Deep Learning for algorithmic trading using Python
- 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 Deep Learning (Deep neural network, Recurrent neural network).
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:
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Artificial intelligence algorithm
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Apply Deep Learning in Live Trading
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Predict stock prices using Deep Learning
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Live trading implementation
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Import financial data using MetaTrader 5 or Yahoo finance
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DNN Algorithm
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RNN algorithm to analyze and predict time series behavior
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How to do a backtest a strategy using the programming language Python
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Numpy, Pandas, Matplotlib
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Sharpe, Sortino ratios
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Alpha, Beta coefficients
Why this course and not another?
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It is not a programming course nor a trading course. It is a course in which programming is used for trading.
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A data scientist does not create this course, but a degree in mathematics and economics specialized in Machine learning for finance.
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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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3IntroductionVideo lesson
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4Type of object: NumberVideo lesson
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5Type of object: StringVideo lesson
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6Type of object: Logical operations / BooleanVideo lesson
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7Type of object: Variable assignmentVideo lesson
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8Type of object: Tuple and listVideo lesson
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9Type of object: DictionaryVideo lesson
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10Type of object: SetVideo lesson
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11Python structures: IF / ELIF / ELSEVideo lesson
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12Python structures: FORVideo lesson
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13Python structures: WHILEVideo lesson
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14Functions: Basics of functionVideo lesson
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15Functions: Local variableVideo lesson
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16Functions: Global variableVideo lesson
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17Functions: Lambda functionVideo lesson
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18IntroductionVideo lesson
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19Numpy: ArrayVideo lesson
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20Numpy: RandomVideo lesson
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21Numpy: Indexing / Slicing / transformationVideo lesson
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22Pandas: Serie and DataFrameVideo lesson
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23Pandas: Cleaning and selection dataVideo lesson
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24Pandas: Conditional selectionVideo lesson
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25Matplotlib: GraphVideo lesson
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26Matplotlib: ScatterVideo lesson
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27Matplotlib: ToolboxVideo lesson
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31IntroductionVideo lesson
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32Get stock pricesVideo lesson
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33Create a simple moving average (SMA)Video lesson
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34Create a moving standard deviation (MSD)Video lesson
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35Use the Technical analysis library to compute the RSI indicatorVideo lesson
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36Automatization of the features engineering processVideo lesson
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37IntroductionVideo lesson
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38Quick recap of the DNN theoryVideo lesson
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39Data import & Features engineeringVideo lesson
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40Train / Test set split (to fit the DNN model)Video lesson
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41Why and how to standardize the featuresVideo lesson
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42Create a DNN using Tensorflow 2.0Video lesson
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43Use the DNN predictions to create a trading strategyVideo lesson
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44Automate the processVideo lesson
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45The stochastic initialization problemVideo lesson
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46How to fix the stochastic initialization problemVideo lesson
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47Bagging method using the different ANNsVideo lesson
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48IntroductionVideo lesson
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49Sortino ratio computationVideo lesson
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50Beta ratio computation (CAPM metric)Video lesson
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51Alpha ratio computation (CAPM metric)Video lesson
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52Drawdown: function creationVideo lesson
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53Drawdown: applicationVideo lesson
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54Backtesting function (1)Video lesson
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55Backtesting function (2)Video lesson
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56Backtest a trading strategy based on DNNVideo lesson
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57IntroductionVideo lesson
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58Theory behind RNNsVideo lesson
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59Recap from the DNN chapterVideo lesson
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60How to transform 2-dimensional data into 3-dimensional dataVideo lesson
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61How to create a RNN using TensorFlow 2.0Video lesson
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62Dropout LayerVideo lesson
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63RNN prediction to create a trading strategyVideo lesson
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64Automate the processVideo lesson
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65Find the best models throughout all the stochastic initializationVideo lesson
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