Free Download Position Sizing in Trading By QuantInsti
Learn how to design, test, and apply multiple position sizing approaches such as Kelly, Optimal f, and volatility targeting within a trading strategy. Evaluate how these methods influence overall performance and risk. Gain an understanding of money management concepts, uncover hidden risks in financial markets, and integrate everything learned to build a conservative and reliable position sizing framework.
APPLY POSITION SIZING STRATEGIES
- Implement and backtest techniques like Kelly, CPPI, TIPP, and volatility targeting on a sample index reversal system.
- Break down the risks involved in Kelly and Optimal f.
- Highlight the importance of money management while explaining essential money management terms.
- Practice through both paper trading and live trading using the strategies covered in the program.
- Review core methods such as fixed units, fixed fraction, fixed percentage, and fixed sum.
- Visualize leverage levels and the share of capital deployed under each position sizing model.
- Explore market risks including non-stationarity and fat-tail distributions.
- Apply statistical simulations like Bootstrapping and Monte Carlo.
- Build a conservative allocation framework to calculate how much capital to commit per trade.
SKILLS REQUIRED TO LEARN POSITION SIZING IN TRADING
- Position Sizing & Simulation Models
- Kelly & Optimal f
- Volatility Targeting
- CPPI & TIPP
- Bootstrapping & Monte Carlo
- Martingale Strategies
Programming & Data Skills:
- Python, Pandas, NumPy, Matplotlib
- Functions & Loops
Money Management Knowledge:
- Drawdowns & Volatility
- Position Size
- Win/Loss Ratio
- Returns & System Expectancy
LEARNING TRACK 7
This program is included in the track: Portfolio Management and Position Sizing with Quantitative Approaches.
PREREQUISITES TO LEARN POSITION SIZING IN TRADING
Learners should have a foundational understanding of financial market terms. Python is used throughout the course to demonstrate techniques, but it is not a strict requirement—you can implement the same concepts using spreadsheets or another programming language of your choice.
POSITION SIZING IN TRADING COURSE
Introduction
Position sizing is not a magical wand which can make any strategy profitable. In this section, you will understand the course structure, and the various teaching tools used to make your learning experience smooth and untroubled. These tools include videos, quizzes, strategy codes and capstone projects. The interactive methods used help you to not only understand the concepts, but also how to implement the strategies in the live markets.
- Course Introduction 5m 7s
- Course Structure 3m 41s
- Course Structure Flow Diagram 10m
- Getting Started With Quantra 4m 9s
What is Position Sizing and Money Management?
It is always amazing to hear about stalwarts putting all their money on one asset and winning big. Unfortunately, these are more exceptions than the norm. A rational trader would always diversify and allocate capital to different trades. Position sizing techniques have been developed to help the trader allocate their capital efficiently. In this section, you will be introduced to the concept of position sizing and what it can and cannot do.
- Importance of Position Sizing and Money Management 1m 50s
- Primary Purpose of Money Management 2m
- Ideal Allocated Capital 2m
- Drawback of Absolute Fixed Allocation 2m
- Allocation of Capital 2m
- Use of Money Management 2m 38s
- Objective of Successful Money Management 2m
- Properties of Coin Toss Game 2m
- Ideal Bet Size 2m
- Transformation of Profitable to Unprofitable Strategy 2m
Position Sizing Terms
Before moving to position sizing techniques, it is crucial to understand the position sizing performance measures. In this section, you will learn about the common position sizing measures such as trading system expectancy, win/loss ratio and trade size. Along with these, you will also learn about the most common measures used in risk management, such as volatility and maximum drawdown.
- Performance Measure Terms 3m 41s
- Trade Size 2m
- Win/Loss Ratio 2m
- Trading System Expectancy 2m
- Percentage of Winning Trades 2m
- Trading Systems 2m
- Leverage 10m
- Broker and Asset Leverage 2m
- Total Leverage Calculation 2m
- Risk Management Terms 1m 15s
- Volatility 2m
- Volatile Strategy 2m
- Maximum Drawdown 2m
- Calculate Maximum Drawdown 2m
- Using Jupyter Notebook 1m 54s
- Calculate Volatility & Drawdown in Python 10m
- Calculate Volatility 5m
- Calculate Drawdown 5m
Trading Strategy
To apply the position sizing techniques, you need to have a trading strategy. In this section, you will learn about the pillars of the index-reversal strategy. The trading rules of the index-reversal strategy are also covered in this section.
- Index Reversal Strategy 5m 13s
- Popularity of Indices 2m
- The Behaviour of Asset Price 2m
- Pillars of the Index Reversal Strategy 2m
- Short-term Reversal Strategy 2m
- Strategy Implementation 3m 8s
- Overnight Returns vs Intraday Returns 2m
- Capturing the Overnight Returns 2m
- Price Jump After Local Minimum Price 2m
- Index Reversal Strategy Trading Rules 2m
- Local Minimum Day 2m
- Market Exposure in Index Reversal Strategy 2m
- Determining Strategy Entry Signal 2m
- Trading Instruments 10m
- Additional Reading for Trading Strategy 10m
Implementation of the Trading Strategy
In this section, you will learn to apply the index-reversal strategy in a Jupyter notebook. The strategy performance metrics are also calculated and a utility function is created to make the analysis of other strategy returns easier.
- Index Reversal Strategy Implementation 10m
- Trading Signals 5m
- Cumulative Strategy Returns 5m
- Portfolio Value 5m
- Annualised Returns 5m
Basic Position Sizing: Fixed Units and Fixed Sum
This section will introduce you to one of the two most common position sizing techniques which are fixed units and fixed sum. You will learn about the intuition behind fixed units and fixed sum methods with their pros and cons. Along with the concept, you will also learn to apply both methods on the index reversal strategy and analyse how these position sizing methods affect the strategy performance.
- Fixed Units and Fixed Sum 4m 14s
- Number of Units 2m
- Overall Weight 2m
- Disadvantages of Fixed Units Method 2m
- Number of Units Using Fixed Sum Method 2m
- Advantages of Fixed Sum Method 2m
- Implementation: Fixed Units 2m 18s
- Number of Units in the Fixed Units 2m
- Leverage in Fixed Units 2m
- Fixed Units Implementation 10m
- Fixed Units 5m
- Implementation: Fixed Sum 1m 22s
- Fixed Sum Leverage and Portion of Capital 2m
- Number of Units in Fixed Sum 2m
- Fixed Sum Implementation 10m
- Fixed Sum 5m
Basic Position Sizing: Fixed Percentage and Fixed Fraction
Fixed percent and fixed fraction are position sizing techniques, where you spend only a fixed portion of the available capital to place trades. Both these methods will be introduced in the section with the implementation of the fixed percentage method on the index reversal strategy.
- Fixed Percentage and Fixed Fraction 2m 27s
- Number of Units Using Fixed Percentage 2m
- Effect on Trading Size 2m
- Number of Units Using Fixed Fraction 2m
- Implementation: Fixed Percentage 46s
- Returns and Drawdown in Fixed Percentage 2m
- Leverage in Fixed Percentage 2m
- Portion of Capital in Fixed Percentage 2m
- Fixed Percentage Implementation 10m
- Fixed Percentage 5m
Volatility Targeting
Volatility targeting is an advanced position sizing technique. As the volatility of the underlying goes up, the trade size is scaled down. In this section, you will learn about different volatility models and how to calculate volatility using them.
- Introduction to Volatility Targeting 3m 54s
- Level of Volatility 2m
- Effect of Volatility Targeting 2m
- Volatility of Volatility Targeted Portfolio 2m
- Leverage for Volatility Targeted Portfolio 2m
- Performance of Volatility Targeted Portfolio 2m
- Sharpe Ratio for Volatility Targeted Portfolios 2m
- Different Volatility Models 2m 59s
- Volatility Estimation 2m
- Average True Range 2m
- Equal Weighted Returns 2m
- Exponentially Weighted Returns 2m
- Volatility Clustering 2m
- GARCH Model 2m
- Volatility Models 10m
- Calculate EWMA Volatility 5m
- Interpretation of ATR Plot 2m
Application of Volatility Targeting
In this section, you will learn to apply the volatility targeting technique on the index reversal strategy. The performance metrics, leverage ratio, and the portion of capital used by this position sizing technique, are calculated in a Jupyter notebook.
- Application of Volatility Targeting 3m 38s
- Application of Volatility Targeting 10m
- Leverage Based on Volatility Target 5m
- Returns Based on the Leverage 5m
- Risk Exposure in Volatility Targeting 2m
- Portion of Capital in Volatility Targeting 2m
Live Trading on Blueshift
This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, 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 talks about the implementation of the position sizing technique on Blueshift from which you can paper trade and/or live trade.
- Section Overview 10m
- Paper/Live Trading Volatility Targeting Method 10m
- FAQs for Live Trading on Blueshift 10m
Constant Proportion Portfolio Insurance
Constant Proportion Portfolio Insurance is another advanced position sizing technique that protects the downside. It guarantees a minimum return at the end of a period. This section will introduce you to this technique. You will also learn how to implement this in Python and then apply the technique on the index reversal strategy.
- Introduction to Constant Proportion Portfolio Insurance 2m 34s
- Protection Level 2m
- Scale of Exposure in CPPI 2m
- Portfolio Value Minus Floor 2m
- Risky Asset Allocation 2m
- Advantages and Disadvantages of CPPI 2m 8s
- Disadvantages of CPPI 2m
- Gap Risk 2m
- Reason for Gap Risk 2m
- Multiplier 2m
- Exposure to Risky Assets 2m
- Risk to Lose 2m
- Value of the Multiplier 2m
- Allocation to SPY ETF 2m
- Decrease in Portfolio Value 2m
- Allocation to Riskfree Asset 2m
- Application of CPPI 5m 14s
- CPPI Implementation 2m
- Leverage in CPPI 2m
- Implementation of CPPI 10m
- Paper/Live Trading CPPI Method 10m
Time Invariant Portfolio Protection
CPPI guarantees a minimum return at the end of a period, but fails to capture the portfolio highs. In this section, you will learn how to overcome this by adjusting the floor based on the peak value of the portfolio. You will do this by applying the technique on the index reversal strategy.
- Implementing Time Invariant Portfolio Protection 10m
- Limitations of CPPI 2m
- Floor in TIPP 2m
- Additional Reading 10m
- Paper/Live Trading TIPP Method 10m
Kelly Formula
In this section, you will learn about the Kelly formula and how to use it. Kelly formula is a technique used to calculate the trade size that ensures the maximum return without focusing on the risk of return. You will also learn how to implement this in Python. You will further learn the limitations of this technique.
- Kelly Formula 4m 8s
- Objective of Kelly Formula 2m
- Decisions to Open a Position 2m
- Trade Size 2m
- Win/Loss Ratio 2m
- Kelly Percentage 2m
- Implementation of Kelly Criterion 10m
- Calculate Winning Probability and Win/Loss Ratio 5m
- Calculate Kelly Percentage 5m
- Limitations of Kelly Formula 1m 55s
- Kelly Formula in Trading 2m
- Disadvantages of Kelly Formula in Trading 2m
- Additional Reading 10m
Optimal F
The major disadvantage of the Kelly formula is that it is applicable only on binary outcomes, and thus is not directly usable in trading. This is overcome by optimal f. In this section, you will learn about this technique and its implementation in Python. You will also apply this technique on the index reversal strategy.
- Optimal F 1m 39s
- Features of Optimal F 2m
- Range of Optimal F 2m
- Value of Optimal F 2m
- Implementation of Optimal F 10m
- Additional Reading 10m
- Paper/Live Trading Optimal F Method 10m
Theory Is Grey, but Life Is Green
Position sizing techniques can work on a trading strategy exceptionally well and yet falter in the real world. Sometimes, the position sizing techniques, such as Kelly fraction or optimal f, have their own limitations. Other times, the financial markets themselves pose unique challenges to these techniques. In this section, you will look at these challenges and learn how to overcome them.
- Inherent Risk in Kelly Criterion and Optimal F 4m 1s
- Properties of Kelly Criterion Criterion and Optimal F 2m
- Probability of Drawdown 2m
- Reduction in Probability of Drawdown 2m
- Optimal and Real Bet Size 2m
- Relation Between Probability and Drawdown 2m
- Fractional Kelly and Probability of Drawdown 2m
- Fractional Kelly and Profit Potential 2m
- Relation Between Fractional Kelly and Expected Profit 2m
- Optimal Kelly Fraction 2m
- Hidden Risks in Backtesting and Financial Markets 4m 21s
- Out and In Sample Returns 2m
- Compensation of Out of Sample Returns 2m
- Stationarity of Price Series 2m
- Impact of Non-Stationarity 2m
- Significance of Fat Tails 2m
- Black Swan Events 2m
- Focus of Position Sizing 2m
- Dealing with Unprofitable Trading Strategy 1m 44s
- Turning Off Trading Strategy 2m
- Advantage of Multi Strategy Portfolio 2m
- Capital Allocation Based on Performance 2m
- Correlation in Multi-strategy portfolio 2m
- Martingale Trading Strategy 3m 41s
- Use of Martingale Trading Strategy 2m
- Additional Reading 10m
Numerical Methods
Have you ever wondered what would have happened if the past was different? How a trading strategy would perform differently due to some unexpected events in the past. In this section, you will explore alternative trading realities and learn from them. This section will introduce bootstrapping and Monte Carlo methods, which helps you get deeper insights into a trading strategy by exploring multiple scenarios. You will also learn to do bootstrapping simulations on the index reversal strategy and gain more insights.
- Bootstrapping 3m 3s
- Need of Bootstrapping 2m
- Bootstrapping Process 2m
- Inference from Bootstrapping Data-I 2m
- Inference from Bootstrapping Data-II 2m
- Estimate Parameters 2m
- Bootstrapping Results 1m 48s
- Bootstrap Simulation 10m
- Maximal Drawdown Distributions 2m
- Bootstrapping Result Interpretation 2m
- Capital Allocation Based on Bootstrapping Results 2m
- Monte Carlo 2m 33s
- How Much Capital Allocated? 2m
- Monte Carlo Method 2m
- Monte Carlo Process 2m
- Advantages and Disadvantages of Monte Carlo 2m
Conservative Framework for Position Sizing
Once you have gained the knowledge and have applied the position sizing techniques on a trading strategy, you will take the next big step, combining two position sizing techniques! Not only that, you will analyse the trading strategy’s performance as well.
- Testing the Trading Strategy 2m 58s
- Reason for Position Sizing 2m
- Maximisation of Returns through Position Sizing 2m
- Risk and Position Sizing 2m
- Limitations of Backtesting 2m
- Leverage Boundaries 2m
- Long Historical Data 2m
- Conservative Estimate of Leverage 2m
- Revisiting CPPI and Volatility Targeting 3m 19s
- CPPI and Drawdown 2m
- CPPI Based Allocation 2m
- CPPI and Volatility Targeting 2m
- Impact of Volatility on CPPI and Volatility Targeting 2m
- Result of CPPI and Volatility Targeting 2m 9s
- Analysing CPPI and Volatility Targeting Performance 2m
- Implication of Dynamic Position Sizing 2m
- Impact of High Risk Budget and High Leverage 2m
- Position Sizing and Unprofitable Trading Strategy 2m
- Implementing TIPP with Volatility Targeting 10m
- Paper/Live Trading TIPP with Volatility Targeting 10m
Automate Trading Strategy Using IBridgePy
- Additional Reading 10m
- Sample Strategies to Run on Interactive Brokers 2m
Run Codes Locally on Your Machine
Learn to install the Python environment in your local machine.
- Python Installation Overview 1m 59s
- 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
- Installing Ta-Lib 2m
- Quantra Environment 2m
- Troubleshooting Tips For Setting Environment 10m
- How to Run Files in Download Section? 10m
- Troubleshooting For Running Files in Download Section 10m
Capstone Project
In this section, you will undertake a capstone project where you will apply different position sizing techniques to a trading strategy. The performance of the two sizing techniques is compared. This project helps you to practice and apply the concepts learnt in this course.
- Capstone Project: Getting Started 10m
- Problem Statement 10m
- Frequently Asked Questions 10m
- Code Template and Data Files 2m
- Model Solution: Position Sizing Capstone Project 10m
- Capstone Solution Downloadable 2m
Course Summary
In this section, you will go through the different concepts you learnt throughout the course. You will also be able to download all the strategy notebooks as a zip file. You can use these notebooks and modify their contents to create your own unique strategy.
- Course Summary 3m 25s
- Python Data and Codes 2m
ABOUT AUTHOR
Quantpedia
Quantpedia is a comprehensive encyclopedia and database of quantitative and algorithmic trading strategies. Its mission is to transform complex financial research into practical, easy-to-use resources, making it accessible for traders and investors who are searching for innovative quantitative strategy ideas.
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REVIEWS
JEAN-KRISTOFF GRENIER – Canada
Quantra courses are excellent. As someone aiming to become a Junior Trader, the programs have given me an edge by teaching me how to automate trading strategies, setting me apart from others in the field.
ASHOK ARUMUGHAM – Equity Trader, India
What I like most about Quantra courses is the flexibility—they’re completely self-paced, which works perfectly for me. The integration with Jupyter notebooks allowed me to code in Python directly within the platform. The extra reading resources are also very valuable. I especially appreciated the Blueshift platform, which let me research, backtest, and even paper/live trade strategies. I look forward to exploring as many strategies as possible on Blueshift in the future.
