern Recognition in Currency Trading
- Authors: Jai Pal,
- Abstract summary: Wyckoff Phases is a framework devised by Richard D. Wyckoff in the early 20th century.
The research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics.
By dissecting the intricacies of these phases, the study sheds light on the creation of liquidity through market structure.
The study highlights the transformative potential of AI-driven approaches in financial analysis and trading strategies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics and identifying potential trading opportunities. By dissecting the intricacies of these phases, the study sheds light on the creation of liquidity through market structure, offering insights into how traders can leverage this knowledge to anticipate price movements and make informed decisions. The effective detection and analysis of Wyckoff patterns necessitate robust computational models capable of processing complex market data, with spatial data best analyzed using Convolutional Neural Networks (CNNs) and temporal data through Long Short-Term Memory (LSTM) models. The creation of training data involves the generation of swing points, representing significant market movements, and filler points, introducing noise and enhancing model generalization. Activation functions, such as the sigmoid function, play a crucial role in determining the output behavior of neural network models. The results of the study demonstrate the remarkable efficacy of deep learning models in detecting Wyckoff patterns within financial data, underscoring their potential for enhancing pattern recognition and analysis in financial markets. In conclusion, the study highlights the transformative potential of AI-driven approaches in financial analysis and trading strategies, with the integration of AI technologies shaping the future of trading and investment practices.
Related papers
- ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction [8.922126245005336]
This study introduces a novel framework: textbfECC Analyzer, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights.
The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level.
It then uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance.
arXiv Detail & Related papers (2024-04-29T07:11:39Z) - AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data.
We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
arXiv Detail & Related papers (2024-03-19T09:45:33Z) - Financial Time-Series Forecasting: Towards Synergizing Performance And
Interpretability Within a Hybrid Machine Learning Approach [2.0213537170294793]
This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability.
For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting.
arXiv Detail & Related papers (2023-12-31T16:38:32Z) - Enhancing Financial Data Visualization for Investment Decision-Making [0.04096453902709291]
This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics.
The study incorporates multiple features to enhance LSTM's capacity in capturing complex patterns.
The meticulously crafted LSTM incorporates crucial price and volume attributes over a 25-day time step.
arXiv Detail & Related papers (2023-12-09T07:53:25Z) - HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and
Regime-Switch VAE [113.47287249524008]
It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting.
We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors.
Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
arXiv Detail & Related papers (2023-06-05T12:58:13Z) - Futures Quantitative Investment with Heterogeneous Continual Graph
Neural Network [13.882054287609021]
This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks.
The model integrates multi- pricing theories with real-time market dynamics, effectively bypassing the limitations of existing methods.
Empirical tests on 49 commodity futures in China's futures market demonstrate that the proposed model outperforms other state-of-the-art models in terms of prediction accuracy.
arXiv Detail & Related papers (2023-03-29T08:39:36Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z) - Pre-Trained Models: Past, Present and Future [126.21572378910746]
Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI)
By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks.
It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
arXiv Detail & Related papers (2021-06-14T02:40:32Z) - Taking Over the Stock Market: Adversarial Perturbations Against
Algorithmic Traders [47.32228513808444]
We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques.
We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
arXiv Detail & Related papers (2020-10-19T06:28:05Z) - Gaussian process imputation of multiple financial series [71.08576457371433]
Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market.
We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
arXiv Detail & Related papers (2020-02-11T19:18:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.