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| bio | website | |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 1 year, 1 month |
| seen | May 3 at 21:37 | |
| stats | profile views | 24 |
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Apr 11 |
awarded | Yearling |
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Oct 20 |
comment |
Alternative liquidity measures If you explain what Amhiud is, I might give you another one. |
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Oct 19 |
comment |
.NET statistical packages recommendation It's not .NET or C(++/#), but you could use python or call it from these languages. There you could use pandas. If you can write C, you'll learn Python in an hour. Knowing Matlab would be a plus. |
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Oct 17 |
accepted | Order and position management in (semi-)automated trading system |
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Oct 17 |
comment |
Order and position management in (semi-)automated trading system Simple database: save sent orders, save broker/exchange response, i.e. logging. Positions are deduced from responses. History and currently active orders/positions should be separated (flag as active). Maybe I overcomplicated by thinking about design patterns. |
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Oct 17 |
revised |
Order and position management in (semi-)automated trading system added 293 characters in body |
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Oct 17 |
revised |
Order and position management in (semi-)automated trading system added 30 characters in body |
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Oct 16 |
asked | Order and position management in (semi-)automated trading system |
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Oct 14 |
comment |
Resources for performance statistics of trading systems It's just monthly data, but maybe state of trend following is of interest to you. There are monthly posts at least for a couple of years. see automated-trading-system.com/… |
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Oct 14 |
revised |
Analyzing tick data added 216 characters in body |
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Oct 13 |
comment |
Resources for performance statistics of trading systems With 'successful trading systems' I see 2 difficulties: 1) 'successful' is debatetable and depends on your definition (is a year of non-negative returns enough, although ultimately you lost 75% in 3 years?) 2) I believe truely successful strategies are not disclosed and those that are, might suffer from survivorship bias. Maybe looking at (hedge) fund performances would be a viable alternative, although survivorship bias might apply as well. |
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Oct 10 |
comment |
Analyzing tick data 1, 2, 3 & 4 are options, not steps. Either 1, 2, 3 or 4. ad 2) treat time as some variable tightly related to the original time series, maybe forecasting both in order to know where price goes and when it goes there. ad 3) find some small time increment such that all oservations of the original time series roughly fit on some time of your new equidistant time series. ad 4) summarize your data maybe per 500 microseconds and create for example open/high/low/close information for each 500 microsecond batch |
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Oct 9 |
comment |
Switching from Matlab to Python for Quant Trading and Research While I strongly dislike R's syntax and Python gives this pragmatic feeling of "I can get things done [quickly]", Python really lacks R's statistics tools. Python has a mid-size basis of statistics tools, but you need to call R for pretty basic stuff like seasonality/trend-decomposition, not to speak of more advanced statistics, but it partly compares with Matlab, I think. I believe something like the Rmetrics package isn't given anywhere outside of commercial tools yet, so that is definately something Python is lacking currently. |
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Oct 9 |
comment |
Backtesting VaR model violation independence Although you seem to refer to Value at Risk (VaR), I wasn't sure in the first half of your question if you maybe refer to Vector AutoRegressive (VAR) models. |
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Oct 9 |
answered | CARA Utility function expected utility |
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Oct 9 |
awarded | Editor |
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Oct 9 |
revised |
Analyzing tick data added 2 characters in body |
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Oct 9 |
answered | Analyzing tick data |
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Sep 24 |
awarded | Scholar |
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Sep 24 |
awarded | Supporter |
