What Is A Good Success Rate Using Machine Learning For A Beginner?

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poorly_built_human · External communityJun 23, 2014, 4:23 PMPost link

External question — Quantitative Finance Stack Exchange Author: poorly_built_human Original post: https://quant.stackexchange.com/questions/12756 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I know this question will be quickly destroyed and my account summarily banned, but I just have to ask: For a trader using machine-learning algorithms (SVMs, ANNs, GAs, Decision Trees) for quantitative finance, without seasoned financial intuition, what would be considered a good confidence / success rate? I know this will depend on the following, as well as other items I'm not aware of: -Market / Sector (stocks, commodities, FOREX, etc.) -Principal investment -Frequency of trades -Share price -News Volatility of sector -Range of dates used for datasets Please feel free to list other considerations... But in the end, to make the question crystal clear I'd really like a target number. 75%? At 60% I would be roughly taking 1 step forward for every 10 steps taken. Any less and I might as well flip a coin. If it varies, please list the considerations under which they do so. If possible, it would be preferable to use these models to support trades over a period of days rather than seconds/minutes. If you have other suggestions for how to go about things based on low/high, principal, markets to consider, share prices, etc. please feel free. If my question does not make sense, please tell me why. Thank you. UPDATE -At this point I was simply trying to predict up and down movements over a 5 day period. Simple. 45%. -Free Yahoo data be my market data source... daily quotes. Wasn't sure if intra-day information would be helpful. -I've attempted ANNs, SVMs, and some GAs so far. -I wasn't looking for real-time trading, but instead looking to identify regular tides over a several day period. -Maybe if I can get my error high enough, I can simply trade opposite my predictions! (no, seriously though)

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chollida · External communityJun 23, 2014, 8:03 PMPost link

External answer — Quantitative Finance Stack Exchange Author: chollida Original post: https://quant.stackexchange.com/a/12760 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Honestly, if you get 50.1% you should be happy:) Predicting the future is just plain hard and if you can do it reliably then, by definition, you've found a way to make money. Think about how many hedge funds and how many Phd's are working on this right now. The biggest issues you'll come across are curve fitting and survivor ship bias. ie you'll tune your learning model to the data you have for back testing and what often happens is that after a bit of tuning your model perfectly predicts what happened on the day you test it and that's about the only time it accurately predicts what the market does. Sorry if that sounds bleak but its incredibly hard to accurately predict the future, Or put another way, success in machine learning for a beginner is probably just not making things worse than random guessing. Some questions to help flesh out what you are doing... What specifically are you trying to predict? What is your market data source? What ml algorithms are you using? Can they run in real time? As to what percentage of being correct you should really look to target.... it is a function based on how much you lose/gain for each order you put out.

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Louis Marascio · External communityJun 23, 2014, 11:55 PMPost link

External answer — Quantitative Finance Stack Exchange Author: Louis Marascio Original post: https://quant.stackexchange.com/a/12764 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. You're thinking about this the wrong way, in my opinion. Win/loss percentage is worthless in isolation. You must consider the symmetry of your winners and losers. You can have a win % of only 40% and still have a wonderful strategy if your your winners are significantly larger than your losers (this is the classic trend follower PnL distribution). So, you could flip a coin and see 50% prediction accuracy. That would be outstanding if your winners are 2x larger than your losers.

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TonyMorland · External communityJul 17, 2014, 6:32 PMPost link

External answer — Quantitative Finance Stack Exchange Author: TonyMorland Original post: https://quant.stackexchange.com/a/14070 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. "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, and this is excellent if your average win amount is 3 times your average loss amount. In this case your expected return is 0.35x3 - 0.65x(1) = +0.40 times R, where R is the amount you RISKED per trade. Conversely, with a Counter-Trend (CT) / Mean-Reversion trading strategy, where your winning amount per trade might not be more than about 1.2 times R, so you will need a win rate of at least 65% to be about equally profitable, i.e. 0.65x1.2 - 0.35x1 = +0.43 times R per trade. The above numbers are reasonable "ball-park" figures for good real-life trading systems. In fact if you average 0.4*R per trade with either type of system, you will make a LOT of money and you can certainly consider yourself very successful as a trader. As you can see from the example, "Success" does not necessarily equate to a high win rate at all. The win rate that you NEED for financial success in trading will depend entirely on what is your preferred trading style.

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demully · External communitySep 26, 2019, 5:11 AMPost link

External answer — Quantitative Finance Stack Exchange Author: demully Original post: https://quant.stackexchange.com/a/48920 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. OK, in for a penny, in for a pound :-) First, let us assume that you have symmetrical critical levels higher and lower (call them “target” and “stop” if you will). Only in this case is the hit rate relevant. Even, then the hit rate is a function of time. If you take a 5d/1w view, then being 51% right is very different to being to being 51% right on a 21d/1m, which is very different to 51% right on a 65d/3m view. All of these annualise to very different annual hit rates. Hit rates “root time”, like eg volatility. [strictly speaking, they probit-time, but who cares :-)] The punchline is that lower hit rates at higher frequencies are equivalent to higher hit rates at lower frequencies. Theoretically (always a strong caveat!), this need never matter because the “optimal” (Kelly) stake behind any financial risk is not its Sharpe or Information Ratio, ie expected return over expected volatility. It’s expected return over expected variance, which is time-horizon-independent. Except profit maximisation under these conditions contains implicit conditions that scare the pants off most normal human beings. You should eg expect at some point to lose more than a third of your wealth with two-thirds probability! That plus buying every rally and selling every dip levered ~5% tends to put most normal human beings off the mathematically “optimal” strategy (for any set of risk:reward assumptions where the investor actually has confidence).

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What Is A Good Success Rate Using Machine Learning For A Beginner?

What Is A Good Success Rate Using Machine Learning For A Beginner?

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