Neural networks have shown promise for developing profitable investing strategies. As computing power increases and more financial data becomes available, neural networks can find complex patterns that lead to strong returns. This article explores key findings, strategies, and resources for using neural networks in investing available on platforms like GitHub and Reddit as of 2020.
Neural networks can beat the market under the right conditions
Research has found neural networks can consistently beat market returns, but their success depends greatly on factors like quality of input data, network design, and computational power. Studies show strategies based on neural networks tend to perform best in markets with clear trends and patterns, like FOREX and futures trading.
Combining neural networks with traditional strategies boosts performance
Hybird strategies that combine neural networks with traditional technical indicators or fundamental analysis tend to achieve better and more consistent returns compared to using neural networks alone. This complements neural networks’ ability to detect complex patterns with the interpretability of more standard investing approaches.
Open-source GitHub repositories offer neural network investing codebases
For those looking to apply neural networks in investing, GitHub hosts a variety of open-source codebases and frameworks to build off, spanning use cases like stock price prediction, crypto trading bots, and more. These repositories help lower the barriers to integrating neural networks into quantitative strategies.
When applied under the right conditions and design parameters, neural network investing strategies available on platforms like GitHub and Reddit show strong potential to beat the market. Combining neural networks with traditional techniques creates more robust and transparent systems. As data and computing power grow, such AI-driven approaches are likely to become more prevalent.