Sometimes traders struggle with making any profits, and I have been there. But, if you have some edge then it tends to not be hard to make a profit over some period of time: days, weeks, months, or even a year or two. What makes trading hard mathematically is you have to keep the profit and build on it.
That’s why trading is so much different from a job. Day trading is more like if you did great at your job for 6 weeks, 12 weeks, or whatever and got well payed for it. And, then if you have a bad day and all your past profits are taken and now you owe money. Not many people are able to endure that.
Let me back up, a moment: good trading is almost always easy because it means the trader is in-tune with the market and finding opportunity. I tend to trade long side primarily. And, if I start to lose on the day– it often means there aren’t any good long trades to take. The easy side was short or flat.
As I see it, you must be able not just to consistently make profits but you must be able to accumulate profits with high certainty. There’s a big difference between consistently making profits and accumulating significant profits. As I see it, a trader has the highest chance of accumulating profits given the following:
What may be important in any method is to seek out the “ground truth”. Seek out fact based patterns. To summarize, a viable trading methodology must be able to build on profits with a high degree of cumulative certainty: consistency is not enough.
The only get to build cumulative profits is to (1) hold through drawdowns and not take losses likely trading some sort of accumulation or liquidity provision strategy, (2) take trades with significant edge only– only trade large edges– by developing superior systematic systems or discretionary insight, or (3) trade on longer time frames where the R (risk/reward) can be naturally much higher.
The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at email@example.com.
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