este seria el grafico en 1D y el grafico en 5D
Y estos son los datos de ROC1
Vemos que tienen unos ratios de sharpe parecidos, pero la alta volatilidad le afecta a ROC5 pues es como si hubieramos multiplcado por 5 ROC1.
para aplicar nuestros resultados desplazamo un periodo hacia atras nuestro ROC5
encontramos 3 arboles de decision y nos da los siguientes resultados
resultado2 | resultado3 | resultado4 | resultado4a | roc5 | |
Date | |||||
2017-08-23 | 1 | 1 | 1 | -0.010261 | -0.007303 |
2017-08-24 | 1 | 1 | 1 | -0.002079 | -0.001854 |
2017-08-25 | 1 | 1 | 1 | -0.001754 | 0.000226 |
2017-08-28 | 1 | 1 | 1 | 0.014886 | 0.004764 |
2017-08-29 | 1 | 1 | 1 | 0.017669 | -0.023179 |
2017-08-30 | 1 | 1 | 1 | 0.019973 | -0.014114 |
2017-08-31 | 1 | 1 | 1 | 0.013287 | -0.010261 |
2017-09-01 | 0 | 1 | 1 | 0.000000 | -0.002079 |
2017-09-04 | 1 | 1 | 1 | 0.033081 | -0.001754 |
2017-09-05 | 1 | 1 | 1 | 0.027756 | 0.014886 |
2017-09-06 | 0 | 1 | 1 | 0.000000 | 0.017669 |
2017-09-07 | 0 | 1 | 1 | 0.000000 | 0.019973 |
2017-09-08 | 0 | 1 | 1 | 0.000000 | 0.013287 |
2017-09-11 | 0 | 1 | 1 | 0.000000 | 0.030823 |
2017-09-12 | 0 | 1 | 1 | 0.000000 | 0.033081 |
2017-09-13 | 0 | 1 | 1 | 0.000000 | 0.027756 |
2017-09-14 | 1 | 1 | 1 | 0.000000 | 0.019828 |
2017-09-15 | 1 | 1 | 1 | 0.000000 | 0.017460 |
2017-09-18 | 1 | 1 | 1 | 0.000000 | 0.006745 |
2017-09-19 | 1 | 1 | 1 | 0.000000 | 0.000800 |
Sólo entraremos cuando tenemos tres unos, por ejemplo, tenemos 4 operaciones abiertas y parece que con un buen rumbo.
y esta tabla menusual de resultados
Month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | All |
Year | |||||||||||||
2000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.029401 | -0.199037 | -0.056109 | -0.225745 |
2001 | -0.015337 | -0.278239 | 0.000000 | 0.039390 | 0.154319 | -0.089747 | -0.132493 | -0.172211 | 0.000000 | 0.000000 | 0.183154 | 0.104501 | -0.206663 |
2002 | -0.284476 | 0.315031 | -0.050930 | -0.261608 | -0.246437 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -0.075851 | -0.070796 | -0.675066 |
2003 | -0.221124 | 0.000000 | 0.000000 | 0.225896 | 0.248789 | 0.300355 | 0.228768 | 0.272762 | 0.023557 | 0.452112 | 0.071363 | 0.000000 | 1.602477 |
2004 | 0.000000 | 0.063786 | -0.209294 | -0.061734 | -0.011626 | 0.086556 | -0.102504 | -0.099726 | 0.202924 | 0.047288 | 0.081978 | 0.081198 | 0.078847 |
2005 | 0.042069 | 0.042200 | 0.004390 | -0.041498 | 0.204630 | 0.035153 | 0.131189 | 0.108329 | 0.130534 | -0.010067 | 0.170099 | 0.086177 | 0.903204 |
2006 | 0.154408 | 0.073741 | 0.170866 | 0.098604 | -0.214064 | -0.121344 | -0.078097 | 0.169977 | 0.162976 | 0.111210 | 0.059042 | 0.134870 | 0.722189 |
2007 | 0.187596 | -0.154280 | 0.278264 | 0.152601 | 0.148856 | 0.293957 | -0.252756 | 0.053199 | 0.239084 | -0.009063 | 0.020868 | 0.069305 | 1.027632 |
2008 | -0.339935 | 0.000000 | 0.000000 | 0.067104 | 0.033421 | -0.404274 | 0.044904 | -0.100731 | -0.016031 | 0.000000 | 0.000000 | 0.000000 | -0.715543 |
2009 | 0.000000 | 0.000000 | 0.000000 | 0.371037 | 0.183133 | -0.238539 | 0.443064 | 0.154516 | 0.128518 | 0.074242 | 0.227550 | 0.115525 | 1.459046 |
2010 | -0.359526 | 0.289331 | 0.147759 | -0.155499 | 0.164059 | 0.015460 | 0.344941 | -0.008403 | 0.028620 | 0.221579 | 0.189976 | -0.022142 | 0.856154 |
2011 | 0.179239 | -0.018686 | 0.010461 | 0.168888 | -0.227920 | 0.166585 | 0.055085 | 0.000000 | 0.000000 | 0.013371 | -0.013763 | 0.207888 | 0.541148 |
2012 | 0.415858 | 0.061041 | 0.065655 | -0.074021 | -0.168936 | 0.000000 | 0.371307 | 0.154067 | 0.084643 | 0.073840 | 0.120802 | 0.038283 | 1.142540 |
2013 | -0.037233 | 0.180999 | -0.107051 | 0.162207 | -0.062531 | -0.135300 | 0.243270 | -0.022214 | 0.088969 | 0.160207 | 0.104300 | 0.149354 | 0.724977 |
2014 | -0.038278 | 0.084770 | -0.032024 | 0.057134 | 0.190762 | 0.000436 | -0.341870 | 0.116741 | -0.214404 | -0.014309 | 0.132900 | -0.080982 | -0.139124 |
2015 | 0.322181 | 0.142564 | 0.000000 | 0.020943 | -0.000652 | -0.077312 | 0.090454 | -0.156905 | 0.000000 | 0.008406 | 0.043422 | -0.251964 | 0.141137 |
2016 | -0.153872 | 0.000000 | -0.135340 | 0.033606 | 0.212222 | -0.083992 | 0.210452 | 0.207667 | -0.021111 | 0.115399 | 0.157345 | 0.207679 | 0.750056 |
2017 | 0.014322 | 0.113216 | 0.135369 | 0.138368 | 0.025037 | -0.125751 | 0.003774 | -0.008116 | 0.060837 | NaN | NaN | NaN | 0.357057 |
All | -0.134106 | 0.915474 | 0.278124 | 0.941417 | 0.633063 | -0.377756 | 1.259487 | 0.668952 | 0.899115 | 1.273615 | 1.274149 | 0.712788 | 8.344322 |
Podemos comparar las dos series, inicial y final
95% de no perder mas de -0.397773125263 95% de no perder mas de -0.270402900251 95% de no perder mas de -0.397773125263 -0.270402900251 Mediana de la distribucion, trata que sea positiva 0.0735622774365 0.08847237758 media de la distribucion 0.0430925040377 0.0770140705268 maxima ganancia mensual 1.06782979212 0.670358979577 maxima perdida mensual -1.164335774 -0.523092347573 desviacion tipica 0.262964567145 0.202750944228 dos veces la desviacion tipica -0.482836630253 -0.328487817929 tres veces la desviacion tipica -0.745801197398 -0.531238762157 ratio sharpe anualizado 1.11638100334 1.70780588179
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