magnet:?xt=urn:btih:F7D6BAB31FD19C78B45A50A91952CB1E6AC3F242
28. A Machine Learning-powered Strategy A-Z (DNN)/13. Implementation (Oanda & FXCM).mp4 117.9 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/11. How to traceback more complex Errors.mp4 101.5 MB7. Trading with Python and OANDAFXCM - an Introduction/10. OANDA How to place Orders and execute Trades.mp4 89.6 MB22. Implementation and Automation with OANDA (UPDATED!)/17. Trade Monitoring and Reporting.mp4 88.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/56. Customization of Plots.mp4 88.2 MB15. Defining and Backtesting SMA Strategies/4. Finding the optimal SMA Strategy.mp4 86.8 MB11. Financial Data Analysis with Pandas - an Introduction/12. Importing Financial Data from Excel.mp4 84.6 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/12. Inheritance.mp4 82.7 MB20. Advanced Backtesting Techniques/13. Adding the Iterative Backtest Child Class for SMA (Part 2).mp4 82.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/34. Slicing Rows and Columns with loc (label-based indexing).mp4 81.3 MB15. Defining and Backtesting SMA Strategies/5. Generalization with OOP An SMA Backtesting Class in action.mp4 77.2 MB22. Implementation and Automation with OANDA (UPDATED!)/4. Historical Data, real-time Data and Orders (Recap).mp4 76.3 MB10. Introduction to Time Series Data in Pandas/4. Downsampling Time Series with resample().mp4 75.7 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/4. How to create an EC2 Instance.mp4 75.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/61. Categorical Seaborn Plots.mp4 74.2 MB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/4. Defining a Bollinger Bands Mean-Reversion Strategy (Part 2).mp4 74.1 MB3. Day Trading with OANDA A-Z a Deep Dive/7. Margin and Leverage.mp4 74.0 MB3. Day Trading with OANDA A-Z a Deep Dive/6. Trading Costs and Performance Attribution.mp4 72.9 MB27. Working with two or many Strategies (Combination)/8. Strategy Optimization.mp4 71.9 MB12. Advanced Topics/2. Filling NA Values with bfill, ffill and interpolation.mp4 71.7 MB23. Implementation and Automation with FXCM (Updated!)/8. Storing and resampling real-time tick data (Part 2).mp4 71.5 MB4. FOREX Day Trading with FXCM/1. FXCM at a first glance.mp4 70.8 MB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/4. Spot Trading vs. Derivatives Trading (Part 2).mp4 70.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/62. Seaborn Regression Plots.mp4 69.8 MB3. Day Trading with OANDA A-Z a Deep Dive/1. OANDA at a first glance.mp4 69.1 MB23. Implementation and Automation with FXCM (Updated!)/2. Historical Data, real-time Data and Orders (Recap).mp4 69.0 MB20. Advanced Backtesting Techniques/15. OOP Challenge Add Contrarian and Bollinger Strategies.mp4 69.0 MB15. Defining and Backtesting SMA Strategies/7. Creating the Class (Part 2).mp4 68.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/30. Selecting Rows with iloc (position-based indexing).mp4 68.2 MB23. Implementation and Automation with FXCM (Updated!)/6. Storing and resampling real-time tick data (Part 1).mp4 67.7 MB29. Error Handling How to make your Trading Bot more stable and reliable/14. Oanda Error Handling (Part 2).mp4 67.3 MB22. Implementation and Automation with OANDA (UPDATED!)/10. Storing and resampling real-time tick data (Part 4).mp4 67.2 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1. Introduction to OOP and examples for Classes.mp4 66.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/44. Changing Row Index with set_index() and reset_index().mp4 66.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/23. Create your very first Pandas DataFrame (from csv).mp4 65.6 MB3. Day Trading with OANDA A-Z a Deep Dive/10. Our third Trade A-Z - Going Short EURUSD.mp4 64.9 MB22. Implementation and Automation with OANDA (UPDATED!)/22. Machine Learning Strategies (2) - Implementation.mp4 64.6 MB5. Installing Python and Jupyter Notebooks/2. Download and Install Anaconda.mp4 63.8 MB3. Day Trading with OANDA A-Z a Deep Dive/11. Netting vs. Hedging.mp4 63.8 MB16. Defining and Backtesting simple MomentumContrarian Strategies/9. Generalization with OOP A Contrarian Backtesting Class in action.mp4 63.6 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/6. Getting the Instance Ready for Algorithmic Trading.mp4 63.0 MB22. Implementation and Automation with OANDA (UPDATED!)/7. Storing and resampling real-time tick data (Part 1).mp4 62.7 MB3. Day Trading with OANDA A-Z a Deep Dive/3. FOREX Currency Exchange Rates explained.mp4 62.4 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/55. Visualization with Matplotlib (Intro).mp4 62.0 MB16. Defining and Backtesting simple MomentumContrarian Strategies/7. Trades and Trading Costs (Part 1).mp4 61.9 MB22. Implementation and Automation with OANDA (UPDATED!)/25. Running a Python Trader Script.mp4 61.6 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/2. Test your debugging skills!.mp4 61.4 MB11. Financial Data Analysis with Pandas - an Introduction/2. Importing Stock Price Data from Yahoo Finance.mp4 61.3 MB15. Defining and Backtesting SMA Strategies/3. Vectorized Strategy Backtesting.mp4 61.2 MB23. Implementation and Automation with FXCM (Updated!)/14. Placing Orders and Executing Trades.mp4 61.0 MB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3. Spot Trading vs. Derivatives Trading (Part 1).mp4 60.6 MB22. Implementation and Automation with OANDA (UPDATED!)/16. Placing Orders and Executing Trades.mp4 60.3 MB7. Trading with Python and OANDAFXCM - an Introduction/7. OANDA How to load Historical Price Data (Part 1).mp4 60.1 MB20. Advanced Backtesting Techniques/11. Creating an Iterative Base Class (Part 8).mp4 59.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/52. Handling NA Values missing Values.mp4 59.0 MB23. Implementation and Automation with FXCM (Updated!)/15. Trade Monitoring and Reporting.mp4 58.9 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/25. First Data Inspection.mp4 58.7 MB19. Trading Strategies powered by Machine Learning - Classification/8. The Classification Backtesting Class explained (Part 1).mp4 57.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/50. Advanced Filtering with between(), isin() and ~.mp4 57.1 MB23. Implementation and Automation with FXCM (Updated!)/5. Collecting and storing real-time tick data.mp4 56.6 MB23. Implementation and Automation with FXCM (Updated!)/19. Machine Learning Strategies (2) - Implementation.mp4 56.5 MB4. FOREX Day Trading with FXCM/2. How to create an Account.mp4 56.5 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/13. Inheritance and the super() Function.mp4 56.5 MB31. Appendix 1 Python (& Finance) Basics/40. Coding Exercise 3.mp4 56.4 MB5. Installing Python and Jupyter Notebooks/4. How to work with Jupyter Notebooks.mp4 56.1 MB20. Advanced Backtesting Techniques/12. Adding the Iterative Backtest Child Class for SMA (Part 1).mp4 55.7 MB7. Trading with Python and OANDAFXCM - an Introduction/17. FXCM How to load Historical Price Data (Part 1).mp4 54.3 MB22. Implementation and Automation with OANDA (UPDATED!)/15. Defining a simple Contrarian Strategy.mp4 54.2 MB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/1. Mean-Reversion Strategies - Overview.mp4 53.9 MB5. Installing Python and Jupyter Notebooks/3. How to open Jupyter Notebooks.mp4 53.4 MB23. Implementation and Automation with FXCM (Updated!)/4. Preview A Trader Class live in action.mp4 53.3 MB20. Advanced Backtesting Techniques/10. Creating an Iterative Base Class (Part 7).mp4 52.9 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/14. Adding meaningful Docstrings.mp4 52.5 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/5. Omitting cells, changing the sequence and more.mp4 52.4 MB28. A Machine Learning-powered Strategy A-Z (DNN)/3. Installation of Tensorflow & Keras (Part 2).mp4 52.2 MB23. Implementation and Automation with FXCM (Updated!)/11. Working with historical data and real-time tick data (Part 2).mp4 52.0 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/11. How to stop Trading Sessions (OANDA).mp4 51.9 MB22. Implementation and Automation with OANDA (UPDATED!)/12. Working with historical data and real-time tick data (Part 1).mp4 51.8 MB20. Advanced Backtesting Techniques/7. Creating an Iterative Base Class (Part 4).mp4 51.8 MB22. Implementation and Automation with OANDA (UPDATED!)/13. Working with historical data and real-time tick data (Part 2).mp4 51.4 MB3. Day Trading with OANDA A-Z a Deep Dive/5. How to calculate Profit & Loss of a Trade.mp4 51.2 MB10. Introduction to Time Series Data in Pandas/2. Converting strings to datetime objects with pd.to_datetime().mp4 51.2 MB7. Trading with Python and OANDAFXCM - an Introduction/6. OANDA Connecting to the APIServer.mp4 50.7 MB18. Trading Strategies powered by Machine Learning - Regression/2. Linear Regression with scikit-learn - a simple Introduction.mp4 50.6 MB3. Day Trading with OANDA A-Z a Deep Dive/2. How to create an Account.mp4 50.6 MB28. A Machine Learning-powered Strategy A-Z (DNN)/10. Prediction & Out-Sample Forward Testing.mp4 50.6 MB22. Implementation and Automation with OANDA (UPDATED!)/5. Preview A Trader Class live in action.mp4 50.3 MB7. Trading with Python and OANDAFXCM - an Introduction/19. FXCM Streaming high-frequency real-time Data.mp4 50.0 MB23. Implementation and Automation with FXCM (Updated!)/21. Running a Python Script.mp4 50.0 MB28. A Machine Learning-powered Strategy A-Z (DNN)/9. Creating and Fitting the DNN Model.mp4 49.9 MB14. +++ PART 3 Defining and Testing Trading Strategies +++/2. Trading Strategies - an Overview.mp4 49.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/54. Summary Statistics and Accumulations.mp4 49.2 MB31. Appendix 1 Python (& Finance) Basics/13. Coding Exercise 1.mp4 49.0 MB22. Implementation and Automation with OANDA (UPDATED!)/8. Storing and resampling real-time tick data (Part 2).mp4 48.2 MB7. Trading with Python and OANDAFXCM - an Introduction/20. FXCM How to place Orders and execute Trades.mp4 48.2 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/12. How to stop Trading Sessions (FXCM).mp4 48.1 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/11. Adding more methods and performance metrics.mp4 47.6 MB11. Financial Data Analysis with Pandas - an Introduction/8. Financial Time Series - Return and Risk.mp4 47.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/47. Filtering DataFrames (one Condition).mp4 47.0 MB29. Error Handling How to make your Trading Bot more stable and reliable/13. Oanda Error Handling (Part 1).mp4 46.7 MB15. Defining and Backtesting SMA Strategies/1. SMA Crossover Strategies - Overview.mp4 46.1 MB15. Defining and Backtesting SMA Strategies/2. Defining an SMA Crossover Strategy.mp4 45.8 MB7. Trading with Python and OANDAFXCM - an Introduction/18. FXCM How to load Historical Price Data (Part 2).mp4 45.4 MB3. Day Trading with OANDA A-Z a Deep Dive/4. Our second Trade - EURUSD FOREX Trading.mp4 45.0 MB7. Trading with Python and OANDAFXCM - an Introduction/15. FXCM Connecting to the APIServer.mp4 44.9 MB18. Trading Strategies powered by Machine Learning - Regression/8. A simple Linear Model to predict Financial Returns (Part 2).mp4 44.8 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/5. The method get_data().mp4 44.8 MB18. Trading Strategies powered by Machine Learning - Regression/1. Machine Learning - an Overview.mp4 44.6 MB31. Appendix 1 Python (& Finance) Basics/48. Coding Exercise 4.mp4 44.4 MB23. Implementation and Automation with FXCM (Updated!)/13. Defining a Simple Contrarian Trading Strategy.mp4 44.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/67. Splitting with many Keys.mp4 44.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/36. Summary, Best Practices and Outlook.mp4 44.1 MB29. Error Handling How to make your Trading Bot more stable and reliable/17. FXCM Error Handling (Part 1).mp4 43.9 MB23. Implementation and Automation with FXCM (Updated!)/17. SMA Crossover and Bollinger Bands (Solution).mp4 43.8 MB14. +++ PART 3 Defining and Testing Trading Strategies +++/1. Introduction to Part 3.mp4 43.3 MB3. Day Trading with OANDA A-Z a Deep Dive/8. Margin Closeout and more.mp4 43.3 MB11. Financial Data Analysis with Pandas - an Introduction/13. Simple Moving Averages (SMA) with rolling().mp4 43.0 MB31. Appendix 1 Python (& Finance) Basics/43. Intro to Strings.mp4 42.8 MB10. Introduction to Time Series Data in Pandas/3. Indexing and Slicing Time Series.mp4 42.6 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/24. Pandas Display Options and the methods head() & tail().mp4 42.5 MB22. Implementation and Automation with OANDA (UPDATED!)/6. How to collect and store real-time tick data.mp4 42.4 MB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/5. Vectorized Strategy Backtesting.mp4 42.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/68. split-apply-combine.mp4 42.0 MB3. Day Trading with OANDA A-Z a Deep Dive/9. Introduction to Charting.mp4 41.6 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/10. Getting help on StackOverflow.com.mp4 41.4 MB23. Implementation and Automation with FXCM (Updated!)/7. A Trader Class.mp4 41.4 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/66. Understanding the GroupBy Object.mp4 41.3 MB31. Appendix 1 Python (& Finance) Basics/50. Keywords pass, continue and break.mp4 41.3 MB15. Defining and Backtesting SMA Strategies/8. Creating the Class (Part 3).mp4 41.3 MB20. Advanced Backtesting Techniques/8. Creating an Iterative Base Class (Part 5).mp4 41.3 MB3. Day Trading with OANDA A-Z a Deep Dive/12. Market, Limit and Stop Orders.mp4 41.2 MB1. Getting Started/3. Did you know... (what Data can tell us about Day Trading).mp4 41.1 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/10. How to schedule Trading sessions with the Task Scheduler.mp4 41.1 MB16. Defining and Backtesting simple MomentumContrarian Strategies/6. Changing the Window Parameter.mp4 41.1 MB20. Advanced Backtesting Techniques/14. Using Modules and adding Docstrings.mp4 40.8 MB31. Appendix 1 Python (& Finance) Basics/49. Conditional Statements.mp4 40.5 MB31. Appendix 1 Python (& Finance) Basics/37. Adding and removing Elements fromto Lists.mp4 40.4 MB15. Defining and Backtesting SMA Strategies/9. Creating the Class (Part 4).mp4 40.3 MB18. Trading Strategies powered by Machine Learning - Regression/4. Overfitting.mp4 40.3 MB1. Getting Started/1. What is Algorithmic Trading Course Overview.mp4 40.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/51. Intro to NA Values missing Values.mp4 40.0 MB29. Error Handling How to make your Trading Bot more stable and reliable/18. FXCM Error Handling (Part 2).mp4 40.0 MB14. +++ PART 3 Defining and Testing Trading Strategies +++/6. Performance Metrics.mp4 39.9 MB20. Advanced Backtesting Techniques/3. A first Intuition on Iterative Backtesting (Part 2).mp4 39.9 MB31. Appendix 1 Python (& Finance) Basics/23. Coding Exercise 2.mp4 39.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/13. Creating Numpy Arrays from Scratch.mp4 39.7 MB28. A Machine Learning-powered Strategy A-Z (DNN)/5. Adding LabelsFeatures.mp4 39.6 MB1. Getting Started/2. How to get the best out of this course.mp4 39.4 MB27. Working with two or many Strategies (Combination)/4. Combining both Strategies - Alternative 1.mp4 39.3 MB12. Advanced Topics/1. Helpful DatetimeIndex Attributes and Methods.mp4 39.2 MB11. Financial Data Analysis with Pandas - an Introduction/4. Normalizing Time Series to a Base Value (100).mp4 39.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/43. First Steps with Pandas Index Objects.mp4 38.9 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/16. Coding Exercise 3 Create your own Class.mp4 38.8 MB22. Implementation and Automation with OANDA (UPDATED!)/19. Implementing an SMA Crossover Strategy (Solution).mp4 38.8 MB12. Advanced Topics/4. Timezones and Converting (Part 2).mp4 38.7 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/1. Introduction and Preparing the Data.mp4 38.6 MB18. Trading Strategies powered by Machine Learning - Regression/9. A Multiple Regression Model to predict Financial Returns.mp4 38.5 MB15. Defining and Backtesting SMA Strategies/11. Creating the Class (Part 6).mp4 38.1 MB11. Financial Data Analysis with Pandas - an Introduction/3. Initial Inspection and Visualization.mp4 38.1 MB32. Appendix 2 User-defined Functions (required for OOP)/3. What´s the difference between Positional Arguments vs. Keyword Arguments.mp4 38.1 MB29. Error Handling How to make your Trading Bot more stable and reliable/12. Implementation with Oanda V20 Connection Issues.mp4 37.9 MB10. Introduction to Time Series Data in Pandas/5. Coding Exercise 1.mp4 37.6 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/63. Seaborn Heatmaps.mp4 37.4 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/3. Numpy Arrays.mp4 37.4 MB31. Appendix 1 Python (& Finance) Basics/52. Introduction to while loops.mp4 37.4 MB31. Appendix 1 Python (& Finance) Basics/47. Comparison, Logical and Membership Operators in Action.mp4 37.3 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/4. The most commonly made Errors at a glance.mp4 37.2 MB32. Appendix 2 User-defined Functions (required for OOP)/9. Scope - easily explained.mp4 37.0 MB19. Trading Strategies powered by Machine Learning - Classification/9. The Classification Backtesting Class explained (Part 2).mp4 36.9 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/4. The special method __init__().mp4 36.7 MB11. Financial Data Analysis with Pandas - an Introduction/7. Measuring Stock Performance with MEAN Returns and STD of Returns.mp4 36.6 MB20. Advanced Backtesting Techniques/2. A first Intuition on Iterative Backtesting (Part 1).mp4 36.5 MB29. Error Handling How to make your Trading Bot more stable and reliable/15. Oanda Error Handling (Part 3).mp4 36.2 MB23. Implementation and Automation with FXCM (Updated!)/10. Working with historical data and real-time tick data (Part 1).mp4 36.2 MB31. Appendix 1 Python (& Finance) Basics/38. Mutable vs. immutable Objects (Part 1).mp4 36.2 MB10. Introduction to Time Series Data in Pandas/1. Importing Time Series Data from csv-files.mp4 36.1 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/8. The methods plot_prices() and plot_returns().mp4 35.8 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/12. Problems with the Python Installation.mp4 35.7 MB23. Implementation and Automation with FXCM (Updated!)/18. Machine Learning Strategies (1) - Model Fitting.mp4 35.5 MB20. Advanced Backtesting Techniques/9. Creating an Iterative Base Class (Part 6).mp4 35.4 MB22. Implementation and Automation with OANDA (UPDATED!)/21. Machine Learning Strategies (1) - Model Fitting.mp4 35.4 MB31. Appendix 1 Python (& Finance) Basics/20. Calculate FV and PV for many Cashflows.mp4 35.1 MB7. Trading with Python and OANDAFXCM - an Introduction/8. OANDA How to load Historical Price Data (Part 2).mp4 35.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/42. Sorting of Series and Introduction to the inplace - parameter.mp4 35.0 MB31. Appendix 1 Python (& Finance) Basics/21. The Net Present Value - NPV (Theory).mp4 34.9 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/5. How to connect to your EC2 Instance.mp4 34.7 MB11. Financial Data Analysis with Pandas - an Introduction/14. Momentum Trading Strategies with SMAs.mp4 34.6 MB15. Defining and Backtesting SMA Strategies/13. Creating the Class (Part 8).mp4 34.3 MB11. Financial Data Analysis with Pandas - an Introduction/6. The methods diff() and pct_change().mp4 34.3 MB11. Financial Data Analysis with Pandas - an Introduction/11. Simple Returns vs. Log Returns.mp4 34.2 MB7. Trading with Python and OANDAFXCM - an Introduction/13. FXCM How to install the FXCM API Wrapper.mp4 34.1 MB22. Implementation and Automation with OANDA (UPDATED!)/9. Storing and resampling real-time tick data (Part 3).mp4 33.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/2. Modules, Packages and Libraries - No need to reinvent the Wheel.mp4 33.6 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/9. How to start Trading sessions with Batch (.bat) Files.mp4 33.2 MB7. Trading with Python and OANDAFXCM - an Introduction/5. OANDA Getting the API Key & other Preparations.mp4 33.0 MB16. Defining and Backtesting simple MomentumContrarian Strategies/3. Excursus Your FAQs answered.mp4 32.7 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/6. IndexErrors.mp4 32.6 MB31. Appendix 1 Python (& Finance) Basics/42. Dictionaries.mp4 32.5 MB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2. Long Term Investing vs. (Algorithmic) Day Trading.mp4 32.4 MB22. Implementation and Automation with OANDA (UPDATED!)/11. Storing and resampling real-time tick data (Part 5).mp4 32.4 MB3. Day Trading with OANDA A-Z a Deep Dive/14. A more general Example.mp4 32.3 MB18. Trading Strategies powered by Machine Learning - Regression/11. Out-Sample Forward Testing.mp4 31.9 MB22. Implementation and Automation with OANDA (UPDATED!)/20. Implementing a Bollinger Bands Strategy (Solution).mp4 31.9 MB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/5. Overview & the Brokers OANDA and FXCM.mp4 31.7 MB20. Advanced Backtesting Techniques/1. Introduction to Iterative Backtesting (event-driven).mp4 31.6 MB22. Implementation and Automation with OANDA (UPDATED!)/14. Working with historical data and real-time tick data (Part 3).mp4 31.5 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/8. How to run Python Scripts in a Windows Command Prompt.mp4 31.4 MB23. Implementation and Automation with FXCM (Updated!)/12. Working with historical data and real-time tick data (Part 3).mp4 31.4 MB31. Appendix 1 Python (& Finance) Basics/18. For Loops - Iterating over Lists.mp4 31.4 MB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/3. Defining a Bollinger Bands Mean-Reversion Strategy (Part 1).mp4 31.2 MB31. Appendix 1 Python (& Finance) Basics/41. Tuples.mp4 31.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/64. Removing Columns.mp4 31.1 MB11. Financial Data Analysis with Pandas - an Introduction/5. The shift() method.mp4 30.9 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/59. Scatterplots.mp4 30.9 MB12. Advanced Topics/3. Timezones and Converting (Part 1).mp4 30.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/17. How to slice 2-dim Numpy Arrays (Part 1).mp4 30.3 MB16. Defining and Backtesting simple MomentumContrarian Strategies/5. Vectorized Strategy Backtesting.mp4 30.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/58. Histogramms (Part 2).mp4 30.3 MB32. Appendix 2 User-defined Functions (required for OOP)/4. How to work with Default Arguments.mp4 29.9 MB7. Trading with Python and OANDAFXCM - an Introduction/4. OANDA How to install the OANDA API Wrapper.mp4 29.8 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/5. The Impact of Granularity.mp4 29.6 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/11. Advanced Filtering & Bitwise Operators.mp4 29.5 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/46. Renaming Index & Column Labels with rename().mp4 29.3 MB4. FOREX Day Trading with FXCM/7. Order Types at a glance.mp4 28.9 MB19. Trading Strategies powered by Machine Learning - Classification/4. Predicting Market Direction with Logistic Regression.mp4 28.8 MB32. Appendix 2 User-defined Functions (required for OOP)/2. Defining your first user-defined Function.mp4 28.7 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/2. The Financial Analysis Class live in action (Part 1).mp4 28.6 MB20. Advanced Backtesting Techniques/4. Creating an Iterative Base Class (Part 1).mp4 28.5 MB32. Appendix 2 User-defined Functions (required for OOP)/5. The Default Argument None.mp4 28.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/27. Selecting Columns.mp4 27.9 MB28. A Machine Learning-powered Strategy A-Z (DNN)/8. Feature ScalingEngineering.mp4 27.7 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/15. Summary and Debugging Flow-Chart.mp4 27.6 MB32. Appendix 2 User-defined Functions (required for OOP)/7. Sequences as arguments and args.mp4 27.6 MB14. +++ PART 3 Defining and Testing Trading Strategies +++/5. A simple Buy and Hold Strategy.mp4 27.4 MB11. Financial Data Analysis with Pandas - an Introduction/16. Merging Aligning Financial Time Series (hands-on).mp4 27.2 MB29. Error Handling How to make your Trading Bot more stable and reliable/11. Waiting periods between re-tries.mp4 27.1 MB7. Trading with Python and OANDAFXCM - an Introduction/9. OANDA Streaming high-frequency real-time Data.mp4 27.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/49. Filtering DataFrames by many Conditions (OR).mp4 27.0 MB22. Implementation and Automation with OANDA (UPDATED!)/2. Updating the Wrapper Package (Part 2).mp4 26.6 MB31. Appendix 1 Python (& Finance) Basics/27. Build-in Functions.mp4 26.6 MB23. Implementation and Automation with FXCM (Updated!)/9. Storing and resampling real-time tick data (Part 3).mp4 26.5 MB29. Error Handling How to make your Trading Bot more stable and reliable/7. try, except, else.mp4 26.4 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/10. The method set_ticker().mp4 26.4 MB15. Defining and Backtesting SMA Strategies/12. Creating the Class (Part 7).mp4 26.3 MB31. Appendix 1 Python (& Finance) Basics/31. More on Lists.mp4 25.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6. Changing Elements in Numpy Arrays & Mutability.mp4 25.7 MB3. Day Trading with OANDA A-Z a Deep Dive/13. Take-Profit and Stop-Loss Orders.mp4 25.6 MB4. FOREX Day Trading with FXCM/3. Example Trade Buying EURUSD.mp4 25.6 MB31. Appendix 1 Python (& Finance) Basics/29. Floats.mp4 25.5 MB31. Appendix 1 Python (& Finance) Basics/24. Data Types in Action.mp4 25.5 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/31. Slicing Rows and Columns with iloc (position-based indexing).mp4 25.5 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/9. Encapsulation and protected Attributes.mp4 25.2 MB28. A Machine Learning-powered Strategy A-Z (DNN)/1. Project Overview.mp4 24.9 MB11. Financial Data Analysis with Pandas - an Introduction/15. Exponentially-weighted Moving Averages (EWMA).mp4 24.9 MB18. Trading Strategies powered by Machine Learning - Regression/5. Underfitting.mp4 24.7 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/6. The method log_returns().mp4 24.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/12. Determining a Project´s Payback Period with np.where().mp4 23.6 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/20. How to perform row-wise and column-wise Operations.mp4 23.6 MB31. Appendix 1 Python (& Finance) Basics/10. More on Variables and Memory.mp4 23.3 MB27. Working with two or many Strategies (Combination)/7. Combining both Strategies - Alternative 2.mp4 23.2 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/7. String representation and the special method __repr__().mp4 23.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/8. Numpy Array Methods and Attributes.mp4 23.0 MB16. Defining and Backtesting simple MomentumContrarian Strategies/8. Trades and Trading Costs (Part 2).mp4 23.0 MB31. Appendix 1 Python (& Finance) Basics/51. Calculate a Project´s Payback Period.mp4 22.9 MB31. Appendix 1 Python (& Finance) Basics/39. Mutable vs. immutable Objects (Part 2).mp4 22.9 MB16. Defining and Backtesting simple MomentumContrarian Strategies/4. Defining a simple Contrarian Strategy.mp4 22.5 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/33. Selecting Rows with loc (label-based indexing).mp4 22.4 MB29. Error Handling How to make your Trading Bot more stable and reliable/9. Try again (...until it works).mp4 22.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/48. Filtering DataFrames by many Conditions (AND).mp4 22.3 MB15. Defining and Backtesting SMA Strategies/10. Creating the Class (Part 5).mp4 22.1 MB7. Trading with Python and OANDAFXCM - an Introduction/14. FXCM Getting the Access Token & other Preparations.mp4 22.1 MB11. Financial Data Analysis with Pandas - an Introduction/9. Financial Time Series - Covariance and Correlation.mp4 22.1 MB31. Appendix 1 Python (& Finance) Basics/30. How to round Floats (and Integers) with round().mp4 21.9 MB29. Error Handling How to make your Trading Bot more stable and reliable/16. Implementation with FXCM APIServer Issues.mp4 21.8 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/15. Creating and Importing Python Modules (.py).mp4 21.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/41. The copy() method.mp4 21.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/57. Histogramms (Part 1).mp4 21.5 MB15. Defining and Backtesting SMA Strategies/6. Creating the Class (Part 1).mp4 21.2 MB14. +++ PART 3 Defining and Testing Trading Strategies +++/4. Getting the Data.mp4 21.2 MB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1. Introduction and Motivation.mp4 21.2 MB18. Trading Strategies powered by Machine Learning - Regression/10. In-Sample Backtesting and the Look-ahead-bias.mp4 21.2 MB31. Appendix 1 Python (& Finance) Basics/33. Slicing Lists.mp4 21.1 MB29. Error Handling How to make your Trading Bot more stable and reliable/8. finally.mp4 21.1 MB19. Trading Strategies powered by Machine Learning - Classification/6. Out-Sample Forward Testing.mp4 21.1 MB16. Defining and Backtesting simple MomentumContrarian Strategies/1. Simple ContrarianMomentum Strategies - Overview.mp4 20.9 MB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/3. The Financial Analysis Class live in action (Part 2).mp4 20.7 MB29. Error Handling How to make your Trading Bot more stable and reliable/1. Introduction.mp4 20.5 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7. View vs. copy - potential Pitfalls when slicing Numpy Arrays.mp4 20.2 MB31. Appendix 1 Python (& Finance) Basics/6. Calculate Interest Rates and Returns with Python.mp4 20.2 MB28. A Machine Learning-powered Strategy A-Z (DNN)/6. Adding lags.mp4 20.2 MB22. Implementation and Automation with OANDA (UPDATED!)/23. Importing a Trader Module Class.mp4 20.1 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/3. The best time to trade (Part 2).mp4 20.0 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/2. The best time to trade (Part 1).mp4 20.0 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/38. First Steps with Pandas Series.mp4 19.9 MB4. FOREX Day Trading with FXCM/4. Trade Analysis.mp4 19.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/5. Vectorized Operations with Numpy Arrays.mp4 19.6 MB32. Appendix 2 User-defined Functions (required for OOP)/6. How to unpack Iterables.mp4 19.5 MB4. FOREX Day Trading with FXCM/6. Closing Positions vs. Hedging Positions.mp4 19.4 MB11. Financial Data Analysis with Pandas - an Introduction/1. Getting Ready (Installing required library).mp4 19.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/60. First Steps with Seaborn.mp4 19.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/15. How to work with nested Lists.mp4 19.1 MB28. A Machine Learning-powered Strategy A-Z (DNN)/11. Saving Model and Parameters.mp4 19.1 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/10. Boolean Arrays and Conditional Filtering.mp4 19.0 MB31. Appendix 1 Python (& Finance) Basics/7. Introduction to Variables.mp4 19.0 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/22. Intro to Tabular Data Pandas.mp4 19.0 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/6. Conclusions.mp4 18.9 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/45. Changing Column Labels.mp4 18.8 MB9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1. Introduction and Downloads Part 2.mp4 18.8 MB18. Trading Strategies powered by Machine Learning - Regression/7. A simple Linear Model to predict Financial Returns (Part 1).mp4 18.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/9. Numpy Universal Functions.mp4 18.6 MB31. Appendix 1 Python (& Finance) Basics/32. Lists and Element-wise Operations.mp4 18.4 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/13. External Factors and Issues.mp4 18.3 MB31. Appendix 1 Python (& Finance) Basics/12. The print() Function.mp4 18.3 MB31. Appendix 1 Python (& Finance) Basics/44. String Replacement.mp4 18.2 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/1. Introduction.mp4 18.0 MB31. Appendix 1 Python (& Finance) Basics/19. The range Object - another Iterable.mp4 17.9 MB31. Appendix 1 Python (& Finance) Basics/11. Variables - Dos, Don´ts and Conventions.mp4 17.9 MB19. Trading Strategies powered by Machine Learning - Classification/3. Getting and Preparing the Data.mp4 17.9 MB20. Advanced Backtesting Techniques/5. Creating an Iterative Base Class (Part 2).mp4 17.7 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/9. TypeErrors and ValueErrors.mp4 17.6 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/19. Recap Changing Elements in a Numpy Array slice.mp4 17.3 MB31. Appendix 1 Python (& Finance) Basics/2. Intro to the Time Value of Money (TVM) Concept (Theory).mp4 17.3 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/14. Errors related to the course content (Transcription Errors).mp4 17.2 MB18. Trading Strategies powered by Machine Learning - Regression/3. Making Predictions with Linear Regression.mp4 17.0 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/16. 2-dimensional Numpy Arrays.mp4 16.9 MB16. Defining and Backtesting simple MomentumContrarian Strategies/2. Getting the Data.mp4 16.1 MB27. Working with two or many Strategies (Combination)/5. Taking into account busy Trading Hours.mp4 15.9 MB20. Advanced Backtesting Techniques/6. Creating an Iterative Base Class (Part 3).mp4 15.9 MB27. Working with two or many Strategies (Combination)/3. Strategy 2 Mean Reversion.mp4 15.3 MB31. Appendix 1 Python (& Finance) Basics/22. Calculate an Investment Project´s NPV.mp4 15.0 MB31. Appendix 1 Python (& Finance) Basics/5. Interest Rates and Returns (Theory).mp4 14.9 MB19. Trading Strategies powered by Machine Learning - Classification/5. In-Sample Backtesting and the Look-ahead-bias.mp4 14.9 MB23. Implementation and Automation with FXCM (Updated!)/16. Trading other Strategies - Coding Challenge.mp4 14.8 MB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/1. Our very first Trade.mp4 14.6 MB31. Appendix 1 Python (& Finance) Basics/17. Indexing Lists.mp4 14.5 MB4. FOREX Day Trading with FXCM/5. Charting.mp4 14.4 MB29. Error Handling How to make your Trading Bot more stable and reliable/10. How to limit the number of retries.mp4 14.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/4. Indexing and Slicing Numpy Arrays.mp4 14.3 MB32. Appendix 2 User-defined Functions (required for OOP)/8. How to return many results.mp4 14.1 MB27. Working with two or many Strategies (Combination)/2. Strategy 1 SMA.mp4 13.9 MB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/4. Spreads during the busy hours.mp4 13.9 MB31. Appendix 1 Python (& Finance) Basics/36. Sorting and Reversing Lists.mp4 13.8 MB31. Appendix 1 Python (& Finance) Basics/3. Calculate Future Values (FV) with Python Compounding.mp4 13.4 MB22. Implementation and Automation with OANDA (UPDATED!)/18. Trading other Strategies - Coding Challenge.mp4 13.2 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/8. Misuse of function names and keywords.mp4 13.0 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/7. Indentation Errors.mp4 12.8 MB31. Appendix 1 Python (& Finance) Basics/46. Operators (Theory).mp4 12.3 MB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/2. Getting the Data.mp4 12.2 MB27. Working with two or many Strategies (Combination)/1. Introduction.mp4 12.0 MB31. Appendix 1 Python (& Finance) Basics/8. Excursus How to add inline comments.mp4 11.8 MB31. Appendix 1 Python (& Finance) Basics/28. Integers.mp4 11.5 MB27. Working with two or many Strategies (Combination)/6. Strategy Backtesting.mp4 11.4 MB31. Appendix 1 Python (& Finance) Basics/25. The Data Type Hierarchy (Theory).mp4 11.3 MB21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/1. Introduction and Overview.mp4 11.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/53. Exporting DataFrames to csv.mp4 11.1 MB31. Appendix 1 Python (& Finance) Basics/14. TVM Problems with many Cashflows.mp4 11.0 MB25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/1. Overview.mp4 10.7 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/29. Zero-based Indexing and Negative Indexing.mp4 10.7 MB31. Appendix 1 Python (& Finance) Basics/35. Changing Elements in Lists.mp4 10.6 MB31. Appendix 1 Python (& Finance) Basics/4. Calculate Present Values (FV) with Python Discounting.mp4 10.5 MB28. A Machine Learning-powered Strategy A-Z (DNN)/7. Splitting into Train and Test Set.mp4 10.3 MB18. Trading Strategies powered by Machine Learning - Regression/6. Getting the Data.mp4 10.0 MB5. Installing Python and Jupyter Notebooks/1. Introduction.mp4 9.3 MB31. Appendix 1 Python (& Finance) Basics/45. Booleans.mp4 9.3 MB29. Error Handling How to make your Trading Bot more stable and reliable/4. try and except.mp4 9.3 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/18. How to slice 2-dim Numpy Arrays (Part 2).mp4 9.2 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/28. Selecting one Column with the dot notation.mp4 9.0 MB28. A Machine Learning-powered Strategy A-Z (DNN)/4. Getting and Preparing the Data.mp4 8.8 MB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/65. Introduction to GroupBy Operations.mp4 8.5 MB31. Appendix 1 Python (& Finance) Basics/15. Intro to Python Lists.mp4 8.1 MB29. Error Handling How to make your Trading Bot more stable and reliable/5. Catching specific Errors.mp4 7.9 MB31. Appendix 1 Python (& Finance) Basics/16. Zero-based Indexing and negative Indexing in Python (Theory).mp4 7.8 MB30. +++ APPENDIX Python Crash Course +++/1. Overview.mp4 7.2 MB29. Error Handling How to make your Trading Bot more stable and reliable/3. Python Errors (Exceptions).mp4 7.2 MB5. Installing Python and Jupyter Notebooks/5. Tips for Python Beginners.mp4 6.5 MB29. Error Handling How to make your Trading Bot more stable and reliable/6. The Exception class.mp4 5.9 MB31. Appendix 1 Python (& Finance) Basics/9. Variables and Memory (Theory).mp4 5.7 MB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/3. Major reasons for Coding Errors.mp4 5.7 MB31. Appendix 1 Python (& Finance) Basics/26. Excursus Dynamic Typing in Python.mp4 5.5 MB7. Trading with Python and OANDAFXCM - an Introduction/2. Overview.mp4 4.7 MB8. 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 MB9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1.1 Part2_Materials.zip 1.9 MB25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/2.1 Part5_Materials.zip 1.9 MB21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/2.1 Part4_Materials.zip 754.1 kB33. 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 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2.1 Trading_vs_investing.pdf 542.5 kB18. Trading Strategies powered by Machine Learning - Regression/1.1 ML.pdf 504.1 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1.1 OOP.pdf 491.0 kB1. Getting Started/1.1 Overview.pdf 488.2 kB28. A Machine Learning-powered Strategy A-Z (DNN)/1.1 DNN.pdf 441.4 kB1. Getting Started/3.1 did_you_know.pdf 439.6 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/2.1 strategy_overview.pdf 410.7 kB20. Advanced Backtesting Techniques/1.1 Event_Driven_BT.pdf 394.9 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1.1 cloud.pdf 375.2 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3.1 Spot_vs_Futures.pdf 259.8 kB31. Appendix 1 Python (& Finance) Basics/21.1 NPV.pdf 251.6 kB31. Appendix 1 Python (& Finance) Basics/5.1 Interest_Rates.pdf 202.6 kB31. Appendix 1 Python (& Finance) Basics/2.1 TVM.pdf 200.5 kB31. Appendix 1 Python (& Finance) Basics/20.1 PV_FV_many.pdf 199.2 kB31. Appendix 1 Python (& Finance) Basics/14.1 FV_many.pdf 190.4 kB31. Appendix 1 Python (& Finance) Basics/39.1 Python_for_Finance_Mutability.pdf 170.7 kB31. Appendix 1 Python (& Finance) Basics/25.1 Type_Hierarchy.pdf 166.3 kB31. Appendix 1 Python (& Finance) Basics/46.1 Operators.pdf 149.1 kB31. Appendix 1 Python (& Finance) Basics/9.1 Variables.pdf 146.4 kB31. Appendix 1 Python (& Finance) Basics/16.1 Indexing.pdf 125.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7.1 Slicing_arrays.pdf 125.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6.1 Mutability_arrays.pdf 124.7 kB3. Day Trading with OANDA A-Z a Deep Dive/3.1 Currency.pdf 116.4 kB3. Day Trading with OANDA A-Z a Deep Dive/6.1 spread.pdf 114.9 kB31. Appendix 1 Python (& Finance) Basics/34.1 Slicing_cheatsheet.pdf 107.8 kB31. Appendix 1 Python (& Finance) Basics/27.1 Built_in_func.pdf 94.8 kB3. Day Trading with OANDA A-Z a Deep Dive/9.1 Candlestick.pdf 94.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/32.1 pandas_iloc.pdf 73.7 kB31. Appendix 1 Python (& Finance) Basics/11.1 keywords.pdf 71.1 kB33. 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 kB3. Day Trading with OANDA A-Z a Deep Dive/10.1 Short_EUR.xlsx 36.3 kB4. FOREX Day Trading with FXCM/4.1 Long_EUR_fxcm.xlsx 27.0 kB31. Appendix 1 Python (& Finance) Basics/1.1 Appendix1_Materials.zip 19.4 kB5. Installing Python and Jupyter Notebooks/4. How to work with Jupyter Notebooks.srt 18.0 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/61. Categorical Seaborn Plots.srt 17.2 kB10. Introduction to Time Series Data in Pandas/4. Downsampling Time Series with resample().srt 17.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/25. First Data Inspection.srt 14.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/56. Customization of Plots.srt 14.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/62. Seaborn Regression Plots.srt 14.6 kB11. Financial Data Analysis with Pandas - an Introduction/12. Importing Financial Data from Excel.srt 14.2 kB28. A Machine Learning-powered Strategy A-Z (DNN)/13. Implementation (Oanda & FXCM).srt 13.8 kB31. Appendix 1 Python (& Finance) Basics/40. Coding Exercise 3.srt 13.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/52. Handling NA Values missing Values.srt 13.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/47. Filtering DataFrames (one Condition).srt 13.1 kB15. Defining and Backtesting SMA Strategies/4. Finding the optimal SMA Strategy.srt 12.9 kB3. Day Trading with OANDA A-Z a Deep Dive/6. Trading Costs and Performance Attribution.srt 12.8 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/1. Introduction to OOP and examples for Classes.srt 12.6 kB33. 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 kB12. Advanced Topics/2. Filling NA Values with bfill, ffill and interpolation.srt 12.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/44. Changing Row Index with set_index() and reset_index().srt 12.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/54. Summary Statistics and Accumulations.srt 12.0 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/2. Test your debugging skills!.srt 11.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/34. Slicing Rows and Columns with loc (label-based indexing).srt 11.9 kB31. Appendix 1 Python (& Finance) Basics/50. Keywords pass, continue and break.srt 11.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/30. Selecting Rows with iloc (position-based indexing).srt 11.8 kB31. Appendix 1 Python (& Finance) Basics/13. Coding Exercise 1.srt 11.8 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/11. How to traceback more complex Errors.srt 11.8 kB31. Appendix 1 Python (& Finance) Basics/37. Adding and removing Elements fromto Lists.srt 11.7 kB15. Defining and Backtesting SMA Strategies/5. Generalization with OOP An SMA Backtesting Class in action.srt 11.6 kB5. Installing Python and Jupyter Notebooks/3. How to open Jupyter Notebooks.srt 11.4 kB10. Introduction to Time Series Data in Pandas/2. Converting strings to datetime objects with pd.to_datetime().srt 11.4 kB11. Financial Data Analysis with Pandas - an Introduction/11. Simple Returns vs. Log Returns.srt 11.4 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/42. Sorting of Series and Introduction to the inplace - parameter.srt 11.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/51. Intro to NA Values missing Values.srt 11.3 kB31. Appendix 1 Python (& Finance) Basics/48. Coding Exercise 4.srt 11.2 kB11. Financial Data Analysis with Pandas - an Introduction/13. Simple Moving Averages (SMA) with rolling().srt 11.2 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/55. Visualization with Matplotlib (Intro).srt 11.0 kB11. Financial Data Analysis with Pandas - an Introduction/7. Measuring Stock Performance with MEAN Returns and STD of Returns.srt 11.0 kB31. Appendix 1 Python (& Finance) Basics/49. Conditional Statements.srt 10.8 kB23. Implementation and Automation with FXCM (Updated!)/2. Historical Data, real-time Data and Orders (Recap).srt 10.7 kB7. Trading with Python and OANDAFXCM - an Introduction/10. OANDA How to place Orders and execute Trades.srt 10.7 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/23. Create your very first Pandas DataFrame (from csv).srt 10.6 kB11. Financial Data Analysis with Pandas - an Introduction/8. Financial Time Series - Return and Risk.srt 10.6 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/63. Seaborn Heatmaps.srt 10.6 kB31. Appendix 1 Python (& Finance) Basics/38. Mutable vs. immutable Objects (Part 1).srt 10.6 kB11. Financial Data Analysis with Pandas - an Introduction/2. Importing Stock Price Data from Yahoo Finance.srt 10.4 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/4. Spot Trading vs. Derivatives Trading (Part 2).srt 10.4 kB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/4. Defining a Bollinger Bands Mean-Reversion Strategy (Part 2).srt 10.3 kB1. Getting Started/5. Student FAQ.html 10.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/66. Understanding the GroupBy Object.srt 10.3 kB16. Defining and Backtesting simple MomentumContrarian Strategies/7. Trades and Trading Costs (Part 1).srt 10.3 kB31. Appendix 1 Python (& Finance) Basics/23. Coding Exercise 2.srt 10.3 kB27. Working with two or many Strategies (Combination)/8. Strategy Optimization.srt 10.3 kB32. Appendix 2 User-defined Functions (required for OOP)/9. Scope - easily explained.srt 10.2 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/3. Spot Trading vs. Derivatives Trading (Part 1).srt 10.2 kB10. Introduction to Time Series Data in Pandas/1. Importing Time Series Data from csv-files.srt 10.2 kB22. Implementation and Automation with OANDA (UPDATED!)/4. Historical Data, real-time Data and Orders (Recap).srt 10.2 kB20. Advanced Backtesting Techniques/13. Adding the Iterative Backtest Child Class for SMA (Part 2).srt 10.2 kB31. Appendix 1 Python (& Finance) Basics/43. Intro to Strings.srt 10.1 kB3. Day Trading with OANDA A-Z a Deep Dive/3. FOREX Currency Exchange Rates explained.srt 10.0 kB31. Appendix 1 Python (& Finance) Basics/18. For Loops - Iterating over Lists.srt 10.0 kB31. Appendix 1 Python (& Finance) Basics/21. The Net Present Value - NPV (Theory).srt 10.0 kB15. Defining and Backtesting SMA Strategies/3. Vectorized Strategy Backtesting.srt 9.8 kB31. Appendix 1 Python (& Finance) Basics/47. Comparison, Logical and Membership Operators in Action.srt 9.7 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/12. Inheritance.srt 9.6 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/3. Numpy Arrays.srt 9.5 kB22. Implementation and Automation with OANDA (UPDATED!)/12. Working with historical data and real-time tick data (Part 1).srt 9.5 kB22. Implementation and Automation with OANDA (UPDATED!)/17. Trade Monitoring and Reporting.srt 9.4 kB18. Trading Strategies powered by Machine Learning - Regression/2. Linear Regression with scikit-learn - a simple Introduction.srt 9.4 kB3. Day Trading with OANDA A-Z a Deep Dive/1. OANDA at a first glance.srt 9.4 kB23. Implementation and Automation with FXCM (Updated!)/8. Storing and resampling real-time tick data (Part 2).srt 9.4 kB31. Appendix 1 Python (& Finance) Basics/20. Calculate FV and PV for many Cashflows.srt 9.3 kB23. Implementation and Automation with FXCM (Updated!)/6. Storing and resampling real-time tick data (Part 1).srt 9.3 kB5. Installing Python and Jupyter Notebooks/2. Download and Install Anaconda.srt 9.3 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/4. The special method __init__().srt 9.2 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/2. Modules, Packages and Libraries - No need to reinvent the Wheel.srt 9.2 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/50. Advanced Filtering with between(), isin() and ~.srt 9.2 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/4. How to create an EC2 Instance.srt 9.2 kB16. Defining and Backtesting simple MomentumContrarian Strategies/9. Generalization with OOP A Contrarian Backtesting Class in action.srt 9.1 kB29. Error Handling How to make your Trading Bot more stable and reliable/14. Oanda Error Handling (Part 2).srt 9.0 kB22. Implementation and Automation with OANDA (UPDATED!)/10. Storing and resampling real-time tick data (Part 4).srt 9.0 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/15. Summary and Debugging Flow-Chart.srt 8.9 kB11. Financial Data Analysis with Pandas - an Introduction/14. Momentum Trading Strategies with SMAs.srt 8.9 kB11. Financial Data Analysis with Pandas - an Introduction/5. The shift() method.srt 8.9 kB31. Appendix 1 Python (& Finance) Basics/52. Introduction to while loops.srt 8.9 kB28. A Machine Learning-powered Strategy A-Z (DNN)/9. Creating and Fitting the DNN Model.srt 8.8 kB22. Implementation and Automation with OANDA (UPDATED!)/7. Storing and resampling real-time tick data (Part 1).srt 8.8 kB11. Financial Data Analysis with Pandas - an Introduction/6. The methods diff() and pct_change().srt 8.6 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/59. Scatterplots.srt 8.5 kB31. Appendix 1 Python (& Finance) Basics/41. Tuples.srt 8.5 kB23. Implementation and Automation with FXCM (Updated!)/14. Placing Orders and Executing Trades.srt 8.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/67. Splitting with many Keys.srt 8.5 kB7. Trading with Python and OANDAFXCM - an Introduction/7. OANDA How to load Historical Price Data (Part 1).srt 8.5 kB15. Defining and Backtesting SMA Strategies/7. Creating the Class (Part 2).srt 8.5 kB3. Day Trading with OANDA A-Z a Deep Dive/8. Margin Closeout and more.srt 8.4 kB7. Trading with Python and OANDAFXCM - an Introduction/6. OANDA Connecting to the APIServer.srt 8.4 kB3. Day Trading with OANDA A-Z a Deep Dive/7. Margin and Leverage.srt 8.4 kB10. Introduction to Time Series Data in Pandas/3. Indexing and Slicing Time Series.srt 8.3 kB19. Trading Strategies powered by Machine Learning - Classification/8. The Classification Backtesting Class explained (Part 1).srt 8.3 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/16. Coding Exercise 3 Create your own Class.srt 8.3 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/5. Omitting cells, changing the sequence and more.srt 8.3 kB28. A Machine Learning-powered Strategy A-Z (DNN)/10. Prediction & Out-Sample Forward Testing.srt 8.3 kB7. Trading with Python and OANDAFXCM - an Introduction/15. FXCM Connecting to the APIServer.srt 8.3 kB20. Advanced Backtesting Techniques/7. Creating an Iterative Base Class (Part 4).srt 8.2 kB31. Appendix 1 Python (& Finance) Basics/10. More on Variables and Memory.srt 8.2 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/58. Histogramms (Part 2).srt 8.2 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/1. Introduction to Part 3.srt 8.2 kB3. Day Trading with OANDA A-Z a Deep Dive/2. How to create an Account.srt 8.1 kB3. Day Trading with OANDA A-Z a Deep Dive/10. Our third Trade A-Z - Going Short EURUSD.srt 8.1 kB20. Advanced Backtesting Techniques/15. OOP Challenge Add Contrarian and Bollinger Strategies.srt 8.1 kB23. Implementation and Automation with FXCM (Updated!)/5. Collecting and storing real-time tick data.srt 8.1 kB18. Trading Strategies powered by Machine Learning - Regression/1. Machine Learning - an Overview.srt 8.1 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/2. Trading Strategies - an Overview.srt 8.1 kB31. Appendix 1 Python (& Finance) Basics/42. Dictionaries.srt 8.1 kB7. Trading with Python and OANDAFXCM - an Introduction/20. FXCM How to place Orders and execute Trades.srt 8.0 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/24. Pandas Display Options and the methods head() & tail().srt 8.0 kB31. Appendix 1 Python (& Finance) Basics/27. Build-in Functions.srt 7.9 kB15. Defining and Backtesting SMA Strategies/2. Defining an SMA Crossover Strategy.srt 7.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/36. Summary, Best Practices and Outlook.srt 7.9 kB11. Financial Data Analysis with Pandas - an Introduction/4. Normalizing Time Series to a Base Value (100).srt 7.8 kB31. Appendix 1 Python (& Finance) Basics/24. Data Types in Action.srt 7.7 kB28. A Machine Learning-powered Strategy A-Z (DNN)/3. Installation of Tensorflow & Keras (Part 2).srt 7.7 kB3. Day Trading with OANDA A-Z a Deep Dive/11. Netting vs. Hedging.srt 7.7 kB31. Appendix 1 Python (& Finance) Basics/2. Intro to the Time Value of Money (TVM) Concept (Theory).srt 7.7 kB20. Advanced Backtesting Techniques/10. Creating an Iterative Base Class (Part 7).srt 7.7 kB32. Appendix 2 User-defined Functions (required for OOP)/3. What´s the difference between Positional Arguments vs. Keyword Arguments.srt 7.7 kB3. Day Trading with OANDA A-Z a Deep Dive/5. How to calculate Profit & Loss of a Trade.srt 7.6 kB4. FOREX Day Trading with FXCM/2. How to create an Account.srt 7.6 kB32. Appendix 2 User-defined Functions (required for OOP)/5. The Default Argument None.srt 7.6 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/12. Problems with the Python Installation.srt 7.6 kB18. Trading Strategies powered by Machine Learning - Regression/8. A simple Linear Model to predict Financial Returns (Part 2).srt 7.6 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/10. Getting help on StackOverflow.com.srt 7.6 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/14. Adding meaningful Docstrings.srt 7.6 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/6. Performance Metrics.srt 7.5 kB32. Appendix 2 User-defined Functions (required for OOP)/2. Defining your first user-defined Function.srt 7.5 kB15. Defining and Backtesting SMA Strategies/8. Creating the Class (Part 3).srt 7.5 kB18. Trading Strategies powered by Machine Learning - Regression/4. Overfitting.srt 7.5 kB22. Implementation and Automation with OANDA (UPDATED!)/22. Machine Learning Strategies (2) - Implementation.srt 7.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/11. Advanced Filtering & Bitwise Operators.srt 7.4 kB12. Advanced Topics/1. Helpful DatetimeIndex Attributes and Methods.srt 7.4 kB7. Trading with Python and OANDAFXCM - an Introduction/19. FXCM Streaming high-frequency real-time Data.srt 7.4 kB22. Implementation and Automation with OANDA (UPDATED!)/25. Running a Python Trader Script.srt 7.4 kB22. Implementation and Automation with OANDA (UPDATED!)/13. Working with historical data and real-time tick data (Part 2).srt 7.4 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/13. Inheritance and the super() Function.srt 7.3 kB4. FOREX Day Trading with FXCM/1. FXCM at a first glance.srt 7.3 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/11. How to stop Trading Sessions (OANDA).srt 7.3 kB28. A Machine Learning-powered Strategy A-Z (DNN)/5. Adding LabelsFeatures.srt 7.2 kB20. Advanced Backtesting Techniques/11. Creating an Iterative Base Class (Part 8).srt 7.1 kB29. Error Handling How to make your Trading Bot more stable and reliable/13. Oanda Error Handling (Part 1).srt 7.1 kB1. Getting Started/2. How to get the best out of this course.srt 7.1 kB23. Implementation and Automation with FXCM (Updated!)/21. Running a Python Script.srt 7.0 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/60. First Steps with Seaborn.srt 7.0 kB7. Trading with Python and OANDAFXCM - an Introduction/17. FXCM How to load Historical Price Data (Part 1).srt 7.0 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/43. First Steps with Pandas Index Objects.srt 7.0 kB31. Appendix 1 Python (& Finance) Basics/29. Floats.srt 7.0 kB32. Appendix 2 User-defined Functions (required for OOP)/4. How to work with Default Arguments.srt 7.0 kB20. Advanced Backtesting Techniques/2. A first Intuition on Iterative Backtesting (Part 1).srt 6.9 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/5. The method get_data().srt 6.9 kB28. A Machine Learning-powered Strategy A-Z (DNN)/1. Project Overview.srt 6.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/17. How to slice 2-dim Numpy Arrays (Part 1).srt 6.9 kB23. Implementation and Automation with FXCM (Updated!)/10. Working with historical data and real-time tick data (Part 1).srt 6.9 kB23. Implementation and Automation with FXCM (Updated!)/15. Trade Monitoring and Reporting.srt 6.9 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/4. The most commonly made Errors at a glance.srt 6.9 kB22. Implementation and Automation with OANDA (UPDATED!)/16. Placing Orders and Executing Trades.srt 6.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/6. Changing Elements in Numpy Arrays & Mutability.srt 6.8 kB22. Implementation and Automation with OANDA (UPDATED!)/15. Defining a simple Contrarian Strategy.srt 6.8 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/6. Getting the Instance Ready for Algorithmic Trading.srt 6.8 kB31. Appendix 1 Python (& Finance) Basics/30. How to round Floats (and Integers) with round().srt 6.7 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/27. Selecting Columns.srt 6.7 kB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/1. Mean-Reversion Strategies - Overview.srt 6.7 kB18. Trading Strategies powered by Machine Learning - Regression/9. A Multiple Regression Model to predict Financial Returns.srt 6.7 kB20. Advanced Backtesting Techniques/12. Adding the Iterative Backtest Child Class for SMA (Part 1).srt 6.7 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/11. Adding more methods and performance metrics.srt 6.6 kB31. Appendix 1 Python (& Finance) Basics/31. More on Lists.srt 6.6 kB22. Implementation and Automation with OANDA (UPDATED!)/21. Machine Learning Strategies (1) - Model Fitting.srt 6.6 kB23. Implementation and Automation with FXCM (Updated!)/18. Machine Learning Strategies (1) - Model Fitting.srt 6.6 kB27. Working with two or many Strategies (Combination)/4. Combining both Strategies - Alternative 1.srt 6.6 kB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/5. Vectorized Strategy Backtesting.srt 6.5 kB31. Appendix 1 Python (& Finance) Basics/7. Introduction to Variables.srt 6.5 kB32. Appendix 2 User-defined Functions (required for OOP)/7. Sequences as arguments and args.srt 6.5 kB22. Implementation and Automation with OANDA (UPDATED!)/6. How to collect and store real-time tick data.srt 6.4 kB23. Implementation and Automation with FXCM (Updated!)/11. Working with historical data and real-time tick data (Part 2).srt 6.4 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/8. Numpy Array Methods and Attributes.srt 6.4 kB23. Implementation and Automation with FXCM (Updated!)/7. A Trader Class.srt 6.4 kB23. Implementation and Automation with FXCM (Updated!)/4. Preview A Trader Class live in action.srt 6.4 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/64. Removing Columns.srt 6.3 kB29. Error Handling How to make your Trading Bot more stable and reliable/1. Introduction.srt 6.3 kB20. Advanced Backtesting Techniques/8. Creating an Iterative Base Class (Part 5).srt 6.3 kB23. Implementation and Automation with FXCM (Updated!)/19. Machine Learning Strategies (2) - Implementation.srt 6.3 kB1. Getting Started/1. What is Algorithmic Trading Course Overview.srt 6.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/13. Creating Numpy Arrays from Scratch.srt 6.3 kB16. Defining and Backtesting simple MomentumContrarian Strategies/6. Changing the Window Parameter.srt 6.3 kB3. Day Trading with OANDA A-Z a Deep Dive/12. Market, Limit and Stop Orders.srt 6.2 kB11. Financial Data Analysis with Pandas - an Introduction/3. Initial Inspection and Visualization.srt 6.2 kB31. Appendix 1 Python (& Finance) Basics/5. Interest Rates and Returns (Theory).srt 6.2 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/1. Introduction and Preparing the Data.srt 6.2 kB11. Financial Data Analysis with Pandas - an Introduction/16. Merging Aligning Financial Time Series (hands-on).srt 6.2 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/5. Overview & the Brokers OANDA and FXCM.srt 6.1 kB31. Appendix 1 Python (& Finance) Basics/19. The range Object - another Iterable.srt 6.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/12. Determining a Project´s Payback Period with np.where().srt 6.0 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/5. A simple Buy and Hold Strategy.srt 6.0 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/8. The methods plot_prices() and plot_returns().srt 6.0 kB31. Appendix 1 Python (& Finance) Basics/39. Mutable vs. immutable Objects (Part 2).srt 6.0 kB29. Error Handling How to make your Trading Bot more stable and reliable/18. FXCM Error Handling (Part 2).srt 6.0 kB10. Introduction to Time Series Data in Pandas/5. Coding Exercise 1.srt 5.9 kB22. Implementation and Automation with OANDA (UPDATED!)/5. Preview A Trader Class live in action.srt 5.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/7. View vs. copy - potential Pitfalls when slicing Numpy Arrays.srt 5.9 kB20. Advanced Backtesting Techniques/3. A first Intuition on Iterative Backtesting (Part 2).srt 5.9 kB7. Trading with Python and OANDAFXCM - an Introduction/5. OANDA Getting the API Key & other Preparations.srt 5.9 kB22. Implementation and Automation with OANDA (UPDATED!)/8. Storing and resampling real-time tick data (Part 2).srt 5.9 kB3. Day Trading with OANDA A-Z a Deep Dive/9. Introduction to Charting.srt 5.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/49. Filtering DataFrames by many Conditions (OR).srt 5.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/22. Intro to Tabular Data Pandas.srt 5.8 kB15. Defining and Backtesting SMA Strategies/1. SMA Crossover Strategies - Overview.srt 5.8 kB11. Financial Data Analysis with Pandas - an Introduction/15. Exponentially-weighted Moving Averages (EWMA).srt 5.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/10. Boolean Arrays and Conditional Filtering.srt 5.8 kB20. Advanced Backtesting Techniques/14. Using Modules and adding Docstrings.srt 5.7 kB32. Appendix 2 User-defined Functions (required for OOP)/6. How to unpack Iterables.srt 5.7 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/12. How to stop Trading Sessions (FXCM).srt 5.7 kB31. Appendix 1 Python (& Finance) Basics/51. Calculate a Project´s Payback Period.srt 5.7 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/2. Long Term Investing vs. (Algorithmic) Day Trading.srt 5.7 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/20. How to perform row-wise and column-wise Operations.srt 5.7 kB12. Advanced Topics/4. Timezones and Converting (Part 2).srt 5.7 kB11. Financial Data Analysis with Pandas - an Introduction/9. Financial Time Series - Covariance and Correlation.srt 5.6 kB15. Defining and Backtesting SMA Strategies/9. Creating the Class (Part 4).srt 5.6 kB23. Implementation and Automation with FXCM (Updated!)/13. Defining a Simple Contrarian Trading Strategy.srt 5.6 kB29. Error Handling How to make your Trading Bot more stable and reliable/17. FXCM Error Handling (Part 1).srt 5.6 kB1. Getting Started/3. Did you know... (what Data can tell us about Day Trading).srt 5.6 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/57. Histogramms (Part 1).srt 5.5 kB15. Defining and Backtesting SMA Strategies/11. Creating the Class (Part 6).srt 5.5 kB31. Appendix 1 Python (& Finance) Basics/32. Lists and Element-wise Operations.srt 5.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/15. How to work with nested Lists.srt 5.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/31. Slicing Rows and Columns with iloc (position-based indexing).srt 5.5 kB12. Advanced Topics/3. Timezones and Converting (Part 1).srt 5.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/48. Filtering DataFrames by many Conditions (AND).srt 5.5 kB29. Error Handling How to make your Trading Bot more stable and reliable/9. Try again (...until it works).srt 5.4 kB31. Appendix 1 Python (& Finance) Basics/46. Operators (Theory).srt 5.4 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/6. IndexErrors.srt 5.4 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/41. The copy() method.srt 5.4 kB7. Trading with Python and OANDAFXCM - an Introduction/18. FXCM How to load Historical Price Data (Part 2).srt 5.4 kB22. Implementation and Automation with OANDA (UPDATED!)/9. Storing and resampling real-time tick data (Part 3).srt 5.4 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/2. The Financial Analysis Class live in action (Part 1).srt 5.4 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/13. External Factors and Issues.srt 5.3 kB20. Advanced Backtesting Techniques/4. Creating an Iterative Base Class (Part 1).srt 5.3 kB15. Defining and Backtesting SMA Strategies/13. Creating the Class (Part 8).srt 5.3 kB31. Appendix 1 Python (& Finance) Basics/12. The print() Function.srt 5.3 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/14. Errors related to the course content (Transcription Errors).srt 5.2 kB16. Defining and Backtesting simple MomentumContrarian Strategies/3. Excursus Your FAQs answered.srt 5.2 kB31. Appendix 1 Python (& Finance) Basics/33. Slicing Lists.srt 5.1 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/10. How to schedule Trading sessions with the Task Scheduler.srt 5.1 kB20. Advanced Backtesting Techniques/1. Introduction to Iterative Backtesting (event-driven).srt 5.1 kB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/3. Defining a Bollinger Bands Mean-Reversion Strategy (Part 1).srt 5.1 kB29. Error Handling How to make your Trading Bot more stable and reliable/11. Waiting periods between re-tries.srt 5.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/38. First Steps with Pandas Series.srt 5.1 kB18. Trading Strategies powered by Machine Learning - Regression/11. Out-Sample Forward Testing.srt 5.0 kB19. Trading Strategies powered by Machine Learning - Classification/9. The Classification Backtesting Class explained (Part 2).srt 5.0 kB31. Appendix 1 Python (& Finance) Basics/44. String Replacement.srt 5.0 kB31. Appendix 1 Python (& Finance) Basics/6. Calculate Interest Rates and Returns with Python.srt 5.0 kB23. Implementation and Automation with FXCM (Updated!)/17. SMA Crossover and Bollinger Bands (Solution).srt 5.0 kB16. Defining and Backtesting simple MomentumContrarian Strategies/5. Vectorized Strategy Backtesting.srt 4.9 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/5. The Impact of Granularity.srt 4.9 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/9. Numpy Universal Functions.srt 4.9 kB31. Appendix 1 Python (& Finance) Basics/11. Variables - Dos, Don´ts and Conventions.srt 4.9 kB3. Day Trading with OANDA A-Z a Deep Dive/4. Our second Trade - EURUSD FOREX Trading.srt 4.9 kB3. Day Trading with OANDA A-Z a Deep Dive/14. A more general Example.srt 4.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/46. Renaming Index & Column Labels with rename().srt 4.8 kB20. Advanced Backtesting Techniques/9. Creating an Iterative Base Class (Part 6).srt 4.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/16. 2-dimensional Numpy Arrays.srt 4.7 kB18. Trading Strategies powered by Machine Learning - Regression/10. In-Sample Backtesting and the Look-ahead-bias.srt 4.7 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/5. Vectorized Operations with Numpy Arrays.srt 4.7 kB14. +++ PART 3 Defining and Testing Trading Strategies +++/4. Getting the Data.srt 4.7 kB18. Trading Strategies powered by Machine Learning - Regression/5. Underfitting.srt 4.6 kB31. Appendix 1 Python (& Finance) Basics/14. TVM Problems with many Cashflows.srt 4.6 kB31. Appendix 1 Python (& Finance) Basics/36. Sorting and Reversing Lists.srt 4.6 kB22. Implementation and Automation with OANDA (UPDATED!)/11. Storing and resampling real-time tick data (Part 5).srt 4.5 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/5. How to connect to your EC2 Instance.srt 4.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/19. Recap Changing Elements in a Numpy Array slice.srt 4.5 kB16. Defining and Backtesting simple MomentumContrarian Strategies/1. Simple ContrarianMomentum Strategies - Overview.srt 4.5 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/9. Encapsulation and protected Attributes.srt 4.5 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/9. TypeErrors and ValueErrors.srt 4.4 kB31. Appendix 1 Python (& Finance) Basics/3. Calculate Future Values (FV) with Python Compounding.srt 4.4 kB31. Appendix 1 Python (& Finance) Basics/25. The Data Type Hierarchy (Theory).srt 4.4 kB22. Implementation and Automation with OANDA (UPDATED!)/19. Implementing an SMA Crossover Strategy (Solution).srt 4.4 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/15. Creating and Importing Python Modules (.py).srt 4.3 kB29. Error Handling How to make your Trading Bot more stable and reliable/15. Oanda Error Handling (Part 3).srt 4.3 kB29. Error Handling How to make your Trading Bot more stable and reliable/8. finally.srt 4.3 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/7. Indentation Errors.srt 4.3 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/9. How to start Trading sessions with Batch (.bat) Files.srt 4.2 kB22. Implementation and Automation with OANDA (UPDATED!)/14. Working with historical data and real-time tick data (Part 3).srt 4.2 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/3. The Financial Analysis Class live in action (Part 2).srt 4.1 kB29. Error Handling How to make your Trading Bot more stable and reliable/12. Implementation with Oanda V20 Connection Issues.srt 4.1 kB23. Implementation and Automation with FXCM (Updated!)/3. Troubleshooting FXCM Server Connection Issues.html 4.1 kB7. Trading with Python and OANDAFXCM - an Introduction/16. Troubleshooting FXCM Server Connection Issues.html 4.1 kB7. Trading with Python and OANDAFXCM - an Introduction/8. OANDA How to load Historical Price Data (Part 2).srt 4.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/45. Changing Column Labels.srt 4.1 kB7. Trading with Python and OANDAFXCM - an Introduction/9. OANDA Streaming high-frequency real-time Data.srt 4.1 kB18. Trading Strategies powered by Machine Learning - Regression/7. A simple Linear Model to predict Financial Returns (Part 1).srt 4.1 kB23. Implementation and Automation with FXCM (Updated!)/12. Working with historical data and real-time tick data (Part 3).srt 4.1 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/29. Zero-based Indexing and Negative Indexing.srt 4.1 kB4. FOREX Day Trading with FXCM/7. Order Types at a glance.srt 4.1 kB4. FOREX Day Trading with FXCM/4. Trade Analysis.srt 4.1 kB3. Day Trading with OANDA A-Z a Deep Dive/13. Take-Profit and Stop-Loss Orders.srt 4.0 kB31. Appendix 1 Python (& Finance) Basics/28. Integers.srt 4.0 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/7. String representation and the special method __repr__().srt 4.0 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/33. Selecting Rows with loc (label-based indexing).srt 4.0 kB15. Defining and Backtesting SMA Strategies/6. Creating the Class (Part 1).srt 3.9 kB19. Trading Strategies powered by Machine Learning - Classification/4. Predicting Market Direction with Logistic Regression.srt 3.9 kB16. Defining and Backtesting simple MomentumContrarian Strategies/4. Defining a simple Contrarian Strategy.srt 3.9 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/8. How to run Python Scripts in a Windows Command Prompt.srt 3.9 kB18. Trading Strategies powered by Machine Learning - Regression/3. Making Predictions with Linear Regression.srt 3.9 kB31. Appendix 1 Python (& Finance) Basics/17. Indexing Lists.srt 3.9 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/3. The best time to trade (Part 2).srt 3.8 kB29. Error Handling How to make your Trading Bot more stable and reliable/7. try, except, else.srt 3.8 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/1. Introduction.srt 3.8 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/2. The best time to trade (Part 1).srt 3.8 kB7. Trading with Python and OANDAFXCM - an Introduction/4. OANDA How to install the OANDA API Wrapper.srt 3.7 kB31. Appendix 1 Python (& Finance) Basics/22. Calculate an Investment Project´s NPV.srt 3.7 kB19. Trading Strategies powered by Machine Learning - Classification/6. Out-Sample Forward Testing.srt 3.7 kB23. Implementation and Automation with FXCM (Updated!)/9. Storing and resampling real-time tick data (Part 3).srt 3.6 kB28. A Machine Learning-powered Strategy A-Z (DNN)/8. Feature ScalingEngineering.srt 3.6 kB19. Trading Strategies powered by Machine Learning - Classification/3. Getting and Preparing the Data.srt 3.6 kB7. Trading with Python and OANDAFXCM - an Introduction/13. FXCM How to install the FXCM API Wrapper.srt 3.6 kB31. Appendix 1 Python (& Finance) Basics/8. Excursus How to add inline comments.srt 3.6 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/6. The method log_returns().srt 3.6 kB13. Object Oriented Programming (OOP) Creating a Financial Analysis Class/10. The method set_ticker().srt 3.5 kB16. Defining and Backtesting simple MomentumContrarian Strategies/8. Trades and Trading Costs (Part 2).srt 3.5 kB4. FOREX Day Trading with FXCM/3. Example Trade Buying EURUSD.srt 3.5 kB31. Appendix 1 Python (& Finance) Basics/16. Zero-based Indexing and negative Indexing in Python (Theory).srt 3.5 kB7. Trading with Python and OANDAFXCM - an Introduction/14. FXCM Getting the Access Token & other Preparations.srt 3.4 kB32. Appendix 2 User-defined Functions (required for OOP)/8. How to return many results.srt 3.4 kB22. Implementation and Automation with OANDA (UPDATED!)/20. Implementing a Bollinger Bands Strategy (Solution).srt 3.4 kB31. Appendix 1 Python (& Finance) Basics/35. Changing Elements in Lists.srt 3.4 kB9. +++ PART 2 Pandas for Financial Data Analysis and Introduction to OOP +++/1. Introduction and Downloads Part 2.srt 3.4 kB22. Implementation and Automation with OANDA (UPDATED!)/2. Updating the Wrapper Package (Part 2).srt 3.3 kB24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/1. Introduction and Motivation.srt 3.3 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/4. Indexing and Slicing Numpy Arrays.srt 3.3 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/8. Misuse of function names and keywords.srt 3.2 kB15. Defining and Backtesting SMA Strategies/10. Creating the Class (Part 5).srt 3.2 kB32. Appendix 2 User-defined Functions (required for OOP)/1.1 Appendix2_Materials.zip 3.2 kB15. Defining and Backtesting SMA Strategies/12. Creating the Class (Part 7).srt 3.2 kB29. Error Handling How to make your Trading Bot more stable and reliable/4. try and except.srt 3.1 kB31. Appendix 1 Python (& Finance) Basics/4. Calculate Present Values (FV) with Python Discounting.srt 3.1 kB28. A Machine Learning-powered Strategy A-Z (DNN)/11. Saving Model and Parameters.srt 3.1 kB31. Appendix 1 Python (& Finance) Basics/15. Intro to Python Lists.srt 3.1 kB27. Working with two or many Strategies (Combination)/7. Combining both Strategies - Alternative 2.srt 3.0 kB27. Working with two or many Strategies (Combination)/5. Taking into account busy Trading Hours.srt 3.0 kB20. Advanced Backtesting Techniques/5. Creating an Iterative Base Class (Part 2).srt 3.0 kB29. Error Handling How to make your Trading Bot more stable and reliable/10. How to limit the number of retries.srt 3.0 kB16. Defining and Backtesting simple MomentumContrarian Strategies/2. Getting the Data.srt 3.0 kB19. Trading Strategies powered by Machine Learning - Classification/5. In-Sample Backtesting and the Look-ahead-bias.srt 2.9 kB22. Implementation and Automation with OANDA (UPDATED!)/23. Importing a Trader Module Class.srt 2.9 kB27. Working with two or many Strategies (Combination)/3. Strategy 2 Mean Reversion.srt 2.9 kB31. Appendix 1 Python (& Finance) Basics/45. Booleans.srt 2.9 kB29. Error Handling How to make your Trading Bot more stable and reliable/16. Implementation with FXCM APIServer Issues.srt 2.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/28. Selecting one Column with the dot notation.srt 2.8 kB27. Working with two or many Strategies (Combination)/2. Strategy 1 SMA.srt 2.8 kB20. Advanced Backtesting Techniques/6. Creating an Iterative Base Class (Part 3).srt 2.8 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/65. Introduction to GroupBy Operations.srt 2.8 kB28. A Machine Learning-powered Strategy A-Z (DNN)/6. Adding lags.srt 2.7 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/53. Exporting DataFrames to csv.srt 2.7 kB1. Getting Started/6. LEGAL DISCLAIMER (MUST READ!) .html 2.6 kB4. FOREX Day Trading with FXCM/6. Closing Positions vs. Hedging Positions.srt 2.6 kB27. Working with two or many Strategies (Combination)/1. Introduction.srt 2.6 kB11. Financial Data Analysis with Pandas - an Introduction/1. Getting Ready (Installing required library).srt 2.6 kB31. Appendix 1 Python (& Finance) Basics/9. Variables and Memory (Theory).srt 2.5 kB33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/18. How to slice 2-dim Numpy Arrays (Part 2).srt 2.5 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/6. Conclusions.srt 2.5 kB28. A Machine Learning-powered Strategy A-Z (DNN)/7. Splitting into Train and Test Set.srt 2.4 kB25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/1. Overview.srt 2.3 kB17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/2. Getting the Data.srt 2.3 kB23. Implementation and Automation with FXCM (Updated!)/16. Trading other Strategies - Coding Challenge.srt 2.2 kB21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/1. Introduction and Overview.srt 2.2 kB2. +++ PART 1 Day Trading, Online Brokers and APIs +++/1. Our very first Trade.srt 2.2 kB22. Implementation and Automation with OANDA (UPDATED!)/18. Trading other Strategies - Coding Challenge.srt 2.1 kB5. Installing Python and Jupyter Notebooks/1. Introduction.srt 2.1 kB26. Trading Hours, Spreads and Granularity - control and limit Trading Costs!/4. Spreads during the busy hours.srt 2.1 kB31. Appendix 1 Python (& Finance) Basics/26. Excursus Dynamic Typing in Python.srt 2.0 kB27. Working with two or many Strategies (Combination)/6. Strategy Backtesting.srt 2.0 kB29. Error Handling How to make your Trading Bot more stable and reliable/3. Python Errors (Exceptions).srt 2.0 kB29. Error Handling How to make your Trading Bot more stable and reliable/5. Catching specific Errors.srt 1.9 kB18. Trading Strategies powered by Machine Learning - Regression/6. Getting the Data.srt 1.9 kB30. +++ APPENDIX Python Crash Course +++/1. Overview.srt 1.9 kB4. FOREX Day Trading with FXCM/5. Charting.srt 1.5 kB6. Excursus How to avoid and debug Coding Errors (don´t skip!)/3. Major reasons for Coding Errors.srt 1.5 kB7. Trading with Python and OANDAFXCM - an Introduction/14.1 FXCM_firststeps.zip 1.5 kB5. Installing Python and Jupyter Notebooks/5. Tips for Python Beginners.srt 1.4 kB28. A Machine Learning-powered Strategy A-Z (DNN)/4. Getting and Preparing the Data.srt 1.4 kB29. Error Handling How to make your Trading Bot more stable and reliable/6. The Exception class.srt 1.4 kB7. Trading with Python and OANDAFXCM - an Introduction/2. Overview.srt 1.4 kB7. Trading with Python and OANDAFXCM - an Introduction/5.1 Oanda_firststeps.zip 1.3 kB8. Conclusion and Outlook/1. Conclusion and Outlook.srt 928 Bytes28. A Machine Learning-powered Strategy A-Z (DNN)/12. Important Notices.html 822 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/35. Label-based Indexing Cheat Sheets.html 701 Bytes7. Trading with Python and OANDAFXCM - an Introduction/12. FXCM Commands to install required packages.html 626 Bytes3. Day Trading with OANDA A-Z a Deep Dive/15. Trading Challenge.html 569 Bytes28. A Machine Learning-powered Strategy A-Z (DNN)/2. Installation of Tensorflow & Keras (Part 1).html 555 Bytes22. Implementation and Automation with OANDA (UPDATED!)/24. Excursus Printing all ticks in a Command PromptTerminal.html 533 Bytes23. Implementation and Automation with FXCM (Updated!)/20. Excursus Printing all ticks in a Command PromptTerminal.html 533 Bytes4. FOREX Day Trading with FXCM/8. Trading Challenge.html 511 Bytes7. Trading with Python and OANDAFXCM - an Introduction/11. Trading Challenge.html 446 Bytes7. Trading with Python and OANDAFXCM - an Introduction/21. Trading Challenge.html 445 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/32. Position-based Indexing Cheat Sheets.html 440 Bytes7. Trading with Python and OANDAFXCM - an Introduction/3. OANDA Commands to install required packages.html 409 Bytes22. Implementation and Automation with OANDA (UPDATED!)/3. Weekend and Bank Holiday Alert.html 381 Bytes23. Implementation and Automation with FXCM (Updated!)/1. Weekend and Bank Holiday Alert.html 381 Bytes24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/7. Weekend and Bank Holiday Alert.html 381 Bytes22. Implementation and Automation with OANDA (UPDATED!)/1. Updating the Wrapper Package (Part 1).html 359 Bytes1. Getting Started/4. Test your knowledge.html 203 Bytes7. Trading with Python and OANDAFXCM - an Introduction/1. How to maximize your learning experience.html 203 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/26. Coding Exercise 9.html 159 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/37. Coding Exercise 10.html 159 Bytes11. Financial Data Analysis with Pandas - an Introduction/10. Coding Exercise 2.html 158 Bytes31. Appendix 1 Python (& Finance) Basics/53. Coding Exercise 5.html 158 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/14. Coding Exercise 7.html 158 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/21. Coding Exercise 8.html 158 Bytes32. Appendix 2 User-defined Functions (required for OOP)/10. Coding Exercise 6.html 156 Bytes
0. Websites you may like/[Tutorialsplanet.NET].url 128 Bytes12. Advanced Topics/[Tutorialsplanet.NET].url 128 Bytes18. Trading Strategies powered by Machine Learning - Regression/[Tutorialsplanet.NET].url 128 Bytes22. Implementation and Automation with OANDA (UPDATED!)/[Tutorialsplanet.NET].url 128 Bytes27. Working with two or many Strategies (Combination)/[Tutorialsplanet.NET].url 128 Bytes31. Appendix 1 Python (& Finance) Basics/[Tutorialsplanet.NET].url 128 Bytes34. What´s next (outlook and additional resources)/[Tutorialsplanet.NET].url 128 Bytes4. FOREX Day Trading with FXCM/[Tutorialsplanet.NET].url 128 Bytes8. Conclusion and Outlook/[Tutorialsplanet.NET].url 128 Bytes[Tutorialsplanet.NET].url 128 Bytes31. Appendix 1 Python (& Finance) Basics/1. Section Downloads.html 124 Bytes32. Appendix 2 User-defined Functions (required for OOP)/1. Section Downloads.html 124 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/1. Downloads for this Section.html 124 Bytes14. +++ PART 3 Defining and Testing Trading Strategies +++/3. Downloads for Part 3.html 123 Bytes21. +++ PART 4 Real-time Implementation and Automation of Strategies +++/2. Downloads for Part 4.html 123 Bytes25. +++ PART 5 Expert Tips & Tricks, Case Studies and more +++/2. Downloads for PART 5.html 123 Bytes29. Error Handling How to make your Trading Bot more stable and reliable/2. Section Materials Notebooks.html 122 Bytes31. Appendix 1 Python (& Finance) Basics/34. Slicing Cheat Sheet.html 108 Bytes16. Defining and Backtesting simple MomentumContrarian Strategies/10. OOP Challenge Create the Contrarian Backtesting Class (incl. Solution).mp4 0 Bytes16. Defining and Backtesting simple MomentumContrarian Strategies/10. OOP Challenge Create the Contrarian Backtesting Class (incl. Solution).srt 0 Bytes17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/6. Generalization with OOP A Bollinger Bands Backtesting Class in action.mp4 0 Bytes17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/6. Generalization with OOP A Bollinger Bands Backtesting Class in action.srt 0 Bytes17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/7. OOP Challenge Create the Bollinger Bands Backtesting Class (incl. Solution).mp4 0 Bytes17. Defining and Backtesting Mean-Reversion Strategies (Bollinger)/7. OOP Challenge Create the Bollinger Bands Backtesting Class (incl. Solution).srt 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/1. Logistic Regression with scikit-learn - a simple Introduction (Part 1).mp4 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/1. Logistic Regression with scikit-learn - a simple Introduction (Part 1).srt 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/2. Logistic Regression with scikit-learn - a simple Introduction (Part 2).mp4 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/2. Logistic Regression with scikit-learn - a simple Introduction (Part 2).srt 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/7. Generalization with OOP A Classification Backtesting Class in action.mp4 0 Bytes19. Trading Strategies powered by Machine Learning - Classification/7. Generalization with OOP A Classification Backtesting Class in action.srt 0 Bytes24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/2. Demonstration AWS EC2 for Algorithmic Trading live in action.mp4 0 Bytes24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/2. Demonstration AWS EC2 for Algorithmic Trading live in action.srt 0 Bytes24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/3. Amazon Web Services (AWS) - Overview and how to create a Free Trial Account.mp4 0 Bytes24. Cloud Deployment (AWS) Scheduling Trading Sessions Full Automation/3. Amazon Web Services (AWS) - Overview and how to create a Free Trial Account.srt 0 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/39. Analyzing Numerical Series with unique(), nunique() and value_counts().mp4 0 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/39. Analyzing Numerical Series with unique(), nunique() and value_counts().srt 0 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/40. Analyzing non-numerical Series with unique(), nunique(), value_counts().mp4 0 Bytes33. Appendix 3 Numpy, Pandas, Matplotlib and Seaborn Crash Course/40. Analyzing non-numerical Series with unique(), nunique(), value_counts().srt 0 Bytes [Tutorialsplanet.NET] Udemy - CSS - The Complete Guide... 3.4 GB [Tutorialsplanet.NET] Udemy - Artificial Intelligence... 1.6 GB [Tutorialsplanet.NET] Udemy - Java Maven 101 Stop... 1.1 GB [Tutorialsplanet.NET] Udemy - Advanced CSS and Sass... 4.1 GB [Tutorialsplanet.NET] Udemy - Complete SQL + Databases... 15.8 GB [Tutorialsplanet.NET] Udemy - The Ultimate Guide to Game... 9.0 GB [Tutorialsplanet.NET] Udemy - Social Media Marketing... 3.3 GB [Tutorialsplanet.NET] Udemy - Webpack 2 The Complete... 882.2 MB [Tutorialsplanet.NET] Udemy - Self Healing Through... 310.3 MB [Tutorialsplanet.NET] Udemy - Finish Your First Game... 1.2 GB
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