Trading Alphas: Mining, Optimisation, and System Design
1904 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 Limited Time Offer
₹14300/-₹57199/-
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- 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
Autocorrelation5m
Trading Signals5m
Why Did the Signal Fail?5m
Additional Reading on Micro Alphas2m
- 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 Curve5m
Positive Auto-Correlation5m
Positively Correlated Time Series5m
Equation for Auto-Correlation5m
Value of g5m
Strategy for Positive Correlation5m
Types of Backtesting5m
Compounded PnL Curve5m
Auto-Correlation5m
Trending Prices5m
Series of Returns5m
Trend5m
Generate Random Returns5m
Linearly Fit the Autocorrelated Data5m
Backtest the Strategy5m
Additional Reading on Trends2m
- 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 Characteristic5m
Constant g5m
Type of Time Series5m
Correlation of Returns5m
Strategy and Benchmark Returns5m
Strategy Based on Correlation5m
Ideal Metric5m
Annualised Alpha5m
Generate Negatively Autocorrelated Returns5m
Additional Reading on Mean Reversion2m
- 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 Patterns5m
Values of a Candlestick Pattern5m
Backtesting5m
Library for Candlestick Pattern5m
Candlestick Pattern for Micro-Alpha5m
Usefulness of Alpha5m
Equity Asset Returns5m
Chart Patterns5m
Extract the Chart Pattern Function5m
Chart Pattern Signals5m
Calculate Signals5m
Capital Allocation5m
- 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
Correlation5m
Usage of Correlation5m
Cross-Sectional Correlation5m
Fundamental Inefficiencies5m
Inference for Correlation5m
Insider Information5m
Trading View based on Analyst Forecasts5m
Golf and a Company's Performance5m
Correlation5m
Calculate Average Correlation5m
Additional Reading on Correlation2m
- 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 Trading5m
Predictive Model5m
Trading the Spread Curve5m
Spread Strategy Code5m
Cash-Neutral Strategy5m
Cointegration5m
Hedge Ratio5m
Cointegration5m
Create a Spread5m
Additional Reading on Cointegration2m
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 Alpha5m
Time Series Alpha5m
Types of Alpha5m
Problem with Independent Signals5m
Number of Signals5m
Positions for Time-Series Alpha5m
Trading Logic5m
RSI Less than 405m
Shift Returns5m
PnL Curves5m
Strategy vs Benchmark5m
Factor in Time-Series Alpha Calculation5m
RSI Strategy Logic2m
Implementation of RSI Based Trading Strategy5m
Calculate RSI5m
Generate Signals Using RSI5m
Calculate Portfolio Returns5m
Additional Reading on Time Series Alphas2m
- 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 Quantra2m
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 RSI2m
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 Attribute5m
Axis for Cross-Sectional Alpha5m
Arrange in Order5m
Indicator for Trading Signals5m
Sum of Rows5m
Two Ranks5m
Cross-Sectional Approach5m
Cross-Sectional Momentum Strategy Logic2m
Cross-Sectional Momentum Strategy5m
Calculate Momentum5m
Backtest Cross-Sectional Momentum Strategy5m
Calculate PnL5m
Additional Reading on Cross-Sectional Alphas2m
Paper/Live Trade Using Cross Sectional Alpha2m
- 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 Alpha5m
Daytime vs Overnight Returns5m
Persistent Overnight Returns5m
MA Weekday Strategy5m
Advantages of Weekday Strategy5m
Important Timing Events5m
Timing Alphas5m
Calculate Overnight Returns5m
Inference of Cumulative Returns Plot5m
Use of Timing of Alphas5m
Additional Reading on Timing Alphas2m
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 Alpha5m
Alpha Combinations-I5m
Annualised Volatility5m
Alpha Combinations-II5m
Volatility Strategy5m
Upper and Lower Limit5m
Upper and Lower Limit Inference5m
Volatility Based Trading Strategy5m
Calculate Volatility of Stock Returns5m
Backtest Volatility Based Trading Strategy with Lower Limit5m
Additional Reading on Combinations of Alphas2m
Things to Keep In Mind While Combining Strategies4m 15s
Paper/Live Trade Using Volatility2m
- 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 Ideas5m
Alpha #3 Factor5m
Alpha #3 and Alpha #575m
Ranking RSI Values5m
Micro-Alphas From 101 Formulaic Alphas5m
Calculate Alpha #65m
Additional Reading on Finding Micro-Alphas2m
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-Alphas5m
Combination of Alphas5m
Number of Metrics5m
Utility of Sharpe Ratio5m
Strategy Performance5m
Additional Reading on Assessing Results2m
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 Profit5m
Features of Total Profit5m
Differences Between PnL Curves5m
Which Strategy is Riskier?5m
Reinvestment of Profits5m
Realised vs Unrealised PnL5m
Drawbacks of Realised PnL5m
Drawbacks of Total PnL5m
Information Provided by PnLs5m
Limitations of Total Profit5m
Strategy Comparison5m
Impact of Compounded PnL5m
Realised Vs Unrealised Profits5m
Realised PnL of a Strategy5m
Additional Reading on Total Profit2m
- 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 Returns5m
Calculate Sharpe Ratio5m
Risk-Free Rate5m
Exclude Risk-Free Rate5m
Drawbacks of Sharpe Ratio5m
Sortino Ratio5m
Sharpe and Sortino Ratios using Python5m
Implement the Sharpe Ratio5m
Implement the Sortino Ratio5m
Additional Reading on Sharpe and Sortino Ratios2m
- 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 Factor5m
Compare the Profit Factor5m
Drawdown of a Strategy5m
Drawdown Calculation5m
Maximum Drawdown Comparison5m
Profit Factor and Drawdown using Python5m
Implement Profit Factor5m
Additional Reading on Profit Factor and Drawdown2m
- 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 Trade5m
Computing Profit Per Trade5m
Profit Per Trade5m
Additional Reading on Profit Per Trade2m
- 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?5m
Annualise the Sharpe Ratio5m
Evaluate the Skill of a Money Manager5m
Initial Backtest5m
CAGR, Alpha and Beta5m
Additional Reading on CAGR, Alpha and Beta2m
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 Assumptions5m
Shortcomings of Execution on Close5m
Executing Large Quantities5m
Slippage5m
Limitation of Market-on-Close Order5m
Arrival Price Algorithm4m 20s
Execution on the Open5m
Order Type for Arrival Price Algorithm5m
Sources of Transaction Costs5m
Additional Reading on Strategy Execution2m
- 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 Management5m
Micro-Alpha Approach5m
Alphas5m
Combining Alphas - I5m
Generating Signals1m 48s
Combining All Micro-Alphas5m
Paper/Live Trade by Combining Micro-Alphas2m
- 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 Weights5m
Equal Portfolio Weights5m
Efficient Frontier5m
Optimisation5m
Additional Reading2m
- 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 Values5m
Parameter Optimisation5m
Simpson's Paradox5m
Sharpe Ratios5m
Lookback Periods5m
Clustering Algorithms - I5m
Clustering Algorithms - II5m
SPP5m
Additional Reading - I2m
Additional Reading - II2m
- Machine Learning AlphasIn this section, you will learn about machine learning alphas.Machine Learning Alphas1m 58s
Classification5m
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 Signals5m
Number of Winning and Losing Trades5m
Reason for High Number of Losing Trades5m
Advantage of Stop-loss and Profit-take5m
Implementation of Profit-take and Stop-loss5m
Conversion of Long-Short to Long-Only Signals5m
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 Returns5m
Additional Reading2m
- 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 Signals5m
Identification of Entry and Exit Points5m
Inference After Difference of Consecutive Signals5m
Code of Entry Date5m
Region Between Profit Take and Original Signal5m
Individual Trade Profit and Loss5m
Impact of Profit-take and Stop-loss Filters5m
Time Difference Before and After Application of PT/SL Filters5m
Vectorized Backtest with Profit-Takes and Stop-Loss5m
Additional Reading2m
- 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 Backtest5m
Technique to Increase Strategy Returns5m
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 System5m
Paradigms for Creation of a Trading System5m
Build a Trading System5m
Parallel Computing2m 45s
Methods to Implement Parallel Computing5m
GIL and Parallel Computing5m
Effect of Multi-threading on Single Core5m
Data Appended to Queue5m
Market Data Reading5m
Threads with Different Idle Times5m
Execution of Parallel Threads5m
Output of Parallel Threads Process5m
Implementation of Parallel Computing5m
Output for Thread in Parallel Processes5m
Resource Sharing Between Threads5m
Data Structure Shared Between Threads5m
Initialisation of Threads5m
Calculate Square of Datapoints Using Threads5m
Additional Reading2m
- 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 Computing5m
Difference Between Concurrency and Multi-threading5m
Property of Asynchronous Loops5m
Python Keywords for Concurrency5m
Asynchronous Recursion5m
Function of Sleep in Asyncio Package5m
Implementation of Asynchronous Recursion5m
Use of Concurrency5m
Implementation of Asynchronous Computing5m
Difference Between Asynchronous Computing and Threading5m
Keyword to Run Method in Asynchronous Fashion5m
Purpose of Await Keyword5m
Additional Reading2m
- 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 Python5m
Python Packages for Distributed Computing5m
Request Based Client Server Module5m
Subscriber Based Client Server Module5m
Acknowledgement of Order Sent in Trading System5m
Limitation of Message Queue5m
Distributed Computing5m
Additional Reading2m
- 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 Logging5m
Level of Logging5m
Difference in Logging Levels During Backtesting and Live Trading5m
Levels of Logging5m
Set Logging Level5m
Configuration of Logger in Notebook5m
Modification of Logger in Notebook5m
Logging for a Trading System5m
Log Messages at Critical Level5m
Conversion of Raw Data5m
Reason for Storage in Raw Data Format5m
Conversion of Raw Data at Regular Intervals5m
Additional Reading2m
- 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 Hardware5m
Selection of Cost-effective Hardware5m
Selection of Hardware Based on Latency Requirements5m
Selection of Hardware for Multiple Strategies5m
Additional Reading2m
- 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 Sectors5m
Combinations of Micro-Alphas Weights and Industry5m
Separation of Tasks as Micro-Services5m
Order of Task Performance5m
Advantage of Task Separation in Trading System5m
Advantage of Linux OS5m
Presence of GUI and Trading Systems5m
Selection of Operating System5m
- 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 Failures5m
Importance of Unit Tests in Trading Systems5m
Tools for Version Control5m
Implementation of Unit Testing5m
Difference Between Yield and Return5m
Number of Runs for a Unit Test5m
Unit Test Scenarios5m
Additional Reading2m
- 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 Ask5m
Developing Strategy Models5m
Stopping the Program5m
Architecture and Start-Up3m 5s
Mock Exchange5m
Portfolio Manager5m
Starting Up the System5m
Mock Exchange Servers5m
Pooling Option5m
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 Server5m
Subscriber and Data Source5m
PUB/SUB Pattern5m
Instrument Name5m
Execution Server5m
Price Data5m
Auto-correlation5m
Trading Server2m
Class for Trading Server5m
ZeroMQ Sockets5m
REQ/REP Pattern5m
Trading, Data, and Execution Servers5m
Data Sockets5m
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 Logic2m
Run Function5m
Portfolio Manager5m
Price Difference5m
Signals2m
Optimisation5m
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 Operation2m
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 Steps2m
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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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.
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Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
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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.
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- 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.
₹14300
₹57199
