Trading Alphas: Mining, Optimisation, and System Design
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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
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty 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 Alphas
The 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 12sEfficient Market Hypothesis2mOverturn Efficient Market Hypothesis2mAutocorrelation2mAssumption of Technical Indicators2mHow to Use Jupyter Notebook?2m 5sGenerating Price Series at Random5mHow to Use Interactive Exercises?5mGenerate Random Numbers5mScaling5mGenerate Price Data5mStatistical Study on Randomly Generated Price Series5mAutocorrelation5mTrading Signals5mWhy Did the Signal Fail?5mAdditional Reading on Micro Alphas2mMarket Inefficiencies: Trend
By 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 Inefficiencies2mTrends3m 38sCompounded PnL Curve5mPositive Auto-Correlation5mPositively Correlated Time Series5mEquation for Auto-Correlation5mValue of g5mStrategy for Positive Correlation5mTypes of Backtesting5mCompounded PnL Curve5mAuto-Correlation5mTrending Prices5mSeries of Returns5mTrend5mGenerate Random Returns5mLinearly Fit the Autocorrelated Data5mBacktest the Strategy5mAdditional Reading on Trends2mMarket Inefficiencies: Mean Reversion
Is 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 25sMarket Characteristic5mConstant g5mType of Time Series5mCorrelation of Returns5mStrategy and Benchmark Returns5mStrategy Based on Correlation5mIdeal Metric5mAnnualised Alpha5mGenerate Negatively Autocorrelated Returns5mAdditional Reading on Mean Reversion2mTrading with Trends and Mean Reversion
In 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 Data5mCalculate Risk-Adjusted Returns5mMarket Inefficiencies: Chart Patterns
Chart 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 55sDefine Chart Patterns5mValues of a Candlestick Pattern5mBacktesting5mLibrary for Candlestick Pattern5mCandlestick Pattern for Micro-Alpha5mUsefulness of Alpha5mEquity Asset Returns5mChart Patterns5mExtract the Chart Pattern Function5mChart Pattern Signals5mCalculate Signals5mCapital Allocation5mMarket Inefficiencies: Correlation, Fundamental and Alternative
In 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 52sCorrelation5mUsage of Correlation5mCross-Sectional Correlation5mFundamental Inefficiencies5mInference for Correlation5mInsider Information5mTrading View based on Analyst Forecasts5mGolf and a Company's Performance5mCorrelation5mCalculate Average Correlation5mAdditional Reading on Correlation2mMarket Inefficiencies: Cointegration
Cointegration 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 39sAlternative Term for Pairs Trading5mPredictive Model5mTrading the Spread Curve5mSpread Strategy Code5mCash-Neutral Strategy5mCointegration5mHedge Ratio5mCointegration5mCreate a Spread5mAdditional Reading on Cointegration2mTypes of Market Inefficiencies2mTest 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 50sCategories of Alpha5mTime Series Alpha5mTypes of Alpha5mProblem with Independent Signals5mNumber of Signals5mPositions for Time-Series Alpha5mTrading Logic5mRSI Less than 405mShift Returns5mPnL Curves5mStrategy vs Benchmark5mFactor in Time-Series Alpha Calculation5mRSI Strategy Logic2mImplementation of RSI Based Trading Strategy5mCalculate RSI5mGenerate Signals Using RSI5mCalculate Portfolio Returns5mAdditional 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 Quantra2mSection Overview2m 19sLive Trading Overview2m 41sVectorised vs Event Driven2mProcess in Live Trading2mReal-Time Data Source2mBlueshift Code Structure2m 57sImportant API Methods10mSchedule Strategy Logic2mFetch Historical Data2mPlace Orders2mBacktest and Live Trade on Blueshift4m 5sAdditional Reading10mBlueshift 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 RSI2mFAQs 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 38sCommon Attribute5mAxis for Cross-Sectional Alpha5mArrange in Order5mIndicator for Trading Signals5mSum of Rows5mTwo Ranks5mCross-Sectional Approach5mCross-Sectional Momentum Strategy Logic2mCross-Sectional Momentum Strategy5mCalculate Momentum5mBacktest Cross-Sectional Momentum Strategy5mCalculate PnL5mAdditional Reading on Cross-Sectional Alphas2mPaper/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 18sSource of Alpha5mDaytime vs Overnight Returns5mPersistent Overnight Returns5mMA Weekday Strategy5mAdvantages of Weekday Strategy5mImportant Timing Events5mTiming Alphas5mCalculate Overnight Returns5mInference of Cumulative Returns Plot5mUse of Timing of Alphas5mAdditional Reading on Timing Alphas2mMost Suitable Alpha3m 52s
Combinations of Alpha
Is 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 35sCombinations of Alpha5mAlpha Combinations-I5mAnnualised Volatility5mAlpha Combinations-II5mVolatility Strategy5mUpper and Lower Limit5mUpper and Lower Limit Inference5mVolatility Based Trading Strategy5mCalculate Volatility of Stock Returns5mBacktest Volatility Based Trading Strategy with Lower Limit5mAdditional Reading on Combinations of Alphas2mThings to Keep In Mind While Combining Strategies4m 15sPaper/Live Trade Using Volatility2mFinding Micro-Alphas
For 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 19sOther’s Ideas5mAlpha #3 Factor5mAlpha #3 and Alpha #575mRanking RSI Values5mMicro-Alphas From 101 Formulaic Alphas5mCalculate Alpha #65mAdditional Reading on Finding Micro-Alphas2mTest 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 19sPrerequisite for Finding Micro-Alphas5mCombination of Alphas5mNumber of Metrics5mUtility of Sharpe Ratio5mStrategy Performance5mAdditional Reading on Assessing Results2mMost 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 4sCharacteristics of Total Profit5mFeatures of Total Profit5mDifferences Between PnL Curves5mWhich Strategy is Riskier?5mReinvestment of Profits5mRealised vs Unrealised PnL5mDrawbacks of Realised PnL5mDrawbacks of Total PnL5mInformation Provided by PnLs5mLimitations of Total Profit5mStrategy Comparison5mImpact of Compounded PnL5mRealised Vs Unrealised Profits5mRealised PnL of a Strategy5mAdditional 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 33sRisk-Adjusted Returns5mCalculate Sharpe Ratio5mRisk-Free Rate5mExclude Risk-Free Rate5mDrawbacks of Sharpe Ratio5mSortino Ratio5mSharpe and Sortino Ratios using Python5mImplement the Sharpe Ratio5mImplement the Sortino Ratio5mAdditional 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 9sProfit Factor5mCompare the Profit Factor5mDrawdown of a Strategy5mDrawdown Calculation5mMaximum Drawdown Comparison5mProfit Factor and Drawdown using Python5mImplement Profit Factor5mAdditional 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 17sApplication of Profit Per Trade5mComputing Profit Per Trade5mProfit Per Trade5mAdditional 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 30sCompounded or Non-compounded?5mAnnualise the Sharpe Ratio5mEvaluate the Skill of a Money Manager5mInitial Backtest5mCAGR, Alpha and Beta5mAdditional Reading on CAGR, Alpha and Beta2mTest 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 56sImplicit Assumptions5mShortcomings of Execution on Close5mExecuting Large Quantities5mSlippage5mLimitation of Market-on-Close Order5mArrival Price Algorithm4m 20sExecution on the Open5mOrder Type for Arrival Price Algorithm5mSources of Transaction Costs5mAdditional 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 39sTraditional Portfolio Management5mMicro-Alpha Approach5mAlphas5mCombining Alphas - I5mGenerating Signals1m 48sCombining All Micro-Alphas5mPaper/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 58sRebalance the Weights5mEqual Portfolio Weights5mEfficient Frontier5mOptimisation5mAdditional 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 4sTesting Robustness of Strategy5mSelecting Best Parameter Sets3m 30sFinding Best Parameter5mPossible Lookback Values5mParameter Optimisation5mSimpson's Paradox5mSharpe Ratios5mLookback Periods5mClustering Algorithms - I5mClustering Algorithms - II5mSPP5mAdditional Reading - I2mAdditional Reading - II2m
- Machine Learning AlphasIn this section, you will learn about machine learning alphas.Machine Learning Alphas1m 58sClassification5mML 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 34sFactors for Setting Exit Signals5mNumber of Winning and Losing Trades5mReason for High Number of Losing Trades5mAdvantage of Stop-loss and Profit-take5mImplementation of Profit-take and Stop-loss5mConversion of Long-Short to Long-Only Signals5mCreation of Vectorized Backtest5mCalculate the Moving Average Crossover5mGenerating Long-Short Trading Signal5mGenerating Long-Only Trading Signal5mCalculate the Cumulative Sum of Returns5mCalculation of Portfolio Returns5mAdditional 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 2sReplacement of Short Signals5mIdentification of Entry and Exit Points5mInference After Difference of Consecutive Signals5mCode of Entry Date5mRegion Between Profit Take and Original Signal5mIndividual Trade Profit and Loss5mImpact of Profit-take and Stop-loss Filters5mTime Difference Before and After Application of PT/SL Filters5mVectorized Backtest with Profit-Takes and Stop-Loss5mAdditional 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 17sUse Case of Vectorized Backtest5mTechnique to Increase Strategy Returns5mAnalysis After Application of PT and SL5mTest 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 40sSequential Order Based Trading System5mParadigms for Creation of a Trading System5mBuild a Trading System5mParallel Computing2m 45sMethods to Implement Parallel Computing5mGIL and Parallel Computing5mEffect of Multi-threading on Single Core5mData Appended to Queue5mMarket Data Reading5mThreads with Different Idle Times5mExecution of Parallel Threads5mOutput of Parallel Threads Process5mImplementation of Parallel Computing5mOutput for Thread in Parallel Processes5mResource Sharing Between Threads5mData Structure Shared Between Threads5mInitialisation of Threads5mCalculate Square of Datapoints Using Threads5mAdditional 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 51sPython Package for Asynchronous Computing5mDifference Between Concurrency and Multi-threading5mProperty of Asynchronous Loops5mPython Keywords for Concurrency5mAsynchronous Recursion5mFunction of Sleep in Asyncio Package5mImplementation of Asynchronous Recursion5mUse of Concurrency5mImplementation of Asynchronous Computing5mDifference Between Asynchronous Computing and Threading5mKeyword to Run Method in Asynchronous Fashion5mPurpose of Await Keyword5mAdditional 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 45sCommunication Between Systems and Programs Using Python5mPython Packages for Distributed Computing5mRequest Based Client Server Module5mSubscriber Based Client Server Module5mAcknowledgement of Order Sent in Trading System5mLimitation of Message Queue5mDistributed Computing5mAdditional 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 10sApplication of Logging5mLevel of Logging5mDifference in Logging Levels During Backtesting and Live Trading5mLevels of Logging5mSet Logging Level5mConfiguration of Logger in Notebook5mModification of Logger in Notebook5mLogging for a Trading System5mLog Messages at Critical Level5mConversion of Raw Data5mReason for Storage in Raw Data Format5mConversion of Raw Data at Regular Intervals5mAdditional 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 30sFocus During Selection of Hardware5mSelection of Cost-effective Hardware5mSelection of Hardware Based on Latency Requirements5mSelection of Hardware for Multiple Strategies5mAdditional 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 26sMicro-Alphas and Industry Sectors5mCombinations of Micro-Alphas Weights and Industry5mSeparation of Tasks as Micro-Services5mOrder of Task Performance5mAdvantage of Task Separation in Trading System5mAdvantage of Linux OS5mPresence of GUI and Trading Systems5mSelection 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 12sElimination of Unexpected Downstream Failures5mImportance of Unit Tests in Trading Systems5mTools for Version Control5mImplementation of Unit Testing5mDifference Between Yield and Return5mNumber of Runs for a Unit Test5mUnit Test Scenarios5mAdditional Reading2m
Implementation of a Trading System
In 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 System3mQuestions to Ask5mDeveloping Strategy Models5mStopping the Program5mArchitecture and Start-Up3m 5sMock Exchange5mPortfolio Manager5mStarting Up the System5mMock Exchange Servers5mPooling Option5mTest 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 Server2mData Server5mSubscriber and Data Source5mPUB/SUB Pattern5mInstrument Name5mExecution Server5mPrice Data5mAuto-correlation5mTrading Server2mClass for Trading Server5mZeroMQ Sockets5mREQ/REP Pattern5mTrading, Data, and Execution Servers5mData Sockets5mData and Execution Server5mTrading Server5mMock Exchange5mStartup5m
- 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 Logic2mRun Function5mPortfolio Manager5mPrice Difference5mSignals2mOptimisation5mTrading Logic, Portfolio Manager and Signals5mTest 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 Operation2mTesting5m
Capstone Project
In 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 59sFlow Diagram10mInstall Anaconda on Windows10mInstall Anaconda on Mac10mKnow your Current Environment2mTroubleshooting Anaconda Installation Problems10mCreating a Python Environment10mChanging Environments2mQuantra Environment2mTroubleshooting Tips for Setting Up Environment10mHow to Run Files in Downloadable Section?10mTroubleshooting 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 6sCourse Summary and Next Steps2mPython Codes and Data2m
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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.