The video provides valuable, actionable insights into a complex topic (quantitative trading) using clear explanations and visual aids. It addresses a common pain point for traders (strategies failing in live markets) and offers a solution, making it highly relevant and engaging for the target audience.
Summary
This video explains how quantitative traders use "loop engineering" to create and refine trading bots. It highlights the importance of iterative testing and an "out-of-sample gate" to ensure strategies are robust and not just fitting past data.
Structure
1Introduction to loop engineering for trading bots
How Quants use Loop Engineering to create a trading bot with alpha #ai #Tech
More from @ray_fu
More breakout videos from this creator.
The concept of strategy shelf-life and signal decay
6The fix: out-of-sample gate for testing on fresh data
7Call to action for a step-by-step guide
Product placement
The video showcases a software called "Iteron" (visible in the UI elements) which is used for quantitative trading strategy development and backtesting. The presenter explains the features and benefits of this software in the context of building trading bots.
Call to action
Like the video and comment 'Quan' to get a step-by-step guide on building this loop system.
On-screen text
How Quants use Loop Engineering to create a trading bot
most people don't realize that
quant traders never trust a strategy
the first time that it works
they run it through a loop that
test each strategy over and
over again until they find
one that sticks
here's how to build this out
most people come up with an idea
backtest this once and
then once it looks great
they go live and 2 weeks later
they are bleeding money
that strategy died
with nothing iterating on
it quant runs a loop testing strategy instead
you generate a strategy
backtest it score it
against a specific metric
read why it failed feed
that information back in
and then you generate a better version
then you test that version
read the failures and run it again
each round keeps the variants that score well
and refines the ones that did not
the whole edge in quant
trading is the iteration
and most people don't iterate at all
the loop also needs a way
to score your strategy
or else it just wonders
the metric that
quants use is called ICIR
and all it measures is how consistently
your strategy performs over time
a strategy that makes a little bit
money every single week completely beats out
a strategy that makes a lot of money in one
week and loses it all in the next
that consistency scores what
the loop optimizes
towards
and here's the second check
that most people skip
even a real strategy has a shelf life
and the signal decays in two days
you're basically just paying fees to chase noise
if it decays in 50 days
you have a real traceable edge
the loop rejects these short
life signals automatically
and this is the most important part
a loop that optimizes the
same data that was built on
does not find better strategies faster
it just finds prettier noise faster
every extra iteration is just another
chance to accidentally fit the past data
THE FIX
Test survivors on data they've never
seen. An out-of-sample gate.
called an out of sample gate
every strategy that survives
a loop gets tested
one final time
on fresh data that is never seen
if the consistency data holds on fresh data
then the score is real
if it does not
it gets thrown out no matter
how good it looked before
I created a step by step guide
on exactly how to build
this loop system
to help you build trading bots that find alpha
just like the video and
comment Quan
and I'll send it to you
follow for more ways to make money
and run your business
with tech and AI
Transcript
Most people don't realize that Quanticrats never trust the strategy the first time that works. They're running through a loop that tests each strategy over and over again until they find one that sticks. Here's how to build this out. Most people come up with an idea, back touch this once and then once it looks great, they go live and two weeks later they are bleeding money. That strategy died because it was one guest with nothing irritating on it. It Quantic runs a loop testing strategy instead. You generate a strategy, back test it, score it against a specific metric, read why it failed, beat that information back in and then you generate a better version. Then you test that version, score it, read the failures and run it again. Each round keeps the variance that scored well in finds the ones that did not. The whole edge in Quant trading is the iteration and most people don't iterate at all. The loop also means a way to score strategy or else it just wonders. The metric that Quant uses is called ICIR. And all the measures is how consistent your strategy performs over time. A strategy that makes a little bit of money every single week completely beats out a strategy that makes a lot of money and one week can lose it all in the next. That consistency score is what the loop optimizes towards. And here's the second check that most people skip. Even a real strategy has a shelf life. In the sign of the case in two days, you're basically just paying fees to chase noise. If it the case in 50 days, you have a real perishable edge. The loop rejects this short lifts signals automatically. And this is the most important part. A loop that optimizes on the same data that was built on does not find better strategies faster. It just finds pretty noise faster. Every extra iteration is just another chance that accidentally fit the past data. The fix that Quantic uses is called an auto sample gate. Every strategy that survives the loop gets tested one final time on fresh data that is never seen. If the consistency data holds on fresh data, then the score is real. If it does not, it gets thrown out no matter how good it looked before. I created a step by step gotten and exactly how to build the training bots that find Alpha. Just like the video and comment Quantic and I'll send it to you. Follow for more ways to make money and run your business with Tekken AI.
