Stock Trading Using Machine Learning: Complete Guide
Rather than developing new approaches, we will apply our understanding of deep Q-networks to the world of financial trading to solve real-world problems.
Even though I can’t guarantee that the code will make you a millionaire on the stock market or in Forex, my intention is far more modest — to demonstrate how reinforcement learning can be extended beyond Atari games and into practical applications.
With OpenAI Gym, we’ll create a customized stock market environment for maximizing our profits. We will train an agent using the DQN method. Our approach here will give us the best chance of making money with stock trades.
Trading
Each day, commodities, equities, and currencies inundate the markets. Even weather predictions can be traded for money using weather derivatives in the modern world. Global financial markets are intricate, which causes this phenomenon. To protect your company against risks, consider acquiring weather derivatives if it depends on weather conditions to earn profits. Prices fluctuate for a variety of assorted items. As a result of trading, one purchases and sells financial instruments for various purposes, including generating profits (investment), mitigating price fluctuations (hedging), or simply fulfilling their own needs (for example, procuring steel or converting USD to JPY to fulfil contractual obligations).
Forecasting future price fluctuations in financial markets has been a steadfast fascination throughout history. An enduring curiosity about the stock market comes from its enticing prospects, such as the possibility of unexpected profits and the ability to guard against drastic market changes.
Countless financial advisors, investment firms, banks, and independent traders work tirelessly to forecast when to buy and sell to maximize profits.
Can we examine this issue from a reinforcement learning perspective? Let’s say we have some insights about the market and are contemplating purchasing, selling, or holding. A price rise before we buy will result in a loss; however, a price decline will result in a positive reward. The ultimate goal of our business is to maximize profits. There is a striking parallel between market trading and reinforcement learning.
Data
For example, we will use the stock market prices of Russia in 2015–2016. A list of fees can be found in the stock-prediction-rl/data/ch08-small-quotes.tgz. We should extract these prices before training the model.