Algo Trading - Why Simple Algos Are Better
Summary
TLDRKevin Davey, a Champion chair, emphasizes the superiority of simple algorithms in trading due to their reliability and performance over complicated ones that often fail in live scenarios. He shares insights from his Strategy Factory Club, where strategies are tested for six months, and highlights the importance of avoiding high slippage markets and scalping strategies. Davey suggests focusing on simple strategies with a few rules and variables for better results, supported by statistical data from over 1500 submitted strategies.
Takeaways
- 😌 Simple algorithms are often the best because they are less likely to fall apart when put into live trading compared to complex ones.
- 📉 Over-optimization in algorithmic trading can lead to a 'curve fitting' effect, which may not hold up in real-world trading scenarios.
- 🤔 Even experienced traders can have strategies that look great in backtesting but fail in live trading, often due to unnecessary complexity.
- 🚀 Kevin Davey's Strategy Factory Club offers a unique way to gain access to a variety of proven trading strategies by submitting and analyzing student submissions.
- 📊 Davey has analyzed over 1500 strategies and found that simplicity is key to successful algorithmic trading.
- 💡 The 'easy way' in algorithmic trading involves avoiding high slippage markets, not overcomplicating strategies, and focusing on longer-term trading bars.
- 🛑 High slippage markets, such as Lumber, Orange Juice, and Palladium, are difficult for algorithmic trading due to the costs involved in order execution.
- 📈 Low slippage markets, like certain stock indices and currencies, are easier for algorithmic strategies and tend to yield better results.
- ⏳ Scalping strategies that use very short time frames (e.g., 1-minute bars) are generally less profitable and harder to develop successfully.
- 📊 Longer-term strategies, such as those using daily bars, are easier to develop and more likely to be profitable, according to Davey's analysis.
- 🔍 The 'sweet spot' for algorithmic strategies lies in the middle ground—simple strategies with a few rules and variables, but not overly simplistic or complex.
Q & A
Why does Kevin Davey believe simple algorithms are the best for trading?
-Kevin Davey believes simple algorithms are the best because they are less prone to fall apart when going live, unlike more complicated ones that may look good in back tests but fail in real-time trading.
What is the common issue with algorithms that have too many parameters and optimizations?
-Algorithms with too many parameters and optimizations often result in overly smooth backtest curves, which can lead to strategies that perform poorly in live trading, sometimes losing money or barely breaking even.
What does Kevin refer to as the 'hard way' in algorithmic trading?
-The 'hard way' in algorithmic trading, according to Kevin, involves trying to reinvent the wheel by making overly complicated strategies and not taking the easier, more straightforward approach.
What is the main advantage of using simple strategies according to the speaker?
-The main advantage of using simple strategies is that they tend to perform better in live trading, avoiding the pitfalls of over-optimization and complexity that can lead to failure.
How does Kevin Davey gather data to support his views on algorithmic trading strategies?
-Kevin gathers data from his Strategy Factory Club, where students submit strategies that he analyzes in real-time for six months. If they pass performance criteria, the students receive a variety of proven strategies in return.
What does Kevin suggest is the 'sweet spot' for strategy complexity in terms of performance?
-The 'sweet spot' for strategy complexity lies in the middle, with simple strategies that have a few rules and variables but avoid excessive optimization.
Why are high slippage markets difficult for algorithmic trading?
-High slippage markets are difficult because an algorithm must first overcome the slippage to have a chance at profitability, which is challenging and can lead to higher costs and lower success rates.
What are some examples of markets with high slippage mentioned in the script?
-Examples of markets with high slippage mentioned are Lumber, Orange Juice, milk, Palladium, and some stock indices with higher slippage.
What type of strategies does Kevin advise against for algorithmic trading?
-Kevin advises against developing scalping strategies and very complicated strategies, as well as strategies for markets with high slippage, due to their difficulty and potential for lower profitability.
What does Kevin suggest as an alternative to high slippage and scalping strategies?
-Kevin suggests focusing on markets with low slippage and longer-term bars, such as daily bars, and using simple strategies for easier and more profitable algorithmic trading.
How does Kevin define 'simple strategies' in the context of algorithmic trading?
-In the context of algorithmic trading, 'simple strategies' are those with a few rules and variables, avoiding excessive parameters and optimization, which makes them more robust and easier to manage.
Outlines
😲 The Pitfalls of Complicated Trading Algorithms
In this paragraph, Kevin Davey, the Champion Chair, introduces the topic of why simple algorithms are preferable in trading. He explains that complex algorithms often result in an impressive backtest but fail when deployed in live trading, leading to financial losses. This is attributed to over-optimization and the addition of too many parameters. Kevin reassures viewers that even experienced traders face such challenges and emphasizes the importance of simplicity in strategy development. He also mentions his 'Strategy Factory Club,' where he analyzes strategies submitted by his course students, providing insights based on a large dataset of over 1500 strategies. The key takeaway is to avoid unnecessary complexity and to focus on developing strategies that are easy to understand and implement.
📉 Overcoming Challenges in Algorithmic Trading
The second paragraph delves into the specific difficulties of algorithmic trading, such as high slippage in certain markets, the challenge of developing scalping strategies, and the inherent complexity of creating effective algorithms. Kevin discusses the high slippage in markets like Lumber, Orange Juice, and Palladium, which makes it hard for algorithms to be profitable due to the significant cost of trading in these markets. He contrasts this with the success his students have had in markets with lower slippage, such as stock indices and certain agricultural commodities. The paragraph also highlights the ineffectiveness of scalping strategies with short time frames and the benefits of focusing on longer-term bars like daily bars. Kevin concludes with a call to action for viewers to share their thoughts and comments, reinforcing the message that simple strategies, low slippage markets, and longer-term bars are the keys to successful algorithmic trading.
Mindmap
Keywords
💡Simple Algos
💡Backtest
💡Optimization
💡Slippage
💡Low Slippage Markets
💡Scalping Strategies
💡Bar Size
💡Strategy Complexity
💡High Frequency Trading
💡Strategy Factory Club
💡Algorithmic Trading
Highlights
Simple algorithms are often the best because they avoid over-complication and the pitfalls of optimization that can lead to unrealistic backtest results.
Complicated algorithms tend to perform well in backtesting but often fail in live trading, a common issue known as 'curve fitting'.
The speaker, Kevin Davey, shares insights from his Strategy Factory Club, where he has analyzed over 1500 strategies submitted by students.
The 'take it easy' approach is recommended for algorithmic trading, suggesting that traders should not overcomplicate their strategies.
Developing strategies for markets with high slippage is challenging and often not profitable due to the costs involved.
Slippage is particularly high in markets like Lumber, Orange Juice, Milk, and Palladium, making algorithmic trading in these markets difficult.
Low slippage markets, such as stock indices and certain currencies, are easier for algorithmic traders to develop profitable strategies.
Scalping strategies, which trade on very short time frames, are generally not profitable due to the slippage costs.
Longer term strategies, such as those based on daily bars, are easier to develop and tend to be more profitable.
The speaker's data shows that very simple strategies and very complicated strategies have lower pass rates, with a 'sweet spot' in the middle.
Strategies with a few rules and variables, but not over-optimized, have the highest success rate according to the speaker's analysis.
Avoiding over-complication is key to developing successful algorithmic trading strategies.
The speaker emphasizes the importance of choosing the right markets and time frames to increase the ease and success of algorithmic trading.
The transcript provides practical advice for traders looking to develop their own algorithms, based on real-world data and experience.
The speaker invites viewers to share their comments and opinions on the topic, encouraging an open discussion on algorithmic trading strategies.
Transcripts
hi there I'm Champion chair Kevin Davey
and today I'm going to talk about why
simple Algos are best so let's get
started
[Music]
we're going to talk about why simple
Algos are the best
and the reason
I think simple Algos are the best
because when you start getting
complicated with your Algos you start to
experience this where you build a nice
looking back test and the more
parameters you add the more optimization
you add probably the straighter the
Curve
and that's what you end up during
development with but when you go live
maybe your strategy does something like
this where it just seems to fall apart
and lose money or you know maybe break
even
that's a lot of times a symptom of
having Algos that are too complicated
or that just are not based on Simplicity
but you don't feel bad about this
having strategies like this everybody's
had them even good Traders I certainly
have had some of these over the years
where they just look great and then they
just fall apart and a lot of times I've
found that the more complicated the
strategy
the more it falls apart so don't feel
bad now I'm going to give you a couple
tips and the source for these tips comes
from my strategy Factory Club this is
part of my strategy Factory course
students of my course
create strategies they submit them to me
and then I watch them in real time for
six months and then if it passes some
performance criteria after six months
they get a bunch of strategies in return
so it's a great way for students to get
a lot of different strategies that are
all proven to work
as part of this club I obviously have a
lot of Statistics I have over 1500
strategies that people have submitted
and so I've been able to analyze the
data and come up with some things of hey
this works hey this doesn't work so
that's going to be one of the sources of
data that I'm going to show you
here
but the key with all this is what I call
take it easy
most algo Traders think they have to
reinvent the wheel and they have to make
something super complicated and they try
doing things the hard way
you know there's the easy way and
there's the hard way well always
try to go the easy way go to the right
there don't go to the left
that's the hard way and that's what you
want to avoid
so let me show you what's hard in algo
trading because if you know it's hard
you'll know what to avoid
one thing that's really hard is
developing strategies for markets that
have high slippage a lot of people like
these markets
but it doesn't work and people like
developing scalping strategies that's
also really hard in algo trading unless
you're a high frequency firm
and then finally developing complicated
strategies that's the other part of it
so let's take a look at all those and
talk about why they're hard and how to
get around it
okay slippage
a lot of people like
markets like Lumber Orange Juice milk
Palladium
those all have a lot of slippage and
what that means is if you're going to
build an algo for those
your algo has to First overcome that
slippage to even have a chance at
profitability
what are we talking about well Lumber
just as an example I've estimated based
on my own trading and data analysis that
it's about 240 dollars per round turn
trade in slippage that means if you were
to buy and then immediately sell you'd
probably be out 240 dollars plus
commissions
it's not a very liquid Market yeah it's
had some great Trends over the years and
that's what a lot of people fixate on
because it looks like hey I just follow
that Trend I can make it a lot of money
that's possible but realize there's a
lot of slippage and the more slippage
you have the tougher it's going to be to
come up with an algo that makes money
over the long run
and I could go through that for orange
juice and Palladium and other markets
too but that just gives you a sense of
what's going on high slippage is a lot
tougher
what my students have found is if they
stick to a lot of the markets with a low
slippage
they tend to do a lot better and what
are those markets well some of the stock
indices are fairly low es the mini Dow
ym rty not as much but uh its slippage
isn't ridiculously high some of the
currencies they have pretty low slippage
and then some AGS
Bean soybean oil
Kansas City hard red winter wheat
students who take my class have a lot
more success in those markets than they
do in some of the higher slippage ones
so there's a definite correlation with
low slippage and
ease of building strategies
those are good markets
now let's talk about scalping strategies
so what I have here is the x-axis is the
bar size in minutes so you have one
minute bars over here you have 14 40
minute which are basically daily bars
over here
and what it shows over a whole bunch of
strategies and I did this in my algo
trading cheat code book
the average net profit is really
negative for
one minute two minute five minute
strategy so the low time frames are
really hard because you've got to
overcome that slippage especially if
you're trading a lot where you can see
it's hard to see here but it actually
goes positive as you go out to the
bigger numbers so what it's saying if
you want ease in building Algos
look to daily bars look to 720 minute
bars you know look to the longer term
bars and stay away from the shorter term
ones
the scalping strategies usually lead to
bigger overall losses and a lot of
people don't realize that they think
well hey I'll only risk a tick or two
and therefore I'm better off well you
risk your ticker to again and again and
again and you keep losing
you're gonna be in trouble
a lot of times it's better to go with
the bigger bar sizes
it's easier to develop Algos and they
tend to be more profitable so that's the
second part about
being easy with these Algos
and the third part is the complexity of
the strategies and again this data comes
from students of mine
and this is audit sample performance so
I broke them up into four different
categories very simple strategies
simple strategies complicated strategies
and very complicated and then I have a
pass rate here in percentages and so
what you can see are the simple
strategies
those tend to do the best if it's too
simple you know if it's just maybe buy
and hold
that would be very simple
those tend not to do too well but then
on the other side if you start getting
very complicated strategies strategies
with 10 20 rules with a lot of
parameters to optimize those tend not to
do well either
The Sweet Spot is right in the middle
with what I'd call Simple strategies you
have a few rules maybe a few variables
you optimize but you don't do too much
that is the problem with developing
complicated strategies it just gets out
of control pretty quickly
so if you're going to build strategies
take the easy way
go for low slippage markets go for
longer term bars like daily bars and go
for simple strategies those are all
proven by my students to lead to more
success
if you have a comment you have a concern
you want to voice your opinion hey I'd
love to hear from you
just leave the comment below
I'm Champion Jerry Kevin Davey have a
great day
[Music]
[Music]
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