| 1. Algorithmic Trading Section Introduction.mp4 | 14 MB | ||
| 1. Algorithmic Trading Section Introduction.srt | 3.6 KB | ||
| 1. Anaconda Environment Setup.mp4 | 180.8 MB | ||
| 1. Anaconda Environment Setup.srt | 19.7 KB | ||
| 1. Colab Notebooks.html | 307.2 B | ||
| 1. Financial Basics Section Introduction.mp4 | 29 MB | ||
| 1. Financial Basics Section Introduction.srt | 7.4 KB | ||
| 1. How to Code by Yourself (part 1).mp4 | 71.8 MB | ||
| 1. How to Code by Yourself (part 1).srt | 22.6 KB | ||
| 1. How to Succeed in this Course (Long Version).mp4 | 35.2 MB | ||
| 1. How to Succeed in this Course (Long Version).srt | 14.7 KB | ||
| 1. Introduction and Outline.mp4 | 46.8 MB | ||
| 1. Introduction and Outline.srt | 9.6 KB | ||
| 1. Portfolio Optimization Section Introduction.mp4 | 24.4 MB | ||
| 1. Portfolio Optimization Section Introduction.srt | 4.8 KB | ||
| 1. Reinforcement Learning Section Introduction.mp4 | 40.9 MB | ||
| 1. Reinforcement Learning Section Introduction.srt | 8.7 KB | ||
| 1. Statistical Factor Models (Beginner).mp4 | 63.4 MB | ||
| 1. Statistical Factor Models (Beginner).srt | 21.2 KB | ||
| 1. Time Series Analysis Section Introduction.mp4 | 31.8 MB | ||
| 1. Time Series Analysis Section Introduction.srt | 9.3 KB | ||
| 1. Trading APIs and Deploying Your Strategy in the Real World.mp4 | 31.9 MB | ||
| 1. Trading APIs and Deploying Your Strategy in the Real World.srt | 7.6 KB | ||
| 1. Trend-Following Strategy with Reinforcement Learning API.mp4 | 49.6 MB | ||
| 1. Trend-Following Strategy with Reinforcement Learning API.srt | 15.7 KB | ||
| 1. What is the Appendix.mp4 | 16.4 MB | ||
| 1. What is the Appendix.srt | 3.8 KB | ||
| 1. Why Sequence Models (pt 1).mp4 | 49.4 MB | ||
| 1. Why Sequence Models (pt 1).srt | 18.7 KB | ||
| 10. Adjusted Close (Code).mp4 | 20.9 MB | ||
| 10. Adjusted Close (Code).srt | 4.6 KB | ||
| 10. Epsilon-Greedy.mp4 | 41.6 MB | ||
| 10. Epsilon-Greedy.srt | 7.4 KB | ||
| 10. Maximum and Minimum Portfolio Return in Code.mp4 | 28 MB | ||
| 10. Maximum and Minimum Portfolio Return in Code.srt | 5.8 KB | ||
| 10. Simple Exponential Smoothing for Forecasting (Code).mp4 | 59.2 MB | ||
| 10. Simple Exponential Smoothing for Forecasting (Code).srt | 12.6 KB | ||
| 11. Back to Returns (Code).mp4 | 45.7 MB | ||
| 11. Back to Returns (Code).srt | 9.1 KB | ||
| 11. Holt's Linear Trend Model (Theory).mp4 | 33 MB | ||
| 11. Holt's Linear Trend Model (Theory).srt | 10.1 KB | ||
| 11. Mean-Variance Optimization.mp4 | 31.6 MB | ||
| 11. Mean-Variance Optimization.srt | 10 KB | ||
| 11. Q-Learning.mp4 | 66.9 MB | ||
| 11. Q-Learning.srt | 18 KB | ||
| 12. Holt's Linear Trend Model (Code).mp4 | 18.7 MB | ||
| 12. Holt's Linear Trend Model (Code).srt | 3.4 KB | ||
| 12. How to Learn Reinforcement Learning.mp4 | 40.4 MB | ||
| 12. How to Learn Reinforcement Learning.srt | 7.6 KB | ||
| 12. QQ-Plots.mp4 | 20.7 MB | ||
| 12. QQ-Plots.srt | 7.2 KB | ||
| 12. The Efficient Frontier.mp4 | 30.7 MB | ||
| 12. The Efficient Frontier.srt | 9.5 KB | ||
| 13. Holt-Winters (Theory).mp4 | 48.8 MB | ||
| 13. Holt-Winters (Theory).srt | 15 KB | ||
| 13. Mean-Variance Optimization And The Efficient Frontier in Code.mp4 | 53.3 MB | ||
| 13. Mean-Variance Optimization And The Efficient Frontier in Code.srt | 11.2 KB | ||
| 13. QQ-Plots (Code).mp4 | 35.3 MB | ||
| 13. QQ-Plots (Code).srt | 9.9 KB | ||
| 14. Global Minimum Variance (GMV) Portfolio.mp4 | 8.6 MB | ||
| 14. Global Minimum Variance (GMV) Portfolio.srt | 2.3 KB | ||
| 14. Holt-Winters (Code).mp4 | 52.5 MB | ||
| 14. Holt-Winters (Code).srt | 9.8 KB | ||
| 14. The t-Distribution.mp4 | 19.7 MB | ||
| 14. The t-Distribution.srt | 5.1 KB | ||
| 15. Autoregressive Models - AR(p).mp4 | 53.6 MB | ||
| 15. Autoregressive Models - AR(p).srt | 16.7 KB | ||
| 15. Global Minimum Variance (GMV) Portfolio in Code.mp4 | 13.6 MB | ||
| 15. Global Minimum Variance (GMV) Portfolio in Code.srt | 2.5 KB | ||
| 15. The t-Distribution (Code).mp4 | 50.7 MB | ||
| 15. The t-Distribution (Code).srt | 10.5 KB | ||
| 16. Moving Average Models - MA(q).mp4 | 10.9 MB | ||
| 16. Moving Average Models - MA(q).srt | 4.2 KB | ||
| 16. Sharpe Ratio.mp4 | 37.6 MB | ||
| 16. Sharpe Ratio.srt | 10 KB | ||
| 16. Skewness and Kurtosis.mp4 | 34.6 MB | ||
| 16. Skewness and Kurtosis.srt | 10.4 KB | ||
| 17. ARIMA.mp4 | 42.7 MB | ||
| 17. ARIMA.srt | 13.8 KB | ||
| 17. Confidence Intervals.mp4 | 38.8 MB | ||
| 17. Confidence Intervals.srt | 13.7 KB | ||
| 17. Maximum Sharpe Ratio in Code.mp4 | 43.7 MB | ||
| 17. Maximum Sharpe Ratio in Code.srt | 8.3 KB | ||
| 18. ARIMA in Code (pt 1).mp4 | 135.2 MB | ||
| 18. ARIMA in Code (pt 1).srt | 24.5 KB | ||
| 18. Confidence Intervals (Code).mp4 | 12.3 MB | ||
| 18. Confidence Intervals (Code).srt | 2.8 KB | ||
| 18. Portfolio with a Risk-Free Asset and Tangency Portfolio.mp4 | 37.8 MB | ||
| 18. Portfolio with a Risk-Free Asset and Tangency Portfolio.srt | 12.4 KB | ||
| 19. Risk-Free Asset and Tangency Portfolio in Code.mp4 | 13.6 MB | ||
| 19. Risk-Free Asset and Tangency Portfolio in Code.srt | 2.6 KB | ||
| 19. Stationarity.mp4 | 49.6 MB | ||
| 19. Stationarity.srt | 16.3 KB | ||
| 19. Statistical Testing.mp4 | 60.7 MB | ||
| 19. Statistical Testing.srt | 20.8 KB | ||
| 2. BONUS Lecture.mp4 | 37.8 MB | ||
| 2. BONUS Lecture.srt | 7.8 KB | ||
| 2. Efficient Market Hypothesis.mp4 | 54.8 MB | ||
| 2. Efficient Market Hypothesis.srt | 16.1 KB | ||
| 2. Elements of a Reinforcement Learning Problem.mp4 | 105.2 MB | ||
| 2. Elements of a Reinforcement Learning Problem.srt | 25.9 KB | ||
| 2. Getting Financial Data.mp4 | 41.9 MB | ||
| 2. Getting Financial Data.srt | 9.9 KB | ||
| 2. High Frequency Trading (HFT).mp4 | 22 MB | ||
| 2. High Frequency Trading (HFT).srt | 5.3 KB | ||
| 2. How to Code by Yourself (part 2).mp4 | 49.2 MB | ||
| 2. How to Code by Yourself (part 2).srt | 13.2 KB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 150.6 MB | ||
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt | 14.1 KB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 105.6 MB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 31.9 KB | ||
| 2. Statistical Factor Models (Intermediate).mp4 | 40.5 MB | ||
| 2. Statistical Factor Models (Intermediate).srt | 13.1 KB | ||
| 2. The S&P500.mp4 | 11.7 MB | ||
| 2. The S&P500.srt | 3.3 KB | ||
| 2. Trend-Following Strategy Revisited (Code).mp4 | 55.8 MB | ||
| 2. Trend-Following Strategy Revisited (Code).srt | 10.5 KB | ||
| 2. Trend-Following Strategy.mp4 | 55.9 MB | ||
| 2. Trend-Following Strategy.srt | 18 KB | ||
| 2. VIP Finance Enthusiasts, Beware of Marketers!.mp4 | 13.5 MB | ||
| 2. VIP Finance Enthusiasts, Beware of Marketers!.srt | 2.8 KB | ||
| 2. Where to get the code.mp4 | 44.2 MB | ||
| 2. Where to get the code.srt | 12.4 KB | ||
| 2. Why Sequence Models (pt 2).mp4 | 41.2 MB | ||
| 2. Why Sequence Models (pt 2).srt | 16 KB | ||
| 2.1 Github Link.html | 102.4 B | ||
| 20. Capital Asset Pricing Model (CAPM).mp4 | 52.2 MB | ||
| 20. Capital Asset Pricing Model (CAPM).srt | 16 KB | ||
| 20. Stationarity Code.mp4 | 64.5 MB | ||
| 20. Stationarity Code.srt | 10.8 KB | ||
| 20. Statistical Testing (Code).mp4 | 41.9 MB | ||
| 20. Statistical Testing (Code).srt | 9.1 KB | ||
| 21. ACF (Autocorrelation Function).mp4 | 37.3 MB | ||
| 21. ACF (Autocorrelation Function).srt | 13 KB | ||
| 21. Covariance and Correlation.mp4 | 32.9 MB | ||
| 21. Covariance and Correlation.srt | 10.8 KB | ||
| 21. Problems with Markowitz Portfolio Theory and Robust Estimation.mp4 | 48.1 MB | ||
| 21. Problems with Markowitz Portfolio Theory and Robust Estimation.srt | 12.2 KB | ||
| 22. Covariance and Correlation (Code).mp4 | 38.9 MB | ||
| 22. Covariance and Correlation (Code).srt | 7.1 KB | ||
| 22. PACF (Partial Autocorrelation Funtion).mp4 | 26.2 MB | ||
| 22. PACF (Partial Autocorrelation Funtion).srt | 8 KB | ||
| 22. Portfolio Optimization Section Conclusion.mp4 | 17.5 MB | ||
| 22. Portfolio Optimization Section Conclusion.srt | 2.9 KB | ||
| 23. ACF and PACF in Code (pt 1).mp4 | 42.3 MB | ||
| 23. ACF and PACF in Code (pt 1).srt | 9.3 KB | ||
| 23. Alpha and Beta.mp4 | 28.8 MB | ||
| 23. Alpha and Beta.srt | 9.3 KB | ||
| 24. ACF and PACF in Code (pt 2).mp4 | 35.4 MB | ||
| 24. ACF and PACF in Code (pt 2).srt | 8 KB | ||
| 24. Alpha and Beta (Code).mp4 | 45.8 MB | ||
| 24. Alpha and Beta (Code).srt | 10.4 KB | ||
| 25. Auto ARIMA and SARIMAX.mp4 | 40.6 MB | ||
| 25. Auto ARIMA and SARIMAX.srt | 12.3 KB | ||
| 25. Mixture of Gaussians.mp4 | 29.3 MB | ||
| 25. Mixture of Gaussians.srt | 9.2 KB | ||
| 26. Mixture of Gaussians (Code).mp4 | 33.5 MB | ||
| 26. Mixture of Gaussians (Code).srt | 8.2 KB | ||
| 26. Model Selection, AIC and BIC.mp4 | 47.3 MB | ||
| 26. Model Selection, AIC and BIC.srt | 13.5 KB | ||
| 27. ARIMA in Code (pt 2).mp4 | 109.8 MB | ||
| 27. ARIMA in Code (pt 2).srt | 16.7 KB | ||
| 27. Volatility Clustering.mp4 | 18.5 MB | ||
| 27. Volatility Clustering.srt | 3.9 KB | ||
| 28. ARIMA in Code (pt 3).mp4 | 112 MB | ||
| 28. ARIMA in Code (pt 3).srt | 18.1 KB | ||
| 28. Price Simulation.mp4 | 12 MB | ||
| 28. Price Simulation.srt | 4 KB | ||
| 29. ACF and PACF for Stock Returns.mp4 | 48.9 MB | ||
| 29. ACF and PACF for Stock Returns.srt | 8.4 KB | ||
| 29. Price Simulation (Code).mp4 | 12.2 MB | ||
| 29. Price Simulation (Code).srt | 3.1 KB | ||
| 3. Getting Financial Data (Code).mp4 | 62.6 MB | ||
| 3. Getting Financial Data (Code).srt | 9.5 KB | ||
| 3. HMM Parameters.mp4 | 37.4 MB | ||
| 3. HMM Parameters.srt | 12.4 KB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 | 79.6 MB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt | 16.8 KB | ||
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 | 69.4 MB | ||
| 3. Proof that using Jupyter Notebook is the same as not using it.srt | 14 KB | ||
| 3. Q-Learning in an Algorithmic Trading Context.mp4 | 29.7 MB | ||
| 3. Q-Learning in an Algorithmic Trading Context.srt | 9.3 KB | ||
| 3. Random Walk Hypothesis.mp4 | 71.5 MB | ||
| 3. Random Walk Hypothesis.srt | 19.1 KB | ||
| 3. Scope of the course.mp4 | 24.4 MB | ||
| 3. Scope of the course.srt | 5.2 KB | ||
| 3. States, Actions, Rewards, Policies.mp4 | 44.2 MB | ||
| 3. States, Actions, Rewards, Policies.srt | 11.7 KB | ||
| 3. Statistical Factor Models (Advanced).mp4 | 73.1 MB | ||
| 3. Statistical Factor Models (Advanced).srt | 25.4 KB | ||
| 3. Trend-Following Strategy in Code (pt 1).mp4 | 63.8 MB | ||
| 3. Trend-Following Strategy in Code (pt 1).srt | 9.4 KB | ||
| 3. What is Risk.mp4 | 30.5 MB | ||
| 3. What is Risk.srt | 9.4 KB | ||
| 30. Financial Basics Section Summary.mp4 | 10 MB | ||
| 30. Financial Basics Section Summary.srt | 3.1 KB | ||
| 30. Forecasting.mp4 | 39.3 MB | ||
| 30. Forecasting.srt | 12.1 KB | ||
| 31. Suggestion Box.mp4 | 16.1 MB | ||
| 31. Suggestion Box.srt | 4.7 KB | ||
| 31. Time Series Analysis Section Conclusion.mp4 | 18.1 MB | ||
| 31. Time Series Analysis Section Conclusion.srt | 5.3 KB | ||
| 4. HMM Tasks and the Viterbi Algorithm.mp4 | 65 MB | ||
| 4. HMM Tasks and the Viterbi Algorithm.srt | 19.2 KB | ||
| 4. How to Practice.mp4 | 24.5 MB | ||
| 4. How to Practice.srt | 5.2 KB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 | 108.2 MB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt | 23.5 KB | ||
| 4. Markov Decision Processes (MDPs).mp4 | 50.7 MB | ||
| 4. Markov Decision Processes (MDPs).srt | 12.7 KB | ||
| 4. Representing States.mp4 | 32.6 MB | ||
| 4. Representing States.srt | 9.6 KB | ||
| 4. Statistical Factor Models (Code).mp4 | 101 MB | ||
| 4. Statistical Factor Models (Code).srt | 18.6 KB | ||
| 4. The Naive Forecast.mp4 | 30.9 MB | ||
| 4. The Naive Forecast.srt | 9.2 KB | ||
| 4. Trend-Following Strategy in Code (pt 2).mp4 | 69.1 MB | ||
| 4. Trend-Following Strategy in Code (pt 2).srt | 12 KB | ||
| 4. Understanding Financial Data.mp4 | 28.5 MB | ||
| 4. Understanding Financial Data.srt | 6.6 KB | ||
| 4. Why Diversify.mp4 | 33.2 MB | ||
| 4. Why Diversify.srt | 10.5 KB | ||
| 5. Describing a Portfolio (pt 1).mp4 | 36.5 MB | ||
| 5. Describing a Portfolio (pt 1).srt | 12.3 KB | ||
| 5. HMM for Modeling Volatility Clustering in Code.mp4 | 102 MB | ||
| 5. HMM for Modeling Volatility Clustering in Code.srt | 24.1 KB | ||
| 5. Machine Learning-Based Trading Strategy.mp4 | 33.3 MB | ||
| 5. Machine Learning-Based Trading Strategy.srt | 10.3 KB | ||
| 5. Q-Learning for Algorithmic Trading in Code.mp4 | 103 MB | ||
| 5. Q-Learning for Algorithmic Trading in Code.srt | 18.5 KB | ||
| 5. Simple Moving Average (Theory).mp4 | 19.9 MB | ||
| 5. Simple Moving Average (Theory).srt | 5.7 KB | ||
| 5. The Return.mp4 | 23.5 MB | ||
| 5. The Return.srt | 6.3 KB | ||
| 5. Understanding Financial Data (Code).mp4 | 75.5 MB | ||
| 5. Understanding Financial Data (Code).srt | 15.1 KB | ||
| 5. Warmup (Optional).mp4 | 23.2 MB | ||
| 5. Warmup (Optional).srt | 6.1 KB | ||
| 6. Dealing with Missing Data.mp4 | 28.2 MB | ||
| 6. Dealing with Missing Data.srt | 7.8 KB | ||
| 6. Describing a Portfolio (pt 2).mp4 | 22.8 MB | ||
| 6. Describing a Portfolio (pt 2).srt | 7.9 KB | ||
| 6. Machine Learning-Based Trading Strategy in Code.mp4 | 69.5 MB | ||
| 6. Machine Learning-Based Trading Strategy in Code.srt | 10.4 KB | ||
| 6. Simple Moving Average (Code).mp4 | 55.7 MB | ||
| 6. Simple Moving Average (Code).srt | 9.5 KB | ||
| 6. Value Functions and the Bellman Equation.mp4 | 47.9 MB | ||
| 6. Value Functions and the Bellman Equation.srt | 12.6 KB | ||
| 7. Classification-Based Trading Strategy in Code.mp4 | 25.1 MB | ||
| 7. Classification-Based Trading Strategy in Code.srt | 4.2 KB | ||
| 7. Dealing with Missing Data (Code).mp4 | 37.7 MB | ||
| 7. Dealing with Missing Data (Code).srt | 8.9 KB | ||
| 7. Exponentially-Weighted Moving Average (Theory).mp4 | 37.8 MB | ||
| 7. Exponentially-Weighted Moving Average (Theory).srt | 14.6 KB | ||
| 7. Visualizing Random Portfolios and Monte Carlo Simulation (pt 1).mp4 | 73.4 MB | ||
| 7. Visualizing Random Portfolios and Monte Carlo Simulation (pt 1).srt | 16.2 KB | ||
| 7. What does it mean to “learn”.mp4 | 31.8 MB | ||
| 7. What does it mean to “learn”.srt | 8.9 KB | ||
| 8. Exponentially-Weighted Moving Average (Code).mp4 | 54.3 MB | ||
| 8. Exponentially-Weighted Moving Average (Code).srt | 14.7 KB | ||
| 8. Returns.mp4 | 29.2 MB | ||
| 8. Returns.srt | 11.7 KB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 | 42.8 MB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt | 12.4 KB | ||
| 8. Using a Random Forest Classifier for Machine Learning-Based Trading.mp4 | 33 MB | ||
| 8. Using a Random Forest Classifier for Machine Learning-Based Trading.srt | 5.5 KB | ||
| 8. Visualizing Random Portfolios and Monte Carlo Simulation (pt 2).mp4 | 88.7 MB | ||
| 8. Visualizing Random Portfolios and Monte Carlo Simulation (pt 2).srt | 18.4 KB | ||
| 9. Adjusted Close, Stock Splits, and Dividends.mp4 | 47.4 MB | ||
| 9. Adjusted Close, Stock Splits, and Dividends.srt | 16.2 KB | ||
| 9. Algorithmic Trading Section Summary.mp4 | 29.9 MB | ||
| 9. Algorithmic Trading Section Summary.srt | 7.6 KB | ||
| 9. Maximum and Minimum Portfolio Return.mp4 | 32.7 MB | ||
| 9. Maximum and Minimum Portfolio Return.srt | 12.5 KB | ||
| 9. Simple Exponential Smoothing for Forecasting (Theory).mp4 | 36.3 MB | ||
| 9. Simple Exponential Smoothing for Forecasting (Theory).srt | 13.9 KB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 | 57.2 MB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt | 14.8 KB | ||
| [Tutorialsplanet.NET].url | 102.4 B | ||
| ▲ 281 total files | |||
Udemy - Financial Engineering and Artificial Intelligence in Python
Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?
Today, you can stop imagining, and start doing.
This course will teach you the core fundamentals of financial engineering, with a machine learning twist.
We will cover must-know topics in financial engineering, such as:
Exploratory data analysis, significance testing, correlations, alpha and beta
Time series analysis, simple moving average, exponentially-weighted moving average
Holt-Winters exponential smoothing model
ARIMA and SARIMA
For more Udemy Courses: https://tutorialsplanet.net
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