We all understand the idea of expectancy: Expectancy = (Probability of Win * Avg. Win Size) - (Probability of Loss * Avg. Loss Size) Namely, over the long run, with positive expectancy, your average winners will be greater than your average losers. Now... how does this relate to money management and risk management? 1. Money management deals with optimizing or maximizing profit. IE the (Probability of Win * Avg. Win Size) part. In this sense, you can either increase your probability of wins or increase the average size of your wins, both while holding risk constant (in order to isolate this part of the equation). To increase win rate (and thus reduce loss rate), one can (a) create better entries, or (b) lower price targets to more probable hit rates; both increase probability of win. Option (b) has the added problem of reducing average win sizes. To increase average win size, one can (c) increase position size, thus increasing avg. win size but at the risk of increasing avg loss size, or (d) increase the price target of trades, therefore increasing average win size. But as the price target grows greater, the probability of it being hit before you are stopped out is smaller, which would serve to decrease win probability. As such, the fine line or 'art' part of this is finding the optimum win rate and avg win size targets. Again, this is all done while attempting to hold risk constant. 2. Risk management deals with reducing or minimizing loss. IE the (Probability of Loss * Avg. Loss Size) part. In this sense, you can either (c) decrease your probability of loss or (d) decrease avg. loss size. As mentioned above, the only way to decrease your probability of loss is to increase your win rate or probability of wins. As such, the main part of risk management deals with part (d), decreasing your average loss size. In this aspect of it, one can set smaller (tighter) stops, but at the risk that they get triggered more often and actually increase probability for loss. Another aspect would be setting smaller position sizes, but this again comes at the cost of reducing average wins if the trade goes in your favor. Putting it together: Now that we've got the general idea of expectancy, we have to do three things: 1. Find better entry points - usually dealt with by effective technical analysis and whatever entry criteria you use, timing is critical. 2. Find the fine balance between setting price targets for profitable exit and stops to exit bad trades -- this will be different for everyone, depending on trading time frame as well as risk tolerance. This is the risk/reward aspect of it. (For me, this means ideally 2:1 or greater R/R) 3. Find the optimum position size to take on when entering a trade or adding on to a trade. Again, different for everyone, as some like to scale into positions and some like to enter whole and scale out. Too great of a position size, and the risk will be high if you get stopped out -- this will increase your average loss size. Too small of a position size and the reward will be too small -- you're not maximizing your average win size. For me, maximum position size is found by my 2% risk per trade rule. I divide 2% of the entire portfolio by the risk per share/contract to get maximum position. Again, achieving these 3 things will be different for everyone, and that is why there is not a one-fits-all system. For most, once you decide what type of risk you're willing to take, then most things fall into place. Perhaps the hardest part of all of this is that these factors often change as the trade develops. The good part is, usually the downside doesn't change unless you start averaging down or *gasp* move your stop lower if you're long or higher if you're short... both generally very bad moves.