## Why, how and when you Can Use Aggressive Money Management

RobertBric Przesłąno 22 Maj in #Money #Correlation #Statistics #Risk #Management #Aggressive #Portfolio #Monte #Carlo #SimulationYou

will see proof that using only one strategy with positive expectancy can make

your rich but can also make you poor. Trading results depend upon the

statistical distribution of trades which nobody can control.

What

you CAN control are many other factors, amongst them is your investment portfolio

structure, or better, the various strategy-market combinations. Doing this

right increases the chance of your trading being a profitable activity. See how

it is done.

*Note of caution. This article is fora serious trader who is prepared to put to use parts of his brain that processmathematics and related sciences. Traders need to use mathematics and statistics forproper analysis in order to develop proper trading approaches. *

Please note also that numbers are shown using the European format ie. a comma is used as a decimal separator and a full stop as a thousands separator.

**What is the standard advice you hear/read about money management (MM)?**

The standard

advice one gets when s/he starts trading is to trade (very) small. “Risk 1% or

less per trade. Maybe 2%. Maximum 3% not too often!”

Such advice

is normally not supported by any serious background information. Why not risk

0,1% or 10% for example?

By the way,

there is nothing wrong with 10%, 1% or 0,1% or x% as long as one understands

why s/he uses that and what to expect as a probable result. Most traders don’t

understand this from a mathematical point of view because they hate mathematics

or find it boring, not to mention statistics or probability calculations. And

most importantly, nobody ever explained this to them.

Most MM

seems to be based on the logic that:

- Most

traders lose therefore a lower risk per trade will at least**prolong their trading.** - Most

traders cannot control their emotions and by risking more they**wipe out their account**

unnecessarily fast so they should risk less per trade.

Furthermore,

very few traders seem to understand more than the simplest MM aspects. Of those

who understand MM, not many are prepared to go through **an emotional rollercoaster of a large drawdown** which is **INEVITABLE** with aggressive MM

techniques.

However,

such a drawdown can be optimised with a proper portfolio structure which is

described below.

**We will compare theperformance/profitability of 3 strategies**

Backtesting

of these strategies showed the following parameters:

__StrategyA__

- Win% =

40%, RRR (Reward to Risk Ratio) = 3 - Expectancy

= 60%

A

simple explanation of expectancy is that it defines how much of your risk you

get back per trade if the statistical distribution of trades is ideal (which

never is!)

One

possible formula for calculating expectancy =

(Win% * RRR – Loss% * 1)/100%

__StrategyB__

- Win% =

75%, RRR = 1 - Expectancy

= 50%.

__StrategyC__

- Win% =

50%, RRR = 1.5 - Expectancy

= 25%.

We have one BIG problem with these strategies:

- Strategies

A and B are proprietary and we don’t know how they function. We only know

their results. - Strategy

C is our newly developed strategy. According to the numbers from

backtesting it is inferior to the

proprietary strategies. But those two strategies are out of our reach so

what can we do to compete with them (if this is what we need to do)?

**What happens when we apply standard MM?**

Let’s assume

we have an account with $100.000. I used a Monte Carlo

simulation of 1.000 iterations of 200 trades with trades distributed in a

random fashion but in line with the theoretical W/L ratio of each strategy.

*Note: This Monte Carlo simulation workedin a mathematically ideal trading world with no spread, commission, slippage, broker stop hunting, central bank interventions and without the changing mind of the market.*

Why 200

trades? If you have 1 trade per day this is about a year’s worth of trading.

Here are the

results, based on Excel calculations using RAND()

function.

Not surprisingly, strategies line up in

profitability according to their expectancy.

If

we run 10.000 simulations, we should expect wider extremes but also an average

which is not too far from the one calculated in 1.000 iterations.

The

results are below but only for 2% risk per trade and only for the first 2

strategies. We already know that Strategy C cannot compete with the other two

strategies due to its low expectancy.

If

we want to compete with Strategies A and B as far as profitability is concerned

we need to employ aggressive money management. Let’s say we decide to risk 5%

per trade.

The

results of Monte Carlo simulations are below.

The average

and highest final equity of Strategy C is now the best. Not surprising with a

risk of 5%!

However, we

have a **PROBLEM**. The lowest final

equity is way below the initial equity of $100.000. So we can see that by

applying aggressive money management it is possible to make as much money and

more with Strategy C as with the other two strategies but also to lose a lot of

money.

**How can we solve a problem of the potential negative extremes in theportfolio equity?**

So far we

have used one strategy in one account. If we got hit by a statistically “good”

set of 200 trades, our performance was good. If we got hit by a “bad” set of

trades, we lost money.

We cannot

control the win/loss distribution. **Themarket does what the market wants to do.** So if we trade this way with

aggressive MM, we will rely on LUCK a lot! We may end up extremely rich or

extremely poor. Not a persuasive enough argument if one cannot afford to lose

much money.

In the next

section you can see how **we can minimiseour reliance on luck **and increase our chance of success.

What happens if we use a different

portfolio structure?

One good way

to approach this is to show what happens when we divide our account into 10

sub-accounts of $10.000, each running one strategy-market combination which is

not correlated with other strategy-market combinations. *(I told you it was a simulation in an ideal trading world. We need to start with a simple example to be able to understand it!)*

All

strategies in those sub-accounts have the same parameters as Strategy C to keep

things simple. Of course, the trade outcome distribution is randomly

distributed in line with W/L ratio. We still risk 5% of the sub-account per

trade.

The results

after 10.000 iterations are shown below. Previous results are also included for

comparison.

The average

final amount in the portfolio is similar to the average final amount when

trading Strategy C on one large account. The BIG difference is in the highest

and the lowest final equity!!!

While we

lose a lot on the upside, we also gain a lot on the downside. In fact, we

sacrifice the upside to minimise the downside potential. But as capital

preservation should be a prime concern (no profit can be generated with no money!)

this is a good outcome.

The worst

case scenario in this particular portfolio situation is not any more a loss of

the initial equity but a decent profit which is higher that the

lowest final equity with strategies A and B.

*The explanation of why this is so is beyond the scope of this article.The reader who is interested in exploring the topic of money management in moredetail will surely find appropriate books for his/her perusal.*

**What is a possible conclusion of this simulation/study?**

A conclusion

is simple: a diversified portfolio of non-correlated strategy-market

combinations minimises the profit and loss potential but allows us to use an

aggressive MM approach.

Please do

not forget that in trading we are always talking probabilities. Nothing is 100%

certain. An optimised portfolio structure cannot eliminate the possibility of a

failure. We can still lose it all despite our knowledge, experience and an

optimised portfolio structure if we get hit by a long enough losing streak.

However, the described approach can at least increase our chances of success.

**What does this mean for YOURTRADING?**

If

you have never looked at money management, strategy correlation and market correlation in

more detail I suggest you do it if you want to be successful in the long run.

While you can be successful without this knowledge and understanding, the

chances are vastly improved if you become more proficient in the mentioned

topics.

An

added benefit is that you will be able to participate actively in any

discussion about 0,1% or 10% or whatever % risk per trade. Trading "gurus" will

not be able to pull wool over you eyes.

Last

but not least: The more you know in trading the less luck and reliance on

others is required.

Wishing

you profitable trading with whatever money management you use.