Fixed Contract Position Sizing

The fixed-share method is a non-risk-based method. In this case we simply buy the same number of contracts (1) for each signal. I use this technique all the time when developing a strategy. But it's probably not the best way to trade something live.

Contracts = 1

Percent Risk Position Sizing

The percent-risk method is a strategy grounded in risk management. In this approach, we allocate a fixed percentage of our equity—2% in this example—to risk on each trading signal. The choice of 2% is guided by a widely accepted principle in trading that suggests risking anywhere from 0.5% to 2% of your account balance on any single trade. This strategy aims to mitigate the impact of a series of losses, preventing them from significantly damaging your trading account. We then use this predetermined risk percentage to calculate the specific dollar amount you're willing to risk on an individual trade.

Example:

Contracts  = (2% of Account Equity) / ( Stop Loss Per Contract)
Contracts  = (2% * $100,000)/ $500
Contracts = $2,000 / $500
Contracts = 4

In the example provided, we have the methodology to calculate the number of contracts to purchase while maintaining a consistent risk level of 2% of the account size per trade. As mentioned, the stop-loss value for the 'Better Breakout' strategy adjusts based on recent market volatility, as detailed in the original article.

This adaptive approach means that if market volatility increases, our stop-loss threshold will widen. Consequently, to adhere to our 2% risk per trade guideline, we might find ourselves buying fewer contracts. Conversely, in periods of reduced volatility, our stop-loss tightens, allowing us to purchase more contracts without breaching the 2% risk rule.

The essence of this strategy is that we never risk more than 2% of our account on a single trade. This flexibility enables us to adjust the number of contracts traded in response to the volatility of the market, effectively normalizing our risk exposure relative to market conditions.

But what implications does this have for our trading outcomes?

Performance Comparison

In our analysis, we observed an increase in profits while maintaining the same number of trades (almost, there is one extra trade), without escalating our maximum intraday drawdown. We also increased every other metric!. 

This is a clear win. More profit. Identical risk. 

It's important to note that in our Percent Risk model, we are not reinvesting profits. We started trading with an account balance of $100,000 which remains constant for the purpose of calculating position sizes.

Why adopt this approach?

The goal was to evaluate the impact of scaling trades based on market volatility and to compare its performance with that of the Fixed Model. Incorporating profits into our calculations would undoubtedly enhance our returns further. However, I preferred to avoid complicating the analysis with additional variables. This decision was made to provide a clearer comparison and to focus solely on the influence of the position sizing model.

Pitfalls To Avoid

Don't Using Position Sizing To Improve A Poor System

First, let’s tackle a common misconception: thinking position sizing can turn a mediocre system into a winner.

Here’s the truth – position sizing isn’t a magic fix. If your trading system doesn’t have a solid foundation, even the best position sizing approach won’t make it profitable in the long run.

Imagine trying to fix a car with no engine by just repainting it; it might look better, but it’s still not going anywhere. You should always start with a positive expectancy system, meaning it’s capable of generating returns based on the market behavior it was designed to exploit.

Think of position sizing as the tool that can amplify or protect your returns, not create them from scratch. So, before diving into advanced sizing techniques, ensure your system is already well-tuned and thoroughly backtested. Only then can you expect position sizing to help elevate your results.

Don’t Risk Too Much Per Trade

Now, here’s a mistake that every beginner should be wary of risking too much per trade. It’s easy to think that a higher risk per trade means faster growth, but the opposite is true – the more you risk, the faster you can blow up your account.

In professional trading, a common rule is to keep risk per trade around 1-2% of your account. Why? Because this allows you to survive the inevitable losing streaks. Even the best trading systems will experience losses, sometimes back-to-back.

If you’re risking 5%, 10%, or more on a single trade, it won’t take long for a few bad trades to wipe out a significant portion of your capital. Here’s the bottom line: keeping your risk per trade at around 2% or lower gives you the breathing room to recover from losses and make adjustments if needed. It’s all about protecting your account so you can stay in the game for the long haul.

Remember, in trading, slow and steady does win the race.

Conclusion

Alright, let’s recap the main points.

We started with the concept of position sizing and why it’s essential for traders who want consistent, long-term results. We saw how adjusting trade size based on a percent-risk approach – rather than a fixed contract method – can help align your trades with market conditions, providing flexibility and stability in a volatile environment.

We also dug into the two common pitfalls to avoid: first, don’t rely on position sizing to make up for a weak system. Build a solid foundation with a proven strategy, and then let position sizing enhance it. Second, keep your risk per trade at a reasonable level, around 2%, to avoid those devastating losses that can sideline even the most promising traders.

Key takeaway? Strategic position sizing is a powerful tool when used with a robust trading system and disciplined risk management. It’s about managing your exposure, protecting your capital, and allowing your account to grow consistently.

Good luck with your trading.

Testing the "Buy in November, Sell in May" Strategy

Test Parameters and Assumptions

Strong Seasonality Trade (November-May) With Filters

Strong Seasonality Trade (November-May) With Filters

Utilizing the MACD filter and comparing it to our baseline system, we can see we don't make as much more as the simple moving average filter, however, we increased our risk adjusted return with a NP vs DD of 3.8. So, if drawdown is your concern and not just pure profit it looks the MACD filter is slightly better.

RSI Filter

For our final filter I will try the RSI indicator with its default loopback period of 14. Again, like the MACD calculation, I want price moving in our direction so I want the RSI calculation to be above zero when opening a position and below zero when closing a position.

Strong Seasonality Trade (November-May) With Filters

Weak Seasonality Trade (May-November) With Filters

The Night Session

The night session strategy is just as simple but it opens a new position at the close of the daily bar. It then closes that position at the open of the next bar. Thus, we are capturing the points gained or lost during the night session. The equity curve is a sum of the points gained or lost during the night session since 2006. Below is the equity curve of this trading system.

As you can see there is a clear difference between the night session and the day session. What does this mean to you? There seems to be an edge in exploiting long positions by riding the overnight session. My hypothesis is because so many active traders do not trade the overnight session, the market will often move in such a way as to lock them out from gains. Most people are familiar with the market shakeouts that rattle the faith of bullish participants, thus forcing them to lose their position.

You’ve seen it where the market moves down to take out your stop loss only to reverse in your favor. A painful experience. However, the market does have another subtle trick that messes with your psychology. That trick is making you miss the bull move altogether. Put another way, not only does the market attempt to shakeout weak hands (stopping you out), it also does a fantastic job of making people miss the rally! I think there is some of that going on here.

Recent Performance Of The Overnight Edge

A few years ago the overnight edge appeared to be fading. But when we take a closer look at the past the last few years the edge has been OK but as you can see, it’s been weak over the past four years, the edge seems to be holding up.

For comparison below is the equity curve of points captured during the day sesson.

The overnight session still may hold a bullish edge. To take advantage of this some system traders devise a trading system to enter long trades in the overnight session or continue to hold trades through the night session that they would otherwise close.

Environmental Settings

I coded the above rules in EasyLanguage and tested it on the E-mini S&P futures market going back to 1998. Before getting into the details of the results let me say this: all the tests within this article are going to use the following assumptions:

• Starting account size of $25,000
• Dates tested are from January 1, 2000 through December 31, 2018
• One contract was traded for each signal
• The P&L is not accumulated to the starting equity
• $30 was deducted per round trip for slippage and commissions
• There are no stops

Baseline Results

Below is the equity curve for trading the E-mini futures contract based upon the rules as defined above. The equity curve looks very similar to the one from the original article. Below the equity curve is the weekly drawdown as a percentage of equity. We can see it reaches down in to the 40% range.

Would anyone really trade this? Of course not, but that’s not the point. The point is simple rules can be rather powerful and be the start of a great system. Can we take this trading concept and take it to a few more steps closer to a real system? Let’s see…

Bull/Bear Regime Filter

You can probably guess this would be my first line of attack. That is, broadly divide the market into two distinct modes: bullish or bearish. Often a simple indicator such as a simple moving average applied to a daily bar chart can be very effective in dividing a market into a bullish or bearish regime. The idea in this case is to only take long trades when we are in a bull market. I will be using a simple moving average to act as my regime filter.

In this case I'm gonig to use a 200-day simple moving average for the bull/bear regime filter. After applying the regime filter we get the following results:

So, does this look like an improvement? While we make less money, the system is more effective overall, in my opinion. The profit factor increases as does the average net profit per trade. We also reduce the max drawdown by a lot. I would guess we are eliminating a number of losing trades by only taking trades during a bull market.

Volatility Filter

Another way to divide the market is through volatility. Markets go through periods of rising volatility and falling volatility. In general, market volatility rises and peeks as the market falls and makes new lows. Some systems do well during these high volatility times while others do better during more quiet times. How are we going to measure volatility? We’re going to use the VIX index.  The VIX is a popular measure of the implied volatility of S&P 500 index options and is often called the fear index. In general, the VIX represents a measure of the market’s expectation of volatility over the next 30 days. The VIX has an inverse relationship to the price action on the S&P. Thus, we often see the VIX making new highs as the market is making new lows.

I’m going use the VIX in a very simple manner. I’m going to take the average of the daily VIX value over a number of days to create a simple moving average. Trade will only be opened when the current VIX is below the moving average. This should help the system by only taking trades when the VIX is likely to be falling.

But what value to use for the look-back? Using TradeStation’s optimization feature I’m going to test look-back values between 5 – 60 in increments of 5. The results are depicted in the bar graph below where the net profit is on the y-axis and the look-back period is on the x-axis.

The value 30 looked like a reasonable value to pick. The results of using 30 for the simple moving average for the VIX is below.

Like the regime filter we see an improvement in profit factor, and drawdown from the baseline. But when comparied to the Regime Filter, this volatility filter underperforms in my opinion. 

Just for curiosity I performed one more test with VIX. That involved inverting the filter. That is, only take trades when VIX is above its moving average. Below are the results. 

I think the inverted run shows the least improvement against the baseline. It's also clearly not as good when compared to the standard Volatility Filter. What I really don't like is the increased drawdown and reduction in profit factor. More money is made with the inverted volatility filter vs the standard voltaility filter but, at a cost!

Overall, I like the equity curve and performance of the Regime Filter over the VIX Filter. Both represent an improvement over the baseline concept and demonstrate how simple concepts can produce positive results. Added one of the filters to the baseline concept was a “next step” to a potential profitable system. Clearly, more work needs to be done

• 200-Period ROC calculation must be above zero.
• Buy on close when RSI(2) is below 10.
• Exit when RSI(2) is above 65.

• If TSI >= 1.65 Exit when RSI(2) of the close of current day is above 95

Now let’s take a look at the performance of this strategy.

Conclusion

There you have it. A strategy that adapts to several different market conditions. I did not cover the bear market regime, but you’re certainly free to explore that. Likewise, you can expand this concept to create a strategy that more drastically changes its trading behavior based on which of the four market environments we defined. Maybe during a strongly trending bull market you will want to use a completely different exit method than used during a non-trending market. In closing, this should provide some idea on how you can use two different types of indicators to divide the market into four different market environments.

As you can see, the traditionally weak period took off! Only in the last few weeks did it start giving back some of those gains. The weak period returned a $3,375 (3.4%) gain.

We are now entering a traditionally strong season for the stock index market. This is a well known seasonality period. In this article I would like to take a closer look at the seasonality bias which so many investors and traders talk about. 

Seasonality Bias

Testing Environment

Seasonality Trade

Seasonlity Trade

Seasonality Trade

The price proxy does what is should do – reduce the number of trades, increase the percent winners and increased the average profit per trade. This was accomplished by reducing whipsaw trades. This results in slightly less overall net profit but generates a better profit factor. So our reducing the number of losing trades comes at a cost.

Keep in mind the number of shares traded between the two systems is not identical. Trades are entered at different times based upon volatility and price so, the number of shares traded will not be identical. Does the scale-in technique of the Market Phase system perform better than our Death/Golden Cross system? It’s a close call. Both systems are very similar, which is expected since both systems produce similar trading signals.

However, we enter the trade earlier with the Market Phase system which is why it produces slightly more profit. It also has more losing trades. Again, this is due to the fact we are scaling into the position. Clearly the Market Phase with the price proxy does not add much value. It all comes down to what you are looking for. For me, the Market Phase does not add enough value. I like to keep things simple and it does not get much simpler than the Golden Cross. Both the Market Phase system and the Golden Cross system are long term trend following systems. They do OK on SPY because it’s such a broad based index. However, they don’t fair so well on major stock index ETFs such as NASDAQ, DIA or IWM. They also perform poorly when trading individual stocks.

So, what else can we use these systems on? Commodity ETFs appear to be the best bet since they tend to have smoother trending characteristics which trend following systems can take advantage of. Note, many times a lot of cash is left on the table as we wait for the Bear Phase to close our long positions. Trading this type of system, which is typical for a long-term trend following system, one must have a lot of psychological strength to sit and watch profits evaporate while waiting for your exit signal. This is mentally difficult to do! This also makes me wonder if scaling out of the trades may work better to capture more profit.

In closing, both of these systems are not trade able systems as they stand. For example, they do not utilize protective stops! However, it appears they could provide a basis for a complete system with some modification. Another idea would be to use the Market Phase as a regime filter.

Below are the results of this strategy.

Now let's try a complementary indicator. One technique I would like to utilize a lot is the use of a simple moving average to divide the market into two different regimes: bull market and bear market. Often a 200-period moving average applied to a daily chart will work just fine. However, Folger suggested an SMA crossover method to determine the market regime. A 50-period moving average and a 60-period moving average. If the 50-period SMA is above the 60-period SMA the market is considered in a bullish regime. Otherwise the market is considered in a bearish regime. Let's apply this filter here.

• Go Long when the SlowD line crosses above 20 and within Bull Market
• Go Short when the SlowD line crosses below 80 and within Bear market

With these rules added to our buy condition we have introduced a trend-based filter. This should reduce unproductive trades by only taking trades in the direction of the dominate market regime. As a result this should reduce the total number of trades and increase the profitability of our strategy.

Below are the results of this strategy.

Complementary Indicators

As you can see using two complementing indicators can really improve the results. Keep this in mind when developing a trading system. The example trading strategy is, of course, not a tradable system. It's only an example of how applying a complementary indicator to filter trades can improve the trading system's performance. I personally use this technique a lot. It really can do wonders for a trading system. You will find below the code used in this article along with a TradeStation workspace.

Looking at the trading rules, you will also notice it’s a long-only system. Later in this article I will also reverse the rules and test it on the short-side. But for now, let’s look at the performance for the long-only system. At this point I’m going to test it on the S&P cash index ($SPX.X for TradeStation).

Unless otherwise stated, all the tests performed in this article will be based on the following assumptions:

• Starting account size of  $100,000.
• The number of shares traded will be based on volatility estimation and risking no more than $2,000 per trade.
• Volatility is estimated with a five times 20-day ATR calculation. This is done to normalize the amount of risk per trade.
• The P&L is not accumulated to the starting equity.
• There are no deductions for commissions and slippage.
• There are no stops.

Here is the position sizing formula used:

Shares = $2,000 per trade / 5 * ATR(20) * Big_Point_Value ) 

This looks very promising. Keep in mind, the system has no stops.

Is The 7-Day Optimized?

Let’s look at changing the 7-day look-back period for two reasons. First, I would like to see if the default seven value is optimized. Secondly, I would like to know if other nearby values are used, would the system remain viable. In short, I would like to test the robustness of  the look-back period. For example, if we change the 7-day low value to six, I don’t want to see the system’s equity curve drastically change. Likewise, if we increase the 7-day low value to eight, I don’t want to see a drastic change in results. The neighboring values around seven should still produce positive results. In fact, it would be great to see the system  remain profitable over a wide range of values.

I will use TradeStation’s optimization feature to optimize the look-back period over the values 2-20 in increments of one. Keep in mind this single input value controls two look-back periods. The first is the entry look-back and the second is the exit look-back. As the trading system is defined, both these variables use the same value.  The results of the test are in the graph below. You can click the image to see a larger view.  The x-axis contains the look-back period while the y-axis contains the trading systems total P&L.

This looks great. Any value you choose produces positive results. Each change in the look-back period also does not drastically change from neighboring values. The default value of seven is near the peek, which is six, but it’s not the most optimal number. It’s also important to keep in mind we will be using the default value of seven for other instruments as well and it’s highly unlikely the bar graph for those instruments would look exactly like what we have here. In any event, I think this brings a lot of confidence to the look-back period.

Is The Regime Look-Back Optimized?

Just as we did for the look-back period for our entry trigger, let’s perform the same type of test on the regime look-back period. Again, I will use TradeStation’s optimization feature to optimize the look-back period over the values 20-200 in increments of ten. The results are in the graph below. You can click the image to see a larger view.  The x-axis contains the look-back period while the y-axis contains the trading systems total return.

This looks great as well. All values produce positive returns. In general, the longer the look-back period the more profit the system generates. While I did not study the numbers just beyond 200, I feel confident that a 200-period look-back is not optimized.

Taken both these tests we can feel confident that this system does not appear to be optimized and it’s robust given a wide variety of input values.

Going Short

The original system is a long-only system. Let’s try to use the current rules to short the market. We can do this by simply reversing the rules. In other words, we will modify the regime filter to only open trades when price is below the 200-day moving average. Trades will then open when price makes a new 7-day high and close when they make a new 7-day low.

• The instrument must be below its 200 day moving average.
• If the instrument closes at a 7-day high – sell short.
• If a position is open and the instrument closes at a 7-day low – buy to cover.

I created a separate trading system to study only the short-side. The results of the system can be seen in the equity graph below.

This is not so great. Most of the time the equity curve is below the zero line. It’s choppy and ugly.  Clearly the market participants’ psychology during a bear market is different than simply a mirror image of those found in a bull market. One would think shorting new highs in a bear is a good idea, and maybe it is, but this system is not successful at capturing profit. Taking a wild guess simply from past experience, I have found that exiting quickly during bear markets tends to work better than in bull markets. Thus, maybe a modification of the exit rules to only hold a trade until the first profitable day or only hold for a maximum of three days may produce significantly better results. However, such modifications are best left for another day.

To further test our shorting idea, let’s now look at testing a range of look-back periods just like we did during our bull market. If our shorting concept is truly flawed, I would expect to see not much improvement in modification of our look-back period. In fact, since my wild guess is we need to exit quickly to remain profitable, I would expect to see greater losses as we continue to increase our look-back period. Likewise, I would expect to see greater profit as we shorten our look-back period. I will optimize the look-back period over the values 2-20 in increments of one. The results are in the graph below. You can click the image to see a larger view.

Just as I expected. In general the shorter the look-back period, the more profitable. However, since this is our first look at the Double Seven Strategy, I don’t want to over complicate it by introducing a shorting component. We’ll keep it simple for now and apply our long-only strategy to several major market ETFs.

Double Seven Strategy

Confident the long-only trading rules are robust and appear to hold potential, let’s now test this system on several major ETFs. Let’s test, SPY, QQQ, DIA, and IWM. For this test we are going to use all the same trading assumptions and position sizing as we did above except for the following modifications.

• $100,000 starting account
• Risk 2% of account equity per trade
• P&L is not reinvested

Double Seven System

Conclusion

The Double Seven Strategy does produce positive results across the four major market ETFs we tested. Is this system tradable with real money as is? Probably not. Remember, there are no stops. However, this does appear to be a great start to a viable system. I would imagine if some of the larger losing trades could be eliminated by some type of stop method, this system could be viable with real money.

 Get The Book

The strategy is far from profitable as the equity curve spends most of its time in negative territory. What this suggests is that short term bursts of strength are soon followed by falling prices. Remember, after we buy we only hold our position for five days and sell. It seems when we sell, price is often lower than when we bought.

Buy Into Weakness – Buy Pullbacks

Now let’s look at buying into pullbacks. We will use the same premise as buying into strength, but look to buy into a falling market.  That is, we will look for three consecutive lower closes. A lower close is a closing price which is lower than yesterday’s closing price. At that point we will buy the open of the next bar. This strategy will represent our buying into weakness.

What a difference! This strategy equity graph looks completely different and suggests that short-term weakness is often followed by price moving higher. If the two equity graphs are not enough to prove this point, let’s compare strategy performance .

ES Buy Strength or Weakness

It’s important to remember these strategies are not trading systems per say. For example, both of these strategies have no stops so you would not blindly trade them. Think of these strategies as market indicators designed to highlight market behavior which might lead to a profitable trading edge. In this case there appears to be a significant bullish bias when buying into weakness as opposed to buying into strength.

How Stable Is Lookback Period?

The two strategies we tested above both used a 3-day lookback period and hold the position five days before closing it. Maybe this is a fluke? What does this test look like over different lookback periods? Are different lookback periods still profitable? While I want the holding period to remain short (five days) to mimic a short-term trading system, let’s look at different lookback periods and see if our strategy will remain profitable of several different values.

The first graph below shows buying into short-term strength with a lookback period of one through 10 days.

Notice the first two days after three consecutive days up produce some profit. But even then, the profit over such a long test period is not significant. Most of the bars show negative profit or profit around zero. In short, this does not appear to hold a market edge.

The next graph below shows buying into short-term weakness with a lookback period of one through 10 days. Notice with a loookback period between one and five we have profitable results. This shows some stability with the market behavior. However, beyond five days (one trading week), price often shows weakness.

So, it appears our short-term lookback period when buying into weakness has a nice profitable region and gives more credence to the market behavior of buying into weakness. The optimal lookback period appears to be four days. After four consecutive lower closes price can often have a strong rebound.

The idea of buying into short-term weakness seems to still hold merit for the S&P. The market edge appears for the 1-5 consecutive down days in both the futures and equity markets. This type of a market edge can lead to building a profitable trading system. Adding a market regime filter, dynamic stop, position sizing model and maybe a dynamic exit could turn this into a winning system.

What About Other Markets?

Lets take a look at some other popular markets. Lets first start with two other major stock indexes, the DOW and NASDAQ.

YM Buy Strength or Weakness

NQ Buy Strength or Weakness

Now lets turn our attention to currency futures

EC Buy Strength or Weakness

Finally, lets take a look at some other popular futures. First silver and gold.

SI Buy Strength or Weakness

GC Buy Strength or Weakness

Finally, oil and U.S. 30-Year Treasuries.

US Buy Strength or Weakness

CL Buy Strength or Weakness