Algorithmic Trading using Technical Indicators

Salkar, Tanishq; Shinde, Aditya; Tamhankar, Neelaya; Bhagat, Narendra

doi:10.1109/iccict50803.2021.9510135

Cited by 12 publications

(2 citation statements)

References 9 publications

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“…To calculate a linear MACD, an exponential moving average with a longer period is subtracted from the moving average price (usually an exponential moving average with a shorter period is taken). In most cases, the result is smoothed using an exponential moving average (EMA) to eliminate random fluctuations [37][38][39][40]:…”

Section: Methods For Calculating Technical Indicatorsmentioning

confidence: 99%

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Barlybayev,

Zhetkenbay,

Karimov

et al. 2023

EEJET

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Adaptive neuro-fuzzy inference system (ANFIS) it is a type of neural network that combines the strengths of both fuzzy logic and artificial neural networks. ANFIS is particularly useful in stock trading because it can handle uncertainty and imprecision in the data, which is common in stock market data. In stock trading, ANFIS can be used for a variety of purposes, such as predicting stock prices, identifying profitable trades, and detecting stock market trends. One of the key advantages of using ANFIS for stock trading is that it can handle both linear and non-linear relationships in the data. This is particularly useful in the stock market, where the relationships between different variables are often complex and non-linear. ANFIS can also be updated and retrained as new data becomes available, which allows it to adapt to changing market conditions. Therefore, the main hypothesis of this work is to understand whether it is possible to predict the dynamics of prices for stocks of companies in the electric vehicle (EV) sector using technical analysis indicators. The purpose of this work is to create a model for predicting the prices of companies in the EV sector. The technical analysis indicators were processed by several machine learning models. Linear models generally perform worse than more advanced techniques. Decision trees, whether fine or coarse, tend to yield poorer performance results in terms of RMSE, MSE and MAE. After conducting a data analysis, the ANFIS and Bayesian regularization back propagation Neural Network (BR-BPNN) models were seen to be the most effective. The ANFIS was trained for 2000 epochs which yielded a minimum RMSE of 5.90926

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Section: Methods For Calculating Technical Indicatorsmentioning

confidence: 99%

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Barlybayev,

Zhetkenbay,

Karimov

et al. 2023

EEJET

View full text Add to dashboard Cite

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“…Existem quatro tipos principais de indicadores técnicos: indicadores de tendência, indicadores de momentum, indicadores de volatilidade e indicadores de volume (SALKAR et al, 2021).…”

Section: Indicadores Técnicosunclassified

Sistema de tomada de decisão no mercado de ações utilizando aprendizado de máquina.

Santos

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Este trabalho apresenta a aplicação de modelos de aprendizado de máquina baseados em árvores de decisão na avaliação dos momentos ideais para compra e venda de ativos no mercado de ações brasileiro. O aprendizado dos modelos é conduzido utilizando indicadores de mercado, calculados a partir da série histórica de preços. O trabalho apresenta uma aplicação prática, abordando o desafio do tratamento de variáveis não estacionárias, bem como a seleção das melhores variáveis para o modelo. Além de avaliar a capacidade de classificação dos melhores momentos de compra e venda, o estudo também inclui uma análise da aplicação dos modelos na geração de ordens de compra e venda, com a realização do backtesting no período de 2017 até 2023. Os resultados obtidos são comparados com a estratégia conhecida como Moving Average Crossover e uma estratégia baseada em ordens aleatórias, além de serem comparados com o Buy and Hold.

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Algorithmic Forex Trading Using Q-learning

Zahrah

Tirtawangsa

2023

IFIP Advances in Information and Communication Technology

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Algorithmic Trading using Technical Indicators

Cited by 12 publications

References 9 publications

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Sistema de tomada de decisão no mercado de ações utilizando aprendizado de máquina.

Algorithmic Forex Trading Using Q-learning

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