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About Algorithmic Financial Trading With Deep CNN Preprint
This document discusses a novel approach to algorithmic stock trading using deep convolutional neural networks. Specifically, it proposes converting financial time series data into 2D images by using 15 technical indicators over a 15 day period, resulting in 15x15 pixel images. These images are then classified as buy, sell, or hold signals. The model is found to outperform traditional trading strategies like buy and hold, as well as other machine learning models, over long out-of-sample periods on stock and ETF data. This represents one of the first attempts to apply convolutional neural networks to financial time series forecasting by converting the data into an image-like format.
Detailed Information
| Author: | ['quoc tu'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 30 |
| Language: | English |
| Format: | |
| Price: | FREE |
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