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datacamp.txt
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datacamp.txt
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#### Structure of a DataCamp course
-----------------
[DataCamp github repo](https://github.com/Data-Camp/datacamp)
[How to create a DataCamp course](https://www.datacamp.com/create/how)
<br/> <br/>
- [ ] 5 chapters at 50 minutes/chapter = 4 hours, 10 minutes total
- [ ] Each 50-minute chapter contains
4 videos at 3.5 minutes/video = 14 minutes
12 interactive exercises (multiple choice + coding) at 3 minutes/exercise = 36 minutes
- [ ] Each chapter begins with a video and videos are separated by 3 interactive exercises
<br/> <br/>
General course concept:
- each chapter should contain a collection of vignettes,
- each vignette should explain a particular topic relating to investment management practice,
- the topics should be illustrated using examples of numerical techniques and investment strategies using historical market data,
- the vignettes should contain complete R code allowing users to reproduce all the calculations from start to finish, including data loading, formatting, model building, and visualization,
- all R code samples should rely on fast, vectorized code,
- the course should teach users how to use popular R packages, such as xts, PerformanceAnalytics, PortfolioAnalytics,
<br/> <br/>
Potential course themes:
- Machine Learning for Systematic Investing
- Investment Portfolio Optimization with R
<br/> <br/>
#### explore and adapt:
- http://www.inside-r.org/pretty-r
- https://developers.google.com/chart/
<br/> <br/>
#### Datacamp course *Systematic Investment Strategies*
=================
<br/> <br/>
#### Chapter 1: Asset pricing
-----------------
Estimating risk measures: dispersion (volatility, MAD), skewness, tail risk (VaR, CVaR),
Calculating confidence intervals using bootstrap
Estimating risk-return performance ratios: Sharpe, Sortino, Calmar, package PerformanceAnalytics,
CAPM model: regressions of asset returns, alpha, beta,
Deriving the Capital Market Line (CML) and Security Market Line (SML)
Beta-adjusted risk-return measures: Treynor ratio, Jensen's alpha,
Fundamental factor models: Fama-French, Barra,
Statistical factor models: principal component factors
Estimating covariance and correlation matrices
Applying factor model regularization (shrinkage)
<br/> <br/>
#### Chapter 2: Forecasting returns and volatility
-----------------
Models of asset returns: t-distribution, Pareto distribution
Performing rolling (running) risk and regression calculations over a sliding interval
Calculating rolling aggregations over a sliding interval
Forecasting returns and volatility using stochastic volatility models: CEV, GARCH, Heston,
Calibrating the forecasting model parameters to maximize forecasting performance
Forecasting returns using value, size, and momentum factors
Forecasting returns using the momentum factor and volatility
Measuring forecastability using the Hurst exponent
Performing rolling PCA analysis over a sliding interval
Constructing highly forecastable portfolios
<br/> <br/>
#### Chapter 3: Backtesting techniques (cross-validation)
-----------------
Performing cross-validation (backtesting) of forecasting models
Creating heatmaps of model parameters using expand.grid
Performing grid search of model parameters on heatmap
Using random model parameters to determine worst case losses
Using data resampling to determine distribution of possible future model performance
Performing regularization of model parameters to control overfitting
Backtesting with quantile optimization
Controlling data snooping (leaking)
Controlling data mining (significance inflation, multiple testing) and the false discovery rate using the Bonferroni method and Sidak correction
Performing metaparameter selection: lookback window, forecast horizon, and rebalancing frequency
Controlling metaparameter data mining to decrease false-discovery rate
<br/> <br/>
#### Chapter 4: Portfolio optimization
-----------------
Estimating covariance and correlation matrices
Calculating correlation coefficient uncertainty using bootstrap
Performing correlation matrix shrinkage
Optimizing portfolios under different correlation assumptions
Calculating optimal portfolio uncertainty using bootstrap
Performing constrained portfolio optimization with weight shrinkage
Constructing efficient frontier portfolios
Proporties of the Market Portfolio under the CAPM model
Optimizing portfolios under different asset constraints and objective functions
<br/> <br/>
#### Chapter 5: Active investment strategies
-----------------
Simulating the terminal distribution of stock prices
Comparing performance of equal-weighted and cap-weighted stock indexes
Performing single-period portfolio selection
Simulating passive asset allocation strategies: all weather, dollar parity, risk parity, portfolio rebalancing, CPPI, minimum variance, low beta,
Simulating dynamic investment and consumption strategies
Simulating smart beta and factor investing strategies
Benchmarking portfolio management skill using random portfolios
Performing rolling portfolio optimization over a sliding interval
Measuring out-of-sample performance of optimized portfolios
Simulating active investment strategies: beta rotation, momentum, tactical asset allocation,
Performing performance attribution of investment strategies
Measuring manager market timing skill
Benchmarking portfolio management skill using random investment choices
Determining stop-loss policy parameters using sequential hypothesis testing
<br/> <br/>