Algorithmic trading for beginners: from zero to hero
- Description
- Curriculum
- FAQ
- Reviews
Do you want to create algorithmic trading strategies?
You already have some trading knowledge and you want to learn about quantitative trading/finance?
You are simply a curious person who wants to get into this subject to monetize and diversify your knowledge?
If you answer at least one of these questions, I welcome you to this course. All the applications of the course will be done using Python. However, for beginners in Python, don’t panic! There is a FREE python crash course included to master Python.
In this course, you will learn how to use technical analysis to create robust strategies. You will perform quantitative analysis to find patterns in the data. Once you will have many profitable strategies, we will learn how to perform vectorized backtesting. Then you will apply portfolio and risk management techniques to reduce the drawdown and maximize your returns.
You will learn and understand quantitative analysis used by portfolio managers and professional traders:
- Modeling: Technical analysis (Moving average, RSI) and condition combination.
- Backtesting: Do a backtest properly without error and minimize the computation time (Vectorized Backtesting).
- Risk management: Manage the drawdown(Stop loss), combine strategies properly (Strategies portoflio).
Why this course and not another?
- This is not a programming course nor a trading course or a machine learning course. It is a course in which statistics, programming and financial theory are used for trading.
- This course is not created by a data scientist but by a degree in mathematics and economics specializing in mathematics applied to finance.
- You can ask questions or read our quantitative finance articles simply by registering on our free Discord forum.
Without forgetting that the course is satisfied or refunded for 30 days. Don’t miss an opportunity to improve your knowledge of this fascinating subject.
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10IntroductionVideo lesson
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11Type of object: NumberVideo lesson
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12Type of object: StringVideo lesson
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13Type of object: Logical operations / BooleanVideo lesson
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14Type of object: Variable assignmentVideo lesson
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15Type of object: Tuple and listVideo lesson
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16Type of object: DictionaryVideo lesson
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17Type of object: SetVideo lesson
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18Python structures: If / Elif / ElseVideo lesson
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19Python structures: ForVideo lesson
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20Python structures: WhileVideo lesson
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21Functions: Basics of functionVideo lesson
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22Functions: Local variableVideo lesson
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23Functions: Global variableVideo lesson
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24Functions: Lambda functionVideo lesson
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25IntroductionVideo lesson
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26Numpy: ArrayVideo lesson
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27Numpy: RandomVideo lesson
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28Numpy: Indexing / Slicing / transformationVideo lesson
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29Pandas: Serie and DataFrameVideo lesson
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30Pandas: Cleaning and selection dataVideo lesson
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31Pandas: Conditional selectionVideo lesson
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32Matplotlib: GraphVideo lesson
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33Matplotlib: ToolboxVideo lesson
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34Matplotlib: ScatterVideo lesson
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35IntroductionVideo lesson
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36Population versus sampleVideo lesson
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37Application: create google stock price sampleVideo lesson
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38Central tendency measure: The meanVideo lesson
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39Application: Compute mean Google return + Annualization of returnsVideo lesson
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40Central tendency measure: The medianVideo lesson
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41Extreme value problem? Compute the medianVideo lesson
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42Central tendency measure: The percentileVideo lesson
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43Application: Understand Google return distributionVideo lesson
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44Dispersion measure: The varianceVideo lesson
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45Application: Compute variance returns + Variance annualizationVideo lesson
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46Dispersion measure: The standard deviationVideo lesson
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47Application: Compute the volatility + Annualize the volatilityVideo lesson
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48Relationship measure: Covariance / covariance matrixVideo lesson
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49Application: Assets covarianceVideo lesson
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50Relationship measure: CorrelationVideo lesson
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51Application: Assets correlationVideo lesson
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52DOWNLOAD summary sheet about descriptive statisticsText lesson
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56IntroductionVideo lesson
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57Simple moving averageVideo lesson
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58Strategy explanationVideo lesson
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59How to verify our trading position?Video lesson
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60Compute the profit of a trading strategyVideo lesson
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61How to automate the strategy?Video lesson
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62Most important video: Performance depending of the data!Video lesson
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63IntroductionVideo lesson
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64Sortino ratio computationVideo lesson
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65Beta ratio computation (CAPM metric)Video lesson
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66Alpha ratio computation (CAPM metric)Video lesson
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67Drawdown function: creationVideo lesson
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68Drawdown function: applicationVideo lesson
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69Backtesting function (1)Video lesson
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70Backtesting function (2)Video lesson
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71Backtest our strategyVideo lesson
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72IntroductionVideo lesson
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73RecapVideo lesson
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74Compute the RSIVideo lesson
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75Add multiple conditions to take a positionVideo lesson
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76Verify if the positions are correctly implementedVideo lesson
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77Compute the profitsVideo lesson
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78Apply a stop loss (SL) on your returnsVideo lesson
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79Automate the strategyVideo lesson
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80Compare the same strategy using different data sourcesVideo lesson
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81Create a portfolio of trading strategiesVideo lesson
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82IntroductionVideo lesson
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83What is the Hurst exponent?Video lesson
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84How to find if is this asset is Trending or not?Video lesson
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85How to find if is this asset is Mean reverting or not?Video lesson
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86How to find if is this asset follows a random walk or not?Video lesson
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87Adapt your strategy to your data!Video lesson
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