  Foreign Exchange (Forex) is the largest financial market in the world. The
daily trading volume of the Forex market is much higher than that of stock and
futures markets. Therefore, it is of great significance for investors to
establish a foreign exchange forecast model. In this paper, we propose a
Wavelet Denoised-ResNet with LightGBM model to predict the rate of change of
Forex price after five time intervals to allow enough time to execute trades.
All the prices are denoised by wavelet transform, and a matrix of 30 time
intervals is formed by calculating technical indicators. Image features are
obtained by feeding the maxtrix into a ResNet. Finally, the technical
indicators and image features are fed to LightGBM. Our experiments on 5-minutes
USDJPY demonstrate that the model outperforms baseline modles with MAE:
0.240977x10EXP-3 MSE: 0.156x10EXP-6 and RMSE: 0.395185x10EXP-3. An accurate
price prediction after 25 minutes in future provides a window of opportunity
for hedge funds algorithm trading. The code is available from
https://mkhushi.github.io/
