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Before backtesting can begin, your trading thinking needs to be changed to trading principles that are objective, reproducible, and also equipped to be further optimized. A common mistake is to try and test a trading plan or thinking that is based on subjectivity. Many popular techniques get out of the essential parameters you need to guess. For example, forms under the umbrella of “Elliott Wave Counting” are notorious for being difficult to test, since the fact that the tide is measured profoundly impacts the results of the recoil test much more than the procedure itself.

As you create trading rules, you will be impressed by how many trading slogans like “The trend is your friend” become useless and why they may not measure up to cold, hard trading principles. Because of this, the criteria for spotting a trend change considerably in trading strategies.

Location of the most suitable system

After creating the first set of business rules, you can begin to mimic what would happen if they had been followed over time. The period is the range of times and dates that the trading platform will analyze. The fitness function is a part or step that you use to evaluate the coverages and how you maximize the parameters of your program. For example, a gym can be a net profit or loss

Quick backtesting using Excel

First, backtesting can be done quickly in Excel. Paste your historical time series into Excel, then enter your formulation and use it in each of the cells in the time string. The easiest way to say this is to simply assign each market type a -1 (market), 0 (market) or even a 1 (buy). Then calculate the profit or loss by subtracting a spread and the trade price.
I suggest evaluating Excel thoroughly before purchasing an expensive tool. This ensures that you know how it works from the bottom up. Backtesting articles often indicate two different principles for the dimensions of historical data collection. Also, it is frequently stated that you should check your trading platform under conditions like those of the current industry. Subtly enough, these tips introduce subjectivity.

Instead of the trading rules being subjective to the owner of the trading platform, the terms of the current market become completely subjective. For example, read on a website on a trading platform with a 22 percent annual return. He's had a permanent winning record for the last 12 months, and he's poised to buy the platform (probably by a lot!). Once you get the machine, you exchange the principles of the machine correctly. When you fail to achieve a 22 percent return and possibly even earn a negative return, you are informed that the market condition has changed. Therefore, the principles of the trading system cannot predict the requirements of the market other than forecasting future costs based on the above. This phenomenon shows another common error created when backtesting. Curve matching is a phrase drawn from data, usually used to refer to nonlinear regression. I will explain it using an example. You are testing safe trading thinking that requires two parameters. However, because you continue to alter the parameters, you find that specific values produce larger positive returns. If you go for both parameters that provide the most significant gains, then you are primarily predicting that the market information you collect at the time will show up as your historical assessment in the future. How can you mitigate this underlying problem?

There are many methods to reduce curve matching in a backtest. The first strategy is to keep your trading thinking intact. If you can't express your trading thinking, not only in market action but also in dimensions of market activity, you need to go back to the drawing board and then continue to work on your own trading thinking. In addition, you can backtest multiple niches and go through the backtest window back and forth to find market requirements, releases, or designs that are ideal for your own system. For example, you might want to back test only at times when a different financial index is released. Back testing with the latest information can capitalize on current market shocks. Advanced mathematics provides many back-testing methods that create results, noting how volatility and quantity show short-term memory. This is because the markets comprise all the data held by people with positions in the market, who intuitively take the short term into account above. This is why long-term back testing, while initially instinctive, can lead to over-optimization and curve matching.

In conclusion, one can summarize designing a trading system in 6 simple steps:

1. Time Frame
Before anything, you should ask yourself this: Am I a day trader, swing trader or a long-term trader?

2. Find the right indicators
There are so many indicators and different traders use different ones to identify a trend.

3. Double check your indicator.
A trader has to make sure they don’t fall into following a false trend. Thus, a second or even a third indicator is used for reassurance.

4. Know your risks
It is extremely important to know how much you are willing to lose on one trade. In fact, the best traders think about the potential loss before thinking about the potential profit

5. Decide on the entry and exit points
This is completely subjective. Some traders like to sell at support while others like to buy and vice versa.

6. Discipline!
Create a system, define the rules and FOLLOW your plan! And never forget.. Don’t get greedy!

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