**The Simple Trader**

(The Simple Art of Being a Trader)

Copyright Michael Colantoni 2020

**Principles**

A trend of any degree tends to continue in the same direction.

The trend may pause or correct before continuing.

Trends occur within trends. They are a function of time frame and degree.

A single point on the chart, with no other information, has a 50% probability of moving either up or down from its current position.

If we guess right 50% of the time, and make a profit each time of 1R when correct or a loss of 1R when wrong, we will breakeven.

If we increase the profit above 1R when we guess right but keep the losses at 1R, then we will make a net profit over time.

If we guess correctly more than 50% of the time, we will improve our profit over time.

If we make multiples of 1R when we guess correctly, but only lose 1R when we guess incorrectly, then we will **dramatically** improve our profit over time.

**Guessing** right is the result of **analysis**.

Making a profit in multiples of 1R is the result of **Trade Management.**

**Trade Management** is **more effective** at improving the bottom line than trying to guess right more often by **improving the analysis**.

**The Simple Facts**

We start with a single point on the chart, anywhere on the time-price plane with no information leading into that point.

There are 2 dimensions visible: Time and Price.

The 3rd dimension is Pattern, ie. the movement in time and price.

At this point, there is no information about the 3rd dimension.

We have no other information. We cannot see the bars leading into this point.

The only conclusion we can come to is that the future, immediate price action following this point is just as likely to go up as to go down. There is no indicated bias.

The probability of bullishness in any time frame is 50%.

The probability of bearishness in any time frame is 50%.

Throwing dice is just as likely to give the right answer as any other method, if we have no other information.

**Can we make money from this situation?**

Over many trades, say 100, the probability of success is 1:2 ie. 50%.

If profit is 1R when we guess correctly, and loss is 1R when wrong, the net profit will be 0 over time.

BUT, if profit is 2R when correct, and loss is 1R when wrong, then

the net profit over the 100 trades will be 2R x 50 – 1R x 50 = 50R.

So this simple illustration shows that, to make a net profit, we do not have to improve the odds of guessing correctly, beyond the random probability. What we need is a method to make an average profit greater than the average loss.

This is no brainer trading!

**So the ****secret**** of the ****simplest trading approach**** is ****not**** in working out how to be right. It is in working out how to ****make more profit on winning trades**** than the loss on losing trades.**

This is the art and science of Trade Management.

Then:

If we can add a filter to our trade selection that reduces the probability of being wrong about trade direction, our success factor will increase above the random 50%. This we call our “**EDGE**”.

And further:

If we can devise a selection process that increases the probability of, when we are correct, being in a trade that will provide a profit of >1R, preferably multiples of 1R, then we will have a list of criteria to identify trades that provide higher than even potential. This improves our edge.

**So now we have a 3 part process which we call our system.**

- Select trades that are
**likely**, if we pick the correct direction, of**returning multiples of 1R**in profit. - Have an
**EDGE**that helps**filter**trades that are**less likely to succeed**. - Manage the trade, so that:
- We
**never**lose more than 1R. - On average, we stay in the trade to realise the
**maximum profit being offered**by the market.

- We

**What is an “EDGE”?**

An **Edge** is the **advantage** created by a **strategy** that **identifies** a setup which results in an **entry** managed according to a **trading plan** that leads to an **average return per trade** **greater than zero**.

A **Strategy** is the result of **observation** of **market behaviour** over time that **identifies** **times or prices or patterns** which frequently lead to **particular outcomes**. A strategy allows us to **anticipate** market action, **not forecast** it.

By **backtesting** a strategy over many historical examples, it is reduced to a **statistical probability** representing the **likely outcome over many trades**.

A **Setup** is a pattern of price, at a given time, that represents the **behaviour** anticipated by the **strategy**. It is then **anticipated** that an entry triggered by this setup will have a **statistical probability** of generating profit as **expected** by the tested strategy.

The factors identified as forming a setup we call the **INITIAL CONDITIONS**.

With a given statistical probability of **not making a loss** described by the strategy, we then need a **trading plan** that includes a **TRADE MANAGEMENT STRATEGY** which maximises the probability of capturing the **maximum profit** generated by a given entry setup. The plan is based on the most likely management approach to yield >1R profit on average over many trades. The aim is to devise a plan that captures multiples of 1R on average over many trades leading to a **predictable outcome from the given trading plan**.

All of these plans and strategies are the result of observation and testing. They are then reduced to a set of criteria and tactics which have been tested for their statistical reliability and reduced to a single figure that we call Expectancy. we rely on this Expectancy to forecast the change to our trading account over time. If we follow our rules and persist, the outcome is acceptably predictable within the usual variances of statistics. However, we cannot predict the market itself in any single instance. This is statistically unreliable, so the outcome of a single trade is never more reliable than the random 50% probability.

The outcome of a single trade cannot be predicted with an acceptable degree of reliability. The outcome of 10 trades has a degree of predictability only slightly better than a single trade. The outcome of 100 trades is somewhat predictable, much more so than 1 trade, but the variance is still significant. The outcome of 1000 trades has a predictability approaching 100%. We will never reach certainty no matter how much backtesting, review or technical analysis is applied.

So our strategy is based on the statistical probability of 1000 trades. This is the basis of our backtesting. However, we expect the outcome of 100 trades to closely match that. Within acceptable tolerances, this is the basis of our Expectancy. So we always think in terms of 100 trades.

We have no interest in the outcome of a single trade. We literally **do not care**.

We are **not forecasting**.

We are looking for recognisable patterns in time and price that yield an Edge and we then apply our trading plan to manage the trade. We know that, over at least 100 trades, we are **reasonably certain** of the outcome.

**Sample Scenario**

If we aim for a probability of **not losing** of about 60% ie.

40% of trades yield a 1R loss.

and say

20% of trades yield a breakeven.

20% of trades yield a 1R profit.

20% of trades yield a 3R profit.

Note: this is just a sample scenario to illustrate the “mathematics of certainty”.

Let’s call this the 40/20/20/20 scenario.

Then, over about 100 trades, this results as follows:

**Trades** **Profit** **Loss** **Total Profit**

40 – 1R -40R

20 – – 0

20 1R – +20R

20 3R – +60R

+40R

So that **strategy** and **trading plan** combination (ie. Edge + Management) gives a **predictable** result of +40R.

As long as we **stick to our trading plan**, we are **virtually** certain of the result. We are **forecasting** a result, but we are not forecasting the market.

Look at another possible scenario – a high risk strategy of say 70/10/10/10 ie. it loses 70% of the time such that:

70 trades yield -1R.

10 trades yield 0.

10 trades yield 2R.

10 trades yield 5R.

High risk usually equates to high potential return, but less frequently.

The result then is:

**Trades** **Profit** **Loss** **Total Profit**

70 – 1R -70R

10 – – 0

10 2R – +20R

10 5R – +50R

0

By doing our statistics for the E+M combination that defines the trading plan/strategy, we have shown that this plan is likely to break even over time. Therefore, the Expectancy of this strategy is zero. We obviously would NOT trade this strategy if we are interested in making a profit, as we are. However, the plan has huge potential because, in its current form, it is failing 70% of the time.

The potential lies in our ability to filter out some of the failing trades, thereby changing the ratio of success to failure in our favour. If we can reduce the number of failing trades that we enter into without missing any of the good ones, we can dramatically improve the result.

This is where the filters come in.

We use our technical analysis tools, such as indicators, trend lines, averages, bar patterns, gaps etc to identify characteristics (factors) that exist in the setups that fail which **do not appear in the successful setups**. We then filter out (reject) any setups that include these characteristics (factors).

This will **reduce** the number of losing trades and so the **ratio** of **wins to losses** will increase.

So we might be able to transform the initial form of the strategy so that the new strategy that has evolved from it results in:

**Trades** **Profit** **Loss** **Total Profit**

35 – 1R -35R

20 – – 0

20 1R – +40R

25 5R – +50R

+55R

Again, this is only an illustration but it shows the potential power of applying filters to our trade selection process. That is, we do not just look for a setup containing the factors we do want. We also **reject** those that contain factors that we **do not want**, thereby **improving the win/loss ratio**.