278 lines
7.6 KiB
Plaintext
278 lines
7.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import math\n",
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"import pandas as pd \n",
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"from IPython.display import Image, display\n",
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"\n",
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"#Newest_York = r'Newest_York.png'\n",
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"\n",
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"#display(Image(filename=Newest_York, width=700, height=500))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h2>I + II) Function for getting <b style=\"color:DodgerBlue;\">Estimated Growth & Past Performance </b> Scores --> <b>Sigmoid fucntion</b></h2>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6694062359364cc2ab6544bc4c5c8130",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(FloatSlider(value=5.746777777777778, description='x', max=200.0, min=-200.0, step=1.0), …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.plot_function(x)>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
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"\n",
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"\n",
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"average_score = not_normalized['Estimated Growth'].str.rstrip('%').astype(float).mean() #STARTING POINT (Analyt's yearly estimated growth (%) for next 5 years)\n",
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"\n",
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"slope = 0.1\n",
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"\n",
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"def plot_function(x):\n",
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" y = (200 / (1 + math.e**(-slope * (-average_score + x))))\n",
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" plt.plot(x_values, y_values)\n",
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" plt.scatter(x, y, color='red', label=f'x={x}')\n",
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" plt.xlabel('Estimated Growth (yearly in %)')\n",
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" plt.ylabel('Stockingly Score')\n",
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" plt.title('Estimated growth Score = sigmoid(Estimated growth %)')\n",
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" plt.legend()\n",
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" plt.grid(False) \n",
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" plt.axhline(0, color='black') \n",
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" plt.axvline(0, color='black') \n",
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"\n",
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" plt.axhline(y, color='green') \n",
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"\n",
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" plt.xlim(-200, 200) \n",
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" plt.ylim(-10, 250) \n",
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"\n",
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" plt.show()\n",
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"\n",
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"# Generate x values from -100 to 100\n",
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"x_values = np.linspace(-200, 200, 200)\n",
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"y_values = (200 / (1 + np.exp(-slope * (-average_score + x_values))))\n",
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"\n",
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"widgets.interact(plot_function, x=widgets.FloatSlider(min=-200, max=200, step=1, value=average_score))\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h2>III) Function for getting <b style=\"color:DodgerBlue;\">Financial health --> </b> score: <b>reverse exponential</b></h2>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5306c1272eb748aeb69bdd401c9262ed",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(FloatSlider(value=4.78006822089688, description='x', max=50.0, step=0.5), Output()), _do…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.plot_function(x)>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import sys\n",
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"sys.path.append('../group-1')\n",
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"import math\n",
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"import pandas as pd\n",
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"import numpy as np \n",
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"\n",
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"not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
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"\n",
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"\n",
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"average_score = not_normalized['Financial Health'].mean() #STARTING POINT (Mean Assets/debts)\n",
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"\n",
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"\n",
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"slope = 0.1\n",
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"\n",
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"def plot_function(x):\n",
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" y = 200 - 200*math.e**(-0.138 * (x))\n",
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" plt.plot(x_values, y_values)\n",
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" plt.scatter(x, y, color='red', label=f'x={x}')\n",
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" plt.xlabel('Total Assets/Total Debt')\n",
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" plt.ylabel('Stockingly Score')\n",
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" plt.title('Financial Health Score = ReverseExp(TOT Assets/TOT Debts)')\n",
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" plt.legend()\n",
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" plt.grid(False) \n",
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" plt.axhline(0, color='black') \n",
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" plt.axvline(0, color='black') \n",
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" plt.axhline(y, color='blue') \n",
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" plt.xlim(0, 100) \n",
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" plt.ylim(-10, 250) \n",
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" plt.show()\n",
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"\n",
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"x_values = np.linspace(-200, 200, 200)\n",
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"y_values = 200 - 200*math.e**(-0.138 * (x_values))\n",
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"\n",
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"\n",
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"widgets.interact(plot_function, x=widgets.FloatSlider(min=0, max=50, step=0.5, value=average_score))\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h2> IV) Function for getting <b style=\"color:DodgerBlue;\">valuation$ score</b> from PEG: --><b>Custom Fractal</b></h2>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5e249d428e5a4413969ea198ea92763e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(FloatSlider(value=2.8344899999999997, description='x', max=30.0), Output()), _dom_classe…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.plot_function(x)>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import sys\n",
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"sys.path.append('../group-1')\n",
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"import math\n",
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"import pandas as pd\n",
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"import numpy as np \n",
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"\n",
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"not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
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"\n",
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"average_score = not_normalized['Valuation'].mean() #STARTING POINT (MEAN PEG RATIO)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"def plot_function(x):\n",
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" y = (200/(1+(1/9*x**2)))\n",
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" plt.plot(x_values, y_values)\n",
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" plt.scatter(x, y, color='red', label=f'x={x}')\n",
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" plt.xlabel('PEG value')\n",
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" plt.ylabel('Stockingly Score Valuation')\n",
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" plt.title('Valuation Score = FractalPolynomial(PEG)')\n",
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" plt.legend()\n",
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" plt.grid(False) \n",
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" plt.axhline(0, color='black') \n",
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" plt.axvline(0, color='black') \n",
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" plt.axhline(y, color='blue') \n",
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" plt.xlim(0, 100) \n",
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" plt.ylim(-10, 250) \n",
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" plt.show()\n",
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"\n",
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"x_values = np.linspace(0, 200, 2000)\n",
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"\n",
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"y_values = (200/(1+(1/9*x_values**2)))\n",
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"\n",
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"widgets.interact(plot_function, x=widgets.FloatSlider(min=0, max=30, step=0.1, value=average_score))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "analyticvisual",
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"language": "python",
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"name": "visualanalytics"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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