Deep Reinforcement Learning test results for Litecoin, Ripple, and Binance Coin

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 […]

Deep Reinforcement Learning for algorithmic trading (Test results 2)

After publishing the deep Q-learning test results, I have received several responses and questions. I found most of them useful and based on them I prepared a new test configuration. In this article, I’m going to share new results on the subject deep reinforcement learning for algorithmic trading. First, I want to list some of […]

Feature extraction class version 2: Adding choppiness index and x indicator

In this post, I’m going to talk about the new version of our feature extraction class. You may have a look at the old version by browsing the following link: A complete feature extraction class for algorithmic trading In the old version, the features were ‘rsi’,’bandwidth’,’percent-b’, ‘cv’ and the rate of change values such as, […]

Deep Q-learning test results for algorithmic trading

In the previous post, we trained our deep Q-network using Litecoin historical data. In this post, we are going to learn how to test the network using a test data. We will also see whether we can make money or not using AI. Before starting, I highly recommend you to read the previous post, because […]

Deep Q-learning for algorithmic trading

In this post, I’m going to briefly present the deep Q-learning method which is the combination of reinforcement learning (RL) and deep neural networks. After that, I’m going to show how I applied this technique to algorithmic trading. At the end of the post, I’m going to share the python implementation of the training procedure. […]

A complete feature extraction class for algorithmic trading

In this post, I’m going to share a complete feature extraction class for algorithmic trading with you. You may have a look at the technical meaning of the features by browsing the following past articles: Feature extraction for algorithmic trading Additional features for algorithmic tading After training and testing my deep Q-agent with several financial […]

Creating a custom gym (OpenAi) environment for algorithmic trading

Gym is a library which provides environments for the reinforcement learning algorithms. There are plenty of environments included in the library such as classic control, 2D and 3D robots, and atari games. An environment includes some public functions containing ‘step’, ‘reset’, and ‘render’. When the ‘step’ function is called, it returns four values, namely ‘obervation’, […]

Additional features for algorithmic trading

In this post, we are going to continue to extract useful features for algoritmic trading. I really recommend you to read the previous posts. The links are; How to get trading data from binance Feature extraction for algorithmic trading In the previous post, we extracted only two features using Bollinger Bands, which were ‘percent b’ […]

Feature extraction for algorithmic trading

In this post, I’m going to show how to extract useful features from the raw trading data. First, you need to obtain the raw trading data. For this, you may have a look at my previous post namely ‘How to get trading data from binance‘. Let us describe what I mean by the useful features. […]

How to get trading data from binance

In this article, I’m going to show how to obtain raw trading data from Binance. Please make sure that ‘Anaconda’ and ‘Python 3.7’ are installed in your computer. Before starting, we need to install ‘python-binance’ module. It can be installed using pip. Go to anaconda prompt and type the following code: Now we can import […]