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## Turning A Trading Idea Into A Trading System in Forex

Before the next test begins, your trading idea needs to be transformed into trading principles that are objective, reproducible, and also optimized. One common mistake is to try and back-test a trading plan or idea based on subjectivity. Many popular techniques reveal the basic parameters you need to estimate. For example, the methods under the umbrella of “Elliott wave counting” are notorious for being difficult to backtest, as the intensity measured at depth affects the backtest results much more than the procedure itself.

When you create trading rules, you will be affected by the number of trading slogans like “Tendency is your friend” that are useless, and because they may not measure up to hard and cold trading principles. Therefore, the criteria for finding a trend in trading strategies vary greatly.

**Location of the Fittest System**

After the first set of trade lines is created, you can start to track what happens if it goes over time. The period is the set of times and dates that you will analyze the trading platform. A fitness function is a component or step that you use to evaluate coverage and how you are maximizing the parameters of your program. For example, a sports year may result in a net profit or loss.

**Quick return using Excel**

First, back tests can be done quickly in Excel. Paste your historical time series into Excel, then paste your formula, and apply it to each of the cells in the timeline. The easiest way to say this is to separate each type of market position with –1 (market), 0 (out of market), or 1 (buy). Then calculate the profit or loss, subtract a spread and trade price.

I recommend that you consider Excel carefully before you buy an expensive tool. This ensures that you know how it works from the bottom up. Articles on backtesting often outline two different principles for the criteria for collecting your historical data. In addition, it is often said that you need to check your trading platform under conditions similar to the current sector. Strictly speaking, these tips introduce subjectivity.

Instead of subjective trading rules being embedded in the trading platform, today’s market conditions are completely subjective. For example, you read about a site on a trading platform with an annual yield of 22 percent. Have a consistent track record of success in the previous 12 months, and that you are willing to buy the platform (probably a lot!). When you get the machine, you trade the principles of the machine right. When you don’t get a 22 percent yield and maybe even a negative return, you are advised that the market situation has changed! Therefore, the principles of the trading system cannot predict the needs of the market from the prediction of future costs that depend on the past! This phenomenon represents another frequent mistake made during backtesting. Arch fit is a data-derived equation, often used for nonlinear regression. I will explain with an example. You are testing the idea of a safe trade that requires two parameters. However, as you continue to vary the parameters, you find that specific values produce larger, more positive outputs. If you choose the two metrics that provide the most significant gains, then you can generally predict that the time-accumulation of market information will look similar to your historical assessment in the future. How can you alleviate this fundamental problem?

There are several ways to reduce the arch consistency in a back test. The first strategy is to keep your trading idea harmless. If you can’t define your trading idea, not only in market action, but also in terms of market action, you should go back to the drawing board and then continue working on your trading idea. Additionally, you can backtest on a variety of niches and move back and forth through the backtest window to find market needs, specifications, or designs that are ideal for your system. For example, you may want to back-test only on occasions when a different financial statement is published. Back testing the latest information can capitalize on current market shocks. Advanced mathematics provides a variety of back-testing methods that generate results, showing how randomness and numbers reflect short-term memory. This is because the market is made up of all the data held by people who have positions on the market, which they intuitively remember in the short term. It is for this reason that long-term backtesting, although initially modest, may lead to over-optimization and convergence.

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