├── 1) Monte Carlo valuation of European call option in py2.ipynb
├── 2) mcs_vector_numpy in py27.ipynb
├── 3) Data Visualisation.ipynb
├── 4) Financial Time Series in Py27.ipynb
├── 5) Input-Output Operations (I-O) in py27.ipynb
├── 6) Mathematical Tools.ipynb
├── 7) Excel Spreadsheet Interaction in Py27.ipynb
├── 8) Object Orientation and Graphical User Interfaces in Py27.ipynb
├── 9) Web Integeration.ipynb
├── A) Data Types and Structure in Py27.ipynb
├── C) Dates and Times.ipynb
├── README.md
├── cash_flow_series_GUI.PNG
├── cash_flow_series_GUI.py
├── es.txt
├── es50.txt
├── extended_short_rate_GUI.PNG
├── extended_short_rate_GUI.py
├── short rate GUI.PNG
├── short_rate_GUI.py
├── stock.csv
├── tradechat
    ├── static
    │   └── style.css
    ├── table.sql
    ├── templates
    │   ├── layout.html
    │   ├── login.html
    │   ├── register.html
    │   └── show_entries.html
    ├── tradechat.db
    └── tradechat.py
└── vs.txt


/1) Monte Carlo valuation of European call option in py2.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "# Monte Carlo valuation of European call option\n",
 8 |     "in Black-Scholes-Merton model"
 9 |    ]
10 |   },
11 |   {
12 |    "cell_type": "code",
13 |    "execution_count": 6,
14 |    "metadata": {},
15 |    "outputs": [
16 |     {
17 |      "name": "stdout",
18 |      "output_type": "stream",
19 |      "text": [
20 |       "Value of the European Call Option 7.996\n"
21 |      ]
22 |     }
23 |    ],
24 |    "source": [
25 |     "\n",
26 |     "import numpy as np\n",
27 |     "\n",
28 |     "# Parameter Values\n",
29 |     "S0 = 100.  # initial index level\n",
30 |     "K = 105.  # strike price\n",
31 |     "T = 1.0  # time-to-maturity\n",
32 |     "r = 0.05  # riskless short rate\n",
33 |     "sigma = 0.2  # volatility\n",
34 |     "\n",
35 |     "I = 100000  # number of simulations\n",
36 |     "\n",
37 |     "# Valuation Algorithm\n",
38 |     "z = np.random.standard_normal(I)  # pseudorandom numbers\n",
39 |     "ST = S0 * np.exp((r - 0.5 * sigma ** 2) * T + sigma * np.sqrt(T) * z)\n",
40 |     "  # index values at maturity\n",
41 |     "hT = np.maximum(ST - K, 0)  # inner values at maturity\n",
42 |     "C0 = np.exp(-r * T) * np.sum(hT) / I  # Monte Carlo estimator\n",
43 |     "\n",
44 |     "# Result Output\n",
45 |     "print \"Value of the European Call Option %5.3f\" % C0"
46 |    ]
47 |   }
48 |  ],
49 |  "metadata": {
50 |   "kernelspec": {
51 |    "display_name": "Python [conda env:py27]",
52 |    "language": "python",
53 |    "name": "conda-env-py27-py"
54 |   },
55 |   "language_info": {
56 |    "codemirror_mode": {
57 |     "name": "ipython",
58 |     "version": 2
59 |    },
60 |    "file_extension": ".py",
61 |    "mimetype": "text/x-python",
62 |    "name": "python",
63 |    "nbconvert_exporter": "python",
64 |    "pygments_lexer": "ipython2",
65 |    "version": "2.7.15"
66 |   }
67 |  },
68 |  "nbformat": 4,
69 |  "nbformat_minor": 2
70 | }
71 | 


--------------------------------------------------------------------------------
/2) mcs_vector_numpy in py27.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "### Monte Carlo valuation of European call option with NumPy"
 8 |    ]
 9 |   },
10 |   {
11 |    "cell_type": "code",
12 |    "execution_count": 37,
13 |    "metadata": {},
14 |    "outputs": [
15 |     {
16 |      "name": "stdout",
17 |      "output_type": "stream",
18 |      "text": [
19 |       "European Option Value   8.037\n",
20 |       "Duration in Seconds     2.519\n"
21 |      ]
22 |     }
23 |    ],
24 |    "source": [
25 |     "# mcs_vector_numpy.py\n",
26 |     "import math\n",
27 |     "import numpy as np\n",
28 |     "from time import time\n",
29 |     "\n",
30 |     "np.random.seed(20000)\n",
31 |     "t0 = time()\n",
32 |     "\n",
33 |     "# Parameters\n",
34 |     "S0 = 100.; K = 105.; T = 1.0; r = 0.05; sigma = 0.2\n",
35 |     "M = 50; dt = T / M; I = 250000\n",
36 |     "\n",
37 |     "# Simulating I paths with M time steps\n",
38 |     "S = np.zeros((M + 1, I))\n",
39 |     "S[0] = S0\n",
40 |     "for t in range(1, M + 1):\n",
41 |     "    z = np.random.standard_normal(I)  # pseudorandom numbers\n",
42 |     "    S[t] = S[t - 1] * np.exp((r - 0.5 * sigma ** 2) * dt\n",
43 |     "                                  + sigma * math.sqrt(dt) * z)\n",
44 |     "      # vectorized operation per time step over all paths\n",
45 |     "\n",
46 |     "# Calculating the Monte Carlo estimator\n",
47 |     "C0 = math.exp(-r * T) * np.sum(np.maximum(S[-1] - K, 0)) / I\n",
48 |     "\n",
49 |     "# Results output\n",
50 |     "tnp1 = time() - t0\n",
51 |     "print \"European Option Value %7.3f\" % C0\n",
52 |     "print \"Duration in Seconds   %7.3f\" % tnp1"
53 |    ]
54 |   },
55 |   {
56 |    "cell_type": "code",
57 |    "execution_count": 39,
58 |    "metadata": {},
59 |    "outputs": [
60 |     {
61 |      "name": "stdout",
62 |      "output_type": "stream",
63 |      "text": [
64 |       "European Option Value   8.037\n",
65 |       "Duration in Seconds     2.208\n"
66 |      ]
67 |     }
68 |    ],
69 |    "source": [
70 |     "%run mcs_vector_numpy.py"
71 |    ]
72 |   }
73 |  ],
74 |  "metadata": {
75 |   "kernelspec": {
76 |    "display_name": "Python [conda env:py27]",
77 |    "language": "python",
78 |    "name": "conda-env-py27-py"
79 |   },
80 |   "language_info": {
81 |    "codemirror_mode": {
82 |     "name": "ipython",
83 |     "version": 2
84 |    },
85 |    "file_extension": ".py",
86 |    "mimetype": "text/x-python",
87 |    "name": "python",
88 |    "nbconvert_exporter": "python",
89 |    "pygments_lexer": "ipython2",
90 |    "version": "2.7.15"
91 |   }
92 |  },
93 |  "nbformat": 4,
94 |  "nbformat_minor": 2
95 | }
96 | 


--------------------------------------------------------------------------------
/6) Mathematical Tools.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Approximation "
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import numpy as np\n",
 17 |     "import matplotlib.pyplot as plt\n",
 18 |     "%matplotlib inline"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 2,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "def f(x):\n",
 28 |     "    return np.sin(x) + 0.5 * x"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 3,
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "x = np.linspace(-2 * np.pi, 2 * np.pi, 50)"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 4,
 43 |    "metadata": {},
 44 |    "outputs": [
 45 |     {
 46 |      "data": {
 47 |       "text/plain": [
 48 |        "Text(0,0.5,'f(x)')"
 49 |       ]
 50 |      },
 51 |      "execution_count": 4,
 52 |      "metadata": {},
 53 |      "output_type": "execute_result"
 54 |     },
 55 |     {
 56 |      "data": {
 57 |       "image/png": "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\n",
 58 |       "text/plain": [
 59 |        "<Figure size 432x288 with 1 Axes>"
 60 |       ]
 61 |      },
 62 |      "metadata": {},
 63 |      "output_type": "display_data"
 64 |     }
 65 |    ],
 66 |    "source": [
 67 |     "plt.plot(x, f(x), 'b')\n",
 68 |     "plt.grid(True)\n",
 69 |     "plt.xlabel('x')\n",
 70 |     "plt.ylabel('f(x)')"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "markdown",
 75 |    "metadata": {},
 76 |    "source": [
 77 |     "### REGRESSION"
 78 |    ]
 79 |   },
 80 |   {
 81 |    "cell_type": "code",
 82 |    "execution_count": 6,
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "reg = np.polyfit(x, f(x), deg=1)\n",
 87 |     "ry = np.polyval(reg, x)"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": 7,
 93 |    "metadata": {},
 94 |    "outputs": [
 95 |     {
 96 |      "data": {
 97 |       "text/plain": [
 98 |        "Text(0,0.5,'f(x)')"
 99 |       ]
100 |      },
101 |      "execution_count": 7,
102 |      "metadata": {},
103 |      "output_type": "execute_result"
104 |     },
105 |     {
106 |      "data": {
107 |       "image/png": "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\n",
108 |       "text/plain": [
109 |        "<Figure size 432x288 with 1 Axes>"
110 |       ]
111 |      },
112 |      "metadata": {},
113 |      "output_type": "display_data"
114 |     }
115 |    ],
116 |    "source": [
117 |     "plt.plot(x, f(x), 'b', label='f(x)')\n",
118 |     "plt.plot(x, ry, 'r.', label='regression')\n",
119 |     "plt.legend(loc=0)\n",
120 |     "plt.grid(True)\n",
121 |     "plt.xlabel('x')\n",
122 |     "plt.ylabel('f(x)')"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": 8,
128 |    "metadata": {},
129 |    "outputs": [],
130 |    "source": [
131 |     "reg = np.polyfit(x, f(x), deg=5)\n",
132 |     "ry = np.polyval(reg, x)"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "code",
137 |    "execution_count": 10,
138 |    "metadata": {},
139 |    "outputs": [
140 |     {
141 |      "data": {
142 |       "text/plain": [
143 |        "Text(0,0.5,'f(x)')"
144 |       ]
145 |      },
146 |      "execution_count": 10,
147 |      "metadata": {},
148 |      "output_type": "execute_result"
149 |     },
150 |     {
151 |      "data": {
152 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd8VGXa//HPTSDAUldAdMWluYiCGgiWWVadgAVXH9xd9eeyPnalPIJlxa7oiooF7AUbKipG1xV7JTKySlCqAiLKAio2iqs0Q0hy/f64JxhwJnVmzkzyfb9e8wqTOXPOdSfhXHN3Z2aIiIg0CjoAERFJD0oIIiICKCGIiEiUEoKIiABKCCIiEqWEICIigBKCiIhEKSGIiAighCAiIlGNgw6gJtq3b29dunRJ+Hk3bdpEixYtEn7eVMn0+CHzy5Dp8UPmlyHT44fklWHu3LlrzaxDVcdlVELo0qULc+bMSfh5I5EI4XA44edNlUyPHzK/DJkeP2R+GTI9fkheGZxzn1fnODUZiYgIoIQgIiJRSggiIgJkWB9CLFu3bmXVqlUUFRXV+hxt2rRhyZIlCYwqtZIZf7NmzejUqRNNmjRJyvlFJH1kfEJYtWoVrVq1okuXLjjnanWODRs20KpVqwRHljrJit/MWLduHatWraJr164JP7+IpJeMbzIqKiqiXbt2tU4GEp9zjnbt2tWp9iUimSPjEwKgZJBE+tmKpIHCQhg3zn9NooxvMhIRqdcKC2HgQCguhuxsKCiAUCgpl6oXNYSg3Xnnney1116cdNJJPP/881x77bWVHj969GjefvvtFEUnIhktEvHJoLTUf41EknYp1RAS4N577+W1116ja9eu/P73v+fFF1+s9PhRo0Zx9tlnM2DAgBRFKCIZKxz2NYPyGkISZ2OrhlBHw4cPZ/ny5QwePJibbrqJpk2b0r59ewCOPfZYJk+eDMD999/PSSedBEDnzp1Zt24d3377bWBxi0iGCIV8M9HYsUltLoJ6VkM4/3xYsKDm7ystbU5WVuzXcnLg9tvjv3fixIm8/vrrTJ8+nZdeeom+fftue+2BBx6gf//+dO3alQkTJjBr1qxtr/Xt25f33nuP4447ruYBi0jDEgolNRGUq1cJIWjffPMNHTr8vKBgx44dufbaa8nLy2Pq1KnstNNO217beeed+frrr4MIU0QkpnqVECr7JF+ZDRt+SsjErubNm/Pjjz9u972FCxfSrl27X9z8i4qKaN68eZ2vKSKSKOpDSKC99tqLZcuWbXv+wQcf8NprrzF//nzGjx/PihUrtr326aef0rt37yDCFBGJSQkhgQ455BDmz5+PmbFlyxbOPvtsJk2axG9+8xsmTJjAGWecgZmxdetWli1bRr9+/YIOWURkm3rVZBSUlStXbvv3YYcdRkFBAYcddhgffvjhtu8PHjyYwYMHA/Dyyy9z/PHH07ixfvwikj5UQ0iwyy+/nM2bN1d6TElJCRdeeGGKIhIRqR59RE2wjh07bqsJxHPCCSekKBoRkepTDUFEJI0VFcEll8Dq1cm/lhKCiEgau+02uPnm2k26ranAEoJzrplz7gPn3IfOucXOuX8EFYuISDpatQquuw7+9Cc44ojkXy/IPoQtwAAz2+icawK865x7zcxmVfVGEZGG4KKLoKwMbr01NdcLrIZg3sbo0ybRhwUVT30zZswYpk2bFnQYIlJL77wD+flw8cWQqh1snVlw92DnXBYwF9gDuMfMLolxzFBgKEDHjh1z8/Pzt3u9TZs27LHHHnWKo7S0lKx4q9vVkJlhZjRqVLtcW1JSUuP5CYmMP5Zly5b9YkmORNu4cSMtW7ZM6jWSKdPjh8wvQ6bHDz+XobTUMXRoLps2NebRRz+gWbOyOp03Ly9vrplVPRO2/AYW5ANoC0wHeld2XG5uru3o448//sX3qjRzptkNN/ivZrZ+/fqan6OCFStWWM+ePW3EiBGWk5Njjz76qB100EHWp08fO/74423Dhg1mZvbKK6/Ynnvuaf3797dRo0bZ0UcfbWZmV199tZ199tl2+OGH25AhQ6ykpMRGjx5t/fr1s3322ccmTpxoZmZff/21HXzwwbbffvtZr169bMaMGVZSUmJ/+9vfrFevXta7d2+79dZbzczs1FNPtX/+859mZjZt2jTLycmx3r172+mnn25FRUVmZta5c2cbM2aM9enTx3r37m1LliyJWb5a/YxraPr06Um/RjJlevxmmV+GTI/f7Ocy3H23GZhF/wvXGTDHqnEvTotRRmb2AxABBiX9YuXb0V11lf+aoD1Kly5dyimnnMJbb73Fww8/zLRp05g3bx79+vXj1ltvpaioiGHDhvHaa6/x7rvvsmbNmu3eP3fuXF544QWmTJnCww8/TJs2bZg9ezazZ8/mwQcfZMWKFUyZMoUjjzySBQsW8OGHH5KTk8OCBQv45ptvWLRoEQsXLuT000/f7rxFRUWcdtppPP300yxcuJCSkhLuu+++ba+3b9+eefPmMWLECMaPH5+Qn4WI1N7atf72NGAApHp1/CBHGXVwzrWN/rs5cBjwSdIvnKTt6Dp37sxBBx3ErFmz+Pjjj+nfvz85OTk89thjfP7553zyySd069aNrtHGwCFDhmz3/sGDB29b/fTNN99k8uTJ5OTkcOCBB7Ju3To+++wz9t9/fx555BGuueYaFi5cSKtWrejWrRsrVqxg1KhRvP7667Ru3Xq78y5dupSuXbvSo0cPAE499VRmzJix7fW//OUvAOTm5m63BIeIBOPKK2H9erjzTnAutdcOcpTRrsBj0X6ERsAzZvZy0q+apO3oWrRoAfgmuMMPP5ynnnpqu9fnz59frfeXn+Ouu+7iyCOP/MVxM2bM4JVXXuHkk0/moosu4pRTTmHmzJnMnDmTe+65h2eeeYZJkyZtd67KNG3aFICsrCxKSkoqL6SIJNWnn7bkgQfg3HOhV6/UXz/IUUYfmVkfM9vXzHqbWeU70ydKkrejO+igg3jvvfe2LYO9efNmPv30U3r27Mny5cu3fQp/+umn457jyCOP5L777mPr1q2AXyp706ZNfP755+y8886cffbZnHnmmcybN4+1a9dSVlbGcccdx9ixY5k3b9525+rZsycrV67cFs/jjz/OoYcemtAyi0jd2cxCNl/1EoPaFHLNNcHE0DDXMkridnQdOnTg0UcfZciQIWzZsgWA6667jh49enDvvfcyaNAg2rdvzwEHHBD3HGeddRYrV66kb9++mBkdOnTg+eefJxKJcMstt9CkSRNatmzJ5MmT+eqrrzj11FO3vXfcuHHbnatZs2Y88sgjnHDCCZSUlLD//vszfPjwpJRdRGqpsJDSvIGMLC5mZJNbaLwkuXsnx1Wdnud0eSRslNEO6jrKqLrKRxuVlZXZiBEjto0Iqqtkx69RRlXL9PjNMr8MmRz/5qtusK1kmYGVZWX5UZAJRCaNMmooHnzwQXJycujVqxc//vgjw4YNCzokEUkDd34Upphsyhpl4RLYt1lTDbPJKCAXXHABF1xwQdBhiEgamTULLn0hRNP/V8Dg1pPodsYZwTQXUU8SgpnhUj0+q4GwAGeyi9R3JSUwfDjsthuc+VCIuXO30C2gZAD1YPnrZs2asW7dOt24ksDMWLduHc2aNQs6FJF66a674MMP4Y47oFWroKOpBzWETp06sWrVql/M/K2JoqKijL7pJTP+Zs2a0alTp6ScW6QhW7UKxoyBo46C6PzQwGV8QmjSpMm22b+1FYlE6NOnT4IiSr1Mj1+kITr/fN9kdPfdqZ+RHE/GJwQRkUzz6qvwr3/B9ddDt25BR/OzjO9DEBHJJJs3w8iR0LMnjB4ddDTbUw1BRCSFrrsOVqyA6dP9cmrpRDUEEZEUmT0bbr4ZTjstsLlnlVJCEBFJgaIiOPVU2HVXuO22oKOJTU1GIiIpMGYMLFkCr78ObdsGHU1sqiGIiCTZzJkwfjwMHQoxtjlJG0oIIiJJtHmz7zP47W99UkhnajISEUmiyy+Hzz6Dt9+OLk9RWOi37g2HA1vELh4lBBGRJHnnHb9O0ciRkJeHTwYDB/68hW8Sdm2si8CajJxzuzvnpjvnljjnFjvnzgsqFhGRRNu4EU4/Hbp3hxtvjH4zEvHJoLTUf41EAozwl4KsIZQAF5rZPOdcK2Cuc+4tM/s4wJhERBJi9GhYuRJmzIAWLaLfDId9zaC8hpBmkxECSwhm9g3wTfTfG5xzS4DdACUEEcloU6bA/ff7pPCHP1R4IRTyzUTqQ4jPOdcF6AO8H2wkIiJ1s3AhnH22TwQ33BDjgFAo7RJBORf0xjLOuZbAO8D1ZvZcjNeHAkMBOnbsmJufn5/wGDZu3EjLli0Tft5UyfT4IfPLkOnxQ+aXIR3i37gxi+HDc/nppyweeGAu7doV1/D9ySlDXl7eXDPrV9VxgSYE51wT4GXgDTO7tarj+/XrZ3PmzEl4HJFIhHCateXVRKbHD5lfhkyNf+1a3869aRMUFn5E9+77snGjf96sGfTpA717Q9OmQUdataB/B2VlfqObV17xC9dt11RUTckqg3OuWgkhsCYj5zdBfhhYUp1kICKJsXIlTJ3qH++9529k3r4xj2/cGHr1gr594ai2hRxUFKHT/4Zxv9+h2SPe+Po0HnefSDfdBC+84IeZ1iYZpIMg+xD6AycDC51zC6Lfu9zMXg0wJpF66ZNP4J//hOeegwXR/2377gtXXeVv9C1bwtKlcznkkFxatPCjYtavh/nzYd48//XbqYUc/cNAsimmaGI2zwwt4LCrQuy2G/HH16f5uPtEeestuPJKGDIERo0KOpraC3KU0btAmmwcJ1I/ff89XHGFH/EC/l58yy3w5z/78fEVNWq0gV69ok8KC+kQidA9HOb44/0N3G6IwJhiXGkpWDGf3B/hjAdDDBoEN7WN0Ks4+lr5+PpQKPa4+/KEUE9qDl984RPB3nvDgw+mz3aYtZEWo4xEJLHKyuCxx+Dii+G//4XzzvP/3nXXarw5zqd6lxfeNoa+cXY25zwWJutDePRROPurMAVk09QV0yg7G1feDh5v3H09qTls2gTHHQdbt/otMbfNN4CMTHhKCCL1zEcfwf/9n+8f+P3v4d57Yb/94hxc8aZVLt6n+h3G0HcKhbjuBPjHP+DNN0OMua6AJjMjzG8SZsC/Q5yzL7SIN+6+HtQcfvoJBg/2TWpTp0KPHhVezNCEp4QgUk8UFfmF1O68E379a5g0yW/I0ijeAjU73LRa33KLvwlXNps2xhj6rCw46ig46qgQH3wQYsHVcMklMGGC/zpiRIjmO94MM7zmsGWLb3abPh0ef9wnhu1UlvDSmJa/FqkH/vtfOOIIvxPXWWfB0qV+HZ24yQB+cdNqW97bXP6pfuzYGt+QDzgAXnsN3n0X9tkHLrwQunWDiROhpKTCgfGuUdlaP4WFMG6c/xqg4mI44QR44w146CE46aQYB5UnvKystFyiIh7VEEQy3Jdf+k/on30G+flw4onVfOMOn9J/yMn5+bU6zqbt3x+mTfOrfV55JYwYAXff7WsN2zaIiXWNNK85lJTA3/4GL73km+LOOCPOgWm+REU8SggiGWzRIhg0CDZs8Fsz5uXFOTBWu/wON631W7YkPL5DD/WLu02dChdd5GM96ii/Uczee8d4Q236HFKktNQ3wf3rX74mNmJEFW9I4yUq4lFCEMlQ77wDxx4Lv/qVv+lW2nEc79N1xZtWkpZids7P4D36aLjrLt9KtO++MGyY75Bu336HN9Sk5lCusJDfPvmkn1KdhJtwWZlvipsyxS9lff75Cb9EWlAfgkgGevZZ32ewyy7+fh83GUDarMHftKlf/XPZMp8M7r/fz4UYN85vM1mpyvo1ogmv66RJPvEluI9h9Wpfs3n0UbjmGt9Rvp006dtIBCUEkQwzeTL8v/8Hubl+aGnnzlW8Ic06ODt0gHvu8cNjw2E/MqpHD3jkEZ+z4gqF4LLLflkDiCY8V1aW8IQ3Y4Zfz2nGDJ/AxozZ4YDy2tdVVyUlGaWaEoJIBnn7bTjzTN9XMG0atGtXjTfVYdRQMu29t1/75513YLfdfAdtTo4fpVSjNTejCa+sUaNfJrxafnovK/Nvy8vzk83efx+GDo0xCzlNal+JooQgkiGWLPFt8T16+I7NX/0qxkHxboDxPl2ngUMOgVmz4Jln/GSvP/7R34hff72aiSGa8Faeccb2Ca+Wn97XrPExXH65r4nNnVtJk1ya1b7qSglBJAN8952/STVrBq++Cm3bxjgog5svnPNj+z/+2E+s++wzPxpp3339EhzFVW0rEArxxUknbZ/wajinobTU98306eMPve8+34ncqlXs48uvm461r9rSKCORNLd5s58J+913vnklbp9BGgzNrKvsbL9a6LBhfk7F+PFw2mn+0/q55/rvx0yGsVRzTsOWVwt4dGmI8eOh/bJC/t4hwtETw+x5Wijm8b+48Wfg8NJ4VEMQSWNlZXDyyTB7tv+0uv/+lRxcj5ovsrPhlFPgww9909Hee8Oll/q+hj//2c8Q/uabKk5SjdnQpUXFjD8mwvDhcEiTQv6dPZALvr+KPf+vQg2rnvUTVEY1BJE0dsklfg+DW2+FP/2pioMzdHZsZZzzM5uPPNLv4/DAA/Dyy/D88/713Fw45hjfnLZ+fWPKynZYriP66d0MvvzCj2z6/oswJ5JNI4rZatms2yfM9HFw6MwIbkyMGlZVcyDqESUEkTT14IO+yeScc3aYCFXZaqD1qPliRzk5frmIe+7xM7Rfftk/rr3WT3CDP5CV5Se6tW/vh7e2bw/ffus3vv/xx/IzhXh+lwKG/CZCn/PD3Hpy9OfVNBz7xl8PE208SggiaWjhQt+WfuSRcPvtFYY7psmaPkFyzi+ct88+fuDU2rV+1dF33llGmzZ7sHatHym0dq1PHO3b+wXoyt/Tuze0aRMCYiTTeDf+epxoK1JCEEkzP/3kd+Bq29ZPQmtc8X9pPeg4TrT27f0IpQ4dVhEO71G3kzWQG388SggiaWb0aFi82C+vvPPOO7zYgNqzJfUCTQjOuUnAMcBqM+sdZCwi6eCFF3w7+YUX+rWKfqEBtWdL6gVdQ3gUuBuYHHAcIoH76iu/fEOfPnD99ZUc2MCbNSR5Ap2HYGYzgO+DjEEkHZSW+nH3RUXw1FN+ZVCRVHNWo1WkkhCAc12Al+M1GTnnhgJDATp27Jibn5+f8Bg2btxIy5YtE37eVMn0+CHzy1DX+KdM+S0PPtiN0aM/4eijvwWg9eLFtF2wgB9ycljfq1eiQo2rof8O0kGyypCXlzfXzPpVeaCZBfoAugCLqnNsbm6uJcP06dOTct5UyfT4zTK/DHWJ//33zRo3NjvhBLOysug3Z840a97cLCvLf505MyFxVqYh/w7SRbLKAMyxatxjtXSFSIB++gn+93/hN7/x6+1vm2/QgJZLkPQRdKeySIP2j3/4lT0LCuDXv67wgoaXSgCCHnb6FBAG2jvnVgFXm9nDQcYkkioLFvilKc44AwYM2OFFDS+VAASaEMxsSJDXFwlKSYnftL19e7jlljgHaXippJiajEQCcMcdfieuZ56BnXYKOhoRT53KIim2fLnf1GzwYDj+eGq9769IoqmGIJJCZn7Xr8aN/TLObpZWL5X0oRqCSApNngzTpsFNN0GnTmh4qaQVJQSRFPnuO7jgAujf39cSgHq17aVkPjUZiaTI+efDpk1+J7Rt2zxqeKmkESUEkRR4803Iz/cT0fbaa4cXNbxU0oSajESSbMsWvx3mHnvAJZcEHY1IfEoIIkl2223w6afw+P8V0vRWDS+V9KUmI5Ek+vJLGDsWLjmkkIOu0PBSSW+qIYgk0d//DmVlcMmBEQ0vlbSnhCCSJNOmwbPPwuWXw6//HNbwUkl7ajISSYLiYhg5Erp3h4suApppeKmkPyUEkSS4/XZYuhReeQWaNYt+U8NLJc2pyUgkwVatgmuv9YvX/fGPQUcjUn1KCCIJduGFvu/49tuDjkSkZqrVZOSc2xnoD/wG+AlYhN+0uSyJsYlknDl3FdLtmQj3nBWma1c1D0lmqTQhOOfygEuBnYD5wGqgGfAnoLtz7llggpmtT3agIumu5N+F9DpvIDkUk/VkNpyhuQaSWaqqIfwRONvMvtjxBedcY+AY4HDgX7W5uHNuEHAHkAU8ZGY31uY8Iungg5sjHGDFNKbCXAMlBMkglSYEM7uoktdKgOdre2HnXBZwDz6hrAJmO+deNLOPa3tOkaD88EMT7o2EebFRNlmuGKe5BpKBqtWp7Jx73DnXpsLzLs65gjpe+wBgmZktN7NiIB84to7nFAnEww93ZXpRiG+fKMCNHaulKSQjVXcewrvA+865vwO7ARcBF9bx2rsBX1Z4vgo4sI7nFEm5efPglVd25bzzoMuQEKBEIJnJmVn1DnTuD8B0YC3Qx8y+rdOFnTsBONLMzoo+Pxk4wMxG7XDcUGAoQMeOHXPz8/PrctmYNm7cSMuWLRN+3lTJ9Pghc8tgBuee24cvv2zGE0/MpmXLkqBDqrVM/R2Uy/T4IXllyMvLm2tm/ao80MyqfAAnA58CQ4BxwDxgv+q8t5JzhoA3Kjy/DLissvfk5uZaMkyfPj0p502VTI/fLHPL8OSTZmA2evSSoEOps0z9HZTL9PjNklcG/DSBKu/L1W0yOg74g5mtBp5yzk0FHgX61CBJ7Wg28DvnXFfgK+CvwN/qcD6R1CksZMsbEfLvCdOvX4ijjvoW6Bl0VCJ1Uq2EYGZ/2uH5B865OrX3m1mJc24k8AZ+2OkkM1tcl3OKpERhIQwcSOOiYvItm/9cX8A6zfmXeqDSP2Pn3JXOuZ1ivWZmxc65Ac65Y2p7cTN71cx6mFl3M7u+tucRSalIBNtSTJaV0tQVs8+6SNARiSREVTWEhcBLzrkifL/BGvxM5d8BOcA04IakRiiSbsJhismmEcVkNY3ON9iyJeioROqsqoru8WbWH9+ssxjftLMeeAI/IugCM1uT5BhF0spLa0OEywqYddRYGr2t+QZSf1RVQ8h1znUGTgLydnitOX6hO5EG46ef4LzzoPneIQ56IQRNgo5IJHGqSggTgdeBbsCcCt93gEW/L9Jg3HQTrFgBb78NTZQMpJ6ptMnIzO40s73wI4C6VXh0NTMlA2lQli+HG2+EE0+EvB3ryyL1QHWHnY5IdiAiaamwcNs+yOePC9G4MYwfH3RQIsmhPZVF4onON6C4mNKsbNYUFzDmphCdOgUdmEhyaDqNSDyRiN/XoLQUKy7mhA4Rzj8/6KBEkkcJQSSecBiysyl1WRSTzcFXhsnODjookeRRk5FIPKEQXz9ewH1/jVB2SJjrz9V8A6nflBBE4jCD4Y+FKMgO8cmjQUcjknxKCCJxPPssvPQS3HIL7L570NGIJJ/6EETAjygaN85/Bb7/HkaOhL59UUeyNBiqIYhUGF5KdjYUFHDRQyHWrYPXX4fG+l8iDYRqCCIVhpdSXMyyhyJMmgSjR0OfumwBJZJhlBBEosNLycrCsrO57I0w3bvD1VcHHZhIaqkyLBIKQUEBRCLcuzjMs0+GePttaN486MBEUksJQQQgFGJe0xDnXQVnnqnF66RhUpORCFBSAmedBe3b+2GmIg1RIAnBOXeCc26xc67MOdcviBikgdpheGm5226D+fPh7rvh178OKDaRgAXVZLQI+Atwf0DXl4YoxvBSQiEWLYIxY2DwYDjuuKCDFAlOIDUEM1tiZkuDuLY0YDsMLyUS4aefYMgQaN0aHngAnAs6SJHgqFNZGo7y4aXlNYRwmIsvhkWL4LXXoGPHoAMUCZYzs+Sc2LlpwC4xXrrCzF6IHhMBRpvZnBjHlZ9nKDAUoGPHjrn5+fkJj3Xjxo20bNky4edNlUyPH1JXhtaLF9N2wQJ+yMnh9R8P4Yor9uH447/knHP+U6fz6ncQvEyPH5JXhry8vLlmVnV/rZkF9gAiQL/qHp+bm2vJMH369KScN1UyPX6z1Jfhq6/M2rUz228/s6Kiup9Pv4PgZXr8ZskrAzDHqnGP1bBTaXDKyuDUU2HzZnjqKWjaNOiIRNJDUMNO/+ycWwWEgFecc28EEYfUU3GGlpabMAGmTYM77oC99kpxbCJpLJBOZTObCkwN4tpSz8UZWlpuzhy4/HI/vPSsswKMUyQNaZRRGjODzz+Hjz6ChQv9aJiyMujQwc+obd/e/3vVqrZ0765NXICYQ0vLE8KPP8Lf/ga77KIhpiKxKCEEpbDQ36zC4e0+wS5ZAhMn+k+yCxfChg0/v6VrV/+hd80av4HLz3IYPRr22QeOOcY/DjwQsrJSVJZ0EmNoKcDWrXD88bBiha807LRToFGKpCUlhGSLdeOP0azxXlmIm2+GF1+EQ7MLOfU3ETYcEabVESH22Qd694ZWrX4+bUmJTwpr1sBbby2gpCSHV16Bm2/2zeft2sFRR8Gf/gTHHtuANnmpsHJp+c/cDEaM8P0GkybBIYcEHaRIemoot4lgxGvPrtCsUbalmAf+GmHEFyHatYMHzyjkzCkDcV8Ww3fZcOH2beDlGjeGnXf2jzVrfiAc9hu6/PADvPEGvPyyn2z1xBPQubPfBvLMM7dPKhkvTi2LUGi75zfdBA8/DFdcAaefnvIoRTKGhp0mU6z2bIBwmNLG2ZSQRVFZNm8Wh7n7bvjiCzhrjwhua4z3QJWjZwDatoUTT4THH4fvvoMXXoDf/hYuuMB/vewy+PrrZBY6RcqT7VVX+a9xfibPPOPLPGQIjB2b4hhFMowSQjJV2ImrvD27rAzGRUIcUlzA7TuN5b1/FPDMlyHOOQd+9avY7wGqfQOsKCvLL9g2YwbMmgWHHeablLp08Z+Uly9PXtGTLl6yrWDmTDjlFPjDH3xTkTqRRSqnhJAosT69l7dnjx0LBQWs2SPEH//ohz3+9sQQw1ZexuFjQtu37+/wnm1NH9W4AVbmwAPhn/+ETz+FYcPg6aehZ09fc1i3rq6FD0C8xBn1n//4vpPdd4epU6FZs0CiFMko6kNIhMrGvkfbs999F/7aB9au9aOIhg6t5BPrDm3gQNzRM+DX56Gw8Jdt6TF07w533QVRxWwvAAAOLklEQVSXXgrXXAN33gmPPOKbVc49N4O2jYzReVxu7Vo4+mg/bPfVV/3wXBGpmmoIiVDJp/eyMrjxRn/Pat7c37eHDatF80W8mkNhIftdeGGNmpIAdtsNHnzQz3E4+GCfIHr0gMce88VIK/H6TkIhn8kqJIOVK30T0cqV8Pzz8LvfpTRSkYymhJAIcZovSkv9mjmXXeZnxs6dC3361OE6MW6ARCI02rq11k1JvXrBSy/B9Omw665w2mnQr59/nhZq0HeyYIH/0Xz3Hbz1lk8MIlJ9SgiJEOPTe1mZbxZ64gn/7fx8vwlLwoXDlDVpErstvRqjkiqchvff94u9/fe/MGCAn8Pw2WdJiLkmqtl3UlDg5xc0bgzvvutrPSJSM+pDSJQK7f5mMHKkH9kyZgxceWVyr/vhhAn0Xb++yslvVfUvOAd//avvjL39drjhBth7b1+WMWMC2mu4kr6Tck895Wtie+7p51506pTyKEXqBdUQEswM/v53uO8+uPhi33GbbOt79YrZlBT3k3UVNYfmzf3pli3zw1PvvBP22MNvRP/TT0ksSDVGau2Y1CZM8OsThULw738rGYjUhRJCTVVyMzXzQ0pvv92P2LnxxgDHvidgPkPHjn4RuPnzITfXJ7ru3eHuu2HLlsSG23rx4vhxxeg7Wb3a1wpGj/ZrFL3xhp+UJyK1p4RQE1XcTK+91ieBYcN8Ugh0IlQC5zPsuy+8+aY/9He/g1GjfI3h/vv9KRKh7YIF1YqrtNQP291zT5gyxTfH5edrnoFIIigh1EQlN9Px433z0Gmnwb33psms2FijkqqY0FVZDejQQ32Rp03zE76GD/dDVSdOhPXraxBXjGv8kJNTeVz4FWAPOsgvVNenjx8yO3ZsA13VVSQJ1KlcE3E6ON94w/cXnHACPPQQNErnNFvJhK5KO6KjC8m5cJiBA0MMGODLPWaMv0H//e++/Gee6Uf4uFlxFp6Lc431vXrFjev7731NYOJE34w1ZYrv/E6LpCtSjygh1ESMm+nKlb5Ts3dvP+M3Iz6txpoJDfE3l4lxE3ehEIMGwZGtC1n1RIQpX4e5fmqIyZPh+N0KmbJ6II3LinE7JpZKNrCpGNfq1X4p8KlTfY2kpMT3y/zjH9CmTQp+RiINkBJCTVW4aRUV+QlnpaXw3HPQokXAsdVVvCGelSQKd9hAdi8u5pLsbM57pYB/rgpRdLVfsdVRSslPxUw5I8LyE0P06QMH7RVm5+xsXPQaW/uH2fA9rF7dlIULfe547jl47z0/y7trVz/s9bTT/AZAIpI8gSQE59wtwP8AxcB/gNPN7IcgYqmLkSNh3jy/xPQeewQdTQLEa06qZqJoNivCyZeFYI8wZQOyKdtSTGmjbF4vCvP0WH+DhxCHtSjgD40jTCsO8+6h5TWVn2ss++7r++3//Gf/bzUNiaRGUDWEt4DLzKzEOXcTcBlwSUCx1MpDD/286crgwUFHk0CxmpNqmihCIRq97Y9vGg4zJRTiwU2+E3j+fFi8OMT3jUMc0hKOauFrVqtWfUJubk/2398PbRWR1AskIZjZmxWezgKODyKO2po9G845B444wrdpNwg1SRQxjm/RIn7XBUAk8i3hcM+Ehy0i1efMLNgAnHsJeNrMnojz+lBgKEDHjh1z8/PzEx7Dxo0badmyZbWO/fHHJgwblgvA/ffPoU2bkoTHU1M1iT9dZXoZMj1+yPwyZHr8kLwy5OXlzTWzflUeaGZJeQDTgEUxHsdWOOYKYCrRxFTVIzc315Jh+vTp1TqutNTsiCPMmjY1mz07KaHUSnXjT2eZXoZMj98s88uQ6fGbJa8MwByrxj02aU1GZnZYZa87504FjgEGRgNOLzE2cL/3Xj9jd+JEv0S0iEh9EtQoo0H4TuRDzWxzEDFUKsa4+2UdQlxyCQwa5Je1FhGpb4KaU3s30Ap4yzm3wDk3MaA4YtthOGXZ2xFOOw2aNPG7jGkYpIjUR0GNMkrvUfs7DKd8+rsw773nt5fU8soiUl9ppnIsFYZTruwS5vTTQwweDCefHHRgIiLJo4QQTyhEyf4hTuzvx9Dff7+aikSkflNCqMT48fDBB36Lxl12CToaEZHkSueFmgO1aBFcfbXfjevEE4OORkQk+ZQQYti61W/P2KZNGm12IyKSZGoyiuGOO/wqps8+Cx06BB2NiEhqqIawgy++8Fth/s//wF/+EnQ0IiKpo4Swg/POAzO46y41FYlIw6Imowpeegmefx5uvBE6dw46GhGR1FINIWrTJhg1Cnr18hvGi4g0NKohRI0dC59/DjNm+DWLREQaGtUQgBUrfsWECXD66XDwwUFHIyISjAafEMzg9tt70Lo13Hxz0NGIiASnYSeEwkLmnTCOX320hJtvhvbtgw5IRCQ4DbcPobAQGzCQ/YqKedtl07RnARBnB3gRkQag4dYQIhHKthTTmFKaumIazYgEHZGISKAabEJYumuYLZZNqcvCmjT2m+KIiDRgDbLJyAzOfChE67YF/GtUhCUdW9M3pOYiEWnYAkkIzrmxwLFAGbAaOM3Mvk7V9Z98Et57Dx56KETzM0Osj0RSdWkRkbQVVJPRLWa2r5nlAC8DY1J14Q0b4OKLYf/9/bwDERHxAqkhmNn6Ck9bAJaqa193HXzzDUydCo0abA+KiMgvBdaH4Jy7HjgF+BHIS8U1ly6F227zNYMDD0zFFUVEMoczS86Hc+fcNCDWTsRXmNkLFY67DGhmZlfHOc9QYChAx44dc/Pz82sVjxlceuk+LF7chsmT32ennbZue23jxo20bNmyVudNB5keP2R+GTI9fsj8MmR6/JC8MuTl5c01s35VHmhmgT6AzsCi6hybm5trtfXCC2Zgduutv3xt+vTptT5vOsj0+M0yvwyZHr9Z5pch0+M3S14ZgDlWjXtsIK3ozrnfVXg6GPgkmdcrKoILLoC994aRI5N5JRGRzBVUH8KNzrk98cNOPweGJ/NiEybA8uXw1lta2lpEJJ6gRhkdl8rr7bEHjBgBhx2WyquKiGSWBjFT+cQT/UNEROLTSHwREQGUEEREJEoJQUREACUEERGJUkIQERFACUFERKKUEEREBFBCEBGRqIaREAoLYdw4/1VERGKq/zOVCwth4EAoLobsbCgoAO2fLCLyC/W/hhCJ+GRQWuq/av9kEZGY6n9CCId9zSAry38Nh4OOSEQkLdX/JqNQyDcTRSI+Gai5SEQkpvqfEMAnASUCEZFK1f8mIxERqRYlBBERAZQQREQkSglBREQAJQQREYlSQhAREQCcmQUdQ7U559YAnyfh1O2BtUk4b6pkevyQ+WXI9Pgh88uQ6fFD8srQ2cw6VHVQRiWEZHHOzTGzfkHHUVuZHj9kfhkyPX7I/DJkevwQfBnUZCQiIoASgoiIRCkheA8EHUAdZXr8kPllyPT4IfPLkOnxQ8BlUB+CiIgAqiGIiEiUEkIFzrlRzrmlzrnFzrmbg46nNpxzo51z5pxrH3QsNeWcu8U594lz7iPn3FTnXNugY6oO59yg6N/NMufcpUHHUxPOud2dc9Odc0uif/fnBR1TbTnnspxz851zLwcdS00559o6556N/v0vcc4FsjyzEkKUcy4POBbY18x6AeMDDqnGnHO7A4cDXwQdSy29BfQ2s32BT4HLAo6nSs65LOAe4Chgb2CIc27vYKOqkRLgQjPbCzgIOCfD4q/oPGBJ0EHU0h3A62bWE9iPgMqhhPCzEcCNZrYFwMxWBxxPbdwGXAxkZMeQmb1pZiXRp7OATkHGU00HAMvMbLmZFQP5+A8WGcHMvjGzedF/b8DfiHYLNqqac851Ao4GHgo6lppyzrUGDgEeBjCzYjP7IYhYlBB+1gM42Dn3vnPuHefc/kEHVBPOucHAV2b2YdCxJMgZwGtBB1ENuwFfVni+igy8oQI457oAfYD3g42kVm7HfxgqCzqQWugGrAEeiTZ5PeScaxFEIA1jx7Qo59w0YJcYL12B/1n8Gl9t3h94xjnXzdJoGFYV8V8OHJHaiGqusjKY2QvRY67AN2U8mcrYasnF+F7a/M1Ul3OuJfAv4HwzWx90PDXhnDsGWG1mc51z4aDjqYXGQF9glJm975y7A7gUuCqIQBoMMzss3mvOuRHAc9EE8IFzrgy/rsiaVMVXlXjxO+f2AboCHzrnwDe1zHPOHWBm36YwxCpV9jsAcM6dChwDDEynZFyJVcDuFZ53Ar4OKJZacc41wSeDJ83suaDjqYX+wGDn3B+BZkBr59wTZva/AcdVXauAVWZWXjN7Fp8QUk5NRj97HhgA4JzrAWSTIQtlmdlCM9vZzLqYWRf8H1jfdEsGVXHODQIuAQab2eag46mm2cDvnHNdnXPZwF+BFwOOqdqc/wTxMLDEzG4NOp7aMLPLzKxT9G//r8DbGZQMiP4//dI5t2f0WwOBj4OIpUHVEKowCZjknFsEFAOnZsgn1PrkbqAp8Fa0pjPLzIYHG1LlzKzEOTcSeAPIAiaZ2eKAw6qJ/sDJwELn3ILo9y43s1cDjKkhGgU8Gf1QsRw4PYggNFNZREQANRmJiEiUEoKIiABKCCIiEqWEICIigBKCiIhEKSGIiAighCAiIlFKCCJ14JzbP7p/QzPnXIvongK9g45LpDY0MU2kjpxz1+HX0GmOX5NmXMAhidSKEoJIHUWXG5gNFAG/N7PSgEMSqRU1GYnU3U5AS6AVvqYgkpFUQxCpI+fci/id0roCu5rZyIBDEqkVrXYqUgfOuVOAEjObEt1feaZzboCZvR10bCI1pRqCiIgA6kMQEZEoJQQREQGUEEREJEoJQUREACUEERGJUkIQERFACUFERKKUEEREBID/D7YIivXTXLaiAAAAAElFTkSuQmCC\n",
153 |       "text/plain": [
154 |        "<Figure size 432x288 with 1 Axes>"
155 |       ]
156 |      },
157 |      "metadata": {},
158 |      "output_type": "display_data"
159 |     }
160 |    ],
161 |    "source": [
162 |     "plt.plot(x, f(x), 'b', label='f(x)')\n",
163 |     "plt.plot(x, ry, 'r.', label='regression')\n",
164 |     "plt.legend(loc=0)\n",
165 |     "plt.grid(True)\n",
166 |     "plt.xlabel('x')\n",
167 |     "plt.ylabel('f(x)')"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": 11,
173 |    "metadata": {},
174 |    "outputs": [
175 |     {
176 |      "data": {
177 |       "text/plain": [
178 |        "Text(0,0.5,'f(x)')"
179 |       ]
180 |      },
181 |      "execution_count": 11,
182 |      "metadata": {},
183 |      "output_type": "execute_result"
184 |     },
185 |     {
186 |      "data": {
187 |       "image/png": "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\n",
188 |       "text/plain": [
189 |        "<Figure size 432x288 with 1 Axes>"
190 |       ]
191 |      },
192 |      "metadata": {},
193 |      "output_type": "display_data"
194 |     }
195 |    ],
196 |    "source": [
197 |     "reg = np.polyfit(x, f(x), 7)\n",
198 |     "ry = np.polyval(reg, x)\n",
199 |     "plt.plot(x, f(x), 'b', label='f(x)')\n",
200 |     "plt.plot(x, ry, 'r.', label='regression')\n",
201 |     "plt.legend(loc=0)\n",
202 |     "plt.grid(True)\n",
203 |     "plt.xlabel('x')\n",
204 |     "plt.ylabel('f(x)')"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "markdown",
209 |    "metadata": {},
210 |    "source": [
211 |     "### A brief check reveals that the result is not perfect:"
212 |    ]
213 |   },
214 |   {
215 |    "cell_type": "code",
216 |    "execution_count": 12,
217 |    "metadata": {},
218 |    "outputs": [
219 |     {
220 |      "data": {
221 |       "text/plain": [
222 |        "False"
223 |       ]
224 |      },
225 |      "execution_count": 12,
226 |      "metadata": {},
227 |      "output_type": "execute_result"
228 |     }
229 |    ],
230 |    "source": [
231 |     "np.allclose(f(x), ry)"
232 |    ]
233 |   },
234 |   {
235 |    "cell_type": "markdown",
236 |    "metadata": {},
237 |    "source": [
238 |     "### the mean squared error (MSE) is not too large—at least, over this narrow range of x values:"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": 13,
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "data": {
248 |       "text/plain": [
249 |        "0.0017769134759517793"
250 |       ]
251 |      },
252 |      "execution_count": 13,
253 |      "metadata": {},
254 |      "output_type": "execute_result"
255 |     }
256 |    ],
257 |    "source": [
258 |     "np.sum((f(x) - ry) ** 2) / len(x)"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "markdown",
263 |    "metadata": {},
264 |    "source": [
265 |     "### Individual basis functions"
266 |    ]
267 |   },
268 |   {
269 |    "cell_type": "code",
270 |    "execution_count": 14,
271 |    "metadata": {},
272 |    "outputs": [],
273 |    "source": [
274 |     "matrix = np.zeros((3 + 1, len(x)))\n",
275 |     "matrix[3, :] = x ** 3\n",
276 |     "matrix[2, :] = x ** 2\n",
277 |     "matrix[1, :] = x\n",
278 |     "matrix[0, :] = 1"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 15,
284 |    "metadata": {},
285 |    "outputs": [],
286 |    "source": [
287 |     "#### The sublibrary numpy.linalg provides the function lstsq to solve least-squares optimization problems"
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "code",
292 |    "execution_count": 16,
293 |    "metadata": {},
294 |    "outputs": [
295 |     {
296 |      "name": "stderr",
297 |      "output_type": "stream",
298 |      "text": [
299 |       "C:\\ProgramData\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
300 |       "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
301 |       "  if __name__ == '__main__':\n"
302 |      ]
303 |     }
304 |    ],
305 |    "source": [
306 |     "reg = np.linalg.lstsq(matrix.T, f(x))[0]"
307 |    ]
308 |   },
309 |   {
310 |    "cell_type": "code",
311 |    "execution_count": 17,
312 |    "metadata": {},
313 |    "outputs": [
314 |     {
315 |      "data": {
316 |       "text/plain": [
317 |        "array([ 1.52685368e-14,  5.62777448e-01, -1.11022302e-15, -5.43553615e-03])"
318 |       ]
319 |      },
320 |      "execution_count": 17,
321 |      "metadata": {},
322 |      "output_type": "execute_result"
323 |     }
324 |    ],
325 |    "source": [
326 |     "reg"
327 |    ]
328 |   }
329 |  ],
330 |  "metadata": {
331 |   "kernelspec": {
332 |    "display_name": "Python [conda env:py27]",
333 |    "language": "python",
334 |    "name": "conda-env-py27-py"
335 |   },
336 |   "language_info": {
337 |    "codemirror_mode": {
338 |     "name": "ipython",
339 |     "version": 2
340 |    },
341 |    "file_extension": ".py",
342 |    "mimetype": "text/x-python",
343 |    "name": "python",
344 |    "nbconvert_exporter": "python",
345 |    "pygments_lexer": "ipython2",
346 |    "version": "2.7.15"
347 |   }
348 |  },
349 |  "nbformat": 4,
350 |  "nbformat_minor": 2
351 | }
352 | 


--------------------------------------------------------------------------------
/7) Excel Spreadsheet Interaction in Py27.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Basic Spreadsheet Interaction"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 1,
  13 |    "metadata": {},
  14 |    "outputs": [],
  15 |    "source": [
  16 |     "import numpy as np\n",
  17 |     "import pandas as pd\n",
  18 |     "import xlrd, xlwt\n",
  19 |     "import xlsxwriter\n",
  20 |     "path = 'C:/Users/Taymour/Python For Finance/'"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 2,
  26 |    "metadata": {},
  27 |    "outputs": [],
  28 |    "source": [
  29 |     "wb = xlwt.Workbook()"
  30 |    ]
  31 |   },
  32 |   {
  33 |    "cell_type": "code",
  34 |    "execution_count": 3,
  35 |    "metadata": {},
  36 |    "outputs": [
  37 |     {
  38 |      "data": {
  39 |       "text/plain": [
  40 |        "<xlwt.Workbook.Workbook at 0x8024518>"
  41 |       ]
  42 |      },
  43 |      "execution_count": 3,
  44 |      "metadata": {},
  45 |      "output_type": "execute_result"
  46 |     }
  47 |    ],
  48 |    "source": [
  49 |     "wb"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "code",
  54 |    "execution_count": 4,
  55 |    "metadata": {},
  56 |    "outputs": [
  57 |     {
  58 |      "data": {
  59 |       "text/plain": [
  60 |        "<xlwt.Worksheet.Worksheet at 0x8042080>"
  61 |       ]
  62 |      },
  63 |      "execution_count": 4,
  64 |      "metadata": {},
  65 |      "output_type": "execute_result"
  66 |     }
  67 |    ],
  68 |    "source": [
  69 |     "wb.add_sheet('first_sheet', cell_overwrite_ok=True)"
  70 |    ]
  71 |   },
  72 |   {
  73 |    "cell_type": "code",
  74 |    "execution_count": 5,
  75 |    "metadata": {},
  76 |    "outputs": [
  77 |     {
  78 |      "data": {
  79 |       "text/plain": [
  80 |        "0"
  81 |       ]
  82 |      },
  83 |      "execution_count": 5,
  84 |      "metadata": {},
  85 |      "output_type": "execute_result"
  86 |     }
  87 |    ],
  88 |    "source": [
  89 |     "wb.get_active_sheet()"
  90 |    ]
  91 |   },
  92 |   {
  93 |    "cell_type": "code",
  94 |    "execution_count": 6,
  95 |    "metadata": {},
  96 |    "outputs": [
  97 |     {
  98 |      "data": {
  99 |       "text/plain": [
 100 |        "<xlwt.Worksheet.Worksheet at 0x8042080>"
 101 |       ]
 102 |      },
 103 |      "execution_count": 6,
 104 |      "metadata": {},
 105 |      "output_type": "execute_result"
 106 |     }
 107 |    ],
 108 |    "source": [
 109 |     "ws_1 = wb.get_sheet(0)\n",
 110 |     "ws_1"
 111 |    ]
 112 |   },
 113 |   {
 114 |    "cell_type": "code",
 115 |    "execution_count": 7,
 116 |    "metadata": {},
 117 |    "outputs": [],
 118 |    "source": [
 119 |     "ws_2 = wb.add_sheet('second_sheet')"
 120 |    ]
 121 |   },
 122 |   {
 123 |    "cell_type": "code",
 124 |    "execution_count": 8,
 125 |    "metadata": {},
 126 |    "outputs": [],
 127 |    "source": [
 128 |     "data = np.arange(1, 65).reshape((8, 8))"
 129 |    ]
 130 |   },
 131 |   {
 132 |    "cell_type": "code",
 133 |    "execution_count": 9,
 134 |    "metadata": {},
 135 |    "outputs": [
 136 |     {
 137 |      "data": {
 138 |       "text/plain": [
 139 |        "array([[ 1,  2,  3,  4,  5,  6,  7,  8],\n",
 140 |        "       [ 9, 10, 11, 12, 13, 14, 15, 16],\n",
 141 |        "       [17, 18, 19, 20, 21, 22, 23, 24],\n",
 142 |        "       [25, 26, 27, 28, 29, 30, 31, 32],\n",
 143 |        "       [33, 34, 35, 36, 37, 38, 39, 40],\n",
 144 |        "       [41, 42, 43, 44, 45, 46, 47, 48],\n",
 145 |        "       [49, 50, 51, 52, 53, 54, 55, 56],\n",
 146 |        "       [57, 58, 59, 60, 61, 62, 63, 64]])"
 147 |       ]
 148 |      },
 149 |      "execution_count": 9,
 150 |      "metadata": {},
 151 |      "output_type": "execute_result"
 152 |     }
 153 |    ],
 154 |    "source": [
 155 |     "data"
 156 |    ]
 157 |   },
 158 |   {
 159 |    "cell_type": "code",
 160 |    "execution_count": 10,
 161 |    "metadata": {},
 162 |    "outputs": [],
 163 |    "source": [
 164 |     "ws_1.write(0, 0, 100) # write 100 in cell \"A1\""
 165 |    ]
 166 |   },
 167 |   {
 168 |    "cell_type": "code",
 169 |    "execution_count": 11,
 170 |    "metadata": {},
 171 |    "outputs": [],
 172 |    "source": [
 173 |     "for c in range(data.shape[0]):\n",
 174 |     "    for r in range(data.shape[1]):\n",
 175 |     "        ws_1.write(r, c, data[c, r])\n",
 176 |     "        ws_2.write(r, c, data[r, c])"
 177 |    ]
 178 |   },
 179 |   {
 180 |    "cell_type": "code",
 181 |    "execution_count": 12,
 182 |    "metadata": {},
 183 |    "outputs": [],
 184 |    "source": [
 185 |     "wb.save(path + 'workbook.xls')"
 186 |    ]
 187 |   },
 188 |   {
 189 |    "cell_type": "markdown",
 190 |    "metadata": {},
 191 |    "source": [
 192 |     "## GENERATING WORKBOOKS (.XSLX)"
 193 |    ]
 194 |   },
 195 |   {
 196 |    "cell_type": "code",
 197 |    "execution_count": 13,
 198 |    "metadata": {},
 199 |    "outputs": [],
 200 |    "source": [
 201 |     "wb = xlsxwriter.Workbook(path + 'workbook.xlsx')"
 202 |    ]
 203 |   },
 204 |   {
 205 |    "cell_type": "code",
 206 |    "execution_count": 14,
 207 |    "metadata": {},
 208 |    "outputs": [],
 209 |    "source": [
 210 |     "ws_1 = wb.add_worksheet('first_sheet')\n",
 211 |     "ws_2 = wb.add_worksheet('second_sheet')"
 212 |    ]
 213 |   },
 214 |   {
 215 |    "cell_type": "code",
 216 |    "execution_count": 15,
 217 |    "metadata": {},
 218 |    "outputs": [],
 219 |    "source": [
 220 |     "for c in range(data.shape[0]):\n",
 221 |     "    for r in range(data.shape[1]):\n",
 222 |     "        ws_1.write(r, c, data[c, r])\n",
 223 |     "        ws_2.write(r, c, data[r, c])"
 224 |    ]
 225 |   },
 226 |   {
 227 |    "cell_type": "code",
 228 |    "execution_count": 16,
 229 |    "metadata": {},
 230 |    "outputs": [],
 231 |    "source": [
 232 |     "wb.close()"
 233 |    ]
 234 |   },
 235 |   {
 236 |    "cell_type": "code",
 237 |    "execution_count": 17,
 238 |    "metadata": {},
 239 |    "outputs": [],
 240 |    "source": [
 241 |     "wb = xlsxwriter.Workbook(path + 'chart.xlsx')\n",
 242 |     "ws = wb.add_worksheet()\n",
 243 |     "\n",
 244 |     "# write cumsum of random values in first column\n",
 245 |     "values = np.random.standard_normal(15).cumsum()\n",
 246 |     "ws.write_column('A1', values)\n",
 247 |     "\n",
 248 |     "# create a new chart object\n",
 249 |     "chart = wb.add_chart({'type': 'line'})\n",
 250 |     "\n",
 251 |     "# add a series to the chart\n",
 252 |     "chart.add_series({'values': '=Sheet1!$A$1:$A$15',\n",
 253 |     "                  'marker': {'type': 'diamond'},})\n",
 254 |     "# series with markers (here: diamond)\n",
 255 |     "\n",
 256 |     "# insert the chart\n",
 257 |     "ws.insert_chart('C1', chart)\n",
 258 |     "\n",
 259 |     "wb.close()"
 260 |    ]
 261 |   },
 262 |   {
 263 |    "cell_type": "markdown",
 264 |    "metadata": {},
 265 |    "source": [
 266 |     "## READING FROM WORKBOOKS"
 267 |    ]
 268 |   },
 269 |   {
 270 |    "cell_type": "code",
 271 |    "execution_count": 18,
 272 |    "metadata": {},
 273 |    "outputs": [
 274 |     {
 275 |      "data": {
 276 |       "text/plain": [
 277 |        "<xlrd.book.Book at 0x80b5d30>"
 278 |       ]
 279 |      },
 280 |      "execution_count": 18,
 281 |      "metadata": {},
 282 |      "output_type": "execute_result"
 283 |     }
 284 |    ],
 285 |    "source": [
 286 |     "book = xlrd.open_workbook(path + 'workbook.xlsx')\n",
 287 |     "book"
 288 |    ]
 289 |   },
 290 |   {
 291 |    "cell_type": "code",
 292 |    "execution_count": 19,
 293 |    "metadata": {},
 294 |    "outputs": [
 295 |     {
 296 |      "data": {
 297 |       "text/plain": [
 298 |        "[u'first_sheet', u'second_sheet']"
 299 |       ]
 300 |      },
 301 |      "execution_count": 19,
 302 |      "metadata": {},
 303 |      "output_type": "execute_result"
 304 |     }
 305 |    ],
 306 |    "source": [
 307 |     "book.sheet_names()"
 308 |    ]
 309 |   },
 310 |   {
 311 |    "cell_type": "code",
 312 |    "execution_count": 20,
 313 |    "metadata": {},
 314 |    "outputs": [
 315 |     {
 316 |      "data": {
 317 |       "text/plain": [
 318 |        "<xlrd.sheet.Sheet at 0x80bf128>"
 319 |       ]
 320 |      },
 321 |      "execution_count": 20,
 322 |      "metadata": {},
 323 |      "output_type": "execute_result"
 324 |     }
 325 |    ],
 326 |    "source": [
 327 |     "sheet_1 = book.sheet_by_name('first_sheet')\n",
 328 |     "sheet_2 = book.sheet_by_index(1)\n",
 329 |     "sheet_1"
 330 |    ]
 331 |   },
 332 |   {
 333 |    "cell_type": "code",
 334 |    "execution_count": 21,
 335 |    "metadata": {},
 336 |    "outputs": [
 337 |     {
 338 |      "data": {
 339 |       "text/plain": [
 340 |        "u'second_sheet'"
 341 |       ]
 342 |      },
 343 |      "execution_count": 21,
 344 |      "metadata": {},
 345 |      "output_type": "execute_result"
 346 |     }
 347 |    ],
 348 |    "source": [
 349 |     "sheet_2.name"
 350 |    ]
 351 |   },
 352 |   {
 353 |    "cell_type": "code",
 354 |    "execution_count": 22,
 355 |    "metadata": {},
 356 |    "outputs": [
 357 |     {
 358 |      "data": {
 359 |       "text/plain": [
 360 |        "(8, 8)"
 361 |       ]
 362 |      },
 363 |      "execution_count": 22,
 364 |      "metadata": {},
 365 |      "output_type": "execute_result"
 366 |     }
 367 |    ],
 368 |    "source": [
 369 |     "sheet_1.ncols, sheet_1.nrows"
 370 |    ]
 371 |   },
 372 |   {
 373 |    "cell_type": "code",
 374 |    "execution_count": 26,
 375 |    "metadata": {},
 376 |    "outputs": [
 377 |     {
 378 |      "data": {
 379 |       "text/plain": [
 380 |        "1.0"
 381 |       ]
 382 |      },
 383 |      "execution_count": 26,
 384 |      "metadata": {},
 385 |      "output_type": "execute_result"
 386 |     }
 387 |    ],
 388 |    "source": [
 389 |     "cl = sheet_1.cell(0, 0)\n",
 390 |     "cl.value"
 391 |    ]
 392 |   },
 393 |   {
 394 |    "cell_type": "code",
 395 |    "execution_count": 28,
 396 |    "metadata": {},
 397 |    "outputs": [
 398 |     {
 399 |      "data": {
 400 |       "text/plain": [
 401 |        "2"
 402 |       ]
 403 |      },
 404 |      "execution_count": 28,
 405 |      "metadata": {},
 406 |      "output_type": "execute_result"
 407 |     }
 408 |    ],
 409 |    "source": [
 410 |     "cl.ctype"
 411 |    ]
 412 |   },
 413 |   {
 414 |    "cell_type": "code",
 415 |    "execution_count": 29,
 416 |    "metadata": {},
 417 |    "outputs": [
 418 |     {
 419 |      "data": {
 420 |       "text/plain": [
 421 |        "[number:25.0,\n",
 422 |        " number:26.0,\n",
 423 |        " number:27.0,\n",
 424 |        " number:28.0,\n",
 425 |        " number:29.0,\n",
 426 |        " number:30.0,\n",
 427 |        " number:31.0,\n",
 428 |        " number:32.0]"
 429 |       ]
 430 |      },
 431 |      "execution_count": 29,
 432 |      "metadata": {},
 433 |      "output_type": "execute_result"
 434 |     }
 435 |    ],
 436 |    "source": [
 437 |     "sheet_2.row(3)"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 30,
 443 |    "metadata": {},
 444 |    "outputs": [
 445 |     {
 446 |      "data": {
 447 |       "text/plain": [
 448 |        "[number:4.0,\n",
 449 |        " number:12.0,\n",
 450 |        " number:20.0,\n",
 451 |        " number:28.0,\n",
 452 |        " number:36.0,\n",
 453 |        " number:44.0,\n",
 454 |        " number:52.0,\n",
 455 |        " number:60.0]"
 456 |       ]
 457 |      },
 458 |      "execution_count": 30,
 459 |      "metadata": {},
 460 |      "output_type": "execute_result"
 461 |     }
 462 |    ],
 463 |    "source": [
 464 |     "sheet_2.col(3)"
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "code",
 469 |    "execution_count": 31,
 470 |    "metadata": {},
 471 |    "outputs": [
 472 |     {
 473 |      "data": {
 474 |       "text/plain": [
 475 |        "[28.0, 29.0, 30.0, 31.0]"
 476 |       ]
 477 |      },
 478 |      "execution_count": 31,
 479 |      "metadata": {},
 480 |      "output_type": "execute_result"
 481 |     }
 482 |    ],
 483 |    "source": [
 484 |     "sheet_1.col_values(3, start_rowx=3, end_rowx=7)"
 485 |    ]
 486 |   },
 487 |   {
 488 |    "cell_type": "code",
 489 |    "execution_count": 32,
 490 |    "metadata": {},
 491 |    "outputs": [
 492 |     {
 493 |      "data": {
 494 |       "text/plain": [
 495 |        "[28.0, 36.0, 44.0, 52.0]"
 496 |       ]
 497 |      },
 498 |      "execution_count": 32,
 499 |      "metadata": {},
 500 |      "output_type": "execute_result"
 501 |     }
 502 |    ],
 503 |    "source": [
 504 |     "sheet_1.row_values(3, start_colx=3, end_colx=7)"
 505 |    ]
 506 |   },
 507 |   {
 508 |    "cell_type": "code",
 509 |    "execution_count": 33,
 510 |    "metadata": {},
 511 |    "outputs": [
 512 |     {
 513 |      "name": "stdout",
 514 |      "output_type": "stream",
 515 |      "text": [
 516 |       "1 2 3 4 5 6 7 8\n",
 517 |       "9 10 11 12 13 14 15 16\n",
 518 |       "17 18 19 20 21 22 23 24\n",
 519 |       "25 26 27 28 29 30 31 32\n",
 520 |       "33 34 35 36 37 38 39 40\n",
 521 |       "41 42 43 44 45 46 47 48\n",
 522 |       "49 50 51 52 53 54 55 56\n",
 523 |       "57 58 59 60 61 62 63 64\n"
 524 |      ]
 525 |     }
 526 |    ],
 527 |    "source": [
 528 |     "for c in range(sheet_1.ncols):\n",
 529 |     "    for r in range(sheet_1.nrows):\n",
 530 |     "        print '%i' % sheet_1.cell(r, c).value,\n",
 531 |     "    print"
 532 |    ]
 533 |   },
 534 |   {
 535 |    "cell_type": "markdown",
 536 |    "metadata": {},
 537 |    "source": [
 538 |     "## USING OPENPYXL"
 539 |    ]
 540 |   },
 541 |   {
 542 |    "cell_type": "code",
 543 |    "execution_count": 38,
 544 |    "metadata": {},
 545 |    "outputs": [],
 546 |    "source": [
 547 |     "import openpyxl as oxl"
 548 |    ]
 549 |   },
 550 |   {
 551 |    "cell_type": "code",
 552 |    "execution_count": 39,
 553 |    "metadata": {},
 554 |    "outputs": [],
 555 |    "source": [
 556 |     "wb = oxl.Workbook()"
 557 |    ]
 558 |   },
 559 |   {
 560 |    "cell_type": "code",
 561 |    "execution_count": 40,
 562 |    "metadata": {},
 563 |    "outputs": [],
 564 |    "source": [
 565 |     "ws = wb.create_sheet(index=0, title='oxl_sheet')"
 566 |    ]
 567 |   },
 568 |   {
 569 |    "cell_type": "code",
 570 |    "execution_count": 47,
 571 |    "metadata": {},
 572 |    "outputs": [],
 573 |    "source": [
 574 |     "wb = oxl.load_workbook(path + 'workbook.xlsx')"
 575 |    ]
 576 |   },
 577 |   {
 578 |    "cell_type": "code",
 579 |    "execution_count": 48,
 580 |    "metadata": {},
 581 |    "outputs": [
 582 |     {
 583 |      "name": "stderr",
 584 |      "output_type": "stream",
 585 |      "text": [
 586 |       "C:\\ProgramData\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:1: DeprecationWarning: Call to deprecated function get_active_sheet (Use the .active property).\n",
 587 |       "  if __name__ == '__main__':\n"
 588 |      ]
 589 |     }
 590 |    ],
 591 |    "source": [
 592 |     "ws = wb.get_active_sheet()"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 49,
 598 |    "metadata": {},
 599 |    "outputs": [],
 600 |    "source": [
 601 |     "cell = ws['B4']"
 602 |    ]
 603 |   },
 604 |   {
 605 |    "cell_type": "code",
 606 |    "execution_count": 50,
 607 |    "metadata": {},
 608 |    "outputs": [
 609 |     {
 610 |      "data": {
 611 |       "text/plain": [
 612 |        "'B'"
 613 |       ]
 614 |      },
 615 |      "execution_count": 50,
 616 |      "metadata": {},
 617 |      "output_type": "execute_result"
 618 |     }
 619 |    ],
 620 |    "source": [
 621 |     "cell.column"
 622 |    ]
 623 |   },
 624 |   {
 625 |    "cell_type": "code",
 626 |    "execution_count": 51,
 627 |    "metadata": {},
 628 |    "outputs": [
 629 |     {
 630 |      "data": {
 631 |       "text/plain": [
 632 |        "4"
 633 |       ]
 634 |      },
 635 |      "execution_count": 51,
 636 |      "metadata": {},
 637 |      "output_type": "execute_result"
 638 |     }
 639 |    ],
 640 |    "source": [
 641 |     "cell.row"
 642 |    ]
 643 |   },
 644 |   {
 645 |    "cell_type": "code",
 646 |    "execution_count": 52,
 647 |    "metadata": {},
 648 |    "outputs": [
 649 |     {
 650 |      "data": {
 651 |       "text/plain": [
 652 |        "12L"
 653 |       ]
 654 |      },
 655 |      "execution_count": 52,
 656 |      "metadata": {},
 657 |      "output_type": "execute_result"
 658 |     }
 659 |    ],
 660 |    "source": [
 661 |     "cell.value"
 662 |    ]
 663 |   },
 664 |   {
 665 |    "cell_type": "code",
 666 |    "execution_count": 53,
 667 |    "metadata": {},
 668 |    "outputs": [
 669 |     {
 670 |      "data": {
 671 |       "text/plain": [
 672 |        "((<Cell u'first_sheet'.B1>,),\n",
 673 |        " (<Cell u'first_sheet'.B2>,),\n",
 674 |        " (<Cell u'first_sheet'.B3>,),\n",
 675 |        " (<Cell u'first_sheet'.B4>,))"
 676 |       ]
 677 |      },
 678 |      "execution_count": 53,
 679 |      "metadata": {},
 680 |      "output_type": "execute_result"
 681 |     }
 682 |    ],
 683 |    "source": [
 684 |     "ws['B1':'B4']"
 685 |    ]
 686 |   },
 687 |   {
 688 |    "cell_type": "code",
 689 |    "execution_count": 54,
 690 |    "metadata": {},
 691 |    "outputs": [
 692 |     {
 693 |      "name": "stdout",
 694 |      "output_type": "stream",
 695 |      "text": [
 696 |       "9\n",
 697 |       "10\n",
 698 |       "11\n",
 699 |       "12\n"
 700 |      ]
 701 |     }
 702 |    ],
 703 |    "source": [
 704 |     "for cell in ws['B1':'B4']:\n",
 705 |     "    print cell[0].value"
 706 |    ]
 707 |   },
 708 |   {
 709 |    "cell_type": "markdown",
 710 |    "metadata": {},
 711 |    "source": [
 712 |     "## USING PANDAS FOR READING AND WRITING"
 713 |    ]
 714 |   },
 715 |   {
 716 |    "cell_type": "code",
 717 |    "execution_count": 59,
 718 |    "metadata": {},
 719 |    "outputs": [],
 720 |    "source": [
 721 |     "df_1 = pd.read_excel(path + 'workbook.xlsx',\n",
 722 |     "                     'first_sheet', header=None)\n",
 723 |     "df_2 = pd.read_excel(path + 'workbook.xlsx',\n",
 724 |     "                     'second_sheet', header=None)"
 725 |    ]
 726 |   },
 727 |   {
 728 |    "cell_type": "code",
 729 |    "execution_count": 60,
 730 |    "metadata": {},
 731 |    "outputs": [
 732 |     {
 733 |      "data": {
 734 |       "text/plain": [
 735 |        "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']"
 736 |       ]
 737 |      },
 738 |      "execution_count": 60,
 739 |      "metadata": {},
 740 |      "output_type": "execute_result"
 741 |     }
 742 |    ],
 743 |    "source": [
 744 |     "import string\n",
 745 |     "columns = []\n",
 746 |     "for c in range(data.shape[0]):\n",
 747 |     "    columns.append(string.uppercase[c])\n",
 748 |     "columns"
 749 |    ]
 750 |   },
 751 |   {
 752 |    "cell_type": "code",
 753 |    "execution_count": 61,
 754 |    "metadata": {},
 755 |    "outputs": [],
 756 |    "source": [
 757 |     "df_1.columns = columns\n",
 758 |     "df_2.columns = columns"
 759 |    ]
 760 |   },
 761 |   {
 762 |    "cell_type": "code",
 763 |    "execution_count": 62,
 764 |    "metadata": {},
 765 |    "outputs": [
 766 |     {
 767 |      "data": {
 768 |       "text/html": [
 769 |        "<div>\n",
 770 |        "<style scoped>\n",
 771 |        "    .dataframe tbody tr th:only-of-type {\n",
 772 |        "        vertical-align: middle;\n",
 773 |        "    }\n",
 774 |        "\n",
 775 |        "    .dataframe tbody tr th {\n",
 776 |        "        vertical-align: top;\n",
 777 |        "    }\n",
 778 |        "\n",
 779 |        "    .dataframe thead th {\n",
 780 |        "        text-align: right;\n",
 781 |        "    }\n",
 782 |        "</style>\n",
 783 |        "<table border=\"1\" class=\"dataframe\">\n",
 784 |        "  <thead>\n",
 785 |        "    <tr style=\"text-align: right;\">\n",
 786 |        "      <th></th>\n",
 787 |        "      <th>A</th>\n",
 788 |        "      <th>B</th>\n",
 789 |        "      <th>C</th>\n",
 790 |        "      <th>D</th>\n",
 791 |        "      <th>E</th>\n",
 792 |        "      <th>F</th>\n",
 793 |        "      <th>G</th>\n",
 794 |        "      <th>H</th>\n",
 795 |        "    </tr>\n",
 796 |        "  </thead>\n",
 797 |        "  <tbody>\n",
 798 |        "    <tr>\n",
 799 |        "      <th>0</th>\n",
 800 |        "      <td>1</td>\n",
 801 |        "      <td>9</td>\n",
 802 |        "      <td>17</td>\n",
 803 |        "      <td>25</td>\n",
 804 |        "      <td>33</td>\n",
 805 |        "      <td>41</td>\n",
 806 |        "      <td>49</td>\n",
 807 |        "      <td>57</td>\n",
 808 |        "    </tr>\n",
 809 |        "    <tr>\n",
 810 |        "      <th>1</th>\n",
 811 |        "      <td>2</td>\n",
 812 |        "      <td>10</td>\n",
 813 |        "      <td>18</td>\n",
 814 |        "      <td>26</td>\n",
 815 |        "      <td>34</td>\n",
 816 |        "      <td>42</td>\n",
 817 |        "      <td>50</td>\n",
 818 |        "      <td>58</td>\n",
 819 |        "    </tr>\n",
 820 |        "    <tr>\n",
 821 |        "      <th>2</th>\n",
 822 |        "      <td>3</td>\n",
 823 |        "      <td>11</td>\n",
 824 |        "      <td>19</td>\n",
 825 |        "      <td>27</td>\n",
 826 |        "      <td>35</td>\n",
 827 |        "      <td>43</td>\n",
 828 |        "      <td>51</td>\n",
 829 |        "      <td>59</td>\n",
 830 |        "    </tr>\n",
 831 |        "    <tr>\n",
 832 |        "      <th>3</th>\n",
 833 |        "      <td>4</td>\n",
 834 |        "      <td>12</td>\n",
 835 |        "      <td>20</td>\n",
 836 |        "      <td>28</td>\n",
 837 |        "      <td>36</td>\n",
 838 |        "      <td>44</td>\n",
 839 |        "      <td>52</td>\n",
 840 |        "      <td>60</td>\n",
 841 |        "    </tr>\n",
 842 |        "    <tr>\n",
 843 |        "      <th>4</th>\n",
 844 |        "      <td>5</td>\n",
 845 |        "      <td>13</td>\n",
 846 |        "      <td>21</td>\n",
 847 |        "      <td>29</td>\n",
 848 |        "      <td>37</td>\n",
 849 |        "      <td>45</td>\n",
 850 |        "      <td>53</td>\n",
 851 |        "      <td>61</td>\n",
 852 |        "    </tr>\n",
 853 |        "    <tr>\n",
 854 |        "      <th>5</th>\n",
 855 |        "      <td>6</td>\n",
 856 |        "      <td>14</td>\n",
 857 |        "      <td>22</td>\n",
 858 |        "      <td>30</td>\n",
 859 |        "      <td>38</td>\n",
 860 |        "      <td>46</td>\n",
 861 |        "      <td>54</td>\n",
 862 |        "      <td>62</td>\n",
 863 |        "    </tr>\n",
 864 |        "    <tr>\n",
 865 |        "      <th>6</th>\n",
 866 |        "      <td>7</td>\n",
 867 |        "      <td>15</td>\n",
 868 |        "      <td>23</td>\n",
 869 |        "      <td>31</td>\n",
 870 |        "      <td>39</td>\n",
 871 |        "      <td>47</td>\n",
 872 |        "      <td>55</td>\n",
 873 |        "      <td>63</td>\n",
 874 |        "    </tr>\n",
 875 |        "    <tr>\n",
 876 |        "      <th>7</th>\n",
 877 |        "      <td>8</td>\n",
 878 |        "      <td>16</td>\n",
 879 |        "      <td>24</td>\n",
 880 |        "      <td>32</td>\n",
 881 |        "      <td>40</td>\n",
 882 |        "      <td>48</td>\n",
 883 |        "      <td>56</td>\n",
 884 |        "      <td>64</td>\n",
 885 |        "    </tr>\n",
 886 |        "  </tbody>\n",
 887 |        "</table>\n",
 888 |        "</div>"
 889 |       ],
 890 |       "text/plain": [
 891 |        "   A   B   C   D   E   F   G   H\n",
 892 |        "0  1   9  17  25  33  41  49  57\n",
 893 |        "1  2  10  18  26  34  42  50  58\n",
 894 |        "2  3  11  19  27  35  43  51  59\n",
 895 |        "3  4  12  20  28  36  44  52  60\n",
 896 |        "4  5  13  21  29  37  45  53  61\n",
 897 |        "5  6  14  22  30  38  46  54  62\n",
 898 |        "6  7  15  23  31  39  47  55  63\n",
 899 |        "7  8  16  24  32  40  48  56  64"
 900 |       ]
 901 |      },
 902 |      "execution_count": 62,
 903 |      "metadata": {},
 904 |      "output_type": "execute_result"
 905 |     }
 906 |    ],
 907 |    "source": [
 908 |     "df_1"
 909 |    ]
 910 |   },
 911 |   {
 912 |    "cell_type": "code",
 913 |    "execution_count": 63,
 914 |    "metadata": {},
 915 |    "outputs": [
 916 |     {
 917 |      "data": {
 918 |       "text/html": [
 919 |        "<div>\n",
 920 |        "<style scoped>\n",
 921 |        "    .dataframe tbody tr th:only-of-type {\n",
 922 |        "        vertical-align: middle;\n",
 923 |        "    }\n",
 924 |        "\n",
 925 |        "    .dataframe tbody tr th {\n",
 926 |        "        vertical-align: top;\n",
 927 |        "    }\n",
 928 |        "\n",
 929 |        "    .dataframe thead th {\n",
 930 |        "        text-align: right;\n",
 931 |        "    }\n",
 932 |        "</style>\n",
 933 |        "<table border=\"1\" class=\"dataframe\">\n",
 934 |        "  <thead>\n",
 935 |        "    <tr style=\"text-align: right;\">\n",
 936 |        "      <th></th>\n",
 937 |        "      <th>A</th>\n",
 938 |        "      <th>B</th>\n",
 939 |        "      <th>C</th>\n",
 940 |        "      <th>D</th>\n",
 941 |        "      <th>E</th>\n",
 942 |        "      <th>F</th>\n",
 943 |        "      <th>G</th>\n",
 944 |        "      <th>H</th>\n",
 945 |        "    </tr>\n",
 946 |        "  </thead>\n",
 947 |        "  <tbody>\n",
 948 |        "    <tr>\n",
 949 |        "      <th>0</th>\n",
 950 |        "      <td>1</td>\n",
 951 |        "      <td>2</td>\n",
 952 |        "      <td>3</td>\n",
 953 |        "      <td>4</td>\n",
 954 |        "      <td>5</td>\n",
 955 |        "      <td>6</td>\n",
 956 |        "      <td>7</td>\n",
 957 |        "      <td>8</td>\n",
 958 |        "    </tr>\n",
 959 |        "    <tr>\n",
 960 |        "      <th>1</th>\n",
 961 |        "      <td>9</td>\n",
 962 |        "      <td>10</td>\n",
 963 |        "      <td>11</td>\n",
 964 |        "      <td>12</td>\n",
 965 |        "      <td>13</td>\n",
 966 |        "      <td>14</td>\n",
 967 |        "      <td>15</td>\n",
 968 |        "      <td>16</td>\n",
 969 |        "    </tr>\n",
 970 |        "    <tr>\n",
 971 |        "      <th>2</th>\n",
 972 |        "      <td>17</td>\n",
 973 |        "      <td>18</td>\n",
 974 |        "      <td>19</td>\n",
 975 |        "      <td>20</td>\n",
 976 |        "      <td>21</td>\n",
 977 |        "      <td>22</td>\n",
 978 |        "      <td>23</td>\n",
 979 |        "      <td>24</td>\n",
 980 |        "    </tr>\n",
 981 |        "    <tr>\n",
 982 |        "      <th>3</th>\n",
 983 |        "      <td>25</td>\n",
 984 |        "      <td>26</td>\n",
 985 |        "      <td>27</td>\n",
 986 |        "      <td>28</td>\n",
 987 |        "      <td>29</td>\n",
 988 |        "      <td>30</td>\n",
 989 |        "      <td>31</td>\n",
 990 |        "      <td>32</td>\n",
 991 |        "    </tr>\n",
 992 |        "    <tr>\n",
 993 |        "      <th>4</th>\n",
 994 |        "      <td>33</td>\n",
 995 |        "      <td>34</td>\n",
 996 |        "      <td>35</td>\n",
 997 |        "      <td>36</td>\n",
 998 |        "      <td>37</td>\n",
 999 |        "      <td>38</td>\n",
1000 |        "      <td>39</td>\n",
1001 |        "      <td>40</td>\n",
1002 |        "    </tr>\n",
1003 |        "    <tr>\n",
1004 |        "      <th>5</th>\n",
1005 |        "      <td>41</td>\n",
1006 |        "      <td>42</td>\n",
1007 |        "      <td>43</td>\n",
1008 |        "      <td>44</td>\n",
1009 |        "      <td>45</td>\n",
1010 |        "      <td>46</td>\n",
1011 |        "      <td>47</td>\n",
1012 |        "      <td>48</td>\n",
1013 |        "    </tr>\n",
1014 |        "    <tr>\n",
1015 |        "      <th>6</th>\n",
1016 |        "      <td>49</td>\n",
1017 |        "      <td>50</td>\n",
1018 |        "      <td>51</td>\n",
1019 |        "      <td>52</td>\n",
1020 |        "      <td>53</td>\n",
1021 |        "      <td>54</td>\n",
1022 |        "      <td>55</td>\n",
1023 |        "      <td>56</td>\n",
1024 |        "    </tr>\n",
1025 |        "    <tr>\n",
1026 |        "      <th>7</th>\n",
1027 |        "      <td>57</td>\n",
1028 |        "      <td>58</td>\n",
1029 |        "      <td>59</td>\n",
1030 |        "      <td>60</td>\n",
1031 |        "      <td>61</td>\n",
1032 |        "      <td>62</td>\n",
1033 |        "      <td>63</td>\n",
1034 |        "      <td>64</td>\n",
1035 |        "    </tr>\n",
1036 |        "  </tbody>\n",
1037 |        "</table>\n",
1038 |        "</div>"
1039 |       ],
1040 |       "text/plain": [
1041 |        "    A   B   C   D   E   F   G   H\n",
1042 |        "0   1   2   3   4   5   6   7   8\n",
1043 |        "1   9  10  11  12  13  14  15  16\n",
1044 |        "2  17  18  19  20  21  22  23  24\n",
1045 |        "3  25  26  27  28  29  30  31  32\n",
1046 |        "4  33  34  35  36  37  38  39  40\n",
1047 |        "5  41  42  43  44  45  46  47  48\n",
1048 |        "6  49  50  51  52  53  54  55  56\n",
1049 |        "7  57  58  59  60  61  62  63  64"
1050 |       ]
1051 |      },
1052 |      "execution_count": 63,
1053 |      "metadata": {},
1054 |      "output_type": "execute_result"
1055 |     }
1056 |    ],
1057 |    "source": [
1058 |     "df_2"
1059 |    ]
1060 |   },
1061 |   {
1062 |    "cell_type": "markdown",
1063 |    "metadata": {},
1064 |    "source": [
1065 |     "# Scripting Excel with Python Using DataNitro Add-ins"
1066 |    ]
1067 |   },
1068 |   {
1069 |    "cell_type": "code",
1070 |    "execution_count": null,
1071 |    "metadata": {},
1072 |    "outputs": [],
1073 |    "source": [
1074 |     "#\n",
1075 |     "# Plotting with DataNitro in Excel\n",
1076 |     "# dn_plotting.py\n",
1077 |     "#\n",
1078 |     "import pandas.io.data as web\n",
1079 |     "import nitroplot as nplt\n",
1080 |     "  # wrapper for matplotlib.pyplot (plt)\n",
1081 |     "\n",
1082 |     "# make a new workbook\n",
1083 |     "\n",
1084 |     "wb = new_wkbk()\n",
1085 |     "active_wkbk(wb)\n",
1086 |     "rename_sheet(\"Sheet1\", \"Apple_Stock\")\n",
1087 |     "\n",
1088 |     "# read Apple Inc. stock data\n",
1089 |     "\n",
1090 |     "aapl = web.DataReader('aapl', data_source='yahoo')[['Open', 'Close']]\n",
1091 |     "\n",
1092 |     "# write the data to the new workbook\n",
1093 |     "\n",
1094 |     "Cell(\"A1\").df = aapl\n",
1095 |     "\n",
1096 |     "# generate matplotlib plot\n",
1097 |     "\n",
1098 |     "nplt.figure(figsize=(8, 4))\n",
1099 |     "nplt.plot(Cell(\"A2\").vertical, Cell(\"C2\").vertical, label='AAPL')\n",
1100 |     "nplt.legend(loc=0)\n",
1101 |     "nplt.grid(True)\n",
1102 |     "nplt.xticks(rotation=35)\n",
1103 |     "\n",
1104 |     "# expose plot to Excel spreadsheet\n",
1105 |     "\n",
1106 |     "nplt.graph()\n",
1107 |     "  # as plt.show()\n",
1108 |     "\n",
1109 |     "# save the new workbook with data and plot\n",
1110 |     "\n",
1111 |     "save('dn_plot.xlsx')"
1112 |    ]
1113 |   }
1114 |  ],
1115 |  "metadata": {
1116 |   "kernelspec": {
1117 |    "display_name": "Python [conda env:py27]",
1118 |    "language": "python",
1119 |    "name": "conda-env-py27-py"
1120 |   },
1121 |   "language_info": {
1122 |    "codemirror_mode": {
1123 |     "name": "ipython",
1124 |     "version": 2
1125 |    },
1126 |    "file_extension": ".py",
1127 |    "mimetype": "text/x-python",
1128 |    "name": "python",
1129 |    "nbconvert_exporter": "python",
1130 |    "pygments_lexer": "ipython2",
1131 |    "version": "2.7.15"
1132 |   }
1133 |  },
1134 |  "nbformat": 4,
1135 |  "nbformat_minor": 2
1136 | }
1137 | 


--------------------------------------------------------------------------------
/9) Web Integeration.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 4,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import ftplib\n",
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd\n",
 12 |     "import httplib\n",
 13 |     "import urllib\n",
 14 |     "import bokeh.plotting as bp\n",
 15 |     "import time\n",
 16 |     "import datetime as dt\n",
 17 |     "import requests\n",
 18 |     "from bokeh.objects import Glyph\n",
 19 |     "import os\n",
 20 |     "from sqlite3 import dbapi2 as sqlite3\n",
 21 |     "from flask import Flask, request, session, g, redirect, url_for, abort, \\\n",
 22 |     "render_template, flash\n",
 23 |     "from jinja2 import Template\n",
 24 |     "from IPython.display import HTML\n",
 25 |     "from scipy.stats import ncx2\n",
 26 |     "from werkzeug.wrappers import Request, Response"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 1,
 32 |    "metadata": {},
 33 |    "outputs": [],
 34 |    "source": [
 35 |     "import ftplib\n",
 36 |     "import numpy as np"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": 11,
 42 |    "metadata": {},
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "import urllib"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 14,
 51 |    "metadata": {},
 52 |    "outputs": [],
 53 |    "source": [
 54 |     "url = 'https://ichart.finance.yahoo.com/table.csv?g=d&ignore=.csv'\n",
 55 |     "url += '&s=YHOO&a=01&b=1&c=2014&d=02&e=6&f=2014'"
 56 |    ]
 57 |   },
 58 |   {
 59 |    "cell_type": "code",
 60 |    "execution_count": 20,
 61 |    "metadata": {},
 62 |    "outputs": [],
 63 |    "source": [
 64 |     "url = 'https://github.com/datasets/s-and-p-500-companies/blob/master/data/constituents.csv'"
 65 |    ]
 66 |   },
 67 |   {
 68 |    "cell_type": "code",
 69 |    "execution_count": 21,
 70 |    "metadata": {},
 71 |    "outputs": [],
 72 |    "source": [
 73 |     "connect = urllib.urlopen(url)"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "code",
 78 |    "execution_count": 22,
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "data = connect.read()"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "markdown",
 87 |    "metadata": {},
 88 |    "source": [
 89 |     "# Web Plotting\n",
 90 |     "## STATIC PLOTS"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "code",
 95 |    "execution_count": 1,
 96 |    "metadata": {},
 97 |    "outputs": [],
 98 |    "source": [
 99 |     "import numpy as np\n",
100 |     "import pandas as pd\n",
101 |     "%matplotlib inline"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": 2,
107 |    "metadata": {},
108 |    "outputs": [],
109 |    "source": [
110 |     "url = 'https://raw.githubusercontent.com/alperenguman/csv-stock-price-processor/master/examples/table.csv'\n",
111 |     "data = pd.read_csv(url, parse_dates=['Date'])"
112 |    ]
113 |   },
114 |   {
115 |    "cell_type": "code",
116 |    "execution_count": 3,
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "data": {
121 |       "text/html": [
122 |        "<div>\n",
123 |        "<style scoped>\n",
124 |        "    .dataframe tbody tr th:only-of-type {\n",
125 |        "        vertical-align: middle;\n",
126 |        "    }\n",
127 |        "\n",
128 |        "    .dataframe tbody tr th {\n",
129 |        "        vertical-align: top;\n",
130 |        "    }\n",
131 |        "\n",
132 |        "    .dataframe thead th {\n",
133 |        "        text-align: right;\n",
134 |        "    }\n",
135 |        "</style>\n",
136 |        "<table border=\"1\" class=\"dataframe\">\n",
137 |        "  <thead>\n",
138 |        "    <tr style=\"text-align: right;\">\n",
139 |        "      <th></th>\n",
140 |        "      <th>Date</th>\n",
141 |        "      <th>Open</th>\n",
142 |        "      <th>High</th>\n",
143 |        "      <th>Low</th>\n",
144 |        "      <th>Close</th>\n",
145 |        "      <th>Volume</th>\n",
146 |        "      <th>Adj Close</th>\n",
147 |        "    </tr>\n",
148 |        "  </thead>\n",
149 |        "  <tbody>\n",
150 |        "    <tr>\n",
151 |        "      <th>0</th>\n",
152 |        "      <td>2013-02-08</td>\n",
153 |        "      <td>474.00</td>\n",
154 |        "      <td>478.81</td>\n",
155 |        "      <td>468.25</td>\n",
156 |        "      <td>474.98</td>\n",
157 |        "      <td>22597100</td>\n",
158 |        "      <td>474.98</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>1</th>\n",
162 |        "      <td>2013-02-07</td>\n",
163 |        "      <td>463.25</td>\n",
164 |        "      <td>470.00</td>\n",
165 |        "      <td>454.12</td>\n",
166 |        "      <td>468.22</td>\n",
167 |        "      <td>25163600</td>\n",
168 |        "      <td>468.22</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>2</th>\n",
172 |        "      <td>2013-02-06</td>\n",
173 |        "      <td>456.47</td>\n",
174 |        "      <td>466.50</td>\n",
175 |        "      <td>452.58</td>\n",
176 |        "      <td>457.35</td>\n",
177 |        "      <td>21203800</td>\n",
178 |        "      <td>454.70</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>3</th>\n",
182 |        "      <td>2013-02-05</td>\n",
183 |        "      <td>444.05</td>\n",
184 |        "      <td>459.74</td>\n",
185 |        "      <td>442.22</td>\n",
186 |        "      <td>457.84</td>\n",
187 |        "      <td>20476700</td>\n",
188 |        "      <td>455.19</td>\n",
189 |        "    </tr>\n",
190 |        "    <tr>\n",
191 |        "      <th>4</th>\n",
192 |        "      <td>2013-02-04</td>\n",
193 |        "      <td>453.91</td>\n",
194 |        "      <td>455.94</td>\n",
195 |        "      <td>442.00</td>\n",
196 |        "      <td>442.32</td>\n",
197 |        "      <td>17039900</td>\n",
198 |        "      <td>439.76</td>\n",
199 |        "    </tr>\n",
200 |        "  </tbody>\n",
201 |        "</table>\n",
202 |        "</div>"
203 |       ],
204 |       "text/plain": [
205 |        "        Date    Open    High     Low   Close    Volume  Adj Close\n",
206 |        "0 2013-02-08  474.00  478.81  468.25  474.98  22597100     474.98\n",
207 |        "1 2013-02-07  463.25  470.00  454.12  468.22  25163600     468.22\n",
208 |        "2 2013-02-06  456.47  466.50  452.58  457.35  21203800     454.70\n",
209 |        "3 2013-02-05  444.05  459.74  442.22  457.84  20476700     455.19\n",
210 |        "4 2013-02-04  453.91  455.94  442.00  442.32  17039900     439.76"
211 |       ]
212 |      },
213 |      "execution_count": 3,
214 |      "metadata": {},
215 |      "output_type": "execute_result"
216 |     }
217 |    ],
218 |    "source": [
219 |     "data.head()"
220 |    ]
221 |   },
222 |   {
223 |    "cell_type": "code",
224 |    "execution_count": 4,
225 |    "metadata": {},
226 |    "outputs": [
227 |     {
228 |      "data": {
229 |       "text/plain": [
230 |        "<matplotlib.axes._subplots.AxesSubplot at 0x8d91630>"
231 |       ]
232 |      },
233 |      "execution_count": 4,
234 |      "metadata": {},
235 |      "output_type": "execute_result"
236 |     },
237 |     {
238 |      "data": {
239 |       "image/png": "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\n",
240 |       "text/plain": [
241 |        "<Figure size 432x288 with 1 Axes>"
242 |       ]
243 |      },
244 |      "metadata": {},
245 |      "output_type": "display_data"
246 |     }
247 |    ],
248 |    "source": [
249 |     "data.plot(x='Date', y='Close')"
250 |    ]
251 |   }
252 |  ],
253 |  "metadata": {
254 |   "kernelspec": {
255 |    "display_name": "Python [conda env:py27]",
256 |    "language": "python",
257 |    "name": "conda-env-py27-py"
258 |   },
259 |   "language_info": {
260 |    "codemirror_mode": {
261 |     "name": "ipython",
262 |     "version": 2
263 |    },
264 |    "file_extension": ".py",
265 |    "mimetype": "text/x-python",
266 |    "name": "python",
267 |    "nbconvert_exporter": "python",
268 |    "pygments_lexer": "ipython2",
269 |    "version": "2.7.15"
270 |   }
271 |  },
272 |  "nbformat": 4,
273 |  "nbformat_minor": 2
274 | }
275 | 


--------------------------------------------------------------------------------
/C) Dates and Times.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Python"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [
 15 |     {
 16 |      "data": {
 17 |       "text/plain": [
 18 |        "datetime.datetime(2018, 8, 9, 14, 36, 51, 331000)"
 19 |       ]
 20 |      },
 21 |      "execution_count": 1,
 22 |      "metadata": {},
 23 |      "output_type": "execute_result"
 24 |     }
 25 |    ],
 26 |    "source": [
 27 |     "import datetime as dt\n",
 28 |     "dt.datetime.now()"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 2,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/plain": [
 39 |        "datetime.datetime(2018, 8, 9, 15, 0, 1, 783000)"
 40 |       ]
 41 |      },
 42 |      "execution_count": 2,
 43 |      "metadata": {},
 44 |      "output_type": "execute_result"
 45 |     }
 46 |    ],
 47 |    "source": [
 48 |     "to = dt.datetime.today()\n",
 49 |     "to"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 3,
 55 |    "metadata": {},
 56 |    "outputs": [
 57 |     {
 58 |      "data": {
 59 |       "text/plain": [
 60 |        "3"
 61 |       ]
 62 |      },
 63 |      "execution_count": 3,
 64 |      "metadata": {},
 65 |      "output_type": "execute_result"
 66 |     }
 67 |    ],
 68 |    "source": [
 69 |     "to.weekday() # zero-based numbering; 0 = Monday"
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 4,
 75 |    "metadata": {},
 76 |    "outputs": [
 77 |     {
 78 |      "data": {
 79 |       "text/plain": [
 80 |        "datetime.datetime(2016, 10, 31, 10, 5, 30, 500000)"
 81 |       ]
 82 |      },
 83 |      "execution_count": 4,
 84 |      "metadata": {},
 85 |      "output_type": "execute_result"
 86 |     }
 87 |    ],
 88 |    "source": [
 89 |     "d = dt.datetime(2016, 10, 31, 10, 5, 30, 500000)\n",
 90 |     "d"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "code",
 95 |    "execution_count": 5,
 96 |    "metadata": {},
 97 |    "outputs": [
 98 |     {
 99 |      "name": "stdout",
100 |      "output_type": "stream",
101 |      "text": [
102 |       "2016-10-31 10:05:30.500000\n"
103 |      ]
104 |     }
105 |    ],
106 |    "source": [
107 |     "print d"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 7,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "name": "stdout",
117 |      "output_type": "stream",
118 |      "text": [
119 |       "2016\n",
120 |       "10\n",
121 |       "31\n",
122 |       "10\n"
123 |      ]
124 |     }
125 |    ],
126 |    "source": [
127 |     "print d.year\n",
128 |     "print d.month\n",
129 |     "print d.day\n",
130 |     "print d.hour"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "code",
135 |    "execution_count": 9,
136 |    "metadata": {},
137 |    "outputs": [
138 |     {
139 |      "data": {
140 |       "text/plain": [
141 |        "736268"
142 |       ]
143 |      },
144 |      "execution_count": 9,
145 |      "metadata": {},
146 |      "output_type": "execute_result"
147 |     }
148 |    ],
149 |    "source": [
150 |     "o = d.toordinal()\n",
151 |     "o"
152 |    ]
153 |   },
154 |   {
155 |    "cell_type": "code",
156 |    "execution_count": 10,
157 |    "metadata": {},
158 |    "outputs": [
159 |     {
160 |      "data": {
161 |       "text/plain": [
162 |        "datetime.datetime(2016, 10, 31, 0, 0)"
163 |       ]
164 |      },
165 |      "execution_count": 10,
166 |      "metadata": {},
167 |      "output_type": "execute_result"
168 |     }
169 |    ],
170 |    "source": [
171 |     "dt.datetime.fromordinal(o)"
172 |    ]
173 |   },
174 |   {
175 |    "cell_type": "code",
176 |    "execution_count": 11,
177 |    "metadata": {},
178 |    "outputs": [
179 |     {
180 |      "data": {
181 |       "text/plain": [
182 |        "datetime.time(10, 5, 30, 500000)"
183 |       ]
184 |      },
185 |      "execution_count": 11,
186 |      "metadata": {},
187 |      "output_type": "execute_result"
188 |     }
189 |    ],
190 |    "source": [
191 |     "t = dt.datetime.time(d)\n",
192 |     "t"
193 |    ]
194 |   },
195 |   {
196 |    "cell_type": "code",
197 |    "execution_count": 12,
198 |    "metadata": {},
199 |    "outputs": [
200 |     {
201 |      "data": {
202 |       "text/plain": [
203 |        "datetime.date(2016, 10, 31)"
204 |       ]
205 |      },
206 |      "execution_count": 12,
207 |      "metadata": {},
208 |      "output_type": "execute_result"
209 |     }
210 |    ],
211 |    "source": [
212 |     "dd = dt.datetime.date(d)\n",
213 |     "dd"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "code",
218 |    "execution_count": 13,
219 |    "metadata": {},
220 |    "outputs": [
221 |     {
222 |      "data": {
223 |       "text/plain": [
224 |        "datetime.datetime(2016, 10, 31, 10, 5)"
225 |       ]
226 |      },
227 |      "execution_count": 13,
228 |      "metadata": {},
229 |      "output_type": "execute_result"
230 |     }
231 |    ],
232 |    "source": [
233 |     "d.replace(second=0, microsecond=0)"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 14,
239 |    "metadata": {},
240 |    "outputs": [
241 |     {
242 |      "data": {
243 |       "text/plain": [
244 |        "datetime.timedelta(-648, 68294, 339000)"
245 |       ]
246 |      },
247 |      "execution_count": 14,
248 |      "metadata": {},
249 |      "output_type": "execute_result"
250 |     }
251 |    ],
252 |    "source": [
253 |     "td = d - dt.datetime.now()\n",
254 |     "td"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": null,
260 |    "metadata": {},
261 |    "outputs": [],
262 |    "source": [
263 |     "td.days"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": 15,
269 |    "metadata": {},
270 |    "outputs": [
271 |     {
272 |      "name": "stdout",
273 |      "output_type": "stream",
274 |      "text": [
275 |       "-648\n",
276 |       "68294\n",
277 |       "339000\n",
278 |       "-55918905.661\n"
279 |      ]
280 |     }
281 |    ],
282 |    "source": [
283 |     "print td.days\n",
284 |     "print td.seconds\n",
285 |     "print td.microseconds\n",
286 |     "print td.total_seconds()"
287 |    ]
288 |   },
289 |   {
290 |    "cell_type": "code",
291 |    "execution_count": 16,
292 |    "metadata": {},
293 |    "outputs": [
294 |     {
295 |      "data": {
296 |       "text/plain": [
297 |        "'2016-10-31T10:05:30.500000'"
298 |       ]
299 |      },
300 |      "execution_count": 16,
301 |      "metadata": {},
302 |      "output_type": "execute_result"
303 |     }
304 |    ],
305 |    "source": [
306 |     "d.isoformat()"
307 |    ]
308 |   },
309 |   {
310 |    "cell_type": "code",
311 |    "execution_count": 18,
312 |    "metadata": {},
313 |    "outputs": [
314 |     {
315 |      "name": "stdout",
316 |      "output_type": "stream",
317 |      "text": [
318 |       "Monday, 31. October 2016 10:05AM\n",
319 |       "2017-03-31 00:00:00\n",
320 |       "2016-04-30 00:00:00\n"
321 |      ]
322 |     }
323 |    ],
324 |    "source": [
325 |     "print d.strftime(\"%A, %d. %B %Y %I:%M%p\")\n",
326 |     "print dt.datetime.strptime('2017-03-31', '%Y-%m-%d') # year first and four-digit year\n",
327 |     "print dt.datetime.strptime('30-4-16', '%d-%m-%y') # day first and two-digit year"
328 |    ]
329 |   },
330 |   {
331 |    "cell_type": "code",
332 |    "execution_count": 19,
333 |    "metadata": {},
334 |    "outputs": [
335 |     {
336 |      "data": {
337 |       "text/plain": [
338 |        "'2016-10-31 10:05:30.500000'"
339 |       ]
340 |      },
341 |      "execution_count": 19,
342 |      "metadata": {},
343 |      "output_type": "execute_result"
344 |     }
345 |    ],
346 |    "source": [
347 |     "ds = str(d)\n",
348 |     "ds"
349 |    ]
350 |   },
351 |   {
352 |    "cell_type": "code",
353 |    "execution_count": 20,
354 |    "metadata": {},
355 |    "outputs": [
356 |     {
357 |      "data": {
358 |       "text/plain": [
359 |        "datetime.datetime(2016, 10, 31, 10, 5, 30, 500000)"
360 |       ]
361 |      },
362 |      "execution_count": 20,
363 |      "metadata": {},
364 |      "output_type": "execute_result"
365 |     }
366 |    ],
367 |    "source": [
368 |     "dt.datetime.strptime(ds, '%Y-%m-%d %H:%M:%S.%f')"
369 |    ]
370 |   },
371 |   {
372 |    "cell_type": "code",
373 |    "execution_count": 21,
374 |    "metadata": {},
375 |    "outputs": [
376 |     {
377 |      "name": "stdout",
378 |      "output_type": "stream",
379 |      "text": [
380 |       "2018-08-09 15:12:52.277000\n",
381 |       "2018-08-09 10:12:52.277000\n"
382 |      ]
383 |     }
384 |    ],
385 |    "source": [
386 |     "print dt.datetime.now()\n",
387 |     "print dt.datetime.utcnow()"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 22,
393 |    "metadata": {},
394 |    "outputs": [
395 |     {
396 |      "data": {
397 |       "text/plain": [
398 |        "u'United States'"
399 |       ]
400 |      },
401 |      "execution_count": 22,
402 |      "metadata": {},
403 |      "output_type": "execute_result"
404 |     }
405 |    ],
406 |    "source": [
407 |     "import pytz\n",
408 |     "pytz.country_names['US']"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "code",
413 |    "execution_count": 23,
414 |    "metadata": {},
415 |    "outputs": [
416 |     {
417 |      "data": {
418 |       "text/plain": [
419 |        "[u'Europe/Brussels']"
420 |       ]
421 |      },
422 |      "execution_count": 23,
423 |      "metadata": {},
424 |      "output_type": "execute_result"
425 |     }
426 |    ],
427 |    "source": [
428 |     "pytz.country_timezones['BE']"
429 |    ]
430 |   },
431 |   {
432 |    "cell_type": "code",
433 |    "execution_count": 24,
434 |    "metadata": {},
435 |    "outputs": [
436 |     {
437 |      "data": {
438 |       "text/plain": [
439 |        "[u'Asia/Karachi']"
440 |       ]
441 |      },
442 |      "execution_count": 24,
443 |      "metadata": {},
444 |      "output_type": "execute_result"
445 |     }
446 |    ],
447 |    "source": [
448 |     "pytz.country_timezones['PK']"
449 |    ]
450 |   },
451 |   {
452 |    "cell_type": "code",
453 |    "execution_count": 25,
454 |    "metadata": {},
455 |    "outputs": [
456 |     {
457 |      "data": {
458 |       "text/plain": [
459 |        "['Pacific/Wake',\n",
460 |        " 'Pacific/Wallis',\n",
461 |        " 'US/Alaska',\n",
462 |        " 'US/Arizona',\n",
463 |        " 'US/Central',\n",
464 |        " 'US/Eastern',\n",
465 |        " 'US/Hawaii',\n",
466 |        " 'US/Mountain',\n",
467 |        " 'US/Pacific',\n",
468 |        " 'UTC']"
469 |       ]
470 |      },
471 |      "execution_count": 25,
472 |      "metadata": {},
473 |      "output_type": "execute_result"
474 |     }
475 |    ],
476 |    "source": [
477 |     "pytz.common_timezones[-10:]"
478 |    ]
479 |   },
480 |   {
481 |    "cell_type": "markdown",
482 |    "metadata": {},
483 |    "source": [
484 |     "# Numpy"
485 |    ]
486 |   },
487 |   {
488 |    "cell_type": "code",
489 |    "execution_count": 26,
490 |    "metadata": {},
491 |    "outputs": [
492 |     {
493 |      "data": {
494 |       "text/plain": [
495 |        "numpy.datetime64('2015-10-31')"
496 |       ]
497 |      },
498 |      "execution_count": 26,
499 |      "metadata": {},
500 |      "output_type": "execute_result"
501 |     }
502 |    ],
503 |    "source": [
504 |     "import numpy as np\n",
505 |     "nd = np.datetime64('2015-10-31')\n",
506 |     "nd"
507 |    ]
508 |   },
509 |   {
510 |    "cell_type": "code",
511 |    "execution_count": 27,
512 |    "metadata": {},
513 |    "outputs": [
514 |     {
515 |      "data": {
516 |       "text/plain": [
517 |        "'2015-10-31'"
518 |       ]
519 |      },
520 |      "execution_count": 27,
521 |      "metadata": {},
522 |      "output_type": "execute_result"
523 |     }
524 |    ],
525 |    "source": [
526 |     "np.datetime_as_string(nd)"
527 |    ]
528 |   },
529 |   {
530 |    "cell_type": "code",
531 |    "execution_count": 28,
532 |    "metadata": {},
533 |    "outputs": [
534 |     {
535 |      "data": {
536 |       "text/plain": [
537 |        "('D', 1)"
538 |       ]
539 |      },
540 |      "execution_count": 28,
541 |      "metadata": {},
542 |      "output_type": "execute_result"
543 |     }
544 |    ],
545 |    "source": [
546 |     "np.datetime_data(nd)"
547 |    ]
548 |   },
549 |   {
550 |    "cell_type": "code",
551 |    "execution_count": 29,
552 |    "metadata": {},
553 |    "outputs": [
554 |     {
555 |      "data": {
556 |       "text/plain": [
557 |        "datetime.datetime(2016, 10, 31, 10, 5, 30, 500000)"
558 |       ]
559 |      },
560 |      "execution_count": 29,
561 |      "metadata": {},
562 |      "output_type": "execute_result"
563 |     }
564 |    ],
565 |    "source": [
566 |     "d"
567 |    ]
568 |   },
569 |   {
570 |    "cell_type": "code",
571 |    "execution_count": 30,
572 |    "metadata": {},
573 |    "outputs": [
574 |     {
575 |      "data": {
576 |       "text/plain": [
577 |        "numpy.datetime64('2016-10-31T10:05:30.500000')"
578 |       ]
579 |      },
580 |      "execution_count": 30,
581 |      "metadata": {},
582 |      "output_type": "execute_result"
583 |     }
584 |    ],
585 |    "source": [
586 |     "nd = np.datetime64(d)\n",
587 |     "nd"
588 |    ]
589 |   },
590 |   {
591 |    "cell_type": "code",
592 |    "execution_count": 34,
593 |    "metadata": {},
594 |    "outputs": [],
595 |    "source": [
596 |     "dtl = np.arange('2016-01-01T00:00:00', '2016-01-02T00:00:00',\n",
597 |     "                dtype='datetime64[h]')"
598 |    ]
599 |   },
600 |   {
601 |    "cell_type": "code",
602 |    "execution_count": 37,
603 |    "metadata": {},
604 |    "outputs": [
605 |     {
606 |      "data": {
607 |       "image/png": "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\n",
608 |       "text/plain": [
609 |        "<Figure size 1080x576 with 1 Axes>"
610 |       ]
611 |      },
612 |      "metadata": {},
613 |      "output_type": "display_data"
614 |     }
615 |    ],
616 |    "source": [
617 |     "import matplotlib.pyplot as plt\n",
618 |     "%matplotlib inline\n",
619 |     "\n",
620 |     "np.random.seed(3000)\n",
621 |     "rnd = np.random.standard_normal(len(dtl)).cumsum() ** 2\n",
622 |     "\n",
623 |     "\n",
624 |     "fig = plt.figure(figsize= (15, 8))\n",
625 |     "plt.plot(dtl.astype(dt.datetime), rnd) # convert np.datetime to datetime.datetime\n",
626 |     "plt.grid(True)\n",
627 |     "fig.autofmt_xdate() # autoformatting of datetime x-ticks"
628 |    ]
629 |   }
630 |  ],
631 |  "metadata": {
632 |   "kernelspec": {
633 |    "display_name": "Python [conda env:py27]",
634 |    "language": "python",
635 |    "name": "conda-env-py27-py"
636 |   },
637 |   "language_info": {
638 |    "codemirror_mode": {
639 |     "name": "ipython",
640 |     "version": 2
641 |    },
642 |    "file_extension": ".py",
643 |    "mimetype": "text/x-python",
644 |    "name": "python",
645 |    "nbconvert_exporter": "python",
646 |    "pygments_lexer": "ipython2",
647 |    "version": "2.7.15"
648 |   }
649 |  },
650 |  "nbformat": 4,
651 |  "nbformat_minor": 2
652 | }
653 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Python - for - Finance
 2 | Simulation of Financial Models  
 3 | Valuation Framework  
 4 | Derivatives and Portfolio Valuation  
 5 | Volatility Options  
 6 | 
 7 | ## Python 2.7 is used for Computation & Analytics
 8 | 
 9 | 
10 | ## SHORT RATE CLASS WITH GUI 
11 | ### short_rate_GUI.py  
12 | #### Description  
13 | Guided User Ineterface for Short Rate Class below:  
14 |   
15 | ![short rate gui](https://user-images.githubusercontent.com/30389323/43771663-5c40ccae-9a59-11e8-8018-328fc3e3ac1f.PNG)  
16 | 
17 | ## Extended SHORT RATE CLASS WITH GUI 
18 | ### extended_short_rate_GUI.py  
19 | #### Description  
20 | Guided User Ineterface for Extended Short Rate Class below:  
21 | 
22 | ![extended_short_rate_gui](https://user-images.githubusercontent.com/30389323/43772860-11b35054-9a5d-11e8-828a-56e70beac4c6.PNG)  
23 | 
24 | ## CASH FLOW SERIES CLASS WITH GUI 
25 | ### cash_flow_series_GUI.py  
26 | #### Description  
27 | Guided User Ineterface for CASH FLOW SERIES CLASS WITH GUI below:  
28 | 
29 | ![cash_flow_series_gui](https://user-images.githubusercontent.com/30389323/43773198-62309ea0-9a5e-11e8-907f-94fbce795e02.PNG)  
30 | 
31 | 
32 | ## Cash Flow Series Result GUI
33 | 
34 | ![cahs flow series result gui](https://user-images.githubusercontent.com/30389323/43773646-d2917498-9a5f-11e8-89d5-205b3f8920e4.PNG)
35 |   
36 |     
37 | # TradeChat Room  
38 | ## Allow Users to sign in and chat with market brokers
39 | ## A simple example for a web-based chat room based on Flask and SQLite3.
40 | Usage From Command Line  
41 | taymour$- python tradechat.py  
42 | ### table.sql  
43 | Schema design for SQLite3    
44 | 
45 | ### End GUI
46 | #### Home Page
47 | ![tradechat1](https://user-images.githubusercontent.com/30389323/43886724-1c5c5f9e-9bd6-11e8-928f-3ba4b248d0cf.PNG)  
48 |   
49 | #### Register Page  
50 | ![tradechat2](https://user-images.githubusercontent.com/30389323/43886942-b2f4a5d8-9bd6-11e8-81fd-56476fa5ebb9.PNG)  
51 |   
52 | #### Chat Page  
53 | ![tradechat3](https://user-images.githubusercontent.com/30389323/43886946-b97a1e1a-9bd6-11e8-83c8-cf15393aa5e3.PNG)  
54 |     
55 | #### Posts with time and user name
56 | ![tradechat4](https://user-images.githubusercontent.com/30389323/43886956-c38a24ea-9bd6-11e8-836a-218ff990ffe1.PNG)
57 | 


--------------------------------------------------------------------------------
/cash_flow_series_GUI.PNG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/taymourniazi/Python-for-Finance/5f43f9774915d266244cec560baba9c5fdec80c7/cash_flow_series_GUI.PNG


--------------------------------------------------------------------------------
/cash_flow_series_GUI.py:
--------------------------------------------------------------------------------
 1 | import traits.api as trapi
 2 | import traitsui.api as trui
 3 | import numpy as np
 4 | 
 5 | class cash_flow_series(trapi.HasTraits):
 6 |     name = trapi.Str
 7 |     short_rate = trapi.Range(0.0, 0.5, 0.05)
 8 |     time_list = trapi.Array(dtype=np.float, shape=(1, 6))
 9 |     cash_flows = trapi.Array(dtype=np.float, shape=(1, 6))
10 |     disc_values = trapi.Array(dtype=np.float, shape=(1, 6))
11 |     present_values = trapi.Array(dtype=np.float, shape=(1, 6))
12 |     net_present_value = trapi.Float
13 |     update = trapi.Button
14 |     def _update_fired(self):
15 |         self.disc_values = np.exp(-self.short_rate * self.time_list)
16 |         self.present_values = self.disc_values * self.cash_flows
17 |         self.net_present_value = np.sum(self.present_values)
18 |     v = trui.View(trui.Group(trui.Item(name = 'name'),
19 |                              trui.Item(name='short_rate'),
20 |                              trui.Item(name='time_list', label='Time List'),
21 |                              trui.Item(name='cash_flows', label='Cash Flows'),
22 |                              trui.Item('update', show_label=False),
23 |                              trui.Item(name='disc_values',
24 |                                        label='Discount Factors'),
25 |                              trui.Item(name='present_values',
26 |                                        label='Present Values'),
27 |                              trui.Item(name='net_present_value',
28 |                                        label='Net Present Value'),
29 |                              show_border=True, label='Calculate Present Values'),
30 |                              buttons = [trui.OKButton, trui.CancelButton],
31 |                              resizable = True)
32 | 
33 | cfs = cash_flow_series()
34 | cfs.configure_traits()


--------------------------------------------------------------------------------
/extended_short_rate_GUI.PNG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/taymourniazi/Python-for-Finance/5f43f9774915d266244cec560baba9c5fdec80c7/extended_short_rate_GUI.PNG


--------------------------------------------------------------------------------
/extended_short_rate_GUI.py:
--------------------------------------------------------------------------------
 1 | import traits.api as trapi
 2 | import traitsui.api as trui
 3 | import numpy as np
 4 | 
 5 | class short_rate(trapi.HasTraits):
 6 |     name = trapi.Str
 7 |     rate = trapi.Float
 8 |     time_list = trapi.Array(dtype=np.float, shape=(1, 5))
 9 |     disc_list = trapi.Array(dtype=np.float, shape=(1, 5))
10 |     update = trapi.Button
11 |     def _update_fired(self):
12 |         self.disc_list = np.exp(-self.rate * self.time_list)
13 |     v = trui.View(trui.Group(trui.Item(name = 'name'),
14 |                              trui.Item(name='rate'),
15 |                              trui.Item(name='time_list',
16 |                                        label='Insert Time List Here'),
17 |                              trui.Item('update', show_label=False),
18 |                              trui.Item(name='disc_list',
19 |                                        label='Press Update for Factors'),
20 |                              show_border=True, label='Calculate Discount Factors'),
21 |                   buttons = [trui.OKButton, trui.CancelButton],
22 |                   resizable = True)
23 | 
24 | sr = short_rate()
25 | sr.configure_traits()


--------------------------------------------------------------------------------
/short rate GUI.PNG:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/taymourniazi/Python-for-Finance/5f43f9774915d266244cec560baba9c5fdec80c7/short rate GUI.PNG


--------------------------------------------------------------------------------
/short_rate_GUI.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import traits.api as trapi
 3 | 
 4 | 
 5 | class short_rate(trapi.HasTraits):
 6 |     name = trapi.Str
 7 |     rate = trapi.Float
 8 |     time_list = trapi.Array(dtype=np.float, shape=(5,))
 9 |     def get_discount_factors(self):
10 |         return np.exp(-self.rate * self.time_list)
11 | 
12 | sr = short_rate()
13 | sr.configure_traits()


--------------------------------------------------------------------------------
/stock.csv:
--------------------------------------------------------------------------------
  1 | date,close_price,high_price,low_price,open_price,volume
  2 | 07/08/2017,10.2435,10.2997,10.2247,10.2622,35013023
  3 | 08/08/2017,10.2153,10.3185,10.2059,10.2716,26322101
  4 | 09/08/2017,10.2435,10.2716,10.159,10.2059,32155872
  5 | 10/08/2017,10.1028,10.2341,10.1028,10.2059,31903827
  6 | 11/08/2017,10.1028,10.1684,10.0934,10.1215,32300117
  7 | 14/08/2017,10.2341,10.2716,10.1215,10.1497,26413474
  8 | 15/08/2017,10.1684,10.3185,10.1637,10.2435,29266299
  9 | 16/08/2017,10.1309,10.2247,10.1121,10.1872,28316308
 10 | 17/08/2017,9.9808,10.1309,9.9433,10.1215,35518834
 11 | 18/08/2017,9.9058,9.9714,9.8213,9.962,43998743
 12 | 21/08/2017,9.9151,9.9949,9.8682,9.8776,30152540
 13 | 22/08/2017,9.9902,10.0652,9.9527,9.9527,28676062
 14 | 23/08/2017,10.0465,10.1121,9.9339,9.9527,35405768
 15 | 24/08/2017,10.0465,10.1309,10.0277,10.084,33584388
 16 | 25/08/2017,10.1497,10.2059,10.0934,10.1028,35926827
 17 | 28/08/2017,10.1215,10.2059,10.0652,10.1872,22307395
 18 | 29/08/2017,10.1684,10.2153,10.0652,10.084,29807261
 19 | 30/08/2017,10.2622,10.2622,10.1028,10.1497,23629293
 20 | 31/08/2017,10.3466,10.5061,10.2716,10.2904,54208921
 21 | 01/09/2017,10.6468,10.7219,10.4498,10.4874,64560278
 22 | 05/09/2017,10.6562,10.6843,10.5436,10.6562,44000160
 23 | 06/09/2017,10.7875,10.8157,10.6834,10.6843,43279041
 24 | 07/09/2017,10.6937,10.8063,10.6281,10.8063,30271005
 25 | 08/09/2017,10.6562,10.6843,10.5905,10.6374,26633932
 26 | 11/09/2017,10.7031,10.7406,10.675,10.6937,43375019
 27 | 12/09/2017,10.872,10.947,10.6843,10.7219,41386669
 28 | 13/09/2017,10.9001,10.9095,10.8063,10.8813,23302152
 29 | 14/09/2017,10.8438,10.9564,10.7688,10.8813,31696274
 30 | 15/09/2017,10.9001,10.9095,10.7781,10.825,30865470
 31 | 18/09/2017,10.9095,11.0033,10.8766,10.9095,25132356
 32 | 19/09/2017,10.9939,11.0127,10.9001,10.9095,26392861
 33 | 20/09/2017,10.9845,11.0643,10.9142,10.9564,26705319
 34 | 21/09/2017,10.9939,11.0877,10.9658,10.9845,40319429
 35 | 22/09/2017,11.1065,11.1346,10.9939,11.0127,30196459
 36 | 25/09/2017,11.2003,11.3128,11.1065,11.144,40766002
 37 | 26/09/2017,11.1909,11.2566,11.1627,11.2096,30646723
 38 | 27/09/2017,11.2096,11.2753,11.1252,11.2378,28412202
 39 | 28/09/2017,11.219,11.2472,11.1627,11.2096,23105656
 40 | 29/09/2017,11.2284,11.2753,11.1815,11.2284,31227515
 41 | 02/10/2017,11.341,11.341,11.2284,11.2566,36019960
 42 | 03/10/2017,11.5755,11.6599,11.4629,11.5567,65641714
 43 | 04/10/2017,11.538,11.6881,11.5192,11.5661,52909843
 44 | 05/10/2017,11.4911,11.5942,11.3973,11.5755,48145783
 45 | 06/10/2017,11.5473,11.5661,11.4371,11.4442,35091790
 46 | 09/10/2017,11.5755,11.6411,11.4911,11.5661,28924795
 47 | 10/10/2017,11.6224,11.6505,11.4911,11.5755,40586234
 48 | 11/10/2017,11.613,11.6599,11.5473,11.5661,34953438
 49 | 12/10/2017,11.3691,11.5567,11.3597,11.538,45924434
 50 | 13/10/2017,11.3035,11.416,11.1346,11.3128,44597334
 51 | 16/10/2017,11.3691,11.3973,11.219,11.2472,32349675
 52 | 17/10/2017,11.5098,11.5473,11.3691,11.3785,55273235
 53 | 18/10/2017,11.4348,11.5849,11.416,11.5192,30783686
 54 | 19/10/2017,11.4723,11.4911,11.3035,11.3879,29678158
 55 | 20/10/2017,11.4913,11.5293,11.3963,11.5103,28171651
 56 | 23/10/2017,11.4343,11.5768,11.4343,11.5008,21504813
 57 | 24/10/2017,11.5768,11.6432,11.4827,11.5008,34651185
 58 | 25/10/2017,11.4343,11.5388,11.3678,11.5388,47674841
 59 | 26/10/2017,11.6527,11.7192,11.4533,11.5863,49003126
 60 | 27/10/2017,11.4533,11.6243,11.4059,11.6148,33925366
 61 | 30/10/2017,11.4913,11.5388,11.3488,11.4058,36457435
 62 | 31/10/2017,11.6527,11.6812,11.5188,11.5388,42010246
 63 | 01/11/2017,11.7287,11.8427,11.6577,11.7762,43495830
 64 | 02/11/2017,11.7952,11.8047,11.6432,11.7097,40923421
 65 | 03/11/2017,11.7382,11.8332,11.7002,11.7857,33220261
 66 | 06/11/2017,11.7097,11.8047,11.6812,11.7382,20178513
 67 | 07/11/2017,11.5483,11.7572,11.5008,11.7287,41536291
 68 | 08/11/2017,11.4533,11.5483,11.4058,11.5293,34127543
 69 | 09/11/2017,11.4343,11.4438,11.3298,11.4058,26257259
 70 | 10/11/2017,11.4058,11.4818,11.3773,11.3963,35298374
 71 | 13/11/2017,11.5483,11.6053,11.3678,11.4058,30415855
 72 | 14/11/2017,11.4153,11.5483,11.3868,11.4913,25893421
 73 | 15/11/2017,11.3963,11.4248,11.2729,11.3678,28441307
 74 | 16/11/2017,11.4343,11.4438,11.3298,11.3963,28269046
 75 | 17/11/2017,11.4058,11.4533,11.3821,11.4153,31117727
 76 | 20/11/2017,11.5198,11.5388,11.4153,11.4248,32352040
 77 | 21/11/2017,11.5103,11.61,11.5103,11.5293,28510260
 78 | 22/11/2017,11.4628,11.5388,11.4438,11.5008,36184010
 79 | 24/11/2017,11.4913,11.5388,11.4248,11.4913,9549629
 80 | 27/11/2017,11.5008,11.5768,11.4533,11.4818,28525156
 81 | 28/11/2017,11.5958,11.6243,11.4723,11.5388,33199323
 82 | 29/11/2017,11.9282,11.9377,11.6148,11.6243,64865974
 83 | 30/11/2017,11.8902,12.0136,11.7952,11.8997,49224189
 84 | 01/12/2017,11.9471,12.0611,11.7287,11.9851,52458736
 85 | 04/12/2017,11.9946,12.1656,11.9566,12.0136,39558857
 86 | 05/12/2017,11.8047,11.9851,11.7762,11.7952,45527163
 87 | 06/12/2017,11.7572,11.8047,11.6622,11.7477,28453762
 88 | 07/12/2017,11.8997,11.9566,11.7097,11.9424,25417119
 89 | 08/12/2017,11.9756,11.9756,11.8047,11.8997,21981870
 90 | 11/12/2017,11.9471,11.9946,11.8807,11.9377,44194633
 91 | 12/12/2017,11.9661,11.9994,11.8807,11.9661,54370612
 92 | 13/12/2017,11.9946,12.0849,11.9566,11.9756,31858995
 93 | 14/12/2017,11.8332,12.0516,11.7857,12.0136,37073960
 94 | 15/12/2017,11.9471,12.0231,11.8712,11.8807,48655453
 95 | 18/12/2017,12.0231,12.1086,11.9756,12.0231,25022657
 96 | 19/12/2017,12.0516,12.1371,12.0231,12.0706,23056884
 97 | 20/12/2017,12.0801,12.1086,12.0136,12.0801,18989429
 98 | 21/12/2017,11.9946,12.1276,11.9946,12.0991,21004663
 99 | 22/12/2017,11.9471,12.0231,11.9282,12.0231,17876156
100 | 26/12/2017,11.9661,12.0136,11.9187,11.9377,11664564
101 | 27/12/2017,11.8712,11.9471,11.8237,11.9377,17005626
102 | 28/12/2017,11.9471,11.9471,11.8427,11.8522,14793534
103 | 29/12/2017,11.8617,11.9756,11.8617,11.9471,18362455
104 | 02/01/2018,12.0231,12.0231,11.8712,11.8902,20773320
105 | 03/01/2018,12.1181,12.1561,12.0326,12.0421,29765638
106 | 04/01/2018,12.327,12.384,12.1276,12.1371,37478200
107 | 05/01/2018,12.536,12.555,12.384,12.403,46121873
108 | 08/01/2018,12.4885,12.555,12.4505,12.5455,33828330
109 | 09/01/2018,12.422,12.555,12.422,12.517,27924109
110 | 10/01/2018,12.3745,12.478,12.308,12.403,56517139
111 | 11/01/2018,12.498,12.536,12.346,12.365,28342293
112 | 12/01/2018,12.5644,12.6214,12.441,12.441,56979186
113 | 16/01/2018,12.441,12.8019,12.3555,12.6404,53961375
114 | 17/01/2018,11.5673,11.9471,11.4913,11.9471,132412533
115 | 18/01/2018,11.4628,11.5863,11.4153,11.5673,66541729
116 | 19/01/2018,11.3963,11.5103,11.3109,11.5103,51303747
117 | 22/01/2018,11.4153,11.4723,11.3014,11.4153,52851915
118 | 23/01/2018,11.3583,11.4248,11.3014,11.4248,51830968
119 | 24/01/2018,11.4438,11.4818,11.3203,11.3963,63300483
120 | 25/01/2018,10.988,11.3298,10.8645,11.3298,114886820
121 | 26/01/2018,11.0639,11.1114,10.9215,11.007,52496001
122 | 29/01/2018,10.8207,11.1612,10.8109,11.1126,55113971
123 | 30/01/2018,10.7623,10.9277,10.7525,10.7915,52068187
124 | 31/01/2018,10.6747,10.8158,10.6066,10.7915,57644576
125 | 01/02/2018,10.626,10.7039,10.5579,10.6552,41352192
126 | 02/02/2018,10.4217,10.5871,10.3341,10.5871,72312922
127 | 05/02/2018,9.9644,10.5093,9.9644,10.4898,96756955
128 | 06/02/2018,10.4704,10.4995,9.9157,9.9935,139938127
129 | 07/02/2018,10.4704,10.7428,10.412,10.4606,68041787
130 | 08/02/2018,10.1492,10.5677,10.1395,10.4995,73990734
131 | 09/02/2018,10.2465,10.3536,9.9644,10.2855,72103620
132 | 12/02/2018,10.412,10.5287,10.2598,10.3828,49068946
133 | 13/02/2018,10.3049,10.4022,10.2465,10.3439,36884488
134 | 14/02/2018,10.4509,10.4606,10.2174,10.266,27844338
135 | 15/02/2018,10.4704,10.5093,10.3536,10.5093,28208311
136 | 16/02/2018,10.3244,10.5287,10.2368,10.412,41041761
137 | 20/02/2018,10.3439,10.3828,10.266,10.2952,27617865
138 | 21/02/2018,10.3147,10.4606,10.3049,10.3633,36037720
139 | 22/02/2018,10.3439,10.4412,10.2757,10.3147,25409561
140 | 23/02/2018,10.412,10.4217,10.3341,10.3828,21786781
141 | 26/02/2018,10.5969,10.6552,10.412,10.4509,27157756
142 | 27/02/2018,10.3244,10.6942,10.3147,10.6358,43502012
143 | 28/02/2018,10.3244,10.4995,10.2855,10.3925,41321429
144 | 01/03/2018,10.013,10.4022,9.9254,10.3633,75841414
145 | 02/03/2018,10.12,10.1395,9.867,9.9644,60491864
146 | 05/03/2018,10.2952,10.3439,10.0227,10.0325,44021329
147 | 06/03/2018,10.3439,10.3925,10.1882,10.3633,30978946
148 | 07/03/2018,10.3439,10.3536,10.1492,10.2271,30046598
149 | 08/03/2018,10.3244,10.3925,10.1979,10.3633,26947958
150 | 09/03/2018,10.4412,10.4412,10.2855,10.3439,26684375
151 | 12/03/2018,10.519,10.5579,10.3925,10.4022,33800918
152 | 13/03/2018,10.4898,10.6844,10.4412,10.5482,44456812
153 | 14/03/2018,10.7234,11.132,10.7136,10.9472,91985319
154 | 15/03/2018,10.772,10.9472,10.6552,10.7525,47645277
155 | 16/03/2018,10.8499,10.918,10.7623,10.8207,80684224
156 | 19/03/2018,10.7136,10.8693,10.5774,10.8499,63122538
157 | 20/03/2018,10.6942,10.8304,10.6747,10.7525,45612813
158 | 21/03/2018,10.8012,10.8839,10.6358,10.7039,38550957
159 | 22/03/2018,10.4606,10.7623,10.4412,10.7039,51280905
160 | 23/03/2018,10.2757,10.5579,10.2271,10.4995,39950867
161 | 26/03/2018,10.5385,10.5774,10.3925,10.4509,52325512
162 | 27/03/2018,10.5385,10.626,10.4314,10.5579,40677320
163 | 28/03/2018,10.5677,10.6552,10.4412,10.5385,42263662
164 | 29/03/2018,10.7817,10.8304,10.6212,10.6358,39863962
165 | 02/04/2018,10.5677,10.7915,10.412,10.7623,43598094
166 | 03/04/2018,10.8499,10.879,10.6066,10.7136,42523917
167 | 04/04/2018,11.025,11.0542,10.5093,10.5774,44834639
168 | 05/04/2018,11.0445,11.0737,10.9277,11.0347,28755606
169 | 06/04/2018,10.879,11.0445,10.772,10.9764,31920686
170 | 09/04/2018,10.9472,11.0737,10.8985,10.9472,36462658
171 | 10/04/2018,11.1418,11.2391,11.1029,11.132,44005015
172 | 11/04/2018,11.1223,11.1807,10.9958,11.0737,32979038
173 | 12/04/2018,11.0055,11.2294,10.9472,11.1904,47133732
174 | 13/04/2018,10.9764,11.0834,10.9277,11.0445,34598783
175 | 16/04/2018,11.0737,11.1418,10.9666,11.0542,32803268
176 | 17/04/2018,11.0737,11.1418,10.9958,11.1223,30906803
177 | 18/04/2018,11.025,11.171,10.9958,11.0737,46226082
178 | 19/04/2018,10.8081,11.0934,10.6996,11.025,49154173
179 | 20/04/2018,10.67,10.8771,10.6306,10.8574,44930027
180 | 23/04/2018,10.887,10.887,10.6799,10.6897,31936522
181 | 24/04/2018,10.8081,11.0151,10.7785,10.8968,39624986
182 | 25/04/2018,10.956,11.0053,10.7193,10.8081,40356076
183 | 26/04/2018,11.2715,11.3702,11.1335,11.2617,80676755
184 | 27/04/2018,11.3307,11.3997,11.2222,11.242,33375943
185 | 30/04/2018,11.0842,11.5625,11.0645,11.3702,66090895
186 | 01/05/2018,11.1039,11.1927,10.9461,11.094,61430446
187 | 02/05/2018,11.0546,11.1088,10.9658,11.1039,41899467
188 | 03/05/2018,11.0447,11.0546,10.8179,11.0151,38907404
189 | 04/05/2018,11.2025,11.2518,10.9363,11.0053,32043468
190 | 07/05/2018,11.1828,11.2814,11.1532,11.2025,27510279
191 | 08/05/2018,11.1138,11.2124,11.0792,11.1631,25754109
192 | 09/05/2018,10.9067,11.1236,10.8672,11.0546,43731958
193 | 10/05/2018,11.0546,11.094,10.8672,10.887,32023835
194 | 11/05/2018,11.0349,11.0891,11.0053,11.0645,19837675
195 | 14/05/2018,11.025,11.1335,11.0151,11.0743,27903121
196 | 15/05/2018,11.0645,11.094,10.9067,10.9461,26950114
197 | 16/05/2018,11.242,11.242,11.0447,11.0546,28976692
198 | 17/05/2018,11.3011,11.3209,11.2222,11.242,20741087
199 | 18/05/2018,11.1729,11.311,11.1433,11.2814,23893731
200 | 21/05/2018,11.3504,11.3899,11.2321,11.2617,31011916
201 | 22/05/2018,11.3603,11.4737,11.3504,11.4195,22360885
202 | 23/05/2018,11.2814,11.3899,11.1729,11.3406,23595004
203 | 24/05/2018,11.4589,11.4885,11.2814,11.2913,34876334
204 | 25/05/2018,11.3504,11.4441,11.311,11.3997,19337470
205 | 29/05/2018,11.2814,11.4589,11.2025,11.4392,31324274
206 | 30/05/2018,11.3899,11.449,11.311,11.3702,25554593
207 | 31/05/2018,11.3899,11.5723,11.2025,11.4885,61563249
208 | 01/06/2018,11.5477,11.6068,11.449,11.5082,35406189
209 | 04/06/2018,11.5772,11.6216,11.5279,11.5772,25141346
210 | 05/06/2018,11.666,11.703,11.5181,11.5772,31036704
211 | 06/06/2018,11.8041,11.8336,11.6561,11.7054,31110458
212 | 07/06/2018,11.8632,11.9816,11.7942,11.8041,47314073
213 | 08/06/2018,11.9323,11.952,11.8336,11.8731,28371881
214 | 11/06/2018,11.8632,11.9816,11.8534,11.9323,30398078
215 | 12/06/2018,11.9421,11.9816,11.8632,11.9027,51395448
216 | 13/06/2018,11.8534,11.9323,11.7942,11.9323,28406733
217 | 14/06/2018,11.7252,11.8435,11.6759,11.8336,34523019
218 | 15/06/2018,11.7153,11.8632,11.6364,11.6759,56326835
219 | 18/06/2018,11.8238,11.8731,11.6463,11.6561,32706527
220 | 19/06/2018,11.7252,11.7252,11.5082,11.6561,40064785
221 | 20/06/2018,11.7054,11.8829,11.666,11.8041,31964792
222 | 21/06/2018,11.5477,11.6659,11.4786,11.6364,33316702
223 | 22/06/2018,11.4885,11.7054,11.4589,11.5871,38207303
224 | 25/06/2018,11.3406,11.5082,11.2222,11.4688,36101893
225 | 26/06/2018,11.3603,11.4688,11.3307,11.3997,36627672
226 | 27/06/2018,11.2617,11.4786,11.242,11.38,27241994
227 | 28/06/2018,11.1236,11.242,10.9954,11.2124,43836444
228 | 29/06/2018,10.9165,11.2124,10.8771,11.1631,43615655
229 | 02/07/2018,10.9461,10.951,10.7588,10.8672,37892751
230 | 03/07/2018,10.8376,10.9954,10.7883,10.9461,16544777
231 | 05/07/2018,10.8968,10.9856,10.8672,10.9461,32008544
232 | 06/07/2018,10.9067,10.9461,10.7686,10.887,25487567
233 | 09/07/2018,11.0447,11.1039,10.8771,10.9461,23916802
234 | 10/07/2018,11.094,11.1039,11.0151,11.0447,21231537
235 | 11/07/2018,10.9165,11.0151,10.8968,10.9954,30030119
236 | 12/07/2018,10.8968,10.9856,10.8475,10.9461,27025237
237 | 13/07/2018,10.8278,10.8968,10.7588,10.8672,36537177
238 | 16/07/2018,10.6996,10.8179,10.6108,10.7982,46156833
239 | 17/07/2018,10.7094,10.7489,10.6503,10.6503,37584265
240 | 18/07/2018,10.7193,10.744,10.6601,10.6996,36420018
241 | 19/07/2018,10.67,10.7489,10.6207,10.739,40308773
242 | 20/07/2018,10.56,10.72,10.51,10.68,41447134
243 | 23/07/2018,10.47,10.57,10.42,10.56,34941983
244 | 24/07/2018,10.57,10.74,10.5,10.51,47302950
245 | 25/07/2018,10.52,10.57,10.12,10.32,92610535
246 | 26/07/2018,9.89,10.16,9.84,10.14,103893039
247 | 27/07/2018,9.93,10.05,9.91,9.96,45933084
248 | 30/07/2018,10.07,10.1,9.93,9.96,35877009
249 | 31/07/2018,10.04,10.11,10,10.11,38026871
250 | 01/08/2018,9.9,10.0785,9.82,10.06,44126692
251 | 02/08/2018,9.92,9.98,9.84,9.9,40318865
252 | 03/08/2018,10.04,10.06,9.93,9.93,25925122
253 | 


--------------------------------------------------------------------------------
/tradechat/static/style.css:
--------------------------------------------------------------------------------
 1 | body { font-family: sans-serif; background: #eee; }
 2 | a, h1, h2 { color: #0066cc; }
 3 | h1, h2 { font-family: 'Helvetica', sans-serif; margin: 0; }
 4 | h1 { font-size: 1.4em; border-bottom: 2px solid #eee; }
 5 | h2 { font-size: 1.0em; }
 6 | 
 7 | .page { margin: 2em auto; width: 35em; border: 1px solid #ccc;
 8 |                   padding: 0.8em; background: white; }
 9 | .comments { list-style: none; margin: 0; padding: 0; }
10 | .comments li { margin: 0.8em 1.2em; }
11 | .comments li h2 { margin-left: -1em; }
12 | .add-comment { color: #0066cc; font-size: 0.7em; border-bottom: 1px solid #ccc; }
13 | .add-comment dl { font-weight: bold; }
14 | .metanav { text-align: right; font-size: 0.8em; padding: 0.3em;
15 |                   margin-bottom: 1em; background: #fafafa; }
16 | .flash { color: #b9b9b9; font-size: 0.7em; }
17 | .error { color: #ff4629; font-size: 0.7em; padding: 0.5em; }


--------------------------------------------------------------------------------
/tradechat/table.sql:
--------------------------------------------------------------------------------
 1 | drop table if exists comments;
 2 | create table comments (
 3 |   id integer primary key autoincrement,
 4 |   comment text not null,
 5 |   user text not null,
 6 |   time text not null
 7 | );
 8 | 
 9 | drop table if exists users;
10 | create table users (
11 |   id integer primary key autoincrement,
12 |   name text not null,
13 |   password text not null
14 | );
15 | 


--------------------------------------------------------------------------------
/tradechat/templates/layout.html:
--------------------------------------------------------------------------------
 1 | <!doctype html>
 2 | <title>Tradechat</title>
 3 | <link rel=stylesheet type=text/css
 4 |      href="{{ url_for('static', filename='style.css') }}">
 5 | <div class=page>
 6 |   <h1>Tradechat</h1>
 7 |   <div class=metanav>
 8 |   {% if not session.logged_in %}
 9 |     <a href="{{ url_for('login') }}">log in</a><br>
10 |     <a href="{{ url_for('register') }}">register</a>
11 |   {% else %}
12 |     <a href="{{ url_for('logout') }}">log out</a>
13 |   {% endif %}
14 |   </div>
15 |   {% for message in get_flashed_messages() %}
16 |     <div class=flash>{{ message }}</div>
17 |   {% endfor %}
18 |   {% block body %}{% endblock %}
19 | </div>


--------------------------------------------------------------------------------
/tradechat/templates/login.html:
--------------------------------------------------------------------------------
 1 | {% extends "layout.html" %}
 2 | {% block body %}
 3 |   <h2>Login</h2>
 4 |   {% if error %}<p class=error><strong>Error:</strong> {{ error }}{% endif %}
 5 |   <form action="{{ url_for('login') }}" method=post>
 6 |     <dl>
 7 |       <dd><font size="-1">Username</font>
 8 |       <dd><input type=text name=username>
 9 |       <dd><font size="-1">Password</font>
10 |       <dd><input type=password name=password>
11 |       <dd><input type=submit value=Login>
12 |     </dl>
13 |   </form>
14 | {% endblock %}


--------------------------------------------------------------------------------
/tradechat/templates/register.html:
--------------------------------------------------------------------------------
 1 | {% extends "layout.html" %}
 2 | {% block body %}
 3 |   <h2>Register</h2>
 4 |   {% if error %}<p class=error><strong>Error:</strong> {{ error }}{% endif %}
 5 |   <form action="{{ url_for('register') }}" method=post>
 6 |     <dl>
 7 |       <dd><font size="-1">Username</font>
 8 |       <dd><input type=text name=username>
 9 |       <dd><font size="-1">Password</font>
10 |       <dd><input type=password name=password>
11 |       <dd><input type=submit value=Register>
12 |     </dl>
13 |   </form>
14 | {% endblock %}


--------------------------------------------------------------------------------
/tradechat/templates/show_entries.html:
--------------------------------------------------------------------------------
 1 | {% extends "layout.html" %}
 2 | {% block body %}
 3 |   {% if session.logged_in %}
 4 |     <form action="{{ url_for('add_entry') }}" method=post class=add-comment>
 5 |       <dl>
 6 |         <dd>What's up?
 7 |         <dd><textarea name=text rows=3 cols=40></textarea>
 8 |         <dd><input type=submit value=Post>
 9 |       </dl>
10 |     </form>
11 |   {% endif %}
12 |   <ul class=comments>
13 |   {% for comment in comments %}
14 |     <li>{{ comment.comment|safe }}
15 |          <font size="-2">({{ comment.user }} @ {{ comment.time }})</font>
16 |   {% else %}
17 |     <li><em>No comments so far.</em>
18 |   {% endfor %}
19 |   </ul>
20 | {% endblock %}


--------------------------------------------------------------------------------
/tradechat/tradechat.db:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/taymourniazi/Python-for-Finance/5f43f9774915d266244cec560baba9c5fdec80c7/tradechat/tradechat.db


--------------------------------------------------------------------------------
/tradechat/tradechat.py:
--------------------------------------------------------------------------------
  1 | # Tradechat
  2 | # 
  3 | # A simple example for a web-based chat room
  4 | # based on Flask and SQLite3.
  5 | # 
  6 | 
  7 | import os
  8 | import datetime as dt
  9 | from sqlite3 import dbapi2 as sqlite3
 10 | from flask import Flask, request, session, g, redirect, url_for, abort, \
 11 |      render_template, flash
 12 | 
 13 | 
 14 | # the application object from the main Flask class
 15 | app = Flask(__name__)
 16 | 
 17 | # override config from environment variable
 18 | app.config.update(dict(
 19 |     DATABASE=os.path.join(app.root_path, 'tradechat.db'),
 20 |       # the SQLite3 database file ("TC database")
 21 |     DEBUG=True,
 22 |     SECRET_KEY='secret_key',
 23 |       # use secure key here for real applications
 24 | ))
 25 | app.config.from_envvar('TC_SETTINGS', silent=True)
 26 |   # do not complain if no config file exists
 27 | 
 28 | def connect_db():
 29 |     ''' Connects to the TC database.'''
 30 |     rv = sqlite3.connect(app.config['DATABASE'])
 31 |     rv.row_factory = sqlite3.Row
 32 |     return rv
 33 | 
 34 | 
 35 | def get_db():
 36 |     ''' Opens a new connection to the TC database. '''
 37 |     if not hasattr(g, 'sqlite_db'):
 38 |         # open only if none exists yet
 39 |         g.sqlite_db = connect_db()
 40 |     return g.sqlite_db
 41 | 
 42 | 
 43 | def init_db(): 
 44 |     ''' Creates the TC database tables.'''
 45 |     with app.app_context():
 46 |         db = get_db()
 47 |         with app.open_resource('tables.sql', mode='r') as f:
 48 |             db.cursor().executescript(f.read())
 49 |                # creates entries and users tables
 50 |         db.commit()
 51 | 
 52 | 
 53 | @app.teardown_appcontext
 54 | def close_db(error):
 55 |     ''' Closes the TC database at the end of the request. '''
 56 |     if hasattr(g, 'sqlite_db'):
 57 |         g.sqlite_db.close()
 58 | 
 59 | 
 60 | @app.route('/')
 61 | def show_entries():
 62 |     ''' Renders all entries of the TC database. '''
 63 |     db = get_db()
 64 |     query = 'select comment, user, time from comments order by id desc'
 65 |     cursor = db.execute(query)
 66 |     comments = cursor.fetchall()
 67 |     return render_template('show_entries.html', comments=comments)
 68 | 
 69 | 
 70 | @app.route('/register', methods=['GET', 'POST'])
 71 | def register():
 72 |     ''' Registers a new user in the TC database. '''
 73 |     error = None
 74 |     if request.method == 'POST':
 75 |         db = get_db()
 76 |         if request.form['username'] == '' or request.form['password'] == '':
 77 |             error = 'Provide both a username and a password.'
 78 |             # both fields have to be nonempty
 79 |         else:
 80 |             db.execute('insert into users (name, password) values (?, ?)',
 81 |                          [request.form['username'], request.form['password']])
 82 |             db.commit()
 83 |             session['logged_in'] = True
 84 |             # directly log in new user
 85 |             flash('You were sucessfully registered.')
 86 |             app.config.update(dict(USERNAME=request.form['username']))
 87 |             return redirect(url_for('show_entries'))
 88 |     return render_template('register.html', error=error)
 89 | 
 90 | 
 91 | @app.route('/login', methods=['GET', 'POST'])
 92 | def login():
 93 |     ''' Logs in a user. '''
 94 |     error = None
 95 |     if request.method == 'POST':
 96 |         db = get_db()
 97 |         try:
 98 |             query = 'select id from users where name = ? and password = ?'
 99 |             id = db.execute(query, (request.form['username'],
100 |                                     request.form['password'])).fetchone()[0]
101 |               # fails if record with provided username and password
102 |               # is not found
103 |             session['logged_in'] = True
104 |             flash('You are now logged in.')
105 |             app.config.update(dict(USERNAME=request.form['username']))
106 |             return redirect(url_for('show_entries'))
107 |         except:
108 |             error = 'User not found or wrong password.'
109 |     return render_template('login.html', error=error)
110 | 
111 | 
112 | @app.route('/add', methods=['POST'])
113 | def add_entry():
114 |     ''' Adds entry to the TC database. '''
115 |     if not session.get('logged_in'):
116 |         abort(401)
117 |     db = get_db()
118 |     now = dt.datetime.now()
119 |     db.execute('insert into comments (comment, user, time) values (?, ?, ?)',
120 |             [request.form['text'], app.config['USERNAME'], str(now)[:-7]])
121 |     db.commit()
122 |     flash('Your comment was successfully added.')
123 |     return redirect(url_for('show_entries'))
124 | 
125 | 
126 | @app.route('/logout')
127 | def logout():
128 |     ''' Logs out the current user. '''
129 |     session.pop('logged_in', None)
130 |     flash('You were logged out')
131 |     return redirect(url_for('show_entries'))
132 | 
133 | # main routine
134 | if __name__ == '__main__':
135 |     init_db()  # comment out if data in current
136 |                # TC database is to be kept
137 |     app.run()


--------------------------------------------------------------------------------