Available only $72, Mean Reversion Strategies In Python – Dr. Ernest P. Chan-Quantinsti Course

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Offered by Dr. Ernest P Chan, this course will teach you to identify trading opportunities based on Mean Reversion theory. You will create different mean reversion strategies such as Index Arbitrage, Long-short portfolio using market data and advanced statistical concepts. A must-do course for quant traders.nnAPPLY MEAN REVERSION STRATEGIESnnCreate four different types of mean reverting strategiesnPerform statistical test for identifying stationarity and co-integrationnBacktest pairs trading, triplets, index arbitrage and long-short strategynExplain the role of risk managementnPaper trade and live trade your strategies without any installations or downloadsnnIntroduction to the CoursennThis section gives an overview of the mean reversion strategy through examples. You will go through the course structure and understand how the course is structured in videos, quizzes, strategy codes and interactive coding exercises. This will make sure that not only do you understand the mechanics of mean reversion but also implement trading strategies in live markets.nnIntroduction by Dr. Ernest ChannnIntroduction to Mean Reversion StrategynnCourse Structure Flow DiagramnnQuantra Features and GuidancennTypes of Statistical Arbitrage StrategiesnnStationarity of Time SeriesnnStationary is one of the essential concepts upon which pairs trading and other cointegrated trading is built. This section discusses the concept of stationary through real price data and how it is different from the random walk.nnWhat is Stationarity?nnMean Reversion Trading ApproachnnTemporary Mean ReversionnnStationaritynnStatistical Test for StationaritynnAugmented Dickey-Fuller TestnnThis section covers the concept of the augmented dickey-fuller (ADF) test, which is used to check whether the price series is stationary or not. You will learn the mathematics behind the ADF test. You will also learn to check the stationarity of currency pairs in Python.nnPre Reading MaterialsnnWhat is ADF Test?nnPurpose of ADF TestnnADF Test AssumptionnnNull HypothesisnnStationary Price SeriesnnMath Behind ADF Test (Optional)nnCritical Value and Test StatisticsnnHow to Use Jupyter Notebook?nnADF Test on CADUSD PairnnCalculate Test StatisticsnnImport Library and Read CSVnnAdditional ReadingnnFrequently Asked QuestionsnnMean Reversion StrategynnIn this section, you will learn to create and backtest a trading strategy based on the concept of mean reversion. You will learn to use the Bollinger Bands to create a mean reversion strategy on a currency pair.nnMean Reversion StrategynnUpper BandnnTrading Based on Mean ReversionnnMean Reversion Strategy on AUDCADnnCalculate Moving Average and Standard DeviationnnUpper and Lower BandnnLong Entry and ExitnnShort Entry and ExitnnLong and Short PositionsnnForward Fill Missing PositionsnnConsolidate the PositionsnnCompute PnLnnRecapnnFrequently Asked QuestionsnnTest on Mean ReversionnnLive Trading on BlueshiftnnThis section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.nnSection OverviewnnLive Trading OverviewnnVectorised vs Event DrivennnProcess in Live TradingnnReal-Time Data SourcennBlueshift Code StructurennImportant API MethodsnnSchedule Strategy LogicnnFetch Historical DatannPlace OrdersnnBacktest and Live Trade on BlueshiftnnAdditional ReadingnnBlueshift Data FAQsnnLive Trading TemplatennIn this section, a live trading strategy template will be provided to you. The template strategy will be on the mean reversion strategy covered in the previous section. You can tweak the code by changing different currency pairs, date range to backtest and finally analyse the strategy performance in more detail.nnPaper/Live Trading FX Mean Reversion StrategynnFAQs for Live Trading on BlueshiftnnCointegrationnnFree PreviewnnIf a linear combination of two or more price series is stationary, then the individual price series are said to be cointegrated with each other. This section introduces cointegration between two-time series and covers a test for detecting cointegration of a portfolio of instruments called the cointegrated augmented Dickey-Fuller (CADF) test.nnWhat is Cointegration?nnCointegrationnnCorrelationnnWhat is Hedge Ratio?nnPortfolio Formation Using Hedge RationnHedge Ratio CodennImport LibrarynnCalculate Hedge RationnWhat is CADF Test?nnCheck Cointegration using CADF TestnnOrder Dependence of CADFnnPairs TradingnnMost financial instruments are not stationary, and creating a mean reversion strategy is not possible on such a price series. To overcome this issue, you need two price series which are cointegrated with each other. In this section, you will learn to create a pairs trading strategy using the Bollinger Bands. You will also learn to backtest the same in Python.nnMean Reversion Strategy on PairsnnMean Reversion Strategy on GLD-GDXnnTake Long Entry and ExitnnCompute Strategy PnLnnPaper/Live Trading Pair Trading StrategynnAdditional ReadingnnRecapnnMean Reversion StrategynnTripletsnnThis section discusses failure of the mean reversion strategy of the GLD-GDX pair. Based on the possible reason we will arrive at the conclusion of choosing a triplet to improve the mean reversion strategy. The working of Johansen test will be explained to arrive at the hedge ratios for the new mean reversion strategy for triplets. This section also covers the concept of half-life of mean reversion along with Ornstein-Uhlenbeck formula for computing the half-life of mean reversion.nnCointegration Breakdown in the GLD-GDX PairnnReason of Breakdown of CointegrationnnSignificance of CointegrationnnSurviving Breakdown of CointegrationnnHow to Survive Breakdown of Cointegration?nnBreakdown RemediesnnOptimization ProblemsnnEigenvalues and EigenvectorsnnWhat is Johansen Test?nnCADF ShortcomingsnnLinear CombinationnnGLD-GDX Cointegration TestnnMean Reversion on TripletsnnMean Reversion on Triplets CodennGLD-GDX-USO Cointegration TestnnCointegration TestnnTaking PositionsnnRecapnnFrequently Asked QuestionsnnHalf LifennThis section explains how long would it it take for the time series to revert back to the mean. And the importance of half-life to select the right instruments to trade in.nnHalf Life of Mean Reverting Time SeriesnnHalf LifennHalf Life FormulannHalf-Life Using Johansen TestnnCompute Half-Life of GLD-GDXnnFrequently Asked QuestionsnnRisk ManagementnnThis section explains the importance and two common usages of stop loss in mean reverting strategies. Further, you will learn to backtest mean-reverting strategy with and without stop and compare the performance of the strategy.nnStop-LossnnMean Reversion Strategy With Stop LossnnBest Markets to Pair TradennThis section explains the pros and cons of mean reversion strategies in different markets such as exchange traded funds (ETFs), stocks, currencies, and futures. Further, in the section, will understand how economically related pairs do not co-integrate, cover the basic concept of crack spread and test for stationarity of crack spread.nnBest Markets To Pair TradennMean Reversion of ETF PairsnnMean Reversion of Stock PairsnnMean Reversion of Currencies and FuturesnnCointegration Test of CL and BZnnCointegration Test of Crack SpreadnnIdentify Cointegrated Stock PairsnnIndex ArbitragennThis section explains Index Arbitrage Strategy which is an extension of pairs and triplets, how to construct a basket of instruments and see the difficulties of trading an Index Arbitrage strategy.nnIndex Arbitrage StrategynnWorking of Index Arbitrage StrategynnCustom BasketnnIndex Arbitrage Strategy CodennDifficulties in Index ArbitragennLong Short PortfolionnThis section explains the concept of long-short portfolio strategy, how it is different from other mean reversion strategies. Further, will construct a long-short portfolio of stocks in the S&P 500, understand the importance of universe selection of stocks on strategy and learn to refine a strategy.nnLong-Short portfolio StrategynnLong-Short PortfolionnStrategy FormulannLong-Short Portfolio Strategy CodennCalculate Stock ReturnsnnCalculate Market ReturnsnnCalculate Dollar Allocation for Each StocknnCalculate Sharpe RationnPaper/Live Trading Long-Short StrategynnAnalysis of Strategy PerformancennTest on Trading Based on Mean ReversionnnRun Codes Locally on Your MachinennLearn to install the Python environment in your local machine.nnPython Installation OverviewnnFlow DiagramnnInstall Anaconda on WindowsnnInstall Anaconda on MacnnKnow your Current EnvironmentnnTroubleshooting Anaconda Installation ProblemsnnCreating a Python EnvironmentnnChanging EnvironmentsnnQuantra EnvironmentnnTroubleshooting Tips For Setting Up EnvironmentnnHow to Run Files in Downloadable Section?nnTroubleshooting For Running Files in Downloadable SectionnnAutomated Trading Using IBridgePynnA live trading strategy template will be provided to you. You can tweak the template to deploy your strategies on Interactive Brokers using IBridgePy API.nnAdditional ReadingnnSample Strategy to Run on Interactive BrokersnnSummarynnCourse SummarynnPython Codes and DatannTag: Mean Reversion Strategies In Python – Dr. Ernest P. Chan-Quantinsti Download, Mean Reversion Strategies In Python – Dr. Ernest P. Chan-Quantinsti review, Mean Reversion Strategies In Python – Dr. Ernest P. Chan-Quantinsti Discount, mean reversion strategies, short term mean reversion strategies, mean reversion strategies trading, best mean reversion strategies, mean reversion strategies in python 

Trading foreign exchange and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance does not guarantee future results.