3 Proven Trading Strategies You Can Deploy Today

In our latest session with Christos — a seasoned trader with over three decades of experience — we walk through a powerful, real-world trading workflow that takes you from strategy concept to live deployment. You’ll get a behind-the-scenes look at three robust algorithmic strategies built using StrategyQuant X (SQX) and deployed live on AlgoCloud.

What’s unique? These strategies aren’t based on hype or guesswork. They draw from timeless trading concepts published decades ago — yet they still outperform today. From “buying the gap down” setups to breakout patterns and mean-reversion plays, Christos reveals how he builds, tests, and manages over 35 live trading bots across Alpaca accounts.

We show equity curves, backtest stats, and even walk through live trade examples. Whether you’re a beginner or an algo pro, this is a masterclass in turning proven ideas into automated systems — without reinventing the wheel.

👉 Watch the full video and start testing the strategies yourself. And don’t forget to drop your questions — we’ll answer them in an upcoming video!

transcript:

And I want to present the workflow. Here’s the equity curve that we get
doing this particular backtesting. I like the fact that it’s a smooth, somewhat smooth,
upward sloping. I look for stocks that are above $5. Here in this line, I’m saying that
constrained accounts, if the account is less than $25,000, you can still deploy this particular
stock. So here’s the SQX pro. Today, I’m joined by my friend Christos, a true trading veteran
with decades of experience. This session is pre-recorded. However, we are here to answer
your questions if you have any. Christos will share three trading strategies for AlgoCloud,
all built on proven knowledge that’s been around for years. The message is simple. You don’t need
to reinvent the wheel to build reliable trading robots. We are looking forward to your questions
and feedback in the comments. We’ll collect them and return to them together with Christos
in a future video. And just one quick note. Everything you will see is for educational
purposes only, not for financial advice. Christos, over to you. So hello, Christos. Again, nice to
see you. Hello, Cornel. Nice to see you again. Hello, everyone. Today, I’m excited to present
three particular strategies that have developed almost a year ago. And I’ve actually deployed them
into Alpaca Live accounts. And I want to present the workflow that took place from the original
idea inception through the modeling using the SQX language, through the backtesting,
and finally, through the deployment. Before I get to discussing the particulars for these three
strategies, I want to talk a little bit about my background as a trader.
I started my trading career in the late 80s. And initially, I focused on futures trading.
But slowly, I got interested in stocks trading. And even though the road to futures trading
was a little bit challenging, when it came to stocks, it was struggling. I was struggling.
The struggle was due to the fact that I was a discretionary trader. And
as a discretionary trader, I was trying to combine fundamental analysis with technical analysis. And
it was challenging just to find the right mix. It was very time consuming.
And it had a lot of ups and downs. I used to do sector analysis.
I was looking at fundamental analysis, listening to earnings calls, reading news releases,
of course, doing some technicals, such as breakouts with high relative volume.
So as a result of all this, the performance was inconsistent when it came to stock trading.
So I had been looking for years to automate this stock trading effort because
I realized that that was the only way for me to proceed.
In early 2024, I came across the strategy context tools. And I immediately realized that that’s the
solution that I was looking for. It was allowing me to quickly implement an idea,
do extensive back testing. I had the database right there. I had the stress testing part right
there. And lastly, the onboarding of the strategies onto a live account was a very easy process.
Now, I have a total of 35 strategies deployed across four live Alpaca accounts. And
these strategies are a mixture in terms of how they approach stock trading.
Quite a few are mean reversion.
Some of them are trend following. Some of them break out. And a few of them
are seasonal and pairs trading strategies.
The stock universe that I have these strategies deployed on are the S&P 500,
NASDAQ 100, and Russell 3000. It’s important to point out that about 22
are single asset strategies. In other words, they deal with a single stock symbol
and try to capitalize on the availability of leveraged stock ETFs,
such as Apple has a leveraged ETF and NVIDIA and so forth. I have these single asset strategies
deployed as well. Now, it comes to the part where I get the inspiration for the strategy ideas.
Apart from my own strategies, I’m very much interested in searching various books and
various forums for ideas. The books have a distinct advantage, especially the old ones,
that the ideas are out in the open for many, many years. And when you do a back test
today, a lot of the data is out of sample. So, you can see immediately if the
idea stood the test of time or not. The particular books that I found helpful are the
Trading Systems and Methods by Kaufman. This is like an encyclopedia of a lot of good systems.
Also, the Encyclopedia of Trading Systems by Kach. Trade Systems that Work by Streitzman.
The Master Swing Trader by Farley. Particularly useful is the book by John Carter named Mastering
the Trade. And of course, the all-time classic, The Long-Term Secrets to Short-Term Trading.
Also, some additional books that I found helpful is the Old Handbook by Gil Morales.
Trade Like a Hedge Fund by Altucher. How Do Many Make Money in Stocks by O’Neill,
which is also a classic. And Entry and Exit Confessions of a Champion Trader by Kevin Davey,
which is very rich in providing particular entry and exit methods.
I am also looking at various accounts on Twitter, presently X. And one particular account I found
very useful is by Jeff Sun. This is the code name of Jeff Sun. And of course, there are a lot of
algo traders on Twitter. And lastly, on YouTube, I follow the StatOasis channel by Ali Casey, who
has done a wonderful job in presenting various ideas and also how he implements those ideas on
SQX. And also Kevin Davey’s algo trading channel, which also has some good ideas.
Let’s start with the first idea that I implemented. It’s called buying the gap down.
We all know that when people wake up and they see their stock
going down, they get very emotional. And quite a few investors can’t wait to get out of the stock,
seeing the losses piling up. So this particular idea the author is presenting is trying to
take advantage of this emotional decision that stock traders make when they see their stock
gapping down. So it’s worth mentioning this book was published back in October 3, 2004.
So basically, it’s been in existence for 21 years. So all the backtesting will do with
data up to 2025. For these years, it’s basically not a sample test. So here’s the basic idea. You
buy a stock when the day before was down. And today, the stock is opening
with at least a 5% gap relative to the close the day before.
But in addition to that, the QQQ is also gapping down at least half a percent.
And the method for exiting this particular trade is to liquidate the position if the gap is filled
or at the end of the day, whichever comes first.
I decided to modify this particular strategy after some testing.
And also because of the great power of SQX to test a bigger universe of stocks. Because
originally, this idea was had in mind the Nasdaq 100 universe.
So instead of using the QQQ as the market index, I use SPY, the SPY ETF.
And of course, I’m looking at the Russell 3000 universe. And the last modification is that the
strategy gets out of the position on the next day’s open. So there’s no target profit and not
closing at the end of the day. And this particular exit method is friendly for PDT constrained
accounts. If the account is less than $25,000, you can still deploy this particular strategy.
So here’s the SQX code.
So basically, yesterday’s close was below the days before of yesterday close.
The open today is less than 5% of the close of yesterday.
And here, this statement has a subchart, subchart 1. And in subchart 1, I have the SPY ETF. So we’re
saying here that if the open is less than half a percent of the close of yesterday of the SPY ETF,
then it’s a valid condition. I have a setting here that the close is greater than $1 because
we don’t want to deal with very low price stocks. And lastly, on the volume yesterday,
it will be less than $2 million because we want to focus on the small cap and mid cap. Because
I found this particular strategy is very effective on low caps and mid caps and
potentially because the institutional involvement is less. You get a lot of
small investors who get very emotional when their stock caps down 5%.
And we exit after one bar. So we’re not liquidating the position the same day that we’re taking the
position. This slide explains everything I just described. Here’s where I define the subchart 1,
will be the daily SPY closing. This is a setting you need to make on the SQX.
And now we come to the position score, where first of all, the strategy can open
a maximum number of position 5 stocks at the same time. And the ranking function
ranks higher stocks that open down the most. So this expression gets maximized as the
open is lower relative to yesterday’s close. So this slide explains the previous slide.
The money management function I have is $100,000
with no reinvestment. So for the duration of backtesting, we have a fixed capital of $100,000
which we’re not reinvesting. And the period that we’re backtesting is from beginning of 2000
to about the end of 2044. And of course, the Russell 3000 universe.
And this slide here shows the condition. Basically, we enter on bar open
and exiting on bar open. Now, I need to make a comment here that when you
deploy the strategy on a wide range of markets, it will execute on 9.32 AM.
That means that in some instances, the open have happened,
let’s say, right after 9.30. So you’re missing the first two minutes. Now, in practice,
over a year after deploying this strategy for a year, it doesn’t make all that much difference,
but you need to be aware of this. And later on, I’ll explain how you can sort of remedy this
particular discrepancy in the time that the strategy takes position.
Just a comment that in my data bank, there are over 12,000 symbols for the Russell 3000.
So quite a few symbols have been delisted. So the backtesting includes a lot of
delisted stocks. So we don’t have survival bias in our backtesting.
Here’s the equity curve that we get doing this particular backtesting. I
like the fact that it’s a smooth, somewhat smooth, upward sloping equity curve.
In the x-axis, we have the number of trades that have been done over this particular
time interval. It shows a few bumps, and you’ll see that occur in certain years.
Listing the same strategy
with the x-axis as being the time, it shows that for some particular
stretch of time, the performance wasn’t all that great, even though it was positive.
The drawdown is a very respectable 12%, and that’s the open percent drawdown.
And the yellow curve down here is the buy and hold performance on a risk-adapted basis.
Also worth noting is that the exposure time is very small. My screen at the moment is small. I
can’t see very well, but I think the exposure time is the order of 12% to 15%, which means
the capital is available to be deployed elsewhere. Now, going to the particulars
of the performance of this strategy is we see that it’s got a 64% average return
with comparative annual growth of 12%, and a return to drawdown ratio in excess of 46%,
and 62% of the trades are profitable. It’s a very, very nice strategy. I believe
this type of strategy of buying the gap down should be part of the strategy portfolio.
As you can see here, listing the years, the months in a row and the years in a vertical manner,
this particular strategy didn’t have a single down year from 2000 to 2025, which is very remarkable
given some of the bear markets we had experienced, the bear market 2008 or the COVID 2020.
Also, it’s worth noting that this particular strategy had two months in a row as negative
months, but the rest of the months were positive, which means that if we see
two negative months, we soon get disappointed. It’s part of how this particular strategy behaves.
Now, I want to show a particular example of a successful trade for this strategy. Of course,
60% are profitable and 40% are unprofitable. This particular one was picked because
it shows what a perfect trade is for this particular strategy. So, we’ll go back in
April 6th, and we’ll look at the SPY. The SPY had a closing of 505.26 and on April 7th,
it opened with a gap, certainly greater than 0.5%. At the same time, this particular stock,
Rimini, symbol RM and I, had closed this particular day here, lower than the day before,
and on April 7th, the same day that the SPY was gapping down, it got more than 5%.
And the position was entered at the open at $2.11 per share.
The strategy liquidated the position at the next day’s open, which was $3.35.
So, if you deploy this particular strategy,
what you can do is create some offshoots of this strategy, where
they not necessarily buy it at market, but you can put limits, so you can guarantee that you
you can buy it at a favorable price. So, apart from market order at $9.32, you can also have
offshoots of this particular strategy that may open, let’s say, at open minus a very small
percentage of ATR. Okay, let’s go now to the second strategy. This was inspired by
a posting by Jeff Zahn. Jeff Zahn has a good presence on Twitter with many good ideas. He
is a money manager, he has written a lot of filters for trading too,
and is one of the accounts that I’m actually following.
So, one particular idea that I liked was to go long authorizing exponential moving average,
20-day exponential moving average. So, basically, what we want to do is locate stocks that have
an upward sloping exponential moving average for 20 days, but over the recent
few days, they’re experiencing a strange range,
and we’re on the lookout for the stock to close above this particular type range.
So, what I’ve done is on the Russell 3000 universe, I look at the five-day range,
and when I’m talking about range, I mean the highest high and the lowest low not being more
than three percent over this five-day span. We want the close yesterday to be above this range,
and at the same time, we want the 20-day EMA to be climbing to be above the 50-day EMA.
So, basically, I implemented this particular way.
I look for stocks that are above five dollars.
Here in this line, I’m saying that the day before yesterday,
the maximum range for five days was less or equal to three percent.
We want the close yesterday to be above the highest high of the previous five days.
We want the EMA20 yesterday to be above the EMA20 of the day before yesterday. So, basically,
we have an ascending EMA, and lastly, we want the 20-day EMA to be above the 50-day EMA.
So, these conditions are, and how do we exit? We exit either the rate of days becomes negative,
or if that doesn’t happen, we exit anyway after 10 days.
So, in this slide, I’m explaining again what all the lines mean.
Now, let’s get to the ranking function and the maximum number of positions,
the testing interval, and money management.
We basically want to buy at the open, and we want to exit as well.
Now, we take a maximum number of positions to be equal to 10,
and our ranking function
gets larger the closer,
yesterday’s closest to the EMA. So, basically, we want to be as close to the rising EMA as possible
on a closing basis. So, if multiple stocks satisfy the previous conditions,
we select the ones that are the closest to the EMA20.
The interval, the testing interval, is from end of 1999 to mid-2025, August 2025.
Again, the money management is that we have $100,000 capital, which we deploy equally among
these positions, and we do not reinvest the capital. So, it’s like a fixed capital
throughout the testing period. This is the equity curve we get. It’s fairly smooth.
Again, the x-axis is the number of trades that this strategy takes. So, we see that
it takes quite a few of trades through the 25-year period. If we
set our x-axis to be in the time domain,
what’s remarkable is that in years such as 2008, when we had a severe bear market,
and also in 2020, when we had also a severe decline, that we still had a positive slope
in our equity curve, and that’s because of the condition that the 20-year EMA is rising.
Which certainly doesn’t happen in a bear market.
That the 20-year EMA is above the 50-year EMA, which also is a condition that doesn’t occur
often in a bear market. But more importantly, the tight range, the five-day tight range less than 3%,
it doesn’t happen because of severe declines in the stock. So, that condition doesn’t hold either.
So, effectively, this strategy is shutting down when the conditions are not favorable.
Now, the yellow curve here is also the buy and hold
SPY ETF. So, on a risk-adjusted basis, we see that this particular strategy is far outperforming the
buy and hold for the SPY ETF. This is the monthly performance we see from end of 1999 to
August of 2025. There have been two years so far, it remains to be seen what will happen in 2025.
And in addition, we see that this particular strategy may have like one, two, three,
four months in a row that are negative. So, if you deploy this strategy, don’t get this
discouraged if you see like three negative months back to back.
This is the table that shows the particulars of the performance of the strategy. It’s about 14%
annual return. The percent drawdown on an open basis is about 9%.
The profit factor is 1.49% and the return to drawdown ratio is a little bit over 22%.
And about half the trades are losses and half the trades are gainers.
And also, the comparative annual growth rate is 6.25%.
Also, I like the fact that the stagnation period is fairly small.
Here’s another table that shows the performance of the strategy.
Basically, it concurs with the previous slide. It’s mostly profitable with two years being negative
years. Now, this is an example of the strategy working as it should.
We basically have, on this particular day, a rising EMA20, which is the cyan color,
which is also above the blue EMA20.
We see that the previous five days range conforms to our criterion.
And we see that in this particular day, we closed above this tight range. So, we enter the next day
at the open, which is the 11th of July. And basically, we’re holding until either the rate
of change or 10-day rate of change becomes negative or 10 days go by. And obviously,
in this particular instance, the rate of change for 10 days never went negative. So, we’re exiting
here on the 25th of July.
Let’s see. Also, this is another example of how when the strategy works perfectly.
Again, we have an upward sloping EMA20. We have a tight range.
On this particular day, we have a closing outside this range.
We go long at the open of the next day.
And basically, we exit either when the rate of change for 10 days becomes negative or 10 days
go by. And in this particular instance as well, the rate of change never went negative. And we’re
exiting after 10 days. So, basically, these two examples showing when the EMA20
this strategy works perfectly. These are winning trades.
Okay. Let’s discuss now the third and final strategy.
Larry Williams published a classic titled Long-Term Secrets to Short-Term Trading.
And it was published back in 1998. And one of his
mean reversal strategies was the 7-bar rule.
And this particular strategy that he is commenting about, of course, he used it on
trading for futures. So, I decided to test the idea to see how well it will perform
when it comes to stocks.
So, the basic idea is that if today’s closing is the lowest of the last seven trading days,
you buy. And if today’s closing price is the highest of the last seven trading days, you sell.
It’s a very, very simple strategy and impossible to curve fit.
Again, I decided to utilize the power of the SQX and test it on the Russell 3000 universe.
So, it’s also worth noting that the strategy is 27 years out in the open.
So, all our backtesting data is out of sample.
So, here’s how I implemented the SQX code.
Just two lines. Basically, you want today’s close to be the lowest of the last
seven days. So, including today. Today is the seventh day. You want to be the lowest closing.
But I put a filter here. We want a linear regression angle of 100 days for the stock.
Be positive. So, basically, we want stocks that are in an uptrend for the last 100 days.
And we exit either when today, the closing today, is the seventh highest.
Or if this condition is never met, we exit after 20 bars.
Now, here’s the, I’m commenting on the previous slide.
Again, we buy on the bar close. And we also exit on the bar close.
The maximum number of positions we take are 10.
And for ranking function, I decided to select stocks which are satisfying the previous conditions.
But also, the strategy prefers stocks that are experiencing a low standard deviation or low
volatility. So, the lower the volatility here, the higher the value of this expression.
And I did that on purpose because when you’re buying the low of the last seven days,
the stock would be actually in a free fall. And we don’t want to be catching knives.
So, essentially, we want to focus on stocks that experience a mild decline
with a seven-day closing low. And not attempt to get into a stock experiencing high volatility.
In the testing period of 1999 to August of 2025,
the fixed return, again, is $100,000. That is equal to all the positions.
And there’s no reinvestment. The curve is smooth. There are certain years,
because of the severe declines, that we have some downward bumps.
We observe that, in general, this is an upward sloping equity curve.
The performance of this particular strategy is 13% annual rate with 6% compounded 1.44%.
And the return to drawdown ratio is a little bit of 13. And it has a fairly high winning rate.
I want to see something in the previous slide. I did not put the buy and hold for SPY,
but also it’s outperforming the SPY buy and hold strategy.
This is the table of the monthly performance.
Basically, it’s remarkable that it had only one negative year in this particular stretch.
But you may get something like three months of negative performance, like in this particular
year, or this particular year, and so forth. So, getting three consecutive negative months is
to be expected in this particular strategy. Now, let’s look at some examples.
This one has to do with ACLX, the stock here, where the seventh day closing was the lowest.
If you include this bar, plus 1, 2, 3, 4, 5, 6, was the closest low.
And at the same time, the linear regression slope, which is on the lower pane here,
is positive, is above zero. So, that means on a 100-day basis, this particular stock is on an uptrend.
The position got liquidated two days later, because the stock closed at a seventh-day high.
So, this particular strategy, you could be selling actually the next day. The next seven-day high
could happen any time, any time after you take the position. This is another example.
Just like the example before, we’re seeing that this particular stretch has low volatility,
and that’s what we want. Also, in this particular example, we’re experiencing low volatility,
and that’s exactly what we want. This particular day, we’re closing at the seventh-day
closing low. So, we buy at the closing, and we still have a positive 100-day linear regression.
And we sell three days later at the close, because the stock has reached a seven-day closing high.
So, these are my closing remarks. Basically, we’re living in a great era,
where we have access to the internet and a lot of sources for ideas.
And it turns out there are a lot of ideas, even if they’re old, they’re still profitable.
In order to avoid the danger of curve fitting, you can use these old sources and test them on
recent data, because effectively, they’ll be out of sample.
And I like a lot the SQX platform, the algo cloud, because you can very quickly, as I showed,
you can very quickly implement the ideas and do the backtesting. But I think the holy grail is to
find many different ideas that are active during different phases of the market,
and combine them, because that way you will
get a smooth equity curve and will diminish the drawdowns.
Thanks for attending this presentation. Back to you, Kornel.
Thank you. Thank you, Christos, for sharing those strategies.
And if anyone will have any question, just let us know in the comments, or you can ask even via email.
And we are ready to answer or even promise to record some video with the answers.
Right, we can record another video with all the answers or
any comments, any suggestions how to improve the strategies.
Yeah, so thank you again, Christos, and see you next time.
You’re very welcome. Very welcome. It was a pleasure.
And that’s all for today. If you enjoyed this video, don’t forget to hit the like button
and subscribe to the channel for more sessions like this.
You can also start testing all three strategies right now by clicking the link below.
Just create a free trading account at algo cloud and open the strategies directly.
If you have any questions or feedback, drop them in the comments. We discuss them in detail
in one of the upcoming videos. We are looking forward to seeing you in the next session.