Udemy Reinforcement Learning for Algorithmic Trading with Python

[0dayddl]

5.03 GB | 00:11:57 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 - Welcome and Introduction (22.18 MB)
2 - How to get the most out of this course (39.14 MB)
3 - Course Materials Downloads (27.04 MB)
1 - Download and Install Anaconda (63.81 MB)
2 - How to open Jupyter Notebooks (84.37 MB)
3 - How to work with Jupyter Notebooks (81.15 MB)
1 - Introduction (12.52 MB)
2 - Loading and inspecting the Dataset with Pandas (67.21 MB)
3 - Prices and Financial Returns (40.28 MB)
4 - Simple Moving Averages (SMA) (41.4 MB)
5 - Excursus Creating Technical Indicators with Pandas (24.55 MB)
6 - MACD Lines (22.7 MB)
7 - Relative Strength Index (RSI) (18.28 MB)
8 - Stochastic Oscillators & Conclusion (103.33 MB)
1 - What is ChatGPT and how does it work (17.22 MB)
10 - Prompt Engineering Techniques (Part 2) (51.34 MB)
11 - Prompt Engineering Techniques (Part 3) (70.8 MB)
2 - ChatGPT vs Search Engines (33.07 MB)
3 - Artificial Intelligence vs Human Intelligence (25.3 MB)
4 - Creating a ChatGPT account and getting started (51.63 MB)
5 - Update August 2024 (55.64 MB)
6 - Features, Options and Products around GPT models (35.09 MB)
7 - Navigating the OpenAI Website (98.84 MB)
8 - What is a Token and how do Tokens work (63.34 MB)
9 - Prompt Engineering Techniques (Part 1) (107.16 MB)
1 - Introduction and Overview (7.82 MB)
2 - Reinforcement Learning vs traditional Machine Learning (84.44 MB)
3 - Reinforcement Learning - Use Cases (68.66 MB)
4 - Reinforcement Learning - Models and Algorithms (74.07 MB)
1 - Introduction (58.32 MB)
10 - Training an RL Agent with Q-Tables (75.25 MB)
11 - Q-learning explained - the Hyperparameters (107.92 MB)
12 - Q-learning explained - Discretization of the State Space (39.45 MB)
13 - Q-learning explained - the Q-Table (66.28 MB)
14 - Q-learning explained - Visualizing the Q-Table (75.65 MB)
15 - Q-learning explained - Updating the Q-Table (115.95 MB)
16 - Testing the trained Agent (51.07 MB)
17 - Visualizing the trained Agent (31.3 MB)
18 - Improving the Agent Training (Brainstorming) (91.09 MB)
19 - Excursus Randomness and Reproducibility of random events (122.06 MB)
2 - Project Assignment (36.07 MB)
20 - Training and Testing with Reproducibility (random seed) (72.52 MB)
21 - Tuning the Hyperparameters (42.2 MB)
22 - Increasing the number of Training Episodes (50.26 MB)
23 - Visualizing the Training Process and Performance Plateaus (78.41 MB)
24 - Increasing the State Space Discretization (78.48 MB)
25 - Conclusion and Outlook (38.66 MB)
3 - One Random Episode with Human Rendering (60.75 MB)
4 - Defining the maximum number of steps per Episode (36.66 MB)
5 - The code explained - line for line (77.46 MB)
6 - Running multiple random Episodes with human rendering (89.91 MB)
7 - Performance Measurement and Success Evaluation (104.77 MB)
8 - Running multiple Episodes without human Rendering (65.49 MB)
9 - Excursus RGB Rendering with Visualization (49.22 MB)
1 - Introduction (53.13 MB)
10 - SavingLoading and Testing a trained Agent (50.93 MB)
11 - The trained Agent live in Action (20.6 MB)
2 - One Random Episode with Human Rendering (80.14 MB)
3 - Running multiple random Episodes with human rendering (11.04 MB)
4 - Performance Measurement and Success Evaluation (22.54 MB)
5 - Running multiple Episodes without human Rendering (14.29 MB)
6 - Saving and visualizing successful Episodes (24.59 MB)
7 - Creating an appropriate Observation Space for Training (77.47 MB)
8 - State Space Discretization (34.54 MB)
9 - Training a RL Agent for the Lunar Lander (71.44 MB)
1 - Introduction and Assignment (28.92 MB)
10 - Reinforcement Learning with multiple Episodes (95.09 MB)
11 - Hyperparameter Optimization (46.56 MB)
12 - Testing the Agent on new Data (78.21 MB)
13 - The Importance of Trading Costs (38.8 MB)
14 - Modifying the Rewards Function (96.25 MB)
15 - Performance Evaluation and Identifying Overfitting (34.37 MB)
2 - Loading and Preparing the Dataset (22.71 MB)
3 - Splitting into Training and Test Set (20 MB)
4 - Discretization and Quantile Binning (Part 1) (70.3 MB)
5 - Discretization and Quantile Binning (Part 2) (35.99 MB)
6 - Discretization and Quantile Binning (Part 3) (67.52 MB)
7 - Trading Profits and Losses (47.45 MB)
8 - Introduction to Agent Training (one Episode) (61.02 MB)
9 - Training of an Algo Trading Agent - explained (120.11 MB)

Screenshot

Spoiler

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[0dayddl]

5.52 GB | 11min 57s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Welcome and Introduction.mp4 (10.48 MB)
2 Sneak Preview.mp4 (4.26 MB)
3 How to get the most out of this course.mp4 (91.63 MB)
4 Course Overview.mp4 (23.73 MB)
5 Course Materials Downloads (Update January 2025).mp4 (27.04 MB)
1 Download and Install Anaconda.mp4 (81.92 MB)
2 How to open Jupyter Notebooks.mp4 (84.37 MB)
3 How to work with Jupyter Notebooks.mp4 (81.15 MB)
4 Important Set Up for Reinforcement Learning.mp4 (87.49 MB)
1 Introduction.mp4 (12.51 MB)
2 Loading and inspecting the Dataset with Pandas.mp4 (67.21 MB)
3 Prices and Financial Returns.mp4 (40.28 MB)
4 Simple Moving Averages (SMA).mp4 (41.4 MB)
5 Excursus Creating Technical Indicators with Pandas.mp4 (24.54 MB)
6 MACD Lines.mp4 (22.69 MB)
7 Relative Strength Index (RSI).mp4 (18.28 MB)
8 Stochastic Oscillators & Conclusion.mp4 (103.33 MB)
1 What is ChatGPT and how does it work.mp4 (17.21 MB)
2 ChatGPT vs Search Engines.mp4 (33.07 MB)
3 Artificial Intelligence vs Human Intelligence.mp4 (25.29 MB)
4 Creating a ChatGPT account and getting started.mp4 (51.63 MB)
5 Update August 2024.mp4 (55.63 MB)
6 Features, Options and Products around GPT models.mp4 (35.09 MB)
8 Navigating the OpenAI Website.mp4 (98.83 MB)
9 What is a Token and how do Tokens work.mp4 (63.34 MB)
10 Prompt Engineering Techniques (Part 1).mp4 (107.16 MB)
12 Prompt Engineering Techniques (Part 2).mp4 (51.34 MB)
14 Prompt Engineering Techniques (Part 3).mp4 (70.79 MB)
1 Introduction and Overview.mp4 (7.81 MB)
2 Reinforcement Learning vs traditional Machine Learning.mp4 (84.43 MB)
4 Reinforcement Learning - Use Cases.mp4 (68.65 MB)
6 Reinforcement Learning - Models and Algorithms.mp4 (74.07 MB)
1 Introduction.mp4 (58.31 MB)
2 Project Assignment.mp4 (36.07 MB)
4 One Random Episode with Human Rendering.mp4 (60.74 MB)
6 Defining the maximum number of steps per Episode.mp4 (36.66 MB)
8 The code explained - line for line.mp4 (77.45 MB)
9 Running multiple random Episodes with human rendering.mp4 (89.91 MB)
11 Performance Measurement and Success Evaluation.mp4 (104.77 MB)
13 Running multiple Episodes without human Rendering.mp4 (65.49 MB)
15 Excursus RGB Rendering with Visualization.mp4 (49.21 MB)
16 Training an RL Agent with Q-Tables.mp4 (75.24 MB)
18 Q-learning explained - the Hyperparameters.mp4 (107.91 MB)
20 Q-learning explained - Discretization of the State Space.mp4 (39.44 MB)
21 Q-learning explained - the Q-Table.mp4 (66.28 MB)
23 Q-learning explained - Visualizing the Q-Table.mp4 (75.65 MB)
25 Q-learning explained - Updating the Q-Table.mp4 (115.94 MB)
27 Testing the trained Agent.mp4 (51.06 MB)
29 Visualizing the trained Agent.mp4 (31.3 MB)
30 Improving the Agent Training (Brainstorming).mp4 (91.09 MB)
32 Excursus Randomness and Reproducibility of random events.mp4 (122.05 MB)
33 Training and Testing with Reproducibility (random seed).mp4 (72.51 MB)
34 Tuning the Hyperparameters.mp4 (42.2 MB)
36 Increasing the number of Training Episodes.mp4 (50.25 MB)
37 Visualizing the Training Process and Performance Plateaus.mp4 (78.4 MB)
39 Increasing the State Space Discretization.mp4 (78.47 MB)
40 Conclusion and Outlook.mp4 (38.65 MB)
1 Introduction.mp4 (53.13 MB)
2 Project Assignment.mp4 (15.92 MB)
4 One Random Episode with Human Rendering.mp4 (80.14 MB)
5 Running multiple random Episodes with human rendering.mp4 (11.03 MB)
6 Performance Measurement and Success Evaluation.mp4 (22.54 MB)
7 Running multiple Episodes without human Rendering.mp4 (14.29 MB)
8 Saving and visualizing successful Episodes.mp4 (24.58 MB)
9 Creating an appropriate Observation Space for Training.mp4 (77.47 MB)
10 State Space Discretization.mp4 (34.53 MB)
11 Training a RL Agent for the Lunar Lander.mp4 (71.43 MB)
12 SavingLoading and Testing a trained Agent.mp4 (50.93 MB)
13 The trained Agent live in Action.mp4 (20.59 MB)
1 Introduction and Assignment.mp4 (28.91 MB)
2 Loading and Preparing the Dataset.mp4 (22.71 MB)
3 Splitting into Training and Test Set.mp4 (19.99 MB)
4 Discretization and Quantile Binning (Part 1).mp4 (70.29 MB)
5 Discretization and Quantile Binning (Part 2).mp4 (35.99 MB)
6 Discretization and Quantile Binning (Part 3).mp4 (67.51 MB)
7 Trading Profits and Losses.mp4 (47.44 MB)
8 Introduction to Agent Training (one Episode).mp4 (61.02 MB)
9 Training of an Algo Trading Agent - explained.mp4 (120.11 MB)
10 Reinforcement Learning with multiple Episodes.mp4 (95.08 MB)
11 Hyperparameter Optimization.mp4 (46.55 MB)
12 Testing the Agent on new Data.mp4 (78.2 MB)
13 The Importance of Trading Costs.mp4 (38.8 MB)
14 Modifying the Rewards Function.mp4 (96.24 MB)
15 Performance Evaluation and Identifying Overfitting.mp4 (34.36 MB)
16 Analyzing Overfitting and Countermeasures.mp4 (82.55 MB)
18 Update Download of additional files.mp4 (13.77 MB)
19 A Low-Complexity Model and Underfitting.mp4 (83.31 MB)
20 A more balanced Model and Outlook.mp4 (116.76 MB)

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Screenshot

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