Candidacy for Artificial Intelligence in Trading

Candidacy for Artificial Intelligence in Trading

Conquering Volatility for More Consistent Returns

Nolen Reijn Foxx

Executive Summary

The stock market has become more volatile than ever since the dawn of algorithmic trading, with one seasoned trader saying, “What is left is not trading, in my opinion. It’s much more game theory, algorithm-based.”

Margie Teller had been trading for 26 years stating, “…it was social, and you know, people would joke around. You were rarely fighting against other locals. Everybody needed everybody else.” Insinuating that trading was once a people oriented business. When asked, “What advice would you give to someone who’s looking to go into trading [today]?” Margie’s advice, “Don’t do it.” 1

Many traders feel the same with an estimated 80% of all traders failing and quitting and only 6% making the cut as professional traders. 2,5

Despite the rise of volatility and a changing landscape, 62% of wealth managers say they will not make any meaningful changes to advisor discretion over portfolios. Yet their respective firms continue to solicit for investor funds, collecting fortunes in management fees before their clients ever see a return. That decision has already cost investors at least $9 Trillion since July 2022, alone.

3,4,11

“They’re not creating a market function. They’re trying to take their little piece off the top without giving some sort of value back.”

— Margie Teller, Veteran CME Trader1

As fiduciaries, we must first ask ourselves is algorithmic trading hurting or helping the economy? Secondly, how do we prevent further loss amidst advisor negligence?

It starts with admitting the truth. The great majority of RIAs (and financial advisors) are not seasoned traders. The lack of expertise in an otherwise professional field is

a major cause for concern. Despite this fact, wealth managers continue to recruit newer, younger and less skilled advisors. 10

Additionally, training new recruits in the skill of trading has been made more difficult by the lack of available experts on the topic — many of whom trade only for themselves — and the declining predilection towards human skill in the financial markets.

To illustrate, many institutions have chosen to answer this call for action with algorithmic trading. Seeking to replace human expertise with “if/then” boolean computer logic. However, new evidence reveals this solution has not been profitable, with Wall Street now seeing sharp declines in revenue, despite replacing thousands of floor traders with algorithms. Proving the intervention to be more of a detriment than a boon to the entire financial industry, at least thus far. 8

So, how do we deal with this problem? Prospects may be pointing to artificial intelligence.

In this whitepaper, we will explore possible candidacy requirements for trading systems qualified for machine learning and artificial intelligence. With the premise being that if an A.I. imitated the thought process of the 6% of successful traders in the world — traders who regularly and successfully defend themselves against algorithms on a daily basis. Then wealth managers may stand a better chance of managing client funds — despite reluctance to change advisor discretion.

This may be our most viable defense against rapidly increasing volatility in the stock market due to algorithms. The result could see a decrease in unnecessary portfolio loss and an overall stabilization of portfolio growth.

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Criterion

of Winning Trading Systems

In order to qualify a trading system for machine learning and artificial intelligence. We must first answer, is the trading system good enough? Will it achieve the desired result? And for that matter, what is the desired result?

Most investors would like to see a return of 10% or higher from stock market investments. This is on par with the year over year growth of the S&P 500 index. The problem is, that average has been calculated over decades. The indices have never achieved a consistent 10% growth year over year. Which is what investors really want: 10% ROI or higher — with consistency. 7,9

In studying systems that consistently produce 10% APY or higher. We have come down to 3 winning criterion for A.I. candidacy.

utilize this average fluctuation to plot internal profit targets.

Successful traders utilize several technical analysis tools to predict profitable entries and exits in security trading. These tools include (but are not limited to): Candlestick Patterns, Trend Lines, Moving Averages and the Relative Strength Index (RSI).

The most advanced traders never rely on a single analysis tool, but the convergence of many tools along with divergence on the RSI. Allowing these traders to time trade entries and exits down to the precision of a dime.

2. Internal Profit Targeting

Every stock has an average range of movement in a given time period. We call this movement the stock’s average oscillation. The most advanced trading systems

Entering at the edges of the fluctuation and exiting at a significant high or low inside the range. Never needing the entire move for above average market gains.

Finally, advanced trading systems will have a way of detecting overall sentiment for a stock and when that sentiment has undoubtedly changed. Volume can be a clue and while many expert traders use candlestick patterns found on weekly and monthly time intervals as key indicators of trend continuations or reversals — fundamental analysis is almost an afterthought. Even fewer view the news as a viable source of intelligence.

“Fundamentals that you read about are typically useless as the market has already discounted the price, and I call them “funny-mentals.”

—

Ed Seykota, Renowned Trader 15

1. Convergence

3. Market Sentiment Detection

Internal Profit Targets

Convergence

The primary difference between a professional trader and an amateur is in the use of technical indicators. The amateur trader uses more screens than a jet fighter, yet is either keen on only one indicator or knows of many — but has mastered none.

The professional trader or expert may utilize one screen and has become a master of at least 3 indicators (and often needs no more). These indicators usually include:

1) Japanese Candlesticks or Western Bars

2) Trendlines

3) Relative Strength Index or Moving Averages

These are all that are necessary to trade profitably and consistently — and are the foundation of a winning system. Like any other profession, how well a practitioner performs is down to his or her understanding of their own tools. It’s about experience.

What experience eventually teaches a professional is the rule of convergence: when 3 or more indicators are successfully fulfilled, simultaneously.

That is when a candlestick pattern or bar chart pattern signals an entry, at a proven trend line, converging on a moving average or the RSI. This is the most opportune entry, and in the experience of the author, rarely fails.

This is because algorithms are designed to create liquidity or play tricks with the amateur majorly in the middle, where there is no convergence of signals. All to simply create liquidity. Something that is unnecessary at points of convergence because orders are already waiting.

An illustration of convergence is as-follows.

1) A piercing pattern emerges amongst the candles:

2) At or near a trend line (in this case, two):

3) Converging on a moving average:

A real life scenario of this convergence can be seen here, happening twice, highlighted in green:

Exhibit 1.5 Convergence happening in ticker BTU via candlestick pattern, lateral trend line, rising trend line, Bollinger Band Moving Average and Relative Strength Index (RSI).

Internal Profit Targeting

If Convergence is the foundation of a winning trading system. Then Internal Profit Targeting would be the roof — and a house does very little for the man or woman with no roof.

As so, a trading system that can win against the tides of volatility is one that has a target. It typically does not matter what time interval this target is plotted. What matters is that it occurs inside a stock's average oscillation, per that time interval.

You can find any stock’s average oscillation by calculating the average trading range of the last 30 sessions, on any time interval. Staying risk-averse by rounding the resulting number down.

For example, at the time of writing this whitepaper, AAPL has an average daily oscillation of 4.00 (rounded down from 4.267). This would be the extreme trading range.

So if the discerning trader were to enter the stock at the lowest possible price point, on any given day. They could effectively hold that position for a single day — and chances are they will score 4 points on a single trade (as seen in exhibit 1.6). Though “chance” is not a strategy.

“I believe in analysis, not forecasting.”

— Nicholas Darvas, Dancer & Legendary Stock Trader

Instead of exiting on the extreme end, in hopes of a big win. Some successful traders admit to exiting beforehand. If little more than to avoid the rush of retail traders, who often catch the trend at its end.

For example, in AAPL’s case, a professional trader might look for a 1 point internal profit target for a day trade, effectively a fourth of the stock’s average oscillation, and no more than a third. That target is more certain than waiting for the stock’s high.

Buying a stock’s high is precisely the reason amateurs (including amateurs with licenses) lose large sums of money (and their clients’ money) in a very short period of time. While a professional, who is often not licensed at all, can make a full time income with as little as $10,000 or even less.

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“Amateurs think about how much money they could make. Professionals think about how much money they could lose.”

— Jack Schwager, Veteran Trader and Author 14

“Wall Street is the only place that people ride to in a Rolls Royce to get advice from people who take the subway”

—Warren Buffet, The World’s Most Famous Investor 13

Exhibit 1.6 30-Minute chart of AAPL rising 4.65 points in under 5 hours, proceeding an upward trend after bottoming out on the RSI.

Market Sentiment Detection

Keeping our house theme — we now have our foundation, our roof, and we’ve just arrived at the door: Market Sentiment Detection. You cannot walk in or outside of a house without a door — less you break down the walls that keep it standing.

Detecting market sentiment is to the professional trader what checking the wind is to the sailor before embarking on what could otherwise be a dangerous trip. A sailor doesn’t look for excitement and again, likewise, a professional trader doesn’t look for a thrill. In both cases boredom is sought because fun could lead to demise.

Even still, you have your amateur sailors that want to use every bit of technology before setting sail. While the pro simply licks their finger and puts it in the wind. Then you have your amateur trader who subscribes to several different news feeds, is active on one or more forums and reads every article forwarded to him by his or her broker. However the news is rarely a factor in detecting market sentiment; the precursor to a stock’s ultimate price direction. Many times the news is not even real.

Several articles are published throughout the week on stocks. While some of them have a basis. Most of them do not and quite frankly none of it matters. What matters is supply and demand.

Perhaps the world’s greatest advertiser, Eugene Schwartz, stated in his book, Brilliant Advertising, “Copy cannot create desire for a product.” The same is true of news. News cannot create desire nor can it quell it. All the news can do is reveal what is already there. Let’s take a look.

On August 17, 2022, TheStreet.com published an article titled, “Peabody Is Glowing Hot”. This resulted in an immediate hike in the stock’s price from 23.24 to 29.14 in just 7 days (see exhibit 1.7).

Just this month, on December 13, 2022, a similar article was published on titled, “Peabody Energy Stock Burns Hot Near Buy Point” then…crickets:

This article, like the last, was an attempt to create demand. The difference is the rally prior to August 17 revealed the demand was while the bearish engulfing pattern on the week prior to December 13 revealed demand was weak (see exhibit 1.9).

Checking weekly technicals is the equivalent of a trader simply putting their finger in the wind.

We don’t care about ‘why’. Real traders only have the time and interest to care about ‘what’ and ‘when’ and ‘if’ and ‘then’. ‘Why’ is for pretenders.”

— JC Parets, Expert Technical Analyst 14

A Word On Stop Loss

Before we move onto proving the viability of using these 3 criterion explained in the pages prior. The author would like to include his findings on effective stop losses.

First of all, it has come to mind that most RIAs and financial advisers must not know that it is possible to reduce a client’s loss — by simply pulling the client’s money out of the market during “bad times”.

One solution is to use a stop loss.

A stop loss is a core function of any broker’s trading program and is simply a fail safe order to get out, just in case the stock starts moving against you.

“There’s nothing that upsets me more than a guy saying that, ‘This is a very risky trade. I'll use a tight stop loss.’ That's just rubbish because you’ll get stopped out over and over and over again.”

— David Paul, Veteran Institutional Trader 6

Next, there is the theory that you want to keep your stop loss very far. This is the most far fetched of either theory because if the stop loss is hit, the manager will lose significant money, and thus value to a client’s portfolio.

The best solution the author has come up with, which is agreed upon by only the best traders in the world. Is the use of no stop loss, at all.

It sounds ridiculous.

However after over 4,000 hours of testing, it was found that not using a stop loss forces the trader to make better decisions with a client’s money. Knowing you do not have any brakes, you will likely take the slower, safer road and avoid the highway.

And so it is with the professional trader. By not using a stop loss, the trader will work harder to be more certain that a trade fits all criteria to a perfect T (as the reader will see in the next few pages).

Now there is much debate over the use of a stop loss. Many claim you want to keep the stop loss tight. However, in testing this theory many times, it would seem algorithms are semi-aware of stop loss orders and recognize them as discounted liquidity: shares available at a better price.

On most occasions, the stop loss will be hit and a trader would soon be out of the trade with less money than he or she started with. So close stops have been ruled out of a truly winning trading strategy.

Once the stock begins to move off your entry, that entry then becomes the stop loss. Allowing the trader to “be wrong for free” if worse came to worse.

A trend line can then be developed at the lows of the proceeding sessions (as depicted in Exhibit 2.0), and if crossed prior to reaching the internal profit target, the trader can then exit — having secured the majority of the profit.

Trading this way has been able to reduce losses by an estimated 90%.

Exhibit 2.0 Depiction of the stop-loss process where 1 is the order entry point and 2 is the stop loss range. If the trend line from 1 to 2 is broken, the stop loss order is engaged and the trader exits without incurring significant loss.

Demonstrating Short-Term Viability

Trifecta™ is a trading system that was developed by the author over the period of nearly 2 years and over 6,000 hours of consistent trading. It fits all criteria of a winning trading system. Now, we will backlog a trade to determine viability.

At 10:45AM on August 8, Trifecta™ detected an entry according to all criteria above and an order was placed for the stock at 19.02.

preference for a bullish entry.

The order was then filled for 1600 shares at 10:47AM and the market sought no lower during this session. It also sought no lower for the next 14 days as the stock climbed to 29.14. It should be noted that the stock still has sought no lower at the time of this writing -- topping out at 32.89.

This demonstrates how effective Trifecta™ can be in terms of timing the market within the accuracy of a penny — even for long holds. Although that was beyond the scope of this test.

Next we will look at the viability of these criteria by testing Trifecta’s™ performance over an entire month.

Exhibit 2.1 Screenshot of piercing pattern occurring August 7, 2022

Exhibit 2.2 Screenshot of hammer occurring the week of July 8, 2022

Exhibit 2.3 Screenshot of order just filled at 10:45:13 AM, August 8, 2022

Demonstrating Long-Term Viability

To test Trifecta™ for longer holds, we will back log trades for an entire month from late June into July 2022. When the system was being "run hot”.

With the same behaviors leading to the same actions every day, guarded by the criteria explained in previous sections. Trifecta™ has been able to double an account within one month, beating the Dow Jones by an astounding 3,816%.

Little more than saying that if packaged and distributed to willing wealth managers and their suite of advisors, Trifecta™ could certainly reduce client portfolio loss and increase stability — generating more profit for clients in less time, with greater consistency. “Especially if run at a more realistic level”, Nolen adds.

Overall, this is a true example of how powerful technical analysis, alone, can be and why it should continue to be the only basis for any augmentation in the application of machine learning — in pursuing the manufacture of artificial intelligence for securities trading.

However promising, upon a deeper look, the testing did reveal a flaw.

This was all done while under the constraint of only 3 day trades per week, and to the author’s opinion, has even greater potential if left without restriction.

“The test served to prove that an effective trading system can be tremendously profitable. Despite the negative effects of algorithmic trading” Nolen says. “At times the system seemed to have even taken advantage of the algorithms. Using their own predictive behavior against them to land better entries and avoid unnecessary loss.”

In the next page we will examine this flaw and how Artificial Intelligence might remedy the factor.

Exhibit 2.4 Screenshot of Trifecta’s™ day-to-day performance from late June into July — revealing a flaw.

Exhibit 2.4 Screenshot of Trifecta™ ahead of Dow Jones (DJI), which at the time saw a 2.76% loss over the last month while Trifecta™ was ahead at 102.58% — a 3,816% rate difference.

The Advent of Machine Learning in Trading

Now on the pointed end of this Whitepaper, the author will reflect on a previously unpublished interview concerning the “red days” of the testing period.

“Trader fatigue is real and that’s something we just have to deal with.

“On those days I believe I was trying to make money rather than make the move and mis-timed my entries. Although losses were made up in a very short period of time by adhering to the criteria more strictly. Even so, that’s what happens when you run a system too hot -errors occur.

“We have to be honest about the implication of the human element in something that will clearly, one day, be completely dominated by a machine.”

Despite the human element being involved here. Adhering to the system did manage to beat the major indices by more than 3,800%. So even the human element is not a complete deficiency, but it does leave the application open for artificial intelligence.

“Silicon valley has already started implementing machine learning in trading. In fact, that’s what algorithms are based on. It’s currently estimated that 75% of all stock trading is algorithmic. Yet, these algorithms aren't good enough. We need to take the matter beyond machine learning and into the realm of true A.I.”

Machine Learning gives a computer the ability to learn. It is the foundation of A.I. but is considered rudimentary in application in that it only pertains to the use and repetition of simplistic patterns. This is the level of A.I. used in most technology today including Netflix, Siri and Erica.

As we can agree, a seasoned trader behind the wheel of a qualified trading system still must make “intuitive” decisions.

“There are times I might stay out of a trade simply because the chart doesn’t feel right.”

—Nolen Reijn Foxx

And in that response we find the problem. Computers do not yet feel. This feeling the author spoke of during the interview is the result of his neural network, including his sympathetic nervous system, having been disciplined over a long period of time. Wherein mistakes made in the past were compartmentalized and stored in his nervous system as “feelings” or unconscious competence.

And so it is with others alike. As a professional trader’s experience grows, so does his or her vast network of neurons. Forming deeper, more robust connections, as the neurons augment systematically. This process provides the difference between an expert and a novice, and at this stage, trading algorithms are still novices.

So how do we advance the process to give automated trading the same "gut feeling” a disciplined human trader might have developed? That’s where deep learning comes into practice.

Exhibit 2.4 Neurons making new connections, enabling unconscious competence, giving the ability to trade with “gut feeling”

Beyond Machine Learning

Deep learning is a sub-category of machine learning. The primary difference being whereas machine learning gives a computer the ability to recognize and imitate patterns — and is at the core of modern day trading algorithms.

Deep learning allows a computer to mimic a human's neural network. At its best, this application may birth a trading A.I. with “gut feelings” — though with the ability to be emotionless otherwise.

The latter part of that statement is what will give an artificial intelligence, trained to trade, an advantage over a human. While the best human traders can work without experiencing burnout or having personal life events cloud their judgement. That isn’t the case for the majority, and we can’t forget that wealth managers do not plan to change advisor discretion, let alone train advisors to take an active role in their client’s funds.

Adopting an artificial intelligence that can process complex layers of human thought — imitating the profitable intuition of a seasoned trader — could assist these money managers in portfolio recovery, limit loss and avert risk -- leading to greater, more consistent client portfolio growth.

Though, the author would be reluctant to remove the human element from the equation entirely (albeit that’s where the future will inevitably take us). For now, keeping the advisor or lesser experienced trader on as a “pilot” would be a more holistic and economically responsible decision, in terms of providing jobs.

The author is looking forward to Trifecta™ becoming a leader in the marketplace for such an application.

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16. Cover art by “and machines”.

Note from the author:

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