diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a8c213f --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +__pycache__/ +.pytest_cache +.vscode/ +test_save.html diff --git a/PORTOFOLIO ANALYSIS.ipynb b/PORTOFOLIO ANALYSIS.ipynb index 82d5600..b3f69fb 100644 --- a/PORTOFOLIO ANALYSIS.ipynb +++ b/PORTOFOLIO ANALYSIS.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "76af6d01", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "403e2576", "metadata": {}, "outputs": [], @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "ffc2315d", "metadata": {}, "outputs": [], @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "50848654", "metadata": {}, "outputs": [], @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "8cb9a833", "metadata": {}, "outputs": [], @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "id": "b438bbd4", "metadata": { "collapsed": true @@ -137,7 +137,7 @@ "outputs": [ { "data": { - "image/png": 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", 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gALa2tk2maIjIPF6BExFJilfgRESSYoATEUnK6o+Tvf5m/86g1WpNPvDSE/X0Mejp/Qc4BjdT/728vJpdzytwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJWf2TmETUPdTPn9plbas3f9Zlbd9MeAVORCQpBjgRkaQY4EREkmKAExFJigFORCQpBjgRkaQY4EREkmKAExFJigFORCQpBjgRkaQY4EREkmKAExFJigFORCQpBjgRkaQY4EREkmKAExFJigFORCQpBjgRkaTM/km10tJSJCUloby8HCqVCnq9HpMnT0ZycjK++uoruLi4AABmzJiBUaNGdXrBRER0ldkAV6vVeOKJJ+Dn54eamhosX74cwcHBAIApU6Zg6tSu+7t6REQ9mdkAd3d3h7u7OwDAwcEB3t7eKCsr6/TCiIjoxtr0V+lLSkpQUFCAgIAAnDlzBvv370daWhr8/Pwwa9YsaDSaJvsYDAYYDAYAQHx8PLRarWUqvwFbW1urtNOd9fQx6On9B8yPQbEVa7kec8AyVEII0ZoNa2trERsbi2nTpmHs2LEoLy9X5r937twJo9GIZ555xuxxfvnll45V3AparRalpaWd3k531tPHoKf3HzA/BvXzu276U735s05v42Y6B7y8vJpd36q7UOrq6pCQkIDx48dj7NixAAA3NzfY2NjAxsYGkyZNQl5enuWqJSIis8wGuBACb731Fry9vXHfffcp641Go/L1d999hwEDBnROhURE1Cyzc+DZ2dlIS0uDj48Pnn/+eQBXbxk8cuQICgsLoVKp4OnpiaioqE4vloiI/sdsgA8dOhTJyclN1vOebyKirsVPYhIRSapNtxESkeV11t0gXXmbIFkHr8CJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikpStuQ1KS0uRlJSE8vJyqFQq6PV6TJ48GVVVVUhMTMSFCxfg6emJJUuWQKPRWKNmIiJCKwJcrVbjiSeegJ+fH2pqarB8+XIEBwfj0KFDGDFiBCIiIrBnzx7s2bMHkZGR1qiZiIjQiikUd3d3+Pn5AQAcHBzg7e2NsrIypKenQ6fTAQB0Oh3S09M7t1IiIjJh9gq8sZKSEhQUFCAgIAAVFRVwd3cHALi5uaGioqLZfQwGAwwGAwAgPj4eWq22gyWbZ2tra5V2urOePgYy9b+4qwvoAswBy2h1gNfW1iIhIQFz5syBo6OjyWsqlQoqlarZ/fR6PfR6vbJcWlrazlJbT6vVWqWd7qynj0FP7393xxxoGy8vr2bXt+oulLq6OiQkJGD8+PEYO3YsAMDV1RVGoxEAYDQa4eLiYqFSiYioNcwGuBACb731Fry9vXHfffcp60NDQ5GamgoASE1NxejRozuvSiIiasLsFEp2djbS0tLg4+OD559/HgAwY8YMREREIDExEQcOHFBuIyQiIusxG+BDhw5FcnJys6+tWrXK4gUREVHr8JOYRESSYoATEUmKAU5EJCkGOBGRpBjgRESSYoATEUmKAU5EJCkGOBGRpBjgRESSYoATEUmKAU5EJCkGOBGRpBjgRESSYoATEUmKAU5EJCkGOBGRpNr0V+mJblb186d2dQlEbcYrcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpKU2WehbNy4ESdPnoSrqysSEhIAAMnJyfjqq6/g4uICAJgxYwZGjRrVuZUSEZEJswEeHh6Oe++9F0lJSSbrp0yZgqlT+QAgIqKuYnYKJTAwEBqNxhq1EBFRG7T7cbL79+9HWloa/Pz8MGvWrBZD3mAwwGAwAADi4+Oh1Wrb22Sr2draWqWd7qynj0Fb+1/cibVQU8wBy2hXgP/pT3/Cww8/DADYuXMnPvjgAzzzzDPNbqvX66HX65Xl0tLS9jTZJlqt1irtdGc9fQx6ev+7O+ZA23h5eTW7vl13obi5ucHGxgY2NjaYNGkS8vLyOlQcERG1XbsC3Gg0Kl9/9913GDBggMUKIiKi1jE7hbJ+/XpkZWWhsrISCxYswKOPPorMzEwUFhZCpVLB09MTUVFR1qiViIgaMRvg0dHRTdZNnDixM2ohIqI24CcxiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSTHAiYgkxQAnIpIUA5yISFIMcCIiSdma22Djxo04efIkXF1dkZCQAACoqqpCYmIiLly4AE9PTyxZsgQajabTiyUiov8xewUeHh6OFStWmKzbs2cPRowYgb///e8YMWIE9uzZ01n1ERFRC8wGeGBgYJOr6/T0dOh0OgCATqdDenp651RHREQtMjuF0pyKigq4u7sDANzc3FBRUdHitgaDAQaDAQAQHx8PrVbbnibbxNbW1irtdGc9fQza2v/iTqyFmmIOWEa7ArwxlUoFlUrV4ut6vR56vV5ZLi0t7WiTZmm1Wqu005319DHo6f3v7pgDbePl5dXs+nbdheLq6gqj0QgAMBqNcHFxaX9lRETULu0K8NDQUKSmpgIAUlNTMXr0aIsWRURE5pmdQlm/fj2ysrJQWVmJBQsW4NFHH0VERAQSExNx4MAB5TZCIiKyLrMBHh0d3ez6VatWWboWIiJqA34Sk4hIUgxwIiJJMcCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACcikhQDnIhIUh1+GiGRJdXPn2qR4/DxsNQT8AqciEhSDHAiIkkxwImIJMUAJyKSFAOciEhSDHAiIkkxwImIJMUAJyKSFAOciEhSDHAiIkkxwImIJMUAJyKSFAOciEhSDHAiIkkxwImIJMUAJyKSFAOciEhSDHAiIkl16E+qLVy4EPb29rCxsYFarUZ8fLyl6iIiIjM6/DcxY2Nj4eLiYolaiIioDTiFQkQkqQ5fgcfFxQEA7r77buj1+iavGwwGGAwGAEB8fDy0Wm1HmzTL1tbWKu10Z7KOAf+afM/AHLAMlRBCtHfnsrIyeHh4oKKiAmvXrsWTTz6JwMDAG+7zyy+/tLe5VtNqtSgtLe30drozWcegfv7Uri6BrEC9+bNOb0PW74HmeHl5Nbu+Q1MoHh4eAABXV1eMHj0aubm5HTkcERG1QbsDvLa2FjU1NcrXp0+fho+Pj8UKIyKiG2v3HHhFRQX+9re/AQDq6+sRFhaGkJAQS9VFRERmtDvA+/Xrh9dee82StRARURvwNkIiIkl1+DZCIqK2ssbdRs3dkmqNu1+siVfgRESSYoATEUmKAU5EJCkGOBGRpBjgRESSYoATEUmKAU5EJCneB05N8ImAdLPqynO7M+5B5xU4EZGkGOBERJJigBMRSYoBTkQkKQY4EZGkGOBERJJigBMRSYoBTkQkKQY4EZGkGOBERJJigBMRSYoBTkQkKQY4EZGkpHkaYVueItbcX6PuaTgGRDc/XoETEUmKAU5EJCkGOBGRpBjgRESS6tCbmBkZGdi6dSsaGhowadIkREREWKgsIiIyp91X4A0NDXjnnXewYsUKJCYm4siRI/j5558tWRsREd1AuwM8NzcX/fv3R79+/WBra4tx48YhPT3dkrUREdENtHsKpaysDH369FGW+/Tpg5ycnCbbGQwGGAwGAEB8fDy8vLza1+C+4+3bj4joJtXpb2Lq9XrEx8cjPj6+s5tSLF++3GptdVc9fQx6ev8BjkFP6H+7A9zDwwMXL15Uli9evAgPDw+LFEVEROa1O8D9/f1RVFSEkpIS1NXV4ejRowgNDbVkbUREdAPtngNXq9WYO3cu4uLi0NDQgAkTJmDAgAGWrK3d9Hp9V5fQ5Xr6GPT0/gMcg57Qf5UQQnR1EURE1Hb8JCYRkaQY4EREkpLmeeDXq6qqQmJiIi5cuABPT08sWbIEGo2myXZxcXHIycnB0KFDTW4rSkpKQlZWFhwdHQEACxcuxKBBg6xVvkV0dAxKSkqwfv16VFZWws/PD4sWLYKtrTynRGv7f+jQIezatQsAMG3aNISHhwMAVq9eDaPRiN69ewMAVq5cCVdXV6vV3xHmHmNx5coVbNiwAfn5+XB2dkZ0dDT69u0LANi9ezcOHDgAGxsbPPnkkwgJCbF+Bzqovf0vKSnBkiVLlM+jDB48GFFRUV3QAwsRktq2bZvYvXu3EEKI3bt3i23btjW73enTp0V6erpYt26dyfoNGzaIb775prPL7FQdHYOEhARx+PBhIYQQmzZtEvv37+/Uei2tNf2vrKwUCxcuFJWVlSZfCyFEbGysyM3NtWbJFlFfXy+effZZ8euvv4orV66IZcuWifPnz5ts88UXX4hNmzYJIYQ4fPiweP3114UQQpw/f14sW7ZMXL58WRQXF4tnn31W1NfXW70PHdGR/hcXF4vnnnvO6jV3FmmnUNLT06HT6QAAOp2uxY/xjxgxAg4ODtYszWo6MgZCCGRmZuL2228HAISHh0v3KITW9D8jIwPBwcHQaDTQaDQIDg5GRkaGlSu1rNY8xuL48ePKbxq33347fvjhBwghkJ6ejnHjxqFXr17o27cv+vfvj9zc3C7oRft1pP83G3l+X75ORUUF3N3dAQBubm6oqKho8zE+/PBDfPLJJwgKCsLjjz+OXr16WbrMTtWRMaisrISjoyPUajWAqx/MKisr65Q6O0tr+n/9Ix+u7+fGjRthY2ODsWPH4qGHHoJKper8wjuoNY+xaLyNWq2Go6MjKisrUVZWhsGDByvbyfj/3pH+A1enDv/v//4PDg4OmD59OoYNG2a94i2sWwf4mjVrUF5e3mT99OnTTZZVKlWbv/FmzpwJNzc31NXVYdOmTdi7dy8efvjhjpTbKTpzDGTQmf1fvHgxPDw8UFNTg4SEBKSlpSlX9HRzcnd3x8aNG+Hs7Iz8/Hy89tprSEhIUN4Lk023DvCYmJgWX3N1dYXRaIS7uzuMRiNcXFzadOxrV269evXChAkT8M9//rNDtXaWzhoDZ2dnVFdXo76+Hmq1GmVlZd3yUQgd7b+HhweysrKU5bKyMgQGBiqvAYCDgwPCwsKQm5srRYC35jEW17bp06cP6uvrUV1dDWdn5yb7dtf/9xvpSP9VKpXym7afnx/69euHoqIi+Pv7W7UPliLtHHhoaChSU1MBAKmpqRg9enSb9jcajQCgzAt2l0+RtkVHxkClUmH48OE4duwYgKt3asj2KITW9D8kJASnTp1CVVUVqqqqcOrUKYSEhKC+vh6XLl0CANTV1eHEiRPSnAOteYzFbbfdhkOHDgEAjh07huHDh0OlUiE0NBRHjx7FlStXUFJSgqKiIgQEBHRBL9qvI/2/dOkSGhoaAADFxcUoKipCv379rN0Fi5H2k5iVlZVITExEaWmpyS1keXl5+PLLL7FgwQIAwKpVq/Df//4XtbW1cHZ2xoIFCxASEoKXXnpJ+QYeOHAgoqKiYG9v35VdarOOjkFxcTHWr1+Pqqoq+Pr6YtGiRVK9D9Da/h84cAC7d+8GcPU2wgkTJqC2thaxsbGor69HQ0MDRowYgdmzZ8PGRo5rmpMnT+L9999XHmMxbdo07Ny5E/7+/ggNDcXly5exYcMGFBQUQKPRIDo6WgmqXbt24eDBg7CxscGcOXMwcuTILu5N27W3/8eOHUNycjLUajVsbGzwyCOPSHfh0pi0AU5E1NPJcblBRERNMMCJiCTFACcikhQDnIhIUgxwIiJJMcCJiCTFACciktT/A1yX7zvMNyyXAAAAAElFTkSuQmCC", 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", 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", + "image/png": 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", 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", 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AgeLh4SGRkZGybdu2Gi9ibty40Wq8G9+fSPULhmvXrpUePXqIl5eXtGrVSqKjoyU5Odnmexw2bJi4ublJUFCQrFixwuqipYj1RcyCggJ55JFHJCgoSFxdXeWOO+6QZ5991nKXh8i1C+ahoaGi1WolJCRERGq+8FjT/KrpjRs3SkhIiLi5ucmQIUPk+PHjVut9+OGHEhYWJu7u7jJ8+HDZtGmT1UVMEZFZs2aJv7+/ALBcUL7xIqY9+1xISIgsWbLEat6UKVMkJibmpr1VjUaEf5GnuVVUVKBjx44YNWpUtSNXqi4jIwNdunTBjz/+aHVBkKil4ymUZpCamorCwkJ0794d586dQ2JiIvLz82+br203lcuXL8NsNmP+/PkYPHgww5voBgzwZlBRUYHXX38dOTk5cHFxQZcuXbBnz56b3v1AwKZNmzB58mR07twZf//73x1dDtEth6dQiIgUxdsIiYgUxQAnIlJUs58Dv/4LEc3JYDDAbDY7ZGxVsEf2YZ9sY49sq0uPqh7BcCMegRMRKYoBTkSkKAY4EZGiGOBERIpigBMRKYoBTkSkKAY4EZGiGOBERIpigBMRKYpPIyQCUDF1lN3LNvafxdUmfd7IW6SWgkfgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESnK5uNkr1y5gri4OJSXl6OiogJ9+/bFmDFjUFhYiBUrVuDcuXMIDw/H888/D2dnPp2WiKi52ExcFxcXxMXFwd3dHeXl5YiNjUVUVBS++OILPPTQQ+jfvz/ef/997N69G/fdd19z1ExERLDjFIpGo4G7uzsAoKKiAhUVFdBoNMjIyEDfvn0BAIMGDUJ6enrTVkpERFbsOudRWVmJV155Bb/99hvuv/9+BAQEwMPDA1qtFgDg5+eH4uLiJi2UiIis2RXgTk5OePvtt3HhwgUsW7YMBQUFdg9gMplgMpkAAPHx8TAYDPWrtIGcnZ0dNrYqWnKPGvvPpNXF7djzlvxZsldj9KhOVx1btWqFzp07IysrCxcvXkRFRQW0Wi2Ki4vh5+dX4zpGoxFGo9EybTabG1RwfRkMBoeNrQr2yDFux57zs2RbXXoUGBhY43yb58DPnj2LCxcuALh2R8rhw4cRFBSEzp07Iy0tDQCwd+9eREdH21s3ERE1AptH4CUlJVi9ejUqKyshIrjnnnvQs2dPtGvXDitWrMCnn36KsLAwDBkypDnqJSKi/89mgIeEhOCtt96qNj8gIABvvvlmkxRFRES28ZuYRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpCgGOBGRohjgRESKYoATESmKAU5EpChnWwuYzWasXr0aZ86cgUajgdFoxIMPPogtW7Zg165d8Pb2BgCMHz8ePXr0aPKCiYjoGpsBrtVqMWHCBISHh+PSpUuYN28e7r77bgDAQw89hFGjRjV5kUREVJ3NAPf19YWvry8AQKfTISgoCMXFxU1eGBER3ZzNAL9eYWEh8vLyEBkZiZ9//hk7d+5EamoqwsPD8fTTT8PT07PaOiaTCSaTCQAQHx8Pg8HQOJXXkbOzs8PGVkVL7tHvDhz7dux5S/4s2asxeqQREbFnwbKyMsTFxeHRRx9Fnz59cObMGcv5782bN6OkpAQzZsywuZ2CgoIGFVxfBoMBZrPZIWOroiX3qGKq404FapM+d9jYTaUlf5bsVZceBQYG1jjfrrtQysvLkZCQgAEDBqBPnz4AAB8fHzg5OcHJyQlDhw5Fbm6unWUTEVFjsBngIoK1a9ciKCgII0aMsMwvKSmx/Hzw4EEEBwc3TYVERFQjm+fAMzMzkZqaivbt2+Pll18GcO2WwQMHDiA/Px8ajQb+/v6YNm1akxdLRET/YzPAO3bsiC1btlSbz3u+iYgci9/EJCJSFAOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMUAJyJSVJ3+pBpRU3PkX8YhUg2PwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkXZ/CKP2WzG6tWrcebMGWg0GhiNRjz44IM4f/48EhMTUVRUBH9/f8yZMweenp7NUTMREcGOANdqtZgwYQLCw8Nx6dIlzJs3D3fffTf27t2Lrl27YvTo0UhOTkZycjKeeuqp5qiZiIhgxykUX19fhIeHAwB0Oh2CgoJQXFyM9PR0xMTEAABiYmKQnp7etJUSEZGVOj0LpbCwEHl5eYiMjERpaSl8fX0BAD4+PigtLa1xHZPJBJPJBACIj4+HwWBoYMn14+zs7LCxVXEr9Oh3h47uGI7ueVO4FT5Lt7rG6JHdAV5WVoaEhARMmjQJHh4eVq9pNBpoNJoa1zMajTAajZZps9lcz1IbxmAwOGxsVbBHjnE79pyfJdvq0qPAwMAa59t1F0p5eTkSEhIwYMAA9OnTBwCg1+tRUlICACgpKYG3t7ddhRARUeOwGeAigrVr1yIoKAgjRoywzI+OjkZKSgoAICUlBb169Wq6KomIqBqbp1AyMzORmpqK9u3b4+WXXwYAjB8/HqNHj0ZiYiJ2795tuY2QiIiaj80A79ixI7Zs2VLja7GxsY1eEBER2YffxCQiUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUlSd/qQaETW+iqmjHDKuNulzh4xLjYdH4EREimKAExEpigFORKQoBjgRkaIY4EREimKAExEpigFORKQoBjgRkaIY4EREirL5Tcz33nsPhw4dgl6vR0JCAgBgy5Yt2LVrF7y9vQEA48ePR48ePZq2UiIismIzwAcNGoThw4dj9erVVvMfeughjBrlmK8AExGRHadQOnXqBE9Pz+aohYiI6qDeD7PauXMnUlNTER4ejqeffrrWkDeZTDCZTACA+Ph4GAyG+g7ZIM7Ozg4bWxW3Qo9+d+joLUtT/q5vhc/Sra4xelSvAL/vvvvw+OOPAwA2b96Mjz/+GDNmzKhxWaPRCKPRaJk2m831GbLBDAaDw8ZWBXvUsjTl75qfJdvq0qPAwMAa59frLhQfHx84OTnByckJQ4cORW5ubn02Q0REDVCvAC8pKbH8fPDgQQQHBzdaQUREZB+bp1BWrFiBo0eP4ty5c5g+fTrGjBmDjIwM5OfnQ6PRwN/fH9OmTWuOWomI6Do2A3z27NnV5g0ZMqQpaiEiojrgNzGJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUhQDnIhIUQxwIiJFMcCJiBTFACciUpSzrQXee+89HDp0CHq9HgkJCQCA8+fPIzExEUVFRfD398ecOXPg6enZ5MUSEdH/2DwCHzRoEBYsWGA1Lzk5GV27dsW7776Lrl27Ijk5uanqIyKiWtgM8E6dOlU7uk5PT0dMTAwAICYmBunp6U1THRER1crmKZSalJaWwtfXFwDg4+OD0tLSWpc1mUwwmUwAgPj4eBgMhvoM2WDOzs4OG1sVt0KPfnfo6C1LU/6ub4XP0q2uMXpUrwC/nkajgUajqfV1o9EIo9FomTabzQ0dsl4MBoPDxlYFe9SyNOXvmp8l2+rSo8DAwBrn1+suFL1ej5KSEgBASUkJvL2967MZIiJqgHoFeHR0NFJSUgAAKSkp6NWrV6MWRUREttk8hbJixQocPXoU586dw/Tp0zFmzBiMHj0aiYmJ2L17t+U2QiIial42A3z27Nk1zo+NjW3sWoiIqA74TUwiIkUxwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkU1+C/y0O2nYuooR5dARHbgETgRkaIY4EREimKAExEpigFORKQoBjgRkaIY4EREimKAExEpigFORKSoBn2RZ+bMmXB3d4eTkxO0Wi3i4+Mbqy4iIrKhwd/EjIuLg7e3d2PUQkREdcBTKEREimrwEfjSpUsBAMOGDYPRaKz2uslkgslkAgDEx8fDYDA0dMh6cXZ2dtjYqqjq0e+OLoSaRVPuD9zfbGuMHmlEROq7cnFxMfz8/FBaWorXX38dzzzzDDp16nTTdQoKCuo7XIMYDAaYzWaHjK2Kqh7xYVYtgzbp8ybbNvc32+rSo8DAwBrnN+gUip+fHwBAr9ejV69eyMnJacjmiIioDuod4GVlZbh06ZLl58OHD6N9+/aNVhgREd1cvc+Bl5aWYtmyZQCAiooK3HvvvYiKimqsuoiIyIZ6B3hAQADefvvtxqyFiIjqgLcREhEpigFORKQoBjgRkaIY4EREimKAExEpigFORKQoBjgRkaIa/DCrlqClPBuED7EiUguPwImIFMUAJyJSFAOciEhRDHAiIkUxwImIFMW7UIio2bWUO7uu1xR/AYlH4EREimKAExEpigFORKQoBjgRkaIY4EREimKAExEpSpnbCBt62xEf1EREtxsegRMRKYoBTkSkqAadQvnxxx+xfv16VFZWYujQoRg9enQjlUVERLbU+wi8srISH374IRYsWIDExEQcOHAAJ0+ebMzaiIjoJuod4Dk5OWjbti0CAgLg7OyMfv36IT09vTFrIyKim6j3KZTi4mK0bt3aMt26dWtkZ2dXW85kMsFkMgEA4uPjERgYWL8B//Fd/dYjIoe46b7O/RmAjR7ZockvYhqNRsTHxyM+Pr6ph7qpefPmOXR8FbBH9mGfbGOPbGuMHtU7wP38/HD69GnL9OnTp+Hn59fggoiIyD71DvCIiAicOnUKhYWFKC8vxzfffIPo6OjGrI2IiG6i3ufAtVotJk+ejKVLl6KyshKDBw9GcHBwY9bWqIxGo6NLuOWxR/Zhn2xjj2xrjB5pREQaoRYiImpm/CYmEZGiGOBERIpS5mmEdXX+/HkkJiaiqKgI/v7+mDNnDjw9Pa2Wyc/PR1JSEi5dugQnJyc8+uij6Nevn4Mqbn729AgAli5diuzsbHTs2LHF3B5m6zERV69exapVq3D8+HF4eXlh9uzZaNOmjWOKdSBbfTp69Cg2bNiAEydOYPbs2ejbt69jCnUgWz364osvsGvXLmi1Wnh7e+O5556Dv7+/fRuX29TGjRtlx44dIiKyY8cO2bhxY7Vlfv31VykoKBARkdOnT8vUqVPl/PnzzVmmQ9nTIxGRw4cPS3p6urz55pvNWJ3jVFRUyJ/+9Cf57bff5OrVq/LSSy/JL7/8YrXMv/71L1m3bp2IiOzfv1+WL1/uiFIdyp4+/f7775Kfny8rV66Ub7/91kGVOo49PTpy5IiUlZWJiMjOnTvr9Fm6bU+hpKenIyYmBgAQExNT49f8AwMDcccddwC4dl+7Xq/H2bNnm7VOR7KnRwDQtWtX6HS65izNoex5TMR3332HQYMGAQD69u2Ln376CdLC7gewp09t2rRBSEgINBqNg6p0LHt61KVLF7i5uQEAOnTogOLiYru3f9sGeGlpKXx9fQEAPj4+KC0tvenyOTk5KC8vR0BAQHOUd0uoa49aipoeE3HjTnX9MlqtFh4eHjh37lyz1ulo9vSppatrj3bv3o2oqCi7t6/0OfAlS5bgzJkz1eaPGzfOalqj0dz0CKCkpAQrV67EzJkz4eR0e/0/rbF6RERNKzU1FcePH8fixYvtXkfpAF+0aFGtr+n1epSUlMDX1xclJSXw9vaucbmLFy8iPj4e48ePx5133tlUpTpMY/SopbHnMRFVy7Ru3RoVFRW4ePEivLy8mrtUh+LjNGyzt0eHDx/Gjh07sHjxYri4uNi9/dvrcPM60dHRSElJAQCkpKSgV69e1ZYpLy/HsmXLMHDgwBZ5ddyeHrVE9jwmomfPnti7dy8AIC0tDZ07d25x/4Lh4zRss6dHeXl5SEpKwv/93/9Br9fXafu37Tcxz507h8TERJjNZqtb5HJzc/H1119j+vTpSE1NxZo1a9CuXTvLejNnzkRoaKjjCm9G9vQIAGJjY/Hrr7+irKwMXl5emD59ep3O06no0KFD2LBhg+UxEY8++ig2b96MiIgIREdH48qVK1i1ahXy8vLg6emJ2bNnt6jrJ1Vs9SknJwfLli3DhQsX4OLiAh8fHyxfvtzRZTcrWz1asmQJ/vvf/8LHxwcAYDAY8Morr9i17ds2wImIbne37SkUIqLbHQOciEhRDHAiIkUxwImIFMUAJyJSFAOciEhRDHAiIkX9P2/zyQj467QzAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "id": "348dd5d0", "metadata": { "collapsed": true @@ -198,31 +198,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.3799288669850971\n" + "the volatility of your portfolio over the last 90 days is: 0.4224507297523766\n", + "the expected returns of your portfolio are: 0.5204592808527991\n" ] - }, - { - "data": { - "text/plain": [ - "0.8148242093567409" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ "T = 90\n", "###\n", - "print('the volatility of your portfolio over the last 90 days is: 'get_volt(weights, covM, T))\n", - "print('the expected returns of your portfolio are: 'get_returns(mret, weights, T))" + "print('the volatility of your portfolio over the last 90 days is: ' + str(get_volt(weights, covM, T)))\n", + "print('the expected returns of your portfolio are: ' + str(get_returns(mret, weights, T)))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.5 64-bit", "language": "python", "name": "python3" }, @@ -236,7 +227,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.5" + }, + "vscode": { + "interpreter": { + "hash": "301da6f0e7e53b7955fa2d3d6c4b0305db26df81f353cd714eada29af4753e7a" + } } }, "nbformat": 4, diff --git a/page.html b/page.html new file mode 100644 index 0000000..18d6879 --- /dev/null +++ b/page.html @@ -0,0 +1,5274 @@ + + + + + + + +Top Crypto Gainers and Losers | CoinGecko + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +
    +TRY +
    +Turkish Lira +
    +
    • +
  • +
    +
    +UAH +
    +Ukrainian hryvnia +
    +
    • +
  • +
    +
    +VEF +
    +Venezuelan bolívar fuerte +
    +
    • +
  • +
    +
    +VND +
    +Vietnamese đồng +
    +
    • +
  • +
    +
    +ZAR +
    +South African Rand +
    +
    • +
  • +
    +
    +XDR +
    +IMF Special Drawing Rights +
    +
    • +
+
+Commodities +
+
    +
  • +
    +
    +XAG +
    +Silver - Troy Ounce +
    +
    • +
  • +
    +
    +XAU +
    +Gold - Troy Ounce +
    +
    • +
+
+
+
+
+ +
+1 Hour +24 Hours +7 Days +14 Days +30 Days +60 Days +One Year +
+
+
+ +
+All Coins +Top 100 +Top 300 +
+
+
+
+
+

Top Gainers

+* 24h Volume is above USD$50,000 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CoinVolumePrice
+
+
+metaversepay (MVP) +MVP +
+
+ +MetaversePay +MVP +
+
+		 + +$97,156 + + +$0.00000280 + +1673.1% +
+
+
+space crypto (SPG) +SPG +
+
+ +Space Crypto +SPG +
+
+		 + +$234,142 + + +$0.04231660 + +379.6% +
+
+
+netcoin (NET) +NET +
+
+ +Netcoin + NET +
+
+		 + +$350,523 + + +$0.00157690 + +264.4% +
+
+
+pumptopia (PTPA) +PTPA +
+
+ +Pumptopia +PTPA +
+
+		 + +$61,845 + + +$0.02433867 + +196.4% +
+
+
+luna inu (LINU) +LINU +
+
+ +Luna Inu +LINU +
+
+		 + +$204,595 + + +$0.000000001256 + +184.8% +
+
+
+aura finance (AURA) +AURA +
+
+ +Aura Finance +AURA +
+
+		 + +$1,275,236 + + +$1.88 + +158.8% +
+
+
+dogemetaverse (DOGEMETA) +DOGEMETA +
+
+ +Dogemetaverse +DOGEMETA +
+
+		 + +$790,503 + + +$0.000000000793561 + +140.5% +
+
+
+lever network (LEV) +LEV +
+
+ +Lever Network +LEV +
+
+		 + +$98,450 + + +$0.01782953 + +124.6% +
+
+
+motiv protocol (MOV) +MOV +
+
+ +MOTIV Protocol +MOV +
+
+		 + +$2,688,205 + + +$0.01371877 + +124.2% +
+
+
+muse dao (MUSE) +MUSE +
+
+ +Muse DAO +MUSE +
+
+		 + +$11,779,534 + + +$13.61 + +107.6% +
+
+
+graphen (ELTG) +ELTG +
+
+ +Graphen +ELTG +
+
+		 + +$94,046 + + +$0.00013673 + +100.9% +
+
+
+meerkat shares (MSHARE) +MSHARE +
+
+ +Meerkat Shares +MSHARE +
+
+		 + +$6,819,865 + + +$322.48 + +98.4% +
+
+
+silva (SILVA) +SILVA +
+
+ +Silva +SILVA +
+
+		 + +$265,031 + + +$0.000000000888075 + +96.8% +
+
+
+food bank (FOOD) +FOOD +
+
+ +Food Bank +FOOD +
+
+		 + +$427,935 + + +$0.000000000222327 + +88.2% +
+
+
+global gold (GGT) +GGT +
+
+ +Global Gold +GGT +
+
+		 + +$7,051,004,705 + + +$0.00041718 + +85.3% +
+
+
+octopus protocol (OPS) +OPS +
+
+ +Octopus Protocol +OPS +
+
+		 + +$171,057 + + +$0.00201466 + +83.4% +
+
+
+muu inu (MINU) +MINU +
+
+ +Muu Inu +MINU +
+
+		 + +$70,045 + + +$0.000000366263 + +73.4% +
+
+
+laeeb (LAEEB) +LAEEB +
+
+ +LaEeb +LAEEB +
+
+		 + +$413,497 + + +$0.000000019119 + +73.3% +
+
+
+dfi.money (YFII) +YFII +
+
+ +DFI.money +YFII +
+
+		 + +$180,010,699 + + +$1,070.52 + +69.0% +
+
+
+swello (SWLO) +SWLO +
+
+ +Swello +SWLO +
+
+		 + +$64,996 + + +$0.827385 + +65.4% +
+
+
+stepg (STEPG) +STEPG +
+
+ +STEPG +STEPG +
+
+		 + +$594,165 + + +$0.00192079 + +64.7% +
+
+
+supe infinity (SUPE) +SUPE +
+
+ +Supe Infinity +SUPE +
+
+		 + +$580,432 + + +$0.265668 + +60.9% +
+
+
+vr blocks (VRBLOCKS) +VRBLOCKS +
+
+ +VR Blocks +VRBLOCKS +
+
+		 + +$87,123 + + +$0.096148 + +58.8% +
+
+
+jupiter (JUP) +JUP +
+
+ +Jupiter +JUP +
+
+		 + +$19,572,811 + + +$0.01756991 + +58.7% +
+
+
+supersonic finance (SSN) +SSN +
+
+ +Supersonic Finance +SSN +
+
+		 + +$64,159 + + +$0.000000000289431 + +58.1% +
+
+
+3x long matic token (MATICBULL) +MATICBULL +
+
+ +3X Long Matic Token +MATICBULL +
+
+		 + +$191,400 + + +$0.00036858 + +54.3% +
+
+
+scrap (SCRAP) +SCRAP +
+
+ +Scrap +SCRAP +
+
+		 + +$70,976 + + +$1.18 + +54.1% +
+
+
+islamicoin (ISLAMI) +ISLAMI +
+
+ +ISLAMICOIN +ISLAMI +
+
+		 + +$1,881,497 + + +$0.00169230 + +53.2% +
+
+
+block commerce protocol (BCP) +BCP +
+
+ +Block Commerce Protocol +BCP +
+
+		 + +$708,801 + + +$0.00412910 + +53.1% +
+
+
+ndn link (NDN) +NDN +
+
+ +NDN Link +NDN +
+
+		 + +$81,278 + + +$0.00213158 + +50.4% +
+
+
+
+

Top Losers

+* 24h Volume is above USD$50,000 +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CoinVolumePrice
+
+
+movex (MOVX) +MOVX +
+
+ +MoveX +MOVX +
+
+		 + +$124,835 + + +$0.00001171 + +-69.8% +
+
+
+euphoria (WAGMI) +WAGMI +
+
+ +Euphoria +WAGMI +
+
+		 + +$409,612 + + +$0.827443 + +-64.0% +
+
+
+tranquil finance (TRANQ) +TRANQ +
+
+ +Tranquil Finance +TRANQ +
+
+		 + +$113,062 + + +$0.01172057 + +-45.2% +
+
+
+goldario (GLD) +GLD +
+
+ +Goldario +GLD +
+
+		 + +$105,256 + + +$0.214956 + +-42.1% +
+
+
+aag ventures (AAG) +AAG +
+
+ +AAG Ventures +AAG +
+
+		 + +$289,770 + + +$0.00991541 + +-40.9% +
+
+
+moonwell artemis (WELL) +WELL +
+
+ +Moonwell Artemis +WELL +
+
+		 + +$5,532,992 + + +$0.04250236 + +-40.7% +
+
+
+skyrim finance (SKYRIM) +SKYRIM +
+
+ +Skyrim Finance +SKYRIM +
+
+		 + +$143,631 + + +$0.00591340 + +-40.2% +
+
+
+podo point (POD) +POD +
+
+ +Podo Point +POD +
+
+		 + +$73,308 + + +$0.00042845 + +-36.2% +
+
+
+tranquil staked one (STONE) +STONE +
+
+ +Tranquil Staked ONE +STONE +
+
+		 + +$188,403 + + +$0.01789727 + +-36.0% +
+
+
+ziktalk (ZIK) +ZIK +
+
+ +Ziktalk +ZIK +
+
+		 + +$129,150 + + +$0.00334386 + +-34.4% +
+
+
+kitty coin solana (KITTY) +KITTY +
+
+ +Kitty Coin Solana +KITTY +
+
+		 + +$319,321 + + +$0.00005988 + +-32.9% +
+
+
+fira (FIRA) +FIRA +
+
+ +FIRA +FIRA +
+
+		 + +$116,540 + + +$0.262271 + +-32.7% +
+
+
+obrok (OBROK) +OBROK +
+
+ +OBRok +OBROK +
+
+		 + +$832,763 + + +$0.000000037526 + +-32.4% +
+
+
+game on players (GOPX) +GOPX +
+
+ +Game On Players +GOPX +
+
+		 + +$64,731 + + +$0.149353 + +-32.1% +
+
+
+greenzonex (GZX) +GZX +
+
+ +GreenZoneX +GZX +
+
+		 + +$100,975 + + +$0.00010687 + +-31.9% +
+
+
+zenc coin (ZENC) +ZENC +
+
+ +Zenc Coin +ZENC +
+
+		 + +$744,658 + + +$0.00692784 + +-31.2% +
+
+
+math (MATH) +MATH +
+
+ +MATH +MATH +
+
+		 + +$8,977,916 + + +$0.242111 + +-30.7% +
+
+
+dragonsb (SB) +SB +
+
+ +DragonSB +SB +
+
+		 + +$111,891 + + +$0.01641603 + +-30.1% +
+
+
+i will poop it nft (SHIT) +SHIT +
+
+ +I will poop it NFT +SHIT +
+
+		 + +$14,875,467 + + +$0.000000204906 + +-30.0% +
+
+
+shinjiro (SHOX) +SHOX +
+
+ +Shinjiro +SHOX +
+
+		 + +$57,181 + + +$0.000000000001546 + +-29.4% +
+
+
+parallel (PAR) +PAR +
+
+ +Parallel +PAR +
+
+		 + +$125,755 + + +$1.06 + +-29.1% +
+
+
+vulcano (VULC) +VULC +
+
+ +Vulcano +VULC +
+
+		 + +$72,877 + + +$0.02010151 + +-27.7% +
+
+
+moonbirds nft index by mexc (NMOON) +NMOON +
+
+ +Moonbirds NFT Index by MEXC +NMOON +
+
+		 + +$475,049 + + +$0.112662 + +-27.4% +
+
+
+smart block chain city (SBCC) +SBCC +
+
+ +Smart Block Chain City +SBCC +
+
+		 + +$251,105 + + +$1.13 + +-26.8% +
+
+
+unitrade (TRADE) +TRADE +
+
+ +Unitrade +TRADE +
+
+		 + +$74,515 + + +$0.053122 + +-26.5% +
+
+
+golfrochain (GOLF) +GOLF +
+
+ +Golfrochain +GOLF +
+
+		 + +$51,511 + + +$0.090821 + +-26.1% +
+
+
+dungeon (DGN) +DGN +
+
+ +Dungeon +DGN +
+
+		 + +$53,988 + + +$0.00000314 + +-24.7% +
+
+
+xjewel (XJEWEL) +XJEWEL +
+
+ +xJEWEL +XJEWEL +
+
+		 + +$251,204 + + +$0.271800 + +-23.6% +
+
+
+wjewel (WJEWEL) +WJEWEL +
+
+ +WJEWEL +WJEWEL +
+
+		 + +$2,311,616 + + +$0.152728 + +-23.5% +
+
+
+belrium (BEL) +BEL +
+
+ +Belrium +BEL +
+
+		 + +$57,020 + + +$5.74 + +-23.3% +
+
+
+
+
+ +
+
+ + + + + + + +
+ + +
+
+
+
+
+
+
+
+
+ +
+
+ +
+ + + +
+
+ + + + + + + + + + + + + + \ No newline at end of file diff --git a/trending cryptocurrencies webscraper.py b/trending cryptocurrencies webscraper.py index d444775..da979f0 100644 --- a/trending cryptocurrencies webscraper.py +++ b/trending cryptocurrencies webscraper.py @@ -1,68 +1,359 @@ +#!/usr/bin/env python +# ^ GCH usually you put this as the first line of a script in +# in Linux. +# If you have this line and the file is executable, you do not +# need to call explicitly python. +# You just call the script by name and the right python interpreter +# (or also other interpreted languages) is called by +# the command line shell + +# GCH +# There is difference between COMMENTs (lines starting with # like this one) +# and documentation. +# Read: https://realpython.com/documenting-python-code/ +# Documentation in python is done using the docstring syntax. +# Look for example here: https://python-sprints.github.io/pandas/guide/pandas_docstring.html +# Docstring is important because it is understood by the python interpreter and by tools +# to display documentation of your code. +# For example, in visual code, if you hoover with the mouse over a call, the docstring is displayed. + +''' +This program loads data about crypto currencies from a web page with specific format +or from a local file and creates a table with data for the top 10 currencies. +Run: + python '.\new trending cryptocurrencies webscraper.py' --help +to get help +or + python '.\new trending cryptocurrencies webscraper.py' --test +to run tests +''' + import requests from bs4 import BeautifulSoup from selenium import webdriver import time import pandas as pd +import argparse + + +def get_data_list(soup, how_many, tag_name, tag_attrs, strip_char=""): + ''' + Function to extract data from an BeautifulSoup document. + + :param BeautifulSoup soup: the input beautiful document + :param int how_many: how many of the filtered items shall be processed + :param string tag_name: the name of the tag to filter using BeautifulSoup.find_all() + :param dictionary tag_attrs: a distionary of 'attribute': 'value' for filtering + :param string strip_char: a string to be filtered out, if needed, from the result + :return: a list with all filtered record.span.string content (stripped if needed) + :rtype: list or strings + + >>> html_snippet = """ + ... [ + ... + ... $439,632 + ... , + ... + ... $473,416 + ... , + ... + ... $92,706 + ... ] + ... """ + >>> soup = BeautifulSoup(html_snippet, 'lxml') + >>> #samp_data = soup.find_all('td', {'class': 'td-liquidity_score lit'}) + >>> get_data_list(soup, 3, 'td', {'class': 'td-liquidity_score lit'}, "$") + ['439,632', '473,416', '92,706'] + + ''' + + out_vec = [] + all_found_records = soup.find_all(tag_name, tag_attrs) + needed_records = all_found_records[0:how_many] + for record in needed_records: + record_soup = BeautifulSoup(str(record), 'lxml') + out_vec.append(record_soup.span.string.replace(strip_char,"")) + + return out_vec + +def load_from_web(url = 'https://www.coingecko.com/en/coins/trending', save_in_file = None): + ''' + load the trending page from the web and parses data into soup format using driver + + If save_in_file is != None it also save the loaded page for reuse or testing + + >>> # as a test, loads the page from the default url and saves it in file + >>> # then counts that there are al least 10 td tags + >>> soup = load_from_web(save_in_file = "test_save.html") + Loading data from web url: https://www.coingecko.com/en/coins/trending + >>> td_tags = soup.find_all('td', {'class': 'td-change24h change24h stat-percent text-center'}) + >>> found_tags = len(td_tags) + >>> found_tags >= 10 + True + >>> # now loads the file + >>> new_soup = load_from_file("test_save.html") + Loading data from file: test_save.html + >>> new_td_tags = soup.find_all('td', {'class': 'td-change24h change24h stat-percent text-center'}) + >>> found_tags == len(new_td_tags) + True + ''' + + print("Loading data from web url: " + url) -def get_data(): - # get the trending page parsed data into soup format using driver - url = 'https://www.coingecko.com/en/coins/trending' option = webdriver.ChromeOptions() option.add_argument('headless') driver = webdriver.Chrome(options=option) driver.get(url) + + if save_in_file != None: + with open(save_in_file, "w", encoding="utf-8") as text_file: + text_file.write(driver.page_source) + soup = BeautifulSoup(driver.page_source, 'lxml') driver.close() - #get the pct change (daily) of the top 10 gainers - aa = soup.find_all('td', {'class': 'td-change24h change24h stat-percent text-center'}) - aa = aa[0:10] - bb = [str(x) for x in aa] - pct = [] - for x in bb: - x = x.split('aed":', 1)[1] - x = x.split(',', 1)[0] - x = round(float(x),3) - pct.append(x) + return soup + +def load_from_file(file_name): + ''' + load the trending page from a file + + The file has been typically save by calling load_from_web() + + >>> # as a test, loads an existing file + >>> # and counts the td tags found there + >>> soup = load_from_file("page.html") + Loading data from file: page.html + >>> td_tags = soup.find_all('td', {'class': 'td-change24h change24h stat-percent text-center'}) + >>> len(td_tags) + 60 + ''' + print("Loading data from file: " + file_name) + + contents = "" + with open(file_name, "r", encoding="utf-8") as text_file: + contents = text_file.read() + + soup = BeautifulSoup(contents, 'lxml') + + return soup + +def load_from_string(contents): + ''' + load the trending page from a string + + The string has been created in some way, by loading from a source os as a text string. + This is very useful for small and quick regression tests, in particula doctest. + + :param str contents: the input html.... describe the format + :return: the parsed data + :rtype: BeautifulSoup + + + >>> html_snippet = """ + ... + ... + ... $439,632 + ... + ... """ + >>> soup = load_from_string(html_snippet) + Loading data from string + >>> print(soup) + + + $439,632 + + + + ''' + print("Loading data from string") + + soup = BeautifulSoup(contents, 'lxml') + + return soup + +def set_args(parser): + ''' + Defines specific application command line parameters. + It is called by the startup utility runOrTest(). + In our case it adds exclusive command line options to load data from a + file or from the web. + + :param ArgumentParser parser: the ArgumentParser to be estended + + ''' + group = parser.add_mutually_exclusive_group() + group.add_argument("--file", type=str, const=None, help="Load data from a local file") + group.add_argument("--url", type=str, const=None, help="Load data from web at the given url") + +def get_data(args): # argv unused now, but required. To be cleaned up + ''' + Get the trending page parsed data into soup format using driver + + This is the actual program. + It can load data from a file or from the web. + Loading from a file is essential to be able to test with a know set of data, + so that it is possible to reproduce tests. + + :param list args: arguments parsed by ArgumentParser + + ''' + + # GCH + # I have split access to internet to read the page + # (in general all data collection for a more complex application) + # from the data manipulation . + # This makes it easier to write tests. + # + + # GCH: + # Loads data from a file or from a web url, depending + # on the command line parameters. + if args.file != None: + # If a filename is given, use it + full_soup = load_from_file(args.file) + elif args.url != None: + # If a url is given, use it + full_soup = load_from_web(args.url) + else: + # otherwise it will default to the standard url + full_soup = load_from_web() + + # GCH: made names of variables "speaking". + # It is also bad practice to reuse the same variable for things + # of different type + # You can syntactically do it in Python, but not in other + # strongly typed languages. + # Also, variable name of 1/2 characters are not searchable and therefore difficult + # to debug. # get the names of the top 10 coins - aa = soup.find_all('span', {'class': 'd-lg-none font-bold'}) - n = [str(x) for x in aa] - n = n[0:10] - for x in range(0, len(n)): - n[x] = n[x].replace('', '') - n[x] = n[x].replace('', '') - - #get price - aa = soup.find_all('td', {'class': 'td-price price'}) - aa = aa[0:10] - aa = [str(x) for x in aa] - p = [] - for x in aa: - x = x.split('$', 1)[1] - x = x.split('<', 1)[0] - p.append(x) - - #get volume - aa = soup.find_all('td', {'class': 'td-liquidity_score lit'}) - aa = aa[0:10] - aa = [str(x) for x in aa] - v = [] - for x in aa: - x = x.split('$', 1)[1] - x = x.split('<', 1)[0] - v.append(x) - - #create dataframe to print - dataframe = {'Coin Names': n, 'Daily % Change': pct, 'Price': p, 'Daily Volume': v} + name = get_data_list(full_soup, 10, + 'span', {'class': 'd-lg-none font-bold'} ) + + # get the pct change (daily) of the top 10 gainers + # This is a simpler parsing just taking the value from the span tag + # instead of parsing the jason string. + # It has only 1 decimal (do you really need 3?) + # but can use the same parsing scheme as all other filters. + pct = get_data_list(full_soup, 10, + 'td', {'class': 'td-change24h change24h stat-percent text-center'}, "%" ) + + # get prices + price = get_data_list(full_soup, 10, + 'td', {'class': 'td-price price'}, "$" ) + + # get volumes + vol = get_data_list(full_soup, 10, + 'td', {'class': 'td-liquidity_score lit'}, "$" ) + + # create dataframe to print + dataframe = {'Coin Names': name, 'Daily % Change': pct, 'Price': price, 'Daily Volume': vol} df = pd.DataFrame(dataframe) print(df) + # The end + +############################################################ +# +# These should go in a separate library of utilities +# +########################################################### +import os, sys + +def runOrTest(argv=sys.argv, fname=None, main=None, app_args=None): + """ + Utility function to allow running automatically all doctest and unittest tests in a file. + If the argv list contains the --test argument, the system will try to run the tests. + Otherwise, if not None, it will execute the function passed in the main argument. + This allows to selectively execute or test also executable python scripts. + + Args: + argv: The command line arguments. If contains --test, the tests are executed. + Default are the command line arguments coming from sys.argv. + fname: The name of this same file. Must be ..... + main: A function with the signature main(argv) to be executed if not testing. + The original argv list is passed. + Default is None. + app_args: function setting application specific command line arguments. + + + Examples: + # Just put the following code (uncommented) at the end of your file: + if __name__ == '__main__': + import m1tools.test.testRunner + runOrTest(main=mainForTest) + + ------------- + + >>> runOrTest(argv=[""], main=mainForTest) + Running mainForTest with arguments: Namespace(test=False) + + """ + import argparse + import pytest + + parser = argparse.ArgumentParser() + parser.add_argument("--test", action="store_true", help="Run doctest and unitest tests, if exist") + if app_args != None: + app_args(parser) + args, unknown = parser.parse_known_args(argv) + + if args.test: + # If no filename is given (the default) take the filename of the main + if fname == None: + import __main__ as main + fname = main.__file__ + + # --doctest-tests to ensure we test also for modules/files that contain the 'test' string in the name + # pytestargs = ["", "-v", "--with-doctest", "--doctest-tests", "--with-xunit", "--xunit-file=" + fname + ".xml", fname] + pytestargs = ["-v", "--doctest-modules", fname] + pytest.main(pytestargs) + else: + if main != None: + main(args) + +def mainForTest(args): + """ + Just a simple example of main(), used for test and documentation purposes of runOrTest(). + Prints on stdout the passed arguments, typically command line arguments. + + Args: + argv: arguments passed on the command line + + >>> mainForTest(['AAA', 'BBB']) + Running mainForTest with arguments: ['AAA', 'BBB'] + """ + print ('Running mainForTest with arguments: %s' % str(args)) +#################### +###################################### +# Unit test +# +# This is an example of unittest +# +###################################### +import unittest +class MyTest(unittest.TestCase): + + + def test_true(self): + """ + This test loads the standard data test file + and verifies that it can parse exactly 60 records. + """ + soup = load_from_file("page.html") + td_tags = soup.find_all('td', {'class': 'td-change24h change24h stat-percent text-center'}) + self.assertEqual(len(td_tags),60) + +# End pytest test() # +##################### if __name__ == "__main__": - get_data() + runOrTest(main=get_data, app_args = set_args) + +# __oOo__ \ No newline at end of file