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| 1. Introduction.mp4 |
10.38Мб |
| 1. Introduction.mp4 |
5.24Мб |
| 1. Introduction.mp4 |
9.72Мб |
| 1. Introduction.mp4 |
21.45Мб |
| 1. Introduction.mp4 |
12.41Мб |
| 1. Introduction.mp4 |
7.37Мб |
| 1. Introduction.mp4 |
8.99Мб |
| 1. Introduction.mp4 |
8.93Мб |
| 1. READ ME.html |
1.01Кб |
| 10. How to fix the stochastic initialization problem.mp4 |
40.34Мб |
| 10. Live Trading strategy based on ANN.mp4 |
25.56Мб |
| 10. Matplotlib Toolbox.mp4 |
17.58Мб |
| 10. Python structures FOR.mp4 |
29.93Мб |
| 11. Bagging method using the different ANNs.mp4 |
57.47Мб |
| 11. Live Trading strategy based on RNN.mp4 |
26.27Мб |
| 11. Python structures WHILE.mp4 |
14.34Мб |
| 12. Functions Basics of function.mp4 |
32.71Мб |
| 13. Functions Local variable.mp4 |
14.27Мб |
| 14. Functions Global variable.mp4 |
12.75Мб |
| 15. Functions Lambda function.mp4 |
13.17Мб |
| 2. Get stock prices.mp4 |
16.65Мб |
| 2. Import & manage data from Metatrader 5.mp4 |
104.44Мб |
| 2. Install a library on Jupyter.mp4 |
2.78Мб |
| 2. Install the environments.html |
1.13Кб |
| 2. Numpy Array.mp4 |
60.10Мб |
| 2. Quick recap of the DNN theory.mp4 |
85.79Мб |
| 2. Sortino ratio computation.mp4 |
57.53Мб |
| 2. Theory behind RNNs.mp4 |
34.06Мб |
| 2. Type of object Number.mp4 |
23.97Мб |
| 3. Beta ratio computation (CAPM metric).mp4 |
72.70Мб |
| 3. Create a simple moving average (SMA).mp4 |
42.95Мб |
| 3. Data import & Features engineering.mp4 |
30.81Мб |
| 3. Import & manage data from Yahoo Finance.mp4 |
43.91Мб |
| 3. Initialize the platform.mp4 |
14.71Мб |
| 3. Numpy Random.mp4 |
54.14Мб |
| 3. Recap from the DNN chapter.mp4 |
51.63Мб |
| 3. Type of object String.mp4 |
83.58Мб |
| 4. Alpha ratio computation (CAPM metric).mp4 |
23.59Мб |
| 4. Create a moving standard deviation (MSD).mp4 |
20.64Мб |
| 4. Get data broker.mp4 |
23.10Мб |
| 4. How to transform 2-dimensional data into 3-dimensional data.mp4 |
41.19Мб |
| 4. Numpy Indexing Slicing transformation.mp4 |
85.41Мб |
| 4. Train Test set split (to fit the DNN model).mp4 |
42.45Мб |
| 4. Type of object Logical operations Boolean.mp4 |
25.68Мб |
| 5. Drawdown function creation.mp4 |
28.69Мб |
| 5. How to create a RNN using TensorFlow 2.0.mp4 |
67.14Мб |
| 5. Pandas Serie and DataFrame.mp4 |
31.24Мб |
| 5. Send orders on the market using Python.mp4 |
44.48Мб |
| 5. Type of object Variable assignment.mp4 |
37.02Мб |
| 5. Use the Technical analysis library to compute the RSI indicator.mp4 |
29.98Мб |
| 5. Why and how to standardize the features.mp4 |
34.59Мб |
| 6.1 assets.csv |
3.09Мб |
| 6. Automatization of the features engineering process.mp4 |
19.19Мб |
| 6. Create a DNN using Tensorflow 2.0.mp4 |
68.73Мб |
| 6. Drawdown application.mp4 |
27.41Мб |
| 6. Dropout Layer.mp4 |
16.65Мб |
| 6. Get current positions.mp4 |
44.48Мб |
| 6. Pandas Cleaning and selection data.mp4 |
81.57Мб |
| 6. Type of object Tuple and list.mp4 |
49.84Мб |
| 7. Backtesting function (1).mp4 |
21.28Мб |
| 7. Pandas Conditional selection.mp4 |
25.97Мб |
| 7. RNN prediction to create a trading strategy.mp4 |
60.68Мб |
| 7. Run structure creation.mp4 |
32.71Мб |
| 7. Type of object Dictionary.mp4 |
46.75Мб |
| 7. Use the DNN predictions to create a trading strategy.mp4 |
59.36Мб |
| 8. Automate the process.mp4 |
35.24Мб |
| 8. Automate the process.mp4 |
17.25Мб |
| 8. Backtesting function (2).mp4 |
24.22Мб |
| 8. Close all positions.mp4 |
10.85Мб |
| 8. Matplotlib Graph.mp4 |
19.72Мб |
| 8. Type of object Set.mp4 |
28.72Мб |
| 9. Backtest a trading strategy based on DNN.mp4 |
15.83Мб |
| 9. Find the best models throughout all the stochastic initialization.mp4 |
35.58Мб |
| 9. Live Trading application random signals.mp4 |
32.80Мб |
| 9. Matplotlib Scatter.mp4 |
14.35Мб |
| 9. Python structures IF ELIF ELSE.mp4 |
40.93Мб |
| 9. The stochastic initialization problem.mp4 |
24.97Мб |
| Bonus Resources.txt |
386б |
| Get Bonus Crypto Downloads Here.url |
185б |