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[FreeCourseSite.com] Udemy - Algorithmic Trading A-Z with Python, Machine Learning & AWS
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[FreeCourseSite.com] Udemy - Algorithmic Trading A-Z with Python, Machine Learning & AWS
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文件列表
28. A Machine Learning-powered Strategy A-Z (DNN)/13. Implementation (Oanda & FXCM).mp4
117.9 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/11. How to traceback more complex Errors.mp4
101.5 MB
7. Trading with Python and OANDAFXCM - an Introduction/10. OANDA How to place Orders and execute Trades.mp4
89.6 MB
22. Implementation and Automation with OANDA (UPDATED!)/17. Trade Monitoring and Reporting.mp4
88.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/56. Customization of Plots.mp4
88.2 MB
15. Defining and Backtesting SMA Strategies/4. Finding the optimal SMA Strategy.mp4
86.8 MB
11. Financial Data Analysis with Pandas - an Introduction/12. Importing Financial Data from Excel.mp4
84.6 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/12. Inheritance.mp4
82.7 MB
20. Advanced Backtesting Techniques/13. Adding the Iterative Backtest Child Class for SMA (Part 2).mp4
82.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/34. Slicing Rows and Columns with loc (label-based indexing).mp4
81.3 MB
15. Defining and Backtesting SMA Strategies/5. Generalization with OOP An SMA Backtesting Class in action.mp4
77.2 MB
19. Trading Strategies powered by Machine Learning - Classification/7. Generalization with OOP A Classification Backtesting Class in action.mp4
77.0 MB
22. Implementation and Automation with OANDA (UPDATED!)/4. Historical Data, real-time Data and Orders (Recap).mp4
76.3 MB
10. Introduction to Time Series Data in Pandas/4. Downsampling Time Series with resample().mp4
75.7 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/4. How to create an EC2 Instance.mp4
75.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/61. Categorical Seaborn Plots.mp4
74.2 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/4. Defining a Bollinger Bands Mean-Reversion Strategy (Part 2).mp4
74.1 MB
3. Day Trading with OANDA A-Z a Deep Dive/7. Margin and Leverage.mp4
74.0 MB
3. Day Trading with OANDA A-Z a Deep Dive/6. Trading Costs and Performance Attribution.mp4
72.9 MB
27. Working with two or many Strategies (Combination)/8. Strategy Optimization.mp4
71.9 MB
12. Advanced Topics/2. Filling NA Values with bfill, ffill and interpolation.mp4
71.7 MB
23. Implementation and Automation with FXCM (Updated!)/8. Storing and resampling real-time tick data (Part 2).mp4
71.5 MB
4. FOREX Day Trading with FXCM/1. FXCM at a first glance.mp4
70.8 MB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/4. Spot Trading vs. Derivatives Trading (Part 2).mp4
70.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/39. Analyzing Numerical Series with unique(), nunique() and value_counts().mp4
70.4 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/62. Seaborn Regression Plots.mp4
69.8 MB
3. Day Trading with OANDA A-Z a Deep Dive/1. OANDA at a first glance.mp4
69.1 MB
23. Implementation and Automation with FXCM (Updated!)/2. Historical Data, real-time Data and Orders (Recap).mp4
69.0 MB
20. Advanced Backtesting Techniques/15. OOP Challenge Add Contrarian and Bollinger Strategies.mp4
69.0 MB
15. Defining and Backtesting SMA Strategies/7. Creating the Class (Part 2).mp4
68.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/30. Selecting Rows with iloc (position-based indexing).mp4
68.2 MB
23. Implementation and Automation with FXCM (Updated!)/6. Storing and resampling real-time tick data (Part 1).mp4
67.7 MB
29. Error Handling How to make your Trading Bot more stable and reliable/14. Oanda Error Handling (Part 2).mp4
67.3 MB
22. Implementation and Automation with OANDA (UPDATED!)/10. Storing and resampling real-time tick data (Part 4).mp4
67.2 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1. Introduction to OOP and examples for Classes.mp4
66.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/44. Changing Row Index with set_index() and reset_index().mp4
66.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/23. Create your very first Pandas DataFrame (from csv).mp4
65.6 MB
3. Day Trading with OANDA A-Z a Deep Dive/10. Our third Trade A-Z - Going Short EURUSD.mp4
64.9 MB
22. Implementation and Automation with OANDA (UPDATED!)/22. Machine Learning Strategies (2) - Implementation.mp4
64.6 MB
5. Installing Python and Jupyter Notebooks/2. Download and Install Anaconda.mp4
63.8 MB
3. Day Trading with OANDA A-Z a Deep Dive/11. Netting vs. Hedging.mp4
63.8 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/9. Generalization with OOP A Contrarian Backtesting Class in action.mp4
63.6 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/6. Getting the Instance Ready for Algorithmic Trading.mp4
63.0 MB
22. Implementation and Automation with OANDA (UPDATED!)/7. Storing and resampling real-time tick data (Part 1).mp4
62.7 MB
3. Day Trading with OANDA A-Z a Deep Dive/3. FOREX Currency Exchange Rates explained.mp4
62.4 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/55. Visualization with Matplotlib (Intro).mp4
62.0 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/7. Trades and Trading Costs (Part 1).mp4
61.9 MB
22. Implementation and Automation with OANDA (UPDATED!)/25. Running a Python Trader Script.mp4
61.6 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/2. Test your debugging skills!.mp4
61.4 MB
11. Financial Data Analysis with Pandas - an Introduction/2. Importing Stock Price Data from Yahoo Finance.mp4
61.3 MB
15. Defining and Backtesting SMA Strategies/3. Vectorized Strategy Backtesting.mp4
61.2 MB
23. Implementation and Automation with FXCM (Updated!)/14. Placing Orders and Executing Trades.mp4
61.0 MB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3. Spot Trading vs. Derivatives Trading (Part 1).mp4
60.6 MB
22. Implementation and Automation with OANDA (UPDATED!)/16. Placing Orders and Executing Trades.mp4
60.3 MB
7. Trading with Python and OANDAFXCM - an Introduction/7. OANDA How to load Historical Price Data (Part 1).mp4
60.1 MB
20. Advanced Backtesting Techniques/11. Creating an Iterative Base Class (Part 8).mp4
59.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/52. Handling NA Values missing Values.mp4
59.0 MB
23. Implementation and Automation with FXCM (Updated!)/15. Trade Monitoring and Reporting.mp4
58.9 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/25. First Data Inspection.mp4
58.7 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/2. Demonstration AWS EC2 for Algorithmic Trading live in action.mp4
57.4 MB
19. Trading Strategies powered by Machine Learning - Classification/8. The Classification Backtesting Class explained (Part 1).mp4
57.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/50. Advanced Filtering with between(), isin() and ~.mp4
57.1 MB
23. Implementation and Automation with FXCM (Updated!)/5. Collecting and storing real-time tick data.mp4
56.6 MB
23. Implementation and Automation with FXCM (Updated!)/19. Machine Learning Strategies (2) - Implementation.mp4
56.5 MB
4. FOREX Day Trading with FXCM/2. How to create an Account.mp4
56.5 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/13. Inheritance and the super() Function.mp4
56.5 MB
31. Appendix 1 Python (& Finance) Basics/40. Coding Exercise 3.mp4
56.4 MB
5. Installing Python and Jupyter Notebooks/4. How to work with Jupyter Notebooks.mp4
56.1 MB
20. Advanced Backtesting Techniques/12. Adding the Iterative Backtest Child Class for SMA (Part 1).mp4
55.7 MB
7. Trading with Python and OANDAFXCM - an Introduction/17. FXCM How to load Historical Price Data (Part 1).mp4
54.3 MB
22. Implementation and Automation with OANDA (UPDATED!)/15. Defining a simple Contrarian Strategy.mp4
54.2 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/1. Mean-Reversion Strategies - Overview.mp4
53.9 MB
5. Installing Python and Jupyter Notebooks/3. How to open Jupyter Notebooks.mp4
53.4 MB
23. Implementation and Automation with FXCM (Updated!)/4. Preview A Trader Class live in action.mp4
53.3 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/6. Generalization with OOP A Bollinger Bands Backtesting Class in action.mp4
53.2 MB
20. Advanced Backtesting Techniques/10. Creating an Iterative Base Class (Part 7).mp4
52.9 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/14. Adding meaningful Docstrings.mp4
52.5 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/5. Omitting cells, changing the sequence and more.mp4
52.4 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/3. Installation of Tensorflow & Keras (Part 2).mp4
52.2 MB
23. Implementation and Automation with FXCM (Updated!)/11. Working with historical data and real-time tick data (Part 2).mp4
52.0 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/11. How to stop Trading Sessions (OANDA).mp4
51.9 MB
22. Implementation and Automation with OANDA (UPDATED!)/12. Working with historical data and real-time tick data (Part 1).mp4
51.8 MB
20. Advanced Backtesting Techniques/7. Creating an Iterative Base Class (Part 4).mp4
51.8 MB
22. Implementation and Automation with OANDA (UPDATED!)/13. Working with historical data and real-time tick data (Part 2).mp4
51.4 MB
3. Day Trading with OANDA A-Z a Deep Dive/5. How to calculate Profit & Loss of a Trade.mp4
51.2 MB
10. Introduction to Time Series Data in Pandas/2. Converting strings to datetime objects with pd.to_datetime().mp4
51.2 MB
7. Trading with Python and OANDAFXCM - an Introduction/6. OANDA Connecting to the APIServer.mp4
50.7 MB
18. Trading Strategies powered by Machine Learning - Regression/2. Linear Regression with scikit-learn - a simple Introduction.mp4
50.6 MB
3. Day Trading with OANDA A-Z a Deep Dive/2. How to create an Account.mp4
50.6 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/10. Prediction & Out-Sample Forward Testing.mp4
50.6 MB
22. Implementation and Automation with OANDA (UPDATED!)/5. Preview A Trader Class live in action.mp4
50.3 MB
7. Trading with Python and OANDAFXCM - an Introduction/19. FXCM Streaming high-frequency real-time Data.mp4
50.0 MB
23. Implementation and Automation with FXCM (Updated!)/21. Running a Python Script.mp4
50.0 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/9. Creating and Fitting the DNN Model.mp4
49.9 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/2. Trading Strategies - an Overview.mp4
49.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/54. Summary Statistics and Accumulations.mp4
49.2 MB
31. Appendix 1 Python (& Finance) Basics/13. Coding Exercise 1.mp4
49.0 MB
22. Implementation and Automation with OANDA (UPDATED!)/8. Storing and resampling real-time tick data (Part 2).mp4
48.2 MB
7. Trading with Python and OANDAFXCM - an Introduction/20. FXCM How to place Orders and execute Trades.mp4
48.2 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/12. How to stop Trading Sessions (FXCM).mp4
48.1 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/11. Adding more methods and performance metrics.mp4
47.6 MB
11. Financial Data Analysis with Pandas - an Introduction/8. Financial Time Series - Return and Risk.mp4
47.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/47. Filtering DataFrames (one Condition).mp4
47.0 MB
29. Error Handling How to make your Trading Bot more stable and reliable/13. Oanda Error Handling (Part 1).mp4
46.7 MB
15. Defining and Backtesting SMA Strategies/1. SMA Crossover Strategies - Overview.mp4
46.1 MB
15. Defining and Backtesting SMA Strategies/2. Defining an SMA Crossover Strategy.mp4
45.8 MB
7. Trading with Python and OANDAFXCM - an Introduction/18. FXCM How to load Historical Price Data (Part 2).mp4
45.4 MB
3. Day Trading with OANDA A-Z a Deep Dive/4. Our second Trade - EURUSD FOREX Trading.mp4
45.0 MB
7. Trading with Python and OANDAFXCM - an Introduction/15. FXCM Connecting to the APIServer.mp4
44.9 MB
18. Trading Strategies powered by Machine Learning - Regression/8. A simple Linear Model to predict Financial Returns (Part 2).mp4
44.8 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/5. The method get_data().mp4
44.8 MB
18. Trading Strategies powered by Machine Learning - Regression/1. Machine Learning - an Overview.mp4
44.6 MB
31. Appendix 1 Python (& Finance) Basics/48. Coding Exercise 4.mp4
44.4 MB
23. Implementation and Automation with FXCM (Updated!)/13. Defining a Simple Contrarian Trading Strategy.mp4
44.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/67. Splitting with many Keys.mp4
44.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/36. Summary, Best Practices and Outlook.mp4
44.1 MB
29. Error Handling How to make your Trading Bot more stable and reliable/17. FXCM Error Handling (Part 1).mp4
43.9 MB
23. Implementation and Automation with FXCM (Updated!)/17. SMA Crossover and Bollinger Bands (Solution).mp4
43.8 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/1. Introduction to Part 3.mp4
43.3 MB
3. Day Trading with OANDA A-Z a Deep Dive/8. Margin Closeout and more.mp4
43.3 MB
11. Financial Data Analysis with Pandas - an Introduction/13. Simple Moving Averages (SMA) with rolling().mp4
43.0 MB
31. Appendix 1 Python (& Finance) Basics/43. Intro to Strings.mp4
42.8 MB
10. Introduction to Time Series Data in Pandas/3. Indexing and Slicing Time Series.mp4
42.6 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/24. Pandas Display Options and the methods head() & tail().mp4
42.5 MB
22. Implementation and Automation with OANDA (UPDATED!)/6. How to collect and store real-time tick data.mp4
42.4 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/5. Vectorized Strategy Backtesting.mp4
42.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/68. split-apply-combine.mp4
42.0 MB
3. Day Trading with OANDA A-Z a Deep Dive/9. Introduction to Charting.mp4
41.6 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/10. Getting help on StackOverflow.com.mp4
41.4 MB
23. Implementation and Automation with FXCM (Updated!)/7. A Trader Class.mp4
41.4 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/66. Understanding the GroupBy Object.mp4
41.3 MB
31. Appendix 1 Python (& Finance) Basics/50. Keywords pass, continue and break.mp4
41.3 MB
15. Defining and Backtesting SMA Strategies/8. Creating the Class (Part 3).mp4
41.3 MB
20. Advanced Backtesting Techniques/8. Creating an Iterative Base Class (Part 5).mp4
41.3 MB
3. Day Trading with OANDA A-Z a Deep Dive/12. Market, Limit and Stop Orders.mp4
41.2 MB
1. Getting Started/3. Did you know... (what Data can tell us about Day Trading).mp4
41.1 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/10. How to schedule Trading sessions with the Task Scheduler.mp4
41.1 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/6. Changing the Window Parameter.mp4
41.1 MB
20. Advanced Backtesting Techniques/14. Using Modules and adding Docstrings.mp4
40.8 MB
31. Appendix 1 Python (& Finance) Basics/49. Conditional Statements.mp4
40.5 MB
31. Appendix 1 Python (& Finance) Basics/37. Adding and removing Elements fromto Lists.mp4
40.4 MB
15. Defining and Backtesting SMA Strategies/9. Creating the Class (Part 4).mp4
40.3 MB
18. Trading Strategies powered by Machine Learning - Regression/4. Overfitting.mp4
40.3 MB
1. Getting Started/1. What is Algorithmic Trading Course Overview.mp4
40.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/51. Intro to NA Values missing Values.mp4
40.0 MB
29. Error Handling How to make your Trading Bot more stable and reliable/18. FXCM Error Handling (Part 2).mp4
40.0 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/6. Performance Metrics.mp4
39.9 MB
20. Advanced Backtesting Techniques/3. A first Intuition on Iterative Backtesting (Part 2).mp4
39.9 MB
31. Appendix 1 Python (& Finance) Basics/23. Coding Exercise 2.mp4
39.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/13. Creating Numpy Arrays from Scratch.mp4
39.7 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/5. Adding LabelsFeatures.mp4
39.6 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/10. OOP Challenge Create the Contrarian Backtesting Class (incl. Solution).mp4
39.5 MB
1. Getting Started/2. How to get the best out of this course.mp4
39.4 MB
27. Working with two or many Strategies (Combination)/4. Combining both Strategies - Alternative 1.mp4
39.3 MB
12. Advanced Topics/1. Helpful DatetimeIndex Attributes and Methods.mp4
39.2 MB
11. Financial Data Analysis with Pandas - an Introduction/4. Normalizing Time Series to a Base Value (100).mp4
39.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/43. First Steps with Pandas Index Objects.mp4
38.9 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/16. Coding Exercise 3 Create your own Class.mp4
38.8 MB
22. Implementation and Automation with OANDA (UPDATED!)/19. Implementing an SMA Crossover Strategy (Solution).mp4
38.8 MB
12. Advanced Topics/4. Timezones and Converting (Part 2).mp4
38.7 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/1. Introduction and Preparing the Data.mp4
38.6 MB
18. Trading Strategies powered by Machine Learning - Regression/9. A Multiple Regression Model to predict Financial Returns.mp4
38.5 MB
15. Defining and Backtesting SMA Strategies/11. Creating the Class (Part 6).mp4
38.1 MB
11. Financial Data Analysis with Pandas - an Introduction/3. Initial Inspection and Visualization.mp4
38.1 MB
32. Appendix 2 User-defined Functions (required for OOP)/3. What´s the difference between Positional Arguments vs. Keyword Arguments.mp4
38.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/40. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp4
37.9 MB
29. Error Handling How to make your Trading Bot more stable and reliable/12. Implementation with Oanda V20 Connection Issues.mp4
37.9 MB
10. Introduction to Time Series Data in Pandas/5. Coding Exercise 1.mp4
37.6 MB
19. Trading Strategies powered by Machine Learning - Classification/2. Logistic Regression with scikit-learn - a simple Introduction (Part 2).mp4
37.5 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/63. Seaborn Heatmaps.mp4
37.4 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/3. Numpy Arrays.mp4
37.4 MB
31. Appendix 1 Python (& Finance) Basics/52. Introduction to while loops.mp4
37.4 MB
31. Appendix 1 Python (& Finance) Basics/47. Comparison, Logical and Membership Operators in Action.mp4
37.3 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/4. The most commonly made Errors at a glance.mp4
37.2 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/7. OOP Challenge Create the Bollinger Bands Backtesting Class (incl. Solution).mp4
37.0 MB
32. Appendix 2 User-defined Functions (required for OOP)/9. Scope - easily explained.mp4
37.0 MB
19. Trading Strategies powered by Machine Learning - Classification/9. The Classification Backtesting Class explained (Part 2).mp4
36.9 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/4. The special method __init__().mp4
36.7 MB
11. Financial Data Analysis with Pandas - an Introduction/7. Measuring Stock Performance with MEAN Returns and STD of Returns.mp4
36.6 MB
20. Advanced Backtesting Techniques/2. A first Intuition on Iterative Backtesting (Part 1).mp4
36.5 MB
29. Error Handling How to make your Trading Bot more stable and reliable/15. Oanda Error Handling (Part 3).mp4
36.2 MB
23. Implementation and Automation with FXCM (Updated!)/10. Working with historical data and real-time tick data (Part 1).mp4
36.2 MB
31. Appendix 1 Python (& Finance) Basics/38. Mutable vs. immutable Objects (Part 1).mp4
36.2 MB
10. Introduction to Time Series Data in Pandas/1. Importing Time Series Data from csv-files.mp4
36.1 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/8. The methods plot_prices() and plot_returns().mp4
35.8 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/12. Problems with the Python Installation.mp4
35.7 MB
23. Implementation and Automation with FXCM (Updated!)/18. Machine Learning Strategies (1) - Model Fitting.mp4
35.5 MB
20. Advanced Backtesting Techniques/9. Creating an Iterative Base Class (Part 6).mp4
35.4 MB
22. Implementation and Automation with OANDA (UPDATED!)/21. Machine Learning Strategies (1) - Model Fitting.mp4
35.4 MB
31. Appendix 1 Python (& Finance) Basics/20. Calculate FV and PV for many Cashflows.mp4
35.1 MB
7. Trading with Python and OANDAFXCM - an Introduction/8. OANDA How to load Historical Price Data (Part 2).mp4
35.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/42. Sorting of Series and Introduction to the inplace - parameter.mp4
35.0 MB
31. Appendix 1 Python (& Finance) Basics/21. The Net Present Value - NPV (Theory).mp4
34.9 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/5. How to connect to your EC2 Instance.mp4
34.7 MB
11. Financial Data Analysis with Pandas - an Introduction/14. Momentum Trading Strategies with SMAs.mp4
34.6 MB
15. Defining and Backtesting SMA Strategies/13. Creating the Class (Part 8).mp4
34.3 MB
11. Financial Data Analysis with Pandas - an Introduction/6. The methods diff() and pct_change().mp4
34.3 MB
11. Financial Data Analysis with Pandas - an Introduction/11. Simple Returns vs. Log Returns.mp4
34.2 MB
7. Trading with Python and OANDAFXCM - an Introduction/13. FXCM How to install the FXCM API Wrapper.mp4
34.1 MB
22. Implementation and Automation with OANDA (UPDATED!)/9. Storing and resampling real-time tick data (Part 3).mp4
33.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/2. Modules, Packages and Libraries - No need to reinvent the Wheel.mp4
33.6 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/9. How to start Trading sessions with Batch (.bat) Files.mp4
33.2 MB
7. Trading with Python and OANDAFXCM - an Introduction/5. OANDA Getting the API Key & other Preparations.mp4
33.0 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/3. Excursus Your FAQs answered.mp4
32.7 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/6. IndexErrors.mp4
32.6 MB
31. Appendix 1 Python (& Finance) Basics/42. Dictionaries.mp4
32.5 MB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2. Long Term Investing vs. (Algorithmic) Day Trading.mp4
32.4 MB
22. Implementation and Automation with OANDA (UPDATED!)/11. Storing and resampling real-time tick data (Part 5).mp4
32.4 MB
3. Day Trading with OANDA A-Z a Deep Dive/14. A more general Example.mp4
32.3 MB
18. Trading Strategies powered by Machine Learning - Regression/11. Out-Sample Forward Testing.mp4
31.9 MB
22. Implementation and Automation with OANDA (UPDATED!)/20. Implementing a Bollinger Bands Strategy (Solution).mp4
31.9 MB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/5. Overview & the Brokers OANDA and FXCM.mp4
31.7 MB
20. Advanced Backtesting Techniques/1. Introduction to Iterative Backtesting (event-driven).mp4
31.6 MB
22. Implementation and Automation with OANDA (UPDATED!)/14. Working with historical data and real-time tick data (Part 3).mp4
31.5 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/8. How to run Python Scripts in a Windows Command Prompt.mp4
31.4 MB
23. Implementation and Automation with FXCM (Updated!)/12. Working with historical data and real-time tick data (Part 3).mp4
31.4 MB
31. Appendix 1 Python (& Finance) Basics/18. For Loops - Iterating over Lists.mp4
31.4 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/3. Defining a Bollinger Bands Mean-Reversion Strategy (Part 1).mp4
31.2 MB
31. Appendix 1 Python (& Finance) Basics/41. Tuples.mp4
31.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/64. Removing Columns.mp4
31.1 MB
11. Financial Data Analysis with Pandas - an Introduction/5. The shift() method.mp4
30.9 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/59. Scatterplots.mp4
30.9 MB
12. Advanced Topics/3. Timezones and Converting (Part 1).mp4
30.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/17. How to slice 2-dim Numpy Arrays (Part 1).mp4
30.3 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/5. Vectorized Strategy Backtesting.mp4
30.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/58. Histogramms (Part 2).mp4
30.3 MB
32. Appendix 2 User-defined Functions (required for OOP)/4. How to work with Default Arguments.mp4
29.9 MB
7. Trading with Python and OANDAFXCM - an Introduction/4. OANDA How to install the OANDA API Wrapper.mp4
29.8 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/5. The Impact of Granularity.mp4
29.6 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/11. Advanced Filtering & Bitwise Operators.mp4
29.5 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/46. Renaming Index & Column Labels with rename().mp4
29.3 MB
19. Trading Strategies powered by Machine Learning - Classification/1. Logistic Regression with scikit-learn - a simple Introduction (Part 1).mp4
29.3 MB
4. FOREX Day Trading with FXCM/7. Order Types at a glance.mp4
28.9 MB
19. Trading Strategies powered by Machine Learning - Classification/4. Predicting Market Direction with Logistic Regression.mp4
28.8 MB
32. Appendix 2 User-defined Functions (required for OOP)/2. Defining your first user-defined Function.mp4
28.7 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/2. The Financial Analysis Class live in action (Part 1).mp4
28.6 MB
20. Advanced Backtesting Techniques/4. Creating an Iterative Base Class (Part 1).mp4
28.5 MB
32. Appendix 2 User-defined Functions (required for OOP)/5. The Default Argument None.mp4
28.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/27. Selecting Columns.mp4
27.9 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/8. Feature ScalingEngineering.mp4
27.7 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/15. Summary and Debugging Flow-Chart.mp4
27.6 MB
32. Appendix 2 User-defined Functions (required for OOP)/7. Sequences as arguments and args.mp4
27.6 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/5. A simple Buy and Hold Strategy.mp4
27.4 MB
11. Financial Data Analysis with Pandas - an Introduction/16. Merging Aligning Financial Time Series (hands-on).mp4
27.2 MB
29. Error Handling How to make your Trading Bot more stable and reliable/11. Waiting periods between re-tries.mp4
27.1 MB
7. Trading with Python and OANDAFXCM - an Introduction/9. OANDA Streaming high-frequency real-time Data.mp4
27.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/49. Filtering DataFrames by many Conditions (OR).mp4
27.0 MB
22. Implementation and Automation with OANDA (UPDATED!)/2. Updating the Wrapper Package (Part 2).mp4
26.6 MB
31. Appendix 1 Python (& Finance) Basics/27. Build-in Functions.mp4
26.6 MB
23. Implementation and Automation with FXCM (Updated!)/9. Storing and resampling real-time tick data (Part 3).mp4
26.5 MB
29. Error Handling How to make your Trading Bot more stable and reliable/7. try, except, else.mp4
26.4 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/10. The method set_ticker().mp4
26.4 MB
15. Defining and Backtesting SMA Strategies/12. Creating the Class (Part 7).mp4
26.3 MB
31. Appendix 1 Python (& Finance) Basics/31. More on Lists.mp4
25.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6. Changing Elements in Numpy Arrays & Mutability.mp4
25.7 MB
3. Day Trading with OANDA A-Z a Deep Dive/13. Take-Profit and Stop-Loss Orders.mp4
25.6 MB
4. FOREX Day Trading with FXCM/3. Example Trade Buying EURUSD.mp4
25.6 MB
31. Appendix 1 Python (& Finance) Basics/29. Floats.mp4
25.5 MB
31. Appendix 1 Python (& Finance) Basics/24. Data Types in Action.mp4
25.5 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/31. Slicing Rows and Columns with iloc (position-based indexing).mp4
25.5 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/9. Encapsulation and protected Attributes.mp4
25.2 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/1. Project Overview.mp4
24.9 MB
11. Financial Data Analysis with Pandas - an Introduction/15. Exponentially-weighted Moving Averages (EWMA).mp4
24.9 MB
18. Trading Strategies powered by Machine Learning - Regression/5. Underfitting.mp4
24.7 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/6. The method log_returns().mp4
24.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/12. Determining a Project´s Payback Period with np.where().mp4
23.6 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/20. How to perform row-wise and column-wise Operations.mp4
23.6 MB
31. Appendix 1 Python (& Finance) Basics/10. More on Variables and Memory.mp4
23.3 MB
27. Working with two or many Strategies (Combination)/7. Combining both Strategies - Alternative 2.mp4
23.2 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/7. String representation and the special method __repr__().mp4
23.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/8. Numpy Array Methods and Attributes.mp4
23.0 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/8. Trades and Trading Costs (Part 2).mp4
23.0 MB
31. Appendix 1 Python (& Finance) Basics/51. Calculate a Project´s Payback Period.mp4
22.9 MB
31. Appendix 1 Python (& Finance) Basics/39. Mutable vs. immutable Objects (Part 2).mp4
22.9 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/4. Defining a simple Contrarian Strategy.mp4
22.5 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/33. Selecting Rows with loc (label-based indexing).mp4
22.4 MB
29. Error Handling How to make your Trading Bot more stable and reliable/9. Try again (...until it works).mp4
22.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/48. Filtering DataFrames by many Conditions (AND).mp4
22.3 MB
15. Defining and Backtesting SMA Strategies/10. Creating the Class (Part 5).mp4
22.1 MB
7. Trading with Python and OANDAFXCM - an Introduction/14. FXCM Getting the Access Token & other Preparations.mp4
22.1 MB
11. Financial Data Analysis with Pandas - an Introduction/9. Financial Time Series - Covariance and Correlation.mp4
22.1 MB
31. Appendix 1 Python (& Finance) Basics/30. How to round Floats (and Integers) with round().mp4
21.9 MB
29. Error Handling How to make your Trading Bot more stable and reliable/16. Implementation with FXCM APIServer Issues.mp4
21.8 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/15. Creating and Importing Python Modules (.py).mp4
21.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/41. The copy() method.mp4
21.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/57. Histogramms (Part 1).mp4
21.5 MB
15. Defining and Backtesting SMA Strategies/6. Creating the Class (Part 1).mp4
21.2 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/4. Getting the Data.mp4
21.2 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1. Introduction and Motivation.mp4
21.2 MB
18. Trading Strategies powered by Machine Learning - Regression/10. In-Sample Backtesting and the Look-ahead-bias.mp4
21.2 MB
31. Appendix 1 Python (& Finance) Basics/33. Slicing Lists.mp4
21.1 MB
29. Error Handling How to make your Trading Bot more stable and reliable/8. finally.mp4
21.1 MB
19. Trading Strategies powered by Machine Learning - Classification/6. Out-Sample Forward Testing.mp4
21.1 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/1. Simple ContrarianMomentum Strategies - Overview.mp4
20.9 MB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/3. The Financial Analysis Class live in action (Part 2).mp4
20.7 MB
29. Error Handling How to make your Trading Bot more stable and reliable/1. Introduction.mp4
20.5 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7. View vs. copy - potential Pitfalls when slicing Numpy Arrays.mp4
20.2 MB
31. Appendix 1 Python (& Finance) Basics/6. Calculate Interest Rates and Returns with Python.mp4
20.2 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/6. Adding lags.mp4
20.2 MB
22. Implementation and Automation with OANDA (UPDATED!)/23. Importing a Trader Module Class.mp4
20.1 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/3. The best time to trade (Part 2).mp4
20.0 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/2. The best time to trade (Part 1).mp4
20.0 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/38. First Steps with Pandas Series.mp4
19.9 MB
4. FOREX Day Trading with FXCM/4. Trade Analysis.mp4
19.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/5. Vectorized Operations with Numpy Arrays.mp4
19.6 MB
32. Appendix 2 User-defined Functions (required for OOP)/6. How to unpack Iterables.mp4
19.5 MB
4. FOREX Day Trading with FXCM/6. Closing Positions vs. Hedging Positions.mp4
19.4 MB
11. Financial Data Analysis with Pandas - an Introduction/1. Getting Ready (Installing required library).mp4
19.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/60. First Steps with Seaborn.mp4
19.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/15. How to work with nested Lists.mp4
19.1 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/11. Saving Model and Parameters.mp4
19.1 MB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/3. Amazon Web Services (AWS) - Overview and how to create a Free Trial Account.mp4
19.1 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/10. Boolean Arrays and Conditional Filtering.mp4
19.0 MB
31. Appendix 1 Python (& Finance) Basics/7. Introduction to Variables.mp4
19.0 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/22. Intro to Tabular Data Pandas.mp4
19.0 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/6. Conclusions.mp4
18.9 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/45. Changing Column Labels.mp4
18.8 MB
9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1. Introduction and Downloads Part 2.mp4
18.8 MB
18. Trading Strategies powered by Machine Learning - Regression/7. A simple Linear Model to predict Financial Returns (Part 1).mp4
18.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/9. Numpy Universal Functions.mp4
18.6 MB
31. Appendix 1 Python (& Finance) Basics/32. Lists and Element-wise Operations.mp4
18.4 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/13. External Factors and Issues.mp4
18.3 MB
31. Appendix 1 Python (& Finance) Basics/12. The print() Function.mp4
18.3 MB
31. Appendix 1 Python (& Finance) Basics/44. String Replacement.mp4
18.2 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/1. Introduction.mp4
18.0 MB
31. Appendix 1 Python (& Finance) Basics/19. The range Object - another Iterable.mp4
17.9 MB
31. Appendix 1 Python (& Finance) Basics/11. Variables - Dos, Don´ts and Conventions.mp4
17.9 MB
19. Trading Strategies powered by Machine Learning - Classification/3. Getting and Preparing the Data.mp4
17.9 MB
20. Advanced Backtesting Techniques/5. Creating an Iterative Base Class (Part 2).mp4
17.7 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/9. TypeErrors and ValueErrors.mp4
17.6 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/19. Recap Changing Elements in a Numpy Array slice.mp4
17.3 MB
31. Appendix 1 Python (& Finance) Basics/2. Intro to the Time Value of Money (TVM) Concept (Theory).mp4
17.3 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/14. Errors related to the course content (Transcription Errors).mp4
17.2 MB
18. Trading Strategies powered by Machine Learning - Regression/3. Making Predictions with Linear Regression.mp4
17.0 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/16. 2-dimensional Numpy Arrays.mp4
16.9 MB
16. Defining and Backtesting simple MomentumContrarian Strategies/2. Getting the Data.mp4
16.1 MB
27. Working with two or many Strategies (Combination)/5. Taking into account busy Trading Hours.mp4
15.9 MB
20. Advanced Backtesting Techniques/6. Creating an Iterative Base Class (Part 3).mp4
15.9 MB
27. Working with two or many Strategies (Combination)/3. Strategy 2 Mean Reversion.mp4
15.3 MB
31. Appendix 1 Python (& Finance) Basics/22. Calculate an Investment Project´s NPV.mp4
15.0 MB
31. Appendix 1 Python (& Finance) Basics/5. Interest Rates and Returns (Theory).mp4
14.9 MB
19. Trading Strategies powered by Machine Learning - Classification/5. In-Sample Backtesting and the Look-ahead-bias.mp4
14.9 MB
23. Implementation and Automation with FXCM (Updated!)/16. Trading other Strategies - Coding Challenge.mp4
14.8 MB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/1. Our very first Trade.mp4
14.6 MB
31. Appendix 1 Python (& Finance) Basics/17. Indexing Lists.mp4
14.5 MB
4. FOREX Day Trading with FXCM/5. Charting.mp4
14.4 MB
29. Error Handling How to make your Trading Bot more stable and reliable/10. How to limit the number of retries.mp4
14.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/4. Indexing and Slicing Numpy Arrays.mp4
14.3 MB
32. Appendix 2 User-defined Functions (required for OOP)/8. How to return many results.mp4
14.1 MB
27. Working with two or many Strategies (Combination)/2. Strategy 1 SMA.mp4
13.9 MB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/4. Spreads during the busy hours.mp4
13.9 MB
31. Appendix 1 Python (& Finance) Basics/36. Sorting and Reversing Lists.mp4
13.8 MB
31. Appendix 1 Python (& Finance) Basics/3. Calculate Future Values (FV) with Python Compounding.mp4
13.4 MB
22. Implementation and Automation with OANDA (UPDATED!)/18. Trading other Strategies - Coding Challenge.mp4
13.2 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/8. Misuse of function names and keywords.mp4
13.0 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/7. Indentation Errors.mp4
12.8 MB
31. Appendix 1 Python (& Finance) Basics/46. Operators (Theory).mp4
12.3 MB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/2. Getting the Data.mp4
12.2 MB
27. Working with two or many Strategies (Combination)/1. Introduction.mp4
12.0 MB
31. Appendix 1 Python (& Finance) Basics/8. Excursus How to add inline comments.mp4
11.8 MB
31. Appendix 1 Python (& Finance) Basics/28. Integers.mp4
11.5 MB
27. Working with two or many Strategies (Combination)/6. Strategy Backtesting.mp4
11.4 MB
31. Appendix 1 Python (& Finance) Basics/25. The Data Type Hierarchy (Theory).mp4
11.3 MB
21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/1. Introduction and Overview.mp4
11.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/53. Exporting DataFrames to csv.mp4
11.1 MB
31. Appendix 1 Python (& Finance) Basics/14. TVM Problems with many Cashflows.mp4
11.0 MB
25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/1. Overview.mp4
10.7 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/29. Zero-based Indexing and Negative Indexing.mp4
10.7 MB
31. Appendix 1 Python (& Finance) Basics/35. Changing Elements in Lists.mp4
10.6 MB
31. Appendix 1 Python (& Finance) Basics/4. Calculate Present Values (FV) with Python Discounting.mp4
10.5 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/7. Splitting into Train and Test Set.mp4
10.3 MB
18. Trading Strategies powered by Machine Learning - Regression/6. Getting the Data.mp4
10.0 MB
5. Installing Python and Jupyter Notebooks/1. Introduction.mp4
9.3 MB
31. Appendix 1 Python (& Finance) Basics/45. Booleans.mp4
9.3 MB
29. Error Handling How to make your Trading Bot more stable and reliable/4. try and except.mp4
9.3 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/18. How to slice 2-dim Numpy Arrays (Part 2).mp4
9.2 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/28. Selecting one Column with the dot notation.mp4
9.0 MB
28. A Machine Learning-powered Strategy A-Z (DNN)/4. Getting and Preparing the Data.mp4
8.8 MB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/65. Introduction to GroupBy Operations.mp4
8.5 MB
31. Appendix 1 Python (& Finance) Basics/15. Intro to Python Lists.mp4
8.1 MB
29. Error Handling How to make your Trading Bot more stable and reliable/5. Catching specific Errors.mp4
7.9 MB
31. Appendix 1 Python (& Finance) Basics/16. Zero-based Indexing and negative Indexing in Python (Theory).mp4
7.8 MB
30. +++ APPENDIX Python Crash Course +++/1. Overview.mp4
7.2 MB
29. Error Handling How to make your Trading Bot more stable and reliable/3. Python Errors (Exceptions).mp4
7.2 MB
5. Installing Python and Jupyter Notebooks/5. Tips for Python Beginners.mp4
6.5 MB
29. Error Handling How to make your Trading Bot more stable and reliable/6. The Exception class.mp4
5.9 MB
31. Appendix 1 Python (& Finance) Basics/9. Variables and Memory (Theory).mp4
5.7 MB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/3. Major reasons for Coding Errors.mp4
5.7 MB
31. Appendix 1 Python (& Finance) Basics/26. Excursus Dynamic Typing in Python.mp4
5.5 MB
7. Trading with Python and OANDAFXCM - an Introduction/2. Overview.mp4
4.7 MB
8. Conclusion and Outlook/1. Conclusion and Outlook.mp4
4.1 MB
14. +++ PART 3 Defining and Testing Trading Strategies +++/3.1 Part3_Materials.zip
2.3 MB
9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1.1 Part2_Materials.zip
1.9 MB
25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/2.1 Part5_Materials.zip
1.9 MB
21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/2.1 Part4_Materials.zip
754.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/1.1 Appendix3_Materials.zip
672.0 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/5.1 Brokers.pdf
567.0 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2.1 Trading_vs_investing.pdf
542.5 kB
18. Trading Strategies powered by Machine Learning - Regression/1.1 ML.pdf
504.1 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1.1 OOP.pdf
491.0 kB
1. Getting Started/1.1 Overview.pdf
488.2 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/1.1 DNN.pdf
441.4 kB
1. Getting Started/3.1 did_you_know.pdf
439.6 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/2.1 strategy_overview.pdf
410.7 kB
20. Advanced Backtesting Techniques/1.1 Event_Driven_BT.pdf
394.9 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1.1 cloud.pdf
375.2 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3.1 Spot_vs_Futures.pdf
259.8 kB
31. Appendix 1 Python (& Finance) Basics/21.1 NPV.pdf
251.6 kB
31. Appendix 1 Python (& Finance) Basics/5.1 Interest_Rates.pdf
202.6 kB
31. Appendix 1 Python (& Finance) Basics/2.1 TVM.pdf
200.5 kB
31. Appendix 1 Python (& Finance) Basics/20.1 PV_FV_many.pdf
199.2 kB
31. Appendix 1 Python (& Finance) Basics/14.1 FV_many.pdf
190.4 kB
31. Appendix 1 Python (& Finance) Basics/39.1 Python_for_Finance_Mutability.pdf
170.7 kB
31. Appendix 1 Python (& Finance) Basics/25.1 Type_Hierarchy.pdf
166.3 kB
31. Appendix 1 Python (& Finance) Basics/46.1 Operators.pdf
149.1 kB
31. Appendix 1 Python (& Finance) Basics/9.1 Variables.pdf
146.4 kB
31. Appendix 1 Python (& Finance) Basics/16.1 Indexing.pdf
125.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7.1 Slicing_arrays.pdf
125.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6.1 Mutability_arrays.pdf
124.7 kB
3. Day Trading with OANDA A-Z a Deep Dive/3.1 Currency.pdf
116.4 kB
3. Day Trading with OANDA A-Z a Deep Dive/6.1 spread.pdf
114.9 kB
31. Appendix 1 Python (& Finance) Basics/34.1 Slicing_cheatsheet.pdf
107.8 kB
31. Appendix 1 Python (& Finance) Basics/27.1 Built_in_func.pdf
94.8 kB
3. Day Trading with OANDA A-Z a Deep Dive/9.1 Candlestick.pdf
94.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/32.1 pandas_iloc.pdf
73.7 kB
31. Appendix 1 Python (& Finance) Basics/11.1 keywords.pdf
71.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/35.1 Pandas_loc.pdf
69.4 kB
3. Day Trading with OANDA A-Z a Deep Dive/5.1 Long_EUR.xlsx
41.0 kB
3. Day Trading with OANDA A-Z a Deep Dive/10.1 Short_EUR.xlsx
36.3 kB
4. FOREX Day Trading with FXCM/4.1 Long_EUR_fxcm.xlsx
27.0 kB
31. Appendix 1 Python (& Finance) Basics/1.1 Appendix1_Materials.zip
19.4 kB
5. Installing Python and Jupyter Notebooks/4. How to work with Jupyter Notebooks.srt
18.0 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/61. Categorical Seaborn Plots.srt
17.2 kB
10. Introduction to Time Series Data in Pandas/4. Downsampling Time Series with resample().srt
17.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/39. Analyzing Numerical Series with unique(), nunique() and value_counts().srt
16.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/25. First Data Inspection.srt
14.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/56. Customization of Plots.srt
14.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/62. Seaborn Regression Plots.srt
14.6 kB
11. Financial Data Analysis with Pandas - an Introduction/12. Importing Financial Data from Excel.srt
14.2 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/13. Implementation (Oanda & FXCM).srt
13.8 kB
31. Appendix 1 Python (& Finance) Basics/40. Coding Exercise 3.srt
13.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/52. Handling NA Values missing Values.srt
13.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/47. Filtering DataFrames (one Condition).srt
13.1 kB
15. Defining and Backtesting SMA Strategies/4. Finding the optimal SMA Strategy.srt
12.9 kB
3. Day Trading with OANDA A-Z a Deep Dive/6. Trading Costs and Performance Attribution.srt
12.8 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1. Introduction to OOP and examples for Classes.srt
12.6 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/68. split-apply-combine.srt
12.5 kB
34. What´s next (outlook and additional resources)/1. Bonus Lecture.html
12.3 kB
12. Advanced Topics/2. Filling NA Values with bfill, ffill and interpolation.srt
12.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/44. Changing Row Index with set_index() and reset_index().srt
12.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/54. Summary Statistics and Accumulations.srt
12.0 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/2. Test your debugging skills!.srt
11.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/34. Slicing Rows and Columns with loc (label-based indexing).srt
11.9 kB
19. Trading Strategies powered by Machine Learning - Classification/7. Generalization with OOP A Classification Backtesting Class in action.srt
11.9 kB
31. Appendix 1 Python (& Finance) Basics/50. Keywords pass, continue and break.srt
11.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/30. Selecting Rows with iloc (position-based indexing).srt
11.8 kB
31. Appendix 1 Python (& Finance) Basics/13. Coding Exercise 1.srt
11.8 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/11. How to traceback more complex Errors.srt
11.8 kB
31. Appendix 1 Python (& Finance) Basics/37. Adding and removing Elements fromto Lists.srt
11.7 kB
15. Defining and Backtesting SMA Strategies/5. Generalization with OOP An SMA Backtesting Class in action.srt
11.6 kB
5. Installing Python and Jupyter Notebooks/3. How to open Jupyter Notebooks.srt
11.4 kB
10. Introduction to Time Series Data in Pandas/2. Converting strings to datetime objects with pd.to_datetime().srt
11.4 kB
11. Financial Data Analysis with Pandas - an Introduction/11. Simple Returns vs. Log Returns.srt
11.4 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/42. Sorting of Series and Introduction to the inplace - parameter.srt
11.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/51. Intro to NA Values missing Values.srt
11.3 kB
31. Appendix 1 Python (& Finance) Basics/48. Coding Exercise 4.srt
11.2 kB
11. Financial Data Analysis with Pandas - an Introduction/13. Simple Moving Averages (SMA) with rolling().srt
11.2 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/55. Visualization with Matplotlib (Intro).srt
11.0 kB
11. Financial Data Analysis with Pandas - an Introduction/7. Measuring Stock Performance with MEAN Returns and STD of Returns.srt
11.0 kB
31. Appendix 1 Python (& Finance) Basics/49. Conditional Statements.srt
10.8 kB
23. Implementation and Automation with FXCM (Updated!)/2. Historical Data, real-time Data and Orders (Recap).srt
10.7 kB
7. Trading with Python and OANDAFXCM - an Introduction/10. OANDA How to place Orders and execute Trades.srt
10.7 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/23. Create your very first Pandas DataFrame (from csv).srt
10.6 kB
11. Financial Data Analysis with Pandas - an Introduction/8. Financial Time Series - Return and Risk.srt
10.6 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/63. Seaborn Heatmaps.srt
10.6 kB
31. Appendix 1 Python (& Finance) Basics/38. Mutable vs. immutable Objects (Part 1).srt
10.6 kB
11. Financial Data Analysis with Pandas - an Introduction/2. Importing Stock Price Data from Yahoo Finance.srt
10.4 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/4. Spot Trading vs. Derivatives Trading (Part 2).srt
10.4 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/4. Defining a Bollinger Bands Mean-Reversion Strategy (Part 2).srt
10.3 kB
1. Getting Started/5. Student FAQ.html
10.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/66. Understanding the GroupBy Object.srt
10.3 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/7. Trades and Trading Costs (Part 1).srt
10.3 kB
31. Appendix 1 Python (& Finance) Basics/23. Coding Exercise 2.srt
10.3 kB
27. Working with two or many Strategies (Combination)/8. Strategy Optimization.srt
10.3 kB
32. Appendix 2 User-defined Functions (required for OOP)/9. Scope - easily explained.srt
10.2 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3. Spot Trading vs. Derivatives Trading (Part 1).srt
10.2 kB
10. Introduction to Time Series Data in Pandas/1. Importing Time Series Data from csv-files.srt
10.2 kB
22. Implementation and Automation with OANDA (UPDATED!)/4. Historical Data, real-time Data and Orders (Recap).srt
10.2 kB
20. Advanced Backtesting Techniques/13. Adding the Iterative Backtest Child Class for SMA (Part 2).srt
10.2 kB
31. Appendix 1 Python (& Finance) Basics/43. Intro to Strings.srt
10.1 kB
3. Day Trading with OANDA A-Z a Deep Dive/3. FOREX Currency Exchange Rates explained.srt
10.0 kB
31. Appendix 1 Python (& Finance) Basics/18. For Loops - Iterating over Lists.srt
10.0 kB
31. Appendix 1 Python (& Finance) Basics/21. The Net Present Value - NPV (Theory).srt
10.0 kB
15. Defining and Backtesting SMA Strategies/3. Vectorized Strategy Backtesting.srt
9.8 kB
31. Appendix 1 Python (& Finance) Basics/47. Comparison, Logical and Membership Operators in Action.srt
9.7 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/12. Inheritance.srt
9.6 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/3. Numpy Arrays.srt
9.5 kB
22. Implementation and Automation with OANDA (UPDATED!)/12. Working with historical data and real-time tick data (Part 1).srt
9.5 kB
22. Implementation and Automation with OANDA (UPDATED!)/17. Trade Monitoring and Reporting.srt
9.4 kB
18. Trading Strategies powered by Machine Learning - Regression/2. Linear Regression with scikit-learn - a simple Introduction.srt
9.4 kB
3. Day Trading with OANDA A-Z a Deep Dive/1. OANDA at a first glance.srt
9.4 kB
23. Implementation and Automation with FXCM (Updated!)/8. Storing and resampling real-time tick data (Part 2).srt
9.4 kB
31. Appendix 1 Python (& Finance) Basics/20. Calculate FV and PV for many Cashflows.srt
9.3 kB
23. Implementation and Automation with FXCM (Updated!)/6. Storing and resampling real-time tick data (Part 1).srt
9.3 kB
5. Installing Python and Jupyter Notebooks/2. Download and Install Anaconda.srt
9.3 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/4. The special method __init__().srt
9.2 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/2. Modules, Packages and Libraries - No need to reinvent the Wheel.srt
9.2 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/50. Advanced Filtering with between(), isin() and ~.srt
9.2 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/4. How to create an EC2 Instance.srt
9.2 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/9. Generalization with OOP A Contrarian Backtesting Class in action.srt
9.1 kB
29. Error Handling How to make your Trading Bot more stable and reliable/14. Oanda Error Handling (Part 2).srt
9.0 kB
22. Implementation and Automation with OANDA (UPDATED!)/10. Storing and resampling real-time tick data (Part 4).srt
9.0 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/15. Summary and Debugging Flow-Chart.srt
8.9 kB
11. Financial Data Analysis with Pandas - an Introduction/14. Momentum Trading Strategies with SMAs.srt
8.9 kB
11. Financial Data Analysis with Pandas - an Introduction/5. The shift() method.srt
8.9 kB
31. Appendix 1 Python (& Finance) Basics/52. Introduction to while loops.srt
8.9 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/9. Creating and Fitting the DNN Model.srt
8.8 kB
22. Implementation and Automation with OANDA (UPDATED!)/7. Storing and resampling real-time tick data (Part 1).srt
8.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/40. Analyzing non-numerical Series with unique(), nunique(), value_counts().srt
8.7 kB
11. Financial Data Analysis with Pandas - an Introduction/6. The methods diff() and pct_change().srt
8.6 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/59. Scatterplots.srt
8.5 kB
31. Appendix 1 Python (& Finance) Basics/41. Tuples.srt
8.5 kB
23. Implementation and Automation with FXCM (Updated!)/14. Placing Orders and Executing Trades.srt
8.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/67. Splitting with many Keys.srt
8.5 kB
7. Trading with Python and OANDAFXCM - an Introduction/7. OANDA How to load Historical Price Data (Part 1).srt
8.5 kB
15. Defining and Backtesting SMA Strategies/7. Creating the Class (Part 2).srt
8.5 kB
3. Day Trading with OANDA A-Z a Deep Dive/8. Margin Closeout and more.srt
8.4 kB
7. Trading with Python and OANDAFXCM - an Introduction/6. OANDA Connecting to the APIServer.srt
8.4 kB
3. Day Trading with OANDA A-Z a Deep Dive/7. Margin and Leverage.srt
8.4 kB
10. Introduction to Time Series Data in Pandas/3. Indexing and Slicing Time Series.srt
8.3 kB
19. Trading Strategies powered by Machine Learning - Classification/8. The Classification Backtesting Class explained (Part 1).srt
8.3 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/16. Coding Exercise 3 Create your own Class.srt
8.3 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/5. Omitting cells, changing the sequence and more.srt
8.3 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/10. Prediction & Out-Sample Forward Testing.srt
8.3 kB
7. Trading with Python and OANDAFXCM - an Introduction/15. FXCM Connecting to the APIServer.srt
8.3 kB
20. Advanced Backtesting Techniques/7. Creating an Iterative Base Class (Part 4).srt
8.2 kB
31. Appendix 1 Python (& Finance) Basics/10. More on Variables and Memory.srt
8.2 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/58. Histogramms (Part 2).srt
8.2 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/1. Introduction to Part 3.srt
8.2 kB
3. Day Trading with OANDA A-Z a Deep Dive/2. How to create an Account.srt
8.1 kB
3. Day Trading with OANDA A-Z a Deep Dive/10. Our third Trade A-Z - Going Short EURUSD.srt
8.1 kB
20. Advanced Backtesting Techniques/15. OOP Challenge Add Contrarian and Bollinger Strategies.srt
8.1 kB
23. Implementation and Automation with FXCM (Updated!)/5. Collecting and storing real-time tick data.srt
8.1 kB
18. Trading Strategies powered by Machine Learning - Regression/1. Machine Learning - an Overview.srt
8.1 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/2. Trading Strategies - an Overview.srt
8.1 kB
31. Appendix 1 Python (& Finance) Basics/42. Dictionaries.srt
8.1 kB
7. Trading with Python and OANDAFXCM - an Introduction/20. FXCM How to place Orders and execute Trades.srt
8.0 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/24. Pandas Display Options and the methods head() & tail().srt
8.0 kB
31. Appendix 1 Python (& Finance) Basics/27. Build-in Functions.srt
7.9 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/6. Generalization with OOP A Bollinger Bands Backtesting Class in action.srt
7.9 kB
15. Defining and Backtesting SMA Strategies/2. Defining an SMA Crossover Strategy.srt
7.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/36. Summary, Best Practices and Outlook.srt
7.9 kB
11. Financial Data Analysis with Pandas - an Introduction/4. Normalizing Time Series to a Base Value (100).srt
7.8 kB
31. Appendix 1 Python (& Finance) Basics/24. Data Types in Action.srt
7.7 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/3. Installation of Tensorflow & Keras (Part 2).srt
7.7 kB
3. Day Trading with OANDA A-Z a Deep Dive/11. Netting vs. Hedging.srt
7.7 kB
31. Appendix 1 Python (& Finance) Basics/2. Intro to the Time Value of Money (TVM) Concept (Theory).srt
7.7 kB
20. Advanced Backtesting Techniques/10. Creating an Iterative Base Class (Part 7).srt
7.7 kB
32. Appendix 2 User-defined Functions (required for OOP)/3. What´s the difference between Positional Arguments vs. Keyword Arguments.srt
7.7 kB
3. Day Trading with OANDA A-Z a Deep Dive/5. How to calculate Profit & Loss of a Trade.srt
7.6 kB
4. FOREX Day Trading with FXCM/2. How to create an Account.srt
7.6 kB
32. Appendix 2 User-defined Functions (required for OOP)/5. The Default Argument None.srt
7.6 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/12. Problems with the Python Installation.srt
7.6 kB
18. Trading Strategies powered by Machine Learning - Regression/8. A simple Linear Model to predict Financial Returns (Part 2).srt
7.6 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/10. Getting help on StackOverflow.com.srt
7.6 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/14. Adding meaningful Docstrings.srt
7.6 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/6. Performance Metrics.srt
7.5 kB
32. Appendix 2 User-defined Functions (required for OOP)/2. Defining your first user-defined Function.srt
7.5 kB
15. Defining and Backtesting SMA Strategies/8. Creating the Class (Part 3).srt
7.5 kB
18. Trading Strategies powered by Machine Learning - Regression/4. Overfitting.srt
7.5 kB
22. Implementation and Automation with OANDA (UPDATED!)/22. Machine Learning Strategies (2) - Implementation.srt
7.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/11. Advanced Filtering & Bitwise Operators.srt
7.4 kB
12. Advanced Topics/1. Helpful DatetimeIndex Attributes and Methods.srt
7.4 kB
19. Trading Strategies powered by Machine Learning - Classification/2. Logistic Regression with scikit-learn - a simple Introduction (Part 2).srt
7.4 kB
7. Trading with Python and OANDAFXCM - an Introduction/19. FXCM Streaming high-frequency real-time Data.srt
7.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/25. Running a Python Trader Script.srt
7.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/13. Working with historical data and real-time tick data (Part 2).srt
7.4 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/13. Inheritance and the super() Function.srt
7.3 kB
4. FOREX Day Trading with FXCM/1. FXCM at a first glance.srt
7.3 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/11. How to stop Trading Sessions (OANDA).srt
7.3 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/5. Adding LabelsFeatures.srt
7.2 kB
20. Advanced Backtesting Techniques/11. Creating an Iterative Base Class (Part 8).srt
7.1 kB
29. Error Handling How to make your Trading Bot more stable and reliable/13. Oanda Error Handling (Part 1).srt
7.1 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/2. Demonstration AWS EC2 for Algorithmic Trading live in action.srt
7.1 kB
1. Getting Started/2. How to get the best out of this course.srt
7.1 kB
23. Implementation and Automation with FXCM (Updated!)/21. Running a Python Script.srt
7.0 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/60. First Steps with Seaborn.srt
7.0 kB
7. Trading with Python and OANDAFXCM - an Introduction/17. FXCM How to load Historical Price Data (Part 1).srt
7.0 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/43. First Steps with Pandas Index Objects.srt
7.0 kB
31. Appendix 1 Python (& Finance) Basics/29. Floats.srt
7.0 kB
32. Appendix 2 User-defined Functions (required for OOP)/4. How to work with Default Arguments.srt
7.0 kB
20. Advanced Backtesting Techniques/2. A first Intuition on Iterative Backtesting (Part 1).srt
6.9 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/5. The method get_data().srt
6.9 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/1. Project Overview.srt
6.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/17. How to slice 2-dim Numpy Arrays (Part 1).srt
6.9 kB
23. Implementation and Automation with FXCM (Updated!)/10. Working with historical data and real-time tick data (Part 1).srt
6.9 kB
23. Implementation and Automation with FXCM (Updated!)/15. Trade Monitoring and Reporting.srt
6.9 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/4. The most commonly made Errors at a glance.srt
6.9 kB
22. Implementation and Automation with OANDA (UPDATED!)/16. Placing Orders and Executing Trades.srt
6.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6. Changing Elements in Numpy Arrays & Mutability.srt
6.8 kB
22. Implementation and Automation with OANDA (UPDATED!)/15. Defining a simple Contrarian Strategy.srt
6.8 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/6. Getting the Instance Ready for Algorithmic Trading.srt
6.8 kB
31. Appendix 1 Python (& Finance) Basics/30. How to round Floats (and Integers) with round().srt
6.7 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/27. Selecting Columns.srt
6.7 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/1. Mean-Reversion Strategies - Overview.srt
6.7 kB
18. Trading Strategies powered by Machine Learning - Regression/9. A Multiple Regression Model to predict Financial Returns.srt
6.7 kB
20. Advanced Backtesting Techniques/12. Adding the Iterative Backtest Child Class for SMA (Part 1).srt
6.7 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/11. Adding more methods and performance metrics.srt
6.6 kB
31. Appendix 1 Python (& Finance) Basics/31. More on Lists.srt
6.6 kB
22. Implementation and Automation with OANDA (UPDATED!)/21. Machine Learning Strategies (1) - Model Fitting.srt
6.6 kB
23. Implementation and Automation with FXCM (Updated!)/18. Machine Learning Strategies (1) - Model Fitting.srt
6.6 kB
27. Working with two or many Strategies (Combination)/4. Combining both Strategies - Alternative 1.srt
6.6 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/5. Vectorized Strategy Backtesting.srt
6.5 kB
31. Appendix 1 Python (& Finance) Basics/7. Introduction to Variables.srt
6.5 kB
32. Appendix 2 User-defined Functions (required for OOP)/7. Sequences as arguments and args.srt
6.5 kB
22. Implementation and Automation with OANDA (UPDATED!)/6. How to collect and store real-time tick data.srt
6.4 kB
23. Implementation and Automation with FXCM (Updated!)/11. Working with historical data and real-time tick data (Part 2).srt
6.4 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/8. Numpy Array Methods and Attributes.srt
6.4 kB
23. Implementation and Automation with FXCM (Updated!)/7. A Trader Class.srt
6.4 kB
23. Implementation and Automation with FXCM (Updated!)/4. Preview A Trader Class live in action.srt
6.4 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/64. Removing Columns.srt
6.3 kB
29. Error Handling How to make your Trading Bot more stable and reliable/1. Introduction.srt
6.3 kB
20. Advanced Backtesting Techniques/8. Creating an Iterative Base Class (Part 5).srt
6.3 kB
23. Implementation and Automation with FXCM (Updated!)/19. Machine Learning Strategies (2) - Implementation.srt
6.3 kB
1. Getting Started/1. What is Algorithmic Trading Course Overview.srt
6.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/13. Creating Numpy Arrays from Scratch.srt
6.3 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/6. Changing the Window Parameter.srt
6.3 kB
3. Day Trading with OANDA A-Z a Deep Dive/12. Market, Limit and Stop Orders.srt
6.2 kB
11. Financial Data Analysis with Pandas - an Introduction/3. Initial Inspection and Visualization.srt
6.2 kB
31. Appendix 1 Python (& Finance) Basics/5. Interest Rates and Returns (Theory).srt
6.2 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/1. Introduction and Preparing the Data.srt
6.2 kB
19. Trading Strategies powered by Machine Learning - Classification/1. Logistic Regression with scikit-learn - a simple Introduction (Part 1).srt
6.2 kB
11. Financial Data Analysis with Pandas - an Introduction/16. Merging Aligning Financial Time Series (hands-on).srt
6.2 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/5. Overview & the Brokers OANDA and FXCM.srt
6.1 kB
31. Appendix 1 Python (& Finance) Basics/19. The range Object - another Iterable.srt
6.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/12. Determining a Project´s Payback Period with np.where().srt
6.0 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/5. A simple Buy and Hold Strategy.srt
6.0 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/8. The methods plot_prices() and plot_returns().srt
6.0 kB
31. Appendix 1 Python (& Finance) Basics/39. Mutable vs. immutable Objects (Part 2).srt
6.0 kB
29. Error Handling How to make your Trading Bot more stable and reliable/18. FXCM Error Handling (Part 2).srt
6.0 kB
10. Introduction to Time Series Data in Pandas/5. Coding Exercise 1.srt
5.9 kB
22. Implementation and Automation with OANDA (UPDATED!)/5. Preview A Trader Class live in action.srt
5.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7. View vs. copy - potential Pitfalls when slicing Numpy Arrays.srt
5.9 kB
20. Advanced Backtesting Techniques/3. A first Intuition on Iterative Backtesting (Part 2).srt
5.9 kB
7. Trading with Python and OANDAFXCM - an Introduction/5. OANDA Getting the API Key & other Preparations.srt
5.9 kB
22. Implementation and Automation with OANDA (UPDATED!)/8. Storing and resampling real-time tick data (Part 2).srt
5.9 kB
3. Day Trading with OANDA A-Z a Deep Dive/9. Introduction to Charting.srt
5.8 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/10. OOP Challenge Create the Contrarian Backtesting Class (incl. Solution).srt
5.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/49. Filtering DataFrames by many Conditions (OR).srt
5.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/22. Intro to Tabular Data Pandas.srt
5.8 kB
15. Defining and Backtesting SMA Strategies/1. SMA Crossover Strategies - Overview.srt
5.8 kB
11. Financial Data Analysis with Pandas - an Introduction/15. Exponentially-weighted Moving Averages (EWMA).srt
5.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/10. Boolean Arrays and Conditional Filtering.srt
5.8 kB
20. Advanced Backtesting Techniques/14. Using Modules and adding Docstrings.srt
5.7 kB
32. Appendix 2 User-defined Functions (required for OOP)/6. How to unpack Iterables.srt
5.7 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/12. How to stop Trading Sessions (FXCM).srt
5.7 kB
31. Appendix 1 Python (& Finance) Basics/51. Calculate a Project´s Payback Period.srt
5.7 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2. Long Term Investing vs. (Algorithmic) Day Trading.srt
5.7 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/20. How to perform row-wise and column-wise Operations.srt
5.7 kB
12. Advanced Topics/4. Timezones and Converting (Part 2).srt
5.7 kB
11. Financial Data Analysis with Pandas - an Introduction/9. Financial Time Series - Covariance and Correlation.srt
5.6 kB
15. Defining and Backtesting SMA Strategies/9. Creating the Class (Part 4).srt
5.6 kB
23. Implementation and Automation with FXCM (Updated!)/13. Defining a Simple Contrarian Trading Strategy.srt
5.6 kB
29. Error Handling How to make your Trading Bot more stable and reliable/17. FXCM Error Handling (Part 1).srt
5.6 kB
1. Getting Started/3. Did you know... (what Data can tell us about Day Trading).srt
5.6 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/57. Histogramms (Part 1).srt
5.5 kB
15. Defining and Backtesting SMA Strategies/11. Creating the Class (Part 6).srt
5.5 kB
31. Appendix 1 Python (& Finance) Basics/32. Lists and Element-wise Operations.srt
5.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/15. How to work with nested Lists.srt
5.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/31. Slicing Rows and Columns with iloc (position-based indexing).srt
5.5 kB
12. Advanced Topics/3. Timezones and Converting (Part 1).srt
5.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/48. Filtering DataFrames by many Conditions (AND).srt
5.5 kB
29. Error Handling How to make your Trading Bot more stable and reliable/9. Try again (...until it works).srt
5.4 kB
31. Appendix 1 Python (& Finance) Basics/46. Operators (Theory).srt
5.4 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/6. IndexErrors.srt
5.4 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/41. The copy() method.srt
5.4 kB
7. Trading with Python and OANDAFXCM - an Introduction/18. FXCM How to load Historical Price Data (Part 2).srt
5.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/9. Storing and resampling real-time tick data (Part 3).srt
5.4 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/2. The Financial Analysis Class live in action (Part 1).srt
5.4 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/13. External Factors and Issues.srt
5.3 kB
20. Advanced Backtesting Techniques/4. Creating an Iterative Base Class (Part 1).srt
5.3 kB
15. Defining and Backtesting SMA Strategies/13. Creating the Class (Part 8).srt
5.3 kB
31. Appendix 1 Python (& Finance) Basics/12. The print() Function.srt
5.3 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/14. Errors related to the course content (Transcription Errors).srt
5.2 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/3. Excursus Your FAQs answered.srt
5.2 kB
31. Appendix 1 Python (& Finance) Basics/33. Slicing Lists.srt
5.1 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/10. How to schedule Trading sessions with the Task Scheduler.srt
5.1 kB
20. Advanced Backtesting Techniques/1. Introduction to Iterative Backtesting (event-driven).srt
5.1 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/3. Defining a Bollinger Bands Mean-Reversion Strategy (Part 1).srt
5.1 kB
29. Error Handling How to make your Trading Bot more stable and reliable/11. Waiting periods between re-tries.srt
5.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/38. First Steps with Pandas Series.srt
5.1 kB
18. Trading Strategies powered by Machine Learning - Regression/11. Out-Sample Forward Testing.srt
5.0 kB
19. Trading Strategies powered by Machine Learning - Classification/9. The Classification Backtesting Class explained (Part 2).srt
5.0 kB
31. Appendix 1 Python (& Finance) Basics/44. String Replacement.srt
5.0 kB
31. Appendix 1 Python (& Finance) Basics/6. Calculate Interest Rates and Returns with Python.srt
5.0 kB
23. Implementation and Automation with FXCM (Updated!)/17. SMA Crossover and Bollinger Bands (Solution).srt
5.0 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/5. Vectorized Strategy Backtesting.srt
4.9 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/5. The Impact of Granularity.srt
4.9 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/9. Numpy Universal Functions.srt
4.9 kB
31. Appendix 1 Python (& Finance) Basics/11. Variables - Dos, Don´ts and Conventions.srt
4.9 kB
3. Day Trading with OANDA A-Z a Deep Dive/4. Our second Trade - EURUSD FOREX Trading.srt
4.9 kB
3. Day Trading with OANDA A-Z a Deep Dive/14. A more general Example.srt
4.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/46. Renaming Index & Column Labels with rename().srt
4.8 kB
20. Advanced Backtesting Techniques/9. Creating an Iterative Base Class (Part 6).srt
4.8 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/7. OOP Challenge Create the Bollinger Bands Backtesting Class (incl. Solution).srt
4.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/16. 2-dimensional Numpy Arrays.srt
4.7 kB
18. Trading Strategies powered by Machine Learning - Regression/10. In-Sample Backtesting and the Look-ahead-bias.srt
4.7 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/5. Vectorized Operations with Numpy Arrays.srt
4.7 kB
14. +++ PART 3 Defining and Testing Trading Strategies +++/4. Getting the Data.srt
4.7 kB
18. Trading Strategies powered by Machine Learning - Regression/5. Underfitting.srt
4.6 kB
31. Appendix 1 Python (& Finance) Basics/14. TVM Problems with many Cashflows.srt
4.6 kB
31. Appendix 1 Python (& Finance) Basics/36. Sorting and Reversing Lists.srt
4.6 kB
22. Implementation and Automation with OANDA (UPDATED!)/11. Storing and resampling real-time tick data (Part 5).srt
4.5 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/5. How to connect to your EC2 Instance.srt
4.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/19. Recap Changing Elements in a Numpy Array slice.srt
4.5 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/1. Simple ContrarianMomentum Strategies - Overview.srt
4.5 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/9. Encapsulation and protected Attributes.srt
4.5 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/9. TypeErrors and ValueErrors.srt
4.4 kB
31. Appendix 1 Python (& Finance) Basics/3. Calculate Future Values (FV) with Python Compounding.srt
4.4 kB
31. Appendix 1 Python (& Finance) Basics/25. The Data Type Hierarchy (Theory).srt
4.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/19. Implementing an SMA Crossover Strategy (Solution).srt
4.4 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/15. Creating and Importing Python Modules (.py).srt
4.3 kB
29. Error Handling How to make your Trading Bot more stable and reliable/15. Oanda Error Handling (Part 3).srt
4.3 kB
29. Error Handling How to make your Trading Bot more stable and reliable/8. finally.srt
4.3 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/7. Indentation Errors.srt
4.3 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/9. How to start Trading sessions with Batch (.bat) Files.srt
4.2 kB
22. Implementation and Automation with OANDA (UPDATED!)/14. Working with historical data and real-time tick data (Part 3).srt
4.2 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/3. The Financial Analysis Class live in action (Part 2).srt
4.1 kB
29. Error Handling How to make your Trading Bot more stable and reliable/12. Implementation with Oanda V20 Connection Issues.srt
4.1 kB
23. Implementation and Automation with FXCM (Updated!)/3. Troubleshooting FXCM Server Connection Issues.html
4.1 kB
7. Trading with Python and OANDAFXCM - an Introduction/16. Troubleshooting FXCM Server Connection Issues.html
4.1 kB
7. Trading with Python and OANDAFXCM - an Introduction/8. OANDA How to load Historical Price Data (Part 2).srt
4.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/45. Changing Column Labels.srt
4.1 kB
7. Trading with Python and OANDAFXCM - an Introduction/9. OANDA Streaming high-frequency real-time Data.srt
4.1 kB
18. Trading Strategies powered by Machine Learning - Regression/7. A simple Linear Model to predict Financial Returns (Part 1).srt
4.1 kB
23. Implementation and Automation with FXCM (Updated!)/12. Working with historical data and real-time tick data (Part 3).srt
4.1 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/29. Zero-based Indexing and Negative Indexing.srt
4.1 kB
4. FOREX Day Trading with FXCM/7. Order Types at a glance.srt
4.1 kB
4. FOREX Day Trading with FXCM/4. Trade Analysis.srt
4.1 kB
3. Day Trading with OANDA A-Z a Deep Dive/13. Take-Profit and Stop-Loss Orders.srt
4.0 kB
31. Appendix 1 Python (& Finance) Basics/28. Integers.srt
4.0 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/7. String representation and the special method __repr__().srt
4.0 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/33. Selecting Rows with loc (label-based indexing).srt
4.0 kB
15. Defining and Backtesting SMA Strategies/6. Creating the Class (Part 1).srt
3.9 kB
19. Trading Strategies powered by Machine Learning - Classification/4. Predicting Market Direction with Logistic Regression.srt
3.9 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/4. Defining a simple Contrarian Strategy.srt
3.9 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/8. How to run Python Scripts in a Windows Command Prompt.srt
3.9 kB
18. Trading Strategies powered by Machine Learning - Regression/3. Making Predictions with Linear Regression.srt
3.9 kB
31. Appendix 1 Python (& Finance) Basics/17. Indexing Lists.srt
3.9 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/3. The best time to trade (Part 2).srt
3.8 kB
29. Error Handling How to make your Trading Bot more stable and reliable/7. try, except, else.srt
3.8 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/1. Introduction.srt
3.8 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/2. The best time to trade (Part 1).srt
3.8 kB
7. Trading with Python and OANDAFXCM - an Introduction/4. OANDA How to install the OANDA API Wrapper.srt
3.7 kB
31. Appendix 1 Python (& Finance) Basics/22. Calculate an Investment Project´s NPV.srt
3.7 kB
19. Trading Strategies powered by Machine Learning - Classification/6. Out-Sample Forward Testing.srt
3.7 kB
23. Implementation and Automation with FXCM (Updated!)/9. Storing and resampling real-time tick data (Part 3).srt
3.6 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/8. Feature ScalingEngineering.srt
3.6 kB
19. Trading Strategies powered by Machine Learning - Classification/3. Getting and Preparing the Data.srt
3.6 kB
7. Trading with Python and OANDAFXCM - an Introduction/13. FXCM How to install the FXCM API Wrapper.srt
3.6 kB
31. Appendix 1 Python (& Finance) Basics/8. Excursus How to add inline comments.srt
3.6 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/6. The method log_returns().srt
3.6 kB
13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/10. The method set_ticker().srt
3.5 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/8. Trades and Trading Costs (Part 2).srt
3.5 kB
4. FOREX Day Trading with FXCM/3. Example Trade Buying EURUSD.srt
3.5 kB
31. Appendix 1 Python (& Finance) Basics/16. Zero-based Indexing and negative Indexing in Python (Theory).srt
3.5 kB
7. Trading with Python and OANDAFXCM - an Introduction/14. FXCM Getting the Access Token & other Preparations.srt
3.4 kB
32. Appendix 2 User-defined Functions (required for OOP)/8. How to return many results.srt
3.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/20. Implementing a Bollinger Bands Strategy (Solution).srt
3.4 kB
31. Appendix 1 Python (& Finance) Basics/35. Changing Elements in Lists.srt
3.4 kB
9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1. Introduction and Downloads Part 2.srt
3.4 kB
22. Implementation and Automation with OANDA (UPDATED!)/2. Updating the Wrapper Package (Part 2).srt
3.3 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1. Introduction and Motivation.srt
3.3 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/4. Indexing and Slicing Numpy Arrays.srt
3.3 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/8. Misuse of function names and keywords.srt
3.2 kB
15. Defining and Backtesting SMA Strategies/10. Creating the Class (Part 5).srt
3.2 kB
32. Appendix 2 User-defined Functions (required for OOP)/1.1 Appendix2_Materials.zip
3.2 kB
15. Defining and Backtesting SMA Strategies/12. Creating the Class (Part 7).srt
3.2 kB
29. Error Handling How to make your Trading Bot more stable and reliable/4. try and except.srt
3.1 kB
31. Appendix 1 Python (& Finance) Basics/4. Calculate Present Values (FV) with Python Discounting.srt
3.1 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/11. Saving Model and Parameters.srt
3.1 kB
31. Appendix 1 Python (& Finance) Basics/15. Intro to Python Lists.srt
3.1 kB
27. Working with two or many Strategies (Combination)/7. Combining both Strategies - Alternative 2.srt
3.0 kB
27. Working with two or many Strategies (Combination)/5. Taking into account busy Trading Hours.srt
3.0 kB
20. Advanced Backtesting Techniques/5. Creating an Iterative Base Class (Part 2).srt
3.0 kB
29. Error Handling How to make your Trading Bot more stable and reliable/10. How to limit the number of retries.srt
3.0 kB
16. Defining and Backtesting simple MomentumContrarian Strategies/2. Getting the Data.srt
3.0 kB
19. Trading Strategies powered by Machine Learning - Classification/5. In-Sample Backtesting and the Look-ahead-bias.srt
2.9 kB
22. Implementation and Automation with OANDA (UPDATED!)/23. Importing a Trader Module Class.srt
2.9 kB
27. Working with two or many Strategies (Combination)/3. Strategy 2 Mean Reversion.srt
2.9 kB
31. Appendix 1 Python (& Finance) Basics/45. Booleans.srt
2.9 kB
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/3. Amazon Web Services (AWS) - Overview and how to create a Free Trial Account.srt
2.9 kB
29. Error Handling How to make your Trading Bot more stable and reliable/16. Implementation with FXCM APIServer Issues.srt
2.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/28. Selecting one Column with the dot notation.srt
2.8 kB
27. Working with two or many Strategies (Combination)/2. Strategy 1 SMA.srt
2.8 kB
20. Advanced Backtesting Techniques/6. Creating an Iterative Base Class (Part 3).srt
2.8 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/65. Introduction to GroupBy Operations.srt
2.8 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/6. Adding lags.srt
2.7 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/53. Exporting DataFrames to csv.srt
2.7 kB
1. Getting Started/6. LEGAL DISCLAIMER (MUST READ!) .html
2.6 kB
4. FOREX Day Trading with FXCM/6. Closing Positions vs. Hedging Positions.srt
2.6 kB
27. Working with two or many Strategies (Combination)/1. Introduction.srt
2.6 kB
11. Financial Data Analysis with Pandas - an Introduction/1. Getting Ready (Installing required library).srt
2.6 kB
31. Appendix 1 Python (& Finance) Basics/9. Variables and Memory (Theory).srt
2.5 kB
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/18. How to slice 2-dim Numpy Arrays (Part 2).srt
2.5 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/6. Conclusions.srt
2.5 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/7. Splitting into Train and Test Set.srt
2.4 kB
25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/1. Overview.srt
2.3 kB
17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/2. Getting the Data.srt
2.3 kB
23. Implementation and Automation with FXCM (Updated!)/16. Trading other Strategies - Coding Challenge.srt
2.2 kB
21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/1. Introduction and Overview.srt
2.2 kB
2. +++ PART 1 Day Trading, Online Brokers and APIs +++/1. Our very first Trade.srt
2.2 kB
22. Implementation and Automation with OANDA (UPDATED!)/18. Trading other Strategies - Coding Challenge.srt
2.1 kB
5. Installing Python and Jupyter Notebooks/1. Introduction.srt
2.1 kB
26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/4. Spreads during the busy hours.srt
2.1 kB
31. Appendix 1 Python (& Finance) Basics/26. Excursus Dynamic Typing in Python.srt
2.0 kB
27. Working with two or many Strategies (Combination)/6. Strategy Backtesting.srt
2.0 kB
29. Error Handling How to make your Trading Bot more stable and reliable/3. Python Errors (Exceptions).srt
2.0 kB
29. Error Handling How to make your Trading Bot more stable and reliable/5. Catching specific Errors.srt
1.9 kB
18. Trading Strategies powered by Machine Learning - Regression/6. Getting the Data.srt
1.9 kB
30. +++ APPENDIX Python Crash Course +++/1. Overview.srt
1.9 kB
4. FOREX Day Trading with FXCM/5. Charting.srt
1.5 kB
6. Excursus How to avoid and debug Coding Errors (don´t skip!)/3. Major reasons for Coding Errors.srt
1.5 kB
7. Trading with Python and OANDAFXCM - an Introduction/14.1 FXCM_firststeps.zip
1.5 kB
5. Installing Python and Jupyter Notebooks/5. Tips for Python Beginners.srt
1.4 kB
28. A Machine Learning-powered Strategy A-Z (DNN)/4. Getting and Preparing the Data.srt
1.4 kB
29. Error Handling How to make your Trading Bot more stable and reliable/6. The Exception class.srt
1.4 kB
7. Trading with Python and OANDAFXCM - an Introduction/2. Overview.srt
1.4 kB
7. Trading with Python and OANDAFXCM - an Introduction/5.1 Oanda_firststeps.zip
1.3 kB
8. Conclusion and Outlook/1. Conclusion and Outlook.srt
928 Bytes
28. A Machine Learning-powered Strategy A-Z (DNN)/12. Important Notices.html
822 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/35. Label-based Indexing Cheat Sheets.html
701 Bytes
7. Trading with Python and OANDAFXCM - an Introduction/12. FXCM Commands to install required packages.html
626 Bytes
3. Day Trading with OANDA A-Z a Deep Dive/15. Trading Challenge.html
569 Bytes
28. A Machine Learning-powered Strategy A-Z (DNN)/2. Installation of Tensorflow & Keras (Part 1).html
555 Bytes
22. Implementation and Automation with OANDA (UPDATED!)/24. Excursus Printing all ticks in a Command PromptTerminal.html
533 Bytes
23. Implementation and Automation with FXCM (Updated!)/20. Excursus Printing all ticks in a Command PromptTerminal.html
533 Bytes
4. FOREX Day Trading with FXCM/8. Trading Challenge.html
511 Bytes
7. Trading with Python and OANDAFXCM - an Introduction/11. Trading Challenge.html
446 Bytes
7. Trading with Python and OANDAFXCM - an Introduction/21. Trading Challenge.html
445 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/32. Position-based Indexing Cheat Sheets.html
440 Bytes
7. Trading with Python and OANDAFXCM - an Introduction/3. OANDA Commands to install required packages.html
409 Bytes
22. Implementation and Automation with OANDA (UPDATED!)/3. Weekend and Bank Holiday Alert.html
381 Bytes
23. Implementation and Automation with FXCM (Updated!)/1. Weekend and Bank Holiday Alert.html
381 Bytes
24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/7. Weekend and Bank Holiday Alert.html
381 Bytes
22. Implementation and Automation with OANDA (UPDATED!)/1. Updating the Wrapper Package (Part 1).html
359 Bytes
1. Getting Started/4. Test your knowledge.html
203 Bytes
7. Trading with Python and OANDAFXCM - an Introduction/1. How to maximize your learning experience.html
203 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/26. Coding Exercise 9.html
159 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/37. Coding Exercise 10.html
159 Bytes
11. Financial Data Analysis with Pandas - an Introduction/10. Coding Exercise 2.html
158 Bytes
31. Appendix 1 Python (& Finance) Basics/53. Coding Exercise 5.html
158 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/14. Coding Exercise 7.html
158 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/21. Coding Exercise 8.html
158 Bytes
32. Appendix 2 User-defined Functions (required for OOP)/10. Coding Exercise 6.html
156 Bytes
0. Websites you may like/[FreeCourseSite.com].url
127 Bytes
9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/0. Websites you may like/[FreeCourseSite.com].url
127 Bytes
31. Appendix 1 Python (& Finance) Basics/1. Section Downloads.html
124 Bytes
32. Appendix 2 User-defined Functions (required for OOP)/1. Section Downloads.html
124 Bytes
33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/1. Downloads for this Section.html
124 Bytes
14. +++ PART 3 Defining and Testing Trading Strategies +++/3. Downloads for Part 3.html
123 Bytes
21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/2. Downloads for Part 4.html
123 Bytes
25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/2. Downloads for PART 5.html
123 Bytes
0. Websites you may like/[CourseClub.Me].url
122 Bytes
29. Error Handling How to make your Trading Bot more stable and reliable/2. Section Materials Notebooks.html
122 Bytes
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122 Bytes
31. Appendix 1 Python (& Finance) Basics/34. Slicing Cheat Sheet.html
108 Bytes
0. Websites you may like/[GigaCourse.Com].url
49 Bytes
9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/0. Websites you may like/[GigaCourse.Com].url
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