Algo Trading - Why Simple Algos Are Better

Summary

Kevin 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

00:00

😲 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.

05:02

📉 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.

Keywords

💡Simple Algos

Simple Algos refers to trading algorithms that are straightforward and not overly complex. The video emphasizes that simple algorithms are often more effective because they are less prone to failure when deployed in live trading environments. The script mentions that as algorithms become more complicated, they tend to have a 'nice looking back test' but may 'fall apart' in real-world use, illustrating the contrast between theoretical performance and practical application.

💡Backtest

Backtesting is the process of evaluating a trading strategy using historical data to see how it would have performed in the past. In the context of the video, a 'nice looking back test' suggests that a strategy might appear successful when tested against past market conditions, but this does not guarantee its effectiveness in live trading, which is a key point in the argument for favoring simpler algorithms.

💡Optimization

Optimization in trading algorithms refers to the process of fine-tuning parameters to enhance the strategy's historical performance. The video warns against excessive optimization, suggesting it can lead to overfitting, where an algorithm performs well on historical data but fails to adapt to new, unseen market conditions.

💡Slippage

Slippage is the difference between the expected price of a trade and the actual price at which the trade is executed, often due to market volatility or low liquidity. The script identifies high-slippage markets as challenging for algorithmic trading, as algorithms must overcome this slippage to be profitable, using examples like lumber, orange juice, and palladium markets.

💡Low Slippage Markets

Low slippage markets are those where the difference between the expected and actual trade execution prices is minimal. The video suggests that sticking to these markets, such as certain stock indices and currencies, can lead to better algorithmic trading performance because they are easier to model and have less unpredictable price movement.

💡Scalping Strategies

Scalping is a trading strategy that aims to profit from small price changes by making many trades in a single trading session. The video argues that scalping strategies are difficult for algorithmic trading due to the high frequency of trades, which can lead to significant slippage and transaction costs, as illustrated by the negative average net profit for one-minute to five-minute strategies.

💡Bar Size

Bar size in trading refers to the time frame of a candlestick chart, which can range from one minute to several hours or even days. The script uses bar size to discuss the effectiveness of different time frame strategies, suggesting that longer-term bars, such as daily or 720-minute bars, are easier for developing profitable algorithms.

💡Strategy Complexity

Strategy complexity pertains to the number of rules and variables within an algorithmic trading strategy. The video presents data suggesting that simple strategies, with a moderate level of complexity, have a higher success rate than very simple or very complicated strategies, indicating a 'sweet spot' in strategy design.

💡High Frequency Trading

High frequency trading (HFT) is a type of trading that involves a large number of trades executed in very short time frames, using complex algorithms. The video mentions HFT in the context of scalping strategies, noting that such strategies are challenging for most traders unless they operate at the level of a high-frequency trading firm.

💡Strategy Factory Club

The Strategy Factory Club is a part of Kevin Davey's Strategy Factory course, where students create and submit trading strategies for review. The video uses the club as a source of data and insights, having analyzed over 1500 strategies submitted by students, to support the argument for the effectiveness of simple algorithms.

💡Algorithmic Trading

Algorithmic trading, also known as algo trading, is a method of executing orders using pre-programmed trading instructions that use various algorithms to determine order entry and exit points. The video's main theme revolves around the advantages of simple algo trading strategies over more complex ones, highlighting the practical experiences and data from the Strategy Factory Club.

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

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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

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