Trading Alphas: Mining, Optimisation, and System Design
2249 Learners
15 Hours
Why should you choose micro alpha models over other trading strategies such as traditional factor models, risk-parity, or trend following? In short, these models, if built well, can provide better performance, stability, and risk management than other trading systems. In this course, you will learn where micro-alphas reside and how to write the most efficient codes to quickly analyse, backtest, optimise and go live with your trading strategy in the least amount of time possible.
Level
Advanced
Author
Dr. Thomas Starke
Price Lifetime Access
₹24984₹32031(Additional 22% off)
Original Price: ₹57199
No Cost EMI available
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Backtesting, adding stop-loss and profit-take using vectorised approach
- Mining micro-alphas using trends, mean-reversion, correlation across assets, and cointegration
- Metrics for analysing strategy which include total profit, sharpe ratio, sortino ratio, profit factor, drawdown, and profit per trade
- Parameter optimisation using machine learning techniques such as clustering
- Building a trading system from scratch
- Explain software architecture, logging, storage, hardware, testing and version control
- Brief study on execution models, implement parallel computing and describe different levels of logging
Skills Covered
Strategies
- Mean-reversion
- K-Nearest Neighbours
- Time series & cross sectional alphas
- Candlestick Patterns
- Vectorized SL & TP
Concepts & Trading
- Cointegration
- Correlation
- Execution
- Building a trading platform
- System Parameter Permutation
Python
- Pandas
- Matplotlib
- Ta-lib
- Sklearn
- Asyncio
learning track 8
This course is a part of the Learning Track: Advanced Algorithmic Trading Strategies
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Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
Fluency with Python including python libraries like pandas, numpy, matplotlib and concepts of machine learning such as clustering, prediction, in-sample, out of sample and features. Working knowledge to place orders to buy and sell exchange traded assets.
Syllabus
- IntroductionThis course will serve as a step-by-step guide that helps you find the trades based on micro alpha opportunities in the markets today. The interactive methods used in this course will help you not only understand the concepts but also answer all questions about micro alphas. This section also covers the course structure as well as the various teaching tools used in the course, such as videos, quizzes, coding exercises, and the capstone project.
Micro AlphasThe efficient market hypothesis states that all information available to the market is contained in the current price. This creates a scenario where it would be impossible to consistently generate profits since the price movements are random and unpredictable. However, there exist ways to exploit market inefficiencies and make money. This section helps you take the first step towards studying micro alphas by establishing a baseline.
Micro Alphas3m 12s
Efficient Market Hypothesis2m
Overturn Efficient Market Hypothesis2m
Autocorrelation2m
Assumption of Technical Indicators2m
How to Use Jupyter Notebook?2m 5s
Generating Price Series at Random5m
How to Use Interactive Exercises?5m
Generate Random Numbers5m
Scaling5m
Generate Price Data5m
Statistical Study on Randomly Generated Price Series5m
Autocorrelation3m
Trading Signals3m
Why Did the Signal Fail?3m
Additional Reading on Micro Alphas5m
- Market Inefficiencies: TrendBy employing some level of technical expertise, you too can stand a chance of benefiting from inefficiencies in the markets. Market trends are one of these inefficiencies. In this section, you will study how market trends take place. You will also learn how to formulate a strategy based on the relationship between past and current returns.Market Inefficiencies2m
Trends3m 38s
Compounded PnL Curve3m
Positive Auto-Correlation3m
Positively Correlated Time Series3m
Equation for Auto-Correlation3m
Value of g3m
Strategy for Positive Correlation3m
Types of Backtesting3m
Compounded PnL Curve3m
Auto-Correlation3m
Trending Prices3m
Series of Returns3m
Trend5m
Generate Random Returns5m
Linearly Fit the Autocorrelated Data5m
Backtest the Strategy5m
Additional Reading on Trends5m
- Market Inefficiencies: Mean ReversionIs there a correlation between a stock's present and past returns that can point to its mean-reverting characteristics? The answer is yes. In this section, you will learn about the type of correlation that leads to mean reversion, how to form a strategy based on the mean-reverting properties of a stock, and also how to combine two strategies to get better results.
Mean Reversion4m 25s
Market Characteristic3m
Constant g3m
Type of Time Series3m
Correlation of Returns3m
Strategy and Benchmark Returns3m
Strategy Based on Correlation3m
Ideal Metric3m
Annualised Alpha3m
Generate Negatively Autocorrelated Returns5m
Additional Reading on Mean Reversion5m
- Trading with Trends and Mean ReversionIn this section, you will learn to create and backtest strategies around market inefficiencies such as trend and mean reversion using real-world data. You will also learn how to compare the strategy returns with the market returns to analyse its performance.
Trading with Autocorrelated Data5m
Calculate Risk-Adjusted Returns5m
- Market Inefficiencies: Chart PatternsChart patterns are often used by traders to predict price movements. It's a type of market inefficiency that can be exploited to gain excess returns (alpha). In this section, you will learn how to backtest multiple patterns at the same time. You will also learn how to formulate a strategy based on the backtested results and assess its performance.
Chart Patterns3m 55s
Define Chart Patterns3m
Values of a Candlestick Pattern3m
Backtesting3m
Library for Candlestick Pattern3m
Candlestick Pattern for Micro-Alpha3m
Usefulness of Alpha3m
Equity Asset Returns3m
Chart Patterns5m
Extract the Chart Pattern Function3m
Chart Pattern Signals3m
Calculate Signals5m
Capital Allocation3m
- Market Inefficiencies: Correlation, Fundamental and AlternativeIn this section, you will learn about a few types of market inefficiencies such as correlation, fundamental data, and alternative data. You will also learn how they impact the price movement and how they can be used to gain excess returns.
Correlation, Fundamental and Alternative2m 52s
Correlation3m
Usage of Correlation3m
Cross-Sectional Correlation3m
Fundamental Inefficiencies3m
Inference for Correlation3m
Insider Information3m
Trading View based on Analyst Forecasts3m
Golf and a Company's Performance3m
Correlation5m
Calculate Average Correlation5m
Additional Reading on Correlation5m
- Market Inefficiencies: CointegrationCointegration is the basis of statistical arbitrage. In this section, you will learn how to implement a pairs trading strategy. You will also learn some of the traps of statistical arbitrage and how they can be avoided.Cointegration5m 39s
Alternative Term for Pairs Trading3m
Predictive Model3m
Trading the Spread Curve3m
Spread Strategy Code3m
Cash-Neutral Strategy3m
Cointegration3m
Hedge Ratio3m
Cointegration5m
Create a Spread5m
Additional Reading on Cointegration5m
Types of Market Inefficiencies2m
Test on Micro Alphas and Market Inefficiencies16m
- Time Series AlphasThere are multiple sources of alphas, and the best known, as well as the most widely used alpha is the time-series alpha. This section will help you generate alpha with signals along the time axis. You will learn how you can use historical time series data to create an RSI-based strategy.Time Series Alphas3m 50s
Categories of Alpha3m
Time Series Alpha3m
Types of Alpha3m
Problem with Independent Signals3m
Number of Signals3m
Positions for Time-Series Alpha3m
Trading Logic3m
RSI Less than 403m
Shift Returns3m
PnL Curves3m
Strategy vs Benchmark3m
Factor in Time-Series Alpha Calculation3m
RSI Strategy Logic5m
Implementation of RSI Based Trading Strategy5m
Calculate RSI5m
Generate Signals Using RSI5m
Calculate Portfolio Returns5m
Additional Reading on Time Series Alphas5m
- Live Trading on BlueshiftLearn 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.Uninterrupted Learning Journey with Quantra5m
Section Overview2m 19s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateThis section includes a live trading strategy template that uses the RSI indicator to generate entry and exit signals. You can tweak the code by changing securities or the strategy parameters. You can also analyse the strategy's performance in more detail.Paper/Live Trade Using RSI5m
FAQs for Live Trading on Blueshift5m
- Cross-Sectional AlphasAlphas can be generated not just with signals along the time axis, but also with signals along the instrument axis. In this section, you will learn to generate alpha by ranking assets based on their momentum along the instrument axis.Cross-Sectional Alpha2m 38s
Common Attribute3m
Axis for Cross-Sectional Alpha3m
Arrange in Order3m
Indicator for Trading Signals3m
Sum of Rows3m
Two Ranks3m
Cross-Sectional Approach3m
Cross-Sectional Momentum Strategy Logic5m
Cross-Sectional Momentum Strategy5m
Calculate Momentum5m
Backtest Cross-Sectional Momentum Strategy5m
Calculate PnL3m
Additional Reading on Cross-Sectional Alphas5m
Paper/Live Trade Using Cross Sectional Alpha5m
- Timing AlphasPutting on trades at the right time, hour, weekday, or month can be a significant source of alpha in some cases. In this section, you will learn about the importance as well as the impact of timing the alpha. You will also implement the concepts in a Jupyter notebook.Timing Alpha2m 18s
Source of Alpha3m
Daytime vs Overnight Returns3m
Persistent Overnight Returns3m
MA Weekday Strategy3m
Advantages of Weekday Strategy3m
Important Timing Events3m
Timing Alphas5m
Calculate Overnight Returns3m
Inference of Cumulative Returns Plot3m
Use of Timing of Alphas3m
Additional Reading on Timing Alphas5m
Most Suitable Alpha3m 52s
- Combinations of AlphaIs it possible to combine the different categories of alphas to create a trading strategy? Yes, in this section, you will learn about the various combinations of alphas. You will also implement a volatility-based trading strategy.
Combinations of Alpha1m 35s
Combinations of Alpha3m
Alpha Combinations-I3m
Annualised Volatility3m
Alpha Combinations-II3m
Volatility Strategy3m
Upper and Lower Limit3m
Upper and Lower Limit Inference3m
Volatility Based Trading Strategy5m
Calculate Volatility of Stock Returns5m
Backtest Volatility Based Trading Strategy with Lower Limit5m
Additional Reading on Combinations of Alphas5m
Things to Keep In Mind While Combining Strategies4m 15s
Paper/Live Trade Using Volatility5m
- Finding Micro-AlphasFor finding micro-alphas, creativity is an indispensable prerequisite, and even slight modifications to old ideas can often deliver great results. In this section, you will be introduced to the research paper “100 Formulaic Alphas" which was published by Kakushadze in 2015. You will learn about some of the alphas and implement them in a Jupyter notebook.
Finding Micro-Alphas4m 19s
Other’s Ideas3m
Alpha #3 Factor3m
Alpha #3 and Alpha #573m
Ranking RSI Values3m
Micro-Alphas From 101 Formulaic Alphas5m
Calculate Alpha #65m
Additional Reading on Finding Micro-Alphas5m
Test on Alphas14m
- Assessing ResultsTo understand how well your strategy is working, you need to do a full assessment of the strategy. While it is very important to develop an intuitive sense of the nature of what we are looking at on a chart, this is by no means sufficient for a full assessment. In this section, you will learn about the importance of combining different metrics, which will help you understand a variety of aspects of strategy performance.Assessing Results2m 19s
Prerequisite for Finding Micro-Alphas3m
Combination of Alphas3m
Number of Metrics3m
Utility of Sharpe Ratio3m
Strategy Performance3m
Additional Reading on Assessing Results5m
Most Ideal Performance Metric5m 53s
- Total ProfitIn this section, you will learn about total profit, which is by far the simplest and the most used performance metric. You will learn about compounded and non-compounded as well as realised and unrealised profits. You will also implement these concepts in a Jupyter notebook.Total Profit3m 4s
Characteristics of Total Profit3m
Features of Total Profit3m
Differences Between PnL Curves3m
Which Strategy is Riskier?3m
Reinvestment of Profits3m
Realised vs Unrealised PnL3m
Drawbacks of Realised PnL3m
Drawbacks of Total PnL3m
Information Provided by PnLs3m
Limitations of Total Profit5m
Strategy Comparison3m
Impact of Compounded PnL3m
Realised Vs Unrealised Profits5m
Realised PnL of a Strategy3m
Additional Reading on Total Profit5m
- Sharpe and Sortino RatiosThe Sharpe ratio and Sortino ratios help you compare the risk-adjusted performance of different portfolios or trading strategies and determine the most feasible of them all. In this section, you will learn about the two ratios in depth and implement the same using Python.Sharpe and Sortino Ratios5m 33s
Risk-Adjusted Returns3m
Calculate Sharpe Ratio3m
Risk-Free Rate3m
Exclude Risk-Free Rate3m
Drawbacks of Sharpe Ratio3m
Sortino Ratio3m
Sharpe and Sortino Ratios using Python5m
Implement the Sharpe Ratio5m
Implement the Sortino Ratio5m
Additional Reading on Sharpe and Sortino Ratios5m
- Profit Factor and DrawdownThe Sharpe or Sortino ratios are not suited to evaluate high confidence strategies that take less-frequent but highly profitable trades. In this section, you will learn about the profit factor, which is a good metric to use when we find such types of Alphas. Additionally, the drawdown metric can help us estimate how much we can expect to be underwater at any given time.Profit Factor and Drawdown3m 9s
Profit Factor3m
Compare the Profit Factor3m
Drawdown of a Strategy3m
Drawdown Calculation3m
Maximum Drawdown Comparison3m
Profit Factor and Drawdown using Python5m
Implement Profit Factor5m
Additional Reading on Profit Factor and Drawdown5m
- Profit Per TradeThe profit per trade metric helps you understand the average value you can expect to win or lose per trade. In this section, you will learn about the correct approach for computing profit per trade and you will also learn to compute the same using python.Profit Per Trade4m 17s
Application of Profit Per Trade3m
Computing Profit Per Trade3m
Profit Per Trade5m
Additional Reading on Profit Per Trade5m
- CAGR, Alpha, and BetaIn this section, you will learn about three popular metrics - CAGR, Alpha and Beta. CAGR helps us determine how much return our strategy is realistically able to generate annually. Alpha shows us how much of the strategy’s return is independent of the benchmark. And the Beta provides us with some insight into our exposure to the underlying market.CAGR, Alpha and Beta3m 30s
Compounded or Non-compounded?3m
Annualise the Sharpe Ratio3m
Evaluate the Skill of a Money Manager3m
Initial Backtest3m
CAGR, Alpha and Beta5m
Additional Reading on CAGR, Alpha and Beta5m
Test on Strategy Results14m
- Strategy ExecutionYou need to be aware of the assumptions you will be making in order to avoid spending time on strategies that are not feasible in the real world or are too costly or complex to implement. Through this section, we will discuss a number of such common assumptions that traders make and how we can deal with them. You will also learn about some interesting execution algorithms such as the arrival price algorithm that may help to enhance your execution performance.Strategy Execution3m 56s
Implicit Assumptions3m
Shortcomings of Execution on Close3m
Executing Large Quantities3m
Slippage3m
Limitation of Market-on-Close Order3m
Arrival Price Algorithm4m 20s
Execution on the Open3m
Order Type for Arrival Price Algorithm3m
Sources of Transaction Costs3m
Additional Reading on Strategy Execution5m
- Micro-Alpha PortfolioSo far we have discussed how to research, test, evaluate and execute individual alphas. However, the great strength of the micro-alpha approach lies in the combination of many individual alphas. In this section, you will combine multiple alphas and create a combined alpha strategy.Combining Alphas2m 39s
Traditional Portfolio Management3m
Micro-Alpha Approach3m
Alphas3m
Combining Alphas - I3m
Generating Signals1m 48s
Combining All Micro-Alphas5m
Paper/Live Trade by Combining Micro-Alphas5m
- Portfolio OptimisationIn this section, you will analyse various portfolio optimisation techniques, such as manual optimisation and mean-variance optimisation, by practically applying them to the combined alpha portfolio.Portfolio Optimisation3m 58s
Rebalance the Weights3m
Equal Portfolio Weights3m
Efficient Frontier3m
Optimisation5m
Additional Reading5m
- Advanced Alpha MiningIn this section, you will learn about more advanced alpha mining concepts, such as system parameter permutation and optimisation.Testing Robustness Across Parameter Space3m 4s
Testing Robustness of Strategy5m
Selecting Best Parameter Sets3m 30s
Finding Best Parameter5m
Possible Lookback Values3m
Parameter Optimisation3m
Simpson's Paradox3m
Sharpe Ratios3m
Lookback Periods3m
Clustering Algorithms - I3m
Clustering Algorithms - II3m
SPP3m
Additional Reading - I5m
Additional Reading - II5m
- Machine Learning AlphasIn this section, you will learn about machine learning alphas.Machine Learning Alphas1m 58s
Classification3m
ML Alphas5m
- Basics of Vectorized BacktestBacktests can be done either with loops or in the vectorized format. While a vectorized backtest is relatively complex, the gains in execution speed are well worth the effort. A looped backtest might take hours to run a single backtest, which will be executed in minutes in the vectorized format. In this section, you will backtest a simple moving-average crossover strategy in the vectorized format.Creating a Basic Backtest2m 34s
Factors for Setting Exit Signals3m
Number of Winning and Losing Trades3m
Reason for High Number of Losing Trades3m
Advantage of Stop-loss and Profit-take3m
Implementation of Profit-take and Stop-loss3m
Conversion of Long-Short to Long-Only Signals3m
Creation of Vectorized Backtest5m
Calculate the Moving Average Crossover5m
Generating Long-Short Trading Signal5m
Generating Long-Only Trading Signal5m
Calculate the Cumulative Sum of Returns5m
Calculation of Portfolio Returns3m
Additional Reading5m
- Adding Vectorized Stop-loss and Profit-takesHow can you make a good strategy better? You can incorporate profit-take and stop-loss levels which will help your strategy withstand black swan events. In this section, you will backtest the moving-average crossover strategy before and after adding a stop-loss and profit-take.Application Of Profit-Take And Stop-Loss Filters3m 2s
Replacement of Short Signals3m
Identification of Entry and Exit Points3m
Inference After Difference of Consecutive Signals3m
Code of Entry Date3m
Region Between Profit Take and Original Signal3m
Individual Trade Profit and Loss3m
Impact of Profit-take and Stop-loss Filters3m
Time Difference Before and After Application of PT/SL Filters3m
Vectorized Backtest with Profit-Takes and Stop-Loss5m
Additional Reading5m
- Impact of Profit Take and Stop Loss on StrategyIn this section, you will analyse the backtested strategy after adding stop-loss and profit-take, as well as look at different measures which can be taken to optimise your capital allocation in the strategy.Strategy Analysis After Application of Profit Take and Stop Loss2m 17s
Use Case of Vectorized Backtest3m
Technique to Increase Strategy Returns3m
Analysis After Application of PT and SL5m
Test on Combined Alpha, Advanced Alpha Mining Concepts and Backtesting14m
- Designing a Trading SystemAt its heart, a trading system consists of various sub-processes which are inter-connected with each other and perform various tasks in order to execute a trade as per the strategy. To make sure everything works correctly, you cannot run these processes in sequential order. In this section, you will get a brief on three types of computing architecture, which are parallel, asynchronous and distributed computing. You will also delve deeper into parallel computing architecture.Software Architecture in Trading Systems1m 40s
Sequential Order Based Trading System3m
Paradigms for Creation of a Trading System3m
Build a Trading System3m
Parallel Computing2m 45s
Methods to Implement Parallel Computing3m
GIL and Parallel Computing3m
Effect of Multi-threading on Single Core3m
Data Appended to Queue3m
Market Data Reading3m
Threads with Different Idle Times3m
Execution of Parallel Threads3m
Output of Parallel Threads Process3m
Implementation of Parallel Computing5m
Output for Thread in Parallel Processes3m
Resource Sharing Between Threads3m
Data Structure Shared Between Threads3m
Initialisation of Threads3m
Calculate Square of Datapoints Using Threads5m
Additional Reading5m
- Asynchronous ComputingAsynchronous computing uses the concept of multi-threading to implement threads in a concurrent fashion. In this section, you will understand how to build an asynchronous computing-based trading system.Asynchronous Computing2m 51s
Python Package for Asynchronous Computing3m
Difference Between Concurrency and Multi-threading3m
Property of Asynchronous Loops3m
Python Keywords for Concurrency3m
Asynchronous Recursion3m
Function of Sleep in Asyncio Package3m
Implementation of Asynchronous Recursion3m
Use of Concurrency3m
Implementation of Asynchronous Computing5m
Difference Between Asynchronous Computing and Threading3m
Keyword to Run Method in Asynchronous Fashion3m
Purpose of Await Keyword3m
Additional Reading5m
- Distributed ComputingIn this section, you will take the concept of parallel computing further and see how you can build a distributed computing architecture which communicates with different programmes which could be run in different systems as well. You will also build a sample trading system in Python using distributed computing.Distributed Computing2m 45s
Communication Between Systems and Programs Using Python3m
Python Packages for Distributed Computing3m
Request Based Client Server Module3m
Subscriber Based Client Server Module3m
Acknowledgement of Order Sent in Trading System3m
Limitation of Message Queue3m
Distributed Computing5m
Additional Reading5m
- Importance of Logging and StorageOften, a trader tends to ignore logging of messages which could have been used later for debugging as well as improving the trading strategy. In this section, you will learn how to create logging messages. Further, you will understand the storage requirements as well as the advantages and disadvantages of storing market data in raw and compressed formats.Logging And Storage4m 10s
Application of Logging3m
Level of Logging3m
Difference in Logging Levels During Backtesting and Live Trading3m
Levels of Logging3m
Set Logging Level3m
Configuration of Logger in Notebook3m
Modification of Logger in Notebook3m
Logging for a Trading System5m
Log Messages at Critical Level5m
Conversion of Raw Data3m
Reason for Storage in Raw Data Format3m
Conversion of Raw Data at Regular Intervals3m
Additional Reading5m
- Hardware Elements of a Trading SystemIn this section, you will look at the various types of hardware systems built for trading systems and analyse their pros and cons in terms of execution speed, latency, and computing resources.Hardware of a Trading System3m 30s
Focus During Selection of Hardware3m
Selection of Cost-effective Hardware3m
Selection of Hardware Based on Latency Requirements3m
Selection of Hardware for Multiple Strategies3m
Additional Reading5m
- Software Elements of a Trading SystemWhen you build a trading system, it is important to separate the processes and understand how they are interacting with each other. Further, you will also have to select the right operating system according to your needs.Micro-Services and Operating System4m 26s
Micro-Alphas and Industry Sectors3m
Combinations of Micro-Alphas Weights and Industry3m
Separation of Tasks as Micro-Services3m
Order of Task Performance3m
Advantage of Task Separation in Trading System3m
Advantage of Linux OS3m
Presence of GUI and Trading Systems3m
Selection of Operating System3m
- Testing and Version ControlA trading system is actually built with a number of components and processes. It is always easier to rectify an error while it is small than to set about rectifying it once the entire system is built. Thus, unit tests should be undertaken to make sure that your trading system does not falter due to certain reasons which could have been easily avoided. Further, you should also make sure the various packages used can be run with each other, which is where version control helps in documenting and understanding the processes.Testing and Version Control2m 12s
Elimination of Unexpected Downstream Failures3m
Importance of Unit Tests in Trading Systems3m
Tools for Version Control3m
Implementation of Unit Testing5m
Difference Between Yield and Return3m
Number of Runs for a Unit Test3m
Unit Test Scenarios3m
Additional Reading5m
- Implementation of a Trading SystemIn this section, you will learn about the points that you should keep in mind before you begin with the development of a trading system. You will also learn about the components and structure of the system and how it has to be started.
Prerequisites for Implementing a Trading System3m
Questions to Ask3m
Developing Strategy Models3m
Stopping the Program3m
Architecture and Start-Up3m 5s
Mock Exchange3m
Portfolio Manager3m
Starting Up the System3m
Mock Exchange Servers3m
Pooling Option3m
Test on Designing and Implementation of Trading System18m
- Types of ServersIn this section, you will learn about the types of servers involved in a trading system, such as the data server, trading server, and execution server. You will also learn how these servers communicate and work with each other.Data Server and Execution Server2m
Data Server3m
Subscriber and Data Source3m
PUB/SUB Pattern3m
Instrument Name3m
Execution Server3m
Price Data3m
Auto-correlation3m
Trading Server2m
Class for Trading Server3m
ZeroMQ Sockets3m
REQ/REP Pattern3m
Trading, Data, and Execution Servers3m
Data Sockets3m
Data and Execution Server5m
Trading Server5m
Mock Exchange5m
Startup5m
- Trading LogicThis section talks about two important classes, trading logic, which contains a set of functions directly related to the trading actions and signals, which is the centrepiece of the entire platform and is responsible for signal generation.Trading Logic5m
Run Function3m
Portfolio Manager3m
Price Difference3m
Signals5m
Optimisation3m
Trading Logic, Portfolio Manager and Signals5m
Test on Servers and Trading Logic14m
- Testing and OperationIn this section, you will learn about the importance of testable code. You will also learn about the operational challenges one can face while running this kind of architecture, and how to overcome them.Testing and Operation5m
Testing5m
- Capstone ProjectIn this section, you will apply the knowledge you have gained in the course. You will pick up a capstone project where you will combine a range of Alphas.
- Run Codes Locally on Your MachineIn 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 Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips for Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting for Running Files in Downloadable Section10m
- SummaryThis section includes a course summary and downloadable zipped folder with all the codes and notebooks for easy access.Summary5m 6s
Course Summary and Next Steps5m
Python Codes and Data2m
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about author
Dr. Thomas Starke
Dr Thomas Starke is the CEO of the financial consultancy firm AAAQuants. With a remarkable career spanning working with Boronia Capital, Vivienne Court Trading and Rolls-Royce, he has worked on the development of high-frequency stat-arb strategies for index futures and AI-based sentiment strategy. As an academic, he was a senior research fellow and lecturer at Oxford University. A tech aficionado, he takes a keen interest in new technologies such as AI, quantum computing and blockchain. He holds a PhD in Physics from Nottingham University (UK).
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Reviews
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- Sergei Belov
United StatesGood overview of existing literature for combining alphas and an especially good section on execution. - Tan Kwan Hong
Senior Professor,SingaporeExcellent! Great knowledge! - Dylan Knights
CanadaMy favourite course so far. Many ideas and concepts that I look forward to experimenting with. - Artur Barreiros
Professor at Instituto Superior Técnico,PortugalExcellent and unique content. - Dileep Kumar
Senior Physiotherapist,IndiaGreat to learn the concepts about Alpha. - Donald Yang
Hong KongGood class and Good to learn.
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Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.