The trader that survives has a distribution of trades with the following outcomes:
Note – the professional eliminates the potential for the big (catastrophic) loss. This is not done via "will power" or mental toughness. This is systemic and built into the pipelines that manage the traders money. Even if he wants to trade past a small loss, the system will not let him. You must have mechanisms in place that eliminate the possibility of the catastrophic loss.
As demonstration of the hole that large losses dig, see Figure B:
Large losses require even larger rebounds – and thats just to be back to where you started – not to mention doing so can be an uphill battle as you dig yourself out of a hole with limited confidence and fear running the show.
How Risk Management is Edge
Given the traders expectancy formula:
The only components of this equation the trader has control over is the size of winners and losers. The rate at which each of those occur is dependent on the market and will vary, typically, with the asymmetry on the trade. That is to say, risk:reward has an inverse relationship to win rate.
Given these constraints... the best we can do is cap the downside and let randomness provide upside skew within the return distribution of our trades.
This breaks down to something such as:
Here, the majority of trades are -1R (standard small losses). Then the winners have a large variance and spread in returns. This allows for the small winners to cancel out the losers – then capture excess returns via the outsized winners. When targeting larger asymmetric trades you have a requirement for risk:reward – and a win rate expectation via forward testing. Win rate suffering could be an indication that the conditions of that market are not supportive for the ranges you are attempting to capture.
Low volatility requires higher accuracy with respect to pinning risk. A better idea of knowing where you are wrong means you can still capture large R. This also tends to mean low vol products require a bit more sophistication.