Description

A comprehensive training on leveraging Python for effective cryptocurrency trading. Students will delve into various strategies including the Simple Moving Averages Trading Strategy, the Big Three Trading Strategy, and the Exponential Moving Average Trading Strategy. Additionally, the course covers building autocorrelation models for crypto stock prediction, providing learners with essential skills to thrive in the dynamic world of crypto trading.

• 1. Course overview
  • 00 Course Overview - Python Crypto Trading Strategies

    4:40

• 2. Introduction to Blockchain (Prerequisite)
  • 00 Blockchain Introduction - (Prerequisite) Introduction to Blockchain

    8:32

  • 01 What Are Blockchains And Distributed Ledgers - (Prerequisite) Introduction to Blockchain

    3:49

  • 02A What Are Bitcoin And Ethereum - (Prerequisite) Introduction to Blockchain

    5:29

• 3. Introduction to the Stock Market
  • 01 What Do Day Traders Trade - (Pre-requisite) Stock Market

    9:46

  • 02 What Is Volatility And Standard Deviation - (Pre-requisite) Stock Market

    3:33

  • 03 What Are The Best Assets To Day Trade - (Pre-requisite) Stock Market

    4:54

  • 04 Strategies For Stock Market Trading - (Pre-requisite) Stock Market

    2:38

  • 06 Stock Market Indicators

    1:57

  • 07 Stock Market Lifecycle Trend Phases - (Pre-requisite) Stock Market

    2:21

• 4. (Prerequisite) Introduction to Python
  • 00. Introduction

    4:48

  • 01. Intro To Python

    5:46

  • 02.01 What is Google Colab

    4:25

  • 02.02 What If I Get Errors

    2:40

  • 02.03 How Do I Terminate a Session

    2:40

  • 01. Variables

    19:20

  • 02. Type Conversion Examples

    10:07

  • 03. Operators

    28:54

  • 04. Collections

    8:25

  • 05. List Examples

    19:41

• 5. Introduction to DataFrames with Pandas Python Library
  • 00. Setting Up Pandas - Intro to Dataframes with Pandas Pyplot Library

    2:25

  • 01. Creating A Dataframe - 01. Intro to Dataframes with Pandas Pyplot Library

    22:43

  • 02. Sorting And Series - 01. Intro to Dataframes with Pandas Pyplot Library

    19:19

  • 03. Expanding A Dataframe - 01. Intro to Dataframes with Pandas Pyplot Library

    17:15

  • 01 Getting Values And Dealing With Nan Values - 02. Intro to Dataframes with Pandas Pyplot Library

    21:29

  • 02 Dropping Rows And Columns - 02. Intro to Dataframes with Pandas Pyplot Library

    23:57

  • 01 Reading From Csv - 03. Intro to Dataframes with Pandas Pyplot Library

    19:41

  • 02 Writing To Csv - 03. Intro to Dataframes with Pandas Pyplot Library

    20:42

  • 01 Starting With An Analysis - 04. Intro to Dataframes with Pandas Pyplot Library

    21:23

  • 02 Locating Data By Labels - 04. Intro to Dataframes with Pandas Pyplot Library

    20:16

  • 03 Statistical Description Of Data - 04. Intro to Dataframes with Pandas Pyplot Library

    19:51

  • 04 Histogram Plots In Pandas - 04. Intro to Dataframes with Pandas Pyplot Library

    21:56

  • 05 Starting An Analysis Of All Our Data - 04. Intro to Dataframes with Pandas Pyplot Library

    21:13

  • 06 Continuing An Analysis Of All Our Data - 04. Intro to Dataframes with Pandas Pyplot Library

    16:52

• 6. Introduction to Data Science with NumPy Python Library
  • 01. Introduction - 00. Intro to Data Science with Numpy Python Library

    2:34

  • 01 Creating And Reshaping Numpy Arrays - 01. Intro to Data Science with Numpy Python Library

    21:04

  • 02 Creating Standard Numpy Arrays - 02. Intro to Data Science with Numpy Python Library

    21:05

  • 03 Creating Standard 2D Arrays - 01. Intro to Data Science with Numpy Python Library

    13:52

  • 04 Attributes On Numpy Arrays - Intro to Data Science with Numpy Python Library

    19:09

  • 05 Resizing Arrays - 01. Intro to Data Science with Numpy Python Library

    13:20

  • 01 Writing An Array To File And Formating Strings - 02. Intro to Data Science with Numpy Python Library

    28:30

  • 02 Random Numbers - 02. Intro to Data Science with Numpy Python Library

    18:09

  • 03 Sorting In Numpy - 02. Intro to Data Science with Numpy Python Library

    22:57

  • 01 Calculations Within Numpy Arrays - 03. Intro to Data Science with Numpy Python Library

    22:30

  • 02 Math Functions With Numpy - 03. Intro to Data Science with Numpy Python Library

    22:57

  • 03 Integrating With Numpy - 03. Intro to Data Science with Numpy Python Library

    21:55

  • 04 Statistics With Numpy - 03. Intro to Data Science with Numpy Python Library

    20:40

  • 05 Polynomials - 03. Intro to Data Science with Numpy Python Library

    21:06

  • 06 Polynomials (Cont'd) - 03. Intro to Data Science with Numpy Python Library

    19:48

• 7. Simple Moving Averages Trading Strategy
  • 04-00 Backtesting Simple Moving Averages Explained

    2:05

  • 01 Load data for backtesting SMA with vectorbt

    3:21

  • 02 Build trading strategies with vectorbt

    3:41

  • 03 Simulate portfolio with vectorbt

    3:41

  • 04 Visualize trading strategy with kaleido

    4:40

• 8. Big Three Trading Strategy
  • 00 What Is The Big Three Trading Strategy

    2:28

  • 01 Big Three Trading Strategy On Binance Coin

    6:58

  • 02 Big Three Trading Strategy On 1 Year Of Stocks

    2:39

• 9. Exponential moving average trading strategy
  • 00 Ema Trading Strategy For Crypto - Overview

    2:58

  • 01 Load Data With Cryptometrics Api

    7:54

  • 02 Visualize Historical Stock Prices With Matplotlib

    6:32

  • 03 Build Exponential Moving Average Trading Strategy

    3:55

• 10. Build autocorrelation for crypto stock prediction
  • 00 Autocorrelation Explained For Crypto Stock Prediction

    2:46

  • 01 Load Data For Crypto Price Autocorrelation With Pandas

    5:52

  • 02 Visualize Crypto Price Data With Pyplot

    3:14

  • 03 Apply Hodrick-Prescott Filter To Data With Python

    4:11

  • 04 Calculate Autocorrelation With Python

    3:26