tcapy_notebooks/a_10_minute_view_of_tcapy.ipynb

{
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fVjA47Hsag/QEmqus5+0nOFRdKxYoPjqrDjMdHoCsjbsT05V3wm5+znzwI2fj/d9QJJHJ53e1qyAuuRvmGV6K9qIEhysKSWJb3Ox8aWp8NZDLl7C70lPIU0PTmHWs1AUt5M5mVxOb3lcuWz4kS3estu/pQXDudeLjn7n4v0BQ/FnXZMx0eCCVyvHXui++5+55GuMUWHjsdSIUldVwiY4fuMI2HESMdiQBfL+rf8GiO2HI8vibRAh+nvvkhaXVNLcKR7xnxCgCLeQeHRM7bUrPgKYlCxeJRIOdHEIskVnc6WyqAxpcZmlf9H8XWERSmLF1IETA2A7DgJSiplmXsYTBI7T4gkuSR2QpiDUoufqmexp+GojaVqyAFDa2pyKs36UaXsGa6oGP/fNzyzgQKTFjT/sLugQzz5474nKfNEEjMzMTKxso2vmSZffCkT623v+9/G7459AS9LoHp7aVv29iFbbDMOClf2diinj2XUoRu40VX7XtYdTyu2EeIcV4H0YITvY7xsGHbQ7Ryo2SOeUcI8WuEJc90qLSatO6JzdloD5oIfeOjo6Y2Ph9e/eB0CdO0DDUM8jNzcPKBgqo7kHKM7vavC85J6+zj8wv55hfDwKR6Xe/1AQe7wothgl3PvW0Qi64GSKWyEVimVtYsVNAQUk116TLYc+5HhSTXVfdxDe8HKB3OSCzVGEKhtpmwYruhxYRovwXv/IgPeUzfdn7D1rIXSKRuLq927tnn+OLlw5Pnx86eHjwnd0bW4VBSWyDLkmde5ty0CFGKJadcU6y+5rlFlKMq8f0WhCa8nzIIZN1bHsUhX8R8HdG7feIsri8hoiMWpFE5hZUBKeHus20cEXwPIDNGZekivp2eFbhbO96Za64q6D17Y+jOGNgOcjhAy3kXldfb2psoj1j5qpVqw319M3NzK9du5GYkDSYl03fYyvmXAtCbe0nXic4fMmSyOTesRUHXsQVV7XhAgJnH5k5LO0z6aUtxJZ+4K0vmV8jygQiKfpZnyPLnLvWu+NwxSXstpVdfQ0ggofQ5eSbxN1PY0iZxo7uXr4MBgxayD0oKGSy5kTi+L0pEye9fPVaLB743b3nlYkLZduT6OoGPjw8BVWtG2wjPoV1jujDeetTBrbPkALCEpLcLW+GSKRy1JACKg9MqJLKOoVfVc+rbxasvh9JNCbxoGMcPjU7gwGDLsGM58dP06f0DGgC6aempmHFA8LeZ1ijO9DUOhAf8ZBd3PwutCeYAS6+EzYcs/pDijlLUe4Q2xCrK+JIQr5Q2ovcL7xNGWvrDgwTaCH3lJTUgMCgwMBgrUmTkdzNzebUDmIBDy5fYmGDtUIiRndHLN7R5aFJ1cQiCKDzKoZ+4qGCylZiTvwxtIRyQF1KYdOvxKqQ1GpzxYFXiNpW/nbe2UyD41CBFnJns9mXr1hnZefs2b0XufYXL14OJnBPLW4melZdK5atdxYqKq3mriUMYEX8+HvoFwupqFMYS3XuXYryD4ItmUVNp5yT8F6TRN7/mh2ZXsvMnDGEoIXca2rrLlyw0hw3Hg/fL160GozcS2q4qE0Gcf3939+jylHwIBBKH/7IwYsQ/3WKRzsOIcQS+Vp7LD6ByNvqXQplrxi+QPouqAg/E5zr70f+9RPj/HnQQu4tLRwPz09I6Ig/fvzCygaEqgYesUPYnGtBbbyePK+oslWbICyg+c2QIW/0gJR0Ufdr3QMv4kC7qqb9qGni738ea0qoCoytAyH0x4oZDB1oIXdAYlIyce4NiOaxggGhupFvShg0NPOiv4AQNyfmNxK1BYQge8jlVdcsMO7u8wg84hTXS30ll3ck5DS4BBSadu3iMcbWf/xjoIXcQQcRhOUmIaQpKCjAygYEkJoZob/hgpshxJaN2Oz62Kw6vBTx25CO14aA2yeuUr87oILkIatErRnrRWJZCbuNx7Q5Dg9oIXeJVLpty1Zc7lMmTiot7a03bJ9oahXO7XpJibjPKY446qe5VRSbU4+XIl55P6j6hATw4ye6B5cA7b2zmYyTDqCF3OVy+YF9+3G5mxgZNzUNqv8TBMoHHOPwt5Kr7SLgAcDKumDZNdsWkcttI4Z2stz4vAb0msnwcsBL/wL7r1mDyL0ZDA3oEsy4u3vgzTJXr14bTLMM4EdMhf6lnpYZ4O8MhVb8MwTXiwixPiS4WPGgwa7n/YyrvOKe9jqo8GVg4a6nMWfeUjREMvjDoIXc+Xz+z1++RvqGE7oUv337jkH2d/8VT55h5rvi/O7pBU0kA20r/9i8IZtS721gkd4l1tbH0ejVEpcv8Y7++yeYpz9oIfeMzCz7+w9/R0XfvHlbZ8ZMnRnaiYlJWNmA4JtQRVLzp3CFto7aRj7JAPjs12B7HePwTew8AYimvkaVRaTVXHiXwuEyvbtGHrSQu58/68yp0+fOntuza9dcszlWVpfr6uqxsgHhY9e07kR+UmzaK2G3mVgHEmdDB+5+FqN+vNHGE+dXtX2LKr/rlfk9toK0GE5sQSM6puXt0MySlv0OMWN8QVOagBZy5/H5b5xd8Xb3NavXcNsHNUPQg6/ZuIgRScGMXN4RkMQ2Irx5BS64HarmfKK1Tfztj6Lwd1WGVwMLqxSmA8ivatXr7jBj753dyBEO9zwfDNQBLeQuFIq+en/bvHEzkvvyZctbBreu6r/dEyrhDEwiDwMFR45mKsVt9C8HFLDbsOJeATviewENrgTkKr6l+hFdjuQOKcGeJ9GVY379XpqAFnJvb293dXWbMXUakjvw1q3bA56MQCCSLrNVGAQETKRKQ1vbxXi3FqDuJVayegNAwVubEd/aWvnf/pRBDIRcAgvx0i8RpcywDJqAFnKHJHWy5kRc68Bdu3YPePxereIrVcQyqtlx88o5xDVeDK8EFKs9OfDn32XEmmHOjeDa5p7Jpr1jeibHc/IdsgyYwSAx8nKXyWSNTU3XrK9PISje2vp6RUUlnz+Q2cpzKhREDNS1YvGpehcKRLIlhG7xIN8EtYf3S6Ty9YSaARx8aAY2vREUHeyaWQDx6c9ctJ3BiGPk5Z6RkXnvni348vj4BGMDIyR3EyNjH1//xsaBvFv1iiQ3y8y7Hkw5GgiiD3YDf7tDz7gnfELdPiGTd5D6zdt0DwKEBIA4sOONP7PgPV1Ai2CGx+M9fPjY0sJylrYukruhnn59wwBf+tz+mI5LDXHlvXA0QlQZfvGVaC5scO13vmR+U/tlkFAsI2oauOdZLPoWd8XBgV8ihn7sCIOBYeTlXltX7+H58ZePb1lZua2tvZG+Ach94QJLgWAg09tCvrjrec9ckIhbn0SjQdAkgIe288pCI4lmXWKllzaDiFU9GCQQpzMAgvTD02qEos465PQbhR4KpP4LDEYQIyz3jo6Oy1aXN2/abGxodPjQkeLikqam5n+PHNXT0a2pGchMn6DXjQ9/E5NI4OHXCaqGVrzvnnMGdrHxyswobs5Vb9xqfkUrfnyg+fXglbbhre1iOIFlikFOWS2zygBdQIPYPTPr0aMntbV1a1atnjZF6/Ztmzt3bTX+Gefs7IpZ9Aeg6op6HmmYcy/dsyobeMRnQ+9ywGmXJHXWCKhvFuB7AU++Stj6OCqf3VrfIiB+u8nVQMosmcGIgBaxe05uXkxs3Cevzyhw1xw/YbrWVD//AKy4P4Cg5bonOXY//CxW1WB+nlCqPLLpJatARhX8EJHS3U0A0fBKwMKbITc+Zdh6ZxNHhW97GKVmdMTgD4AWck9ISDQ1NiEu4LFq+cqBdYoMSakmTWYEXHc/UpXmYPsr/wJjRcXDEVL6apFMLmg86UzuRUwiZAVZJS191xQM/hRoIfeGhkaI3XGtAyG8wcr6idSiJuIcBIg7n8ao8tYCkSy3nHP/G7mPzT2vvqcgDkxik/YiccGNkAGvI8lgODDycpfL5Z8/f7G0sFyzes2mjZs3bthkbjZHT3eW97cfmEV/0MaTLFacRnTf81j3oCKsWAkNrcIr71NfE2amRtzTOZIas6EElFortXiSuMMhholkaIWRl3tHR0cbF3t1X1nFrq6u2bVjFzj4bVu2DqDbDHjTTQ97Vv/SsWKVVHN7meMX8si9j6O3PonGd0Hc7BDdu1JFEpk5odsMJY9D1tv7Q8Pgz4IWwYxQKPz2/aejo+PvqOgjh46geAay1Q8eHzELtdHYKpxPGJQNWWNcTm9d5yGFveCWgtvjXP/wd+/TMqYojYdS5rYHv4d1dRAG/QUt5C6Ty587viD2iEQ8feo0ZqE2EgoaiQ2LkIN+i+tjRo2aJv48pfkZ1z74jcbdqUJ4as3crkXweqHjz9ywlGpsBwY0AC3kDoBs1cTQaOL4CUS5b920ub/xjP3XLKLgDK8ExOX20RkBwg1S/3XgKruI3uesyynn6BGagOAZQyuFEPk+sKicqicmg5ECXeQO4PF4np4fZ2hhPn7KxEksViBWph7EEvlmxSj8rFtyn++MILyOz6nXV2y+XHYvnKd6NZjWdvE6xfmpV9lGnHdLJr3Njc0e1BBEBkMOGskd4O8fML17CT7dmdr97RHZ1CokhSXhaqw5Wt8siEivJflmOI6qMRmQDR9WGi219VEUBEVoBQ6cyQUD6dHJYPhAL7lfsrqMtA48deqMSNS/PC+9pJk4c/Rs66AWNYYRVdXzjr9OMFDs3gg5blUjRW97nkDyL6EvO04dKxYriZ2Q12BiHbjsTtjcG8FwJmdck5ip2WkFGsldIBQuX7IMl7uPr19/J1dyCegZMgfc+ChKnfH/EALt6F5fGyeINTGvEbMggMMVn3Xp7DBM4qq74TYe6RKpPL2kpbKeV1LDPeWc9IpVwLRD0gp0kbtUKs3OyTXs6v0LhJzVxfVtv0YzwbNxTPGt/m31Fl2SyTuOUnUH8AilmIZXKJZlV3BI3WyA1z+kBSWz3wcVQUhT2yxw+Jl70CkeXyGHAU1AF7lXsasfPX4aGxe3ccNGjX/G6enM8vVj9cu7twskxNAZskZWIvUi1CRU1LWbEiaDx3nJnWKSVL/EKmK8hKhzifU9rqK4qg38OiuuEmIYJ7/8Nh4zHJt2oIvcm5tbPn/5WlXFzssvWL92ndakyUVFxViZeqhpFhDnjYHP71T3HSCipJqrrGDgLsdY5Wl7XRRHKiGa3QiubxGAMZpUVZ3+wwxGBHSRe1lZeUsLRyaTwV/be3YQz3zrZ5+Z4JSeBca0rVjWb1PK1RtXwRdJVypN1AHc/ISiH4F/12x4JM65GdzUKkzKa2Am2KA56CJ31HNGIpFwOJxFlgtB7m/fuWNl6uHel56FVCGSmXMtKEa9Zm+ImOy9yT0igZsfU3RVT8xtIJkB9zyNSStqtvuUyXh1moMucpfL5fUNjWERvzOzsiGMmW8+b/269WK1p5oRSWRbHRReMC20CW3jq7t7VhlHOZ6h9O45ZRzSuyTY8WNkmeWtkNB0Zkwq3UEXuSO8fe+empbe2tZ25PC/RgaG4Omxgr7Q1CaarZhu3vqYob6vhQdj0W2FdViB25/FKLeaZ5W0kOQOPPgs9u7nzF7ewjKgCegld1tbO62Jk6ZN1pqiOfHA/gMQymMFfSGEELgD9S6z8ioV5ijtHdmlLaTeB8Cdz2OVB4X4xlcqy/1GV4s7ZsGAxqCR3CF8P3K4s/fvJA3NDx6ev6OipVK1/CXseMUjjai/bU+i+7XyTH5F65GXCcQjAG96ZSjLncMVbSRMHqZ3OeDa2xTrD4Naz57BHwON5A7itrO1t7O/r687KyIyktverqbc24XShYRQBLzvr7hKrExtfI4sw4+A+OxXnnI4xBcoDOU2vBLgEVR80S0ZK2ZAb9ArmBGJRMUlpaGhYcuWLrt+7YZAqNbMSpEZtbj+gBZqT9NOxA/CJKaI82+GcJQaFt3DFNrdLW6FJOY0VFP1rmFAQ9BL7nK5vKa27vuPX9rTZ1hbX1Ons7tM3nFcsQuA/ZcsrKw/gKCceBDEzxEK6102t4nMCCsDA42sA5l1CkYR6CX31ta2qqoqG5u7SxcvDY+IFIv7fmtTXttO7KpueCWweEAjKijl/uR7DlbcheS8RlI/4VmXA1LyKXqSMaAn6CV3Lre9obHR3d0zISHJ3cOzz3HNkKSSJlHa8ZSi9VAdsBIpZtFwZSks3h2RXkNqlgHvXsNEMqMH9JJ7QUFhc3ML+Hjw68WlZX2uaJBf2UoaQfcrvt9JKkJcLnkdbV0r1j3vbOIAbR+lHgQb7kdiZQxGA2gkd3DVAYFBSxYvyS8orKtvaG1rgy1YGRUkUvluxX7q6+9HKieXaqK8vl35xSo8P8SON29CikgGdt+ysTIGowH08u7l5eVLFi12/+D5+o3Lc0cnHk/lCl7wIDgpzoWke4kVkTnw1/hNbSJzpSVuQO5h3cP/EvMbSIGT3uWARCZwH1Wgl9xr6+pbOBy5XA5hTGxsfC/eHSJmkysKvQYsbEIH0yGxc6bsx1HEAyKutosQS+QiceeyNsTACXjgeVy/XmYxGHHQS+6gb1B4YmLSw0ePf/r4SqUqOxE0coRbCOrUsWI9/Z4zyKV6laffAC6xCYPwnZXEVp5X4xmzCs1oA73kjgCuvYpdnZScUt/QoKrbTHEVdx3hZb7yyqYDgHdUOX5AnHOuBYWk1RBnJkOcdYn1exCxE4MRAe3kzucLyisq29q4Eb+j0tMzYmLjsAICithty+4pDMgAT9/7HHfqwOFHDmm5pV747FduK5cZzDHKQDu5C4Wi/IJCCN/BwQuFQuUXqwKhdP4tBV9rfiM4s3RQq2wj2HxM944uN1Iadq3MDQ9+tzEDl0Yh6BjMACCEb2xqbm1rw+UOSWt9iwAi+yDFvr6g9XiqFbEHAGdWQVOr8ITiQmKUVH+BPga0Au3kDhJvaGjMyc3jctuJLTPyjo52vqSkhkucJ2zO9eC04mbMYtAQSztf40bnkN83kTjzon9lbXtiX1NPMqAh6CV3SEx9/FgQxrTzeIlJKaB7rOC//8KSqoVimf2nDAiv9S6xzKyDjK4E+Az0HWov+EyVsBK58GYIPBXMeI7RCNp5d5FYXFBYVFVVLZVKiYG7QCSTd85sIWviioCcdnEDR9j7nNT9BRdqD3bb/a9Zyqs7EXn4ZQK2A4PRBjrG7l2t7310DhsOwMPzM6oc3HZaUfO2J9Gk3mBAqFX2PouNzqrDdmAw2kBHuUNIw66uyc0rCAwMVm6ZGVbwRVJRV43BF0pvfkgjLhkJ3PyIWTVydIOOcgfw+PyU1LTW1jbs/z8LCJkaWgSJOQ0nXyZoW2GKn38zJH3o0mIGIwKayh0cfF19QxVbrUkehxxtPMlL3zz/+MqH33IOvUo44Zr0NqiImSHsLwDt5A756B8OYCiB5w4jlEcwGBbQTu7l5RWh4ZHt7WpN78iAQb9Ax2AmJzevpqbvRWYYMOgv6Cj3isrK0jLmLT3dATHn9+8/GhobxWIxMeCD7bSN/+gody63XZ05CEYX2tt5IpG4z9G3CDweT/0JA4mIiY0nzkVFlB18DgkJ3bRhk4mR8eaNm8PDI0tKy2B7SwuHeLUlEmlWdk5zcwuotrGpicfjt7a1wQcpnJBMBr+isakZNgqFQm47rx0+CQStrW2wF+i+ra2zJS03N7e0tKysvELVyHo4Ez5/ZGYroZfcQQ1wZeFawBWBC1pf39B72lrf0AC7NLe0wN/Ol64dWJrby9MiEAggWAI9we2pqaltampuaGgUiUToJS5RHwh1dfX37eyDgoJhF/gWAFgiM/iLPsAWGXy94qnC+aMfUlJa6uX12VDP4O49+48fvbBi1YAz3LZ1+/v37p4fP0FF13lcFRcBfTv6AD8EfrXji5f+rMCSktLKyip7+/vHjp0oKi6G0wsPC1+6eOlkzYloLSBErYmTbG7b7Nm99/Ub118+vvfu3rt4/sJCC0vNceOna00LDgkrKiqB7XNmmzk5vbx541ZcfCKfz/fzD/jx81d0TFxIaPiPX77FJaUCgbCxsQn0jWZsrqmty8nJK1S9GgU8Ni1qT3Y7tKCX3MGvw2X68vXb1avWvn5+2tNnHD1y9L37h6DgEODPnz7v33+AS+/o+OLr12/R0bGrVqyys38w4X/jjA2Mdu3YBXfr1MlTz5877ti2w8fHFwTXOS1ZcYmvn7+fH8ufFZCenslmV+vO1Dl/7vzZs+dt7tx7+84dbvzMadOXLF6yd/fey5cuv3z56uMnr8ysbBA23Mjz5y4gccDJzJs7z9TYFCxt79nm5xc8eeJw5tQZOL3FCxfv2L7z8WMHOPkH9x+AE/X2/rZh/YYZU6ddsroE2kVH0J+lN8fUjMfnIwWjJwd+NZwmSDw5JdXhiUNsfEJBQeHMqdPRLsuWLjt+/MSB/Qfs7tm+eePs7u4Bv4LDaQWvmpSUzGIFwl+4aPZ29vq6s1YuX6mnowt7afwzDjhFc+LE8ROAa1avWTBvPmmJZmVO0tCEvUyNjD96foKHc92adc7OrnACixYummc+NzEpGS4mnPbZc+fh2f7x0yc8onMWBnjM4W9dfT3UEimpafDgVdfU4lUTVAUJickBAYFwwr2MTftjoF0wk5KSCt4Frv7UyVOIN0NNThyvMWmCBvr88NETZZcGWif+q4qg1N279gQHh07W0CQVIYK80AeQCP4BP3PNcSq1dfzYiWvW13Nz89auXrNr5y7v7z/WrF5rZGA0seu0V65YaT7HvJfdgXPN5ljMt9CaNHnJosUGevqzTWYrSxmKkpJTLOYvwLe8e++OnzOJcObr166HH7t3zz64/nAXDh/qnJt2utbUmNjYisqqbVu3xcbGgQvPyMjy+vIVHHlXZSJpam4ODA4B+zYuNyU1HbZ33cMewANQVcVGM6lgm0YUtJM7xCcGs/SJN2PA1JkxE9fiAAgxLjhUjS4FDzn1dfVIWxBB7m5v36PHBpH4mUjYDhUUaSMiqD86JgYqGeJjADFbUDC2BVzAxg0bwZ2jIqhzCgqLUtPSwTeDZH39WYsXLYZLt3XLVojBa2prIUaHoAWqo7Wr10Jsk56RhYJvsAd/Dx8gCkxOTlFuPoZQEPtED9BO7nAFz545h27DUBEXPbirKZqT8O3guUm+f8QJKnz69Dny9CDN06fObNq0hWiAc+6cuYmJyfABfh3+TELEderk6RXLlkOuuXjhomlTtHBNQ4wBznjm9Bnw+cL5CxA9x8XGPXF4unfvPviblp7RlZP8V1pWnpqWFhsb6+vjC0EjSBxuCgR1kGdDcJiamg6hF6SnaPuoA+3kDohPSFy7Zi14L7jrEICiu4U4ZWKPWFURgplOff8zbuqkznDI3Mz8/v2HoGyIMeDIbm5vkRmEKz9/+Rw7dhz9q0yIi0hbhpxwnjrTZ+KfwctCFqirrWNoYKg5fgKcNpfL3b//oPa0GYZ6+hBAo8gKxD1/7vz7Dx5BWLxu7XrQ/fJly6dPmQpHePjw0YcPnq5ub+FnQryenp4BKQQ6PrhnSEimTtaCaxsbG49d665ZlwMCgyFdycsvAFedlZ2LFfyNoKPcoWYER+Ll9eXLF++EhMT3791tbe3XrFoNedjWrduhBj937gLcy4WWiwxmKYQEEM6amZgGBQb9+uUDaRbU5gvmzodUFWJHsIeM7YnDMwhAz545C4davnQZKyDQ3eMjfIaHas/uvfgixrAFjnz37j145JYtWXrs6HH4AOls51NE+DqcyBnj1PhnvLGB4aQJnZnfIstFkGtqTZwEn0m7wwEtF1jA6V04fxHSwSOHj1RWsYtKSkHlRvqGUMVBdmhrZ79v335wvVHRsdz2ds+PXi9evITflZ6R+fmL98NHDp6enz54fPwdFb1x/cZDhw5DKAhFEJZAQBgWFlFeXrF923aITNavWx8TG19ZVeX9/adAKEQpMo62rkGS1TU1ULVim/5S0FHulIA7BEFkRUUFeCP4FyrrwqISm9t3Thw/8ft3VHx8QkZ6RmFhEaRQ+D2TyWRNTU3oc1sbF7IlANxX2F5TUwtHS0hMYrOrS8vKrly5Cgf5+tV71crV8+fOi42Lh1QSROPg8Mzr81d4uu7ds4uJjZul3dnu0aXUGfBcQVUD0cLePXvfvHE5eODQjRu34KmYPkVr5fIV4EoDAoJYrADI7Xbu2Hn5ivW37z9u3bq90HIh/Hvt+g14gNFXwLl1Rsxl5RAuoM9wevX1DQKBAPQNMQM8n/AXb+tAgF9R39BYW1vL4bSiFlgw43A4X756g+duaWk5evT4xYtW8AMhiWxsauJy26OiYpAldogxiVEjd0rw+XySoxoYQD08Ph9UVcWuRjkZbETKkMpklZWVYrEEHrCKikoQq7OzC8iI09paDJ65pNT9g2eXnrgQh+zbu8/Pn1XFZsOWrgN3AgT36vWbwKAQkG9ySmp2Tm5lZRVWNtTg8fjwYMCZp6Wlh4aGYVsZdGN0y/3PAzWoEZ8xeDaSklJaW1uVm+FwwF65efmoNQMcLMlVM/hjYOTOYAyBkTuDMQRG7gzGEBi5MxhDYOTOYAyBkTuDMQRG7gzGEBi5MxhDYOTOYAyBkTuDMQRG7gzGEIZF7jKZjMvlJqekffv23cnp5X07O+CbN86+vv6FhUVixXkaGDD4YxhKuYOIW1tbP378tG3rtulaU/GO3SSam82xsbnDZlczomfwhzFkcufxeM7OLiZGxiRxAzX+Gac1cdLUyVOIQyd1Zmh7eX2Wq5hSggGD4cDQyD0/v2Dd2nW4lHFqjhtvucDi/Tv3jIzMvLz80JDQndt34oN6NMdP8PDwxA7BgMHwYwjkHhn5W1+HYkaHSRM07t69x+UqjE6HwP3Y0WO4jfb0GRUVQ7++EgMGlBis3KOjY2ZOw+YAInLiBA1HxxeU4xgKi4qJozYfPXqMFTBgMMwYlNyLiovx4ZtEgpovX7oiUTGsTt7RYaDXM5PM+nUbsAIGDIYZA5e7QCBYuWIlrloiFy6wbG5WuYx1R8d/psamuLGhvgFWwIDBMGPgcn/48BEuWSInTdAM7ppITRXq6uuJTTS6M7WxAgYMhhkDlHtJaamqGY42bdgkEvU2IaCn50ei/dLFS7ECBgyGGQOU+8kTp4iSxTlpgkZE18SwqtDa2jZn9hziLhcuXMTKGDAYZgxE7qWlZfgsuyQuslwIMT1mpwTQ+t49e4n2kzU0ExOTsGIGDIYZA5H7vbv3iJIl8s0bZ8yICgKh8OyZc/gspDO0pr50esV0JWDwx9BvuQuFQj0V04RrT5te1teaSjKZvIrNZgUEBQWFNDQ0Mlpn8CfRb7lHR8eQpvbEuXb1mgH3gUlLS/v+7Zsyo35HKT8SHA6HZIbo6+PL5/MxIyrIZLK0tPSHDx5t3brNxNB4+hQtIwPDzZu22Nvfz8jMpDz5kpLS2Ng4ZWZkZmEWXeC0tmZmZcfHJ+Tl5YvFvU0GzePxc3LzOie1zMzi9Xq2vaO9nVdeUZlfUFhWXo5m+YMw0s/Xl3RNEOvrG9BevUAikZaUlsFP8/f3//HtG4vFgpNkV1dTOiQ4IOkrEFn+/miiNYBILM7KzgHHBvclMSkZHCXargoikQgi28ePnuzevXfe3Hk6M2bONjHdsX3nC0cniJ8xo8Gh33K/eeMWSeU4X796gxn1E1KpdOf2HaSjIVpbX8OMCAgICCSZIZoYGrW2tmJGSoBLuXrlamIbKJGQjUD+3dbGxay7sXvXHs1x45V58sRJKJXJ5bFxcUcO/zttihYcGbZPnKAxd475z5+/SG+U4aFNTU07c+bczGnTwabTcvwEAz19FxdXNBmqmiguLnn69NnKFaumaE6cpKEJpw000jd8cP9hZOTvyRoU09WDewLVYvsrAU6ssLDozp17JkbGcCg4MeTO4C98huRq3dr1CUr5lYeHJ/ErcHYmb0IhPPwvXjiZGJnAb+w62jjN8RPMTGenp2dg+ysCXICb21szUzMwIx0QEQJgp5evMOtBoH9yh1Bkzmwz0qkgak2anJGRidn1E+DkQCKkAyL6+PhhRgTctblLMkPcsnkLZXQEj9PjJw6kaakpeejgIdw5AfgCge5MbZINovMb59zcvK1btoImSEVAuMdWF63wQxWXlOzds4/yXoLlnl17uFzyY6aMqir28WPHVT2uQMozAc42ma1q4tjm5pYrl68qr+kwZeIkY0Mj/IDwb0xsHLZP1xNy9vQZ3JjIo/8eZbECYF/SdkTwR8rVb0pK6kKLhegZ64Ua48Y3NTdj+wwU/ZN7eXmFqjYZfR1dUm8w9VFZWUUpBXAtbDaFW9qwYSPJEvHhw0eYBQFwp62tr1NezQXz5v/86ZOekQUyQlvAX8bF90z1X1RcDI8xbk/kwYOH+1z5A2pCkUj8xtmlz1UYoH5Ay9apwi8fX8q+Sepwx7YdlHFaTm6uuWKjMBASMBdXN3j8YJfyior5c+eh7URXApd0xbIV+C5EmpvN6eWBBIaFR6CDAOCAP378pBwaAce/dNHq9KnT+Fpa8POFwn5Ug5Ton9xDQkJVPYU7d+zCjPoPXz9/0tEQoSaBCBUz6gZU/ZQSBFcUEdFzKXE4OjpRur0Vy5ZDnYtswMnhDunGjVtoIyAoKLhPr9ML4XFdv269OkeAmsfXl6IeQ3iruFoTImy5dOlycUlpO4+XlJwCbpXyZwIhM8EOREBmZpbyymSGevpQZWEWXXBzdUNF5nPMcbnDw6DKC/TJ799/oIPA0X798tVScgQQFnp5fYY4AplB1K4zU3vq5Cne3t/QFoSGxkbsU3/QP7nb290nnRzOwXRsvHWTOh/YtGGjcp/K7JxcSgHB019aRk5oEhITScvdIEKIUl3dsw49uKvN3TUGfMC2/vcfRLT4LiTu3LEzIvJ3C4fT3NLi4PAUXwKJRDjVXTt3gUsDszYu9737B1WWK5evIMZROMCvK1d9oGx4BjCLLjQ0NBrqYauPkBgWTn7xV1tXB5k6yWza5CmQm2IW3XByeolKly5eissdQnl8LxJBl7b3bEtKSyExvXAeW6OTSLSCAyAjM1O5vgIHAWkPMsABcVSVYiVfU1sLSQKkHNj/aqN/ct+7dx/p/BDh6gcEBGBG/QRIbYuK1bYePHiIGRHw9ctXkhnibCNjCLUxoy7w+YKli5eQzBA/KA4rgYp7146dqGjjeqyHJtzdVSr6wEGeit97ADyTxE78RBrqG5Deu9na2pNsECm7/oNuZlDFMDdv9lRBCNnZ2ZRRBAiIo7hmL/zYPbv3kMyAtrZ2xB+FEB0Tm5ySmpObF0uI3Z2dXUj7Is7S1olPSMSM/vsvKiqKVOEsW7IMhVWQrS1dRHFrzpw+o+zgcnJyN2/aAl5/2dLlEE7zeDzwgzO0pip7tz7RD7nL5R2qUhCo2sDpYnb9BIgSXxSJxMjIKMyIAKuLl0hmiIcOHsYsuvH1qzdl/Q6OgXRNwa2uW7MWlZ4+fRZthAhHZwa2ThiJX754Ixsc/qwAkg3ORsVqNyMzi2SACKeamamQ60skEoiYSWbAeeZzlV9dv+taD1mZlgssSHkquE+SDdDUyLhZvUQQHomjR46Sdke8csUaM+oCVDgzpk7DS/FICY7g6PgC345zlo6ucqqWmpo2XavnIPfu2u7asQs+gFeSKz2cfaIfcm9tbVWOtBDhV9Wp0bJLibz8AtLREKGmq6+nWJVzoeUikiXiK8VmUHiKFlpYkmwQvypGgQBI+fEu+B8+YI4/OztHVYpZoFSNJqqu3+vq6jCjLpSXV5IMEME3Z2dlY0ZdgMyB0mH7+PhiFt0AAamqXg7uP0D02RBjzDGlaFt74fQSs+gL4J7ndeevJELejxl1IzgkdPu2HRs3bLp75x67O3psaeHoUQ1/u3L5Kql6gbNdMG8+0QbFsXBZlC+COuiH3EvLyqFmJH43Tn1dXfSmYwDw+vyFdDREi/kLlButQJeUCoCAmBR3xsTEUDYiGekbosXMiPjcfQ7gzvGI4rPXZ3wvIqHKVq5wf/n4kcwQ9XRnkTxxamo6yQZRe/rMisqeYEYslixfuoxkA1T21gC4UIssFpIsEd+8ccGMuuDt/Z1kAIRMpqqKjVn0hfr6BsrcCaL20tJSzKhXODu7kvYFTp00OS8vH7PoRkJCImX9vG7NOuWbqA76IXeohVU1XS9dMsBOvPA0W124SDoaIsktIYCmKa81yJSYesKOJ46fJNkgXrt2HTPqBtRaoCFUevbMWRRcAs6epV7NGNwVMiDCxuYOyQxx9crVJHWqekEDIQox94BKnDLJpuyVVFtbp6Pi/UBaWs+bHbgsa1atIRkA9+7ei//qPhEaFk7aHdHMxBRv6eoF4CmWL1tO2he4ZOEi5deuvj6+E5Rut77urLx88oOhJvoh97j4eFUvvSgVoA5ACqtWriIdDdHZxQ0zIuDN6zckM0TL+RZEj9vc0gKRIskGCI9KuuK7MKiUrl61RqUQv+L+FbbPNSO3SSPa2dohGxxgvGHdepIZ4iWrS5hRN86cPkuyQTx18jRm0YUrV66SDIBQX9VRBXgxMbEkS0SdGdqQ2GFGnW+7SpXXvIdr4v2VnIr0ggf3H5COgAjpozrPTFFxCaXDsrOjaC3NyMgkBRQQiaWkpGLF/Uc/5B4aqrLR/eCBQ5hRP8HltkMlSDoaEKowyne0kI+SLBHPn7+AWXQhNSWVMpIxN5tDfCogQ33y5CmKjiBVCA3rWZmRXV2t3bWeujLBvWFG3YD6wdTYhGSG+P2HQrMaVFcL5i8g2SB6eX3GjDr7w7SbULUKqBrX6/D0GckSceXylcS65W33iuFEQt5VXFKCWaiBHduxJiwSHzygeMenjHfv3pN2BMItSEzsadLBAc+Pu/sHYr7r5vYWKxsQ+iF3FoulSu5H/z2GGfUTySmppEMhGszSa2zsWZoUAZRKWiYb4z/jPnp+xIy6AIkX2aaLZ071eFAIfo4fO4G0DsGrr68fMXaCq0+ZJMBz2NREbsEoKCikrPfgCJVVCkuowo+iDEbhmSe+31GVJT+merkBl0VVjyOIzYg/at++/SQDIAQhAkEfnbdwwHdR9qoAYURG/saMesXRfyladSA+qVVM6HHA+RPfJ/j5s7CCAaEfcv/18yf+rSQeOfIvZtRPuHa/tCNx6aLFyrlIWXkFpQRBGWmKfY/2KA4iwen+wROCgfT0jJs3b+vrdj45cJ8WLrBMTk4hygLg5PSKuCPOBfMWkCwBn1QktWamZqRXAdHRMSQbRGMDQw6nJ/B97/6BZIAYG9fTwQEHl8vFX/WT+JnQYAqeUp/KWcAzgFmogcKiYtLuiJBnkx5sVVhIlVJbzl+gKvVsam4h3vSU1DSsYEDoh9x9flG01yLu29uPS0YE3lmFRMoRfaq6MOhMn0Gc+EAgEMxT0eEMJA6OBH+vCY7q3l1b0lsYhN27KF7EAP9VerBB/efPnSeZIW7ZuJkYOwEcnzuSbBAhQiA+RSdPniYZAOGuc9speiVVVVWp6lVBfBFTXVNLefXgCmAWauD7D2qXB3kOMUlQBbg1lA7r8KEjmIUSIGXH60M4f8jKsIIBoR9yDw5W2YFkYKkq3GCL+ViTCIlfqZIne3vqLgxrVq0haqWhsZEyT8UJl2/uHPO7d22r2GxlVw0AVZkqvWNHdHFxxYy6AW5p1QrqbFs5/QK/QLJBfP78BWbRlb4voPLW88znUZ6tqjdcJoqvmSFupLx979+5YxZq4CpVAg08eOAgZtEryisqSDsi3lcd938gVHRw17CtA0U/5B4bG0cZdwIXL1yEGfUHEARTHhCCk6Iiiu4QmzZuJlki3iR06gJUVFYqd8YAzpw6beumzY8ePU5LS1fueUZESUmpqhmMUwntegicFs4sbazXHonhiqPUwdPP0qGYhQpqm4SEnpcGLRyO4SyKx3Xzxs2Ucre5Td0GumHdemKeGhIaRil3n1/kd0O9YKWKB/t1r4M2cahK1T596knTSdi//wBuduL4CWzrQNEPuefm5avq/as7YyaP1++BOZG/o0jHQTQxNFaus3h8/jSqNhyNceN//vyJGXWhqLCIMs/bv3cfKbSgBEhKVTUybYqWcoiZouIWgjGp+0BJaRml4CCmqqmpxYy6+rVTNgopx1EAiUSySUV36Nu3bTCjLnh7e5MMEIMCgzCLvgA+gvLFC2xMTk7GjHpFRMRv0r6IwUHBmIUi2rhcuIy4GYRSWMFA0Q+5gwRVvVTvfCWWX4DZqQ1VLbhrVq1W1mVObi7lw6Y1cRI8h5hRF3JzcynbSYjNMr0gI4Oipx4iKWpCcHGhzrYt5i8gvU8NCAiklPuSRYvx/q6AoqLiaZN77jHOi4qNrQhtbW2q2kADAhU05OnhQTJA/NHdI7d3SKWy69dvkPZF1NPRra7pecfXC4KCgkn7IsYR+p8RERwcgtf/kJ9wWtuwgoGiH3IHLF9K8T4MCHfx/TuF/qh9Iisrewah6w+Rt24puCWEHz9+UmpFe9p0Ul+D7OxsSssL585hFqrR0NCgqqcN0OqiFWbXDblc/u/hIyQzxL2795Beu0DQRbJBJA1QhCtD2Un42tWrmAUBhYVFlD8WXENtbU+NAVAl95dqDIqDOgRuivIrKkTL+Qsouy4rw0/FwIbfVI2YcPW2bd2G2xw9chQrGAT6J3eHJ0/xrydxyaIlyt30VAECAOWxBTjxTlpEXL9+k2SGCN+LWXSjWMUQJMpeCURwOJwtm7eS9iLym5IjFApFixctJpkhkrqsAVQFvh8+eGAWXUhPS6esnS4rvaAFwCmRzBCNDYzaFZtxKDtCAg8fVtkqggARxfHjJygfKsRz5yiqHUoEh4SS9kV8S+Ur4+Lj8focvj0xSa14qXf0T+45uXmUDUlAqHQeP3pC8mfK4PMFTk6vVHU1Q3RVfHMGGg0JCdVVkQ5aLrAkdQTNy8unjEZmm5j20q+Iza5eT7UiA5HlSv3Rm5qaVI3rUX7XrSr9JYUTEAdTamvXjl2kx1UskexTMQLB2JAs95jYOMrD6mnr1KgORRISk1BLOVTFqm79x4+fMOu+AJIl7YsIV550ayBIW7ywp+vrju07endVaqJ/coev3L6N+gUeEJ7FK5ev1ijWoQiwY0VllYuLq5npbHTRIQ1Q1QMHot6CwiL088rLKy6cv6jqQgPhSz998oLaFowhXYbPEEqSbHA6UXVzhUc0LDzC2MAQDCZrTNy4cRNxF5wgIOWMQlW2PUtbV3kOZFXdp4m91iBBohx7AYSkjTioB077/v2HvThdD09P3PtkZ+dsVJHRAg/sPwguHFkiwC9NTUs/dhQbCQ7B1RtnV8rvgh+VqTgHSS+AjJy0OyL4SkisW7tD87q6+h0EmWlNnExKzwaM/skdkJWdTZky4pyiOXHzpi0Qpzo+d3zh+MLO1g6umsUCC2KbI1w4qMHNzahfBgHBeLbp7Hnmc3Ghq0rIgHA07ekzLS0WgiDQLTHUN6C8N3BYB4dn+PtLUENKatqhg4eQMdQ5b5xdLl60Iu6CE+JIZQfzVEVnlSULFyv374OLQDJDhCt2x+YOVPSvX7/p5WcCIa8ABwlBY3V1zdWr1pS/ESeUzjOft2vn7mVLlxMvPiVNjIyvWV9/6fTyyaPHp06exr0SUHPcBEjH4cGm/DoIShsa+jFs1FxFxzsg3L4N6zdu3ryFVGFSDmobGPotd8Dz5xRDUdQnuApn586XNSdV9NElEZ6u588dWayA3u8uTrjBFZWVcKdJ23FCxbJm9RqopkyNTfFjTp00+csXb6hSF1tSdxxXfvsok8n3q4glKFuIrayox2GRuGXTFjOT2aSNOOGEEeEziPjM6bO9ex+c69eud3BQmXqpIlwrcEzwnD9Vse+yJUv7jGCJUNVTWhV379otVaP5WE0MRO7w86D+JZ2WmjTU0w/vnnoBPKuqZB8nROE+Pr5wudvbeb17PiBUBdZXryG3Gh0doyqqVqblAgs0ZLi+vsGA6hUPMDSU3BFSLBbPNZ9LMkP84KHQZQ0hIzOrzyd27+693PZ2d3ePPi3B4OSJU3w+nzTLLCWtLl6CJxmuDN6zXx2aGBpFx8Sik9+zm/pbrl+/iQzUBMRjxB6OvXP9uvWkDGSQGIjcAaA/N7e3qrrIUhIStZs3b7W0KHRQgdBCleLhdm5Yt4E4TC4hIVFVizhQX1fv+/cfRE/j5+ffi30n/xk3c9qMhw8ft3f398jMzKTME8CDks4cABEFpSjhCAUF1G8hnj59riqugFDq+rUbaCoVEPGG9RtIBkTCRTt96jR6sGtr61YuX6Hq8QD/8qvLX3R9/3+lZWWQspNsSIRDwXW7Y3MXD6alUinlsF2wVJ44oE98/vKV8iITCUf+98hR/L4MFQYodwS437dv2RjM0lNVn8Kvgjxjzeq1EJVSjkuA2wBRyrIlyyB+xXUAR1swbz6egBJRVFxy6OBhcNsTJ2igGwz5Ltyba9bXIL/BjAiATPfM6TPwpOH28Bc+Q4K1Ytny16/ekLoZu6jooWm5wJL4Qh4hICCIZIYIWW+L6p5Mfn6sJYsWT+7+vXA+8HuXL10eFhaOixIAme6+vfuULyzYQy7+9u074pspHo/30unV4kWL4XdBrAg/EB4eI32Dhw8fKZ9JY1MTxP3gqojXBC4j7DJDayrkXeDISLO4FBUXUz6lEG0rj7jrE+imzzY2oTwmbDQxNPby+tKvGElNDEruCOCKEhKT3Fzdrl+/cfLk6ePHTpw9ex58w1u3t1FR0aBC4l2kBPywwqLioODgT54fvT5+Sk5O6f21RX1DQ2xcPPhyzw8ekOEp94wnoam5GSrlr1++eHz48PPHT9iXsvkIACfs4uyiTH8/lvKvyMjIIJkhen/1Vn42iIDfW1BQGBgY5Onh8enjJwjqJBIKe5lMBudzyeryyhUrQbtzTGdDWuLq4qpqflM4bHVNLURl8AMzs7J7n3ML8vW4uPhv377BOcDfkJBQiLVa26hfW8Llgkvt+eEDiV8/f1H/ZQsJELP5+vpBlLVu7XqocGbN1LaYv+DYseM/vv8ccqeOYwjkzoDBaAEjdwZjCIzcGYwhMHJnMIbAyJ3BGAIjdwZjCIzcGYwhMHJnMIbAyJ3BGAIjdwZjCIzcGYwhMHJnMIbAyJ3BGAIjdwZjCIzcGYwhMHJnMIbAyJ3BGAIjdwZjBv/99/8kDa6t9Ywk7gAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T09:14:47.623554Z",
     "start_time": "2020-03-13T09:14:47.619119Z"
    }
   },
   "source": [
    "![cuemacro_logo.png](attachment:cuemacro_logo.png)\n",
    "\n",
    "# A 10 minute view of tcapy\n",
    "\n",
    "**Saeed Amen / Founder of Cuemacro**\n",
    "\n",
    "https://www.cuemacro.com / saeed@cuemacro.com / @saeedamenfx / All material is copyright Cuemacro / 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll quickly introduce tcapy showing you how a few lines of code can quickly generate TCA to understand what you're trading costs are. Whilst tcapy supports the use of databases, caching or distributing computation (which we discuss in other notebooks), we'll focus on the basics here. You can also run this notebook interactively in [Binder](https://mybinder.org/v2/gh/cuemacro/tcapy/master?filepath=tcapy_notebooks/a_10_minute_view_of_tcapy.ipynb) to play around with the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is TCA?\n",
    "\n",
    "Transaction cost analysis (TCA) involves looking at your trade/order data combined with market data to understand your trading costs. We can use TCA to calculate metrics like slippage and market impact, which we discuss later.\n",
    "\n",
    "We should be able to understand how trading costs vary depending on upon factors like, asset, liquidity provider and method of execution (manual/algo)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is tcapy?\n",
    "\n",
    "tcapy is one of the first open source Python libraries for doing TCA. Typically, developing a TCA library yourself could cost many hundreds of thousands of dollar. By contrast tcapy is free (of course sponsors for new features are always welcome, and Cuemacro also offers commercial support for tcapy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's the difference between trades and orders?\n",
    "\n",
    "We assume that an order has a start and end time, and hence a duration. It will have fields associated with it such as the\n",
    "ticker traded, the execution price, the account, the execution trader, the broker, the algo used etc.\n",
    " \n",
    "Underneath every order, there might be a number of trades and other events. The difference is that we assume these trade\n",
    "events, are points in time. These trades will tag their respective orders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark\n",
    "\n",
    "What should we consider as our benchmark? We need to have market data which is representative of the market as our reference point to compare our own trades. If we consider the FX spot market there are relatively small number of principal liquidity providers (LPs). At any one time, you might observe different skew in prices from different market makers, because of a number of reasons, such as different forecasts on where spot could go, their own inventory etc. Hence, using the mid of the pricing stream from a single market maker, it may not be representative of the market on aggregate. We are using the mid price as a proxy for the reference price.\n",
    "\n",
    "We've focused on Dukascopy here, mainly because it's free, to allow readers to run this code (and so it works on Binder). However, you can try different data sources to see how it impacts your results.\n",
    "\n",
    "At present tcapy has adapters to download market data from two data sources listed below to use as a benchmark. However, if you can feed whatever market data you want to the library and we hope to add more data sources in the future.\n",
    "* Duksacopy - FX retail broker (free data) - note: doesn't have the past few weeks of data - our examples focus on this given it's free download\n",
    "* New Change FX - data provider (paid data)\n",
    "\n",
    "Once you've decided which market data to use, you can calculate several benchmarks, which are below. Some benchmarks like TWAP are only appropriate for orders, which have a start and finish time. The abstract class `Benchmark` in tcapy can be extended to create your own customised benchmarks.\n",
    "\n",
    "* Mid price (`BenchmarkMarketMid`)\n",
    "* TWAP (`BenchmarkTWAP`)\n",
    "* VWAP (`BenchmarkVWAP`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics\n",
    "\n",
    "Metrics you might wish to calculate the below, and we've indicated the classes which implement these. In tcapy, there is the abstract `Metric` class, which can be extended so you can write your own metrics.\n",
    "\n",
    "* slippage - the difference between your benchmark and the executed price (`MetricSlippage`)\n",
    "* market impact - the price move following a trade/order (`MetricTransientMarketImpact` and `MetricPersistentMarketImpact`)\n",
    "\n",
    "We haven't implemented this yet, but you might also want to look at some flat fees, which are charged depending on the trading venue (although these are much easier to keep track of, as you don't need any market data to calculate, just your trade data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregated statistics\n",
    "\n",
    "As part of TCA, we'd then want to generate statistics based on the metrics like those listed below. An abstract class `ResultsForm` aggregates various statistics. You can extend it to create your own aggregations.\n",
    "* average (`BarResultsForm`)\n",
    "* PDF (`DistResultsForm`)\n",
    "* outlier (`TableResultsForm`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doing the imports\n",
    "\n",
    "Our first step is to add the tcapy library to our `PYTHONPATH`. You may need to change these lines. Note, that in our setup, we are running the Jupyter notebook on Windows, and the backend Celery workers on Linux. If you are running everything in the same environment, the paths are likely to be same. We'll also define some paths for the test data we'll use for trade/orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T16:44:40.767061Z",
     "start_time": "2021-02-01T16:44:40.746057Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "windows_tcapy_path = 'e:/cuemacro/tcapy' # Windows platform\n",
    "linux_tcapy_path = '/home/tcapyuser/cuemacro/tcapy' # Linux platform\n",
    "local_test_data_path = '../test/resources/' # Windows platform\n",
    "remote_test_data_path = '../test/resources/' # WSL drive\n",
    "\n",
    "# Assuming the front end is on Windows\n",
    "sys.path.insert(0, windows_tcapy_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the Python path now includes tcapy, we can do all the imports from it, that we'll need later. We'll also import all the other Python libraries we'll use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T18:46:29.594569Z",
     "start_time": "2021-02-01T18:46:29.573570Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "# This is the entry point for our TCA analysis\n",
    "from tcapy.analysis.tcaengine import TCAEngineImpl\n",
    "\n",
    "# To construct the parameters for our TCA calculation\n",
    "from tcapy.analysis.tcarequest import TCARequest\n",
    "\n",
    "# To access trade and market data\n",
    "from tcapy.data.databasesource import DatabaseSourceCSVBinary, \\\n",
    "    DatabaseSourceDataFrame, DatabaseSourceDukascopy, DatabaseSourceNCFX\n",
    "\n",
    "# Import all the metrics and benchmarks we'll use\n",
    "from tcapy.analysis.algos.benchmark import BenchmarkArrival, BenchmarkMarketSpreadToMid\n",
    "from tcapy.analysis.algos.metric import MetricSlippage, MetricTransientMarketImpact\n",
    "\n",
    "# To aggregate the TCA results\n",
    "from tcapy.analysis.algos.resultsform import TimelineResultsForm, DistResultsForm, BarResultsForm\n",
    "\n",
    "# To help display the output of a TCA calculation\n",
    "from tcapy.vis.tcaresults import TCAResults\n",
    "from tcapy.vis.report.tcareport import TCAReport\n",
    "from tcapy.vis.displaylisteners import PlotRender\n",
    "\n",
    "# General classes\n",
    "from tcapy.conf.constants import Constants\n",
    "from tcapy.util.mediator import Mediator\n",
    "\n",
    "# Prevent requests from displaying debug messages for certain libraries\n",
    "import logging\n",
    "\n",
    "logging.getLogger(\"findatapy\").setLevel(logging.WARNING)\n",
    "logging.getLogger(\"requests\").setLevel(logging.WARNING)\n",
    "logging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n",
    "\n",
    "# For plotting later\n",
    "import plotly.io as pio\n",
    "\n",
    "# For interactive plots (but these disappear when we reopen Jupyter)\n",
    "pio.renderers.default = \"notebook\" \n",
    "\n",
    "# For static plots, we use https://github.com/plotly/Kaleido\n",
    "# pio.renderers.default = \"svg\"\n",
    "pio.renderers.default = \"png\" \n",
    "\n",
    "import plotly\n",
    "\n",
    "from chartpy import Chart, Style\n",
    "\n",
    "# default size for Plotly charts we use\n",
    "chart_width = 800\n",
    "chart_height = 500\n",
    "\n",
    "chart = Chart()\n",
    "constants = Constants()\n",
    "style = Style(width=chart_width, height=chart_height, scale_factor=-1, silent_display=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also set the market data source for use later, either downloading external data from Dukascopy or we can use some mocked up data in a Parquet file in the test folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T18:46:30.344991Z",
     "start_time": "2021-02-01T18:46:30.336990Z"
    }
   },
   "outputs": [],
   "source": [
    "market_data_store = 'dukascopy'\n",
    "# market_data_store = os.path.join(local_test_data_path, 'small_test_market_df.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Format of trade/order data for tcapy and DatabaseSource"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tcapy has a test dataset of randomised trade/order data in CSV format, which we'll use later. We have generated these by taking market bid/ask data and then randomly perturbing the bid/ask for sells/buys respectively. Random sizes and directions have also been constructed. I'd suggest it's probably better to try using your own real trade data, as the output will be more 'realistic'. A lot of the output we'll get might end up looking artifical because of the simple assumptions we've made in the construction of the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T18:46:31.651900Z",
     "start_time": "2021-02-01T18:46:31.641900Z"
    }
   },
   "outputs": [],
   "source": [
    "# The test trade/order data is populated between 25 Apr 2017-05 Jun 2017\n",
    "# with trades/orders for 'EURUSD', 'USDJPY' and 'EURJPY'\n",
    "csv_trade_order_mapping = {'trade_df' : os.path.join(local_test_data_path, 'small_test_trade_df.csv'),\n",
    "                           'order_df' : os.path.join(local_test_data_path, 'small_test_order_df.csv')}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data from Dukascopy\n",
    "\n",
    "The `DatabaseSource` abstract class is extended to provide trade and market data access from internal sources (eg. CSV, Arctic/MongoDB), as well as external sources like Dukascopy. We demonsrate how to use `DatabaseSourceDukascopy` to download market data with quoted bid/ask from Dukascopy (we can't download trade data from that). If there are any timeouts in the external download, `findatapy` displays a warning, and will also retry the download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T18:46:40.893825Z",
     "start_time": "2021-02-01T18:46:32.631817Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "database_source_dukascopy = DatabaseSourceDukascopy()\n",
    "\n",
    "market_df = database_source_dukascopy.fetch_market_data(\n",
    "    ticker='EURUSD', start_date='25 Apr 2017', finish_date='28 Apr 2017')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the market data downloaded to see the format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-01T18:46:42.849305Z",
     "start_time": "2021-02-01T18:46:42.828306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>mid</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:01.466000+00:00</th>\n",
       "      <td>1.08648</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086510</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:01.618000+00:00</th>\n",
       "      <td>1.08650</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086520</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:02.276000+00:00</th>\n",
       "      <td>1.08650</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086520</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:04.081000+00:00</th>\n",
       "      <td>1.08649</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086515</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:05.470000+00:00</th>\n",
       "      <td>1.08650</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086520</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:06.039000+00:00</th>\n",
       "      <td>1.08650</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086520</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:06.567000+00:00</th>\n",
       "      <td>1.08651</td>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.086525</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:07.604000+00:00</th>\n",
       "      <td>1.08652</td>\n",
       "      <td>1.08655</td>\n",
       "      <td>1.086535</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:08.503000+00:00</th>\n",
       "      <td>1.08654</td>\n",
       "      <td>1.08656</td>\n",
       "      <td>1.086550</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 00:00:08.717000+00:00</th>\n",
       "      <td>1.08656</td>\n",
       "      <td>1.08660</td>\n",
       "      <td>1.086580</td>\n",
       "      <td>EURUSD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      bid      ask       mid  ticker\n",
       "Date                                                                \n",
       "2017-04-25 00:00:01.466000+00:00  1.08648  1.08654  1.086510  EURUSD\n",
       "2017-04-25 00:00:01.618000+00:00  1.08650  1.08654  1.086520  EURUSD\n",
       "2017-04-25 00:00:02.276000+00:00  1.08650  1.08654  1.086520  EURUSD\n",
       "2017-04-25 00:00:04.081000+00:00  1.08649  1.08654  1.086515  EURUSD\n",
       "2017-04-25 00:00:05.470000+00:00  1.08650  1.08654  1.086520  EURUSD\n",
       "2017-04-25 00:00:06.039000+00:00  1.08650  1.08654  1.086520  EURUSD\n",
       "2017-04-25 00:00:06.567000+00:00  1.08651  1.08654  1.086525  EURUSD\n",
       "2017-04-25 00:00:07.604000+00:00  1.08652  1.08655  1.086535  EURUSD\n",
       "2017-04-25 00:00:08.503000+00:00  1.08654  1.08656  1.086550  EURUSD\n",
       "2017-04-25 00:00:08.717000+00:00  1.08656  1.08660  1.086580  EURUSD"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Directly loading trade/order data from CSV\n",
    "\n",
    "We can use `DatabaseSourceCSVBinary` to open up the trade and order data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.600053Z",
     "start_time": "2020-09-14T15:28:00.557055Z"
    }
   },
   "outputs": [],
   "source": [
    "trade_df = DatabaseSourceCSVBinary(trade_data_database_csv=csv_trade_order_mapping['trade_df']) \\\n",
    "    .fetch_trade_order_data()\n",
    "\n",
    "order_df = DatabaseSourceCSVBinary(trade_data_database_csv=csv_trade_order_mapping['order_df']) \\\n",
    "    .fetch_trade_order_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring trade/order data\n",
    "\n",
    "Let's look at the trade data so we can understand the type of format that tcapy needs. We can see that each trade is a point in time. In some cases, these are actual `trade` events, other times they are `placement`, `cancel` or `cancel/replace` messages. The times of two different events can be identical, and there are some instances of this in the test datasets of trade/orders. Note, that this can often cause problems with Pandas, so if do create your own `Metric` and `Benchmark` variations, do check that your implementations can manage it (can require some rewriting of the code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.630054Z",
     "start_time": "2020-09-14T15:28:00.603056Z"
    }
   },
   "outputs": [
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       "      <td>...</td>\n",
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       "      <td>18517881</td>\n",
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       "      <td>account3</td>\n",
       "      <td>algo6</td>\n",
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       "    <tr>\n",
       "      <th>2017-04-25 11:09:53.242000+00:00</th>\n",
       "      <td>account3</td>\n",
       "      <td>algo6</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-04-25 11:09:52.384000+00:00_2...</td>\n",
       "      <td>broker6</td>\n",
       "      <td>subbroker6</td>\n",
       "      <td>cancel/replace</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>execution_EURUSD2017-04-25 11:09:53.242000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.088080</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0</td>\n",
       "      <td>portfolio4</td>\n",
       "      <td>pm3</td>\n",
       "      <td>1.088090</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader5</td>\n",
       "      <td>venue3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 11:10:20.251000+00:00</th>\n",
       "      <td>account3</td>\n",
       "      <td>algo6</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-04-25 11:09:52.384000+00:00_2...</td>\n",
       "      <td>broker6</td>\n",
       "      <td>subbroker6</td>\n",
       "      <td>trade</td>\n",
       "      <td>8364696</td>\n",
       "      <td>1.088093</td>\n",
       "      <td>execution_EURUSD2017-04-25 11:10:20.251000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.088125</td>\n",
       "      <td>EUR</td>\n",
       "      <td>8364696</td>\n",
       "      <td>portfolio4</td>\n",
       "      <td>pm3</td>\n",
       "      <td>1.088090</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader5</td>\n",
       "      <td>venue4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 account_id algo_id algo_settings  \\\n",
       "Date                                                                \n",
       "2017-04-25 07:09:31.804000+00:00   account3   algo4       default   \n",
       "2017-04-25 07:09:33.765000+00:00   account3   algo4       default   \n",
       "2017-04-25 11:09:52.384000+00:00   account3   algo6       default   \n",
       "2017-04-25 11:09:53.242000+00:00   account3   algo6       default   \n",
       "2017-04-25 11:10:20.251000+00:00   account3   algo6       default   \n",
       "\n",
       "                                                                ancestor_pointer_id  \\\n",
       "Date                                                                                  \n",
       "2017-04-25 07:09:31.804000+00:00  order_EURUSD2017-04-25 07:09:31.804000+00:00_2...   \n",
       "2017-04-25 07:09:33.765000+00:00  order_EURUSD2017-04-25 07:09:31.804000+00:00_2...   \n",
       "2017-04-25 11:09:52.384000+00:00  order_EURUSD2017-04-25 11:09:52.384000+00:00_2...   \n",
       "2017-04-25 11:09:53.242000+00:00  order_EURUSD2017-04-25 11:09:52.384000+00:00_2...   \n",
       "2017-04-25 11:10:20.251000+00:00  order_EURUSD2017-04-25 11:09:52.384000+00:00_2...   \n",
       "\n",
       "                                 broker_id broker_sub_id      event_type  \\\n",
       "Date                                                                       \n",
       "2017-04-25 07:09:31.804000+00:00   broker5    subbroker1       placement   \n",
       "2017-04-25 07:09:33.765000+00:00   broker5    subbroker1           trade   \n",
       "2017-04-25 11:09:52.384000+00:00   broker6    subbroker6       placement   \n",
       "2017-04-25 11:09:53.242000+00:00   broker6    subbroker6  cancel/replace   \n",
       "2017-04-25 11:10:20.251000+00:00   broker6    subbroker6           trade   \n",
       "\n",
       "                                  executed_notional  executed_price  \\\n",
       "Date                                                                  \n",
       "2017-04-25 07:09:31.804000+00:00                  0        0.000000   \n",
       "2017-04-25 07:09:33.765000+00:00           18517881        1.087965   \n",
       "2017-04-25 11:09:52.384000+00:00                  0        0.000000   \n",
       "2017-04-25 11:09:53.242000+00:00                  0        0.000000   \n",
       "2017-04-25 11:10:20.251000+00:00            8364696        1.088093   \n",
       "\n",
       "                                                                                 id  \\\n",
       "Date                                                                                  \n",
       "2017-04-25 07:09:31.804000+00:00  execution_EURUSD2017-04-25 07:09:31.804000+00:...   \n",
       "2017-04-25 07:09:33.765000+00:00  execution_EURUSD2017-04-25 07:09:33.765000+00:...   \n",
       "2017-04-25 11:09:52.384000+00:00  execution_EURUSD2017-04-25 11:09:52.384000+00:...   \n",
       "2017-04-25 11:09:53.242000+00:00  execution_EURUSD2017-04-25 11:09:53.242000+00:...   \n",
       "2017-04-25 11:10:20.251000+00:00  execution_EURUSD2017-04-25 11:10:20.251000+00:...   \n",
       "\n",
       "                                  ...  market_mid  notional_currency  \\\n",
       "Date                              ...                                  \n",
       "2017-04-25 07:09:31.804000+00:00  ...    1.087965                EUR   \n",
       "2017-04-25 07:09:33.765000+00:00  ...    1.087990                EUR   \n",
       "2017-04-25 11:09:52.384000+00:00  ...    1.088090                EUR   \n",
       "2017-04-25 11:09:53.242000+00:00  ...    1.088080                EUR   \n",
       "2017-04-25 11:10:20.251000+00:00  ...    1.088125                EUR   \n",
       "\n",
       "                                  order_notional portfolio_id  \\\n",
       "Date                                                            \n",
       "2017-04-25 07:09:31.804000+00:00        18517881   portfolio4   \n",
       "2017-04-25 07:09:33.765000+00:00        18517881   portfolio4   \n",
       "2017-04-25 11:09:52.384000+00:00         8364696   portfolio4   \n",
       "2017-04-25 11:09:53.242000+00:00               0   portfolio4   \n",
       "2017-04-25 11:10:20.251000+00:00         8364696   portfolio4   \n",
       "\n",
       "                                  portfolio_manager_id price_limit side  \\\n",
       "Date                                                                      \n",
       "2017-04-25 07:09:31.804000+00:00                   pm2    1.087965   -1   \n",
       "2017-04-25 07:09:33.765000+00:00                   pm2    1.087965   -1   \n",
       "2017-04-25 11:09:52.384000+00:00                   pm3    1.088090   -1   \n",
       "2017-04-25 11:09:53.242000+00:00                   pm3    1.088090   -1   \n",
       "2017-04-25 11:10:20.251000+00:00                   pm3    1.088090   -1   \n",
       "\n",
       "                                  ticker  trader_id   venue  \n",
       "Date                                                         \n",
       "2017-04-25 07:09:31.804000+00:00  EURUSD    trader1  venue4  \n",
       "2017-04-25 07:09:33.765000+00:00  EURUSD    trader1  venue3  \n",
       "2017-04-25 11:09:52.384000+00:00  EURUSD    trader5  venue4  \n",
       "2017-04-25 11:09:53.242000+00:00  EURUSD    trader5  venue3  \n",
       "2017-04-25 11:10:20.251000+00:00  EURUSD    trader5  venue4  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print out all the fields. Some fields are compulsory for use in tcapy, including the index (`Date`), `ticker`, `side` etc. Some fields here are optional such as `market_ask`, `market_bid` and `market_mid`. Similar fields will be autogenerated from the market data and appends to the trade data. `DatabaseSource` does minimal changes to the format of the data. Each of these trade message point to the order which they belong to. There can be multiple trade messages within a single order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.645054Z",
     "start_time": "2020-09-14T15:28:00.632055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['account_id', 'algo_id', 'algo_settings', 'ancestor_pointer_id',\n",
       "       'broker_id', 'broker_sub_id', 'event_type', 'executed_notional',\n",
       "       'executed_price', 'id', 'market_ask', 'market_bid', 'market_mid',\n",
       "       'notional_currency', 'order_notional', 'portfolio_id',\n",
       "       'portfolio_manager_id', 'price_limit', 'side', 'ticker', 'trader_id',\n",
       "       'venue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the order data, one the key differences, which we noted is that each order has a `benchmark_date_start` and `benchmark_date_end`, a start and end time. Note, obviously this randomized data. We also see that it doesn't have columns for `executed_notional` data or `executed_price` data. tcapy can generate this from the underlying trade fills from the earlier table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.675053Z",
     "start_time": "2020-09-14T15:28:00.647057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>algo_id</th>\n",
       "      <th>algo_settings</th>\n",
       "      <th>arrival_price</th>\n",
       "      <th>benchmark_date_end</th>\n",
       "      <th>benchmark_date_start</th>\n",
       "      <th>broker_id</th>\n",
       "      <th>broker_sub_id</th>\n",
       "      <th>id</th>\n",
       "      <th>notional</th>\n",
       "      <th>notional_currency</th>\n",
       "      <th>portfolio_id</th>\n",
       "      <th>portfolio_manager_id</th>\n",
       "      <th>price_limit</th>\n",
       "      <th>side</th>\n",
       "      <th>ticker</th>\n",
       "      <th>trader_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-25 07:09:31.804000+00:00</th>\n",
       "      <td>account3</td>\n",
       "      <td>algo4</td>\n",
       "      <td>default</td>\n",
       "      <td>1.087965</td>\n",
       "      <td>2017-04-25 10:40:16.512000+00:00</td>\n",
       "      <td>2017-04-25 07:09:31.804000+00:00</td>\n",
       "      <td>broker5</td>\n",
       "      <td>subbroker1</td>\n",
       "      <td>order_EURUSD2017-04-25 07:09:31.804000+00:00_2...</td>\n",
       "      <td>18517881</td>\n",
       "      <td>EUR</td>\n",
       "      <td>portfolio4</td>\n",
       "      <td>pm2</td>\n",
       "      <td>1.087965</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 11:09:52.384000+00:00</th>\n",
       "      <td>account3</td>\n",
       "      <td>algo6</td>\n",
       "      <td>default</td>\n",
       "      <td>1.088090</td>\n",
       "      <td>2017-04-25 14:33:06.745000+00:00</td>\n",
       "      <td>2017-04-25 11:09:52.384000+00:00</td>\n",
       "      <td>broker6</td>\n",
       "      <td>subbroker6</td>\n",
       "      <td>order_EURUSD2017-04-25 11:09:52.384000+00:00_2...</td>\n",
       "      <td>8364696</td>\n",
       "      <td>EUR</td>\n",
       "      <td>portfolio4</td>\n",
       "      <td>pm3</td>\n",
       "      <td>1.088090</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25 15:50:41.479000+00:00</th>\n",
       "      <td>account1</td>\n",
       "      <td>algo3</td>\n",
       "      <td>default</td>\n",
       "      <td>1.093765</td>\n",
       "      <td>2017-04-26 06:01:27.935000+00:00</td>\n",
       "      <td>2017-04-25 15:50:41.479000+00:00</td>\n",
       "      <td>broker5</td>\n",
       "      <td>subbroker5</td>\n",
       "      <td>order_EURUSD2017-04-25 15:50:41.479000+00:00_2...</td>\n",
       "      <td>774423</td>\n",
       "      <td>EUR</td>\n",
       "      <td>portfolio1</td>\n",
       "      <td>pm6</td>\n",
       "      <td>1.093765</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 06:48:48.562000+00:00</th>\n",
       "      <td>account4</td>\n",
       "      <td>algo2</td>\n",
       "      <td>default</td>\n",
       "      <td>1.092460</td>\n",
       "      <td>2017-04-26 09:57:57.631000+00:00</td>\n",
       "      <td>2017-04-26 06:48:48.562000+00:00</td>\n",
       "      <td>broker3</td>\n",
       "      <td>subbroker3</td>\n",
       "      <td>order_EURUSD2017-04-26 06:48:48.562000+00:00_2...</td>\n",
       "      <td>16465763</td>\n",
       "      <td>EUR</td>\n",
       "      <td>portfolio3</td>\n",
       "      <td>pm5</td>\n",
       "      <td>1.092460</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26 11:15:50.864000+00:00</th>\n",
       "      <td>account6</td>\n",
       "      <td>algo1</td>\n",
       "      <td>default</td>\n",
       "      <td>1.089585</td>\n",
       "      <td>2017-04-26 14:41:01.382000+00:00</td>\n",
       "      <td>2017-04-26 11:15:50.864000+00:00</td>\n",
       "      <td>broker3</td>\n",
       "      <td>subbroker6</td>\n",
       "      <td>order_EURUSD2017-04-26 11:15:50.864000+00:00_2...</td>\n",
       "      <td>11501881</td>\n",
       "      <td>EUR</td>\n",
       "      <td>portfolio4</td>\n",
       "      <td>pm3</td>\n",
       "      <td>1.089585</td>\n",
       "      <td>-1</td>\n",
       "      <td>EURUSD</td>\n",
       "      <td>trader4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 account_id algo_id algo_settings  \\\n",
       "Date                                                                \n",
       "2017-04-25 07:09:31.804000+00:00   account3   algo4       default   \n",
       "2017-04-25 11:09:52.384000+00:00   account3   algo6       default   \n",
       "2017-04-25 15:50:41.479000+00:00   account1   algo3       default   \n",
       "2017-04-26 06:48:48.562000+00:00   account4   algo2       default   \n",
       "2017-04-26 11:15:50.864000+00:00   account6   algo1       default   \n",
       "\n",
       "                                  arrival_price  \\\n",
       "Date                                              \n",
       "2017-04-25 07:09:31.804000+00:00       1.087965   \n",
       "2017-04-25 11:09:52.384000+00:00       1.088090   \n",
       "2017-04-25 15:50:41.479000+00:00       1.093765   \n",
       "2017-04-26 06:48:48.562000+00:00       1.092460   \n",
       "2017-04-26 11:15:50.864000+00:00       1.089585   \n",
       "\n",
       "                                               benchmark_date_end  \\\n",
       "Date                                                                \n",
       "2017-04-25 07:09:31.804000+00:00 2017-04-25 10:40:16.512000+00:00   \n",
       "2017-04-25 11:09:52.384000+00:00 2017-04-25 14:33:06.745000+00:00   \n",
       "2017-04-25 15:50:41.479000+00:00 2017-04-26 06:01:27.935000+00:00   \n",
       "2017-04-26 06:48:48.562000+00:00 2017-04-26 09:57:57.631000+00:00   \n",
       "2017-04-26 11:15:50.864000+00:00 2017-04-26 14:41:01.382000+00:00   \n",
       "\n",
       "                                             benchmark_date_start broker_id  \\\n",
       "Date                                                                          \n",
       "2017-04-25 07:09:31.804000+00:00 2017-04-25 07:09:31.804000+00:00   broker5   \n",
       "2017-04-25 11:09:52.384000+00:00 2017-04-25 11:09:52.384000+00:00   broker6   \n",
       "2017-04-25 15:50:41.479000+00:00 2017-04-25 15:50:41.479000+00:00   broker5   \n",
       "2017-04-26 06:48:48.562000+00:00 2017-04-26 06:48:48.562000+00:00   broker3   \n",
       "2017-04-26 11:15:50.864000+00:00 2017-04-26 11:15:50.864000+00:00   broker3   \n",
       "\n",
       "                                 broker_sub_id  \\\n",
       "Date                                             \n",
       "2017-04-25 07:09:31.804000+00:00    subbroker1   \n",
       "2017-04-25 11:09:52.384000+00:00    subbroker6   \n",
       "2017-04-25 15:50:41.479000+00:00    subbroker5   \n",
       "2017-04-26 06:48:48.562000+00:00    subbroker3   \n",
       "2017-04-26 11:15:50.864000+00:00    subbroker6   \n",
       "\n",
       "                                                                                 id  \\\n",
       "Date                                                                                  \n",
       "2017-04-25 07:09:31.804000+00:00  order_EURUSD2017-04-25 07:09:31.804000+00:00_2...   \n",
       "2017-04-25 11:09:52.384000+00:00  order_EURUSD2017-04-25 11:09:52.384000+00:00_2...   \n",
       "2017-04-25 15:50:41.479000+00:00  order_EURUSD2017-04-25 15:50:41.479000+00:00_2...   \n",
       "2017-04-26 06:48:48.562000+00:00  order_EURUSD2017-04-26 06:48:48.562000+00:00_2...   \n",
       "2017-04-26 11:15:50.864000+00:00  order_EURUSD2017-04-26 11:15:50.864000+00:00_2...   \n",
       "\n",
       "                                  notional notional_currency portfolio_id  \\\n",
       "Date                                                                        \n",
       "2017-04-25 07:09:31.804000+00:00  18517881               EUR   portfolio4   \n",
       "2017-04-25 11:09:52.384000+00:00   8364696               EUR   portfolio4   \n",
       "2017-04-25 15:50:41.479000+00:00    774423               EUR   portfolio1   \n",
       "2017-04-26 06:48:48.562000+00:00  16465763               EUR   portfolio3   \n",
       "2017-04-26 11:15:50.864000+00:00  11501881               EUR   portfolio4   \n",
       "\n",
       "                                 portfolio_manager_id  price_limit  side  \\\n",
       "Date                                                                       \n",
       "2017-04-25 07:09:31.804000+00:00                  pm2     1.087965    -1   \n",
       "2017-04-25 11:09:52.384000+00:00                  pm3     1.088090    -1   \n",
       "2017-04-25 15:50:41.479000+00:00                  pm6     1.093765    -1   \n",
       "2017-04-26 06:48:48.562000+00:00                  pm5     1.092460    -1   \n",
       "2017-04-26 11:15:50.864000+00:00                  pm3     1.089585    -1   \n",
       "\n",
       "                                  ticker trader_id  \n",
       "Date                                                \n",
       "2017-04-25 07:09:31.804000+00:00  EURUSD   trader1  \n",
       "2017-04-25 11:09:52.384000+00:00  EURUSD   trader5  \n",
       "2017-04-25 15:50:41.479000+00:00  EURUSD   trader3  \n",
       "2017-04-26 06:48:48.562000+00:00  EURUSD   trader5  \n",
       "2017-04-26 11:15:50.864000+00:00  EURUSD   trader4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tcapy can do TCA on trade data on its own (or trade and order data together). Make sure that your trade/order is in a similar format to use with tcapy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TCA computations\n",
    "\n",
    "We earlier created a trade/order mapping, to tell tcapy where to find the trade/order data, which is this case is in CSV files. Note the use of `trade_df` and `order_df`, which you'll have to use each time. We create a `TCAEngineImpl` object which will be used to accept a `TCARequest` for doing the TCA computation. We also get reference to the `VolatileCache` which uses Redis underneath (if Redis isn't installed, calculations will proceed, it'll just be slower)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.720053Z",
     "start_time": "2020-09-14T15:28:00.677056Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:00,713; INFO:tcapy.analysis.tcaengine: Init TCAEngine version: pro - Env: desktop_laptop_linux (tcaengine.py:53)\n"
     ]
    }
   ],
   "source": [
    "tca_version  = constants.tcapy_version\n",
    "tca_engine = TCAEngineImpl(version=tca_version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a `TCARequest`, which will be run on our trade/order CSV files. It allows us to specify our `TCARequest` in a Pythonic like way, as opposed to using some sort of query language directly (like SQL or q). Obviously, the key advantage of this is because tcapy supports all sorts of databases, we don't need to change our `TCARequest` much if we change our underlying data sources.\n",
    "\n",
    "The market data will be externally downloaded from Dukascopy. In this instance, we have set `use_multithreading=False` which avoids the use of Celery, and makes it easier to run on Binder. We can add the below, for calculation:\n",
    "\n",
    "* `Metric` to measure the \"quality\" of each trade/order\n",
    "    * We've added `MetricSlippage` which by default calcualtes the difference between the `mid` price and the `executed_price`\n",
    "* `Benchmark` to capture what the market is around each trade/order\n",
    "    * We've added `BenchmarkArrival` which will the mid price for the arrival price of every trade/order\n",
    "* `ResultForm` objects to aggregate the statistics of various fields. \n",
    "    * We've added `TimelineResultsForm` will calculate the average `slippage` by day and hour. We use a `scalar` of `10000.0` to convert the values into basis points, which are easier to interpret\n",
    "    * By default the slippage will be weighted by the `executed_notional` although we can change the weighting field\n",
    "    \n",
    "Whilst we are asking for ticker `EURUSD`, tcapy will also search for trades/orders booked the \"wrong\" way round ie. `USDEUR` and normalize them so they are converted to `EURUSD` trades/orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:28:00.735054Z",
     "start_time": "2020-09-14T15:28:00.722217Z"
    }
   },
   "outputs": [],
   "source": [
    "# We are conducting a TCA computation for trade/order data between 05 May-10 May 2017 for EURUSD\n",
    "# The trade dataset is from CSV and the market data is downloaded from Dukascopy/or a mocked up file\n",
    "# For metrics, we are calculating slippage and for benchmarks we are adding a field for the arrival price\n",
    "# We are creating a timeline of average slippage by date and hour\n",
    "tca_request = TCARequest(start_date='05 May 2017', finish_date='10 May 2017', ticker=['EURUSD'],\n",
    "                         tca_type='detailed',\n",
    "                         trade_data_store='csv', market_data_store=market_data_store,\n",
    "                         trade_order_mapping=csv_trade_order_mapping,\n",
    "                         metric_calcs=[MetricSlippage()],\n",
    "                         results_form=[TimelineResultsForm(metric_name='slippage', \n",
    "                                                           by_date='datehour', scalar=10000.0)],\n",
    "                         benchmark_calcs=[BenchmarkArrival(), \n",
    "                                          BenchmarkMarketSpreadToMid()],\n",
    "                         use_multithreading=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice there a lot of the log output can be suppressed if we change the logging level to `INFO` in `constants.py`.\n",
    "\n",
    "`calculate_tca` will use several other classes. We describe the rough steps of the calculation before:\n",
    "* `TCAMarketTradeLoaderImpl` manages the computation\n",
    "* A different instance of `TCATickerLoaderImpl` for each ticker will then\n",
    "    * load the market, trade and order data (using `DataFactory`, `DatabaseSource` etc.)\n",
    "    * calculate all the metrics and benchmarks (using `Metric`, `Benchmark` etc.)\n",
    "* `TCAMarketTradeLoaderImpl` will then aggregate the statistics generated (using `ResultsForm`)\n",
    "* dictonary of (mostly) `DataFrame` objects returned to the caller by `calculate_tca`\n",
    "\n",
    "As we noted before if there are any timeouts in downloading the data, it will attempt to retry a few times. **This may take several minutes, if are downloading a lot of data over the web. You might get some warning messages because it is trying to access a cache, which may not exist on Binder, which will also slow down the computation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:15.709540Z",
     "start_time": "2020-09-14T15:28:00.737055Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:00,739; DEBUG:tcapy.analysis.tcamarkettradeloader: Start loading trade/data/computation (tcamarkettradeloader.py:241)\n",
      "2020-09-14 16:28:00,742; DEBUG:tcapy.analysis.tcatickerloaderimpl: Get market and trade/order data for EURUSD from 2017-05-05 00:00:00+00:00 - 2017-05-10 00:00:00+00:00 (tcatickerloaderimpl.py:80)\n",
      "2020-09-14 16:28:00,744; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:28:04,830; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:04,832; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:28:08,914; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:08,917; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-10 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:28:17,171; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:28:17,172; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:28:17,266; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:28:17,302; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:28:21,380; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:28:21,382; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:28:21,478; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:28:21,513; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:25,595; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:28:25,596; DEBUG:tcapy.analysis.tcatickerloader: Get trade order holder for EURUSD from 2017-05-05 00:00:00+00:00 - 2017-05-10 00:00:00+00:00 (tcatickerloader.py:386)\n",
      "2020-09-14 16:28:25,634; WARNING:tcapy.data.datafactory: Dataframe empty for ticker USDEUR (datafactory.py:97)\n",
      "2020-09-14 16:28:25,636; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:29,722; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:29,722; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:28:33,802; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:33,804; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-10 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:28:42,074; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:28:42,075; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:28:42,171; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:28:42,204; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:28:46,287; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:28:46,289; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:28:46,382; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:28:46,416; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:50,504; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:28:50,541; WARNING:tcapy.data.datafactory: Dataframe empty for ticker USDEUR (datafactory.py:97)\n",
      "2020-09-14 16:28:50,542; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:28:54,631; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:54,632; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:28:58,713; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:28:58,715; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-10 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:29:07,058; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:29:07,059; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:29:07,153; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:29:07,185; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:29:11,269; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:29:11,270; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:29:11,363; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 17.08 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:29:11,398; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:443)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:29:15,484; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-10 00:00:00+00:00_market_df_None_comp_size_5698367_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:29:15,488; DEBUG:tcapy.analysis.tcatickerloader: Filter the market date by start/finish date (tcatickerloader.py:761)\n",
      "2020-09-14 16:29:15,495; DEBUG:tcapy.analysis.tcatickerloader: Combine trade/order data (tcatickerloader.py:782)\n",
      "2020-09-14 16:29:15,503; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkMarketSpreadToMid for market data (tcatickerloader.py:600)\n",
      "2020-09-14 16:29:15,527; DEBUG:tcapy.analysis.tcatickerloader: Filter trades by venue (tcatickerloader.py:458)\n",
      "2020-09-14 16:29:15,530; DEBUG:tcapy.analysis.tcatickerloader: Calculating derived fields and benchmarks (tcatickerloader.py:508)\n",
      "2020-09-14 16:29:15,531; DEBUG:tcapy.analysis.tcatickerloader: Calculating execution fields (tcatickerloader.py:510)\n",
      "2020-09-14 16:29:15,543; DEBUG:tcapy.analysis.tcatickerloader: Calculating benchmarks (tcatickerloader.py:536)\n",
      "2020-09-14 16:29:15,545; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkArrival for trade_df (tcatickerloader.py:545)\n",
      "2020-09-14 16:29:15,551; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkArrival for order_df (tcatickerloader.py:545)\n",
      "2020-09-14 16:29:15,556; DEBUG:tcapy.analysis.tcatickerloader: Calculating metrics (tcatickerloader.py:554)\n",
      "2020-09-14 16:29:15,557; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for trade_df (tcatickerloader.py:560)\n",
      "2020-09-14 16:29:15,575; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for order_df (tcatickerloader.py:560)\n",
      "2020-09-14 16:29:15,593; DEBUG:tcapy.analysis.tcatickerloader: Completed derived field calculations for EURUSD (tcatickerloader.py:568)\n",
      "2020-09-14 16:29:15,594; DEBUG:tcapy.analysis.tcatickerloaderimpl: Generating downsampled market data for potentional display (tcatickerloaderimpl.py:275)\n",
      "2020-09-14 16:29:15,615; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-09-14 16:29:15,619; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n",
      "2020-09-14 16:29:15,622; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-09-14 16:29:15,625; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:29:15,661; DEBUG:tcapy.analysis.tcamarkettradeloader: Finished loading data and calculating metrics on individual _tickers (tcamarkettradeloader.py:248)\n",
      "2020-09-14 16:29:15,663; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Constructing results form to summarize analysis... (tcamarkettradeloaderimpl.py:74)\n",
      "2020-09-14 16:29:15,692; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Now join table results... (tcamarkettradeloaderimpl.py:122)\n",
      "2020-09-14 16:29:15,693; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Finished calculating results form and join table results! (tcamarkettradeloaderimpl.py:135)\n"
     ]
    }
   ],
   "source": [
    "# Dictionary of (mostly) dataframes as output from TCA calculation\n",
    "dict_of_df = tca_engine.calculate_tca(tca_request)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting the TCA computation output\n",
    "\n",
    "Let's take a look at the dictionary returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:15.724541Z",
     "start_time": "2020-09-14T15:29:15.711540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['trade_df', 'order_df', 'sparse_market_trade_df', 'sparse_market_order_df', 'market_df_downsampled', 'candlestick_fig', 'timeline_trade_df_slippage_by/mean_datehour/all', 'timeline_order_df_slippage_by/mean_datehour/all', 'market_df'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We explain what each item in the dictionary is below:\n",
    "\n",
    "* for further analysis\n",
    "    * `trade_df` - DataFrame of trades (with added calculated fields like slippage, arrival price etc.)\n",
    "    * `order_df` - DataFrame of orders (with added calculated fields like slippage, arrival price etc.)\n",
    "* for plotting\n",
    "    * `sparse_market_trade_df` - DataFrame with market data and trades\n",
    "    * `sparse_market_order_df` - DataFrame with market data and orders\n",
    "    * `market_df_downsampled` - DataFrame with downsampled market data\n",
    "    * `candlestick_fig` - Plotly JSON of the market data as candlesticks\n",
    "* aggregated data\n",
    "    * `timeline_trade_df_slippage_by/mean_date/all` - a timeline of the slippage for the trades\n",
    "    * `timeline_order_df_slippage_by/mean_date/all` - a timeline of the slippage for the orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the timeline of the slippage by day and hour. During this period the slippage has been between 0.3 and 0.05 basis points (bp), which seems reasonable for EUR/USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:25.553596Z",
     "start_time": "2020-09-14T15:29:15.726542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style.title = 'Timeline of slippage by day (bp)'\n",
    "chart.plot(dict_of_df['timeline_trade_df_slippage_by/mean_datehour/all'], engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explaining output of tcapy calculation in trade/order DataFrames\n",
    "\n",
    "Let's also take a look at the returned `trade_df` DataFrame. Most of the fields are the same, but there now additional fields fields calculated by tcapy. Note, these will vary depending on which `Metric` and `Benchmark` objects you specify in the `TCARequest`:\n",
    "* `arrival` - arrival price from the benchmark at time of the order/just before\n",
    "* `slippage_benchmark` - difference between mid price from the market data, just before/at trade\n",
    "* `spread_to_benchmark` - differnce to the bid (for sells) and to the ask (for buys) - if this data is available\n",
    "* `slippage` - difference between `executed_price` and benchmark\n",
    "* `slippage_anomalous` - 1 if the slippage is beyond a certain \n",
    "\n",
    "We also have fields for the notional in reporting currency (here `USD`) which are autogenerated from the market data combined with the trade data. You can specify you own reporting currency in `Constants`. It is useful to have this field, if you want to calculate total trading costs across many different currency pairs later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using TCAResults class\n",
    "\n",
    "We can use a higher level `TCAResults` object to simplify the output of the TCA calculation. Here we create the Plotly JSON for all the charts which have been requested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:25.689113Z",
     "start_time": "2020-09-14T15:29:25.555597Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:29:25,576; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2583 (displaylisteners.py:988)\n",
      "2020-09-14 16:29:25,624; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2583 and  (displaylisteners.py:998)\n",
      "2020-09-14 16:29:25,627; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2576 (displaylisteners.py:988)\n",
      "2020-09-14 16:29:25,676; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2576 and  (displaylisteners.py:998)\n"
     ]
    }
   ],
   "source": [
    "tca_results = TCAResults(dict_of_df, tca_request, chart_width=chart_width, chart_height=chart_height)\n",
    "tca_results.render_computation_charts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the trade data from the `TCAResults` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:25.719632Z",
     "start_time": "2020-09-14T15:29:25.691114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>algo_id</th>\n",
       "      <th>algo_settings</th>\n",
       "      <th>ancestor_pointer_id</th>\n",
       "      <th>broker_id</th>\n",
       "      <th>broker_sub_id</th>\n",
       "      <th>event_type</th>\n",
       "      <th>executed_notional</th>\n",
       "      <th>executed_price</th>\n",
       "      <th>id</th>\n",
       "      <th>...</th>\n",
       "      <th>notional_reporting_currency_mid</th>\n",
       "      <th>reporting_currency</th>\n",
       "      <th>executed_notional_in_reporting_currency</th>\n",
       "      <th>order_notional_in_reporting_currency</th>\n",
       "      <th>notional</th>\n",
       "      <th>arrival</th>\n",
       "      <th>slippage_benchmark</th>\n",
       "      <th>spread_to_benchmark</th>\n",
       "      <th>slippage</th>\n",
       "      <th>slippage_anomalous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-05 06:27:26.514000+00:00</th>\n",
       "      <td>account5</td>\n",
       "      <td>algo2</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-05 06:26:47.189000+00:00_2...</td>\n",
       "      <td>broker6</td>\n",
       "      <td>subbroker1</td>\n",
       "      <td>trade</td>\n",
       "      <td>18008368</td>\n",
       "      <td>1.097430</td>\n",
       "      <td>execution_EURUSD2017-05-05 06:27:26.514000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.097445</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.976319e+07</td>\n",
       "      <td>1.976319e+07</td>\n",
       "      <td>18008368</td>\n",
       "      <td>1.097445</td>\n",
       "      <td>1.097445</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 11:30:00.791000+00:00</th>\n",
       "      <td>account5</td>\n",
       "      <td>algo3</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-05 11:28:43.816000+00:00_2...</td>\n",
       "      <td>broker3</td>\n",
       "      <td>subbroker3</td>\n",
       "      <td>trade</td>\n",
       "      <td>5370024</td>\n",
       "      <td>1.095723</td>\n",
       "      <td>execution_EURUSD2017-05-05 11:30:00.791000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.095725</td>\n",
       "      <td>USD</td>\n",
       "      <td>5.884070e+06</td>\n",
       "      <td>5.884070e+06</td>\n",
       "      <td>5370024</td>\n",
       "      <td>1.095725</td>\n",
       "      <td>1.095725</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 14:56:49.373000+00:00</th>\n",
       "      <td>account4</td>\n",
       "      <td>algo2</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-05 14:56:33.125000+00:00_2...</td>\n",
       "      <td>broker2</td>\n",
       "      <td>subbroker5</td>\n",
       "      <td>trade</td>\n",
       "      <td>6198753</td>\n",
       "      <td>1.098892</td>\n",
       "      <td>execution_EURUSD2017-05-05 14:56:49.373000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098875</td>\n",
       "      <td>USD</td>\n",
       "      <td>6.811655e+06</td>\n",
       "      <td>6.811655e+06</td>\n",
       "      <td>6198753</td>\n",
       "      <td>1.098875</td>\n",
       "      <td>1.098875</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 20:33:21.932000+00:00</th>\n",
       "      <td>account6</td>\n",
       "      <td>algo2</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-05 20:33:14.677000+00:00_2...</td>\n",
       "      <td>broker5</td>\n",
       "      <td>subbroker6</td>\n",
       "      <td>trade</td>\n",
       "      <td>6270424</td>\n",
       "      <td>1.099439</td>\n",
       "      <td>execution_EURUSD2017-05-05 20:33:21.932000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.099425</td>\n",
       "      <td>USD</td>\n",
       "      <td>6.893861e+06</td>\n",
       "      <td>6.893861e+06</td>\n",
       "      <td>6270424</td>\n",
       "      <td>1.099425</td>\n",
       "      <td>1.099425</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08 10:31:33.011000+00:00</th>\n",
       "      <td>account1</td>\n",
       "      <td>algo3</td>\n",
       "      <td>default</td>\n",
       "      <td>order_EURUSD2017-05-08 10:30:57.526000+00:00_2...</td>\n",
       "      <td>broker4</td>\n",
       "      <td>subbroker2</td>\n",
       "      <td>trade</td>\n",
       "      <td>3276653</td>\n",
       "      <td>1.094703</td>\n",
       "      <td>execution_EURUSD2017-05-08 10:31:33.011000+00:...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.094680</td>\n",
       "      <td>USD</td>\n",
       "      <td>3.586887e+06</td>\n",
       "      <td>3.586887e+06</td>\n",
       "      <td>3276653</td>\n",
       "      <td>1.094680</td>\n",
       "      <td>1.094680</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 account_id algo_id algo_settings  \\\n",
       "Date                                                                \n",
       "2017-05-05 06:27:26.514000+00:00   account5   algo2       default   \n",
       "2017-05-05 11:30:00.791000+00:00   account5   algo3       default   \n",
       "2017-05-05 14:56:49.373000+00:00   account4   algo2       default   \n",
       "2017-05-05 20:33:21.932000+00:00   account6   algo2       default   \n",
       "2017-05-08 10:31:33.011000+00:00   account1   algo3       default   \n",
       "\n",
       "                                                                ancestor_pointer_id  \\\n",
       "Date                                                                                  \n",
       "2017-05-05 06:27:26.514000+00:00  order_EURUSD2017-05-05 06:26:47.189000+00:00_2...   \n",
       "2017-05-05 11:30:00.791000+00:00  order_EURUSD2017-05-05 11:28:43.816000+00:00_2...   \n",
       "2017-05-05 14:56:49.373000+00:00  order_EURUSD2017-05-05 14:56:33.125000+00:00_2...   \n",
       "2017-05-05 20:33:21.932000+00:00  order_EURUSD2017-05-05 20:33:14.677000+00:00_2...   \n",
       "2017-05-08 10:31:33.011000+00:00  order_EURUSD2017-05-08 10:30:57.526000+00:00_2...   \n",
       "\n",
       "                                 broker_id broker_sub_id event_type  \\\n",
       "Date                                                                  \n",
       "2017-05-05 06:27:26.514000+00:00   broker6    subbroker1      trade   \n",
       "2017-05-05 11:30:00.791000+00:00   broker3    subbroker3      trade   \n",
       "2017-05-05 14:56:49.373000+00:00   broker2    subbroker5      trade   \n",
       "2017-05-05 20:33:21.932000+00:00   broker5    subbroker6      trade   \n",
       "2017-05-08 10:31:33.011000+00:00   broker4    subbroker2      trade   \n",
       "\n",
       "                                  executed_notional  executed_price  \\\n",
       "Date                                                                  \n",
       "2017-05-05 06:27:26.514000+00:00           18008368        1.097430   \n",
       "2017-05-05 11:30:00.791000+00:00            5370024        1.095723   \n",
       "2017-05-05 14:56:49.373000+00:00            6198753        1.098892   \n",
       "2017-05-05 20:33:21.932000+00:00            6270424        1.099439   \n",
       "2017-05-08 10:31:33.011000+00:00            3276653        1.094703   \n",
       "\n",
       "                                                                                 id  \\\n",
       "Date                                                                                  \n",
       "2017-05-05 06:27:26.514000+00:00  execution_EURUSD2017-05-05 06:27:26.514000+00:...   \n",
       "2017-05-05 11:30:00.791000+00:00  execution_EURUSD2017-05-05 11:30:00.791000+00:...   \n",
       "2017-05-05 14:56:49.373000+00:00  execution_EURUSD2017-05-05 14:56:49.373000+00:...   \n",
       "2017-05-05 20:33:21.932000+00:00  execution_EURUSD2017-05-05 20:33:21.932000+00:...   \n",
       "2017-05-08 10:31:33.011000+00:00  execution_EURUSD2017-05-08 10:31:33.011000+00:...   \n",
       "\n",
       "                                  ...  notional_reporting_currency_mid  \\\n",
       "Date                              ...                                    \n",
       "2017-05-05 06:27:26.514000+00:00  ...                         1.097445   \n",
       "2017-05-05 11:30:00.791000+00:00  ...                         1.095725   \n",
       "2017-05-05 14:56:49.373000+00:00  ...                         1.098875   \n",
       "2017-05-05 20:33:21.932000+00:00  ...                         1.099425   \n",
       "2017-05-08 10:31:33.011000+00:00  ...                         1.094680   \n",
       "\n",
       "                                  reporting_currency  \\\n",
       "Date                                                   \n",
       "2017-05-05 06:27:26.514000+00:00                 USD   \n",
       "2017-05-05 11:30:00.791000+00:00                 USD   \n",
       "2017-05-05 14:56:49.373000+00:00                 USD   \n",
       "2017-05-05 20:33:21.932000+00:00                 USD   \n",
       "2017-05-08 10:31:33.011000+00:00                 USD   \n",
       "\n",
       "                                  executed_notional_in_reporting_currency  \\\n",
       "Date                                                                        \n",
       "2017-05-05 06:27:26.514000+00:00                             1.976319e+07   \n",
       "2017-05-05 11:30:00.791000+00:00                             5.884070e+06   \n",
       "2017-05-05 14:56:49.373000+00:00                             6.811655e+06   \n",
       "2017-05-05 20:33:21.932000+00:00                             6.893861e+06   \n",
       "2017-05-08 10:31:33.011000+00:00                             3.586887e+06   \n",
       "\n",
       "                                 order_notional_in_reporting_currency  \\\n",
       "Date                                                                    \n",
       "2017-05-05 06:27:26.514000+00:00                         1.976319e+07   \n",
       "2017-05-05 11:30:00.791000+00:00                         5.884070e+06   \n",
       "2017-05-05 14:56:49.373000+00:00                         6.811655e+06   \n",
       "2017-05-05 20:33:21.932000+00:00                         6.893861e+06   \n",
       "2017-05-08 10:31:33.011000+00:00                         3.586887e+06   \n",
       "\n",
       "                                  notional   arrival slippage_benchmark  \\\n",
       "Date                                                                      \n",
       "2017-05-05 06:27:26.514000+00:00  18008368  1.097445           1.097445   \n",
       "2017-05-05 11:30:00.791000+00:00   5370024  1.095725           1.095725   \n",
       "2017-05-05 14:56:49.373000+00:00   6198753  1.098875           1.098875   \n",
       "2017-05-05 20:33:21.932000+00:00   6270424  1.099425           1.099425   \n",
       "2017-05-08 10:31:33.011000+00:00   3276653  1.094680           1.094680   \n",
       "\n",
       "                                  spread_to_benchmark  slippage  \\\n",
       "Date                                                              \n",
       "2017-05-05 06:27:26.514000+00:00            -0.000005 -0.000013   \n",
       "2017-05-05 11:30:00.791000+00:00            -0.000014 -0.000002   \n",
       "2017-05-05 14:56:49.373000+00:00            -0.000014 -0.000016   \n",
       "2017-05-05 20:33:21.932000+00:00            -0.000023 -0.000012   \n",
       "2017-05-08 10:31:33.011000+00:00            -0.000009 -0.000021   \n",
       "\n",
       "                                 slippage_anomalous  \n",
       "Date                                                 \n",
       "2017-05-05 06:27:26.514000+00:00                  1  \n",
       "2017-05-05 11:30:00.791000+00:00                  0  \n",
       "2017-05-05 14:56:49.373000+00:00                  1  \n",
       "2017-05-05 20:33:21.932000+00:00                  0  \n",
       "2017-05-08 10:31:33.011000+00:00                  1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tca_results.trade_order['trade_df'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the timeline of the slippage for trades by accessing the `timeline` property. Note, it won't be populated for date/hours where we haven't traded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:25.734630Z",
     "start_time": "2020-09-14T15:29:25.721814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-05 06:00:00</th>\n",
       "      <td>-0.133895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 11:00:00</th>\n",
       "      <td>-0.017519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 14:00:00</th>\n",
       "      <td>-0.156906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05 20:00:00</th>\n",
       "      <td>-0.122419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08 10:00:00</th>\n",
       "      <td>-0.209665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        value\n",
       "Date                         \n",
       "2017-05-05 06:00:00 -0.133895\n",
       "2017-05-05 11:00:00 -0.017519\n",
       "2017-05-05 14:00:00 -0.156906\n",
       "2017-05-05 20:00:00 -0.122419\n",
       "2017-05-08 10:00:00 -0.209665"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tca_results.timeline['trade_df_slippage_by/mean_datehour/all'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of slippage and average slippage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a `TCARequest` for the same dataset. This time, however, we also calculate the `MetricTransientMarketImpact`, which we've defined as the 5 second move from a trade or start of an order. We also want to calculate the distribution of the slippage by side (ie. buy/sell) with `DistResultsForm` and to calculate the average slippage (weighted by the executed notional) with `BarResultForm`. Note, we've used `scalar` parameters of `10000.0` to convert to basis point to ease understanding.\n",
    "\n",
    "All these `Metric` and `ResultsForm` calculations will be calculated on both trades and orders. However, we can specify for it to be calculated only on for example trades, by instantiating the objects using the `market_trade_order_list` parameter eg. `MetricSlippage(market_trade_order_list=['trade_df'])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:29:25.749648Z",
     "start_time": "2020-09-14T15:29:25.735631Z"
    }
   },
   "outputs": [],
   "source": [
    "tca_request = TCARequest(start_date='05 May 2017', finish_date='20 May 2017', ticker='EURUSD',\n",
    "    tca_type='detailed',\n",
    "    trade_data_store='csv', market_data_store=market_data_store,\n",
    "    trade_order_mapping=csv_trade_order_mapping,\n",
    "    metric_calcs=[MetricSlippage(), \n",
    "        MetricTransientMarketImpact(\n",
    "                             transient_market_impact_gap={'5' : 's'})],\n",
    "    results_form=[DistResultsForm(metric_name='slippage', \n",
    "                  aggregate_by_field='side', scalar=10000.0),\n",
    "        BarResultsForm(metric_name='slippage', \n",
    "            aggregate_by_field='ticker', scalar=10000.0),\n",
    "        BarResultsForm(metric_name='slippage', \n",
    "            aggregate_by_field='broker_id', scalar=10000.0)],\n",
    "    benchmark_calcs=[BenchmarkMarketSpreadToMid()],\n",
    "    use_multithreading=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's kick off the TCA calculation! **Again this can take several minutes to download the data. You might get some warning messages because it is trying to access a cache**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:54.965067Z",
     "start_time": "2020-09-14T15:29:25.751631Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:29:25,756; INFO:tcapy.analysis.tcaengine: More than 1 ticker specified for TCA detailed computation. Only working on first (tcaengine.py:122)\n",
      "2020-09-14 16:29:25,757; DEBUG:tcapy.analysis.tcamarkettradeloader: Start loading trade/data/computation (tcamarkettradeloader.py:241)\n",
      "2020-09-14 16:29:25,758; DEBUG:tcapy.analysis.tcatickerloaderimpl: Get market and trade/order data for EURUSD from 2017-05-05 00:00:00+00:00 - 2017-05-20 00:00:00+00:00 (tcatickerloaderimpl.py:80)\n",
      "2020-09-14 16:29:25,758; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:29:29,846; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:29:29,848; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:29:33,928; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:29:33,930; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-20 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:29:58,402; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:29:58,404; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:29:58,744; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:29:58,873; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:30:02,952; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:30:02,953; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:30:03,295; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:30:03,415; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:30:07,502; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:30:07,502; DEBUG:tcapy.analysis.tcatickerloader: Get trade order holder for EURUSD from 2017-05-05 00:00:00+00:00 - 2017-05-20 00:00:00+00:00 (tcatickerloader.py:386)\n",
      "2020-09-14 16:30:07,545; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:30:11,628; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:30:11,629; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:30:15,710; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:30:15,712; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-20 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:30:40,156; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:30:40,157; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:30:40,531; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:30:40,652; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:30:44,741; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:30:44,758; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:30:45,108; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:30:45,222; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:30:49,303; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:30:49,309; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:30:53,383; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:30:53,384; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:30:57,464; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:30:57,466; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-20 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:31:21,990; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:31:21,991; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:31:22,329; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:31:22,447; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:31:26,523; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:31:26,524; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:31:26,880; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:31:26,996; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:31:31,069; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:31:31,112; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:31:35,197; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:31:35,199; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:31:39,279; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:31:39,282; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-20 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:32:03,729; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:32:03,730; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:32:04,082; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:32:04,201; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:32:08,282; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:32:08,300; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:08,640; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:32:08,760; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:32:12,844; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_USDEUR_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:32:12,849; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:16,923; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:32:16,924; DEBUG:tcapy.data.volatilecache: Attempting to get list from cache: ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] (volatilecache.py:540)\n",
      "2020-09-14 16:32:21,004; WARNING:tcapy.data.volatilecache: Couldn't retrieve ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] from cache: Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it. (volatilecache.py:500)\n",
      "2020-09-14 16:32:21,007; DEBUG:tcapy.data.databasesource: Downloading 2017-05-05 00:00:00 - 2017-05-20 00:00:00 for EURUSD (databasesource.py:3816)\n",
      "2020-09-14 16:32:45,499; WARNING:tcapy.analysis.algos.benchmark: mid not in market data (benchmark.py:90)\n",
      "2020-09-14 16:32:45,500; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n",
      "2020-09-14 16:32:45,848; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:32:45,965; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:32:50,042; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:32:50,043; DEBUG:tcapy.data.volatilecache: Attempting to push ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp'] to cache (volatilecache.py:426)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:50,384; DEBUG:tcapy.util.deltaizeserialize: Pandas dataframe of size: ----------- 63.639 MB ----------- in 1 chunk(s) (deltaizeserialize.py:221)\n",
      "2020-09-14 16:32:50,506; DEBUG:tcapy.data.volatilecache: Now pushing ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:443)\n",
      "2020-09-14 16:32:54,590; DEBUG:tcapy.data.volatilecache: Pushed ['dukascopy_EURUSD_2017-05-05 00:00:00+00:00_2017-05-20 00:00:00+00:00_market_df_None_comp_size_21020283_endsizearrow_'] to cache (volatilecache.py:461)\n",
      "2020-09-14 16:32:54,595; DEBUG:tcapy.analysis.tcatickerloader: Filter the market date by start/finish date (tcatickerloader.py:761)\n",
      "2020-09-14 16:32:54,618; DEBUG:tcapy.analysis.tcatickerloader: Combine trade/order data (tcatickerloader.py:782)\n",
      "2020-09-14 16:32:54,625; DEBUG:tcapy.analysis.tcatickerloader: Calculating BenchmarkMarketSpreadToMid for market data (tcatickerloader.py:600)\n",
      "2020-09-14 16:32:54,680; DEBUG:tcapy.analysis.tcatickerloader: Filter trades by venue (tcatickerloader.py:458)\n",
      "2020-09-14 16:32:54,682; DEBUG:tcapy.analysis.tcatickerloader: Calculating derived fields and benchmarks (tcatickerloader.py:508)\n",
      "2020-09-14 16:32:54,684; DEBUG:tcapy.analysis.tcatickerloader: Calculating execution fields (tcatickerloader.py:510)\n",
      "2020-09-14 16:32:54,693; DEBUG:tcapy.analysis.tcatickerloader: Calculating benchmarks (tcatickerloader.py:536)\n",
      "2020-09-14 16:32:54,694; DEBUG:tcapy.analysis.tcatickerloader: Calculating metrics (tcatickerloader.py:554)\n",
      "2020-09-14 16:32:54,695; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for trade_df (tcatickerloader.py:560)\n",
      "2020-09-14 16:32:54,712; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricTransientMarketImpact for trade_df (tcatickerloader.py:560)\n",
      "2020-09-14 16:32:54,742; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricSlippage for order_df (tcatickerloader.py:560)\n",
      "2020-09-14 16:32:54,760; DEBUG:tcapy.analysis.tcatickerloader: Calculating MetricTransientMarketImpact for order_df (tcatickerloader.py:560)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error 10061 connecting to localhost:6379. No connection could be made because the target machine actively refused it.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:54,784; DEBUG:tcapy.analysis.tcatickerloader: Completed derived field calculations for EURUSD (tcatickerloader.py:568)\n",
      "2020-09-14 16:32:54,786; DEBUG:tcapy.analysis.tcatickerloaderimpl: Generating downsampled market data for potentional display (tcatickerloaderimpl.py:275)\n",
      "2020-09-14 16:32:54,831; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-09-14 16:32:54,835; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n",
      "2020-09-14 16:32:54,837; DEBUG:tcapy.analysis.tcatickerloaderimpl: About to join (tcatickerloaderimpl.py:340)\n",
      "2020-09-14 16:32:54,840; DEBUG:tcapy.analysis.tcatickerloaderimpl: Finished joining (tcatickerloaderimpl.py:356)\n",
      "2020-09-14 16:32:54,865; DEBUG:tcapy.analysis.tcamarkettradeloader: Finished loading data and calculating metrics on individual _tickers (tcamarkettradeloader.py:248)\n",
      "2020-09-14 16:32:54,866; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Constructing results form to summarize analysis... (tcamarkettradeloaderimpl.py:74)\n",
      "2020-09-14 16:32:54,957; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Now join table results... (tcamarkettradeloaderimpl.py:122)\n",
      "2020-09-14 16:32:54,958; DEBUG:tcapy.analysis.tcamarkettradeloaderimpl: Finished calculating results form and join table results! (tcamarkettradeloaderimpl.py:135)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Dictionary of (mostly) dataframes as output from TCA calculation\n",
    "dict_of_df = tca_engine.calculate_tca(tca_request)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the output. This time we have some different DataFrames, in particular:\n",
    "* `dist_trade_df_slippage_by/pdf/side` - distribution of slippage on trades split up by buy/sell\n",
    "* `bar_trade_df_slippage_by/mean/ticker` - average of slippage on trades split by ticker\n",
    "* `bar_trade_df_slippage_by/mean/broker_id` - average of slippage on trades split by broker\n",
    "* `dist_order_df_slippage_by/pdf/side` - distribution of slippage on orders split up by buy/sell\n",
    "* `bar_order_df_slippage_by/mean/ticker` - average of slippage on trades split by ticker\n",
    "* `bar_order_df_slippage_by/mean/broker_id` - average of slippage on trades split by broker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:54.980067Z",
     "start_time": "2020-09-14T15:32:54.966067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['trade_df', 'order_df', 'sparse_market_trade_df', 'sparse_market_order_df', 'market_df_downsampled', 'candlestick_fig', 'dist_trade_df_slippage_by/pdf/side', 'bar_trade_df_slippage_by/mean/ticker', 'bar_trade_df_slippage_by/mean/broker_id', 'dist_order_df_slippage_by/pdf/side', 'bar_order_df_slippage_by/mean/ticker', 'bar_order_df_slippage_by/mean/broker_id', 'market_df'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_of_df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use  the `PlotRender` object (which is used by the GUI) to create different sorts of Ploty charts such as the timelines, distributions etc. In this case we are plotting the distribution of the slippage by buy/sell. Note that if we'd used the `TCAResults` object instead it would have simplified this code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.145072Z",
     "start_time": "2020-09-14T15:32:54.982269Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:54,987; DEBUG:tcapy.vis.displaylisteners: Plotting distribution dist (displaylisteners.py:1239)\n",
      "2020-09-14 16:32:55,005; DEBUG:tcapy.vis.displaylisteners: Rendered distribution dist (displaylisteners.py:1243)\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_render = PlotRender()\n",
    "\n",
    "# Generate Plotly JSON Figure\n",
    "dist_fig = plot_render.plot_dist(dist_df=dict_of_df['dist_order_df_slippage_by/pdf/side'], \n",
    "          metric='slippage', split_by='side', width=chart_width, height=chart_height)\n",
    "   \n",
    "# Render the Plotly JSON Figure to view\n",
    "chart.plot(dist_fig, engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can push the output into a `TCAResults` object, which can simplify the TCA output, in a much easier to use way. We can see that several charts end up getting rendered, such as timeline and distribution plots, together with more descriptive titles. We do not need to get into the low level details of how to do the plotting with Plotly, tcapy does that all for us using chartpy. However, if we want we can of course edit the returned `Plotly Figure` object (see here on do [this](https://plotly.com/python/creating-and-updating-figures))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.325066Z",
     "start_time": "2020-09-14T15:32:55.148067Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-14 16:32:55,151; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2352 (displaylisteners.py:988)\n",
      "2020-09-14 16:32:55,198; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2352 and  (displaylisteners.py:998)\n",
      "2020-09-14 16:32:55,201; DEBUG:tcapy.vis.displaylisteners: Plotting main timeline 2346 (displaylisteners.py:988)\n",
      "2020-09-14 16:32:55,249; DEBUG:tcapy.vis.displaylisteners: Rendered plot 2346 and  (displaylisteners.py:998)\n",
      "2020-09-14 16:32:55,283; DEBUG:tcapy.vis.displaylisteners: Plotting distribution Trades slippage by/pdf/side PDF (displaylisteners.py:1239)\n",
      "2020-09-14 16:32:55,296; DEBUG:tcapy.vis.displaylisteners: Rendered distribution Trades slippage by/pdf/side PDF (displaylisteners.py:1243)\n",
      "2020-09-14 16:32:55,300; DEBUG:tcapy.vis.displaylisteners: Plotting distribution Orders slippage by/pdf/side PDF (displaylisteners.py:1239)\n",
      "2020-09-14 16:32:55,313; DEBUG:tcapy.vis.displaylisteners: Rendered distribution Orders slippage by/pdf/side PDF (displaylisteners.py:1243)\n"
     ]
    }
   ],
   "source": [
    "tca_results = TCAResults(dict_of_df, tca_request, chart_width=chart_width, chart_height=chart_height)\n",
    "tca_results.render_computation_charts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pickup the Plotly JSON `Figure` object ready made to push to `chartpy` for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.460072Z",
     "start_time": "2020-09-14T15:32:55.327068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart.plot(tca_results.dist_charts['trade_df_slippage_by/pdf/side'], engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on what we have specified to be calculated in the `TCARequest` the `TCAReport` can render the following charts (which are available as attributes\n",
    "\n",
    "* `timeline_charts` - timeline aggregates\n",
    "* `sparse_market_charts` - mixture of market data with points to denote trades/orders\n",
    "* `bar_charts` - average aggregates eg. average slippage\n",
    "* `dist_charts` - distribution charts eg. distribution of slippage\n",
    "* `scatter_charts` - scatter charts eg. plotting slippage vs. notional\n",
    "* `styled_tables` - tables in HTML format\n",
    "\n",
    "We can also get the underlying DataFrames using the following properties\n",
    "\n",
    "* `trade_order` - DataFrames of trades/orders\n",
    "* `timeline` - DataFrames of timelines\n",
    "* `sparse_market` - DataFrames with a mixture of market data/trade/order data\n",
    "* `bar` - DataFrames with bar chart style data\n",
    "* `dist` - DataFrames with distribution style data\n",
    "* `scatter` - DataFrames with scatter style data\n",
    "* `table` - DataFrames intended to be displayed as tables\n",
    "* `market` - DataFrames with high frequency tick market data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see inside the `dist_charts` and `bar_charts` to see keys are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.475071Z",
     "start_time": "2020-09-14T15:32:55.462213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['trade_df_slippage_by/pdf/side', 'order_df_slippage_by/pdf/side'])\n",
      "dict_keys(['trade_df_slippage_by/mean/ticker', 'trade_df_slippage_by/mean/broker_id', 'order_df_slippage_by/mean/ticker', 'order_df_slippage_by/mean/broker_id'])\n"
     ]
    }
   ],
   "source": [
    "print(tca_results.dist_charts.keys())\n",
    "print(tca_results.bar_charts.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the average slippage by ticker! In this case, we only have one ticker, so probably just easier to report the number itself, which arount 0.5 bp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.580066Z",
     "start_time": "2020-09-14T15:32:55.477068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                ticker\n",
      "ticker_index          \n",
      "EURUSD       -0.217804\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(tca_results.bar['order_df_slippage_by/mean/ticker'])\n",
    "\n",
    "chart.plot(tca_results.bar_charts['order_df_slippage_by/mean/ticker'], \n",
    "           engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the average slippage by broker. Looks like `broker4` hasn't been particularly good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.685067Z",
     "start_time": "2020-09-14T15:32:55.582227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart.plot(tca_results.bar_charts['order_df_slippage_by/mean/broker_id'], engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot one of the PDF charts too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.805067Z",
     "start_time": "2020-09-14T15:32:55.687067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart.plot(tca_results.dist_charts['trade_df_slippage_by/pdf/side'], \n",
    "           engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try seeing what the `sparse_market_chart` property looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:55.820067Z",
     "start_time": "2020-09-14T15:32:55.807068Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['EURUSD_trade_df', 'EURUSD_order_df'])\n"
     ]
    }
   ],
   "source": [
    "print(tca_results.sparse_market_charts.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the market data benchmark alongside the trades as dots, sized by their executed notional and with different colors for buy (green) and sell (red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:56.090067Z",
     "start_time": "2020-09-14T15:32:55.822297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart.plot(tca_results.sparse_market_charts['EURUSD_trade_df'], engine='plotly', style=style)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because these charts are already Plotly Figure objects in JSON, we can also display them directly, without the `Chart` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:56.255067Z",
     "start_time": "2020-09-14T15:32:56.092068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tca_results.sparse_market_charts['EURUSD_order_df']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we print the charts, we can see inside the Plotly Figure JSON format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:56.315067Z",
     "start_time": "2020-09-14T15:32:56.257069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure({\n",
      "    'data': [{'connectgaps': True,\n",
      "              'line': {'color': 'rgba(0.20392156862745098, 0.5411764705882353, 0.7411764705882353, 1.0)',\n",
      "                       'dash': 'solid',\n",
      "                       'width': 3},\n",
      "              'mode': 'lines',\n",
      "              'name': 'mid',\n",
      "              'text': '',\n",
      "              'type': 'scatter',\n",
      "              'x': [2017-05-05 00:00:00+00:00, 2017-05-05 01:00:00+00:00,\n",
      "                    2017-05-05 02:00:00+00:00, ..., 2017-05-19\n",
      "                    18:19:18.110000+00:00, 2017-05-19 19:00:00+00:00, 2017-05-19\n",
      "                    20:00:00+00:00],\n",
      "              'y': array([1.0979177951812744, 1.0976229906082153, 1.0974935293197632, ..., '',\n",
      "                          1.1204367876052856, 1.1205419301986694], dtype=object)},\n",
      "             {'connectgaps': True,\n",
      "              'line': {'color': 'rgba(0.0, 0.0, 0.0, 1.0)', 'dash': 'solid', 'width': 0.5},\n",
      "              'mode': 'lines',\n",
      "              'name': 'bid',\n",
      "              'text': '',\n",
      "              'type': 'scatter',\n",
      "              'x': [2017-05-05 00:00:00+00:00, 2017-05-05 01:00:00+00:00,\n",
      "                    2017-05-05 02:00:00+00:00, ..., 2017-05-19\n",
      "                    18:19:18.110000+00:00, 2017-05-19 19:00:00+00:00, 2017-05-19\n",
      "                    20:00:00+00:00],\n",
      "              'y': array([1.0978981256484985, 1.0976039171218872, 1.097473382949829, ..., '',\n",
      "                          1.1204227209091187, 1.12051522731781], dtype=object)},\n",
      "             {'connectgaps': True,\n",
      "              'line': {'color': 'rgba(0.0, 0.0, 0.0, 1.0)', 'dash': 'solid', 'width': 0.5},\n",
      "              'mode': 'lines',\n",
      "              'name': 'ask',\n",
      "              'text': '',\n",
      "              'type': 'scatter',\n",
      "              'x': [2017-05-05 00:00:00+00:00, 2017-05-05 01:00:00+00:00,\n",
      "                    2017-05-05 02:00:00+00:00, ..., 2017-05-19\n",
      "                    18:19:18.110000+00:00, 2017-05-19 19:00:00+00:00, 2017-05-19\n",
      "                    20:00:00+00:00],\n",
      "              'y': array([1.0979373455047607, 1.097642183303833, 1.0975137948989868, ..., '',\n",
      "                          1.1204509735107422, 1.1205687522888184], dtype=object)},\n",
      "             {'connectgaps': True,\n",
      "              'line': {'color': 'rgba(0.5568627450980392, 0.7294117647058823, 0.25882352941176473, 1.0)',\n",
      "                       'dash': 'solid',\n",
      "                       'width': 1},\n",
      "              'marker': {'size': [0.0, 0.0, 0.0, ..., 5.466823811857581, 0.0, 0.0]},\n",
      "              'mode': 'markers',\n",
      "              'name': 'buy trade',\n",
      "              'text': '',\n",
      "              'type': 'scatter',\n",
      "              'x': [2017-05-05 00:00:00+00:00, 2017-05-05 01:00:00+00:00,\n",
      "                    2017-05-05 02:00:00+00:00, ..., 2017-05-19\n",
      "                    18:19:18.110000+00:00, 2017-05-19 19:00:00+00:00, 2017-05-19\n",
      "                    20:00:00+00:00],\n",
      "              'y': array(['', '', '', ..., 1.120751000477769, '', ''], dtype=object)},\n",
      "             {'connectgaps': True,\n",
      "              'line': {'color': 'rgba(0.8862745098039215, 0.2901960784313726, 0.2, 1.0)', 'dash': 'solid', 'width': 1},\n",
      "              'marker': {'size': [0.0, 0.0, 0.0, ..., 5.466823811857581, 0.0, 0.0]},\n",
      "              'mode': 'markers',\n",
      "              'name': 'sell trade',\n",
      "              'text': '',\n",
      "              'type': 'scatter',\n",
      "              'x': [2017-05-05 00:00:00+00:00, 2017-05-05 01:00:00+00:00,\n",
      "                    2017-05-05 02:00:00+00:00, ..., 2017-05-19\n",
      "                    18:19:18.110000+00:00, 2017-05-19 19:00:00+00:00, 2017-05-19\n",
      "                    20:00:00+00:00],\n",
      "              'y': array(['', '', '', ..., '', '', ''], dtype=object)},\n",
      "             {'boxpoints': False,\n",
      "              'fillcolor': '#FFFFFF',\n",
      "              'line': {'color': '#808080'},\n",
      "              'name': 'Increasing',\n",
      "              'showlegend': False,\n",
      "              'type': 'box',\n",
      "              'whiskerwidth': 0,\n",
      "              'x': array([datetime.datetime(2017, 5, 5, 2, 0),\n",
      "                          datetime.datetime(2017, 5, 5, 2, 0),\n",
      "                          datetime.datetime(2017, 5, 5, 2, 0), ...,\n",
      "                          datetime.datetime(2017, 5, 19, 20, 0),\n",
      "                          datetime.datetime(2017, 5, 19, 20, 0),\n",
      "                          datetime.datetime(2017, 5, 19, 20, 0)], dtype=object),\n",
      "              'y': [1.0973000526428223, 1.0975000858306885, 1.097825050354004,\n",
      "                    ..., 1.1207250356674194, 1.1207250356674194, 1.120845079421997]},\n",
      "             {'boxpoints': False,\n",
      "              'fillcolor': '#348ABD',\n",
      "              'line': {'color': '#348ABD'},\n",
      "              'name': 'Decreasing',\n",
      "              'showlegend': False,\n",
      "              'type': 'box',\n",
      "              'whiskerwidth': 0,\n",
      "              'x': array([datetime.datetime(2017, 5, 5, 0, 0),\n",
      "                          datetime.datetime(2017, 5, 5, 0, 0),\n",
      "                          datetime.datetime(2017, 5, 5, 0, 0), ...,\n",
      "                          datetime.datetime(2017, 5, 19, 19, 0),\n",
      "                          datetime.datetime(2017, 5, 19, 19, 0),\n",
      "                          datetime.datetime(2017, 5, 19, 19, 0)], dtype=object),\n",
      "              'y': [1.097564935684204, 1.098075032234192, 1.0979499816894531, ...,\n",
      "                    1.1204099655151367, 1.1204099655151367, 1.1211400032043457]}],\n",
      "    'layout': {'height': 500,\n",
      "               'legend': {'bgcolor': '#F5F6F9', 'font': {'color': '#4D5663'}, 'x': 0.05, 'y': 1},\n",
      "               'paper_bgcolor': 'rgba(0,0,0,0)',\n",
      "               'plot_bgcolor': 'rgba(0,0,0,0)',\n",
      "               'showlegend': True,\n",
      "               'template': '...',\n",
      "               'title': {'font': {'color': '#4D5663'}, 'text': 'EURUSD trades'},\n",
      "               'width': 800,\n",
      "               'xaxis': {'gridcolor': '#E1E5ED',\n",
      "                         'showgrid': True,\n",
      "                         'tickfont': {'color': '#4D5663'},\n",
      "                         'title': {'font': {'color': '#4D5663'}, 'text': ''},\n",
      "                         'zerolinecolor': '#E1E5ED'},\n",
      "               'yaxis': {'gridcolor': '#E1E5ED',\n",
      "                         'showgrid': True,\n",
      "                         'tickfont': {'color': '#4D5663'},\n",
      "                         'title': {'font': {'color': '#4D5663'}, 'text': ''},\n",
      "                         'zerolinecolor': '#E1E5ED'}}\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "print(tca_results.sparse_market_charts['EURUSD_trade_df'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can update the properties of a Plotly figure too, such as the title, without having to redraw the whole chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:56.465075Z",
     "start_time": "2020-09-14T15:32:56.317068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trade_fig = tca_results.sparse_market_charts['EURUSD_trade_df']\n",
    "trade_fig.update_layout(title='Look the title is changed!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-14T15:32:56.615073Z",
     "start_time": "2020-09-14T15:32:56.467067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trade_fig"
   ]
  },
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   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "We've seen a quick 10 minute view of tcapy, with some examples of how to call the library. We've also done many other notebooks, showing some of the more advanced functionality of tcapy, which you can look at next. If you are interesting supporting this project, please contact saeed@cuemacro.com."
   ]
  }
 ],
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