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| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| 1. Algorithmic Trading Section Introduction.mp4 |
14.03Мб |
| 1. Algorithmic Trading Section Introduction.srt |
3.60Кб |
| 1. Anaconda Environment Setup.mp4 |
180.77Мб |
| 1. Anaconda Environment Setup.srt |
19.70Кб |
| 1. Colab Notebooks.html |
256б |
| 1. Financial Basics Section Introduction.mp4 |
28.96Мб |
| 1. Financial Basics Section Introduction.srt |
7.41Кб |
| 1. How to Code by Yourself (part 1).mp4 |
71.84Мб |
| 1. How to Code by Yourself (part 1).srt |
22.65Кб |
| 1. How to Succeed in this Course (Long Version).mp4 |
35.21Мб |
| 1. How to Succeed in this Course (Long Version).srt |
14.71Кб |
| 1. Introduction and Outline.mp4 |
46.84Мб |
| 1. Introduction and Outline.srt |
9.58Кб |
| 1. Portfolio Optimization Section Introduction.mp4 |
24.36Мб |
| 1. Portfolio Optimization Section Introduction.srt |
4.83Кб |
| 1. Reinforcement Learning Section Introduction.mp4 |
40.88Мб |
| 1. Reinforcement Learning Section Introduction.srt |
8.67Кб |
| 1. Statistical Factor Models (Beginner).mp4 |
63.37Мб |
| 1. Statistical Factor Models (Beginner).srt |
21.18Кб |
| 1. Time Series Analysis Section Introduction.mp4 |
31.84Мб |
| 1. Time Series Analysis Section Introduction.srt |
9.33Кб |
| 1. Trading APIs and Deploying Your Strategy in the Real World.mp4 |
31.89Мб |
| 1. Trading APIs and Deploying Your Strategy in the Real World.srt |
7.62Кб |
| 1. Trend-Following Strategy with Reinforcement Learning API.mp4 |
49.61Мб |
| 1. Trend-Following Strategy with Reinforcement Learning API.srt |
15.72Кб |
| 1. What is the Appendix.mp4 |
16.38Мб |
| 1. What is the Appendix.srt |
3.77Кб |
| 1. Why Sequence Models (pt 1).mp4 |
49.40Мб |
| 1. Why Sequence Models (pt 1).srt |
18.72Кб |
| 10. Adjusted Close (Code).mp4 |
20.95Мб |
| 10. Adjusted Close (Code).srt |
4.59Кб |
| 10. Epsilon-Greedy.mp4 |
41.63Мб |
| 10. Epsilon-Greedy.srt |
7.44Кб |
| 10. Maximum and Minimum Portfolio Return in Code.mp4 |
28.00Мб |
| 10. Maximum and Minimum Portfolio Return in Code.srt |
5.80Кб |
| 10. Simple Exponential Smoothing for Forecasting (Code).mp4 |
59.17Мб |
| 10. Simple Exponential Smoothing for Forecasting (Code).srt |
12.63Кб |
| 11. Back to Returns (Code).mp4 |
45.65Мб |
| 11. Back to Returns (Code).srt |
9.07Кб |
| 11. Holt's Linear Trend Model (Theory).mp4 |
32.96Мб |
| 11. Holt's Linear Trend Model (Theory).srt |
10.08Кб |
| 11. Mean-Variance Optimization.mp4 |
31.62Мб |
| 11. Mean-Variance Optimization.srt |
10.01Кб |
| 11. Q-Learning.mp4 |
66.91Мб |
| 11. Q-Learning.srt |
18.04Кб |
| 12. Holt's Linear Trend Model (Code).mp4 |
18.66Мб |
| 12. Holt's Linear Trend Model (Code).srt |
3.45Кб |
| 12. How to Learn Reinforcement Learning.mp4 |
40.39Мб |
| 12. How to Learn Reinforcement Learning.srt |
7.56Кб |
| 12. QQ-Plots.mp4 |
20.70Мб |
| 12. QQ-Plots.srt |
7.19Кб |
| 12. The Efficient Frontier.mp4 |
30.70Мб |
| 12. The Efficient Frontier.srt |
9.54Кб |
| 13. Holt-Winters (Theory).mp4 |
48.84Мб |
| 13. Holt-Winters (Theory).srt |
15.00Кб |
| 13. Mean-Variance Optimization And The Efficient Frontier in Code.mp4 |
53.29Мб |
| 13. Mean-Variance Optimization And The Efficient Frontier in Code.srt |
11.23Кб |
| 13. QQ-Plots (Code).mp4 |
35.34Мб |
| 13. QQ-Plots (Code).srt |
9.94Кб |
| 14. Global Minimum Variance (GMV) Portfolio.mp4 |
8.58Мб |
| 14. Global Minimum Variance (GMV) Portfolio.srt |
2.35Кб |
| 14. Holt-Winters (Code).mp4 |
52.48Мб |
| 14. Holt-Winters (Code).srt |
9.82Кб |
| 14. The t-Distribution.mp4 |
19.71Мб |
| 14. The t-Distribution.srt |
5.07Кб |
| 15. Autoregressive Models - AR(p).mp4 |
53.63Мб |
| 15. Autoregressive Models - AR(p).srt |
16.67Кб |
| 15. Global Minimum Variance (GMV) Portfolio in Code.mp4 |
13.64Мб |
| 15. Global Minimum Variance (GMV) Portfolio in Code.srt |
2.47Кб |
| 15. The t-Distribution (Code).mp4 |
50.74Мб |
| 15. The t-Distribution (Code).srt |
10.46Кб |
| 16. Moving Average Models - MA(q).mp4 |
10.93Мб |
| 16. Moving Average Models - MA(q).srt |
4.16Кб |
| 16. Sharpe Ratio.mp4 |
37.59Мб |
| 16. Sharpe Ratio.srt |
9.97Кб |
| 16. Skewness and Kurtosis.mp4 |
34.64Мб |
| 16. Skewness and Kurtosis.srt |
10.36Кб |
| 17. ARIMA.mp4 |
42.69Мб |
| 17. ARIMA.srt |
13.80Кб |
| 17. Confidence Intervals.mp4 |
38.76Мб |
| 17. Confidence Intervals.srt |
13.70Кб |
| 17. Maximum Sharpe Ratio in Code.mp4 |
43.71Мб |
| 17. Maximum Sharpe Ratio in Code.srt |
8.26Кб |
| 18. ARIMA in Code (pt 1).mp4 |
135.23Мб |
| 18. ARIMA in Code (pt 1).srt |
24.49Кб |
| 18. Confidence Intervals (Code).mp4 |
12.32Мб |
| 18. Confidence Intervals (Code).srt |
2.82Кб |
| 18. Portfolio with a Risk-Free Asset and Tangency Portfolio.mp4 |
37.84Мб |
| 18. Portfolio with a Risk-Free Asset and Tangency Portfolio.srt |
12.40Кб |
| 19. Risk-Free Asset and Tangency Portfolio in Code.mp4 |
13.58Мб |
| 19. Risk-Free Asset and Tangency Portfolio in Code.srt |
2.57Кб |
| 19. Stationarity.mp4 |
49.59Мб |
| 19. Stationarity.srt |
16.29Кб |
| 19. Statistical Testing.mp4 |
60.71Мб |
| 19. Statistical Testing.srt |
20.82Кб |
| 2.1 Github Link.html |
116б |
| 2. BONUS Lecture.mp4 |
37.79Мб |
| 2. BONUS Lecture.srt |
7.83Кб |
| 2. Efficient Market Hypothesis.mp4 |
54.80Мб |
| 2. Efficient Market Hypothesis.srt |
16.08Кб |
| 2. Elements of a Reinforcement Learning Problem.mp4 |
105.15Мб |
| 2. Elements of a Reinforcement Learning Problem.srt |
25.92Кб |
| 2. Getting Financial Data.mp4 |
41.85Мб |
| 2. Getting Financial Data.srt |
9.95Кб |
| 2. High Frequency Trading (HFT).mp4 |
22.04Мб |
| 2. High Frequency Trading (HFT).srt |
5.27Кб |
| 2. How to Code by Yourself (part 2).mp4 |
49.16Мб |
| 2. How to Code by Yourself (part 2).srt |
13.18Кб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
150.56Мб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
14.15Кб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
105.57Мб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
31.94Кб |
| 2. Statistical Factor Models (Intermediate).mp4 |
40.50Мб |
| 2. Statistical Factor Models (Intermediate).srt |
13.09Кб |
| 2. The S&P500.mp4 |
11.70Мб |
| 2. The S&P500.srt |
3.34Кб |
| 2. Trend-Following Strategy.mp4 |
55.86Мб |
| 2. Trend-Following Strategy.srt |
17.95Кб |
| 2. Trend-Following Strategy Revisited (Code).mp4 |
55.76Мб |
| 2. Trend-Following Strategy Revisited (Code).srt |
10.53Кб |
| 2. VIP Finance Enthusiasts, Beware of Marketers!.mp4 |
13.51Мб |
| 2. VIP Finance Enthusiasts, Beware of Marketers!.srt |
2.81Кб |
| 2. Where to get the code.mp4 |
44.23Мб |
| 2. Where to get the code.srt |
12.37Кб |
| 2. Why Sequence Models (pt 2).mp4 |
41.20Мб |
| 2. Why Sequence Models (pt 2).srt |
16.04Кб |
| 20. Capital Asset Pricing Model (CAPM).mp4 |
52.23Мб |
| 20. Capital Asset Pricing Model (CAPM).srt |
16.02Кб |
| 20. Stationarity Code.mp4 |
64.55Мб |
| 20. Stationarity Code.srt |
10.76Кб |
| 20. Statistical Testing (Code).mp4 |
41.89Мб |
| 20. Statistical Testing (Code).srt |
9.06Кб |
| 21. ACF (Autocorrelation Function).mp4 |
37.28Мб |
| 21. ACF (Autocorrelation Function).srt |
12.98Кб |
| 21. Covariance and Correlation.mp4 |
32.87Мб |
| 21. Covariance and Correlation.srt |
10.81Кб |
| 21. Problems with Markowitz Portfolio Theory and Robust Estimation.mp4 |
48.06Мб |
| 21. Problems with Markowitz Portfolio Theory and Robust Estimation.srt |
12.18Кб |
| 22. Covariance and Correlation (Code).mp4 |
38.93Мб |
| 22. Covariance and Correlation (Code).srt |
7.14Кб |
| 22. PACF (Partial Autocorrelation Funtion).mp4 |
26.18Мб |
| 22. PACF (Partial Autocorrelation Funtion).srt |
7.97Кб |
| 22. Portfolio Optimization Section Conclusion.mp4 |
17.49Мб |
| 22. Portfolio Optimization Section Conclusion.srt |
2.89Кб |
| 23. ACF and PACF in Code (pt 1).mp4 |
42.27Мб |
| 23. ACF and PACF in Code (pt 1).srt |
9.34Кб |
| 23. Alpha and Beta.mp4 |
28.75Мб |
| 23. Alpha and Beta.srt |
9.26Кб |
| 24. ACF and PACF in Code (pt 2).mp4 |
35.39Мб |
| 24. ACF and PACF in Code (pt 2).srt |
8.02Кб |
| 24. Alpha and Beta (Code).mp4 |
45.80Мб |
| 24. Alpha and Beta (Code).srt |
10.39Кб |
| 25. Auto ARIMA and SARIMAX.mp4 |
40.64Мб |
| 25. Auto ARIMA and SARIMAX.srt |
12.28Кб |
| 25. Mixture of Gaussians.mp4 |
29.33Мб |
| 25. Mixture of Gaussians.srt |
9.17Кб |
| 26. Mixture of Gaussians (Code).mp4 |
33.51Мб |
| 26. Mixture of Gaussians (Code).srt |
8.22Кб |
| 26. Model Selection, AIC and BIC.mp4 |
47.28Мб |
| 26. Model Selection, AIC and BIC.srt |
13.46Кб |
| 27. ARIMA in Code (pt 2).mp4 |
109.85Мб |
| 27. ARIMA in Code (pt 2).srt |
16.66Кб |
| 27. Volatility Clustering.mp4 |
18.52Мб |
| 27. Volatility Clustering.srt |
3.88Кб |
| 28. ARIMA in Code (pt 3).mp4 |
111.97Мб |
| 28. ARIMA in Code (pt 3).srt |
18.05Кб |
| 28. Price Simulation.mp4 |
11.99Мб |
| 28. Price Simulation.srt |
4.02Кб |
| 29. ACF and PACF for Stock Returns.mp4 |
48.93Мб |
| 29. ACF and PACF for Stock Returns.srt |
8.45Кб |
| 29. Price Simulation (Code).mp4 |
12.24Мб |
| 29. Price Simulation (Code).srt |
3.10Кб |
| 3. Getting Financial Data (Code).mp4 |
62.59Мб |
| 3. Getting Financial Data (Code).srt |
9.46Кб |
| 3. HMM Parameters.mp4 |
37.43Мб |
| 3. HMM Parameters.srt |
12.35Кб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
79.64Мб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt |
16.77Кб |
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 |
69.45Мб |
| 3. Proof that using Jupyter Notebook is the same as not using it.srt |
14.05Кб |
| 3. Q-Learning in an Algorithmic Trading Context.mp4 |
29.67Мб |
| 3. Q-Learning in an Algorithmic Trading Context.srt |
9.29Кб |
| 3. Random Walk Hypothesis.mp4 |
71.46Мб |
| 3. Random Walk Hypothesis.srt |
19.10Кб |
| 3. Scope of the course.mp4 |
24.37Мб |
| 3. Scope of the course.srt |
5.16Кб |
| 3. States, Actions, Rewards, Policies.mp4 |
44.24Мб |
| 3. States, Actions, Rewards, Policies.srt |
11.67Кб |
| 3. Statistical Factor Models (Advanced).mp4 |
73.07Мб |
| 3. Statistical Factor Models (Advanced).srt |
25.40Кб |
| 3. Trend-Following Strategy in Code (pt 1).mp4 |
63.78Мб |
| 3. Trend-Following Strategy in Code (pt 1).srt |
9.38Кб |
| 3. What is Risk.mp4 |
30.51Мб |
| 3. What is Risk.srt |
9.35Кб |
| 30. Financial Basics Section Summary.mp4 |
9.98Мб |
| 30. Financial Basics Section Summary.srt |
3.07Кб |
| 30. Forecasting.mp4 |
39.29Мб |
| 30. Forecasting.srt |
12.14Кб |
| 31. Suggestion Box.mp4 |
16.10Мб |
| 31. Suggestion Box.srt |
4.66Кб |
| 31. Time Series Analysis Section Conclusion.mp4 |
18.14Мб |
| 31. Time Series Analysis Section Conclusion.srt |
5.29Кб |
| 4. HMM Tasks and the Viterbi Algorithm.mp4 |
65.01Мб |
| 4. HMM Tasks and the Viterbi Algorithm.srt |
19.23Кб |
| 4. How to Practice.mp4 |
24.52Мб |
| 4. How to Practice.srt |
5.20Кб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
108.17Мб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt |
23.55Кб |
| 4. Markov Decision Processes (MDPs).mp4 |
50.71Мб |
| 4. Markov Decision Processes (MDPs).srt |
12.70Кб |
| 4. Representing States.mp4 |
32.55Мб |
| 4. Representing States.srt |
9.56Кб |
| 4. Statistical Factor Models (Code).mp4 |
101.03Мб |
| 4. Statistical Factor Models (Code).srt |
18.59Кб |
| 4. The Naive Forecast.mp4 |
30.95Мб |
| 4. The Naive Forecast.srt |
9.23Кб |
| 4. Trend-Following Strategy in Code (pt 2).mp4 |
69.09Мб |
| 4. Trend-Following Strategy in Code (pt 2).srt |
12.00Кб |
| 4. Understanding Financial Data.mp4 |
28.53Мб |
| 4. Understanding Financial Data.srt |
6.61Кб |
| 4. Why Diversify.mp4 |
33.24Мб |
| 4. Why Diversify.srt |
10.50Кб |
| 5. Describing a Portfolio (pt 1).mp4 |
36.52Мб |
| 5. Describing a Portfolio (pt 1).srt |
12.35Кб |
| 5. HMM for Modeling Volatility Clustering in Code.mp4 |
101.96Мб |
| 5. HMM for Modeling Volatility Clustering in Code.srt |
24.11Кб |
| 5. Machine Learning-Based Trading Strategy.mp4 |
33.32Мб |
| 5. Machine Learning-Based Trading Strategy.srt |
10.30Кб |
| 5. Q-Learning for Algorithmic Trading in Code.mp4 |
103.04Мб |
| 5. Q-Learning for Algorithmic Trading in Code.srt |
18.50Кб |
| 5. Simple Moving Average (Theory).mp4 |
19.90Мб |
| 5. Simple Moving Average (Theory).srt |
5.73Кб |
| 5. The Return.mp4 |
23.55Мб |
| 5. The Return.srt |
6.32Кб |
| 5. Understanding Financial Data (Code).mp4 |
75.54Мб |
| 5. Understanding Financial Data (Code).srt |
15.10Кб |
| 5. Warmup (Optional).mp4 |
23.17Мб |
| 5. Warmup (Optional).srt |
6.05Кб |
| 6. Dealing with Missing Data.mp4 |
28.20Мб |
| 6. Dealing with Missing Data.srt |
7.76Кб |
| 6. Describing a Portfolio (pt 2).mp4 |
22.75Мб |
| 6. Describing a Portfolio (pt 2).srt |
7.88Кб |
| 6. Machine Learning-Based Trading Strategy in Code.mp4 |
69.54Мб |
| 6. Machine Learning-Based Trading Strategy in Code.srt |
10.38Кб |
| 6. Simple Moving Average (Code).mp4 |
55.75Мб |
| 6. Simple Moving Average (Code).srt |
9.45Кб |
| 6. Value Functions and the Bellman Equation.mp4 |
47.86Мб |
| 6. Value Functions and the Bellman Equation.srt |
12.63Кб |
| 7. Classification-Based Trading Strategy in Code.mp4 |
25.11Мб |
| 7. Classification-Based Trading Strategy in Code.srt |
4.25Кб |
| 7. Dealing with Missing Data (Code).mp4 |
37.68Мб |
| 7. Dealing with Missing Data (Code).srt |
8.95Кб |
| 7. Exponentially-Weighted Moving Average (Theory).mp4 |
37.76Мб |
| 7. Exponentially-Weighted Moving Average (Theory).srt |
14.60Кб |
| 7. Visualizing Random Portfolios and Monte Carlo Simulation (pt 1).mp4 |
73.44Мб |
| 7. Visualizing Random Portfolios and Monte Carlo Simulation (pt 1).srt |
16.22Кб |
| 7. What does it mean to “learn”.mp4 |
31.77Мб |
| 7. What does it mean to “learn”.srt |
8.91Кб |
| 8. Exponentially-Weighted Moving Average (Code).mp4 |
54.34Мб |
| 8. Exponentially-Weighted Moving Average (Code).srt |
14.66Кб |
| 8. Returns.mp4 |
29.22Мб |
| 8. Returns.srt |
11.72Кб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 |
42.79Мб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt |
12.41Кб |
| 8. Using a Random Forest Classifier for Machine Learning-Based Trading.mp4 |
33.02Мб |
| 8. Using a Random Forest Classifier for Machine Learning-Based Trading.srt |
5.54Кб |
| 8. Visualizing Random Portfolios and Monte Carlo Simulation (pt 2).mp4 |
88.72Мб |
| 8. Visualizing Random Portfolios and Monte Carlo Simulation (pt 2).srt |
18.36Кб |
| 9. Adjusted Close, Stock Splits, and Dividends.mp4 |
47.36Мб |
| 9. Adjusted Close, Stock Splits, and Dividends.srt |
16.21Кб |
| 9. Algorithmic Trading Section Summary.mp4 |
29.94Мб |
| 9. Algorithmic Trading Section Summary.srt |
7.56Кб |
| 9. Maximum and Minimum Portfolio Return.mp4 |
32.65Мб |
| 9. Maximum and Minimum Portfolio Return.srt |
12.46Кб |
| 9. Simple Exponential Smoothing for Forecasting (Theory).mp4 |
36.31Мб |
| 9. Simple Exponential Smoothing for Forecasting (Theory).srt |
13.89Кб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 |
57.19Мб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt |
14.78Кб |