Better System Trader

Andrew Gibbs has been involved in the financial markets since 2001 and is the founder and CEO of Halifax New Zealand. Andrew has extensive experience in all forms of equity and derivative contracts, managing millions of dollars and trading a number of markets around the world. In this episode we discuss volatility and methods to trading the VIX plus the benefits and methods of including fundamental data in technical quant models. Topics discussed Instruments you can use to trade volatility and the benefits or disadvantages of each What makes the VIX attractive to trade and why it often trends over time The types of trading styles that suit the VIX The dangers of trading volatility products Seasonality in the VIX How to get started building volatility trading models Fundamental data and the types of fundamental datasets that work well in quantitative models Why some fundamentals work better than others The frequency of fundamental data release and how that dictates trading model style How to account for revisions to data The impact of including fundamental data can have on trading results Technical vs Fundamental data and which tends to be more robust Issues with fundamental data and company reporting accuracy How to reduce the chances of investing in a company that is likely to go bust Combining fundamentals and technical data and how to test How to get started building fundamental quant models   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

29 Nov 201539min

Jay Kaeppel has over 25 years experience in the financial markets. He has worked as the Head Trader for a CTA and published a number of popular trading books on Futures, Options and Stock Market Seasonality. He also spent a number of years writing a weekly column titled “Kaeppel’s Corner” and publishes on his blog “Jay On The Markets”. He is now Portfolio Manager for Alpha Investment Management, offering strategies such as the ‘Alpha Multi-Income Strategy’ to investors. In this episode we discuss a number of seasonal tendencies, how they can be integrated into a trading model, the applications of the Known Trend Index and the reasons why most traders fail. Topics discussed The Santa Claus rally - what it is and how to trade it How to use seasonality to complement other models Seasonality tendencies around holidays Monthly seasonal tendencies and a simple monthly seasonal system that vastly outperforms stock index returns Boiling down the trading process into 4 simple words Using leveraged ETFs for seasonality trades The worst performing month of the year (it’s not October) Converting seasonal tendencies into a trading model A simple seasonal sector system that takes only 6 trades per year Diversification vs Specialisation and the impact it can have on trading and drawdowns Are seasonal trading strategies just data mining? The Known Trends Index (KTI) and how it can be used in trading Why most traders fail   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

22 Nov 201548min

Laurent Bernut was a systematic short seller with Fidelity for 8 years. His mandate was to underperform the longest bear market in modern history: Japanese equities. Prior to that, he worked in the Hedge Fund world for 5 years. He now runs an automated Forex strategy and travels the world with his family. In this episode we talk all about Short selling, creating shorting strategies, the challenges of implementation and how to manage risk. We also discuss the importance of exits, insights into Bear markets, autotrading Forex and why complexity is a form of laziness. Topics discussed The benefits of developing a strategy on the short side first and why long/short symmetry is important Challenges with executing short systems and solutions The most important aspect to worry about when short selling Finding short candidates in a Bull market and why you should ignore absolute performance Tips to creating profitable short strategies The importance of exits and how to test them Insights into Bear markets The 3 wrong questions to ask during a Bear market and the 3 best ones to ask A simple method to identifying Bull and Bear markets Why complexity is a form of laziness Using MT4 as a professional trading platform Why being disciplined is a myth The type of strategies that work in the Forex markets The Common sense Ratio and why it’s more robust than the Sharpe ratio   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

8 Nov 201559min

Thomas Stridsman has over 20 years experience in the financial markets. He was an editor for Futures magazine and published two books on trading system development and money management. He is now a fund manager at Alfakraft, specialising in short-term trend following strategies with a focus on dynamic size allocation and risk distribution algorithms. In this episode we discuss strategy testing, why you need to normalise metrics, tips to creating robust strategies and why he doesn't test entries and exits any more (and what to focus on instead). Topics discussed The differences between short term trend following and long term trend following Why backtesting metrics should be normalised to give an accurate picture of performance Why you should look to restrict the number of consecutive winners and losers The difference between a good model and a profitable one Tips to creating robust systems Trading costs and when to include them in testing Using standard deviation to determine system robustness How his systems development approach has changed over the years The one particular insight that propelled his trading forward Applying Optimalf to position sizing The future of trading   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

7 Nov 201532min

Greg Morris was Sr. Vice President, Chief Technical Analyst, and Chairman of the Investment Committee for Stadion Money Management, overseeing the management of over $5.5 billion in assets.  He has been featured in the media a number of times, being invited to lecture about technical market analysis around the world. He is currently semi-retired, serving as a consultant and working on a few projects, including golf. In this episode we talk about the real definition of risk and how to manage it, the applications of market breadth and how the 'Weight of the evidence' concept can be used in trading. Topics discussed Why defining risk as volatility isn't accurate and what risk really is Can diversification actually be used to minimise risk? Why Rebalancing doesn't make sense The ‘Weight of the evidence’ concept and how it can be used in trading Why it’s important to test indicators over non-standard ranges What market breadth measures can reveal in market tops Different types of breadth and their applications in Trend Following Selecting indicators and why diversification of indicators is vital How often should you tweak your model if something isn't working so well The current state of the market   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

31 Okt 201547min

A couple of weeks ago I went back through all the guests we've had on the show so far and realised how very fortunate we've been to have so many fantastic guests on the show, sharing their knowledge and experience, some of them with more than 50 years of trading experience! To be honest, I’d actually forgotten some of the topics we’d covered so far and going back through them was an excellent reminder of all the valuable information the guests had shared, so for Episode 30 I thought it might be a good idea to revisit some of the highlights from the earlier episodes so that those that haven’t heard them will go and listen to them, and those who have already listened may get some value out of hearing the highlights again. I know when I went back through them it reminded me of some things that I wanted to test or investigate further, and I really found it a valuable exercise so I hope you do too. This episode will cover some of the highlights from episodes 1 to 20; some of my favourites and some of yours. Topics discussed How to find new trading ideas every day Using optimisation to understand market behaviour, not to find the optimal parameters How the level of market noise can indicate the type of strategy to trade Tips to creating robust models Avoiding over-optimised trading strategies Combining multiple conditions or strategies into an ensemble system Why simple systems are better than complex ones How market timing can improve strategy performance The concept of conditional trading and why you need to consider market context Testing the effectiveness of entry and exit rules The type of strategies that should have stops and when stops don't make sense Factors to consider when choosing a position sizing strategy How dual momentum can produce profits and protect in a downturn Trading the equity curve to protect capital Why traders should focus on process and not outcome   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

25 Okt 20151h 7min

Alan Clement is a Certified Financial Technician, full time independent trader, quantitative trading systems designer and private investment consultant. He is also a councillor with the Australian Technical Analysts Association and contributes to the technical analysis articles for Fairfax press. In this episode we talk about Rotational trading systems, the impact of stops on results and alternatives to managing risk. Alan also shares some interesting tips into measuring system health, dynamic position sizing and anticipating trading signals. Topics discussed Rotational trading - entries, exits and managing risk Methods to measure momentum in trend following strategies The impact of stop losses in trading systems and alternatives to managing risk Tips to position sizing without a stop loss Using dynamic profit targets to reduce risk and increase return Why drawdown is not a single number Using Monte Carlo analysis as a dynamic position sizing tool Methods to determining current system health Factors to consider when creating a system health metric Choosing the right Backtesting metrics and using them in live trading Five factors to consider when choosing a strategy to suit your personality Anticipating trading signals, the benefits, challenges and solutions How to anticipate trading signals without reverse-engineering indicators   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

18 Okt 201554min

David Aronson is a pioneer in machine learning and nonlinear trading system development and signal boosting/filtering. He is author of “Evidence Based Technical Analysis” and his most recent book "Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments" is an in-depth look at developing predictive-model-based trading systems. He was also an adjunct professor of finance, regularly teaching MBA and financial engineering students a graduate-level course in technical analysis, data mining and predictive analytics. In this episode David shares research into the effectiveness of indicators to identify Bull and Bear markets; he’s tested a large number of indicators and combinations with some interesting results! We also discuss issues with data mining, conditions where traditional methods of measuring data mining levels can be problematic and then finish up with the future state of Technical Analysis. Topics discussed What the popular Golden and Death Cross can tell us about the probability of a Bull or Bear market Using the RSI indicator to determine market state Methods to reduce the lag the 50/200 Moving Average crossover experiences Using ADX and Price Variance to identify Bull and Bear markets Creating indicators based on the value of other indicators Modifying the McClelland Summation Index indicator to identify market states How datamining increases the chance of good luck in the results Why the White's Reality Check and Monte Carlo Permutation methods breakdown using certain data-mining approaches How the role of Technical Analysis could change over the next 10 years New developments in Machine Learning which may see the end of the role of technicians   Disclaimer: Trading in the financial markets involves a substantial risk of loss and is not suitable for everyone. All content produced by Better System Trader is for informational or educational purposes only and does not constitute trading or investment advice. Past performance is not necessarily indicative of future results.

11 Okt 201555min