Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems. Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.
With the increasing popularity of machine learning, many traders are looking for ways in which they can "teach" a computer to trade for them. This process is called algorithmic trading (sometimes called algo-trading). Algorithmic trading is a hands off strategy for buying and selling stocks that leverages technical indicators instead of human intuition. In order to implement an algorithmic trading strategy though, you have to first narrow down a list of stocks that you want to analyze. This walk-through provides an automated process (using python and logistic regression) for determining the best stocks to algo-trade.
Investing in the stock market used to require a ton of capital and a broker that would take a cut from your earnings. Then Robinhood disrupted the industry allowing you to invest as little as $1 and avoid a broker altogether. Robinhood and apps like it have opened up investing to anyone with a connected device and gave non-investors the opportunity to profit from the newest tech start-up. However, giving those of us who are not economists or accountants the freedom to invest our money in the "hottest" or "trending" stocks is not always the best financial decision. Thousands of companies use software to predict the movement in the stock market in order to aid their investing decisions. The average Robinhood user does not have this available to them.
Before we start this article on machine learning basics, let us take an example to understand the impact of machine learning in the world. We can safely assume that machine learning has been a dominant force in today's world and has accelerated our progress in all fields. No matter which industry you look at, machine learning has dramatically altered it. Let's take an example from the world of trading. Man Group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. Machine learning has become a hot topic today, with professionals all over the world signing up for ML or AI courses for fear of being left behind. But exactly what is machine learning? It will be clear to you when you have reached the end of this article. Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow. Whereas in machine learning, we input a data set through which the machine will learn by identifying and analysing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset.
Putting it all together, the following example shows the equity curve representing cumulative returns of the model strategy, with all values expressed in dollars. To increase the precision of forecasted values, instead of a standard probability of 0.5 (50 percent) we choose a higher threshold value, to be more confident that the model predicts an Up day. As we can see by the chart above, the equity curve is much better than before (Sharpe is 6.5 instead of 3.5), even with fewer round turns. From this point on, we will consider all next models with a threshold higher than a standard value. We can apply our research, as we did previously with the decision tree, into a Logistic Classifier model.