Rui Neves - Böcker
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5 produkter
5 produkter
Financial Data Resampling for Machine Learning Based Trading
Application to Cryptocurrency Markets
Häftad, Engelska, 2021
433 kr
Skickas inom 5-8 vardagar
A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk.
Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation
The Case of S&P 500
Häftad, Engelska, 2021
715 kr
Skickas inom 10-15 vardagar
This book presents a genetic algorithm that optimizes a grid template pattern detector to find the best point to trade in the SP 500.
Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies
Inbunden, Engelska, 2024
1 472 kr
Skickas inom 10-15 vardagar
The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80.
Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies
Häftad, Engelska, 2025
1 472 kr
Skickas inom 10-15 vardagar
This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.
551 kr
Skickas inom 10-15 vardagar
This Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy duty fitness function to a full GPU accelerated GA.