Using CNNs to Classify Forex Trends from Trading Charts

Summary

The study describes a method for classifying forex trends with a convolutional neural network. It first converts quantitative market data into images resembling trading charts, then trains a CNN to learn chart patterns and predict trend classes. Model performance is assessed through classification accuracy, and the resulting predictions are intended to support the construction of trading strategies.

The approach draws an analogy between visual chart reading by people and pattern recognition by a computer. The document gives no accuracy figures, details about the data, definition of trend classes, or evidence of profitability after trading costs. Classification accuracy alone does not establish that a strategy would perform well in live markets, and the description does not explain how personalized strategies are produced or validated. The material is therefore a concise outline of a machine-learning workflow rather than a full evaluation of its trading value.

Key ideas

• The method transforms quantitative forex data into chart-like images before model training.
• A convolutional neural network is trained to classify trends from the images.
• The described performance measure is classification accuracy.
• The document does not report accuracy values or demonstrate profitability from using the classifications.

Tags

# Predict Forex Trend via Convolutional Neural Networks


# Predict Forex Trend via Convolutional Neural Networks









Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images. 2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model. 3. We evaluate the model's performance in terms of the accuracy of classification. A trading model is obtained with this approach to help devise trading strategies. The main application is designed to help clients automatically obtain personalized trading strategies.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.