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All course details below:

Matlab Econometrics Toolbox

Matlab Econometrics Toolbox

Module 1 | Econometrics Toolbox |

Econometrics Toolbox | |

Unit 1 | Algo course conclusion |

Unit 2 | Comparing GARCH fits in Matlab |

Unit 3 | Comparing GARCH fits in Matlab |

Unit 4 | Demo of complete GARCH workflow in estimation, forecasting, simulation, and analysis |

Unit 5 | Demo of random walk in Matlab |

Unit 6 | Estimating GARCH parameters in Matlab |

Unit 7 | Forecast Conditional Mean Response using ARIMA |

Unit 8 | Model construction with GARCH in Matlab |

Unit 9 | Using regression demo for fine tuning your estimating the markets |

Unit 10 | Comparing various GARCH parameters in Matlab |

Unit 11 | Demo of Unit Root testing for stationary time series in Matlab |

Unit 12 | Financial time series GUI tool client demo |

Unit 13 | How to infer residuals with GARCH or ARMAX in Matlab |

Unit 14 | Volatility Simulation with GARCH in Matlab |

Unit 15 | Here is an introductory video to how the R source code walkthroughs will work |

Unit 16 | Model section using GARCH / ARMAX in Matlab |

Unit 17 | Calculate max drawdown and expected max drawdown |

Matlab Financial Toolbox

Matlab Financial Toolbox

Module 1 | Financial Toolbox |

Financial Toolbox | |

Unit 1 | High Low Close and Bollinger Chart Demo |

Unit 2 | Matlab code model walkthrough demo of Mean Reverting, Maximum Likelihood,Ordinary Least Squares, Simple Regression, Greek Analysis |

Unit 3 | Performance metrics with Sharpe Ratio, risk adjusted return, Lower Partial Moments |

Unit 4 | Using ARMA in Matlab |

Unit 5 | Using regression demo for fine tuning your estimating the markets |

Unit 6 | Technical analysis demo with RSI, MACD, Williams %R, OBV |

Unit 7 | Time series demo with Matlab Finance Toolbox |

Unit 8 | Visual financial time series |

Matlab Toolboxes: Signal Processing, Stats, Math

Matlab Toolboxes: Signal Processing, Stats, Math

Module 1 | Signal Processing Toolbox |

Signal Processing Toolbox | |

Unit 1 | Anti-Causal, Zero-Phase Filter Implementation from Matlab Signal Processing |

Unit 2 | Cross correlation from signal processing toolbox |

Module 2 | Stats Toolbox |

Stats Toolbox | |

Unit 1 | Fitting copulas to data |

Unit 2 | ANOVA |

Unit 3 | MANOVA |

Unit 4 | Analysis of covariance tool |

Unit 5 | Cumulative density function with parametric and esimtating empirical cdf |

Unit 6 | Demo of dfittool for distribution fit GUI tool |

Unit 7 | Types of distributions |

Unit 8 | K Means Clustering |

Unit 9 | Markov Chains |

Unit 10 | Portable density function estimating with parameters or no paramaters |

Unit 11 | Princinple Component Analysis |

Module 3 | Math Toolbox |

Math Toolbox | |

Unit 1 | Summation (sum) Matlab sample code |

Unit 2 | Using eigenvector decomposition using eigenvalues or eigenvector |

Unit 3 | Factorization with Cholesky, LU, and DR |

Unit 4 | Fast Fourier Transform with Example of Basic Spectral Analysis |

Unit 5 | Interpolation |

Unit 6 | Ordinary Differential Equations with Single PDE and System of PDEs |

Matlab Strategy Development Demos and Researching WIth Simuilink

Matlab Strategy Development Demos and Researching With Simuilink
Code generation demos to C or C++ demos included
NOTE: This requires a further QuantLabs.net Premium Membership for link references http://quantlabs.net/membership.htm

Module 1 | Matlab Development Module |

Matlab Development Module | |

Unit 1 | Matlab Code Walkthru with Bayesian Analysis for a Logistic Regression Model |

Unit 2 | Demo of Matlab M Code Generation to CPP with Moving Average Algorithm with source code,video of Excel import of market data |

Unit 3 | How to import forex pair into Matlab workspace using Excel IQFeed and QCcollector |

Unit 4 | Video Walkthrough of first Matlab Simulink model with Stateflow and C++ Code Generation |

Unit 5 | Important examples of .NET C# testing examples to call Simulink code generated |

R Course with Technical Analysis

R Course with Technical Analysis

Module 1 | Technical Analysis in R |

Technical Analysis in R | |

Unit 1 | 30 day moving average function |

Unit 2 | 2 sided moving average for mean rolling window |

Unit 3 | R Code Walkthrough Improved Moving Average using intra day for Forex data |

Unit 4 | The improved moving average |

Unit 5 | R Code Wakthrough Simple Moving Averag Strategy with Volatility Filter |

Unit 6 | Love level Improved Moving Average functions with testing code |

Unit 7 | R source code for trading script with update portfolio, position size, MA, cross over, SMA, optimize parameters pt 2 |

Unit 8 | R source code for trading script including MACD, Omega performance, RSI, and Bollinger Band measuring strategy and portfolio performance with plots Pt 3 |

R Course with Quant including GARCH

R Course with Quant including GARCH

Module 1 | Quant trading in R |

Quant trading in R | |

Unit 1 | Walthrough Parallel R Model Prediction Building and Analytics |

Unit 2 | Intro to GARCH forecasting with various R packages |

Unit 3 | How to use GARCH for predict market movements |

Unit 4 | How to use GARCH to predict distributions |

Unit 5 | GARCH trading R script walkthrough with a rolling window |

R Course with Quant

R Course with Quant

Module 1 | Intro |

Intro | |

Unit 2 | An ARMA model R code walkthrough |

Unit 3 | Checklist of forecasting with ARIMA: is time series stationary, differentiate, ARIMA(p,d,q), and which AMRA model to use? |

Unit 4 | R code walkthrough: Detrend to use Auto ARIMA modelling and forecast with statistical data and Ljung BoxTest |

Unit 5 | My first version of ARIMA R script with Forex data and Equity 1 and 5 min frequency |

Unit 6 | Bayesian analysis to Compare algorithms with Gibbs |

Unit 7 | Markov Chain R source code walkthrough |

Unit 8 | Monte Carlo R Walkthrough Demo |

Unit 9 | An alternative to running a Monte Carlo simulation |

Unit 10 | R code walkthrough Mean Absolute Deviation with Efficiency Frontiers Demo |

R Course with Mean Reversion and Pair Trading

R Course with Mean Reversion and Pair Trading

Module 1 | Mean Reversion in R |

Mean Reversion in R | |

Unit 1 | Backtesting a Strategy with Mean Reversion |

Unit 2 | Mean Reversion Euler with Ornstein Uhlenbeck process |

Unit 3 | Pairs trading R source code walkthrough with mean reverting logic, spread and beta calculation |

Module 2 | Pair Trading in R |

Pair Trading in R | |

Unit 1 | Poor mans Pair Trading with Cointegration R Walkthrough |

Unit 2 | Pair trading with S&P 500 companies |

Unit 3 | Pairs trading with testing cointegration |

Unit 4 | Seasonal pair trading |

Unit 5 | Test for stationary in time series with null hypothesis test and p-value using Augmented Dickey Fuller |

Unit 6 | Pairs Trading R Code Walkthrough |

Unit 7 | Pairs trading with a Hedge Ratio Demo |

Unit 8 | R Code Walkthrough Back testing with trading pair with CAPM |

Unit 9 | Gold versus Fear in Cointegration test |

R Course with Arbitrage and Volatility

Arbitrage and Volatility

Module 1 | Arbitrage in R |

Arbitrage in R | |

Unit 1 | Beating a random walk with arbitrage |

Unit 2 | Beating a random walk with arbitrage |

Unit 3 | Time Based Arbitrage Opportunities in Tick Data: Why low latency is needed in HFT? |

Unit 4 | Building a currency graph with arbitrage |

Unit 5 | Arbitrage: Modelling returns with CAPM APT aka Abritrage Pricing Theory |

Unit 6 | Indian equity market index NIFTY anaysis with CAPM vs APT aribitrage pricing theory using PCA and moment analysis |

Module 2 | Volatility in R |

Volatility in R | |

Unit 1 | R Code Walkthrough Adding a volatility filter with VIX |

Unit 2 | R Code Wakthrough Simple Moving Averag Strategy with Volatility Filter |

Unit 3 | Mean Reverting with Volatility Spike |

Unit 4 | Trading with GARCH volatility R script walkthrough demo |

Unit 5 | Jeff Augen volatility spike code |

Quant Algorithm Course including Pair Trading, Arbitrage, Autoregressive,

Quant Algorithm Course including Pair Trading, Arbitrage, Autoregressive,

Module 1 | Arbitrage |

Arbitrage | |

Unit 1 | Event arbitrage using point forecasts, corporate news, and use within forex |

Unit 2 | Market neutral arbitrage using CAPM |

Unit 3 | Statistical arbitrage in high frequency setting Mathematical Foundation |

Unit 4 | Uncovered interest parity arbitrage |

Unit 5 | Liquidity arbitrage |

Module 2 | Pair Trading |

Pair Trading | |

Unit 1 | Cointegration based test on Market efficiency |

Unit 2 | Cointegration with Engle and Granger Test and error correction model |

Module 3 | Autoregressive |

Autoregressive | |

Unit 1 | Autoregressive (AR) estimation models |

Unit 2 | Autocorrelation with t-ratio and Ljung Box tests |

Module 4 | Quant Misc |

Quant Misc | |

Unit 1 | Non linear models with Brownian Motion |

Unit 2 | Nonparametric Estimation of Nonlinear Models |

Unit 3 | Orders Used in Microstructure Trading with Illiquid ratio Amihud |

Unit 4 | Probability of observing exactly k arrivals |

Unit 5 | Random walk theory for market inefficiency |

Unit 6 | Working with tick data for Bid ask spread |

Unit 7 | Core portfolio optimization framework |

Unit 8 | Volatility modelling |

Quant Algorithm Course with Backtesting and Measurement

Quant Algorithm Course with Backtesting and Measurement

Module 1 | Backtesting |

Backtesting | |

Unit 1 | Back-testing trading models with evaluating point forecasts |

Module 2 | Measurement |

Measurement | |

Unit 1 | Executing and monitoring high frequency trading with market aggressiveness selection |

Unit 2 | Market impact costs |

Unit 3 | Maximum number of intraday Sharpe ratio |

Unit 4 | Measuring credit and Counterparty risk |

Unit 5 | Measuring credit and Counterparty risk |

Unit 6 | Measuring Market Risk with Risk Management |

Unit 7 | Information based impact |

Unit 8 | Performance attribution also known as benchmarking |

Unit 9 | Profitability in limit orders |

Unit 10 | Portfolio optimization in the presence of transaction costs |

Unit 11 | Sharpe ratio from Chapter 5 |

Module 3 | Simpler Algo |

Simpler Algo | |

Unit 1 | Simple returns, log return, and average returns |

Unit 2 | Skewness, Kurtosis (fat tail analysis), Volatility is variance of log or simple returns |

Unit 3 | Periodic or simple rate of return |

Open Source Trading Platform Development

Open Source Trading Platform Development
This includes QuantLib, QuantLibXL, and Tradelink platform software. All are open source where QuantLib is developed in C++. Tradelink is developed in Microsoft .NET with C#

Module 1 | Open Source Trading Platform Development Module |

Open Source Trading Platform Development | |

Unit 1 | QuantLib and QuantLibxL Course |

Unit 2 | High Frequency Trading (HFT) Course with Open Source Tradelink |