One of the biggest challenges we face as traders is strategies that either fail or experience a reduction in performance over time.
There are a number of possible causes for this, one obviously being changes in the market.
In podcast Episode 10, Perry Kaufman discussed the change in the markets and how they've become noisier over time, with an increase in erratic movements up and down.
This increase in market noise can have a huge impact on trading strategies and particular trading styles too and we're going to hear a little bit more about that from Perry today, including:
So lets take a listen to Perry.
Last week I finished reading a book called ‘Peak – Secrets from the new science of expertise’.
I wanted to share a concept from that book with you today because it contains a model that has the potential to:
What trader wouldn't want that?
Most people tend to assume that if they keep working on something they will automatically get better at it, but performance scientists have shown that is not necessarily what happens.
A lot of people reach a certain level of competence and plateau...
Others don’t really progress much at all...
And often the process can take a long time to play out, progress can be slow...
So that leads to the question, is it possible to reduce the amount of time that process takes?
Are there techniques that we can use to accelerate the process?
Well, there are and today we’re going to talk about one called "deliberate practice".
Our special guest, Kris Longmore from Robot Wealth, is going to explain what deliberate practice is and how we can leverage it to become better traders, faster.
Machine learning has seen a huge amount of growth over recent years with the increase in available data and processing power.
It's an incredibly powerful toolset for uncovering patterns and relationships in data, however, these tools can be challenging to learn, apply correctly and are also open to abuse.
Our guest for the episode, Kris Longmore from Robot Wealth, specializes in Machine Learning, Algorithmic Trading and Artificial Intelligence.
He is the co-founder and Head of Quantitative Research at Quantify Partners and also provides consulting and educational services through his website Robot Wealth.
In this episode Kris is going to share with us some of his insights into Machine Learning and strategy validation, including:
One of the main challenges with Mean Reversion trading is when to get into a trade, which can have a huge impact on profit and drawdown levels.
How far from the Mean should we wait before considering a trade?
PJ Sutherland shares the approach he uses to:
"My best losing year ever" and how traders can become better by learning from drawdowns, losses and other trading challenges.
With the toolsets we have available to us today it’s really quite easy to create a trading strategy by just mining market data.
As we've just heard in that opening bit of audio and also from previous podcast guests too, if you try enough combinations you can find something that appears to work purely by chance or by luck.
The challenge however is trying to identify something that could be sustainable.
Something that may persist long enough in the future for us to take advantage of, and hopefully make some money from.
Our guest for this episode, Dave Bergstrom from BuildAlpha, has spent years researching, building, testing, and implementing market making and trading strategies for a high frequency trading firm, CTAs, money managers, individual clients, and even aspiring retail traders.
In this episode Dave is going to share some of his insights into strategy development and validation, including: