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MEAN REVERSION STRATEGIES IN PYTHON BY DR. ERNEST P. CHAN(Level: Advanced, Duration: 5 hours)Course Objectives:This course will enable you to: Test for stationarity of a single price series and test for cointegration of two priceseries Create your own mean reversion trading strategies and understand practicalproblems encountered in live trading Create an index arbitrage strategy and difficulties which you need to consider whileimplementing index arbitrage strategy Form a long-short portfolio strategy and how to refine it Understand the role of stop loss while implementing mean reversion strategies Code all the learnt strategies in Python language Implement different statistical tools for checking stationarity and cointegration of aportfolio of instrumentsCourse details:Section 1: Stationarity of time seriesLearn the concept of stationarity, how to test stationarity using augmented dickey fuller [adf]test. Includes the implementation of a mean reversal strategy using bollinger bands.Section 2: CointegrationLearn how to detect co-integrated price series using cointegrated augmented dickey fuller[cadf] test. Calculate hedge ratio using linear regression and practical implementation of amean reversion strategy using bollinger bands on gld-gdx pair in python.Section 3: TripletsLearn why sometimes cointegration between pairs breaks with the example of gld-gdx pairsand learn possible remedies of surviving a breakdown.Section 4: Half lifeUnderstand the concept of half-life of a mean reverting series along with its practicalapplication. Learn to compute the half-life of the gld-gdx spread.Section 5 & 6: Risk management and best markets to pair tradeLearn the strategy behind a stop loss in mean reversion strategies. Know which are the bestmarkets/pairs to trade, along with their pros and cons.Section 7: Index arbitrageUnderstand the concept behind index arbitrage and the difficulties in trading an indexarbitrage strategy.Section 8: Long short portfolioGet introduced to the concept of cross-sectional mean reversion and learn how it is differentfrom time series mean reversion.

NEURAL NETWORKS TRADING BY DR. ERNEST P. CHAN(Level: Advanced, Duration: 8 hours)Course ObjectivesThis course will enable you to: Identify trading opportunities from raw financial markets using neural networks anddeep learning Create automated strategies using those insights and prediction models Code artificial neural networks and deep neural networks using Sklearn and Keras Apply Recurrent Neural Networks and Long Short Term Memory (LSTM) model intrading Test these models on different data sets to optimize your prediction strategies Identify scenarios where these models perform best Install necessary softwares and run the Python strategy codes provided on yourpersonal machinesCourse DetailsSection 1: Neural Networks in TradingHow a neuron works; Forward propagation, Backward propagation; Prediction of next dayreturns using the Sklearn library in PythonSection 2: Deep Learning models in KerasUsing Keras to create deep learning models; Features of Keras: dense, activation, dropout,model checkpoint; Cross Entropy loss functionSection 3: Recurrent Neural networks for financial markets dataUnderstanding the vanishing & exploding gradients problem; Recurrent neural networks as asolution to that problem; continue coding in KerasSection 4: Long Short term memory modelSimplifying the working of the complex LSTM model; modelling it in Keras; and using it infinancial markets to predict the entries and exits of tradeSection 5: Hyperparameter tuning in KerasAutomating the hyperparameter tuning in Keras using Grid search and cross validationtechniques; understanding the different parameters which result in overfitting and poorresults in live markets

ADVANCED OPTIONS TRADING STRATEGIES IN PYTHON(Level: Advanced, Duration: 7 hours)Course ObjectivesThis course will enable you to: Learn dispersion trading in Python Predict option price using machine learning Study Exotic and compound options along with their valuation Manage portfolio risk using options Create scenario analysis to manage risk Learn derivation of Black Scholes Model using Binomial Trees Study derivation of Black Scholes Merton differential equation using Wiener Processand Ito’s LemmaCourse DetailsSection 1: IntroductionLearn essential mathematical concepts to derive options pricing models such as BinomialTrees. Learn Wiener Process and Ito’s Lemma to understand how Robert Merton expandedthe Black Scholes Model to Black Scholes Merton Model or BSM.Section 2: Dispersion TradingLearn a mean-reversion strategy on implied correlation known as Dispersion Trading. Anentire video lecture and an IPython notebook are dedicated to making you understand theworking and application of Dispersion Trading.Section 3: Machine LearningUnderstand how to predict the option price through decision tree classifier and practise itthrough interactive exercises which makes it easier to implement the same in your optionstrading.Section 4: Exotic OptionsLearn the concept and valuation of various exotic and compound options. Also learn riskmeasures such as VaR, Expected Shortfall, and learn how to code them in Python.Section 5: Risk ManagementLearn how Delta of a portfolio can be hedged to protect the portfolio's value from the changein the price of the underlying asset. Also, learn how to preserve the portfolio's value usingGamma Scalping and practise coding the same in Python. Additionally, learn how to make aportfolio Vega neutral.Section 6: Scenario AnalysisA video lecture on scenario analysis and how to use options to earn profits when majorevents impact the financial markets. .Enroll Here

ADVANCED ALGORITHMIC TRADING STRATEGIES IN PYTHON Learn to find alpha in financial markets using artificial intelligence and quantitative techniques. Get trained by experts such as Dr. Ernest P. Chan, with decades of trading experience. Learn to code and backtest your own trading strategy in Python

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