This is the third article written by Mario Cesolini for Unger Academy. Mario is a trader specialized in the conception, programming and development of trading systems, always looking for the perfect algorithm. The search for new strategies never stops. There are times when everything is hectic and times when everything goes in slow motion.

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In the previous article we saw how our system was able to return interesting equity lines on the 4 largest US equity indices.

We have also seen how the lack of risk management (stop loss, take profit, trailing stop, …) and, above all, of a capacious average trade make the system unusable provided you do not want to lose money.

Average Trade Table – 4 ETFs


You don’t believe me? I will prove it. Let’s see what would have happened by trading the system on the ETF SPY if we had to pay the 25 dollars (22 euros) for each trade requested by many Italian brokers.

equity line SPY – commissions

Ratio Table – SPY with and without commissions

Our average trade would have been more than halved and, pay attention, we only considered the commissions without considering slippage (poor execution of orders).

The results would have been even worse on ETF DIA.

DIA equity line – commissions

I hope it is now clearer why we need a larger capacity trade. Recalling that the average trade is calculated by dividing the net profit by the number of trades, from the equity line close to close (which shows the number of transactions on the abscissa axis) it is quite easy to get an idea quickly.

In the case of ETF DIA, $3000 divided into 450 transactions: just under 7 dollars without slippage.


We need to increase the average trade. The easiest ways to go are two:

  1. Use more selective entries;
  2. Allow profits to run longer;

The first technique is based on the selection of the most stringent set-ups to get in position, the second instead is based on choosing more difficult to reach exit points.

We used Tradestation Performance Report (Trade Graph / Entry Efficiency) to analyze the efficiency of our income and expenses.

Trading System on SPY – Entry Analysis


Trading System on SPY – Exit Analysis

I would like to open a small parenthesis for readers who do not know the Tradestation report and who, consequently, may have difficulty reading the last two graphs.

Tradestation clarifies that “Entry Efficiency is defined as a maximum possible realized difference in prices from a trade that has the trade entry price expressed as a part of the total profit potential during that trade. Entry Efficiency shows how well a system enters into a trade. If a trade is long – how close an entry to the lowest point within the trading period, if a trade is short – how close an entry to the highest point within the trading period. The following formula is used to compute Entry Efficiency for a trade”.

Essentially, for long trades, given a certain price movement, the Entry Efficiency gives us the measure of how well we can buy close to the minimum. Similarly, the Exit Efficiency tells us how well we can close the long position considering the maximum movement.

For readers who want to go deeper, these are the formulas with which Tradestation calculates the two values:

Entry Efficiency = Maximum_Possible_Difference_in_Prices_For_This_Entry/Profit_Potential


Maximum_Possible_Difference_in_Prices_For_This_Entry is the difference between the Highest Close Price (for Long Trade or the Lowest Close Price for Short Trade) and Entry Price.


Exit Efficiency = Maximum_Possible_Difference_in_Prices_For_This_Exit/Profit_Potential

In light of what has been said so far, it is clear that exits are much more efficient than our entries (Exit Efficiency has a value of 81% compared to 51% of Entry Efficiency).

Contrary to what one might think, our trading system, despite 72% of profitable trades, has its strong point in exits and not in entries.

When we analyze a trading system we realize (it could not be otherwise) that each algorithm has its strengths and weaknesses. Well, I would like to be clear, however much effort we put into improving the weaknesses, the strengths will remain strengths and weaknesses, however improved, will remain weaknesses.

In our case, it is good to immediately understand that we will get to have entries as effective as the exits.


Let’s see what happens using more selective entries than the value 30 used until now.

We will use the Tradestation optimization function by testing values from 5 to 30 with “step” 5. Naturally, the more selective the entries, the more the number of transactions will decrease and this, in some cases, can cause statistical significance to be lost in the study.

Trading System on SPY – Optimization of entry levels

We note that the lower levels of the indicator allow us to tick larger average trades (column E) and that the number of trades decreases significantly as the input level decreases with a real step between values 5 and 10.

The value 5 returns only 152 operations that are objectively too few in 25 years of history. We are looking for a level that allows us to have an average of at least 10 trades a year: we will use the value 10 as entry level.

The following is the Equity Line and the most significant ratios of the system:

Trading System on SPY – New Equity Line


Ratio Table – 30 and 10 SPY entry levels

As indicated by the green box, all ratios have improved with the exception of the net profit total and this is also logical as our system has gone from 573 operations to 278, less than half.

The question to answer is the following: will an average trade of $71.48 be sufficient to trade our system? In my opinion, this is still not enough value to trade the ETF SPY. Nevertheless, I would like to point out that the system will not be changed from hereon.

In the next article, we will see whether and how it would be possible to use the trading system to operate on other financial instruments.

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Ordinary member of SIAT (Italian Society of Technical Analysis, a branch of IFTA), Mario Cesolini graduated in Financial Market Law at La Sapienza University of Rome in 2003. He began to study financial markets and in particular technical analysis in 1999, specializing in programming trading system, in developing options strategies and in volatility trading on the Contango curve of the VIX Index. In October 2015 he won the SIAT Award as best technical analyst of the year in the Forex category.