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E-Textbook |
Interactive Complementary Learning Resources |
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Theory
2,068 pages
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Case studies
19 cases
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Code
68,537 lines
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Animations
198 clips
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Slides
2,939 slides
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Proofs
1,861 proofs
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Exercises
200 exercises
An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles
Applications to practice beyond the Theory
Your tool to learn by real-world examples
Python coding language, no installation
Your environment to learn by doing
Simulations from data using code
Your device to learn by visualization
Summary of all the learnings
Your source to learn by key items
Proofs to support the analytical results
Your guide to learn by induction
Exercises pen & paper and codes
Your test to verify your knowledge
An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles
Applications to practice beyond the Theory
Your tool to learn by real-world examples
Python coding language, no installation
Your environment to learn by doing
Simulations from data using code
Your device to learn by visualization
Summary of all the learnings
Your source to learn by key items
Proofs to support the analytical results
Your guide to learn by induction
Exercises pen & paper and codes
Your test to verify your knowledge
ARPM Lab - Overview
The Lab is a multi-channel e-textbook, specifically designed to be the complete study resource for the ARPM curriculum.
The curriculum is presented in unified, rigorous, concise mathematical notation and covers:
- Advanced Data Science: Mathematical Statistics for Finance, Linear Mean-Covariance Statistics, Probabilistic Machine Learning, Time Series and Sequential Decisions;
- Quantitative Finance: Financial Engineering, Portfolio and Enterprise Risk Management, Portfolio Construction and Trading.
Theory
An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles
2,068 pages of explanations with formulas.
Case studies
Applications to practice beyond the Theory
Your tool to learn by real-world examples
19 large scale analytics with real data.
Code
Python coding language, no installation
Your environment to learn by doing
68,537 lines of Python code.
Animations
Simulations from data using code
Your device to learn by visualization
198 visualizations of complex models in motion.
Slides
Summary of all the learnings
Your source to learn by key items
2,939 multimedia summaries of all the materials.
Proofs
Proofs to support the analytical results
Your guide to learn by induction
1,861 analytical derivations to support the Theory.
Exercises
Exercises pen & paper and codes
Your test to verify your knowledge
200 analytical exercises to verify the Theory.
