What is price action trading?

Price action trading is based on trading decisions on the price movements of a currency pair. Indicators or other methods of analysis are usually not used, or given very little weight in the trading decision process.

A price action trader believes that the only true source of information is the price itself. If a  currency is going up, that tells the price action trader that people are buying. The trader will then assess, based on how aggressive (volatile) the buying is, whether it is likely to continue. Price action traders are hardly ever  concerned with  “why” something is happen.

Using historical charts and real-time price information (such as bids, offers, volume, velocity and magnitude) the price action trader looks for a favorable entry point.

A favorable entry point is one that allows risk to be controlled, but that also offers a potential profit.

There are many price action strategies. A very common price action strategy is called a breakout. When the price of a currency pair has been moving with a certain tendency, when it breaks that tendency it alerts traders to a new possible trading opportunity.

A breakout doesn’t mean the price will continue in the anticipated direction, often it doesn’t. This is called a false breakout, and also presents a trading opportunity in the opposite direction of the breakout.

Once you know a price action strategy there is little research time required. You find an asset with the specific price conditions you need, or you wait for those conditions to develop. Another benefit is that you often get more favorable entries and exits compared to many indicator based methods. The reason is that indicators are based on price, but lag behind it. By simply focusing on price you get the information in real-time, instead of waiting for a lagging indicator to give you information.

For more information about price action trading, please contact us.

What is Curve fitting ?

One of the advantages of expert advisors is the ability to evaluate their historical performance by “back testing” the strategies based on historical price data. Computers and back adjusted data make it possible to see what a system “would have performed” going back years in history.

The problem, of course, is that the system has been designed on this very same data. Because systems are designed on past data, they are often the victims of what we call “curve fitting”, making the ability to back test results one of the biggest disadvantages of trading systems also. Because the future may look nothing like the past in a particular market – the “fitting” of parameters onto the past “curve” of data may cause big problems on the future data curve, causing the system to be out of phase and potentially causing investors losses.

The easiest way to understand “curve fitting” is through a simple example. Imagine a system that buys or sells eur.usd on a breakout above or below the market high or low for the past X number of days. When testing the system on the past data, the testing may show $1,000 in profits when using a 10 day high/low, $2,000 in profits when using a 20 day high/low, and $5,000 when using a 30 day high/low.

If you were the developer, which value would you use in designing the system, 10, 20, or 30? Most people would use the 30 value, as it gives the highest profit. Now a developer will look at more than just profit, and test for lowest drawdown or most winning months, for example; but whatever your goal for the system, it is human nature to design a system whose parameters produce results as close as possible to those desired. The problem is, just because one parameter worked on the past data does not mean it will work on the future, unknown data. So how would a developer attempt to avoid such a problem?

Another example of curve fitting is adjusting parameters after the fact. Imagine a trading model which is doing well, but suffers a rather big loss on three out of four Wednesday afternoons since live trading began.  A trader might look at those results and come up with a brilliant plan, code the system not to take any trades on Wednesdays after 1:30pm.  Running the code backwards after putting in the new logic would result in those Wednesday losing trades going away, and voila – you have curve fitting.

Some  developers use many tricks such as testing on out of sample data, not optimizing parameters for the best back tested results (instead using logic based parameters – and making sure there are as few parameters as possible in the system (more parameters equals more degrees of freedom = more things to go wrong).

In case you are interested to use an automated trading system, convince yourself that you are not dealing with a tricked curve-fitted Expert Advisor, since it makes the back test useless.

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