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QuantInsti – Backtesting Trading Strategies

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QuantInsti – Backtesting Trading Strategies
Backtesting Trading Strategies

You can improve your likelihood of success in trading by backtesting your trading rules on historical data. This course covers the steps to backtest a trading strategy, including getting financial data, validating the data, applying trading rules, assessing the strategy performance, and applying risk management measures such as stop loss and take profit. Make your backtest more realistic by incorporating slippage and commissions and avoiding common backtesting errors such as survivorship bias.
LIVE TRADING

 Describe the steps involved in backtesting a trading strategy and evaluate the performance of the backtest.
 Explain the fetching and pre-processing of data required for backtesting
 Define trading rules and generate trading signals of the strategy to backtest
 Apply trade-level analytics such as win ratio, average p&l for winning trade, profit factor and average trade duration
 Perform performance analysis of the backtest results using drawdown, sharpe ratio, cagr and equity curve
 Explain the process of improving the backtest by implementing transaction costs and slippage
 Describe the common pitfalls of backtesting such as data snooping etc.
 Paper trade and live trade your strategy

SKILLS COVERED
Statistics

Moving Average

CAGR

Sharpe Ratio

Maximum Drawdown

Profit Factor, Win%
Python

Numpy

Pandas

Line Graphs

Histograms

Vectorised Backtesting
Concepts & Bias

Data Snooping

Survivorship

Trading more than Volume

Stop Loss & Take Profit

Paper Trading
PREREQUISITES

Basic knowledge of Python and Python libraries such as Pandas. The knowledge of financial markets such as placing orders to buy and sell assets will be helpful.
SYLLABUS
Introduction

Backtesting helps to evaluate a trading strategy from different perspectives. The interactive methods used will help you not only grasp the concepts but also answer all questions related to backtesting. This section helps you understand the course structure, and the various teaching tools used in the course: videos, quizzes, coding exercises and also the capstone project.

Introduction 4m 43s

Course Structure 10m

Quantra Features and Guidance 4m 10s
Backtesting

With backtesting, we can evaluate any of our trading strategies objectively. In this section, you will familiarise yourself with the complete process of backtesting. And you will also explore the key difference between backtesting and simulation.

What is Backtesting? 2m 22s

Does Past Reflect Future? 2m

Backtesting Technique 2m

Backtesting vs Simulation 4m 57s

Simulated Data 2m

Generate Simulated Data 2m

Dataset for Backtest 2m

When to Use Simulated Data? 2m

Best Approach to Backtesting 2m

Backtesting Process 2m 21s

Steps in Backtesting 2m

Evaluate the Performance of Backtesting 2m

Need for Backtesting 2m

Drawbacks of Backtesting 2m

Additional Reading 10m
Financial Data

The very first step to backtesting any trading strategy is to get the right data. In this section, you will learn about the different types of financial data that are available. You will also learn to fetch and store the correct data from various web resources. And lastly, you will understand the limitations of working with financial data.

Financial Data

Structured Financial Data 2m

Frequency of the Data 2m

Derivatives Data 2m

Data for Long-term Strategies 2m

Macroeconomic Data 2m

Use of Sentiment Data 2m

Financial Data Storage 4m 28s

Storage Technique – I 2m

Storage Technique – II 2m

Ideal Storage Method 2m

How to Use Jupyter Notebook? 1m 54s

Daily Stock Price Data 5m

Adjusted Data 2m

How to Use Interactive Exercises? 5m

Fetch the Daily Stock Price Data 5m

Limitations of Financial Data 4m 15s

Key Limitation 2m

Overcome the Limitation 2m

Additional Reading 10m
Data Pre-Processing

Validating the data and performing sanity checks is yet another important step in the backtesting process that must not be overlooked. In this section, you will touch upon some techniques that can be used to validate your dataset. You will also learn about the concept of survivorship bias and ways to overcome this challenge.

Data Pre-Processing 3m 56s

Data Pre-Processing Steps 2m

Data Quality Checks 2m

Discrepancies in Data 2m

Data Quality Checks and Data Cleaning 10m

Check for NaN Values 5m

Drop Missing Values 5m

Identify Duplicate Values 5m

Drop Duplicate Values 5m

Survivorship Bias 4m 33s

Survivorship Bias and Trading 2m

Overcome Survivorship Bias 2m

Additional Reading 10m

Test on Creation of a Backtest 10m
Trading Rules

To build a strategy, it’s necessary to have an idea and formulate rules based on these ideas. In this section, you will learn how an idea is converted into the entry and exit rules. These rules act as the foundation for the strategy, you will also learn how they are used to generate trading signals.

Developing Trading Rules 2m 2s

Define Trading Rules 2m

Identify Characteristics of Trading Rules 2m

Identify Trading Rules 2m

Implementing a Trading Strategy 2m

Rule Formulation 2m

Identify the Correct Rule 2m

Entry and Exit Rule 3m 22s

Need for Backtesting 2m

Generate Entry and Exit Signals 10m

Short-Term Moving Average 5m

Trading Signals 5m

Strategy Returns 5m

Backtest and Generate Trade Sheet 10m

Long Crossover Condition 2m

Trade Information of a Long Entr 2m

PnL of Long Trades 5m
Trade Level Analytics

To understand whether your strategy is working, you need to analyse certain metrics. Trade level analytics are computations that depict how well the strategy has performed over a certain period of time. In this section, you will be learning how to calculate and interpret a few widely used analytics.

Trade Level Analytics I 5m

Trade Level Analytics II 4m 21s

Define Win Trades 2m

Calculate Win/Loss Rate 2m

Calculate Average PnL Per Trade 2m

Identify the Correct Strategy-I 2m

Identify the Correct Strategy-II 2m

Limitations of Win Trade 2m

Calculate Average Trade Duration 2m

Interpret the Profit Factor 2m

Calculate the Profit Factor 2m

Trade Level Analytics 10m

Average PnL Per Trade 5m

Limitations of Profit Factor 2m

Win Percentage 5m

Average Trade Duration 5m

Analyse the Strategy Performance 2m

Additional Reading 10m
Performance Metrics

The performance of a strategy is determined not only by its returns but also, by its risk. In this section, you will learn how to evaluate the performance of your strategy based on returns, risk and both. You will learn about some key performance metrics such as Sharpe ratio, CAGR, and maximum drawdown, as well as how to compute and implement them in Python using the Jupyter notebook.

Equity Curve and CAGR 3m 53s

Equity Curve 2m

Equity Curve Interpretation 2m

CAGR Calculation 2m

CAGR and Average Annual Return 2m

Strategy Returns 2m

Sharpe Ratio 2m 10s

Sharpe Ratio of a Strategy 2m

Sharpe Ratio Calculation 2m

Strategy Comparison 2m

Drawback of Sharpe Ratio 2m

Maximum Drawdown 2m 27s

Maximum Drawdown Calculation 2m

Maximum Drawdown Comparison 2m

Maximum Drawdown of a Strategy 2m

Performance Metrics 10m

FAQ on Cumulative Returns 2m

CAGR 5m

Sharpe Ratio 5m

Maximum Drawdown 5m

Additional Reading 10m
Risk Management

Risk management is one of the key elements of a trading strategy. The performance of a trading strategy can be improved with the help of risk management. In this section, you will be learning how to bring down the level of risk of your strategy by applying methods like stop-loss and take-profit levels. You will learn how these levels protect you from extreme losses.

Stop-Loss and Take-Profit 5m

Control the Trading Losses 2m

Applying Risk Management 2m

Risk Management of a Long Trade 2m

Need for Stop-Loss and Take-Profit 2m

Guidelines For Setting Stop-Loss and Take-Profit 3m 27s

Correct Stop-Loss and Take-Profit 2m

Risk Management Parameters 2m

Stop-Loss and Take-Profit Orders 2m

Backtest With Stop-Loss and Take-Profit 10m

Stop-Loss of Long Trades 5m

Take-Profit of Long Trades 5m

Additional Reading 10m
Transaction Costs and Slippage

The journey towards building a good backtest for a strategy idea is incomplete without considering the transaction costs and slippages. In simple words, transaction costs encompass brokerage, commission, etc. Slippage is the difference between the expected and executed price. Learn these concepts and understand how to incorporate them into your backtesting code.

Transaction Costs and Slippage 3m 4s

Calculation of Transaction Cost 2m

Calculation of Slippage 2m

Implementation of Transaction Cost and Slippage 10m

Additional Reading 10m
Paper Trading

Once you have built your backtest and are satisfied with the performance of your strategy, you can move to the next step, paper trading. Paper trading has evolved from simple writing of buy and sell prices on a notepad to a full-fledged system environment which replicates the live trading environment. Learn the importance of paper trading and how it strengthens your confidence in the strategy.

Introduction to Paper Trading 4m 5s

Decrease in Performance During Paper Trading 2m

Sharpe Ratio in Paper Trading 2m

Cost Difference Between Backtesting and Paper Trading 2m

Things to Keep in Mind While Paper Trading 3m 17s

Reasons for Paper Trading 2m

Asset Difference in Paper Trading 2m

Change in Entry Rules in Paper Trading 2m

Reasons to Stop Paper Trading 2m

Reason to Move From Paper to Live Trading 2m

Additional Reading 10m

Test on Evaluation of Strategy 12m
Live Trading on Blueshift

Learn how you can take your backtested strategy live with some important steps. Learn about the code structure, the various functions used to create a strategy, and finally, paper or live trade on Blueshift.

Section Overview 2m 19s

Live Trading Overview 2m 41s

Vectorised vs Event Driven 2m

Process in Live Trading 2m

Real-Time Data Source 2m

Blueshift Code Structure 2m 57s

Important API Methods 10m

Schedule Strategy Logic 2m

Fetch Historical Data 2m

Place Orders 2m

Backtest and Live Trade on Blueshift 4m 5s

Additional Reading 10m

Blueshift Data FAQs 10m
Live Trading Template

This section includes a template of a trading strategy that can be used on Blueshift. This live trading strategy template uses moving average crossover to generate entry and exit signals. You can tweak the code by changing securities or the strategy parameters. You can also analyse the strategy performance in more detail.

Paper/Live Trade Using Moving Averages 10m

FAQs for Live Trading on Blueshift 5m
Common Pitfalls in Backtesting

There exist various biases in Backtesting which include look-ahead, overfitting, and data snooping biases. Learn how to overcome them, and check out the common mistakes while backtesting. Finally, even if your strategy is successful and has been validated by backtesting it, you can never over-rely on it. We explain this with the help of a real-life case study.

Biases to Avoid 4m 7s

Examples of Look Ahead Bias 2m

Paper Trading Based on Exceptional Returns 2m

Example of Overfitting 2m

Common Mistakes Done With Trading Volume 3m 19s

Number of Shares Bought on Basis of Volume 2m

Definition of Illiquid Stock 2m

Trading Based on Volume 2m

Strategy Decision Using Volume 2m

Data Snooping 3m 43s

Example of Data Snooping 2m

Minimisation of Data Snooping 2m

Multiple Iterations on Out of Sample Data 2m

Strategy Performance on In Sample and Out of Sample Data 2m

Over Reliance on Backtesting 2m 12s

Reason of High Leverage in Trading 2m

Possible Flaw in Strategy Idea 2m

Reason for Not Over Relying on Backtesting 2m

Minimise Effect of Extreme Events on Portfolio 2m

Additional Reading 10m
FAQs

In this section, we address some of the most frequently asked questions about backtesting.

Ideal Time Period for Backtesting 2m

Number of Assets to Backtest On 2m 24s

Risk Metrics and Sharpe Ratio 3m 1s

Paper and Live Trading 3m 48s
Capstone Project

This section will help you to develop a moving average crossover strategy and backtest it. You will also create an equal-weighted portfolio and compute its performance metrics.

Capstone Project: Getting Started 10m

Problem Statement 10m

Frequently Asked Questions 10m

Code Template and Data Files 2m

Capstone Project Model Solution 10m

Capstone Solution Downloadable 2m
Run Codes Locally on Your Machine

In this section, you will learn to install the Python environment on your local machine. You will also learn about some common problems while installing python and how to troubleshoot them.

Python Installation Overview 2m 18s

Flow Diagram 10m

Install Anaconda on Windows 10m

Install Anaconda on Mac 10m

Know your Current Environment 2m

Troubleshooting Anaconda Installation Problems 10m

Creating a Python Environment 10m

Changing Environments 2m

Quantra Environment 2m

Troubleshooting Tips for Setting Up Environment 10m

How to Run Files in Downloadable Section? 10m

Troubleshooting for Running Files in Downloadable Section 10m
Course Summary

In this section, we will briefly summarise everything that you have learned in this course.

Summary 2m

Course Summary and Next Steps 2m

Python Codes and Data 2m
ABOUT AUTHOR
QuantInsti®

QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
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