Pair Trading Models - Revision history

Admin: /* Kalman-grid Model (v2) */

2022-07-20T11:09:53Z

‎Kalman-grid Model (v2)

Admin: /* Model Support */

2016-12-21T15:37:10Z

‎Model Support

Admin: /* Model Support */

2016-12-21T15:36:28Z

‎Model Support

Admin: /* Kalman-grid Model (v2) */

2016-12-21T15:36:04Z

‎Kalman-grid Model (v2)

Admin: /* Kalman-Auto Model */

2016-11-08T10:30:27Z

‎Kalman-Auto Model

Admin: /* Kalman-Auto Model */

2016-11-08T10:25:50Z

‎Kalman-Auto Model

Admin: /* Kalman-grid Model (v2) */

2016-11-08T10:02:13Z

‎Kalman-grid Model (v2)

Admin: /* Kalman Model */

2016-11-08T10:01:01Z

‎Kalman Model

Admin: /* Kalman-grid Model (v2) */

2016-11-08T10:00:19Z

‎Kalman-grid Model (v2)

Admin: /* Kalman Model */

2016-11-08T09:47:16Z

‎Kalman Model

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 * if you set entry threshold to 1, exit threshold to -1, model to "simple" and if you allow reversals, the model will be "always-in-position" squeezing the most profit from good pairs
-To give some insight on performance: we took 300 best performing pairs from PTL database in period Jan 2013 - Jan 2016. Then we backtested all 300 pairs using '''out-of-sample''' period (Jan 2016 - Sep 2016) and we compared their performance. Models used: Ratio(per 14), Residual(20), Kalman Grid v1, Kalman Grid v2 using exit threshold of -1 (normal) and 0 (aggressive). Margin 50% (Req-T). Results here:
+To give some insight on performance: we took 300 best performing pairs from PTL database in period Jan 2013 - Jan 2016. Then we backtested all 300 pairs using '''out-of-sample''' period (Jan 2016 - Sep 2016) and we compared their performance. Models used: Ratio(per 14), Residual(20), Kalman Grid v1, Kalman Grid v2 using exit threshold of 0 (normal) and -1 (aggressive). Margin 50% (Req-T). Results here:
 {|
 |'''Model'''

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 === Model Support ===
 * in PTL backtester: '''yes'''
-* in PTL portfolio backtester: '''planned Dec 2016'''
+* in PTL portfolio backtester: '''yes'''
-* in PTL Trader: '''planned Dec 2016'''
+* in PTL Trader: '''since v1.4.0'''
 === Model Neutrality ===

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 === Model Support ===
 * in PTL backtester: '''yes'''
 * in PTL portfolio backtester: '''yes'''

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 * in PTL backtester: '''yes'''
 * in PTL portfolio backtester: '''yes'''
-* in PTL Trader: '''planned Dec 2016'''
+* in PTL Trader: '''since v1.4.0'''
 === Model Parameters ===

← Older revision		Revision as of 10:30, 8 November 2016
Line 262:		Line 262:

	Please note there is no regulation feedback loop inside, the self optimization works just with ring-buffer with follows the current point and it is used for finding best parameters. So do not expect you set the target to 80% and at the end you get 80% portfolio usage. The good working range is <40, 60>.		Please note there is no regulation feedback loop inside, the self optimization works just with ring-buffer with follows the current point and it is used for finding best parameters. So do not expect you set the target to 80% and at the end you get 80% portfolio usage. The good working range is <40, 60>.
		+
		+	Example: [https://www.pairtradinglab.com/backtests/WCGoZyG8rDVDQnY1 Visa/Mastercard backtest] with Usage Target set to 60%. Notice the '''% Days In Trade''' resulted in 64.9%.

	=== Model Support ===		=== Model Support ===

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 == Kalman-Auto Model ==
-'''This section is under construction'''
+This model is very similar to Kalman-grid model, it is like his younger brother. The main difference is that the simple A.I. of this model is not trying to work with profit (which is problem with certain kind of pairs). Instead, it justs tries to find optimum &delta; (transition covariance) of the Kalman filter together with the entry threshold to match the '''margin usage target'''. An example: you can set this model to try to optimize everything so the strategy spends 50% time in position (so if you traded one year, the model spent only half a year in positions). Or you can say the average slot usage (margin usage) is 50%. This is useful for trading whole portfolios of Kalman-Auto based strategies, so you can fine tune the diversification inside the portfolio correctly.
-This model is similar to Kalman-grid model, but it is more simple. This model does not optimize profit, but instead it optimizes margin usage to match predefined target. This model is very robust (is not fooled by glitches in performance) and it automatically optimizes entry rules (thresholds).
+Advantages of this model:
 === Model Support ===
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 === Model Parameters ===
 * Kalman filter observation covariance V<sub>e</sub>, typical value is 0.001
-* Usage Threshold in %, allowed range <20, 80>
+* desired margin usage threshold in %, allowed range <20, 80>, the range <40,60> works the best in terms of how precise is the margin usage matched to the target
 = Deprecated Models =
 * Orthogonal Model
 * Kalman-grid Model (v1)

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 * the simple A.I. inside is profit-based - so not-so-good pairs with waves in profit can "fool" the algorithm so it falls from the optimum
 * model performance is proportional to margin usage
 Specialties:

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 * auxiliary standard deviation period - if equal to zero, Kalman filter is used to estimate standard deviation, if non-zero, auxiliary standard deviation indicator with this period is used (an the estimate from Kalman filter is ignored)
 * unstable period = how many Kalman filter observations are ignored at the beginning (first observations are quite unstable)
-* entry threshold E<sub>n</sub> for Z-score, typical value range is <1.2, 2.5>, 1.5 is used most often
+* entry threshold E<sub>n</sub> for Z-score, typical value range is <1, 2>
-* exit threshold E<sub>x</sub> for Z-score, typical value is <-0.5, 0.5>, 0 is used most often...negative values allowed (allows to exit at other side)
+* exit threshold E<sub>x</sub> for Z-score, typical value is <-1, 0>...negative values allowed (allows to exit at other side)
 * downtick threshold E<sub>d</sub> for Z-score, typical value is <0, 1>...used for downtick mode only as a third band
 * max Z-score E<sub>max</sub> (optional, to filter out extremes, typical value is >10 if used)

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 == Kalman-grid Model (v2) ==
-After one year of experience with the [[Pair Trading Models#Kalman-grid Model (v1)|Kalman-grid Model (v1)]] and researching [https://en.wikipedia.org/wiki/Machine_learning machine learning principles], we have spent weeks of additional work on developing new version of Kalman Grid model, redesigned from scratch. Kalman Grid v2 is now the most advanced model PTL offers and has no competition whatsoever. After proven as stable it will become the default model for the whole PTL ecosystem.
+After one year of experience with the [[Pair Trading Models#Kalman-grid Model (v1)|Kalman-grid Model (v1)]] and researching [https://en.wikipedia.org/wiki/Machine_learning machine learning principles], we have spent weeks of additional work on developing new version of Kalman Grid model, redesigned from scratch. Kalman Grid v2 is now the most advanced model PTL offers.
 This model brings the superior performance of Kalman filter design, but it also deals with all the flaws of Kalman and old Kalman Grid v1:
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 How does it work? Instead of single strategy (and single Kalman filter), the whole grid of Kalman filters is evaluated by a proprietary system at the same time. Then, machine learning principles are applied to rank particular Kalman filter performances. Another algorithm is then used to figure filter outputs (slope, intercept, standard deviation) from the whole grid. The whole system actually behaves like a single Kalman filter, but with no need to provide any parameters.
 Main advantages of this model:
-* it is robust (because there are no parameters to tune)
+* it squeezes all profit from the pair
-* beats any other models in performance (in average)
+* beats any other models in performance (in average) and also in performance per day-in-position
-* no risk of over-fitting in parameter optimization (nothing to optimize)
+* no risk of over-fitting in parameter optimization (nothing to optimize, except thresholds)
 To give some insight on performance: we took 300 best performing pairs from PTL database in period Jan 2013 - Jan 2016. Then we backtested all 300 pairs using '''out-of-sample''' period (Jan 2016 - Sep 2016) and we compared their performance. Models used: Ratio(per 14), Residual(20), Kalman Grid v1, Kalman Grid v2 using exit threshold of -1 (normal) and 0 (aggressive). Margin 50% (Req-T). Results here:
@@ Line 224: / Line 229: @@
 |}
 === Model Support ===
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 * in PTL portfolio backtester: '''yes'''
 * in PTL Trader: '''planned Dec 2016'''
 === Model Neutrality ===
 For Kalman Grid Model, we support both '''dollar neutral''' (equal dollar amount invested to each leg) and '''beta neutral''' regimes.
 == Kalman-Auto Model ==

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 * Kalman filter transition covariance &delta;, typical value is 0.0001 - '''unfortunately this model is very sensitive to this parameter especially when using standard deviation estimate coming from the Kalman filter itself'''
 * Kalman filter observation covariance V<sub>e</sub>, typical value is 0.001
-* Auxiliary standard deviation period - if equal to zero, Kalman filter is used to estimate standard deviation, if non-zero, auxiliary standard deviation indicator with this period is used (an the estimate from Kalman filter is ignored)
+* auxiliary standard deviation period - if equal to zero, Kalman filter is used to estimate standard deviation, if non-zero, auxiliary standard deviation indicator with this period is used (an the estimate from Kalman filter is ignored)
-* Unstable period = how many Kalman filter observations are ignored at the beginning (first observations are quite unstable)
+* unstable period = how many Kalman filter observations are ignored at the beginning (first observations are quite unstable)
 '''This model is provided for education purposes only.'''