Immediately Download Crypto Trading Strategies: Advanced By QuantInsti
This is the second course in the ‘Cryptocurrencies Trading Strategies’ bundle, designed for advanced learners. The program explores machine learning models, statistical arbitrage, and quantitative frameworks to build and implement crypto trading strategies. With a data-driven and algorithmic approach, you’ll gain tools to sharpen your competitive edge in the cryptocurrency markets.
LIVE TRADING
- Build and test a long-only momentum trading model
- Backtest cryptocurrency pairs trading setups
- Develop a clustering strategy using K-means
- Apply Hurst Exponent to fine-tune entries
- Create a complete quantitative trading strategy framework
- Execute strategies in real markets and review outcomes
SKILLS COVERED
Quantitative Strategies
- Pairs Trading
- Momentum (Long-only)
- Hurst Exponent Models
- Bollinger Band Applications
Machine Learning Techniques
- Unsupervised Learning
- K-means Clustering
- Train/Test Dataset Methods
Programming Tools
- Python
- Scikit-learn’s KMeans
- Pandas
- Statsmodels
- TA-Lib
LEARNING TRACK 6
This course belongs to the Learning Track: Algorithmic Trading in Cryptocurrency and Forex.
PREREQUISITES
Before starting, learners should understand essential cryptocurrency concepts like quotes, wallets, and tickers (covered in Cryptocurrency Trading Strategies: Intermediate). Coding skills are not mandatory for grasping the trading logic. However, to program and deploy strategies in Python, prior familiarity with DataFrames is needed—these skills are introduced in the course Python for Trading.
SYLLABUS
Machine Learning in Cryptocurrency Trading
This section explains the working and implementation of an unsupervised machine learning algorithm called K-Means and how it can be used in cryptocurrency trading to capture market trends.
- Introduction to the Course 2m 1s
- Quantra Features and Guidance 3m 48s
- Understanding K-Means Algorithm 3m 44s
- Unsupervised Learning 2m
- Locating the Cluster Center 2m
- Applying K-Means in Python 3m 32s
- K-Means: Indicators 2m
- How to Use Jupyter Notebook? 2m 5s
- Clustering Strategy for Cryptocurrencies 10m
- Frequently Asked Questions 10m
- Cryptocurrency: Strategy Signal 2m
Code the K-Means Strategy
In this section, learn to code the strategy in Python and to use it to predict clusters and generate trading signals.
- Calculating the SMA 5m
- Function for Calculating the Movement 2m
- Instantiating the K-Means 5m
- Unsupervised Learning Algorithms 2m
- Training the K-Means 5m
- Convert the Input Data 2m
- Predicting the Clusters 5m
- Using the Sklearn Prediction 2m
- Trading Signal 5m
Pairs Trading Strategy
In this section, you will learn about the pairs trading strategy and concepts like stationarity, hedge ratio, and co-integration.
- Pairs Trading 2m 1s
- Stationarity 2m
- Cointegration 2m
- Spread 2m
- Hedge Ratio 2m
Code the Pairs Trading Strategy
This section demonstrates the implementation of the pairs trading strategy using Python and how it can be applied in cryptocurrency trading. It also provides the strategy returns to determine the performance.
- Strategy 2m 22s
- ADF Test 10m
- Bollinger Band 2m
- Bollinger Band – Spread 2m
- Pairs Trading Strategy 10m
- Hedge Ratio 5m
- Cointegration 5m
- Moving Standard Deviations 5m
- Buy Signal 5m
- Plot Strategy Returns 5m
- Test on K-Means and Pairs Trading 14m
Hurst Exponent
This section focuses on developing an understanding of a statistical technique called Hurst Exponent, analyzing and calculating it. It also demonstrates how it can be used for cryptocurrency trading.
- Hurst Exponent 2m 59s
- Understanding Market Nature 2m
- Understanding the Hurst Exponent 2m
- Hurst Exponent Calculation 10m
- Analyzing the Hurst Exponent 2m
- Strategy Using Hurst Exponent 2m 27s
- RSI With Hurst 2m
- Crypto Trading Using Hurst Exponent 10m
Code the Hurst Strategy
This section explains the working and implementation of the Hurst Strategy using Python and determines the strategy returns.
- Effect on Net Profit 2m
- Converting to Datetime 5m
- Convert Timestamp 2m
- Calculate the Hurst Exponent 5m
- Understanding the Hurst Function 2m
- Generate the Persistence Signal 5m
- Calculate the RSI 5m
- Understanding the Input 2m
- Calculating the Strategy Returns 5m
- Understanding the Strategy 2m
Quant Strategy Framework
This section covers the quant strategy framework, its components, and its advantages. It also covers topics like Universal Selection Criteria and Capital Allocation.
- Framework Overview 2m 47s
- Advantages of the Framework 2m
- Components of the Framework 2m
- Universe Selection Criteria 2m
- Capital Allocation 2m
- Additional Reading 10m
Code the Long-Only Momentum Strategy
Using the framework explained in the earlier section, a long-only momentum strategy is developed and coded in Python in this section. You can find the downloadable strategy codes in this section.
- Long-Only Momentum Strategy 10s
- Pandas Datetime Function 2m
- Percent Change 2m
- Rank 2-Day Returns 5m
- Combined Alpha Score 5m
- Strategy Returns 5m
- Summary 1m 22s
- Test on Hurst Exponent, Long-Only Momentum Strategy and Quant Strategy Framework 14m
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
- 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
Live Trading on IBridgePy
- Section Overview 2m 2s
- Live Trading Overview 2m 41s
- Vectorised vs Event Driven 2m
- Process in Live Trading 2m
- Real-Time Data Source 2m
- Code Structure 2m 15s
- API Methods 10m
- Schedule Strategy Logic 2m
- Fetch Historical Data 2m
- Place Order 2m
- IBridgePy Course Link 10m
- Additional Reading 10m
- Frequently Asked Questions 10m
Paper and Live Trading
In this section, a live trading strategy template will be provided to you. You can tweak the strategy template to deploy your strategies in the live market!
- Template Documentation 10m
- Template Code Files 2m
Downloadable Resources
You can download the Python strategy codes at the end of the course.
- Python Codes and Data 2m
ABOUT AUTHOR
QuantInsti®
QuantInsti stands as a global leader in quantitative and algorithmic trading education and research, with participants from more than 190 countries. Founded by the creators of iRage, one of India’s top high-frequency trading firms, QuantInsti has been supporting learners for over a decade with its blend of education, research, and financial technology platforms.
WHY QUANTRA®?
- Learn faster and more effectively
- Be guided by experienced market practitioners
- Study at your own pace and schedule
- Access datasets and ready-to-use strategy models for practice
REVIEWS
HOO JUN LUO – Credit Analyst, Malaysia
“Very insightful! I was glad to join during the discount offer. The material is delivered clearly and with practical applications. What I really appreciate is the lifetime access, which lets me return to the lessons whenever I need a refresher.”
GERMAN MONTENEGRO – Argentina
“The instructor explained the concepts well and backed them with real strategies. What I found most valuable is the mix of theory with practical examples, which makes learning far more effective compared to only reading books or articles.”
