Quote from Craig66:
Thanks Mike, the expectancy idea sounds good, here is the P/L distribution. Avg Win/Avg Loss = 0.59, Win% = 80.

Quote from Craig66:
Attached is a histogram of the time taken to close my winning trades vs. the time to close losing trades (the bottom axis is in seconds), it is obvious that the losers and winners have different distributions and that my trading could benefit from adopting a time limit on open trades. My question is to the math guys, given this information how would you systematically decide the length of a time based stops given the overlap and the non-Gaussian nature of the distributions?
Quote from Craig66:
Ok, I've had a think about it.
Having a different time limit for winners and losers has a flaw, in that it assumes we can, at any point, classify a trade as a winner or a loser. Whilst this may be roughly possible, it's going to get inaccurate if a trade is hanging about break even, also, I'm sure we have all seen the trade which is a loser all day then suddenly rallies to a winner in the later stages of the day.
My approach is going to be the following, the first post attachment shows two PDFs, one for winners and one for losers, the axis along the bottom is in seconds, so the x-axis roughly maps to a single trading session. At any point along this axis we can calculate the probability of the trade being a winner or loser by the EOD by integrating the area under the curve between the current point and the RHS end of the x-axis. So the optimal time to get out will be when the P(winner by EOD) < P(loser by EOD). That's the plan anyway...
Quote from mikkom:
What does "density" represent at the curve?
Quote from Mike805:
Ok, from the distribution of trades and the win rate, it appears as if you have a non-normal distribution with few outliers. Most of your PnL comes from a stable mean. That's good, but, my concern would be how any new exit condition will affect your original exit condition. My guess is you have a very specific profit exit condition and implementing a basic time stop might shift your mean significantly. Can you backtest a time based stop?
Quote from Mike805:
Ideally, you'll want to allow the trade to work as long as it has a chance of profit, even it's < 50/50. But, not at the expense of taking a loss. Maybe rather than a strict time based exit, you can tighten exit parameters after time(t) by cutting the trade if goes negative, or already is negative.
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Quote from Mike805:
Something to consider is the volatility of open (unrealized) PnL at each point in time. For example, if this is a mean reversion strategy, you'll find that after the initial entry, the volatility of open PnL will be high, giving you the best chance for profit. After enough time in an mean reversion trade you'll find the price action begin to settle and "flat-line" in terms if vola. These types of trades are strongly reliant on vola for edge, adding a delta(volatility/time) based exit condition might help.
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Quote from Craig66:
Attached is a histogram of the time taken to close my winning trades vs. the time to close losing trades (the bottom axis is in seconds), it is obvious that the losers and winners have different distributions and that my trading could benefit from adopting a time limit on open trades. My question is to the math guys, given this information how would you systematically decide the length of a time based stops given the overlap and the non-Gaussian nature of the distributions?