[ad_1]
Most new traders tend to focus just about all their time and energy on finding nearly perfect “setups”, but trade setups, even very good ones, are *not* the key to successful trading. It’s the *way* you trade your setups that keeps your losses smaller than your gains. And this is the single most essential key to trading success. To me, the process of limiting losses is more than just money management…it is survival.
I can’t give you a list of mechanical survival rules that will take the place of experience and make you a successful trader overnight, but if you stick to the following principles in your trading, you’ll be on track. You’ll be doing just about the opposite of the crowd, and you’ll eventually learn to limit your losses. Limiting your losses is the only way I know to make money in this business.
The following guidelines will sound radical, but they have guided me in making my living from trading for many years.
1. If a trade doesn’t go your way within the first one to five minutes, get out. I usually get out within one or two minutes as soon as my perceived edge is gone.
2. If a trade goes against you in the first few seconds, begin drawing in your hard stop and/ or your target, trying to get out of the trade at break even.
3. Never let your hard stops get hit. When it happens, you may want to take a break and get some fresh air.
4. Hard stops are adjusted to market conditions. At the moment (July 6,2005) I am using 1.50 point hard stops on the SP futures.
5. Never move your hard stop away from your entry point, hoping that a bad trade will turn around.
6. If you find yourself *hoping* as you trade, it is a clear sign that you are not following good survival (money management) principles.
It will be impossible to put these principles together without a set of high-probability setups. Without good setups, trading is just a
flip of the coin.
![Top 10 Leading Countries in AI Research and Technology [current_date format=Y]](https://cryptheory.org/wp-content/uploads/2023/08/ai-strategie.jpg "The strategic approach to AI in the enterprise 2")
