How to use Deep Learning for Real-Time Trading Decisions

Source: Medium

Today, we’re delving into real-time trading using Q-learning, a model-free reinforcement learning algorithm. This approach empowers an agent to discern optimal actions within a given environment by iteratively adjusting Q-values in response to observed rewards.

To facilitate this exploration, we’ll leverage Intrinio’s Nasdaq Basic API, a valuable resource providing real-time data for our analysis and decision-making processes in the dynamic realm of financial markets.

Setting up an Account & Acquiring Data

Before heading to start coding in Python, it is essential to first have an Intrinio account and then purchase the Nasdaq Basic API endpoints. Firstly, to create an account, head over to Intrinio’s homepage and select the Sign Up button on the top-right corner which leads you to the user registration page.

After creating an account, you can purchase the API endpoints from this product page which contains all the details about the real-time stock data that comes with the API: https://intrinio.com/financial-market-data/nasdaq-basic.

Now that you have an Intrinio account which allows you to have your own API keys (vital for extracting data) and ownership of the Nasdaq Basic API for real-time stock data, we can now proceed to coding in Python and get our hands dirty.

Importing Packages & Extracting Data

Let’s start by importing the necessary libraries for our analysis. These libraries will provide the basic tools required to explore and implement our trading strategy. To extract the data, we’ll use Intrinio’s Python SDK, making the process straightforward and efficient.

But before moving further, let’s gain some background about Intrinio’s Nasdaq Basic API. The Nasdaq Basic API, facilitated by Intrinio, allows for efficient extraction of financial data in just a few lines of code. Its user-friendly interface provides flexibility, enabling us to customize its utilization according to our specific needs. Additionally, the API furnishes diverse and accurate information, offering a wide array of options for analysis and application.

The following code imports the necessary packages and extracts the data of Apple stock:

!pip install intrinio-sdk

import intrinio_sdk as intrinio
import numpy as np
import pandas as pd
import time

intrinio.ApiClient().set_api_key('YOUR API KEY') #setting up the API key
#NOTE : Replace YOUR API KEY with you actual API key

identifier = 'AAPL' # Setting up the identifier
source = 'nasdaq_basic' # Setting up the source

response = intrinio.SecurityApi().get_security_realtime_price(identifier, source=source) # Extracting the data
print(response)

To retrieve the data, start by installing the Intrinio Python SDK with a straightforward pip command. After installation, set your API key for authorization. Next, input the identifier (e.g., AAPL) and the data source (in this case, nasdaq_basic). With just one line of code, you’ll receive the desired response. It’s a streamlined process, making data extraction quick and simple.

Now, let’s step back to focus on understanding the Q-learning algorithm. After that, we’ll return to our data extraction process and identify the key features we need from the data.

Q — learning

Reinforcement Learning (RL) is a subfield of machine learning dedicated to training agents to make decisions through iterative interactions with an environment. In the RL paradigm, agents navigate an environment by taking actions and subsequently receiving feedback in the form of rewards or penalties. This feedback guides the agent’s learning process, enabling it to develop a strategy that aims to maximize the cumulative reward over time. Q-learning is a specific type of reinforcement learning method that plays a crucial role in this process, helping agents optimize their decision-making strategies based on the environmental feedback received during the exploration and exploitation phases.

Q-learning stands out as a model-free reinforcement learning algorithm designed to empower an agent in learning optimal actions within a given environment. Through an iterative process, Q-learning updates Q-values, which serve as representations of the anticipated cumulative rewards linked to taking a particular action in a specific state. This method enables the agent to refine its decision-making strategy over time, honing in on the actions that yield the most favorable outcomes based on the learned Q-values. Now, let’s break down the key components of Q-learning.