All courses fdfd
All courses fdfd
All courses fdfd
Access to all courses
These courses are included which is all part of my Elite membership:
All access courses for quant analysis trading and custom platforms
These courses are included which is all part of my Elite membership:
Module 1 VISUALIZATION FOR CUSTOM TRADING PLATFORM MODULE
Unit 1 VISUALIZATION FOR TRADING WORKSHOP PRESENTATION
Custom Trading Course
This is our first version of software components that enable you to: 1. real time data capture via IQFeed 2. ability to build trading strategy logic 3. execute market orders via Interactive Brokers TWS 4. learn various options of different databases with CEP option IMPORTANT NOTE: Each module has an introduction unit (#1) with long videos which demo the remaining units within that module.
Module 1 Market Data Real Time Capture and Historical Backtesting
This module demos: 3rd Party Software for Rapid Historical and Tick Data Storage Open Source Library for Queuing My Architecture Note: All C# demos using Visual Studio 2012 Ultimate Q&A NOTE: Coding is ever evolving so this stuff is never final but works at this time
Unit 1 Market Data Real Time Capture and Historical Backtesting Intro
Unit 2 IQFeed for Real time data capture and backtesting
Unit 3 Third party application for housing real time and historical market data
Unit 4 Open source library for queuing and messaging between software component
Unit 5 My Trading Platform Architecture with Demos
Module 2 Interacting with Interactive Brokers Trader Workstation TWS for automated trading
This module covers: Using Visual Studio 2012 Ultimate for C# and C++ demos 1. Interactive Brokers (IB) Trader Workstation (TWS) 2. 3rd party library interaction to IB API 3. Trading Platform demo Using Visual Studio 2012 Ultimate for C# and C++ demos
Unit 1 Intro to Interacting with Interactive Brokers Trader Workstation TWS for automated trading
Unit 2 Interactive Brokers TWS demo and setup
Unit 3 Interactive Brokers API interfacing to multiple languages with batch testing
Unit 4 Trading Platform Demos
Module 3 Extending Matlab Into a Custom Trading Platform
Extending MATLAB If you are interested in Matlab Algorithm Demos, please refer to our other Matlab courses. Not here to show you how to use MATLAB but will cover: Mathworks Webinars to Showcase Automated and Algorithmic Based Trading Systems Matlab trick ideas converting from research paper to Matlab M script Demo of Matlab toolbox talking with .NET C# program with least square Matlab Coder to C++ All demos use Matlab 2013a and Visual Studio 2010/2012 Ultimate
Unit 1 Extending MATLAB for automated trading
Unit 2 Mathworks Webinars to Showcase Automated and Algorithmic Based Trading Systems
Unit 3 MuPAD from research paper to Matlab M script
Unit 4 Demo of Matlab Builder NE talking with DotNet C# program with least square
Unit 5 Matlab Coder to C++ or C
Unit 6 Other Options …But Wait There’s More!
Module 4 MATLAB Simulink Visual Model Development with C++ and FPGA HDL deployment
This module specifically covers: Simulink Model Demo Matlab Simulink FPGA webinar series (for ultra lowest latency HFT) Simulink Model generation to Code Generation to C++
Unit 1 Intro to MATLAB Simulink Visual Model Development with C++ and FPGA HDL deployment
Unit 2 Simulink Model Demo
Unit 3 Matlab Simulink FPGA webinar series (for ultra lowest latency HFT)
Unit 4 Simulink Model generation to Code Generation to C++
Module 5 Database use for Trading Coursewith MYSQL, NOSQL, SQL Server with T-SQL Schema and CEP uses in DotNet CSharp code
Outline : Database use for Trading Coursewith MYSQL, NOSQL, SQL Server with T-SQL Schema and CEP uses in DotNet CSharp code Video Demos with working .Net and T-SQL code MySQL integration with R, Matlab, .NET, and C++ NOSQL R and Cassandra MongoDB Redis SQL Server SQL Server Market Data with scheme T-SQL code SQL Server StreamInsight for CEP
Unit 1 Intro Video Demo of SQL Database for Algo Trading with HFT Potential with SQL Server MYSQL NOSQL MongoDB Redis Cassandra R Java
Unit 2 MYSQL (free open source) with Matlab, R, .NET, and C++
Unit 3 Open Source NOSQL database options with MongoDB, Big Data, R, Cassandra, Redis, and even ActiveQuant
Unit 4 SQL Server with database schema in T-SQL, StreamInsights for CEP and source code
OPEN SOURCE TRADING PLATFORM AND API TRADING
Open Source Trading and 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#
R TRADING COURSES
Complete R Courses
Complete set of courses including: Technical Analysis in R, R Course with Quant including GARCH, R Course with Quant, R Course with Mean Reversion and Pair Trading, R Course with Arbitrage and Volatility
R Course with Mean Reversion and Pair Trading
R Course with Mean Reversion and Pair Trading unit Details: 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 Technical Analysis
Unit details: 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 Analysis
Unit Details: Unit 1 Checklist of forecasting with Holt Winters with time series analysis by using ACF, SSE, alpha, beta, gamma, Ljung-Box, detrending decomposing 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 Quant Anlysis with GARCH
Unit details: 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 Use GARCH to prove short selling cause a bubble in comparision of stock markets between Shanghai and Hong Kong Unit 6 Calculating RAW GARCH algorithms with this R script Unit 7 GARCH trading R script walkthrough with a rolling window
MATLAB TOOLBOX COURSES
Complete Matlab Courses
Complete Matlab courses including: Matlab Econometrics Toolbox, Matlab Financial Toolbox, Matlab Toolboxes: Signal Processing, Stats, Math, Matlab Toolboxes: System Identification, Wavelet, Optimize, Curve Fitting, Matlab Strategy Development Demos and Researching WIth Simuilink
Matlab Econometrics Toolbox
Unit Details for 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
Financial Toolbox Unit detail: 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
Unit details: 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 Toolboxes: System Identification, Wavelet, Optimize, Curve Fitting
Unit details: Module 1 Wavelet Toolbox Wavelet Toolbox Unit 1 Fractional Brownian Motion Synthesis Using the Command Line Unit 2 Multiple PCA Module 2 Optimize Toolbox Optimize Toolbox Unit 1 Call a nonlinear minimization routine with a xstart Unit 2 Call a nonlinear minimization routine with a xstart Module 3 Curve Fitting Toolbox Curve Fitting Toolbox Unit 1 Using Splines Unit 2 Curve fitting tool demo with analysis, plots, and code generation Unit 3 Various programming curve fitting examples Unit 4 curve fitting tool demo with exclusion and section data Unit 5 Curve fitting tool demo with smoothing Unit 6 Programmatic curve fit demo Unit 7 Rational fit demo Unit 8 Surface fit tool demo Unit 9 Curve Fitting by Optimization Module 4 System Identification Toolbox System Identification Toolbox Unit 1 Constructing an iddata Object for Time-Domain Data Unit 2 Estimate Nonlinear ARX Models Unit 3 Refining an Initial ARMAX Model at the Command Line Unit 4 Resampling Data Without Aliasing Effects Unit 5 Custom regressors Unit 6 Identifying Time-Series Models and Predicting a Time Series Unit 7 Simulating a Continuous-Time State-Space Model at the Command Line
Matlab Strategy Development With Simulink
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 Unit Details: 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
ALGORITHM TRADING COURSES
Quant Algorithm Course including Pair Trading, Arbitrage, Autoregressive
Unit details: 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
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
Millisecond Frequency Trading by Dr Ernie Chan
Dr. Chan currently offers the online course “Millisecond Frequency Trading” (MFT) to a select number of traders and portfolio managers. This is an online workshop conducted in real-time through Adobe Connect. It has 2 focuses:
1) Defence against high frequency traders by utilizing the myriad order types and technologies available to a slower trader;
2) Backtest strategies with milli-second frequency historical tick data using MATLAB.
Free MATLAB trial licenses will be arranged for extensive in-class exercises. No prior knowledge of MATLAB is needed, but some experience with programming is necessary. The math requirement assumed is basic college-level statistics.
Backtesting by Dr Ernie Chan
Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. We will study various common backtest performance metrics. Backtest performance can easily be made unrealistic and un-predictive of future returns due to a long list of pitfalls, which will be examined in this course. The choice of a software platform for backtesting is also important, and criteria for this choice will be discussed. Illustrative examples are drawn from a futures strategy and a stock portfolio trading strategy.
This is a pre-recorded workshop conducted in Adobe Connect by Ernest Chan (www.epchan.com). This workshop focuses on the various practices and pitfalls of backtesting algorithmic trading strategies. Free MATLAB trial licenses will be arranged for extensive in-class exercises. No prior knowledge of MATLAB is assumed, but some programming experience is necessary. The math requirement assumed is basic college-level statistics.
Total hours: 7 hours of recorded session.
A. Overview of Backtesting
1. What is backtesting and how does it differ from “simulations”?
2. The importance of backtesting: Why is backtesting a necessary step for profitable automated trading?
3. The limitations of backtesting: Why is backtesting not a sufficient step to ensure profitability in automated trading?
4. What we can do to increase the predictive power of our backtest results: the avoidance of pitfalls.
5. How to identify good/bad strategies even before a backtest: a preview of various pitfalls through a series of examples.
B. Choosing a backtest platform
1. Criteria for choosing a suitable backtest platform.
2. A list of backtesting platforms.
3. Discussion of pros and cons of each platform.
4. Special note: integrated backtesting and automated execution platforms.
5. Why do we choose MATLAB?
C. Tutorial to MATLAB
1. Survey of syntax.
2. Advantage of array processing.
3. Exercises: building utility functions useful for backtesting.
4. Using toolboxes.
D. Backtesting a single-instrument strategy
1. Exercise: A Bollinger-band strategy for E-mini S&P500 futures (ES) as a prototype mean-reversion strategy.
E. Performance measurement
1. The equity curve.
2. Excess returns and the importance of the Sharpe ratio.
3. Tail risks and maximum drawdown and drawdown duration.
4. The importance of transaction costs estimates.
F. Choosing a historical database
1. Criteria for choosing a good historical database.
2. Equities data: split/dividend adjustments, survivorship bias.
3. Futures data: constructing continuous contracts, settlement vs closing prices.
4. Issues with synchronicity of data.
5. Issues with intraday/tick data.
G. Backtesting a portfolio strategy
1. Exercise: A long-short portfolio strategy of stocks in the S&P 500.
2. Relevance of strategy to 2007 quant funds meltdown.
3. The importance of universe selection: impact of market capitalization, liquidity, and transactions costs on strategies.
4. Strategy refinement: how small changes can make big differences in performance.
H. Detection and elimination of backtesting pitfalls and bias
1. How to detect look-ahead bias?
2. How to avoid look-ahead bias?
3. Data snooping bias: why out-of-sample testing is not a panacea.
4. Parameterless trading.
5. The use of linear models or “averaging-in”: pros and cons.
6. Exercise: linearization of the ES Bollinger band strategy.
7. Impact of noisy data on different types of strategies.
8. Impact of historical or current short-sale constraint.
9. The unavoidable limitation of backtesting: Regime change.
10. What to do when live performance is below expectations?
IMPORTANT: All courses and analytics have been dropped as single items. Only available in our MEMBERSHIPS
PREMUM MEMBERSHIP ARCHIVE
Get access to our massive 5 year archive research technical infrastructure with dozens of algos in MATLAB or R. This is where everything started for me over the years. It will give you a solid foundation to instantly start your trading infrastructure within your control. No need to hand off your highly priced sensitive intectual property investment to other remote servers or cloud solutions to run. Why not run and control all your IP algos and infrastructure with what we provide? Also, you will find exclusive webinar playback videos you cannot even find in my Elite section. Check out this 36 minute video to highlight what is in this archive. Get more details with our spreadsheet to act as your roadmap to our 150+ videos here