NEVER use a backtest to
improve your strategy idea
Stop wasting your time
with backtests! Do this
instead…
NEVER use a backtest to
improve your strategy
idea
Beginners rely on
backtests to tell them
whether or not their
strategy is a good
idea.
While backtests provide
valuable information,
attempting to tweak your
strategy solely to
improve backtest results
can lead to undesirable
outcomes. Some of these
include:
-
Highly inefficient! Your approach isn’t
systematic, so how do
you know you tweaked
it to the right
parameters?
-
Extremely
dangerous!
Trying to improve a
backtest results over
a specific time period
is bound to lead to
overfitting.
-
Massively
unscalable! Are you going to
tweak the parameters
every week? What if
the best parameters
for this week are
different from the
best parameters for
next week?
There is a better, more
efficient, highly
scalable solution.
Automated Strategy
Optimization with
Genetic Algorithms
The initialization step
in genetic
optimization
There are a lot of big
words within this
heading, so I’m going to
break it down.
-
Automated: Operated by computers
without human
intervention
-
Strategy: Planned approach to
achieve long-term
goals or
objectives
-
Optimization: Process of making
something as effective
as possible.
-
Algorithms: A step-by-step
procedure for
completing a task.
Think like a baking
recipe
-
Genetic Algorithms: An algorithm inspired
by natural
selection
Essentially, automated
strategy optimization
uses a
biologically-inspired
step-by-step procedure
to make something as
effective as possible.
We can use these
algorithms to
automatically find the
best parameters for our
portfolio without having
to manually run hundreds
of backtests.
If you’re curious about
how it works under the
hood, check out the
following
articles:
How it works in
general:
From Beaks to Bytes:
Genetic Algorithms
in Trading,
Investing, and
Finance
A Biologist's
Guide to Financial
Evolution Through
Artificial
Intelligence
Pssst, You! The
original story is
on Medium…
How to customize a
genetic
optimization:
Mathematically
Improve Your Trading
Strategy: An
In-Depth Guide
The Most Important
Guide for All
Traders in 2024
Austin Starks ∙ 8
min read ∙ View on
Medium An In-Depth
Guide On…
Using a Genetic
Algorithm as a
Replacement for Manual
Backtests
Genetic algorithms are
the better way of
improving your
strategy’s
parameters.
Notice this is similar
to how you would find
the best parameters for
your strategy, but it
does so automatically
and systematically. It
also has techniques for
limiting overfitting,
such as train/test split
and separating the
training set into
segments. Here’s how you
can apply it in
practice.
Step 1) Create (or
copy) a portfolio of
trading strategies
The step-by-step
guide on
outperforming the
market
Making money
either involves
doing research or
taking risks
Austin Starks in
DataDrivenInvestor
∙ 5 min read ∙
View on…
I’ve written plenty
of articles (like the
linked one above)
about how someone can
create their own trading
rules for their specific
goals. So, this article
will not talk about it
in detail. If you want
to follow along, pick a
random strategy in the
NexusTrade Strategy
Library.
NexusTrade Strategy
Library -
Algorithmic Trading
Strategies
Explore our
collection of
pre-configured
algorithmic
trading
strategies.
Analyze
performance,
initiate
backtests, view…
Step 2) Launch a
genetic optimization.
Iterate, and
improve.
A genetic optimization
with NexusTrade
The process will find a
set of portfolios that
achieve your specified
goals. For example, if
your goal is to minimize
drawdown, it might
create portfolios with
the lowest possible
drawdown — exactly 0,
which means it won’t
trade at all!
Learn the quirks of the
optimization process.
Look at different
generated portfolios,
and see if they match
your goals. Try
different fitness
functions, and learn how
the optimization process
leads to different end
results. Speak the
language of the
optimizer.
This portfolio has a
really low average
drawdown, so it worked
right? (wrong)
Step 3) Experiment with
more advanced
features
After you have a good
feel on how the
different configuration
options
(often called
hyperparameters)
affect the portfolio,
you’re ready to start
using some of the more
advanced options!
By default, the
optimization process is
unbounded; a particular
parameter can be
anything at the end.
But, if we had a
specific strategy (like
a stop loss), we often
want the result to be
similar to original
portfolio. At the very
least, we want our “stop
loss strategy” to still
be a stop loss strategy
at the end.
We can do this by
updating our strategy in
the UI and clicking
“Advanced
Options”.
Advanced strategy
configuration
options
Step 4) Master the
technique and re-run
your
optimizations
As time moves forward,
the best set of
parameters for your
portfolio are going to
change too! By relying
on an automated
approach, we can
continue to evolve our
portfolio with the
turbulence and
ever-changing nature of
the stock market
The Drawbacks of
Genetic Algorithms in
Trading Strategy
Optimization
While genetic
algorithms offer
powerful optimization
capabilities for trading
strategies, they come
with several important
drawbacks that traders
should consider:
-
Overfitting: Genetic
algorithms can easily
lead to strategies
that are overly
tailored to past data.
These overfitted
strategies may perform
exceptionally well in
backtests but often
fail when applied to
live markets with new,
unseen data.
-
Computationally
expensive: Running
genetic optimizations
can be extremely
resource-intensive,
especially for complex
strategies or large
datasets. This can
result in long
processing times and
high computational
costs, potentially
limiting the frequency
of strategy
updates.
-
Over-reliance:
There’s a risk of
becoming too dependent
on automated
optimization,
potentially neglecting
human intuition and
market understanding.
This over-reliance can
lead to a false sense
of security and may
result in strategies
that work well in
theory but lack
real-world
robustness.
In summary, while
genetic algorithms can
be a powerful tool for
strategy optimization,
they are not without
significant drawbacks.
Traders must be aware of
the risks of
overfitting, the
computational demands,
and the danger of
over-relying on
automated processes.
Successful use of
genetic algorithms in
trading requires a
balanced approach that
combines algorithmic
optimization with human
insight and continuous
critical
evaluation.
Concluding
Thoughts
Improving your existing
trading strategy doesn’t
have to be laborious,
boring, and
time-consuming. Just as
deep learning
practitioners use
genetic algorithms to
find the best
hyperparameters for
their AI models, we can
use genetic optimization
to improve our trading
strategies.
With genetic
optimization tools like
those offered by
NexusTrade, traders can
systematically improve
their strategies without
falling into the traps
of manual backtesting.
This approach not only
saves time but also
leads to more robust and
adaptable trading
strategies. By
leveraging genetic
algorithms, you can
explore a vast parameter
space efficiently,
uncovering optimal
configurations that you
might never have
considered through
manual tweaking.
As you become more
comfortable with genetic
optimization, you can
start to incorporate it
into your regular
trading routine.
Consider running
optimizations
periodically to ensure
your strategies remain
effective in changing
market conditions.
Remember, the goal isn’t
to find a “perfect”
strategy that works
forever, but rather to
develop a systematic
process for continually
adapting and improving
your trading approach.
By embracing this
powerful tool, you can
stay ahead of the curve
and potentially achieve
better long-term trading
results.
···
Thank you for reading!
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AI
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in practice?
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