When it comes to using machine learning in the stock market, there are multiple approaches a trader can do to utilize ML models. From determining future risk to predicting stock prices, machine learning can be used for virtually any kind of financial modeling.<\/p>\n\n\n\n
In our previous articles, we delved into the usage of two Time Series<\/em><\/a> models: SARIMAX<\/strong> and Facebook Prophet<\/strong>. We utilized both of these models to forecast the potential, future prices of Bitcoin. Check out the following article if you are interested: https:\/\/towardsdatascience.com\/predicting-prices-of-bitcoin-with-machine-learning-3e83bb4dd35f<\/a><\/p>\n\n\n\n In another article, we used classification models<\/em><\/a> to classify stocks based on their performance in quarterly reports. You can read about the entire process of how we engineered different features from these reports and trained our classification models to determine investment decisions in the article below: https:\/\/medium.com\/swlh\/teaching-a-machine-to-trade-stocks-like-warren-buffett-part-i-445849b208c6<\/a><\/p>\n\n\n\n These are just a couple examples of how we can utilize machine learning models for financial markets.<\/p>\n\n\n\n In these articles, we just used models to predict future prices, however, that is just half the battle. The next step is how do we evaluate these models if they were to actually be used while trading? The answer to that question is called \u2014 Backtesting.<\/em><\/strong><\/p>\n\n\n\n Register & Get Data<\/span><\/a><\/p>\n\n\n\n What is backtesting?<\/em> Backtesting is the process of applying a trading strategy, predictive model, or analytical method to historical data to evaluate its accuracy and performance.<\/p>\n\n\n\n It is very important to note that backtesting does not<\/em><\/strong> 100% accurately, represent live-trading in the past. However, you should only use it to inform your decision to live-trade your strategy or not. Even then, it may be more practical to forward-test<\/strong><\/a> your strategy before trading with real money.<\/p>\n<\/blockquote>\n\n\n\n When we create a machine learning model, we need to backtest the model in order to determine how well it would have potentially performed in the past by feeding it historical data. There are two approaches when it comes to backtesting \u2014 <\/strong>Event-Driven<\/strong><\/a> and Vectorized Backtesting<\/strong><\/a>.<\/p>\n\n\n\n Today, we will be using vectorized backtesting in order to evaluate the performance of our machine learning model. This approach allows us to quickly observe how our ML model might have performed in the past. If you would like to learn more about vectorized backtesting, then we suggest reading the following article by a machine learning researcher which contributed to the outcome of this current project:<\/em> https:\/\/towardsdatascience.com\/backtest-trading-strategies-with-pandas-vectorized-backtesting-26001b0ba3a5<\/a><\/p>\n\n\n\n In order to evaluate the performance of a machine learning model, we\u2019ll first have to construct it in Python. The model we will be using is the AutoARIMA<\/em> time series model from the pmdarima<\/strong><\/a> Python library. This model will quickly find the optimum parameters for us so we won\u2019t need to worry about adjusting any modeling parameters.<\/p>\n\n\n\n However, if you do want to learn more about the parameters and what they mean, check out our previous article<\/a> mentioned above that uses time series modeling to predict Bitcoin prices.<\/p>\n\n\n\nBacktesting<\/h2>\n\n\n\n
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Vectorized Backtesting<\/h3>\n\n\n\n
Coding Our Machine Learning Model<\/h2>\n\n\n\n
Step 1. Importing Necessary Libraries<\/h3>\n\n\n\n
import pandas as pd\nimport numpy as np\nfrom pmdarima.arima import AutoARIMA\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom tqdm.notebook import tqdm\nfrom sklearn.metrics import mean_squared_error\nfrom datetime import date, timedelta\nimport yfinance as yf<\/code><\/pre>\n\n <\/div>\n