Better System Trader

The results of the Brexit decision took a lot of people by surprise and the markets reacted accordingly. What was interesting about this market event is that we all knew the date and time period when the Brexit votes would start rolling in, so we had a rough idea when we might see some type of market reaction, if the market reacted at all.

As systematic traders, what should we do in this type of situation:

• Should we continue trading as usual, following our systems?
• Should we override our systems to reduce exposure, or perhaps temporarily stop trading altogether?
• Are there any other approaches that we should consider?
• How do we go about deciding what to do?

In this episode we're asking 13 system traders and past guests of the podcast (actually it's 12 past guests and one future guest) about their approach to trading around the Brexit vote.

I'll be asking them what their trading plan was going into the Brexit decision and you might be surprised with some of their answers.

I'll also be asking them what factors they considered to reach that decision, whether they were happy with the approach after the event and any key learnings we can get out of this experience.

I personally found it really interesting to hear what they had to say so I'm sure you will too.

• How 13 pro system traders approached the Brexit decision - did they trade through it, reduce exposure, stop trading or something else? Their answers may surprise you!
• The factors they considered when deciding on that approach
• Lessons learned and insights from their results and the market reaction to the Brexit decision

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.

Traders are always looking for an edge and today's guest shares a simple approach he calls an 'unfair trading advantage', that can have a dramatic impact on trading strategy performance.

The guest on this episode has been on the show before, to discuss breakout trading strategies back in Episode 43. In that episde we discussed the steps to building breakout strategies and we even released a breakout strategy toolkit, included an ebook, cheatsheet and EasyLanguage code for 2 breakout trading strategies.

In this episode, our guest Tomas Nesnidal will be sharing a different trading approach, and it's something he likes to call 'an unfair trading advantage'.

He's going to explain to us what it is and why he calls it an 'unfair advantage.' It's something that a lot of traders have probably heard about but perhaps are not aware of how to use it properly or even the positive impact it can have on trading results.

Tomas will explain it in this chat so take a listen!

• An 'unfair trading advantage' all traders should consider for their own trading strategies
• The technique that most traders have probably heard about but don't know how to use properly
• The incredible impacts this technique can have on trading performance

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.

Today we're covering a topic which can really be a concern for traders of all levels, from beginner to pro, and that is the topic of strategy evaluation.

• Have you ever found that real-life performance does not match expected results?
• Or perhaps you have a strategy that is stuck in a drawdown and wondering if it's actually broken?

I'm sure we've all heard of data mining bias, over-optimization and curve fitting and the impacts this can have on our trading accounts.

We may be even using techniques such as Out Of Sample testing, Walk Forward Analysis, Monte Carlo analysis and a number of other measures to identify or reduce the impact of these issues, but do these approaches actually work? Are there limitations or dangers with these techniques? Are there better ways?

In this episode we talk to someone who evaluates trading systems for a living, plus his research into system evaluation techniques has won awards. The guest is Dave Walton.

Dave was the winner of the Wagner award in 2014 for a paper titled 'Know your system – turning data mining bias to benefit through System Parameter Permutation'.

In our chat today we talk about the technique in his paper and how it can be applied to trading strategy evaluation. We also discuss some of the assumptions and limitations of the approach, and he shares with us some valuable insights he's made since publishing the paper which have resulted in an updated approach he now considers a better alternative, so make sure you listen out for that.

• How the typical approaches to system development can introduce datamining bias without you knowing
• The types of systems that can increase the chance of data mining bias and what to look for
• How the method of splitting your out of sample data could be causing you to throw away good strategies
• Out of sample, walk forward analysis and Monte Carlo - do they actually reduce data mining bias?
• The problems with using Monte Carlo analysis to assess strategy performance and why it doesn't protect from overfitting
• System Parameter Permutation - how to use it, why use the median, parameter range selection and new insights since the SPP paper was published
• How System Parameter Randomization solves some of the issues of System Parameter Permutation
• Stochastic modelling and how it can be used to determine if a rule is adding value to your strategy

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.

Nelson Freeburg was the editor of Formula Research, a newsletter that developed systematic timing models for the stock, bond, and commodity markets.

He was also a research consultant working with institutional money managers to design proprietary timing models.

Nelson had been an active trader since 1980 and occasionally spoke about his work to audiences around the world.

In this episode, Linda Raschke shares memories of Nelson, his approach to model development and what we can learn by studying his work.

Topics discussed

• Timing models and the components Nelson used in his models
• Russell growth vs Russell value model
• Out of sample testing and sample size
• Why Nelson focused so much on reducing drawdown
• Nelsons biggest strengths in modelling and what we can learn from his approach
• Voting systems
• The benefits of overlaying 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.

Markets are constantly changing. Trading edges come and go.

In an industry with such a low survival rate, where some areas are changing at an ever increasing rate, what does it actually take to not only survive, but thrive, over an extended period of time?

The guest on this episode, Linda Raschke, has been trading for over 35 years. She traded for several hedge funds before starting her own, ranking 17th out of 4500 hedge funds by Barclays Hedge for 'Best 5 year performance'.

She's experienced a large number of changes in the industry, some of them have been huge, but she's managed to adapt and continues trading even today.

Linda stand out from the crowd for three factors: Performance, Longevity and Consistency, so what does it actually take?

What has she learnt over the years and what can we do to improve our own chances of performance, longevity and consistency?

In our chat with Linda we discuss some of the changes she's experienced over the years and the impacts this has had on trading. We also hear about her approach to modelling the markets, understanding market behavior, trade management, day trading techniques and some fantastic questions submitted by fellow listeners. Make sure you don't miss those!

• Changes in the markets over time and the impacts that has had on strategies and their performance
• How to use modelling to identify market behavior and edges
• AI, machine learning and neural network techniques
• Tips and factors to consider when daytrading
• Reading market behavior throughout the day

PLUS loads of great questions submitted by Better System Trader listeners!

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.

Trading can be tough, markets are noisy and finding signals in the market noise can be challenging. Also, applying indicators to trading strategies can introduce lag, however a lot of traders don't even realize the lag their indicators are introducing or the impact it can have on trading. In fact, the guest in our chat today, John Ehlers said "One of the biggest enemies of traders is lag".

So, what's the solution?

John Ehlers is well known in the commodity futures arena as the Creator of MESA, having pioneered the MESA method of cycle analysis in the late 1970's and becoming the founder of MESA Software.

He is author of four books including Rocket Science for Traders, Cycle Analytics for Traders, Cybernetic Analysis for Stocks and Futures and MESA and Trading Market Cycles.

He has also been a contributing editor of Stocks & Commodities, winning a number of awards for his work.

In our chat with John we discuss the issue of indicator lag, the impact it can have on trading and some solutions. We also talk about applications of Digital Signal Processing in trading, the MESA approach, regime switching, Cycles and the mistakes people make trading cycles.

• MESA and it's application to trading
• Alternatives to the MESA approach and which is best for the markets
• How cycle length can determine indicator length
• Common mistakes people make with cycles
• Cycles and DSP techniques as regime filters
• The problems caused by indicator lag and solutions to reducing lag
• The best low-lag filter and oscillator available
• Getting started with Cycles and DSP

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.

Backtesting and execution are such key parts of algorithmic trading so choosing the wrong platform can have a huge impact on our trading.

There are loads of trading platforms available and a lot of considerations which need to be made when choosing one that suits our needs, so in this episode we'll be discussing backtesting and execution platforms with Nitesh Khandelwal, department head at QuantInsti who also co-founded iRageCapital and iRage Global Advisory Services.

After our chat on algorithmic trading platforms we'll also cover statistical arbitrage, high frequency trading and some interesting audience questions, so listen out for those.

• The 3 key components to an algorithmic trading platform and the basic questions you need to answer before choosing a trading platform
• Why backtesting and execution platforms should be separate
• Choosing a programming language and why python has become a popular choice in trading
• The benefits and drawbacks of using python in trading
• Statistical Arbitrage, how it came about and the benefits of the approach
• The primary risks of statistical arbitrage, especially during times of market stress and how they can be reduced
• The most important factor in stat arb trading
• Common mistakes traders make when building statistical arbitrage 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.

I'm sure we all want to create trading strategies that perform better and last for longer but there are a number of issues we need to look out for when developing robust trading strategies, some are well-known and some perhaps aren't.

In this episode we'll be talking with Perry Kaufman about strategy development and more specifically some of the issues that can catch us out when creating trading strategies. Perry raises some interesting points about optimization that may not be well known plus he shares loads of tips to creating more robust strategies.

Perry writes extensively on markets and strategies, having published fourteen books and has just released a new book on building algorithmic trading strategies, which we'll be discussing in this episode.

He has worked and consulted to a number of successful CTA, investment and prop trading groups, creating systematic trading and hedging programs.

This is also his 2nd appearance on the podcast, appearing as a guest way back in Episode 10.

• The most robust type of systems
• How your choice of optimization values could be misrepresenting your results and how to choose parameters that give a more accurate picture
• The mistakes traders make when analyzing optimization runs and tips to doing it properly
• How to really determine if a new trading rule is robust
• Reducing risk by using multiple parameters
• What the number of profitable runs in an optimization can tell you about the robustness of a strategy
• Why diversifying across strategies instead of across markets could be a better approach
• The challenges of building robust strategies using Genetic Algorithms and Neural Networks

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.

Andrea Unger is the only trader to ever win the World Cup Championship of Futures Trading ®* titles 3 years in a row, with returns of 672% in 2008 (futures division), 115% in 2009 (futures division) and 240% in 2010 (futures & forex division).

This is his 2nd appearance on the podcast, he was also a guest on Episode 16.

In this episode Andrea discusses his approach to trade entries, how the traditional approach to entries can limit our ability to read the market and how he's modified the standard approach to identify entry opportunities.

• The typical approach to entries and how Andrea uses a modified approach to identify and test his entries
• Why starting an entry with a setup can limit your ability to read the markets
• The grouping of setups and how the style of trigger you use can determine the most appropriate setup
• The best timeframes for indicators and the impact lower timeframes can have on indicators
• Combining intraday and daily timeframes for better entries
• How Daily Factor can be used to determine the type of move to expect next
• Symmetrical patterns - when it makes sense to use symmetry and when it doesn't

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.

Back in Episode 32 we had a chat with Laurent Bernut, a systematic short seller who spent years working in the Hedge Fund world specializing in short selling strategies.

He shared loads of knowledge with us in that episode but we actually had a lot more to talk about. We ran out of time back then so in this episode we're going to continue with the chat, covering a bit more on short selling, including common problems and mistakes traders make when short selling, the 5 psychological stages of a bear market, how these stages manifest in market behavior and where we are now.

We also chat about his Convex position sizing model, visualizing your trading edge and how to tilt it more in your favor PLUS he shares with us a special trick to switch our minds from a flight or fight mode back into a state of flow.

We also have some great questions submitted by podcast listeners so listen out for those.

• Common problems traders face when short selling
• When to never short a stock
• The 5 psychological stages of a bear market, how they manifest in the markets and where are we now?
• How Laurents Convex position sizing model adapts position size differently in periods of performance and drawdown
• Visualizing your trading edge and tilting it in your favor based on trading style
• The main components of a short trading strategy
• Why a break of support is often not the best place to enter a short trade and what to do instead
• A simple 'jedi mind trick' that switches your mind from fight or flight into a flow state

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.