Algorithmic Trading Strategies In Python

Master Algorithmic Trading: Unlock Profitable Strategies And Backtesting Using Python

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

Requirements

Python basics familiarity, Pandas and Numpy, basic understanding of Object Oriented Programming in Python

Trading familiarity and simple strategies

Description

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 BasicsTrading Mindset and EmotionsTechnical and Fundamental TradingMean Reversion, Momentum, Statistical Arbitrage StrategiesData Retrieval and Exploratory Analysis in PythonTechnical Indicators with TA-Lib and Pandas_TAStatistical Analysis and ModelingStrategy Development and OptimizationBacktesting for Performance EvaluationExecution and Live Trading with PythonUnlock the power of algorithmic trading today! Enroll and transform your trading strategies with Python expertise.

Overview

Section 1: Course Introduciton

Lecture 1 Introduction

Lecture 2 About This Course

Lecture 3 Course Content

Section 2: Introduction To Algorithmic Trading

Lecture 4 Introduction To Algorithmic Trading

Lecture 5 Emotions and Biases

Lecture 6 A Trading Mindset

Lecture 7 Why To Use Algorithms?

Lecture 8 Emotions In Trading

Lecture 9 Defining Algorithmic Trading

Lecture 10 Revisiting Emotions And Real Life Case

Lecture 11 Overview: Types Of Simple Algorithmic Strategies

Lecture 12 Python Programming As A Prerequisite

Section 3: Trading Concepts

Lecture 13 Technical Vs Fundamental Algorithms

Lecture 14 Bid and Ask Spread and Volume as Market Liquidity Indicators

Lecture 15 Types of Financial Markets

Lecture 16 Market Participants and Big Market Players

Lecture 17 Basic Types of Algorithmic Trading Strategies

Section 4: Financial Data Retrieval And Exploratory Analysis

Lecture 18 Knowing Data Types

Lecture 19 Types Of Financial Data

Lecture 20 Downloading Historical Data For Analysis

Lecture 21 Candlesticks and Indicators Plotting Example

Lecture 22 Additional Visualization Tutorial

Section 5: Technical Indicators Analysis

Lecture 23 Technical Indicators Introduction

Lecture 24 Trend Indicators

Lecture 25 Moving Average And ADX Indicators

Lecture 26 Moving Average And ADX Python Example

Lecture 27 Momentum Indicators : The RSI

Lecture 28 Momentum Indicators : Stochastic Oscillator

Lecture 29 Momentum Indicators Python Examples

Lecture 30 Volatility Indicators: Bollinger Bands

Lecture 31 Volatility Indicators: Average True Range

Lecture 32 Volatility Indicators: Python Examples

Lecture 33 Volume Indicators

Lecture 34 Volume Indicators: CMF

Lecture 35 Volume Indicators: Python Examples

Section 6: Testing Technical Indicators

Lecture 36 Technical Testing Methods I

Lecture 37 Technical Testing Methods II

Lecture 38 Testing Rejection Candle Indicator

Section 7: Building Algorithmic Trading Systems

Lecture 39 Algorithmic Strategy Components

Lecture 40 Trend Detection And Confirmation

Lecture 41 Generating Entry Signals

Lecture 42 Exit Signal Approaches

Lecture 43 Lot Size And Dynamic Sizing

Lecture 44 Python Application: Trend Detection Using The Moving Average Slope

Lecture 45 Python Application: Trend Detection Using 3 Moving Averages Alignment

Lecture 46 Python Application: Consecutive Candles Positions Vs The Moving Average

Lecture 47 Python Application: VWAP And Candles Positions

Lecture 48 Python Application: Trend Confirmation With The ADX

Lecture 49 Python Application: Entry Signal Detection I (Bollinger Bands)

Lecture 50 Python Application: Entry Signal Detection II (Bollinger Bands With RSI)

Lecture 51 Python Application: Entry Signal Detection III (Bollinger Bands With Rejection)

Section 8: Backtesting Trading Strategies

Lecture 52 Backtesting Introduction

Lecture 53 Backtesting Tools And Python Packages

Lecture 54 Backtesting Dot Py Package Python Examples

Lecture 55 Quality Ratios And Backtest Evaluation

Section 9: Python Profitable Strategy Example Step-By-Step

Lecture 56 Strategy Introduction

Lecture 57 Strategy Details: Technical Description

Lecture 58 Detecting Rejection Candles And Support Resistance Levels

Lecture 59 Combining Rejection And Key Levels Signals

Lecture 60 Generating Automated Entry Signal

Lecture 61 Backtest Results: Fixed Stop Loss And Take Profit Values

Lecture 62 Backtest Results: RSI Exit Signal

Lecture 63 Backtest Results: ATR Dependent SL and TP Values

Lecture 64 Backtest Results: Trailing Stop In Python

Lecture 65 Backtest Results: Lot Sizing And Returns Optimization

Section 10: Live Trading Bot: Putting It All Together

Lecture 66 Live Trading Example In Python

Traders, and beginner and intermediate Python developers,Enthusiasts willing to explore the field of financial programming and algorithmic trading