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https://github.com/iblislin/bs_simulate

monte carlo
https://github.com/iblislin/bs_simulate

Last synced: about 1 month ago
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monte carlo

Host: GitHub
URL: https://github.com/iblislin/bs_simulate
Owner: iblislin
Created: 2015-12-03T09:40:58.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2015-12-03T16:42:24.000Z (about 9 years ago)
Last Synced: 2024-10-13T19:47:56.518Z (2 months ago)
Size: 23.6 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.ipynb

Awesome Lists containing this project

README

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import json\n",
"import math\n",
"from random import gauss\n",
"import matplotlib.pyplot as pyplot\n",
"from matplotlib.finance import candlestick_ohlc\n",
"from datetime import date"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def bs_model(S, T, v, mu=0, sigma=1, r=0.02):\n",
" return S * math.exp((r - 0.5 * v**2) * T + v * (T ** 0.5) * gauss(mu, sigma))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('./txf_date') as f:\n",
" data_rel = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 592,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def extract_close(data):\n",
" return np.array(data)[:, -2].astype(float)\n",
"\n",
"\n",
"def extract_ohcl(data):\n",
" return np.array(data)[:, 2:-1].astype(float)\n",
"\n",
"\n",
"# cal volatility\n",
"def volatility(data: 'Single Date', time: 'seconds'=60):\n",
" '''\n",
" >>> volatility(data[-1])\n",
" \n",
" 1/4 day\n",
" '''\n",
" # d = np.array(data)[:, -1].astype(float)\n",
" d = data\n",
" return (((d[1:] - d[:-1]) / d[:-1]).std()) / math.sqrt(time / (252 * 5 * 3600))\n",
"\n",
"\n",
"def gen_k(data, start_price):\n",
" close = extract_close(data)\n",
" S = start_price\n",
" T = 60 / (252 * 5 * 3600) # 1 min\n",
" v = volatility(data)\n",
"\n",
" ret = np.empty((len(data),))\n",
" for i in range(len(data)):\n",
" S = bs_model(S=S, T=T, v=v, r=0.02)\n",
" ret[i] = round(S)\n",
" return ret\n",
"\n",
"def merge_price(d):\n",
" o = d[0]\n",
" c = d[-1]\n",
" h = d.max()\n",
" l = d.min()\n",
" return np.array([o, h, l, c])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def gen_graph(n):\n",
" sim_k = gen_k(data_rel, data_rel[0][0])\n",
" sim_k = np.array([merge_price(i) for i in sim_k.reshape((sim_k.size // 4, 4))])\n",
"\n",
" fig, ax = plt.subplots()\n",
" fig.set_size_inches(20, 8)\n",
" candlestick_ohlc(ax,\n",
" np.hstack((np.arange(sim_k.shape[0]).reshape((sim_k.shape[0], 1)), sim_k)), \n",
" width=0.8,\n",
" colorup='r',\n",
" colordown='green')\n",
" \n",
" filename = 'sim/{}'.format(n)\n",
" fig.savefig(filename)\n",
" sim_k.tofile('{}.raw'.format(filename))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rel = data_rel[-318:-70]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rel_close = np.array([i[-1][-2] for i in rel])"
]
},
{
"cell_type": "code",
"execution_count": 1057,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sim_day():\n",
" day_vol = volatility(rel_close, 5 * 3600)\n",
"\n",
" S = rel[0][0][2]\n",
" T = 1 / 252 # 1 day\n",
"\n",
" mean_col = (S - rel_close[0]) / 252\n",
" sim_close = np.empty((len(rel_close),))\n",
" sim_close[0] = S\n",
" for i in range(1, len(rel_close)):\n",
" S = bs_model(S=S, T=T, v=day_vol, r=0.02) + mean_col\n",
" sim_close[i] = round(S)\n",
" return sim_close"
]
},
{
"cell_type": "code",
"execution_count": 1072,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 1072,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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K17WxsOGL6V8AYG+PQfPTbL/ZHIw/yPsH3uffu/9NQNcAtp3bVm5O+MVw/BzK\n6pJs2SKjtrqU9NXasAHuuKNhP4OaqLdQaJp2BngH2A5sAoIAQz3+akiXUSjKs/7Meib2mIiLtUvN\nkxUtnnv872FVyKpK47rdhM4C7e/oT0hKCCEhmsHCfiBNPBMmwMcfy5pGLi4NL1cRnxWPr7Mb69eD\nc0dnknKSSo8FBEBQELzzDjz5JPQdkYC7rVu58+fPl9FU1eHgYFgoLNtZ8sOsH4jOiOajyR/x2ODH\n2Hx2c7k5yTnJ5f4W1q6FWbPKjtc24qshNCiPQtO0FcAKACHEm8gdQ4oQwknTtJQSs5JuH5WA3HHo\ncCsZq2rcIK+99lrp67FjxzJWV7BF0WrYcm4L03s2TecuReMzs9dMFmxawPmM88RlxfHBwQ94ceSL\nfHn8y3KJg7rezwdDkunbt+qM89dfh+HDZTjoiKqrWRjkRNIJHv3jUXztfPnPuP/QvXN3ErIT8Ojs\nRvv2JUKRXSYUAwbIzG9dnsKvf8aXq9ZbW3RCcflyZVGZ1nNaaQXe2MuxLNq1iKLiIkxNpFc6KSep\n9GeTkyOjrD76qPr77dmzhz179tR5nVXRIKEQQjhompYmhPAAZgGBgBdwP3K3cR+g23NuAFYJIT5C\nmpa8gSOapmlCiEwhxFDgKDAP+KSqe+oLhaL1UVhcyI7oHXw69dPmXorCSHQ078j/jfw/Ar4KoEO7\nDjwx+AnGr5jKUJfhbLz3l9J5Qgj8nfw5eCiELK9/sTlqLlN7Tq10vZ49Zbnw4mLp8K4NQUlBvHfg\nPbZHb+e9ie+x6/wuvjz2Je9OfJf4rHjcrOUuwdnauVx3uoAA6TT28YGuXeXuw83GrYq7VI2DA3z/\nvfSxJCSUD8/Vx7OTJw6WDpxIOsEQ1yEUFheSfjWdvHRHdh2FpCQpjlUVPtRR8SF68eLFdV6zPg3N\nzF4jhOgCFABPaJqWJYR4B/hFCPEgEIuMdELTtDAhxC9AmN58nVlqAbASaA9s0jSt7vV3Fa2CQ/GH\n8OrkVfoEpWgdvDTqJZ4LfI4irYisix15Y8ZTzHjNGmuL8rGc/o7+rMk8xMW8NcRlVV1Oe9Giut3/\n3QPv4mTlxMnHTuJi7YK9pT2fHfkMKP/h79zRmaDkIFJzU1l/Zj2mlrZkZ89m2jQoKi4iJSelXvW1\nHBxg2TIpcj//DI9VE/E9oOsAzlw8wxDXIaTlpmHXwY4H7jfl8GGZRd4cz8oNsm5pmjZa07S+mqYF\naJq2p2TPTmRyAAAgAElEQVQsXdO0CZqm+WqaNknTtMt689/SNM1b07TemqZt0xs/rmmav6ZpPTVN\ne6Yha1LUj8LiQt4/8D5ahfTX3PzcSmONybZz25jcQ0VHt0Y6tOtAR/OOfPAB2HfsxKmTZSJx6ZIM\nH/Wz70ei2+fkF18j81oDuwnpcebiGe7pd0+prb+fUz9OpsjGQfpC4WLtQlJOEi/seIHVp1fz3O6H\n8fYp4uabISU3hS4dutSrZ4mvr0zg+/hj+Pbb6ufqdwlMzknGPL8r2dky0snKqvEjnAyhMrMVgLTd\n/mv7v4i5HFM6VlhcSOA3gbz999tNto5TKadaXDtShfG4ehW++Uba+0/qNXi7+Wb5xG1+2R+sUnGz\ncSMrz0ANjXpQrBUTeSmyXB9xdxt3rhVeIzU3lfiseFxtpN/B2Vr6KLad28ZX07/CuaMzP2wJZcgQ\nmfNR35yee++FVatkN72kJBlJVRXutu7EZ8lA0OScZLISu/Lf/0q/TFCQFIumRgmFApDNgQD2xOwp\nHfs26FvMTMz44OAH7I3ZyxN/PtHou4uISxFN0u9Z0TycPi1zDqZMkTkQ+fnSZn/wIGzeDAlBfphq\n7ZnrP9doQhGXGUfn9p2xtrAuHRNC0M+pHyeSThB5KRJPW5kI4dzRmZMpJzE3NadH5x6McB/BiYuy\ntEhwcjADnOpXZ0sI+WVqKkWxYsa3Pm42bqU7ivjMJHKSuzJ8eL1uazSUUCgA2B2zm0k9JrE3di9p\nuWm8svsVFu1axPKblzOv/zymrprKshPLjPbHa4iCogLOZ5xvtb0lFHIX0b8/WFrKPtZnzsgs7ClT\nZEG7vds7srx3PL52vmTmGcf0dObiGYMPH/0c+/HqnlfxtffFq7MXAE4dnSgsLmRS90kIIRjpPrK0\nBlVQcpBRCjKOHy/NSFWh3072VHQyncy6Ym1d9fymQAlFGyQ1N5ULmRdKv88vymd/3H5eHfMqu2N2\nM3/DfKLSo1h35zoGuQziv+P/S+RTkbjbuJN2xUAwuJGIzojG1ca12sbxiusbnVCA/Dc4GH7/HR55\nRDp6d+6Em260w8bCxmgPJVUKhVM/jiQc4cWRZaFT5qbm2FvaM6mHLEU30mMk+y/o7SiMIBRjx8Lf\nf8uEQUPom57CLiTj5dD8gR1KKNoYv4f/Tp/P+3D3mrIC/kcTjuLdxZvhbsPJK8wjKj2KlTNXMtJD\n1mJub9YeNxs3HKwcSou2GZOLVy5yKuWUMju1QnJzZdZycrL8Xl8oBgyQ/SbCwmSF2ClTZPazgwNN\nIhSjPEcxrec0pnhPKTf+5o1vMtlbBlT42vmSk5/DmYtniLgUQV/HKjIB64CdHfToIfMzDGFvaU9O\nfg5XCq5wPi2Zvp7N38VQCUUb4mjCUR7941E2ztlIdEZ0aXetteFrmeo9FSEEL97wIitnrsTCzKLS\n+Q6WDly8crFBa8jJz+FqwVVAdq9786836flpTyb+MJGTySfLORwV1z/nzskPxAULZNXXU6fKhOKO\nO+D55yE8XJqiHnxQ9r4GsG1vazyhuGRYKHrZ9+LPu/+s1AzokUGPlFZqFULw8MCHuW/dfXTv3J0O\n7RqYBl7CjTdKn4whTIQJrjauxGfFk3olmSF91I5C0USkX03nzt/u5IubvmC4+3AeGPAAy08sJ68w\njx9O/VDaWezZwGer7EZmb2lfo+npqU1P8XPIz1wpuEL/L/uXbqF1PLThITyWeLDgzwVM/3k6GyM3\nEvJ4CAO6DuCTI5+oHUUrIzoaJk6Uu4aFC2W5DV0Npx494Omny5LPuneHGTPkaxsLmwb7KIKTg3H5\nwIX9F/aXlvyuDwtHLeR8xnmjNox6+GFZDXf3bsPH3W3ciUqN54pJMiP7Nb9QqFaorZj0q+m8vOtl\n+jr2ZU34Gm7tfSu39bkNgPkD5zNkmcz89Hfyr5UD2cGyetNTUXERq0JWEZQcBMhQ16+Pf81/xv0H\nkM7qree2snb2WoKSg+jj0IcHAh7Asp0lTwx+glv+d4vaUbQyoqNlb4cvv4Rx48p2EzVhDNPTwbiD\njO8+no+nfEyXDl3qfR0bCxuWz1iOhWnlXXZ96dVLFje8807ZF1y/1/cff4BdOzc2/R2HiW0S3eyV\nUCgaidTcVIYsG8JNPW9iQ8QGOrXvxDsT3ik93r1zd9bMXsM9a+/h4ykf1+qaDlYOlUog63M44TDO\n1s5EpUfx1t9vsWjUIpadWMbLo1/G3NScg/EH6dG5B+O8xjHOa1y5c6f7TGdyj8n4O/nX7w0rWiTR\n0bL8Rffu0oGbkVG784whFCGpIQxxGdIgkdAxw3dGg69RkXHjwNUVDh8u35r1uefAaqY7f5t8h6V1\nB2wsbIx+77qihKKV8sZfbzDTdyafTK2ybBZju43lwnMXMBG1s0A6WDpwOq0sU+hKwRUs21mSeS2T\nkykn2XZuGzN8ZnCl4ArfBn/LS6NeYn/cflaHrmZe/3lsitpUWvysIqYmpmy5R1VuaW1ER0snNcje\n1O7u1c/XYdXOirzCPAqLCzEzqd/HVGhqKLf3ub1e5zYVkyfD1q1lQnHxonT8Fx/15MqIL9gya3sl\nH0pzoHwUrZDojGhWhazi5dEv1zi3tiIBlIt62n9hPw7vOZB5LZNfw35lzMoxfHrkU6b1nMYzgc/w\n8ZSPsWxnyX9v/C8v7niRuMw41p1ZV6VQKFon0dFyN1FXhBBYW1jXe1ehaRqhqaFGiVJqTHRCoePw\nYQgMhPsG3Etg0Ckm9x3SfIvTQ+0oWiHvH3ifJwY/gaNVDSUm64iDpQNpV9LIvJbJPb/fg7mpOaGp\noYSmhvKvEf/C3NSc4e7DMTMxo3tn+ekw3H04M31n4v2pN/P6zWOIS8v4j3+9UFwMWVk19ztobgoL\ny9vZQa49JkYm1tUHnfmpPqajpJwkzEzMjP43YGxGjpRJh1u2gL+/zFAPDISXXrIiM7MZanVUgRKK\nVsbla5dZHbqasAVhRr+2g5UMj10duppBzoPo1L4TIakhhKaG8s8R/6wUj67j/Unvc9+A+wh0CzT6\nmlozmiarjB46JMNKWyqaJnMlliyB0XoFXxMTZRc2S8v6XdfWov4hstfDbgJkJ79HHpE9NhISZPnw\n116T0WENbchkTJTpqZXxbdC3TOs5rVHKdOuing7EH2BSj0n4O/pzKuUUIakh1f5RWplbKZEoISQE\nvvvO8LFLl8r6M4NssXn0qBw/c6Z218/Lg/37G77OurBrlyxWd/iw/F5XDqy+ZicdDXFoh6RU/3+y\nJfHee/J3NmuW/H0PMxyd3qwooWhlrAhewWODqyl23wA6mneksLiQ3ed3M9xtOP2c+rHr/C7yCvPq\n1fWrrVFUJJPKXn+98rFt22QJC/0S1MuWSbG47TZYs6Z299i9G265RZp9mopPPpHmkpAQSEmRpqbi\nYjh7tuFCUd9S4xGXIuht37v+N28G3nlHhsza2TX3SiqjhKIVkZidSEJWAsPdGqfUpBACBysHLl+7\nTB+HPvg7+ZeWNWgJkRktnW++AQsLuUNISSkbz86WWcpTpsjENIC4OGm6CQyUQvHbb7W7R3i4jJwJ\nDoYPP5Rd1RqTtDTZkvS//4XQUPjrL7hwQfazPnkS+vWr+RpV0ZAdRUJ2Qr060TUn5uYwe3Zzr8Iw\nSihaETujd3Kj142lvXYbAwdLBwLdAjE1McXe0h4Xa5frZovf3Pz6q2zdOXx4efPQ7t0weLDsWRAR\nIcc2bZIRMaamcMMNUlhOn4YrV6TDsyrCwmSm86ZN8O671ZezNgahodC3LwwZIs1jujbNwcHSHBUQ\nUP9rN8RHkZSdVK9OdArDKKFoBQQnB7Pl7Ba2R29nQvcJjXovByuHcjuWfk79lFDUkrAwGdkycmR5\nodiyBaZOlV3Q9IXippvka1NT6fD87DN44w0pHC+/XOYL0Cc8HB56SNq9TUzkh3VjEh4OvXtDx47g\n7CzbfE6aBCdOyB1FQ4SiITuKxOzE0m52ioajhKKFsex43Xs+vLzrZW5ZfQvrzqxjYveJjbQyyV1+\nd5VLYvp06qfM6z+vUe95vZKcLP0Ma9fC5cuQmSkTzkaOlFnKR49KM9HmzdLs5OkpTTnp6XKXMVmv\nI+yjj8Lq1bI+0LFj0pcRGVn+fpomxejxx2Unuf/8B2JjZQVXY1FQULZrACkUfUrKKPXtK53xDz4o\nfSqdOsmop/piY2FDam4qC/5cQGFxocE5mqYxduVYIi5GlI4VFhdy6eqlFh8aez2hhKIFUawV89zW\n51h/Zn3p2OnU01X+kQAkZCVwIO4Am+duZrL35NL8hcbigYAHypXZ8O7i3SJKDLQk8vPlzqF3b2lu\n+ve/y568TUxkKOmpU3D33bLmT2GhLK9tagre3lJc/P3LOzWdneH222V5h4AAaaoKqxABnZoqu6h1\n7y7F6Z575D1DQozzvjQNnnpKVj49d06O6d4XyDUPGybfX0QEDBzYsPvZWNiwIngFS48t5XzGeYNz\nLl65yN7YvTy08SGKNenBT8lJwd7Svt4Z3YrKKKFoAeQX5ZNflE90RjS5BblsiNwAwIXMCwz6ehDf\nBVcRTwl8f/J7bu9zO+O8xvHrHb8qp3ILIDxcPnmnp5e090yQDl/dk7elpfQ5REXJAnAffyw/4EGa\nnz79tMzspM/XX0uTE8hrVey7rPvQFgKmT4f27aWoGMv89N57cif0xBMy0kn/niCFb9EiGfVkY9Mw\nsxPIUuOXrl7CzcaNiEsRBudEpUcxyHkQhcWF/HL6F0CZnRoDJRTNxNWCq0RekraDBX8u4JXdr3Ay\n+SSBboFsP7edvMI8Xtr5EmO6jeHdA+9SVFxk8DqrQlZxX//7mnLpihoIDpZP07oeyaNGSZNRb71o\nTZuSTVhgINx6a9m4r68UlmkGKp3oPwP06VN5R6H/oa1jwADjCMUnn0ih2rxZOuR/+EFmXevMabo1\nTZgg1zlsmHxvDcHdxp3pPtO5rfdt5UxL+pxNP4uvvS+39b6NA3EHAJmV7dxRObKNiRKKZmBvzF58\nPvMhcHkgSdlJrD69mk1RmziZcpIJXhPwc/Rj9m+z2R2zm9/u+I3O7Tuz7sy6StdJzE4kMTtRJbO1\nMIKD5Qe0jrFj5Ydqn1q0ROjVS1YUrakct5+fFIqEBHj2WRk+++GH0tGtj7F2FO+/L1uWuruDmxvM\nmSN7R/TqJc1pFfnzT9mHoiGM7z6ejXM24mvnW+WO4mz6Wbw7e9PfqT8nU04CakfRGCihaAY+P/o5\nC29YyHSf6cxYPYNAt0Dis+LZem4r/bv256mhT+Fu486xh49hbWHNXX3vYndM5Q4nO6J3NHo4rKLu\nGBIKqJ1QTJkidx81WRB79ZLObF329nvvweLFcF+FzWXfvlJQGpKAl5oqcz366gW3ffCBNG1V9Z7a\ntav5PdQWX3tfzlw0nJp+Nv0s3l286d+1PyeTT6JpmgyNVTsKo6K8Pc3A0cSjvHHjG4zrNg6/pX78\neOuP2FrYsiZ8Df2d+tPTrid39b2rdH63Tt3YdX5XpevsiN7R6OGwirqhaeX7QoMUjfvvBy+vms93\ncDDsn6iIlRU4OUl/xsGD0pFsCFtbsLeX5TS8a+5NZZDjx8tMaTrat5dVT69erd8160KNO4ou3jha\nOdKhXQfisuJIzE5ksMvgxl9YG0LtKJqYtNw0Mq5m4N3Fm94OvdkwZwO39b6NST0mYdnOkh5delQ6\nx8PWg7isuHJjmqaxI3pHo4fDKupGXJz8EHVyKhszNYUVK+S/xqRPH/lVlUjo8PeXiXH15fhxGDSo\n8njnzuDSBBYeF2sXrhRc4fK1y6VjMZdjyLiaQVR6FD3tegJI81PySemjUMl2RkXtKJqYY4nHGOQy\nqLQPxHSf6YDsoBWXGWewP4SHrQcXMi+UGwtLC8PCzKLRw2EVdSMoqPbtPhvKzJm1qwvk7y9DZG+5\npX73OX4c7rqr5nmNhRBC7iouRpT2c1+4cyHRGdFomoZdB/lD0PkplI/C+KgdRRNzNPGowZ4MXTt2\n5fUbDVSLA+w62HG14Co5+TmlY9ujtzOx+0QVDtvCOHJElrNoCh55RNaBqom+fRuWS3H8uMzbaE4G\ndB3A+oiy/KKk7CTOZ5zHu4t36d9AP6d+fBv0LWcunlFFKo1MmxOKlJwUxn03jo0RG5vkfsk5yeW+\nr0ooqkMIgbutO3GZZeanpijXoag7hw41PCzU2DTE9BQeDjk5DasCawxeH/c6K4JXsDN6JyD/rn66\n7SfenvB26ZzpPtN5efTLbLlnizI9GZlWLRTxWfEsP7Gc/KJ8lhxagtP7Tvgt9cOynSXvHXiPwuJC\nfg75Gc1Q0RwjsOv8Ltw/cudUiuw6o2kaRxOOMsS17o+c+uan/KJ89sXuY7zXeKOuV9EwiopkeY2W\n1k+gVy9ZzTUvr27nZWRIc9U77xgvgqm+OFs78+GkD3ln/zsApOSmMNB5YLmHJdv2tjwY8CCjPUdX\ndRlFPbnufBSaVrv/tLvP7+b2X2/Hq5MXy08s50LmBbbfu50OZh3o1qkbXh97cc/ae/jf6f8x3Wc6\n1hbWRl1nXGYcc9bMYaDzQHZG76SfUz/is+LR0HC3qWWHeT08bMqE4lD8IXzsfLCzbIGF69sw4eHS\nid3S+gmYm0tn9BdfyJyL2vLVV1L0Hn648dZWFwZ0HUDM5RiuFV7jSsEVOrfv3NxLajNcdzuK2Nja\nzfst7DcW3rCQQw8dYpTHKH654xf6OfWjp11P2pm245FBj7A3di+OVo6k5KbUfME68tGhj7i33708\nP/x5dsXI0Fad2ak+fgX9HcX/Qv/HVO+pRl2vouEcOtTydhM6fvxR7gx2V07HqZJz52QBw5aCu607\ncVlxJOck42jlqPxzTch1JxS7D6WXvi4sLuTPyD8Nztt3YR+jPEZhZmLGe5Pe4waP8imrL97wIsce\nPkaPzj0q+RHqw+H4w4xdORaArLwsvjv5HU8Pe5px3caxL3YfhcWF0uxUR/+EDg9bDy5kXSD2ciyr\nT6/mqWFPNXjNCuNy+HDL80/o6NYNnn8e1q+vcWopsbGyom1LoaN5RyzbWRKaGtoorX4VVXPdCcWW\nU8dLX68NX8v0n6eTmpsKyOqru8/vJuNqBucvn2egc9XlK81NzXG1ccWpoxMpOQ3fUQQlB7E3di+n\nUk6x7PgyJnafiIetBw5WDnh28uR44nG5o6iHfwKkUISmhvLMlmd4bNBjqoRyC+TcOVmrqaXi7S0T\n72pLSxMKkH8HRxKOKKFoYq47H8XxpKPARDRN470D7+Fo5cg/P9+Fe+ZdBF1bz2abW1kw5EmGuQ6j\nnWm7Gq/X1aprvXYUmdcy+TPqT8Z4jsHVxpXIS5HYW9rzxl9vsDtmN3vu21M696aeN/H2/rc5lnis\n3juKXva9uHztMkNdhvLCDS/U6xqKxiU5Gbq24M8vL6/aC0VxsWxp6uHRuGuqK562nhxJOHLdtTm9\n3rnudhSxBcfIzoY9MXvIysti4Q0v8evx7eReKea49WLMgh/n86OfMcpjVK2u59TRqV4+ig8Pfsji\nvYvxW+pHUnYSkZciWTRqEb+G/crTQ5/Gz9GvdO6rY14lNz+XTu074WDlUOd7AbjauHLu6XN8Mf0L\n1f+hhZKS0vKF4vx5w53xKpKaCtbWslRIS0LtKJqHBgmFEOI5IUSoEOKUEGKVEMJCCPGqECJeCHGi\n5GuK3vyFQogoIUS4EGKS3vjAkmtECiGWVLtgr318ujKRZ7c+y+Kxi/FmMgUe2+lzz3LcXEzxPfs5\nc3s8y6zes2r1Hrp27Fpn09O1wmt8efxL1t+1npEeIzkYf5DIS5FM8Z7CH3P+qPTEb2Fmwe93/s66\nuypXgFW0DvLzZeG8hnR0a2xsbGQvjNTUmue2RLMTSKHIuJahhKKJqbdQCCFcgKeAgZqm9UOasXSJ\n/h9qmjaw5GtLyfzewGygNzAVWCrKwha+AOZrmuYD+AghJlMFd/V4nH8n+5Gd6MzZdXdy4YQvFu01\nFu9dzPe3fE83T8Fsm4/o59SvVu/DycqJ5Ny6mZ5WnVrFIOdB9LLvRaBrIPti93Eh8wLdO3fnJp+b\nMDc1r3SOlbkVA7oOMHA1RWsgNVUW3zNUcrsloTM/TZ8uW6tWRUsVCk9buSglFE1LQ30UpoCVEKIY\nsAQSgG6Aobi1mcBqTdMKgRghRBQwVAgRC1hrmna0ZN73wC3AVkM3XDFvMbvvdMDXdAYfbRf06QOP\nzPmap+f0wquzFx4e0rZaW+q6o7h45SL/3v1vfpv9GwDD3Ycz7/d5uNm4GRQIResiL0/uHOzty4+3\ndLOTju7dYedOWXE2OFiWAzdUBiQmpmUKhYetdJo4WTnVMFNhTOr9/KNpWiLwAXABKRCXNU3bUXJ4\ngRAiWAixXAhhWzLmCuiXQE0oGXMF4vXG40vGDC/YRBDzv6fYvNqTRx+VrRmfmjIVr86yhrOnZ+1z\nLUD6KOrizH5y05PM6TuHEe4jABjqOpT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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sim_close = sim_day()\n",
"plt.plot(sim_close)\n",
"plt.plot(rel_close)"
]
},
{
"cell_type": "code",
"execution_count": 1074,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sim_intraday(rel_data, close_p, mean):\n",
" def extract_close(data):\n",
" return np.array(data)[:, -2].astype(float)\n",
"\n",
" close = extract_close(rel_data)\n",
" v = volatility(close)\n",
" mean = mean / 600\n",
"\n",
" S = rel_data[0][2]\n",
" T = 60 / (252 * 5 * 3600)\n",
" sim_close = np.empty((len(close),))\n",
" for i in range(len(close)):\n",
" S = bs_model(S=S+mean, T=T, v=v, r=0.02)\n",
" sim_close[i] = round(S)\n",
"\n",
" sim_close += close_p - sim_close[-1]\n",
"\n",
" return sim_close"
]
},
{
"cell_type": "code",
"execution_count": 1075,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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