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2022/05/04阅读：25主题：自定义主题1

# CQF Learning Pathway(二)

## CQF与AI

• 模型构建
• 因子挖掘及生成

Module 4和Module 5的人工智能这两个模块对计算机技术以及编程的要求就比较高了，对于有程序员背景，或工科、理科背景的同学，相对会更能接受些，不会有太吃力的感觉，如果没有接触过计算机技术、编程的同学，CQF课程前导课的Python就显得尤为重要，需要扎实学好，可以多看几遍。

## Module 4

### Data Science and Machine Learning I

This module will introduce the latest techniques used for machine learning in finance. Starting with a comprehensive overview of the topic, the essential mathematical tools followed by a deep dive into the topic of supervised learning , including regression methods, K-Nearest neighbors, Support Vector Machines, Ensemble methods and many more.

• Introduction to Machine Learning: What is Mathematical Modeling, Classic Tools, Principal Techniques, Principal techniques for Machine Learning, Supervised & Unsupervised Learning, Reinforcement Learning.

• Maths Toolbox: Maximum Likelihood Estimation, Cost/Loss Function, Gradient Descent, Stochastic Gradient Descent, Bias & Variance, Lagrange Multipliers, Principal Component Analysis.

• Supervised Learning I: Linear Regression, Penalized Regression: lasso, Ridge & Elastic Net, Logistic, Softmax Regression, Decision Trees, Ensemble Models -Bagging & Boosting.

• Logistic Regression, Support Vector Machines, Cluster Analysis: BIRCH, hierarchical, K-mean, Expectation maximization, DBSCAN, OPTICS and mean shift Kalman filtering.

• Machine Learning Lab: Supervised Learning Implementation, Python - Scikit Learn; Support Vector Machines.

• Trevor Hastie et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009 (2nd edition), Springer
• Martin Odersky et al., Programming in Scala: Updated for Scala 2.12, 2016 (3rd edition), Artima Press
• Macros Lopez de Prado, Advances in Financial Machine Learning, 2018, Wiley
• Christopher Bishop, Pattern Recognition and Machine Learning, 2006, Springer
• Max Kuhn and Kjell Johnson, Applied Predictive Analytics, 2013, Springer

## Module 5

### Data Science and Machine Learning II

In this module we will explore several more methods used for machine learning in finance. Starting with unsupervised learning, Deep learning and Neural networks, we will move into natural language processing and reinforcement learning. You will study the theoretical framework, analyze practical case studies exploring how these techniques are used within finance.

• Machine Learning & Predictive Analytics: Regression, regression in high dimensions, support vector machines, dimension reduction: principal component analysis (PCA), kernel PCA, non-negative matrix decomposition.

• Unsupervised Learning I: K Means Clustering; Self Organizing Maps; Strengths & Weakness of HAC and SOM.

• Unsupervised Learning II: t-SNE; UMAP; Autoencoders.

• Deep Learning & Neural Networks: Structural Building Blocks; Forward & Back Propagation; Multi Output Perceptron; Building Neural Networks.

• Neural Network Architectures: Feedforward, Recurrent, Long Short Term Memory, Convolutional, Generative Adversarial.

• Natural Language Processing: Pre-processing; Word Vectorizations, Word2Vec; Deep Learning & NLP Tools.

• Reinforcement Learning: Multi-armed Bandit; Exploration Strategies; Risk Sensitivity.

• AI Based Algo Trading Strategies Using Python: Financial data analysis with Python and pandas, application of classification algorithms, vectorized backtesting, risk analysis for algo trading strategies.

• William McKinney, Python for Data Analysis, 2013 O’Reilly
• Foster Provost and Tom Fawcett, Data Science for Business, 2013, O’Reilly
• Gareth James et al., An Introduction to Statistical Learning, 2013, Springer
• Yves Hilipisch, Python for Finance, 2014, (2nd edition), O’Reilly

## Module 6

### Fixed Income and Credit

In this module we will review the multitude of interest models used within the industry, focusing on the implementation and limitations of each model. You will learn about credit and how credit risk models are used in quant finance, including structural, reduced form as well as copula models.

• Fixed-Income Products: Fixed and floating rates, bonds, swaps, caps and floors, FRAs and other delta products.

• Yield, Duration and Convexity: Definitions, use and limitations, bootstrapping to build up the yield curve from bonds and swaps.

• Curve Stripping: reference rates & basis spreads, OIS discounting and dual-curve stripping, cross-currency basis curve, cost of funds and the credit crisis.

• Interpolation Methods: piece wise constant forwards, piece wise linear, cubic splines, smart quadratics, quartics, monontone convex splines.

• Current Market Practices: Money vs. scrip, holiday calendars, business day rules, and schedule generation, day count fractions.

• Stochastic Interest Rate Models, one and two factors: Transferring ideas from the equity world, differences from the equity world, popular models, data analysis.

• Calibration: Fitting the yield curve in simple models, use and abuse.

• Heath, Jarrow and Morton Model: Modeling the yield curve. Determining risk factors of yield curve evolution and optimal volatility structure by PCA. Pricing interest rate derivatives by Monte Carlo.

• The Libor Market Model: (Also Brace, Gatarek and Musiela). Calibrating the reference volatility structure by fitting to caplet or swaption data.

• Advanced Monte Carlo Techniques: Low-discrepancy series for numerical quadrature. Use for option pricing, speculation and scenario analysis.

• SABR Arbitrage Free SABR Model: Managing volatility risks, smiles, local volatility models, reduction to the effective forward equation, arbitrage free boundary conditions.

SABR 无套利 SABR 模型：管理波动率风险，微笑，局部波动率模型，简化为有效的远期方程，无套利边界条件。

• Credit Risk and Credit Derivatives: Products and uses, credit derivatives, qualitative description of instruments, applications.

• Structural and Intensity models used for credit risk.

• CDS Pricing, Market Approach: Implied default probability, recovery rate, default time modeling, building a spreadsheet on CDS pricing.

CDS 定价，市场方法：隐含违约概率，回收率，违约时间建模，建立 CDS 定价的电子表格。

• Synthetic CDO Pricing: The default probability distribution, default correlation, tranche sensitivity, pricing spread.

• Implementation: CDO/copula modeling using spreadsheets.

• Correlation and State Dependence: correlation, linear correlation, analyzing correlation, sensitivity and state dependence.

• Risk of Default: The hazard rate, implied hazard rate, stochastic hazard rate and credit rating, capital structure arbitrage.

• Copulas: Pricing basket credit instruments by simulation.

• Statistical Methods in Estimating Default Probability: ratings migration and transition matrices and Markov processes.

• X-Valuation Adjustment: Background, default probability and exposure, collateral, CVA, regulatory requirements, DVA and FVA, Counterparty Lab in excel, credit default swaps, bootstrapping CDS spreads, interest rate swaps.

X-Valuation 调整：背景，违约概率和风险敞口，抵押品，CVA，监管要求，DVA 和 FVA，对手 Lab in excel，信用违约互换，自举 CDS 价差，利率互换。

• Jon Gregory, The xVA Challenge: Counterparty Credit Risk, Funding, Collateral and Capital, third edition, 2015, Wiley (Chapters 4-7, 10, 12)
• Paul Wilmott, Paul Wilmott Introduces Quantitative Finance, 2007, Wiley (Chapters 14-19)
• Paul Wilmott, Paul Wilmott on Quantitative Finance, second edition, 2006, Wiley (Chapters 30-33, 36, 37, 39-42)
• Peter Jaeckel, Monte Carlo Methods in Finance, 2002, Wiley (Chapters 1-14)

• Avinash K. Dixit and Robert S. Pindyck, Investment Under Uncertainty, 1994, Princeton University Press
• Darrell Duffie & Kenneth J. Singleton, Credit Risk: Pricing, Measurement, and Management, 2003, Princeton University Press
• Gunter Loffler and Peter Posche, Credit Risk Modeling using Excel and VBA, 2007, Wiley
• George Chacko et al., Credit Derivatives: A Primer on Credit Risk, Modeling, and Instruments, 2006, Wharton School Publishing (Chapters 3, 5)
• Philipp J. Schoenbucher, Credit Derivatives Pricing Models: Model, Pricing and Implementation, 2003, Wiley (Chapters 2, 4, 5)
• Antulio N. Bomfim, Understanding Credit Derivatives and Related Instruments, 2004, Academic Press (Chapters 15, 16, 17)
• Nassim Taleb, Dynamic Hedging, 1996, Wiley
• John C. Hull, Options, Futures and Other Derivatives, fifth edition, 2002, Prentice-Hall

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