Udemy - The Complete Quantitative Finance & Automatisation Course

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Free Download Udemy - The Complete Quantitative Finance & Automatisation Course
Published: 4/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 22m | Size: 3.32 GB
Master the Essential Tools and Techniques for Quantitative Finance: From Financial Modeling to Algorithmic Trading

What you'll learn
Master Python for financial modeling, data analysis, and risk assessment.
Apply key financial models like Black-Scholes, CAPM, and Markowitz.
Analyze risk using VaR, Sharpe, Sortino, and Calmar Ratios.
Optimize portfolios with quantitative strategies and rebalancing.
Model financial processes using stochastic methods like Wiener and Vasicek.
Visualize and interpret financial data for better decision-making.
Understand statistical concepts like standard deviation and kurtosis.
Implement the Magic Formula and Piotroski F-Score for stock selection.
Use rolling operations to analyze time-series financial data.
Apply the Ornstein-Uhlenbeck process in mean-reverting strategies.
Build and backtest financial models for trading and investment.
Requirements
Basic understanding of finance and investing concepts (helpful but not required).
No prior programming experience needed, Python will be taught from scratch.
A computer with internet access to install Anaconda and Python.
Willingness to learn quantitative finance and apply mathematical concepts.
Description
Welcome to The Complete Quantitative Finance & Automatisation Course, your all-in-one guide to mastering the essential tools and techniques in quantitative finance. Whether you're a beginner or looking to deepen your knowledge, this course will equip you with the skills to navigate the world of financial modeling, analysis, and algorithmic trading using Python.In this comprehensive course, you will:Setup your Environment: Learn how to install and configure Python and Anaconda, the foundation for all the tools you'll use throughout the course.Dive into Statistics: Understand key statistical concepts such as Standard Deviation, Rolling Operations, and Data Visualization, which are crucial for analyzing market data and building models.Explore Mathematical Concepts: Gain in-depth knowledge of important quantitative finance models, including the Markowitz Model, Capital Asset Pricing Model (CAPM), Black-Scholes Model, Piotroski F-Score, and more. You will also learn advanced concepts like Geometric Brownian Motion, Wiener Process, Ornstein-Uhlenbeck Process, and Vasicek Model, all of which play critical roles in financial analysis.Evaluate Portfolio Performance: Learn how to assess the risk-adjusted return of portfolios using metrics like the Sharpe Ratio, Sortino Ratio, and Calmar Ratio. You'll also explore how to implement Portfolio Rebalancing strategies to optimize your investment choices.Through practical, hands-on Python coding exercises and real-world examples, you will develop a strong understanding of financial modeling, data analysis, and how to apply mathematical models to improve investment strategies.By the end of this course, you'll be proficient in applying quantitative finance tools and techniques for risk management, portfolio optimization, and algorithmic trading, enabling you to make informed, data-driven investment decisions.Get ready to transform your financial analysis skills and advance your career in quantitative finance!
Who this course is for
Aspiring quantitative analysts, financial analysts, and data scientists looking to develop technical skills.
Investors, traders, and portfolio managers who want to apply mathematical models and risk management techniques.
Finance and economics students seeking practical knowledge in Python-based financial modeling.
Programmers and engineers interested in transitioning into the world of quantitative finance.
Risk managers and financial professionals looking to enhance their quantitative analysis skills and improve investment decision-making.
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3.33 GB | 38min 33s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 -Summary.mp4 (7.24 MB)
2 -Introduction to Python.mp4 (6.89 MB)
3 -Download Python.mp4 (9.29 MB)
4 -Discover Python.mp4 (4.68 MB)
5 -Introduction to Anaconda.mp4 (28.38 MB)
6 -Download Anaconda.mp4 (8.73 MB)
7 -Anaconda Discover.mp4 (5.92 MB)
1 -Rolling Operations.mp4 (10.43 MB)
2 -Rolling Operation with Python.mp4 (199.6 MB)
3 -Standard Deviation.mp4 (15.59 MB)
4 -Standard Deviation with Python.mp4 (70.5 MB)
5 -Visualization.mp4 (14.44 MB)
6 -Visualization I.mp4 (62.08 MB)
7 -Visualization II.mp4 (26.1 MB)
1 -Kurtosis.mp4 (61.49 MB)
10 -Capital Asset Pricing Model Part 2.mp4 (156.5 MB)
11 -Capital Asset Pricing Model with Python.mp4 (37.22 MB)
12 -Black-Scholes Model.mp4 (179.75 MB)
13 -Black-Scholes Model with Python.mp4 (41.45 MB)
14 -Piotroski F-Score.mp4 (40.16 MB)
15 -Piotrovski F-Score with Python.mp4 (108.08 MB)
16 -Wiener Process.mp4 (246.02 MB)
17 -Wiener Process with Python.mp4 (57.13 MB)
18 -Geometric Brownian with Python.mp4 (113.35 MB)
19 -Geometric Brownian.mp4 (118.16 MB)
2 -Kurtosis with Python.mp4 (41.52 MB)
20 -Geometric Brownian with Python.mp4 (113.32 MB)
21 -Magic Formula.mp4 (81.17 MB)
22 -Magic Formula with Python.mp4 (70.09 MB)
23 -Ornstein-Uhlenbeck Process.mp4 (60.01 MB)
24 -Ornstein-Uhlenbeck Process with Python.mp4 (24.4 MB)
25 -Vasicek Model.mp4 (40.56 MB)
26 -Vasicek Model with Python.mp4 (120.1 MB)
3 -Markowitz Model.mp4 (92.32 MB)
4 -Markowitz Model with Python.mp4 (211.23 MB)
5 -Value At Risk Historical Simulation.mp4 (76.61 MB)
6 -Value At Risk Monte Carlo.mp4 (79.63 MB)
7 -Value At Risk Variance Covariance Parametric Method.mp4 (47.18 MB)
8 -Value At Risk with Python.mp4 (29 MB)
9 -Capital Asset Pricing Model Part 1.mp4 (345.81 MB)
1 -Sharpe Ratio.mp4 (33 MB)
2 -Sharpe Ratio with Python.mp4 (43.13 MB)
3 -Sortino Ratio.mp4 (40.12 MB)
4 -Sortino Ratio with Python.mp4 (13.3 MB)
5 -Calmar Ratio.mp4 (68.42 MB)
6 -Calmar Ratio with Python.mp4 (47.34 MB)
7 -Rebalancing.mp4 (58.19 MB)
8 -Rabalancing with Python.mp4 (43.14 MB)

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