June 24, 2024

You want to win, and you want to win big.

You are on a mission to build an investment and retirement fund for yourself and your family.

**The One Percent a Week Stock Trading Program - Part VI **will unravel more of its hidden potential and show that the strategy is highly scalable. I will also cover leveraging the portfolio for higher profits and even go for market-neutral scenarios.

This series of articles should help you achieve your goals. The last article (**Part V**) ended with a CAGR of 56% over 14.31 years, and I would like this strategy to do even better. But first, you need to understand what the strategy does to gain the confidence and determination to make it work for you.

**Part I** to **Part V** of this series are the foundation for what follows. They provide the context to understand what the strategy can and cannot do. The primary purpose was to evaluate its return potential, validity, and viability based on historical market data and, more importantly, to set realistic expectations for the future.

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If the simulations over past market data had been negative, there would be no interest in ascertaining what it could do in the future. It is why we do simulations in the first place.

If I had a strategy that underperformed SPY on past market data, I would have no evidence to suggest it could improve itself in the future. A trading strategy doesn't suddenly discover its future inner strength as it goes along, nor does it acquire feelings or opinions. A trading script will not do what it is not programmed to do.

Hence, a simulation, on whatever stock market data, has to outperform SPY as a strict minimum; otherwise, SPY would have been an easier and better choice, with the advantage of being a one-decision solution with known long-term expectancies.

What do we know about the * One Percent Per Week *trading strategy?

Every week, you take on trades, lasting at most five trading days, with one entry point and two possible exits. That's it.

You are dealing with two stopping times: one within a time limit and the other as a percent profit target that could occur anytime during the week. As for the time-limit exit, it would be at whatever the price on Friday's close.

This time-limit exit serves the same function as a stop-loss since the position is liquidated no matter what the price might be at the close on that Friday. Thus, the loss, if there was any, is stopped before it extends any further. This stop-all will also liquidate profitable positions that have not yet reached their profit target and still return a profit to your trading account.

With trades lasting at most five trading days, you are only subject to weekly price gyrations, up and down, nothing more.

The task is to improve this strategy and take it to higher levels. It might initially seem like a tall order since the * One Percent Per Week *trading script using TQQQ with a 7% profit target and no other explicit stop-loss has already achieved a remarkable 56% CAGR over the past 14.31 years.

It will be the starting point for this current undertaking.

I want this strategy to do more and go for even higher returns.

From the latest portfolio simulation metrics, I have 381 positive trades averaging a 4.54% profit per trade, while the average loss percent per trade was at -2.67% for the 363 losing trades. The average winning rate was 51.21% on 744 trades, slightly favoring the upside.

In a random environment, we should have had close to the same average profit or loss percent of about |2.7%| on both sides, something close to the average on the losing side of the equation where no stop-loss was set, except for the time-limit exit. A weekly standard deviation in stock prices will apply to both sides of the mean, so the upside variations are about the same as for the downside. Hence, the |2.7%| average.

The higher value of 4.54% on the positive side should have been a direct result of setting a profit target that was often hit, thereby raising the average return per trade. But this is not precisely the case.

When the profit target was removed, the average percent per trade rose to 4.67% on 358 trades, indicating that the target trimmed potential profits. However, this reduced the number of winning trades from 381 to 358 (23 winning trades less).

Another reason for the higher average profit per winning trade should be that those trades are catching part of the historical underlying upward drift, part of the long-term upside bias we see in the average US stocks.

The strategy also benefits from the value-weighted method used in QQQ and its higher concentration in technology stocks; it profits from these long-term tendencies.

QQQ, with its 100 highest-valued stocks on NASDAQ, is also a bet on America and the future of a nation. It follows in the footsteps of Mr. Buffett, who often advocated that he had made his bet on America long ago.

A compromise should be made in favor of a certain profit target level instead of trying to let the profits run. It might all have to be done within the one-week time constraint of this trading strategy. Removing the 7% profit target reduced the number of winning trades by 23. Those 23 trades might have hit the profit target, reversed course, and ended with a loss. The removal of the 7% profit target, as mentioned in **Part IV **reduced the strategy's potential results by over -80%.

Based on the last article (**Part V**), more losses were recorded when a -5% stop-loss was applied. It was as if not leaving the opportunity for those prices to rebound and turn a profit.

This strategy's potential performance declined by -80%, making it quite expensive. Therefore, you were better off not using an explicit stop-loss procedure, even as small as -5%.

I learned early from courses and published academic papers that we need to use a stop-loss to protect our portfolios and that having an automated trading strategy operate without them would be nonsense. However, a simple demonstration of a simple trading strategy shows that it is not always the best course of action. We should revisit some of those "assumptions" and question their validity.

This * One Percent Per Week *trading strategy is so simple you could execute it on your cellphone or tablet in a few minutes on Mondays between two sips of coffee from almost anywhere in the world.

Do not tell me that you do not have the time to do this; tell me that you do not have the money to carry it out, and there I will believe you.

You should find ways to get the money, borrow it, or start saving. The strategy is scalable up or down, and you can start small. It is up to you. But first, study what the strategy can do for you, then decide if it is appropriate. And if not, change it to suit your vision and understanding of the game.

Here is an idea: borrow $1 million and promise to pay it back in one $20 million payment in 20 years. Based on what will follow, you should find it more than easy to honor your promise. No need to cheat anyone. Furthermore, you will owe them a lot more for having helped you succeed.

**Strategy Enhancers**

Before going into the protective mode, I want to study the strategy's potential. How far can it go within reasonable and acceptable limits?

I want to push the strategy to higher levels, observe its behavior, and then set workable parameters that, even if they reduce total performance, remain an acceptable risk in light of the potential rewards. This is a sort of optimization first to get a rough idea of the possibilities and later apply some constraints and protection.

To do this, we need to understand better the inner workings of this * One Percent Per Week *strategy.

How much more risks will we have to take to increase performance?

We have in equation (2) as presented in **Part V**: F(t) = F_{0} ∙ ∏_{i}^{N}(1 + r_{i}), which represent a series of consecutive bets with their respective returns ±r_{i}. The series progresses as *N *increases with new factors added with their respective profit or loss. We could rewrite it as F(t) = F_{0} ∙ (1 + r_{1}) ∙ (1 + r_{2}), ∙∙∙ , (1 + r_{(N-1)}) ∙ (1 + r_{N}) where *N* is again the total number of trades. It is this sequence of trades that is of interest.

Upon closer examination of the bet on each trade b_{i}, it's evident that it's simply the outcome F(t) of the previous position F(t)_{(i-1)}. It implies that the bet size grows at the same rate as F(t), following an exponential function rather than a linear one.

As a result, the bet size becomes a stochastic function with drift and random jumps, just like F(t). This unpredictability extends to the bet size of even the next few bets ahead.

Generalizations, averages, and long-term expected values become important in such a scenario. Designing a betting system that considers these long-term expectancies almost becomes a requirement.

Starting with 50/50 odds, you'll need to gather relevant information that could give you a positive edge. An apparent edge, even though not explicitly stated, is already built in since F(t) has a long-term upward drift, and its average outcome is an exponential curve.

From observations over the last simulation, we can generalize certain notions that could also apply to other strategies. Understanding what the strategy does and mostly why it does it could be rewarding. With these notions, we could enhance specific areas while partially curtailing some of its adverse effects on overall performance.

The objective is not just to raise long-term returns but also to do it as safely as possible. I will repeat: you want to win the long-term game and win it big. Period.

It is up to each of us to set our game plan and how we will proceed in this maze that is out there to get us. It is not that the market has anything against us. Nonetheless, it will be our trading methods that will become the reason we might fail. Therefore, those methods should be justified and justifiable.

If a trading strategy cannot effectively demonstrate its ability to deliver results, it becomes time to consider alternatives.

The stock market has terrible table manners. If you want to give it all your money, it will take it all without blinking an eye or saying thank you. It is up to you to watch your plate.

We can participate in this game with hope; there is nothing wrong with that. But can I say it is not sufficient?

You need a game plan you know will work. You cannot put 30 years into it and then start over again. You have to make sure that what you will do will work to your benefit. Otherwise, why give yourself all that trouble and aggravation?

If the bet size is F(t)_{(i-1)} as in this all-in scenario, you need to know what were the worst circumstances. What were the actual drawdowns instead of just looking at the maximum recorded drawdown? You would be better served with averages, knowing that, at times, the result might be higher or lower. Regardless, only your trading account's cumulative sum of executed trades will matter. The rest is just paper profits and paper losses. It is the recorded trade that matters.

For example, the max drawdown for this strategy was -54.47% on the day of the 2010 flash crash. But that week's trade was recorded with a loss of -22.85% the day after on Friday's close. So, the loss was not -54.47% but -22.85%, which was recorded as a loss in the trading account, where it counts.

It does not minimize the loss, only stating that the maximum favorable or adverse excursions are not what matters. What counts is what is recorded in the trading account: the executed trades with their profits and losses. Your fear is never to see it all fly away.

You cannot escape the high degree of randomness and erratic price movements in stock prices. Even more so in a 3x-leveraged ETF. You can hardly predict future prices with any accuracy over the short term. A reminder: this strategy is operating on the "notion" that a Monday is a sufficient reason to take a trade. Let's be realistic.

It should have been considered nonsense from the start and no better than flipping a coin. The way the trade was placed, even though it was conditional on the session's opening price, it was still executed every Monday.

The reason is simple. The program issued a limit order above the open, making it instantly executable. It is slightly different from a market order in that it will get the ask, but only up to the limit price and no further; otherwise, it would not be a limit order and would be subject to slippage. Every Monday, a trade was taken for no reason other than it was the first trading day of the week.

That is the same as betting heads on every flip of a fair coin. You might face randomness but did not even try to guess the outcome.

Going with the limit order above the open, you almost assured yourself of taking a trade without any assurance of its probable outcome. But you knew there would be an outcome since the trade was time-limited and would be closed at the latest on Friday's close.

It starts to give the trade some properties. It can only benefit if the price rises during the week; otherwise, it might record a loss on Friday's close. Furthermore, with the profit target, the strategy had a second exit and was assured of a profitable one. Once the profit target is triggered, the invested cash and the profits are returned to the trading account and appear on your bottom line. During the weekends, your account is in cash. You have no worries about what Monday's open will be.

**A Trade Loss**

You lose 10% on a trade; you know it can happen, and it does. And since your bet was F(t)_{(i-1)}, you are left with F(t)_{i} = 0.90 ∙ F(t)_{(i-1)}.

Now, you need to recuperate that loss and might generate a new one in the attempt. The second 10% loss would put your portfolio value at 0.90 ∙ 0.90 ∙ F(t)_{(i-1)} = 0.81 ∙ F(t)_{(i-2)}._{ }Already, it is starting to be serious. It would have taken two bets to accomplish this anywhere within the *N* trade sequence. Can it happen? Yes. That is part of the problem we have to solve.

We would not do this with a -10% stop-loss policy since it would guarantee recording the loss. You would still have to dig yourself out of that hole.

In the * One Percent Per Week* strategy, there were 27 trades with losses greater than -10%, so a -10% drop does happen. However, none of those declines were two in a row, which could be considered to our advantage. Repeated drops in price are spread out, reducing their immediate impact since trades could show increases in between.

The strategy uses compounding bets. Bets will rise higher and higher as F(t) progresses. Your 10% loss in the beginning will be much less than the 10% loss near the end. Simply put: 10% of $100k is much less than 10% of $1,000,000 or $10,000,000. Yet, at one point, as your portfolio grows, you will reach that $1,000,000 mark and will suffer a -10% drop over a single week. You can almost count on it.

Since it is your game, the question becomes: what will you do about it?

First, let's start small and see the impact with a theoretical example. You have one million invested and get three trades in a row with a 10% gain. F(t) = $1,000,000 ∙ (1 + 0.10)^{3} = $1,331,000. Three bets, three weeks, 33.1% return, not bad.

Make one of those trades a -10% loss. The order in which they come is irrelevant since what you will get is: F(t) = $1,000,000 ∙ (1 + 0.10)^{2} ∙ (1 - 0.10) = $1,089,000. You still made a profit, but the picture changed.

The point is that it does not take much to derail your expectations. Instead of having a 33.1% return, you now have an 8.9% return because you lost on one trade along the way.

That is a 22.2% portfolio reduction when you only lost 10% on a trade.

The market did not feel sorry for your loss. Any profit you make was supposed to be in someone else's pocket, just as any loss you have is now in the other guy's pocket; it's just not in your pocket. You took a risk and lost, too bad. So, now, what do you do to fix the problem? That is the question.

Should you quit the game and walk away with your 8.9% gain? Or should you try to do something about it? You are only on week #3 of your long-term investment quest that could last hundreds of weeks more.

Your odds have not changed, nor is the market revealing its next move. Isn't the fourth bet expected to be a loss? You are still facing uncertainty, and if your next bet fails, you would be at F(t) = $1,000,000 ∙ (1 + 0.10)^{2} ∙ (1 - 0.10)^{2} = $980,100$, with a -1.99% portfolio loss.

Do you still take the bet knowing your odds are close to 50/50? Losing $100,000 a week is not your idea of fun. Look at it again when you will reach $10,000,000, that 10% loss will be worth $1,000,000. Will you stand up to that and not blink an eye? And, how about when you will reach $10,000,000? Then what?

**The Recovery Plan**

You need a structured recovery plan from the start. Your strategy should deal with it as the series of wins and losses unfold over the years. A weekly trade here and there should not be significant when achieving your goal will take years. You have to look at the problem from the point of view of averages, mostly long-term averages.

If you toss a biased coin with 53/47 odds and always bet heads, your expected number of wins will be 53. You will catch every single one of the head flips. You might have a 53% hit rate, but out of the potential headcount, you would have 53 out of 53. If you used a fair coin to bet on such a biased series, you would still have a 53% expectation.

The * One Percent Per Week* is built the same way. Every Monday, at the open, you bet long and wait for either of two exits. One is for sure at Friday's close, and the other once you have reached a 7% or 8% profit target.

Equation (1) in the previous article (**Part V**) does give a return compensation formula. You lost 10% on a trade, and you compensate with a slightly higher return on the next: (1 - 0.10) ∙ (1 + 0.10 / (1 - 0.10)) = 1.0. The second trade recuperates the loss simply by raising the profit target on that trade to 11.11% instead of letting it at 10%. It is not a big request but sufficient to get you back to even.

Nonetheless, you need to do more. You need to replace the bet you lost with another 10% gain: (1 - 0.10) ∙ (1 + 0.10 / (1 - 0.10))∙ (1 + 0.10) = 1.10, and still add another one to get back on track: (1 + 0.10) ∙ (1 - 0.10) ∙ (1 + 0.10 / (1 - 0.10))∙ (1 + 0.10) ∙ (1 + 0.10) = 1.331. You needed three additional trades to return to the same outcome as your first three profitable 10% trades.

You do have work to do. And this is in an uncertain and random-like environment such as the stock market. Even though you are back on track, you now have 33.1% over 5 weeks instead of 3 previously, thus a lower annualized return. So, you will need to compensate for that too.

Getting you back on track does not have to be on a single trade. It could be haphazardly distributed anywhere within the entire return series. The equation is still the product of successive returns as presented in previous articles: F(t) = F_{0} ∙ ∏_{i}^{N}(1 + r_{i}). You can add compensation trading policies or procedures that could enhance returns, such as the one used in this strategy.

The method waits for the price to rise almost halfway to its profit target before pushing it higher by another 1%. After getting halfway to its profit target potential, that trade is giving signs it could reach its new target and become a more productive bet.

The push is relatively small when the compensation factor could be distributed over hundreds of trades. For instance, that added 1% request for trades meeting the halfway criterion totaled 94 in the trading strategy.

The first such trade added $84.45 to the account, while the last one added $44,614.02 to the pot. That 1% policy helped to add some $20 million to a portfolio that started with $100k (see **Part II**). It is a way of distributing the compensation factor all over the price series and reap its benefits without knowing where prices will end up. You catch them as they go should they reach your profit target. More such trading policies could be implemented.

In the * One Percent Per Week *program, we had 3,591 trading days spread over 14.31 years. It is more than sufficient to start expressing some of the strategy's behavior using averages. You had 744 first trading days of the week. Your trading strategy took them all on the premise that there were more up weeks than down weeks. But you introduced a bias into the equation by always betting on the same side and setting a positive percent profit target, forcing the conversion of the ETF's positive volatility to cash.

Thereby, you won more often than you should have, not by luck but because of the ETF's inherent weekly volatility. QQQ has a weekly volatility close to 3.37%, while TQQQ approaches 9.94%. It justifies the 7% profit target, which could be hit multiple times. We had 37.9% of positive trades that met the minimum 7% request. That is impressive. And positive trades had a hit rate of 51.21%.

You were not playing the odds; you barely knew them. You preferred cashing in when you could, irrelevant to your expected odds.

**Winning And Losing Streaks**

When you look at the strategy's winning and losing streaks, you have 138 of them: 101 winning and 37 losing streaks. That is, 73.19% were winning streaks compared to 26.81% for losing streaks.

That is not what you could call 50/50 odds or anything close to it. Furthermore, the winning streaks were longer. Some 18 of those winning streaks were for six wins in a row or more. Whereas for losing streaks, only two reached 3 in a row, and eight were for two successive losses; the majority have a losing streak of one (see the tiny red bars in the upper panel of the chart below).

**Figure 1: Winning And Losing Streaks**

If you wanted a demonstration that you were not playing a totally random game, this is it. The upside bias is clearly evident; it is right in your face.

You are playing a seemingly random-like game and get winning streak after winning streak. With such odds, you do have an edge. Yet, you did not make any prediction as to the direction of future prices: either up or down. You only took positions as if prices were going up without even providing a forecast or estimate for your decision.

Looking at the bottom panel in Figure #1, it is relatively easy to notice that profitable trades far exceeded losing trades (red bars), and the average profit per trade is in an upward trend, as expressed earlier. The 7% profit target is on a rising bet size as you move along; it follows in step with F(t). Initially, the profit is small, as it should be, then rises and rises. This phenomenon should continue going forward as well.

Another thing that should be taken from Figure #1 is that the losing streaks do not seem to do that much damage. At most, 3 to 4 in a row, with an average loss of -2.67% per trade. And that is what should be considered your portfolio risk.

I am presenting a scenario that would work nicely with no further modification, meaning without even adding more drawdown protection. Nonetheless, after seeing this strategy's upward potential, I will add some protective measures and lower it within my acceptable parameters and trading policies.

We have made only minor changes to the original program, which have had a tremendous impact. Pursuing a higher CAGR will have a price, and the question will become: Are you ready to pay for this higher return?

We already knew we would incur losses and that the strategy would have to pay for those. We also accepted at the same time that we would have drawdowns but also noticed that what mattered the most to the trading account were executed trades, not paper profits and paper losses.

The * One Percent Per Week* strategy had an average loss per trade of -2.67%, not that big a deal after all. Especially since we might only have two or three in a row. Note that the average is a long-term average, and any one loss could be much higher. Nonetheless, it will average a -2.67% loss per trade over the long term (14.31 years).

**Back To Enhancers**

Maybe one of the easiest ways to increase one's CAGR is to apply some leverage IF the trading strategy can support it. And that is a big if. Not all trading strategies do that well over the long term if their leveraging costs take too big a chunk of their potential returns. Even Mr. Buffett will recommend not to short long-term since the market generally has a historical built-in upward bias.

Nonetheless, seeing how this particular strategy would behave under such pressure is relevant. All positions in this trading strategy will last at most five trading days, and that is not what you would call long-term plays.

We also explore the strategy's CAGR potential by looking at how far it could go. Later in the process, we should address finding other enhancers, adding methods to curtail losses, and finding more uses for this strategy.

The objective is to find an acceptable and doable mixture of software routines to increase the overall CAGR. You are not going for a local improvement here and there but for a generalized solution that would also work going forward, like the added 1% to already rising prices nearing the profit target.

A simulation on past market data only gives you an idea of the possibilities. Generalizations in the pursuit of long-term averages will also provide some data, which could help assess and make rough estimates of long-term expectations.

If the average loss per losing trade over 744 trades was -2.67% over the last 14.31 years, and we did not change anything to the program, you should expect the forward average to remain close to that figure for another 15+ years. The same goes for the average gain per trade of 4.54%; in 15+ years, we should also be relatively near that mark. Nonetheless, we should also expect that the variance will increase with time. As Bachelier postulated in his 1900 thesis, it should continue to be proportional to the square root of time. So, that is not something new either.

**Applying Some Leverage**

Leveraging your portfolio will cost you money. The following table attempts to answer the question: What would be the impact of applying this leverage? So, simulations were performed for every level of leverage shown in Table #1:

Raising the leveraging factor from 1.0x to 1.9x increased the total profit. The leverage factor used remained below 2x due to holding overnight trades. As should have been expected, the cost also increased in step with the higher leveraging factors.

**Table #1: Initial Capital: $100k**

* Total profit generated, including paid leveraging fees. (Ver. 5.0).

** Max adverse excursion is the worst paper-loss percent of any trade.

A question: would you pay close to $900,000 to get about $34 million more in profits?

Consider the leveraging expenses a cost of doing business, like any other business. Note that the portfolio profits pay for the leveraging fees as you go along.

From Table #1, the leveraging is increasing your CAGR and max drawdown by about 5 points at the 1.1x level (10% leverage). Is it a fair exchange?

The best gain, worst loss, maximum favorable, and maximum adverse excursions remained the same for all the simulations at all leveraging levels. The reason for this is simple.

For all trades, you got in at the session's opening price on Mondays and had only two ways to exit the trade: either with your minimum 7% profit target or at whatever price on Friday's close. Both the entry and exit prices were the same for all the simulations. None of the simulations changed the actual percent gain or loss.

The application of leverage only changed the number of shares traded, not their entries, exits, or returns.

It slightly changed a prior equation to F(t) = F_{0} ∙ Ʃ_{i}^{N}(L_{i} ∙ b_{i} ∙ r_{i}), where L_{i} is the leveraging factor which raised the position size. With L_{i} = 1.0, you get the no-leverage scenario. Since you could not change any entry or exit price in the trade series, you could only increase the number of shares traded by leveraging your bets.

The leveraging effect is compounding. Profits from previous trades are being redeployed and are also compounding again and again.

In the above table, we should consider the worst loss as the most damaging to our portfolio. If your portfolio has a max drawdown of 50% and you get out with a 20% loss, you did not lose 50%, but only 20%, which is what was at risk.

Nobody saw the 2010 flash crash coming, no matter what they say.

It happened again recently when some 30 stocks, including Berkshire Hathaway, saw its shares drop from $630,000 down to $185.00. The exchange later canceled the trades lower than $608,000. As of this writing, it is back to $615,000.

The above table shows you could raise leverage. You were ready to pay leveraging costs in exchange for higher returns. The understanding is that you are playing with increasing bets since the strategy remains an all-in strategy.

**Figure 2: Winning And Losing Streaks - With 1.9x-Leverage**

The winning streaks depicted in Figure #2 would remain the same in Figure #1, except for the scale on the bottom panel, which would be multiplied by 37.5 in the 1.9x-leveraged scenario.

The * One Percent Per Week* program is highly scalable, near to 100%. The reason is simple. It is built into the above-cited equation: F(t) = F

_{0}∙ Ʃ

_{i}

^{N}(L

_{i}∙ b

_{i}∙ r

_{i}) where the position size follows b

_{i}= F(t)

_{(i-1)}, with return r

_{i}applied on the previous week's portfolio value. You can scale the initial capital using a scaler: c ∙ F

_{0}and apply leverage on every position taken L

_{i}∙ b

_{i}. Since the strategy will hold overnight positions, its leveraging is limited to a maximum factor of 2x, as presented in Table #1 above.

Notice that all the bars in the above chart (green, red, or blue) are the same as in Figure #1. The scaling used in the bottom panel is the only difference.

The higher the leveraging factor, the higher your CAGR.

The * One Percent Per Week* strategy (Ver. 5.0) can do the above without additional modifications. We could improve the strategy's strengths and alleviate some of its weaknesses.

The next question is: Can you improve the strategy and reduce the impact of some drawdowns?

The answer is yes. Either by increasing the winning rate, increasing the average profit per winning trade, or reducing the average loss per losing trade. As illustrated in the above table, there is little we can do about $r_i$ in the present state of the program since we deliberately fixed the entry and exit prices.

Adding a zero to the initial capital in Table #1 would only affect the $-columns, which would also require an added zero.

Even so, this article (**Part VI**) shows part of the strategy's potential. It is now up to you to meet me halfway. Naturally, putting in some leverage will bring its constraints, but there are ways to deal with them too. So, no worry there. It is more that you have to make up your mind about the validity of what is being put forward by testing it all yourself.

As mentioned before, you are not getting into this portfolio-building process to lose. You should want an assured long-term win and one of significance. And if you are not convinced that this strategy can help you achieve your goals, how and why would you apply it?

**The 130/30 Market-Neutral Scenario Revisited**

If you want to start with $1 million instead of $100k, go ahead. Here are the simulations, the same as in Table #1 but using $1 million as initial capital, the same levels of leverage, and the same program version.

In a 130/30 market-neutral scenario, you have a portfolio leveraged at 1.3x on long positions while also having 30% put on short positions. The problem is usually to find inversely correlated stocks.

**Table #2: Initial Capital: $1,000,000**

* Total profit generated includes paid leveraging fees.

The formula is "relatively" simple. You take the equation above and put in the leverage factor at 1.3x: F(t) = F_{0} ∙ Ʃ_{i}^{N}(1.3 ∙ b_{i} ∙ r_{i}). But you also have to contend with the short side, so you get F(t) = F_{0} ∙ Ʃ_{i}^{N}(1.3 ∙ b_{i} ∙ r_{i}) - F_{0} ∙ Ʃ_{i}^{N}(0.3 ∙ b_{i} ∙ r_{i}) where we account for longs and shorts. With this formula, you should remain market-neutral.

The money from the short side (since it is a sale) will provide the funds for the 1.3x-leveraged long positions. Overall, you will make the difference between the long positions minus the sum of the losses on the short positions. You hope you will make more on the long leveraged positions than the shorts since they would be only partially inversely correlated.

We rarely find inversely correlated stocks, and it makes 130/30 market-neutral scenarios "delicate" would be a soft expression.

However, using TQQQ and SQQQ, we have a perfect inverse-correlated match. So, if you go long 1.3x TQQQ and short 0.3x SQQQ, you remain market-neutral and win on all counts.

It makes a slight change to the above equation: F(t) = F_{0} ∙ Ʃ_{i}^{N}(1.3 ∙ b_{i} ∙ r_{i}) - F_{0} ∙ Ʃ_{i}^{N}(0.3 ∙ - b_{s} ∙ r_{s}). Shorting a short ETF is equivalent to having a long position. And since SQQQ is the mirror image of TQQQ, we could replace b_{s} and r_{s} with b_{l} and r_{l} and convert everything to long positions F(t) = F_{0} ∙ Ʃ_{i}^{N}(1.3 ∙ b_{i} ∙ r_{i}) + F_{0} ∙ Ʃ_{i}^{N}(0.3 ∙ b_{i} ∙ r_{i}).

For example, using the 1.3x leverage in the above table with its corresponding CAGR we would have: F(t) = 1,000,000 ∙ (1.7113)^{14.31} + 300,000 ∙ (1.7113)^{14.31} = 2,182,213,366 + 654,664,009 = 2,836,877,375. This would raise the overall CAGR from 71.13% to 74.3% without adding risk since nothing in the last four columns in the above table changed since entry and exit prices did not change. Furthermore, we would have acquired market neutrality in the process.

You will profit from the long side, as shown in Tables #1 and #2, and from the short side. You will profit from the short positions in step with your longs. It is like playing the big game. The leveraging cost gets higher as you raise the leveraging factor. However, when you look at the generated profit column, what is displayed is what remained after paying the leveraging fees. You would need an adjustment to account for the short side.

Leveraging up to 1.9x did not change the best gain percent or the worst loss percent. They remained the same, indicating that the risk was not that high considering the course of action. You will make 10% on your long and 10% on your short position due to the mirror inverse correlation.

Depending on your profit appetite, consider a 150/50 market-neutral scenario. Based on Figures #1 and #2, the strategy is already positively biased in your favor over the long run.

In this series of articles, I presented three charts like Figure #1. They were all the same except for the scale of the lower panel, which showed the average profit per trade.

The risk analysis, in this case, goes crazy. By leveraging TQQQ, you are leveraging the 100 highest-valued stocks on NASDAQ, such as MSFT, AAPL, GOOG, etc. The list of the top 100 stocks in the NDX is always changing and continuously updated, so it is not a direct concern for you to maintain that list.

Using TQQQ, you leverage QQQ by a factor of 3 without paying any leveraging fee. A 3% move in QQQ will translate to a 9% move in TQQQ and a -9% move in SQQQ.

Even though technically you are long and short the same stocks, there is no conflict. You are dealing with QQQ's leveraged proxy ETFs.

If you leverage TQQQ 30%, you would have 1.3 times a 3x-leveraged portfolio, pushing your leveraging to 3.9x, but only pay fees for the 30% that is leveraged.

That gives you a tremendous push upward, especially considering what the abovementioned winning and losing streak chart implies. You could push higher with the 1.9x leverage. The move would leverage your portfolio 5.6 times, and you would still be market-neutral but on steroids.

This strategy can present more opportunities for improvement. You could enhance its performance by digging deeper and finding ways to reduce the strategy's drawdowns, potentially changing its trading structure and behavior.

This potential for improvement should inspire and motivate you to take this strategy to the next level.

I should remind you that it is a matter of choice, your choices. You decide what you can do and how far you want to go.

**The One Percent a Week Stock Trading Program - Part VI** showed that this trading strategy had more hidden potential and was highly scalable. Because of its nature, we could leverage the portfolio for higher profits and even go for market-neutral scenarios.

It allows you to build a retirement fund of substance faster and even provide a legacy fund for your children. Nonetheless, it all remains your choice. I hope you will find ways to use it and enjoy it.

But first, perform the same simulations and verify that it all conforms to the presented equations. It is how you could convince yourself and gain the confidence to carry out your version of this program.

**Related Papers and Articles**:

**The One Percent a Week Stock Trading Program - Part V**

**The One Percent a Week Stock Trading Program - Part IV**

**The One Percent a Week Stock Trading Program - Part III**

**The One Percent a Week Stock Trading Program - Part II**

**The One Percent a Week Stock Trading Program - Part I**

**The Long-Term Stock Trading Problem - Part II**

**The Long-Term Stock Trading Problem - Part I**

**The MoonPhaser Stock Trading Program**

**Anticipating A Stock Portfolio's Long-Term Outcome**

**Sitting On Your Bunnies Might Be Your Best Investment Yet**

**Make Yourself A Glorious Retirement Fund**

**The Age Of The Individual Investor**

**Use QQQ - Make the Money and Keep IT**

**Take the Money and Keep It – II**

**Created: June 24, 2024, © Guy R. Fleury. All rights reserved.**