Udemy - Deep Learning for algorithmic trading using Python

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[ FreeCryptoLearn.com ] Udemy - Deep Learning for algorithmic trading using Python
  • Get Bonus Crypto Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. READ ME.html (1.0 KB)
    • 2. Install the environments.html (1.1 KB)
    2. Python basics
    • 1. Introduction.mp4 (10.4 MB)
    • 10. Python structures FOR.mp4 (29.9 MB)
    • 11. Python structures WHILE.mp4 (14.3 MB)
    • 12. Functions Basics of function.mp4 (32.7 MB)
    • 13. Functions Local variable.mp4 (14.3 MB)
    • 14. Functions Global variable.mp4 (12.7 MB)
    • 15. Functions Lambda function.mp4 (13.2 MB)
    • 2. Type of object Number.mp4 (24.0 MB)
    • 3. Type of object String.mp4 (83.6 MB)
    • 4. Type of object Logical operations Boolean.mp4 (25.7 MB)
    • 5. Type of object Variable assignment.mp4 (37.0 MB)
    • 6. Type of object Tuple and list.mp4 (49.8 MB)
    • 7. Type of object Dictionary.mp4 (46.7 MB)
    • 8. Type of object Set.mp4 (28.7 MB)
    • 9. Python structures IF ELIF ELSE.mp4 (40.9 MB)
    3. Python for data science
    • 1. Introduction.mp4 (5.2 MB)
    • 10. Matplotlib Toolbox.mp4 (17.6 MB)
    • 2. Numpy Array.mp4 (60.1 MB)
    • 3. Numpy Random.mp4 (54.1 MB)
    • 4. Numpy Indexing Slicing transformation.mp4 (85.4 MB)
    • 5. Pandas Serie and DataFrame.mp4 (31.2 MB)
    • 6. Pandas Cleaning and selection data.mp4 (81.6 MB)
    • 6.1 assets.csv (3.1 MB)
    • 7. Pandas Conditional selection.mp4 (26.0 MB)
    • 8. Matplotlib Graph.mp4 (19.7 MB)
    • 9. Matplotlib Scatter.mp4 (14.3 MB)
    4. Import and manage the data
    • 1. Introduction.mp4 (9.7 MB)
    • 2. Import & manage data from Metatrader 5.mp4 (104.4 MB)
    • 3. Import & manage data from Yahoo Finance.mp4 (43.9 MB)
    5. Features engineering
    • 1. Introduction.mp4 (21.4 MB)
    • 2. Get stock prices.mp4 (16.7 MB)
    • 3. Create a simple moving average (SMA).mp4 (43.0 MB)
    • 4. Create a moving standard deviation (MSD).mp4 (20.6 MB)
    • 5. Use the Technical analysis library to compute the RSI indicator.mp4 (30.0 MB)
    • 6. Automatization of the features engineering process.mp4 (19.2 MB)
    6. Deep Neural Networks apply to algorithmic trading
    • 1. Introduction.mp4 (12.4 MB)
    • 10. How to fix the stochastic initialization problem.mp4 (40.3 MB)
    • 11. Bagging method using the different ANNs.mp4 (57.5 MB)
    • 2. Quick recap of the DNN theory.mp4 (85.8 MB)
    • 3. Data import & Features engineering.mp4 (30.8 MB)
    • 4. Train Test set split (to fit the DNN model).mp4 (42.5 MB)
    • 5. Why and how to standardize the features.mp4 (34.6 MB)
    • 6. Create a DNN using Tensorflow 2.0.mp4 (68.7 MB)
    • 7. Use the DNN predictions to create a trading strategy.mp4 (59.4 MB)
    • 8. Automate the process.mp4 (35.2 MB)
    • 9. The stochastic initialization problem.mp4 (25.0 MB)
    7. Vectorized backtesting
    • 1. Introduction.mp4 (7.4 MB)
    • 2. Sortino ratio computation.mp4 (57.5 MB)
    • 3. Beta ratio computation (CAPM metric).mp4 (72.7 MB)
    • 4. Alpha ratio computation (CAPM metric).mp4 (23.6 MB)
    • 5. Drawdown function creation.mp4 (28.7 MB)
    • 6. Drawdown application.mp4 (27.4 MB)
    • 7. Backtesting function (1).mp4 (21.3 MB)
    • 8. Backtesting function (2).mp4 (24.2 MB)
    • 9. Backtest a trading strategy based on DNN.mp4 (15.8 MB)
    8. Recurrent Neural Networks for algorithmic trading
    • 1. Introduction.mp4 (9.0 MB)
    • 2. Theory behind RNNs.mp4 (34.1 MB)
    • 3. Recap from the DNN chapter.mp4 (51.6 MB)
    • 4. How to transform 2-dimensional data into 3-dimensional data.mp4 (41.2 MB)
    • 5. How to create a RNN using TensorFlow 2.0.mp4 (67.1 MB)
    • 6. Dropout Layer.mp4 (16.7 MB)
    • 7. RNN prediction to create a trading strategy.mp4 (60.7 MB)
    • 8. Automate the process.mp4 (17.3 MB)
    • 9. Find the best models throughout all the stochastic initialization.mp4 (35.6 MB)
    9. MetaTrader 5 live trading using Python
    • 1. Introduction.mp4 (8.9 MB)
    • 10. Live Trading strategy based on ANN.mp4 (25.6 MB)
    • 11. Live Trading strategy based on RNN.mp4 (26.3 MB)
    • 2. Install a library on Jupyter.mp4 (2.8 MB)
    • 3. Initialize the platform.mp4 (14.7 MB)
    • 4. Get data broker.mp4 (23.1 MB)
    • 5. Send orders on the market using Python.mp4 (44.5 MB)
    • 6. Get current positions.mp4 (44.5 MB)
    • 7. Run structure creation.mp4 (32.7 MB)
    • 8. Close all positions.mp4 (10.8 MB)
    • 9. Live Trading application random signals.mp4 (32.8 MB)
    • Bonus Resources.txt (0.4 KB)

Description

Deep Learning for algorithmic trading using Python



https://FreeCryptoLearn.com

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.48 GB | Duration: 6h 10m

Artificial intelligence, Backtesting, optimization for algorithmic trading with MetaTrader 5. Bot included

What you'll learn
Create a algorithmic trading strategy based on deep learning algorithms
Put any algorithm in live trading using MetaTrader 5 and Python
Manage financial data using Numpy, Pandas and Matplotlib
Data cleaning using Pandas
Python programming
Deep learning implementation using TensorFlow 2.0
Understand and implement the Deep Neural Networks (DNN)
Understand and implement the Recurrent Neural Networks (RNN)
Import stock price from Yahoo Finance and from your broker

Requirements
Basis in Machine Learning or Deep Learning are very well welcome
Description
You already know python, and you want to monetize and diversify your knowledge?



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Udemy - Deep Learning for algorithmic trading using Python

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2.5 GB
seeders:16
leechers:13
Udemy - Deep Learning for algorithmic trading using Python

Torrent hash: 1EC3C2EE9A199F8EDB6754A7D99B8083321C28AE