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Hey All,

I finished my third live trading system about a year ago and have been trading it with some success for the past six months. However, when I first starting designing and testing it I was still learning the ropes and as such, I ignored in-sample and out-of-sample data periods. So in essence, the system was tested over the entire data set available. I’ve just read Howard Bandy’s Quantitative Trading Systems and as a result I’ve lost the nerve to trade my current system. To be honest, I was using discretion to pick between trades and sometimes ignoring trades if they looked too volatile anyway so perhaps my confidence in the code wasn’t 100% in the first place. My question however, is this: are there situations where it’s acceptable to not have in and out-of sample periods.

I can’t see how the system I’m trading can be curve-fit and I was hoping somebody could enlighten me. I say this because of the following:
The entry code is relatively simple; there are no complex functions or indicators used and there are relatively few actual entry conditions
The system produces a large number of trades (about 400 a year, 4,000 over the last ten)
The holding period is about 2 weeks per trade
It returns a (genuine, I think) 35% per year and is VERY realistic (overly harsh slippage on entry and exit, accurately modeled brokerage, very restrictive volume and liquidity filters, no forward looking etc)
The equity curve is consistent in slope over the past decade, with no major draw downs (11% ish max DD) or volatility (this is partly due to the large number of trades; I can limit position sizes and risk and I think this has a major effect on the overall DD)

I don’t see how it can be dangerous to trade this system and I’d like other people’s opinions (especially yours Mr Bandy if possible). I understand I can wait six months and walk-forward test or perhaps alter the conditions a bit and see if there’s a major change in results, but I’m more interested in the theory of it. If something produces a consistent profit over a sufficiently large number of trades and your equity curve is smooth, I don’t see how it can be curve fit.

To me it seems like if the frequency of trades is high enough over an extended period of time, there wouldn’t be a single static piece of code that would fit that data set well enough to produce consistent performance.

Am I wrong?

By the way, I definitely recommend the book. Nice to see somebody apply some analytical rigor to an industry/genre that seems to be full of people spouting unfounded BS.

Any comments appreciated.

<blockquote data-quote="AMSH" data-source="post: 593818" data-attributes="member: 20635">Hey All,I finished my third live trading system about a year ago and have been trading it with some success for the past six months. However, when I first starting designing and testing it I was still learning the ropes and as such, I ignored in-sample and out-of-sample data periods. So in essence, the system was tested over the entire data set available. I’ve just read Howard Bandy’s Quantitative Trading Systems and as a result I’ve lost the nerve to trade my current system. To be honest, I was using discretion to pick between trades and sometimes ignoring trades if they looked too volatile anyway so perhaps my confidence in the code wasn’t 100% in the first place. My question however, is this: are there situations where it’s acceptable to not have in and out-of sample periods.I can’t see how the system I’m trading can be curve-fit and I was hoping somebody could enlighten me. I say this because of the following:<ul>
<li data-xf-list-type="ul">The entry code is relatively simple; there are no complex functions or indicators used and there are relatively few actual entry conditions</li>
<li data-xf-list-type="ul">The system produces a large number of trades (about 400 a year, 4,000 over the last ten)</li>
<li data-xf-list-type="ul">The holding period is about 2 weeks per trade</li>
<li data-xf-list-type="ul">It returns a (genuine, I think) 35% per year and is VERY realistic (overly harsh slippage on entry and exit, accurately modeled brokerage, very restrictive volume and liquidity filters, no forward looking etc)</li>
<li data-xf-list-type="ul">The equity curve is consistent in slope over the past decade, with no major draw downs (11% ish max DD) or volatility (this is partly due to the large number of trades; I can limit position sizes and risk and I think this has a major effect on the overall DD)</li>
</ul>I don’t see how it can be dangerous to trade this system and I’d like other people’s opinions (especially yours Mr Bandy if possible). I understand I can wait six months and walk-forward test or perhaps alter the conditions a bit and see if there’s a major change in results, but I’m more interested in the theory of it. If something produces a consistent profit over a sufficiently large number of trades and your equity curve is smooth, I don’t see how it can be curve fit. To me it seems like if the frequency of trades is high enough over an extended period of time, there wouldn’t be a single static piece of code that would fit that data set well enough to produce consistent performance.Am I wrong? By the way, I definitely recommend the book. Nice to see somebody apply some analytical rigor to an industry/genre that seems to be full of people spouting unfounded BS.Any comments appreciated.</blockquote>

[QUOTE="AMSH, post: 593818, member: 20635"] Hey All, I finished my third live trading system about a year ago and have been trading it with some success for the past six months. However, when I first starting designing and testing it I was still learning the ropes and as such, I ignored in-sample and out-of-sample data periods. So in essence, the system was tested over the entire data set available. I’ve just read Howard Bandy’s Quantitative Trading Systems and as a result I’ve lost the nerve to trade my current system. To be honest, I was using discretion to pick between trades and sometimes ignoring trades if they looked too volatile anyway so perhaps my confidence in the code wasn’t 100% in the first place. My question however, is this: are there situations where it’s acceptable to not have in and out-of sample periods. I can’t see how the system I’m trading can be curve-fit and I was hoping somebody could enlighten me. I say this because of the following: [LIST] [*]The entry code is relatively simple; there are no complex functions or indicators used and there are relatively few actual entry conditions [*]The system produces a large number of trades (about 400 a year, 4,000 over the last ten) [*]The holding period is about 2 weeks per trade [*]It returns a (genuine, I think) 35% per year and is VERY realistic (overly harsh slippage on entry and exit, accurately modeled brokerage, very restrictive volume and liquidity filters, no forward looking etc) [*]The equity curve is consistent in slope over the past decade, with no major draw downs (11% ish max DD) or volatility (this is partly due to the large number of trades; I can limit position sizes and risk and I think this has a major effect on the overall DD) [/LIST] I don’t see how it can be dangerous to trade this system and I’d like other people’s opinions (especially yours Mr Bandy if possible). I understand I can wait six months and walk-forward test or perhaps alter the conditions a bit and see if there’s a major change in results, but I’m more interested in the theory of it. If something produces a [B]consistent[/B] profit [B]over a sufficiently large number of trades[/B] and your equity curve is smooth, I don’t see how it can be curve fit. [B]To me it seems like if the frequency of trades is high enough over an extended period of time, there wouldn’t be a single static piece of code that would fit that data set well enough to produce consistent performance.[/B] Am I wrong? By the way, I definitely recommend the book. Nice to see somebody apply some analytical rigor to an industry/genre that seems to be full of people spouting unfounded BS. Any comments appreciated. [/QUOTE]

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