English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 67 lectures (6h 58m) | 2.46 GB
Master Algorithmic Trading: Unlock Profitable Strategies And Backtesting Using Python
Welcome to the comprehensive course on Algorithmic Trading Strategies in Python! Join me, Ziad, a seasoned algorithmic trader with over a decade of experience, as I guide you through the fascinating world of algorithmic trading.
In this course, we delve into the fundamentals of algorithmic trading, covering essential concepts, trading mindsets, and the pros and cons of algorithmic trading. Gain a deep understanding of trading terminology, explore technical versus fundamental trading, and grasp basic trading strategies that form the foundation of algorithmic trading.
Discover various types of algorithmic trading strategies, including Mean Reversion, Momentum Trading, and Statistical Arbitrage. Learn how to retrieve and analyze market data using Python, exploring timeframes, ticks data, and utilizing APIs for data retrieval. Dive into the implementation of technical analysis with Python libraries such as TA-Lib and Pandas_TA for effective technical indicators analysis.
Explore advanced topics in statistical analysis and modeling, including Time Series Analysis, Statistical Arbitrage, and Factor Models. Develop and optimize your trading strategies, understanding the main components critical for success. Put your strategies to the test through backtesting, evaluating performance, and ensuring robust results.
Finally, master the execution of trades using Python, transitioning seamlessly from backtesting to live trading. This course is designed to be straight to the point, focusing on numerical trading systems in Python. While some Python background is assumed, if you need to strengthen your skills, I offer separate courses covering Python basics, object-oriented programming, and in-depth training on Numpy and Pandas.
Whether you’re a seasoned developer or just starting your coding journey, this course provides valuable insights, practical knowledge, and a clear roadmap to mastering algorithmic trading in Python. Take the next step in your trading journey and enroll now!
Key Topics:
- Algorithmic Trading Basics
- Trading Mindset and Emotions
- Technical and Fundamental Trading
- Mean Reversion, Momentum, Statistical Arbitrage Strategies
- Data Retrieval and Exploratory Analysis in Python
- Technical Indicators with TA-Lib and Pandas_TA
- Statistical Analysis and Modeling
- Strategy Development and Optimization
- Backtesting for Performance Evaluation
- Execution and Live Trading with Python
Unlock the power of algorithmic trading today! Enroll and transform your trading strategies with Python expertise.
What you’ll learn
- Understand the main essential components of a trading strategy
- Design a targeted trading system
- Implement and backtest a trading system in Python
- Code algorithmic trading strategies in Python
- Backtest and evaluate an automated trading system
Table of Contents
Course Introduciton
1 Introduction
2 About This Course
3 Course Content
Introduction To Algorithmic Trading
4 Introduction To Algorithmic Trading
5 Emotions and Biases
6 A Trading Mindset
7 Why To Use Algorithms
8 Emotions In Trading
9 Defining Algorithmic Trading
10 Revisiting Emotions And Real Life Case
11 Overview Types Of Simple Algorithmic Strategies
12 Python Programming As A Prerequisite
Trading Concepts
13 Technical Vs Fundamental Algorithms
14 Bid and Ask Spread and Volume as Market Liquidity Indicators
15 Types of Financial Markets
16 Market Participants and Big Market Players
17 Basic Types of Algorithmic Trading Strategies
Financial Data Retrieval And Exploratory Analysis
18 Knowing Data Types
19 Types Of Financial Data
20 Downloading Historical Data For Analysis
21 Candlesticks and Indicators Plotting Example
22 Additional Visualization Tutorial
Technical Indicators Analysis
23 Technical Indicators Introduction
24 Trend Indicators
25 Moving Average And ADX Indicators
26 Moving Average And ADX Python Example
27 Momentum Indicators The RSI
28 Momentum Indicators Stochastic Oscillator
29 Momentum Indicators Python Examples
30 Volatility Indicators Bollinger Bands
31 Volatility Indicators Average True Range
32 Volatility Indicators Python Examples
33 Volume Indicators
34 Volume Indicators CMF
35 Volume Indicators Python Examples
Testing Technical Indicators
36 Technical Testing Methods I
37 Technical Testing Methods II
38 Testing Rejection Candle Indicator
Building Algorithmic Trading Systems
39 Algorithmic Strategy Components
40 Trend Detection And Confirmation
41 Generating Entry Signals
42 Exit Signal Approaches
43 Lot Size And Dynamic Sizing
44 Python Application Trend Detection Using The Moving Average Slope
45 Python Application Trend Detection Using 3 Moving Averages Alignment
46 Python Application Consecutive Candles Positions Vs The Moving Average
47 Python Application VWAP And Candles Positions
48 Python Application Trend Confirmation With The ADX
49 Python Application Entry Signal Detection I (Bollinger Bands)
50 Python Application Entry Signal Detection II (Bollinger Bands With RSI)
51 Python Application Entry Signal Detection III (Bollinger Bands With Rejection)
Backtesting Trading Strategies
52 Backtesting Introduction
53 Backtesting Tools And Python Packages
54 Backtesting Dot Py Package Python Examples
55 Quality Ratios And Backtest Evaluation
Python Profitable Strategy Example Step-By-Step
56 Strategy Introduction
57 Strategy Details Technical Description
58 Detecting Rejection Candles And Support Resistance Levels
59 Combining Rejection And Key Levels Signals
60 Generating Automated Entry Signal
61 Backtest Results Fixed Stop Loss And Take Profit Values
62 Backtest Results RSI Exit Signal
63 Backtest Results ATR Dependent SL and TP Values
64 Backtest Results Trailing Stop In Python
65 Backtest Results Lot Sizing And Returns Optimization
Live Trading Bot Putting It All Together
66 Live Trading Example In Python
Extra Strategies Backtest
67 Candles Pattern Strategy
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