Reinforcement Learning for Algorithmic Trading with Python

Install the python data science ecosystem with anaconda, a package manager that simplifies dependencies for data science, machine learning, and algo trading, with Windows, macOS, or Linux installers.

Learn to create technical indicators with pandas in Python, including SMA, SMA ratio, MACD line and signal line, MACD histogram, RSI, and the stochastic oscillator, via an optional Jupyter notebook.

Conclude stochastic oscillator as a momentum indicator for reversals, signaling buy when k> d and sell when k< d, alongside RSI, MACD histogram, SMA ratio, and returns for RL trading.

Explore OpenAI's ecosystem, from ChatGPT and DALL-E to the API, tokens, and safety practices, and learn how to leverage GPT-4 for building and fine-tuning models.

Explain what tokens are in language models like GPT, how tokenization converts text to numbers, and how token counts and limits influence outputs.

Learn iterative refinement prompting to elicit detailed data science steps, including data preprocessing, explanatory data analysis, and Python code for tasks like handling missing values and seaborn heatmaps.

Compare common reinforcement learning algorithms, highlighting q-learning as a simple, model-free method for discrete environments. Explain how deep q-networks extend q-learning to high-dimensional spaces, and discuss exploration versus exploitation.

Explore a hands-on reinforcement learning project using Python, teaching an agent to solve the mountain car task with Q-tables, episodic training, rendering choices, and evaluation.

Explore Q-learning hyperparameters like the learning rate alpha, gamma, and epsilon. Understand how epsilon decay, episodes, and maximum steps shape exploration versus exploitation in the Mountain Car Challenge.

Explore how randomness affects reinforcement learning by using fixed seeds to make random events reproducible, enabling clear assessment of hyperparameter impacts on Q-learning performance.

Extending training episodes beyond 2000 shows mixed effects in mountain car: 2000 episodes achieve 100% success with 149 steps on average, but 7000 episodes drop to 98.75% and 158 steps.

Learn how reinforcement learning trains an agent to maximize rewards in the lunar lander environment. The lunar lander uses discrete actions and an eight-dimensional observation space to land safely.

Save and load a trained q table with numpy, train over 150,000 episodes, then test with 2000 episodes to reach 62.5% success and a 172 average reward.

Explore building a from-scratch reinforcement learning trading agent using hourly eur/usd data, with q-learning, discretized features, and accounting for trading costs and overfitting.

Balance overfitting and underfitting in reinforcement learning trading by tuning instruments, data frequency, training period, and indicators like sma, macd, rsi, and stochastic, and consider q-learning or deep q-learning.