Data Sources and Preprocessing for Financial Machine Learning

This blog covers how to acquire, clean, and transform financial data for machine learning, and discusses the challenges and opportunities of using different data sources and features.

Table of Contents

1. Introduction

Financial machine learning is the application of machine learning techniques to financial problems, such as predicting stock prices, identifying trading opportunities, or detecting fraud. Financial machine learning can help investors, traders, and financial institutions make better decisions and optimize their performance.

However, financial machine learning is not a straightforward task. It requires a lot of data, and not just any data, but high-quality, relevant, and reliable data. Data is the fuel of machine learning, and without it, no algorithm can work effectively. But where can you get financial data? How can you ensure its quality and validity? How can you transform it into a suitable format for machine learning? And how can you extract useful features from it that can capture the complex patterns and dynamics of the financial markets?

In this blog, we will answer these questions and provide you with a comprehensive guide on how to acquire, clean, and transform financial data for machine learning. We will also discuss the challenges and opportunities of using different data sources and features for financial machine learning. By the end of this blog, you will have a solid understanding of how to prepare your data for financial machine learning and how to overcome some of the common pitfalls and difficulties.

Ready to dive into the world of financial data? Let’s get started!

2. Data Sources for Financial Machine Learning

One of the first steps in any financial machine learning project is to acquire the data that you will use to train and test your models. However, finding and accessing financial data is not always easy. There are many different types of financial data, and each one has its own advantages and disadvantages. Moreover, there are various sources of financial data, and each one has its own availability, cost, quality, and reliability. How can you choose the best data sources for your project? What are the criteria that you should consider when selecting financial data?

In this section, we will explore the different types of financial data and the sources that provide them. We will also discuss the pros and cons of each data source and how to evaluate them. By the end of this section, you will have a better understanding of the data landscape for financial machine learning and how to navigate it.

Let’s start by defining what financial data is and why it is important for machine learning.

What is financial data?

Financial data is any data that relates to the performance, behavior, or characteristics of financial markets, instruments, entities, or participants. Financial data can be classified into four main categories:

Market data: This is the data that reflects the prices and volumes of financial instruments, such as stocks, bonds, currencies, commodities, derivatives, etc. Market data can be further divided into two subcategories: historical data and real-time data. Historical data shows the past prices and volumes of financial instruments over a certain period of time, while real-time data shows the current prices and volumes of financial instruments as they change in the market. Market data is essential for machine learning because it allows you to analyze the trends, patterns, and movements of financial instruments and to build predictive models based on them.
Fundamental data: This is the data that reflects the financial performance, health, and value of financial entities, such as companies, countries, sectors, etc. Fundamental data can include metrics such as revenues, earnings, assets, liabilities, cash flows, dividends, ratios, ratings, etc. Fundamental data can also include qualitative information such as news, reports, announcements, etc. Fundamental data is important for machine learning because it allows you to assess the financial strength, growth, and potential of financial entities and to build valuation models based on them.
Alternative data: This is the data that comes from non-traditional sources and provides insights into the behavior, sentiment, and preferences of financial market participants, such as investors, consumers, competitors, etc. Alternative data can include data from social media, web scraping, satellite imagery, geolocation, credit card transactions, etc. Alternative data is valuable for machine learning because it allows you to capture the hidden signals, trends, and opportunities that are not reflected in the conventional data sources and to build innovative models based on them.
Meta data: This is the data that describes the characteristics, quality, and structure of other data sets. Meta data can include information such as the source, format, frequency, size, accuracy, completeness, etc. of the data. Meta data is useful for machine learning because it allows you to understand the properties, limitations, and suitability of the data sets that you use and to improve the data preprocessing and feature engineering steps.

As you can see, financial data is a broad and diverse category that encompasses many types of data. Each type of data has its own benefits and challenges for machine learning. Therefore, it is important to select the data that best fits your project goals, scope, and budget.

But where can you find financial data? What are the sources that provide it? Let’s take a look at the different data sources for financial machine learning and how to compare them.

2.1. Public Data Sources

Public data sources are the data sources that are freely available and accessible to anyone who wants to use them. Public data sources can provide a large amount of financial data, especially market data and fundamental data, from various domains and regions. Public data sources can be a great option for financial machine learning projects that have a limited budget or that want to explore different data sets and possibilities.

Some examples of public data sources for financial machine learning are:

Yahoo Finance: This is one of the most popular and widely used public data sources for financial machine learning. Yahoo Finance provides historical and real-time market data for stocks, bonds, currencies, commodities, indices, etc. from around the world. You can access Yahoo Finance data through its website, mobile app, or API. You can also download the data in CSV format or use libraries such as yfinance or pandas-datareader to access the data in Python.
Quandl: This is another popular and comprehensive public data source for financial machine learning. Quandl provides historical and real-time market data, as well as fundamental data, alternative data, and meta data, from various sources and categories. You can access Quandl data through its website, mobile app, or API. You can also download the data in CSV, JSON, XML, or Excel format or use libraries such as quandl or Quandl-Python to access the data in Python.
Google Finance: This is a simple and easy-to-use public data source for financial machine learning. Google Finance provides real-time market data for stocks, currencies, commodities, indices, etc. from around the world. You can access Google Finance data through its website or mobile app. You can also use libraries such as googlefinance or googlefinance.client to access the data in Python.

These are just some of the many public data sources that you can use for financial machine learning. You can find more public data sources by searching online or by using platforms such as Kaggle, DataHub, or data.world that host and share various data sets.

However, public data sources are not perfect. They have some drawbacks and limitations that you should be aware of. Some of the common challenges of using public data sources are:

Data quality and reliability: Public data sources may not always provide accurate, complete, or consistent data. The data may contain errors, gaps, outliers, or duplicates that can affect the quality and reliability of the data. Moreover, public data sources may not always update or maintain their data regularly, which can lead to outdated or missing data. Therefore, you should always check and validate the data quality and reliability before using it for machine learning.
Data availability and accessibility: Public data sources may not always provide the data that you need or want for your project. The data may be limited in scope, coverage, frequency, or granularity. For example, some public data sources may only provide daily or weekly data, while others may only provide data for certain regions or markets. Moreover, public data sources may not always be easy to access or use. The data may be difficult to find, download, or integrate with your project. For example, some public data sources may have complex or restrictive APIs, while others may have low or limited bandwidth or storage. Therefore, you should always check and compare the data availability and accessibility before using it for machine learning.
Data competitiveness and uniqueness: Public data sources may not always provide the data that gives you a competitive edge or a unique insight for your project. The data may be too common, generic, or widely used by other machine learning practitioners or competitors. For example, some public data sources may provide the same or similar data that everyone else is using, while others may provide the data that is already priced in or reflected in the market. Therefore, you should always check and evaluate the data competitiveness and uniqueness before using it for machine learning.

As you can see, public data sources have their pros and cons for financial machine learning. They can be a great resource for getting started or experimenting with different data sets, but they may not always be the best or the only option for your project. Therefore, you should always weigh the benefits and challenges of using public data sources and consider other data sources that may suit your project better.

In the next section, we will explore another type of data source for financial machine learning: private data sources.

2.2. Private Data Sources

Private data sources are the data sources that are not freely available and accessible to anyone who wants to use them. Private data sources can provide a large amount of financial data, especially alternative data and meta data, from various domains and regions. Private data sources can be a great option for financial machine learning projects that have a high budget or that want to gain a competitive edge or a unique insight from the data.

Some examples of private data sources for financial machine learning are:

Bloomberg: This is one of the most popular and comprehensive private data sources for financial machine learning. Bloomberg provides historical and real-time market data, fundamental data, alternative data, and meta data, from various sources and categories. You can access Bloomberg data through its terminal, website, mobile app, or API. You can also download the data in CSV, JSON, XML, or Excel format or use libraries such as blpapi or blpapi-python to access the data in Python.
Refinitiv: This is another popular and comprehensive private data source for financial machine learning. Refinitiv provides historical and real-time market data, fundamental data, alternative data, and meta data, from various sources and categories. You can access Refinitiv data through its platform, website, mobile app, or API. You can also download the data in CSV, JSON, XML, or Excel format or use libraries such as Refinitiv Data Platform Libraries or Example.RDPAPI.Python to access the data in Python.
Alpha Vantage: This is a simple and easy-to-use private data source for financial machine learning. Alpha Vantage provides historical and real-time market data for stocks, currencies, cryptocurrencies, and technical indicators. You can access Alpha Vantage data through its website or API. You can also download the data in CSV or JSON format or use libraries such as alpha_vantage or alpha-vantage to access the data in Python.

These are just some of the many private data sources that you can use for financial machine learning. You can find more private data sources by searching online or by using platforms such as AlternativeData.org, Eagle Alpha, or Quandl Premium that host and sell various data sets.

However, private data sources are not perfect. They have some drawbacks and limitations that you should be aware of. Some of the common challenges of using private data sources are:

Data cost and accessibility: Private data sources may not always be affordable or accessible for your project. The data may be expensive, requiring a subscription or a license fee to access it. Moreover, private data sources may have strict or complex terms and conditions that limit the use, distribution, or modification of the data. For example, some private data sources may only allow you to use the data for personal or academic purposes, while others may require you to sign a non-disclosure agreement or a data usage agreement. Therefore, you should always check and compare the data cost and accessibility before using it for machine learning.
Data availability and quality: Private data sources may not always provide the data that you need or want for your project. The data may be limited in scope, coverage, frequency, or granularity. For example, some private data sources may only provide data for certain regions or markets, while others may only provide data for certain types or categories of financial instruments. Moreover, private data sources may not always provide accurate, complete, or consistent data. The data may contain errors, gaps, outliers, or duplicates that can affect the quality and reliability of the data. Therefore, you should always check and validate the data availability and quality before using it for machine learning.
Data integration and compatibility: Private data sources may not always be easy to integrate or compatible with your project. The data may be difficult to find, download, or format for your project. For example, some private data sources may have complex or proprietary APIs, while others may have incompatible or inconsistent data formats. Moreover, private data sources may not always be compatible with other data sources or tools that you use for your project. The data may have different standards, conventions, or definitions that can cause conflicts or inconsistencies with other data sets or libraries. Therefore, you should always check and test the data integration and compatibility before using it for machine learning.

As you can see, private data sources have their pros and cons for financial machine learning. They can be a great resource for getting access to exclusive or unique data sets, but they may not always be the best or the only option for your project. Therefore, you should always weigh the benefits and challenges of using private data sources and consider other data sources that may suit your project better.

In the next section, we will explore another type of data source for financial machine learning: alternative data sources.

2.3. Alternative Data Sources

As we have seen, public and private data sources provide a lot of useful information for financial machine learning, but they are not the only ones. There is another category of data sources that can offer a different perspective and a competitive edge for your projects: alternative data sources.

Alternative data sources are data sources that come from non-traditional or unconventional sources and provide insights into the behavior, sentiment, and preferences of financial market participants, such as investors, consumers, competitors, etc. Alternative data sources can include data from social media, web scraping, satellite imagery, geolocation, credit card transactions, etc.

Why are alternative data sources important for financial machine learning? Because they can capture the hidden signals, trends, and opportunities that are not reflected in the conventional data sources and that can give you an advantage over other market players. For example, you can use social media data to measure the popularity, sentiment, and influence of certain stocks, products, or brands. You can use web scraping data to collect information from various websites, such as news, blogs, forums, reviews, etc. You can use satellite imagery data to monitor the activity, production, and inventory of certain companies, sectors, or regions. You can use geolocation data to track the foot traffic, mobility, and demand of certain locations, such as stores, malls, airports, etc. You can use credit card transactions data to analyze the spending patterns, preferences, and loyalty of consumers.

However, alternative data sources also come with some challenges and limitations for financial machine learning. First, alternative data sources are often unstructured, noisy, and incomplete, which means that they require more preprocessing and cleaning than traditional data sources. Second, alternative data sources are often difficult to access, acquire, and store, which means that they may involve higher costs, legal issues, and technical challenges than traditional data sources. Third, alternative data sources are often dynamic, heterogeneous, and complex, which means that they require more advanced methods and techniques for feature engineering and selection than traditional data sources.

Therefore, alternative data sources are not a magic bullet for financial machine learning, but rather a valuable complement to the existing data sources. They can provide you with new insights and opportunities, but they also require more effort and skill to use them effectively.

In the next section, we will discuss how to preprocess the financial data that you have acquired from different sources and how to prepare it for machine learning.

3. Data Preprocessing for Financial Machine Learning

Data preprocessing is the process of transforming the raw data that you have acquired from different data sources into a suitable format for machine learning. Data preprocessing is a crucial step for financial machine learning, as it can affect the quality, validity, and performance of your models. Data preprocessing can involve various tasks, such as data cleaning, data transformation, data normalization, feature engineering, and feature selection. In this section, we will explain what each of these tasks entails and how to perform them for financial machine learning.

Let’s start by defining what data preprocessing is and why it is important for machine learning.

What is data preprocessing?

Data preprocessing is the process of preparing the data for machine learning by modifying, enhancing, or reducing the data. Data preprocessing can have several objectives, such as:

Improving data quality: Data preprocessing can help you to improve the quality of your data by removing or correcting errors, gaps, outliers, or duplicates that can affect the accuracy and reliability of your data.
Improving data validity: Data preprocessing can help you to improve the validity of your data by ensuring that your data meets the assumptions, requirements, or constraints of your machine learning models or algorithms.
Improving data performance: Data preprocessing can help you to improve the performance of your data by enhancing or reducing the features, dimensions, or complexity of your data that can affect the efficiency and effectiveness of your machine learning models or algorithms.

Data preprocessing can be divided into four main tasks: data cleaning, data transformation, data normalization, and feature engineering. Each of these tasks can have different methods, techniques, or tools that you can use to perform them. We will discuss each of these tasks in detail in the following subsections.

Why is data preprocessing important for machine learning?

Data preprocessing is important for machine learning because it can have a significant impact on the outcome and quality of your machine learning models or algorithms. Data preprocessing can help you to:

Avoid data errors or biases: Data preprocessing can help you to avoid data errors or biases that can lead to inaccurate or misleading results or conclusions from your machine learning models or algorithms.
Enhance data features or signals: Data preprocessing can help you to enhance data features or signals that can provide useful or relevant information or insights for your machine learning models or algorithms.
Reduce data noise or redundancy: Data preprocessing can help you to reduce data noise or redundancy that can interfere or obscure the information or insights from your machine learning models or algorithms.
Optimize data size or complexity: Data preprocessing can help you to optimize data size or complexity that can affect the speed, memory, or computational resources of your machine learning models or algorithms.

As you can see, data preprocessing is a vital step for financial machine learning, as it can improve the quality, validity, and performance of your data and your models or algorithms. Therefore, you should always perform data preprocessing before applying machine learning to your data.

In the next subsection, we will explore the first task of data preprocessing: data cleaning and validation.

3.1. Data Cleaning and Validation

Once you have acquired the financial data that you need for your project, the next step is to clean and validate it. Data cleaning and validation are essential steps in any data science or machine learning project, but they are especially important for financial machine learning. Why? Because financial data is often messy, noisy, incomplete, inconsistent, or erroneous, and these issues can affect the quality and reliability of your models and results.

Data cleaning and validation are the processes of detecting, correcting, and removing any errors, anomalies, or inconsistencies in your data. Data cleaning and validation can involve tasks such as:

Handling missing values: This is the task of dealing with the data points that have no value or a null value. Missing values can occur due to various reasons, such as data entry errors, data collection failures, data transmission errors, etc. Missing values can affect the performance and accuracy of your models, so you need to handle them properly. You can handle missing values by deleting them, imputing them, or ignoring them, depending on the context and the amount of missing values.
Handling outliers: This is the task of dealing with the data points that are significantly different from the rest of the data. Outliers can occur due to various reasons, such as measurement errors, data entry errors, data manipulation errors, rare events, etc. Outliers can affect the distribution and statistics of your data, so you need to handle them properly. You can handle outliers by deleting them, transforming them, or keeping them, depending on the context and the impact of outliers.
Handling duplicates: This is the task of dealing with the data points that are identical or very similar to each other. Duplicates can occur due to various reasons, such as data entry errors, data collection errors, data merging errors, etc. Duplicates can affect the size and diversity of your data, so you need to handle them properly. You can handle duplicates by deleting them, aggregating them, or keeping them, depending on the context and the purpose of duplicates.
Handling inconsistencies: This is the task of dealing with the data points that are not consistent with the rest of the data or with the expected format or standard. Inconsistencies can occur due to various reasons, such as data entry errors, data collection errors, data transformation errors, etc. Inconsistencies can affect the validity and comparability of your data, so you need to handle them properly. You can handle inconsistencies by correcting them, converting them, or standardizing them, depending on the context and the type of inconsistencies.

Data cleaning and validation can improve the quality and reliability of your data, which in turn can improve the performance and accuracy of your models and results. Therefore, data cleaning and validation are crucial steps for financial machine learning.

In the next section, we will discuss how to transform and normalize the financial data that you have cleaned and validated and how to prepare it for machine learning.

3.2. Data Transformation and Normalization

After you have cleaned and validated your financial data, the next step is to transform and normalize it. Data transformation and normalization are the processes of changing the format, scale, or distribution of your data to make it more suitable for machine learning. Data transformation and normalization can involve tasks such as:

Resampling: This is the task of changing the frequency or granularity of your data. For example, you can resample your daily data to weekly, monthly, or yearly data, or vice versa. Resampling can help you reduce the noise, volatility, or redundancy of your data, or increase the relevance, consistency, or completeness of your data, depending on your project goals and scope.
Encoding: This is the task of converting your categorical or textual data into numerical data. For example, you can encode your stock symbols, sector names, or sentiment labels into numbers, such as 0, 1, 2, etc. Encoding can help you make your data more compatible with machine learning algorithms, which usually work better with numerical data than with non-numerical data.
Scaling: This is the task of changing the range or magnitude of your numerical data. For example, you can scale your data to have a minimum value of 0 and a maximum value of 1, or to have a mean value of 0 and a standard deviation of 1. Scaling can help you make your data more comparable, consistent, and stable, and avoid the problems of outliers, skewness, or heteroscedasticity.
Transforming: This is the task of changing the shape or distribution of your numerical data. For example, you can transform your data to have a linear, logarithmic, exponential, or power function, or to have a normal, uniform, or binomial distribution. Transforming can help you make your data more linear, symmetrical, or homogeneous, and improve the performance, accuracy, or interpretability of your models.

Data transformation and normalization can improve the suitability and quality of your data for machine learning, which in turn can improve the performance and accuracy of your models and results. Therefore, data transformation and normalization are important steps for financial machine learning.

In the next section, we will discuss how to engineer and select the features from the financial data that you have transformed and normalized and how to prepare it for machine learning.

3.3. Feature Engineering and Selection

The final step in data preprocessing for financial machine learning is feature engineering and selection. Feature engineering and selection are the processes of creating, choosing, and optimizing the features that you will use to train and test your models. Features are the variables or attributes that represent the characteristics, properties, or patterns of your data. Features are the inputs of your models, and they determine the outputs and results of your models.

Feature engineering and selection are important steps for financial machine learning because they can enhance the performance, accuracy, and interpretability of your models. Feature engineering and selection can involve tasks such as:

Creating features: This is the task of generating new features from your existing data or from external sources. For example, you can create features by applying mathematical or statistical operations, such as ratios, differences, averages, etc. You can also create features by applying domain knowledge or business logic, such as indicators, signals, strategies, etc. Creating features can help you capture the relevant and meaningful information from your data and increase the predictive power of your models.
Choosing features: This is the task of selecting the most relevant and useful features from your available features. For example, you can choose features by applying filter methods, such as correlation, variance, etc. You can also choose features by applying wrapper methods, such as forward selection, backward elimination, etc. Choosing features can help you reduce the dimensionality and complexity of your data and avoid the problems of overfitting, multicollinearity, or redundancy.
Optimizing features: This is the task of improving the quality and suitability of your selected features for your models. For example, you can optimize features by applying embedding methods, such as principal component analysis, factor analysis, etc. You can also optimize features by applying regularization methods, such as lasso, ridge, etc. Optimizing features can help you enhance the stability and generalization of your models and avoid the problems of noise, outliers, or heterogeneity.

Feature engineering and selection can improve the suitability and quality of your features for machine learning, which in turn can improve the performance and accuracy of your models and results. Therefore, feature engineering and selection are crucial steps for financial machine learning.

In the next section, we will conclude this blog and summarize the main points that we have covered.

4. Conclusion

In this blog, we have covered the main steps and challenges of data sources and preprocessing for financial machine learning. We have learned how to:

Acquire, clean, and transform financial data from different sources, such as public, private, and alternative data sources.
Engineer and select features from the financial data to capture the relevant and meaningful information for machine learning.
Prepare the financial data for machine learning by resampling, encoding, scaling, and transforming it.

Data sources and preprocessing are essential steps for any financial machine learning project, as they determine the quality and reliability of your data, which in turn determine the performance and accuracy of your models and results. Therefore, it is important to select the best data sources and apply the best data preprocessing techniques for your project goals, scope, and budget.

We hope that this blog has given you a comprehensive and practical guide on how to acquire, clean, and transform financial data for machine learning, and that you have found it useful and informative. If you have any questions, comments, or feedback, please feel free to leave them below. Thank you for reading and happy learning!