This whole argument reminds me of a conversation I had several years back with a buddy of mine who, at the time, was a pit boss in a craps pit at one of the casinos just off the Vegas strip. I asked him if his casino had any standard operating policy to deal with a player, or an entire table, that got hot for a long period of time and really put a small hurt to the pit's shift numbers (for those who don't know, in most casinos, the floor takes numbers every hour or so and they keep track of how much that pit has made or lost for the entire shift). He kind of snickered and said, "hell ya....open up more tables."
Turns out, he wasn't joking at all. When the laws of uneven distribution cause the laws of averages to take a "random walk" so to speak, what we're really saying is that our sample size is not yet large enough for the laws of averages to have "substantial significance." So the only way to combat uneven distribution is by increasing the sample size. In the casino's case -- place more bets; in a mechanical trader's case -- take more trades. As our sample size continues to approach 1,000,000 the laws of averages will continue to become more and more meaningful (over the entire set, not over any small sub-set).
Now the reason why it appears that your chances for success are greater after a series of losses, or less after a series of wins, is not because they really are (because we know, in fact, that they're not -- they're exactly the same), but because we're allowing ourselves to be influenced by what little we do know about "mean reversion theory," which is an entirely different issue all together. Because of mean reversion theory it is reasonable to expect the laws of averages to evenutally gain significance as our sample size increases, however, its important to note that the operative word is "eventually". I'll say it one more time.... EVENTUALLY.
So in a way, it is sort of true that the most important time to trade IS after a series of losses, but not because our chances of success have increased (if we already had an edge, then we don't need them to), but because we need to increase our sample size in order to give the laws of averages a chance to work to our benefit.
Turns out, he wasn't joking at all. When the laws of uneven distribution cause the laws of averages to take a "random walk" so to speak, what we're really saying is that our sample size is not yet large enough for the laws of averages to have "substantial significance." So the only way to combat uneven distribution is by increasing the sample size. In the casino's case -- place more bets; in a mechanical trader's case -- take more trades. As our sample size continues to approach 1,000,000 the laws of averages will continue to become more and more meaningful (over the entire set, not over any small sub-set).
Now the reason why it appears that your chances for success are greater after a series of losses, or less after a series of wins, is not because they really are (because we know, in fact, that they're not -- they're exactly the same), but because we're allowing ourselves to be influenced by what little we do know about "mean reversion theory," which is an entirely different issue all together. Because of mean reversion theory it is reasonable to expect the laws of averages to evenutally gain significance as our sample size increases, however, its important to note that the operative word is "eventually". I'll say it one more time.... EVENTUALLY.
So in a way, it is sort of true that the most important time to trade IS after a series of losses, but not because our chances of success have increased (if we already had an edge, then we don't need them to), but because we need to increase our sample size in order to give the laws of averages a chance to work to our benefit.
