Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance. The neural network model demonstrated the highest accuracy, achieving a 75% success rate, significantly outperforming traditional models. Additionally, trading strategies derived from these ML models yielded a return on investment (ROI) of up to 12%, compared to an 8% benchmark index ROI. These findings underscore the transformative potential of ML in refining trading strategies, providing critical insights for financial analysts, investors, and developers. The study draws on insights from 15 peer-reviewed articles, financial datasets, and industry reports, establishing a robust foundation for future exploration of ML-driven financial forecasting. Tools and Technologies Used †PineScript PineScript, a scripting language integrated within the TradingView platform, was the primary tool used to develop and implement the machine learning models. Its robust features allowed for custom indicator creation, strategy backtesting, and real-time market data analysis. †Python Python was utilized for data preprocessing, model training, and performance evaluation.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
CRB Index increased 16.18 points or 4.53% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Steel decreased 101 Yuan/MT or 3.05% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Steel - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Rhodium increased 1,000 USD/t oz. or 21.86% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rhodium - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Urea increased 41.75 USD/T or 12.37% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urea.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Baltic Dry increased 637 points or 63.89% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Prices for JPYVND Japanese Yen Vietnamese Dong including live quotes, historical charts and news. JPYVND Japanese Yen Vietnamese Dong was last updated by Trading Economics this March 27 of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Uranium decreased 8.70 USD/LBS or 11.92% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Rubber increased 2.60 US Cents/kg or 1.32% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rubber - values, historical data, forecasts and news - updated on March of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Urals Oil decreased 3.02 USD/Bbl or 4.41% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Soda Ash decreased 48 Yuan/MT or 3.14% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soda Ash - values, historical data, forecasts and news - updated on March of 2025.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance. The neural network model demonstrated the highest accuracy, achieving a 75% success rate, significantly outperforming traditional models. Additionally, trading strategies derived from these ML models yielded a return on investment (ROI) of up to 12%, compared to an 8% benchmark index ROI. These findings underscore the transformative potential of ML in refining trading strategies, providing critical insights for financial analysts, investors, and developers. The study draws on insights from 15 peer-reviewed articles, financial datasets, and industry reports, establishing a robust foundation for future exploration of ML-driven financial forecasting. Tools and Technologies Used †PineScript PineScript, a scripting language integrated within the TradingView platform, was the primary tool used to develop and implement the machine learning models. Its robust features allowed for custom indicator creation, strategy backtesting, and real-time market data analysis. †Python Python was utilized for data preprocessing, model training, and performance evaluation.