# Quantitative Momentum Strategies by Dr Ernie Chan

Quantitative Momentum Strategies by Dr Ernie Chan

LIVE

Maximum number of attendees: 6.
Total hours: 12.
Fee: \$1,890.
Dates and times: December 2, 3, 4, 5:30-9:30 pm ET.

SOLD OUT!

This is an online workshop conducted in real-time through Adobe Connect conducted by Ernest Chan (www.epchan.com). The workshop focuses on the theories and practical implementation of momentum strategies using MATLAB. 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.

 Maximum number of attendees: 6.

 Total hours: 12.

Course outline:

1. Causes of momentum

a. Persistence of futures roll returns.

b. Slow diffusion of news.

c. Forced sales and purchases by funds.

d. HFT market manipulation.

2. Tutorial to MATLAB

a. Quick survey of syntax.

b. Exercises: building some utilities useful for trading and plotting simple graphs.

c. Using toolboxes

3. Roll returns as driver of momentum

a. Backwardation vs. contango.

i. Exercise: Estimating spot and roll returns.

b. Time-series vs cross-sectional momentum.

c. Arbitrage between future and spot returns.

i. The case of VX-ES.

d. Statistical tests for time-series momentum.

e. Example futures time-series momentum strategy.

i. Indicators for TS momentum.

f. Example futures cross-sectional momentum strategy.

g. Example stock cross-sectional momentum strategy.

i. Indicators for CS momentum.

ii. News sentiment.

h. The phenomenon of “Momentum Crashes”.

i. The S&P DTI index.

4. Event-driven momentum

i. The shortening of momentum horizon.

b. Other momentum-inducing events.

i. Research from Ravenpack on corporate events.

ii. Macro-economic events.

5. Forced sales and purchases due to funds

a. Hedge funds.

b. Mutual funds.

i. Example strategy using Pressure indicator.

c. Index funds.

d. Levered ETFs.

i. Example strategy.

6. High frequency momentum strategies

b. Ticking.

c. Flipping.

d. Stop hunting.

e. Order flow.

7. Exit Strategies

# Backtesting by Dr Ernie Chan

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.

Course outline:

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.

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.

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.

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?

# Analytics to forecast the markets while trading

Analytics to forecast the markets while trading

Note that all videos are not final output as this is still in testing. This is for all those interested in a preview of this analytics service.

Note we can do mutual fund or indices. We have Australian or British examples to download here

Our Quant Analytics

Welcome!

Our 2 minute trailer

Our longest video with complete overview of the reports/spreadsheets and complete system running to generate these

Detailed videos on each report below

There is virtually no risk to try this out

There are entire set of REPORTS so far!

Demo overall automated reports of European Sentiment Indicator reports

Our entire US Economic view of major Indicators

How to automatically generate trading pair with long and short in the next profitable sector

Full demo and analysis of my real time economic snapshort for profitable market sector AUTOMATED trading

Some statistical analysis for your junkies out there

Preview of the watchlist for prescreening stocks training pair ideas. DO realize this is still in developing and testing but not final:

Demo report of automated positions management on long short trading with stop loss

Self adapting with Kelly Criterion to a portfolio with automated trading performance with P and L

NEW BENEFIT!! Here is a demo of our first video playback with our Trading Club and Technical Pow Wow Meetup Events

# Our Quant Analytics

Our Quant Analytics

Welcome!

We are building and prepping our new Analytics service very soon. Come back soon

There are entire set of REPORTS so far!

Demo overall automated reports of European Sentiment Indicator reports

Our entire US Economic view of major Indicators

# Products

All courses listed included with: EXCLUSIVE Trading Custom Trading Platform course with complete source,, ALL lessons and courses with source code and video walkthrough lited in the QuantLabs.net Academy, Books tips that helped me out, All my notes thus far on building this internal trading system called AK47, Some Excel files to assist my data analytics, Complete links to my math study of Khan Academy, R source code files for all lessons. Bonus and exclusive material: Excel Stock screeners, .NET C# Source code files , and Database examples with schemas for SQL Server. Many more many examples including source code and videos for your trading operations. Access to monthly contributions via monthly subscription.

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Details units: 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

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