Machine Learning for Quant Finance and Algorithmic Trading
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— WELCOME TO THE COURSE —
This comprehensive course is designed for anyone who wants to leverage machine learning techniques in finance. Covering essential topics such as Pandas, NumPy, Matplotlib, and Seaborn, participants will gain a solid foundation in data manipulation and visualization, crucial for analyzing financial datasets.
The curriculum delves into key financial concepts, including derivatives, technical analysis, and asset pricing models, providing learners with the necessary context to apply machine learning effectively. Participants will explore various machine learning methodologies, including supervised and unsupervised learning, deep learning techniques, and their applications in developing trading strategies.
A significant focus of the course is on hands-on coding projects that allow learners to implement machine learning algorithms for trading strategies and backtesting. By the end of the course, students will have practical experience in building predictive models using Python.
Additionally, the course introduces Streamlit, enabling participants to create interactive web applications and dashboards to showcase their quantitative models effectively. This integration of machine learning with web development equips learners with the skills to present their findings dynamically.
Whether you are a finance professional or a data enthusiast, this course empowers you to harness the power of machine learning in quantitative finance and algorithmic trading, preparing you for real-world challenges in the financial markets. Join us to transform your understanding of finance through advanced analytics and innovative technology!
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3
Introduction to Pandas -
4
Pandas Series
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5
DataFrame in Pandas
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6
How to work with CSV and other file types in pandas
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7
Analysing DataFrames in Pandas
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8
Introduction to Numpy
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9
Numpy Arrays
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10
Shape and Reshape Arrays in Numpy
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11
Indexing Arrays
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12
Array Iterating
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13
Slicing Arrays
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14
Searching and Sorting Numpy Arrays
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21
Supervised Machine Learning
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22
Unsupervised Machine Learning
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23
Machine Learning Lifecycle
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24
Train Test Split
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25
Machine Learning Model Evaluation Metrics
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26
Dimensionality in Machine Learning
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27
Regression Analysis
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28
Linear Regression
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29
Logistic Regression
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30
KNN
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31
SVM
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32
Decision Tree
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33
Random Forest
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34
K-Means Clustering
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35
GridSearch CV
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36
Machine Learning Pipeline
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37
Regression Practical Coding Example Mini Project
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38
Classification Practical Coding Example Mini Project
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44
Introduction to Financial Markets
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45
Introduction to Financial Markets Part 2
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46
Time Value of Money
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47
Type of Analysis in Financial Markets
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48
Capital Asset Pricing Model (CAPM)
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49
Modern Portfolio Theory(MPT)
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50
Correlation Theory
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51
Correlation Python Code Practical
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52
Kelly Criterion
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53
Sharpe Ratio
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54
Pair Trading
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55
Arbitrage Trading
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56
Introduction to Financial Derivatives
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57
Futures (Financial Derivatives)
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58
Options (Financial Derivatives)
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59
Black Scholes Option Pricing Model
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60
Introduction to Technical Analysis
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61
CandleStick Chart Python Code Implementation
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62
Support and Resistance
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63
Moving Average
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64
Simple Moving Average(SMA) Python Code Implementation
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65
Exponential Moving Average(EMA) Python Code Implementation
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66
Chart Patterns
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67
Dow Theory
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68
RSI
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69
Working with OHLC Data for Stocks in Python
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70
Apple Stock Price Prediction using Linear Regression
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71
Gold Price Prediction using Machine Learning
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72
Tesla Stock Price Prediction with different ML models
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73
Trading Strategy Development and Backtesting in Python on Amazon Stock
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74
Stock Price Prediction using LSTM Neural Network
