forex-scalping.com

How many trades a challenge target really needs at your average R

Here's a calculation I'd do before buying any challenge, and most people skip it.

Take the profit target in percent. Divide it by your risk per trade in percent. That's the target in R. For example, an 8 percent target with 0.5 percent risk per trade is 16R.

Then take your expectancy per trade in R from your own history. That's the average R you make per trade across winners and losers. Divide the target in R by the expectancy. That's roughly how many trades it takes, on average, to reach the target.

With an expectancy of 0.2R, 16R takes around 80 trades. At 0.1R it's 160. These are averages, so the real number on any attempt could be much higher or lower.

Now compare that with the time limit if there is one, and with how many trades you take on a normal day. If you take three a day and the math says 160, you need more than fifty trading days. That changes how you should approach it.

It also shows what happens if you raise risk to hit the target faster. Doubling risk halves the trades needed but makes the daily and overall loss limits much closer in R terms.

There's no guarantee in any of it. It just stops you from buying a challenge that your own numbers say is unlikely in the time given.

Your drawdown limit can be converted to R the same way. A 10 percent max loss at 0.5 percent risk is 20R. Compare that with your historical worst drawdown in R. If your own record shows drawdowns of 15R happen a couple of times a year, you're operating with only a small margin before failure. If they rarely go beyond 8R, the room is more comfortable. Looking at the target and the limit together in R tells you much more than either on its own. Run the same numbers for your last two quarters separately and see if they agree.

Expectancy from a small sample is shaky. If your history is fifty trades, the true expectancy could be quite different from what you measured. A rough guard is to halve your measured expectancy for this calculation. If the challenge still looks reachable in the time given with half the edge, it's a reasonable bet. If it only works with your best estimate, you're relying on the sample being accurate, and small samples often aren't. If the halved version needs more trading days than the challenge allows, that's a clear signal to wait or choose a different account structure.

Scalpers often have a big advantage here because they take more trades. Two traders with the same expectancy per trade but different frequency will reach the target in very different calendar time. The scalper with ten trades a day gets through 160 trades in a few weeks, while the swing trader might need months. That's why time limited challenges tend to suit high frequency traders better, as long as their costs are low enough that the expectancy survives spreads and commission. A scalper whose edge is mostly eaten by costs gets none of that advantage, so net expectancy is the only number that belongs in the calculation.

A simple simulation adds a lot to the rough math. Take your actual trade results in R, shuffle them randomly, and run them through the challenge rules many times in a spreadsheet or a short Python script. Count how often the target is reached before the daily or total loss limit. That gives you an estimated pass rate based on your own trading. It's still built on past data, but it's far better than guessing, and it shows how sensitive the pass rate is to small changes in risk. A few hundred runs is plenty for a rough answer.

Halving the expectancy is a sensible rough guard. Another way to get to a similar answer is to look at the worst ten week stretch in your history, rather than the average. If that stretch would still have passed the challenge within the time allowed, the plan is robust. If it would have failed, the challenge depends on getting a normal or better run. For a trader who only works one session, say the Tokyo morning, the number of trading days available is also smaller in holiday weeks, which is worth counting before buying.