In the previous post, I shared the trading results for Bitcoin data using deep reinforcement learning approach. I have also tested the model with some other coins using the same trained deep network. In this post, I’m going to share the results for Litecoin, Ripple, and Binance Coin. Remember that the training data was recorded between the dates 01 April, 2019 and 01 Aug, 2019 and its frequency was 1 hour.
In the first test, I used the Litecoin (LTC) data which is recorded between the dates 01 Aug, 2019 and 01 Sep, 2019. In the following figure, you see the testing data, buy/sell instances, and the resulting profit made within 1 month period.
Notice that the data has downtrending characteristics. Its price almost linearly decreases from about 100$ to 70$ within 1 month. We also observe lower peaks during that period. On the other hand, when we have a look at the profit made by the deep Q-network (DQN) agent we observe nearly linear increase. Within the same period it reaches to 25.54 % using only 96 trades (buy/sell) which is quite satisfactory.
In the second test, I used the Ripple (XRP) data which is also recorded recorded between the dates 01 Aug, 2019 and 01 Sep, 2019. The testing data, buy/sell instances, and the resulting profit made within 1 month period are shown in the figure below.
Notice that for this case, the testing data has also downtrending characteristics. In addition, we observe very sharp fall between time indices 300 and 400. This time the profit made by the DQN agent reaches to 20.02% by using only 38 trades (buy/sell).
In the last experiment, I used Binance Coin (BNB) data. The results are shown in the following figure.
In this case, we also have similar results. This time we made 11.76% profit using 70 trades. It is extremely important to test the model using several different testing data. The results in the previous post and this post show that the model we trained works quite well and it might be ready for real trading. If you also want to obtain the same results, please follow the steps given below:
- Training steps: Deep Q-learning for algorithmic trading
- Testing steps: Deep Q-learning test results for algorithmic trading
- Feature extraction: Feature extraction class version 2: Adding choppiness index and x indicator
That’s all for this post. Enjoy your trading!