Van Der Post Hayden, Bisette Vincent - Machine Learning For Finance. Master Financial Strategies With Python Powered Machine Learning.pdf

(2137 KB) Pobierz
MACHINE LEARNING
FOR FINANCE
Master Financial Strategies with Python
Powered Machine Learning
Hayden Van Der Post
Vincent Bisette
Reactive Publishing
CONTENTS
Title Page
Chapter 1: Introduction to Machine Learning and Finance
Chapter 2: Financial Data Handling and Preprocessing
Chapter 3: Supervised Learning Techniques in Finance
Chapter 4: Unsupervised Learning Techniques in Finance
Chapter 5: Time Series Analysis and Forecasting
Chapter 6: Algorithmic Trading and Portfolio Management
Chapter 7: Advanced Topics in Machine Learning for Finance
CHAPTER 1:
INTRODUCTION TO
MACHINE LEARNING
AND FINANCE
M
achine learning stands as one of the most transformative
technologies of the 21st century. At its core, machine learning refers
to the process by which computers use algorithms to analyze data,
learn from that data, and then make informed decisions or predictions
without being explicitly programmed to do so. It essentially allows
computers to get better at tasks with experience, similar to how humans
learn from practice.
To fully grasp the concept of machine learning, it's essential to understand
its foundational components and processes.
1. Core Components of Machine Learning
- Data: The lifeblood of any machine learning model. Data can come in
various forms, such as numerical, categorical, text, images, or even audio.
In finance, data typically includes stock prices, transaction records,
economic indicators, and market sentiment data, among others.
- Algorithms: Algorithms are the mathematical frameworks and procedures
that enable the processing of data. They guide the system on how to identify
patterns and make decisions. Popular algorithms include linear regression,
decision trees, neural networks, and support vector machines.
- Model: The model is essentially the output of a machine learning
algorithm after it has been trained on data. It’s the learned representation
that can be used for making predictions or decisions. For example, a trained
model might predict stock prices or classify transaction types.
- Training: Training involves feeding a machine learning algorithm a large
amount of data and allowing it to adjust its parameters to improve accuracy.
The goal is to minimize the difference between the predicted outcomes and
the actual outcomes.
- Evaluation: Post-training, the model’s performance is evaluated using
various metrics such as accuracy, precision, recall, and F1-score. In finance,
additional metrics like return on investment (ROI) or Sharpe ratio might be
used to assess the model's effectiveness.
2. Types of Machine Learning
Machine learning can be broadly categorized into three types: supervised
learning, unsupervised learning, and reinforcement learning.
- Supervised Learning: Involves training the model on a labeled dataset,
which means that each training example is paired with an output label. For
instance, predicting future stock prices based on historical data is a
supervised learning task. Algorithms such as linear regression, decision
trees, and neural networks are commonly used.
- Unsupervised Learning: Here, the model is trained on an unlabeled dataset
and must find hidden patterns or intrinsic structures in the data. Clustering
of market data to identify different market segments falls into this category.
Techniques like k-means clustering and principal component analysis
(PCA) are often employed.

Plik z chomika:

Inne pliki z tego folderu:

Inne foldery tego chomika:

Zgłoś jeśli naruszono regulamin