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"Success rate", in the sense of winning (W) vs. losing (L) percentage of trades, is almost completely meaningless if taken alone as a trading metric. With a trend-following (TF) trading strategy, where you quickly exit any trades that start to become losers (i.e. cut your losses fast) but let your profits run, a typical win-rate would be around 35% or so, ...

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There are actually a lot of options nowadays. Adjusting your data using historical realized inflation is certainly one way to go. And as @User1996 mentioned, the CPI for All Urban Consumers is the frequently quoted "headline" number. However, to the extent that asset prices reflect inflation expectations, it might be better to use forward-looking ...

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The U.S. Consumer Price Index For All Urban Consumers (http://research.stlouisfed.org/fred2/series/CPIAUCSL) is the CPI you hear in the news, and is the standard inflation number.

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Turns out you can make money where you lose most of the time with Parrondo's paradox! https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=parrondo's%20paradox

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Expected = win rate * avg winner + (1 - win rate) * avg loser - trading costs. if win rate = 1/2; avg winner = 10; avg loser = -5; trading cost = 1 E = 5 - 2.5 - 1 = 1.5

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A prediction model that is correct $50\%$ of the time can be profitable if the model gains more when it is right than it loses when it is wrong. You could simplify it like this: A trading strategy is profitable if your trades have positive expected value. Now suppose that your gains when your model is right equals the losses when your model is wrong. If ...

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std(PPS) PPS = Packets Per Second (wiki article: network packets) The standard deviation of packets per second received from a liquidity source are directly related to the number of quotes per second, or the number of trades per second occurring on that liquidity source. Thus, the higher the number of network / data packets per second, the more volatility ...

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