Algorithmic Trading with Python: Machine Learning strategies
- Description
- Curriculum
- FAQ
- Reviews
You already know python, and you want to monetize and diversify your knowledge?
You already have some trading knowledge, and you want to learn about artificial intelligence in algorithmic trading?
You are simply a curious person who wants to get into this subject?
If you answer at least one of these questions, I welcome you to this course. For beginners in python, don’t panic! There is a python course (small but condensed) to master this python knowledge.
In this course, you will learn how to program strategies from scratch. Indeed, after a crash course in Python, you will learn how to implement a system based on Machine Learning (Linear regression, Support Vector Machine).
Once the strategies are created, we will backtest them using python. So that we know better this strategy using statistics like Sortino ratio, drawdown the beta… Then we will put our best algorithm in live trading.
You will learn about tools used by both portfolio managers and professional traders:
- Artificial intelligence algorithm
- Apply Machine Learning in Live Trading
- Predict stock prices using Machine Learning
- Live trading implementation
- Import financial data
- Linear Regression Algorithm
- Support Vector Machine (SVM)
- How to do a backtest
- The risk of a stock
- Python
- What is a long and short position
- Numpy
- Pandas
- Matplotlib
- Sharpe ratio
- Sortino ratio
- Alpha coefficient
- Beta coefficient
Why this course and not another?
- It is not a programming course nor a trading course. It is a course in which programming is used for trading.
- A data scientist does not create this course, but a degree in mathematics and economics specialized in Machine learning for 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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2IntroductionVideo lesson
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3Type of object: NumberVideo lesson
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4Type of object: StringVideo lesson
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5Type of object: Logical Operations and BooleanVideo lesson
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6Type of object: Variable assignmentVideo lesson
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7Type of object: Tuple and ListVideo lesson
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8Type of object: DictionaryVideo lesson
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9Type of object: SetVideo lesson
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10Python structures: If / Elif/ ElseVideo lesson
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11Python structures: ForVideo lesson
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12Python structures: WhileVideo lesson
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13Functions: Basics of functionVideo lesson
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14Functions: Local variableVideo lesson
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15Functions: Global variableVideo lesson
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16Functions: Lambda functionVideo lesson
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17IntroductionVideo lesson
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18Numpy: ArrayVideo lesson
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19Numpy: RandomVideo lesson
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20Numpy: Indexing / slicing /TransformationVideo lesson
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21Pandas: Serie and DataFrameVideo lesson
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22Pandas: Cleaning and selection dataVideo lesson
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23Pandas: Conditional selectionVideo lesson
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24Matplotlib: GraphVideo lesson
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25Matplotlib: ScatterVideo lesson
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26Matplotlib: ToolsVideo lesson
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30IntroductionVideo lesson
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31Get stock pricesVideo lesson
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32Create a simple moving average (SMA)Video lesson
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33Create a moving standard deviation (MSD)Video lesson
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34Use the technical analysis library to create a RSI indicatorVideo lesson
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35Automatization of the features engineering processVideo lesson
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36IntroductionVideo lesson
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37Linear Regression: TheoryVideo lesson
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38Import the dataVideo lesson
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39Split the datasetVideo lesson
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40Linear Regression: PracticeVideo lesson
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41Predict stock prices using Machine learning predictionsVideo lesson
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42Create trading strategies using Machine learning predictionsVideo lesson
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43Automatize the processVideo lesson
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44IntroductionVideo lesson
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45Sortino ratio computationVideo lesson
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46Beta ratio computation (CAPM metric)Video lesson
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47Alpha ratio computation (CPAM metric)Video lesson
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48Drawdown function: creationVideo lesson
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49Drawdown function: applicationVideo lesson
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50BackTesting FunctionVideo lesson
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51Backtesting Function: CustomizeVideo lesson
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52Application: Machine learningVideo lesson
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53IntroductionVideo lesson
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54SVR: TheroryVideo lesson
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55Features engineering: Create technical indicatorsVideo lesson
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56Features engineering: StandardizationVideo lesson
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57Features engineering: Principal component analysisVideo lesson
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58SVR: PracticeVideo lesson
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59Backtest the strategyVideo lesson
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60AutomatizationVideo lesson

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