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EvanMarie/stock_helpers.ipynb Secret

Created December 28, 2022 15:35
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stock_helpers.ipynb
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stock_helpers.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/EvanMarie/806986d1219bc051cc59bb180d06320a/stock_helpers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"import yfinance as yf\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"pd.options.display.float_format = '{:,.3f}'.format\n",
"\n",
"OUTTER_BACK = '#222222'\n",
"INNER_BACK = '#333333'\n",
"FOREGROUND = 'white'"
],
"metadata": {
"id": "c15Sp_z4EnK5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# dataframe display formatting\n",
"\n",
"def color_negative_red(val):\n",
" color = 'red' if val < 0 else 'black'\n",
" return 'color: %s' % color\n",
"\n",
"def highlight_max(s):\n",
" is_max = s == s.max()\n",
" return ['color: blue' if v else '' for v in is_max]\n",
"\n",
"def style_data(data):\n",
" return data.style.\\\n",
" applymap(color_negative_red).\\\n",
" apply(highlight_max).\\\n",
" set_table_attributes('style=\"font-size: 14px\"').\\\n",
" format(thousands = ',', precision = '2')"
],
"metadata": {
"id": "51ie_WMqEede"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def display_me(df, num, title=None):\n",
" if type(df) != pd.core.frame.DataFrame:\n",
" df = df.to_frame()\n",
" print(\"\")\n",
" highlight(title.upper(), 'cyan', 'black', 3)\n",
" highlight(f'(FIRST {num} ROWS of DATA)', 'cyan', 'black', 3)\n",
" display(style_data(df.head(num)))\n",
" print(\"\")\n",
" highlight(f'(LAST {num} ROWS of DATA)', 'cyan', 'black', 3)\n",
" display(style_data(df.tail(num)))\n",
" print(\"\")"
],
"metadata": {
"id": "bQ6kH6J7Eea-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def head2(data, num_rows):\n",
" return display(style_data(data.head(num_rows)))\n",
"\n",
"def tail2(data, num_rows):\n",
" return display(style_data(data.tail(num_rows)))\n",
"\n",
"def sample2(data, num_rows):\n",
" return display(style_data(data.sample(num_rows)))"
],
"metadata": {
"id": "bsj9e0yoEeYp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def colorprint(text, color='#0038ff', fontsize = 3):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<b><font size={}><text style=color:{}>{}</text></font></b>\".format(fontsize, color, text)))\n",
"\n",
"def redprint(text, fontsize = 3):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<b><font size={}><text style=color:{}>{}</text></font></b>\".format(fontsize, '#ff0000', text)))\n",
"\n",
"def blueprint(text, fontsize = 3):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<b><font size={}><text style=color:{}>{}</text></font></b>\".format(fontsize, 'blue', text)))\n",
"\n",
"def purpleprint(text, fontsize = 3):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<b><font size={}><text style=color:{}>{}</text></font></b>\".format(fontsize, '#8d00ff', text)))\n",
"\n",
"def greenprint(text, fontsize = 3):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<b><font size={}><text style=color:{}>{}</text></font></b>\".format(fontsize, '#00b648', text)))"
],
"metadata": {
"id": "jJjfKNk4EeWN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def highlight(text, highlight_color='yellow', \n",
" text_color='black', fontsize=2):\n",
" from IPython.display import HTML as html_print\n",
" return display(html_print(\"<span style='background-color:{}; padding: 0.5em 0.5em;'><b><font size={}><text style=color:{}>{}</text></font></b></span>\".format(highlight_color, fontsize, text_color, text)))"
],
"metadata": {
"id": "kazdOKFHEeT-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def display_rows(display_list, num_cols, title):\n",
" print(\"........\" * num_cols)\n",
" highlight(f\"{title.upper()}:\", 'cyan', 'black', 3)\n",
" current = 0\n",
" length = len(display_list)\n",
" num_rows = round(length / num_cols)\n",
" for i in range(num_rows + 1):\n",
" print('\\t'.join([str(x) for x in list(display_list[current:current+num_cols])]))\n",
" current = current + num_cols\n",
" print(\"........\" * num_cols)\n",
" print('')"
],
"metadata": {
"id": "0MdOb08dEhYj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def rows_cols(df, df_name):\n",
" df_rows = df.shape[0]\n",
" df_cols = df.shape[1]\n",
" highlight(f'📊 \"{df_name}\" has {df_rows:,} rows and {df_cols} columns.\\n', \n",
" 'yellow', 'black', 3)"
],
"metadata": {
"id": "F-49j7KNEhWU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def print_date_range(df, data_name = None):\n",
" start_year, start_month, start_day = (df.index.min().year, \n",
" df.index.min().month_name(), \n",
" df.index.min().day)\n",
"\n",
" end_year, end_month, end_day = (df.index.max().year, \n",
" df.index.max().month_name(), \n",
" df.index.max().day)\n",
" \n",
" if data_name != None:\n",
" highlight(f'⏰ \"{data_name}\" time range: {start_month} {start_day}, {start_year} to {end_month} {end_day}, {end_year}', 'yellow', 'black', 3)\n",
" else:\n",
" highlight(f\"⏰ Time range: {start_month} {start_day}, {start_year} to {end_month} {end_day}, {end_year}\", 'yellow', 'black', 3)"
],
"metadata": {
"id": "cxn7m6U-EhT_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def get_df_range(df, start, end):\n",
" return df.loc[start : end]"
],
"metadata": {
"id": "nRXfWJdSEhRh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def annualized_data(returns, title = None):\n",
" annualized = returns.agg(['mean', 'std']).T\n",
" annualized['return'] = annualized[\"mean\"] * 250\n",
" annualized['risk'] = annualized['std'] * np.sqrt(250)\n",
" annualized.drop(columns = ['mean', 'std'], inplace = True)\n",
" if title != None:\n",
" highlight(f\"💰 Annualized Return and Risk: {title}\", \n",
" \"lime\", \"black\",fontsize=3)\n",
" else:\n",
" highlight(f\"💰 Annualized Return and Risk\", \n",
" \"lime\", \"black\",fontsize=3)\n",
" display(annualized)\n",
" return annualized"
],
"metadata": {
"id": "4Hw2saHxEhPD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def line_graph(data, start=None, end=None, close_column=\"close\",\n",
" title=None, xlabel=None, ylabel=None):\n",
" \n",
" import matplotlib as mpl\n",
" import matplotlib.pyplot as plt\n",
" import random\n",
" colors = ['tomato', 'yellow', 'peachpuff', 'lime', 'turquoise',\n",
" 'aqua', 'deepskyblue', 'dodgerblue', 'cornflowerblue', 'lavender',\n",
" 'violet', 'fuchsia', 'deeppink']\n",
" \n",
" if (start != None) & (end != None):\n",
" data = data.loc[start : end, close_column].to_frame()\n",
" else:\n",
" data = data\n",
" \n",
" mpl.rcParams['font.family'] = 'monospace'\n",
" fig, ax = plt.subplots(facecolor = OUTTER_BACK, figsize = (13, 7))\n",
" plt.style.use(\"ggplot\");\n",
" ax.plot(data, color = random.choice(colors))\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.65, alpha = 1)\n",
"\n",
" plt.tick_params(labelrotation = 40);\n",
" plt.title(title, fontsize = 23, pad = 20, color=\"white\");\n",
" plt.ylabel(ylabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel(xlabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=12, color=\"white\")\n",
" plt.yticks(fontsize=12, color=\"white\")\n",
" \n",
" print_date_range(data)\n",
" print(\"\")"
],
"metadata": {
"id": "7s9uLmLnEhMj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def plot_threshold_strategies(df, start_threshold=0.00, return_column='dj_return', \n",
" close_column='dj_close', increase_increment=0.01, \n",
" iterations=10, start = None, end = None):\n",
" import warnings\n",
" warnings.filterwarnings(\"ignore\")\n",
" \n",
" if (start != None) & (end != None):\n",
" df = df.loc[start : end, close_column].to_frame()\n",
" else:\n",
" df = df\n",
" \n",
" return_trigger = start_threshold\n",
" results_log = []\n",
" \n",
" for iteration in range(iterations):\n",
" df[\"position\"] = np.where(df[return_column] > return_trigger, -1, 1)\n",
" df[\"strategy_return\"] = df[\"position\"].shift() * df[return_column]\n",
" df['strategy'] = (df.strategy_return.add(1,\n",
" fill_value = 0).cumprod() * \\\n",
" df.iloc[0, 0])\n",
" current_data = (return_trigger, df[['dj_close', 'strategy']])\n",
" results_log.append(current_data)\n",
" return_trigger += increase_increment\n",
" \n",
" rows = round(len(results_log) / 2)\n",
" fig, axs = plt.subplots(nrows=rows, ncols=2, \n",
" figsize = (13, rows*4), facecolor=OUTTER_BACK, \n",
" constrained_layout=True)\n",
" \n",
" print_date_range(df)\n",
" print(\"\")\n",
" plt.style.use(\"ggplot\");\n",
" plt.suptitle(\"Simple Strategy Selling Threshold\", color = \"white\", size = 24)\n",
" for idx, i in enumerate(results_log):\n",
" return_trigger = results_log[idx][0]\n",
" close = results_log[idx][1][close_column]\n",
" returns = results_log[idx][1]['strategy']\n",
" ax = plt.axes();\n",
" ax.remove()\n",
" plt.subplot(rows, 2, idx+1, facecolor = INNER_BACK)\n",
" close.plot(color = 'cyan')\n",
" returns.plot(color = 'yellow')\n",
" plt.grid(color=FOREGROUND, linestyle=':', linewidth=0.65, alpha = 1)\n",
" plt.legend(loc = 2, facecolor = \"#555555\", labelcolor = \"white\")\n",
" plt.yticks(color = FOREGROUND)\n",
" plt.xticks(color = FOREGROUND)\n",
" plt.xlabel(\"years\", color=\"white\", size = 10)\n",
" plt.ylabel('')\n",
" plt.title(f\"Strategy: {(return_trigger*100):.0f}% Sell Threshold\", \n",
" color = \"white\", size = 15, pad = 5)\n",
" \n",
" return results_log "
],
"metadata": {
"id": "o1umHkUyEeRi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def try_strategy(df, return_column=\"dj_return\", close_column=\"dj_close\", \n",
" strategy = 'momentum', threshold = None, start = None,\n",
" end = None):\n",
" \n",
" if (start != None) & (end != None):\n",
" df = df.loc[start : end, close_column].to_frame()\n",
" else:\n",
" df = df\n",
" \n",
" if strategy == 'momentum':\n",
" df['position'] = np.sign(df[return_column])\n",
" elif strategy == 'contrarian':\n",
" df[\"position\"] = -np.sign(df[return_column])\n",
" else:\n",
" df[\"position\"] = np.where(df[return_column] > threshold, -1, 1) \n",
" \n",
" df[\"strategy_return\"] = df[\"position\"].shift() * df['dj_return']\n",
" df['strategy'] = df.strategy_return.add(1, \n",
" fill_value = 0).cumprod() * df.iloc[0,0]\n",
" plt.figure(figsize = (13, 7), facecolor = OUTTER_BACK);\n",
" ax = plt.axes();\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
" \n",
" df[close_column].plot(color = 'deeppink')\n",
" df['strategy'].plot(color = 'yellow')\n",
" plt.legend(loc = 2, facecolor = \"white\", labelcolor = \"black\")\n",
" plt.yticks(color = FOREGROUND, size = 10)\n",
" plt.xticks(color = FOREGROUND, size = 10)\n",
" plt.xlabel(\"years\", color=\"white\", size = 14)\n",
" plt.ylabel('')\n",
" if threshold != None:\n",
" plt.title(f\"Strategy: {(threshold*100):.0f}% Sell Threshold\", \n",
" color = \"white\", size = 22, pad = 20,)\n",
" else:\n",
" plt.title(f\"Strategy: {strategy.capitalize()}\", \n",
" color = \"white\", size = 22, pad = 20,)\n",
" \n",
" \n",
" print_date_range(df)\n",
" print(\"\") \n",
" \n",
" annualized = annualized_data(df[[return_column, 'strategy_return']])\n",
" \n",
" return df, annualized \n"
],
"metadata": {
"id": "9hM1KnVOE_mE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def rolling_average(df, close_column='dj_close', window=50,\n",
" start=None, end=None, legend_loc = 2):\n",
" \n",
" if (start != None) & (end != None):\n",
" df = df.loc[start : end, close_column].to_frame()\n",
" else:\n",
" df = df\n",
" \n",
" rolling_col = \"SMA\" + (str(window))\n",
" df[rolling_col] = df[close_column].rolling(window = window).mean()\n",
" close = df[close_column]\n",
" rolling = df[rolling_col]\n",
" \n",
" fig, ax = plt.subplots(facecolor = OUTTER_BACK, figsize = (13, 7))\n",
" plt.style.use(\"ggplot\");\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
"\n",
" plt.tick_params(labelrotation = 40);\n",
" plt.title(f\"Rolling Averge: {window} day window\", fontsize = 21, pad = 20, color=\"white\");\n",
" plt.ylabel('', fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel('', fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=12, color=FOREGROUND)\n",
" plt.yticks(fontsize=12, color=FOREGROUND)\n",
" close.plot(color='deeppink', linewidth=5, alpha = 0.7)\n",
" rolling.plot(color='cyan') \n",
" plt.legend(fontsize = 13, facecolor = INNER_BACK, \n",
" labelcolor = FOREGROUND, loc = legend_loc)\n",
" \n",
" print_date_range(df)\n",
" print(\"\") "
],
"metadata": {
"id": "hstB1Mg_FEsN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def multiple_rolling_averages(df, close_column='dj_close', \n",
" window_list=[50], start=None,\n",
" end=None, legend_loc = 2):\n",
" import random\n",
" \n",
" colors = ['tomato', 'yellow', 'peachpuff', 'lime', 'turquoise',\n",
" 'aqua', 'deepskyblue', 'dodgerblue', 'cornflowerblue', 'lavender',\n",
" 'violet', 'fuchsia', 'deeppink']\n",
" \n",
" color_list = random.sample(colors, len(window_list))\n",
" \n",
" if (start != None) & (end != None):\n",
" df = df.loc[start : end, close_column].to_frame()\n",
" else:\n",
" df = df\n",
" \n",
" close = df[close_column]\n",
" window_log = []\n",
" \n",
" for i in window_list:\n",
" rolling_col = \"SMA\" + (str(i))\n",
" rolling = df[close_column].rolling(window = i).mean()\n",
" current_data = (rolling_col, rolling)\n",
" window_log.append(current_data)\n",
" \n",
" fig, ax = plt.subplots(facecolor = OUTTER_BACK, figsize = (13, 7))\n",
" plt.style.use(\"ggplot\");\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
" plt.tick_params(labelrotation = 40);\n",
" \n",
" if len(window_list) < 5:\n",
" title_string = ', '.join(str(i) for i in window_list)\n",
" plt.title(f\"Rolling Averges Compared: {title_string} days\", fontsize = 20, pad = 20, color=\"white\");\n",
" else:\n",
" plt.title(f\"Rolling Averges Compared\", fontsize = 22, pad = 20, color=\"white\");\n",
" \n",
" plt.ylabel('', fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel('', fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=12, color=FOREGROUND)\n",
" plt.yticks(fontsize=12, color=FOREGROUND) \n",
" labels = [x[0] for x in window_log]\n",
" \n",
" for idx, i in enumerate(window_log):\n",
" ax.plot(i[1], color = color_list[idx], label = i[0])\n",
" \n",
" plt.legend(loc = legend_loc, fontsize = 13, \n",
" facecolor = INNER_BACK, labelcolor = FOREGROUND)\n",
" \n",
" print_date_range(df)\n",
" print(\"\") "
],
"metadata": {
"id": "9I4ud_OGFEpT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def fancy_plot(data, kind = \"line\", title = None, legend_loc = 'upper right', \n",
" start=None, end=None, xlabel=None, ylabel=None, logy=False,\n",
" cmap = 'viridis', label_rot = None):\n",
" \n",
" import random \n",
" import matplotlib as mpl\n",
" import matplotlib.pyplot as plt\n",
" \n",
" if (start != None) & (end != None):\n",
" data = data.loc[start : end]\n",
" else:\n",
" data = data\n",
" \n",
" mpl.rcParams['xtick.color'] = OUTTER_BACK\n",
" mpl.rcParams['ytick.color'] = OUTTER_BACK\n",
" mpl.rcParams['font.family'] = 'monospace'\n",
" fig = plt.subplots(facecolor = OUTTER_BACK, figsize = (13, 7))\n",
" ax = plt.axes();\n",
" if kind == 'line':\n",
" data.plot(kind='line', ax = ax, rot = label_rot, cmap = cmap)\n",
" else:\n",
" data.plot(kind = kind, ax = ax, rot = label_rot, cmap = cmap);\n",
" plt.style.use(\"ggplot\");\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
" plt.tick_params(labelrotation = 40);\n",
" plt.title(title, fontsize = 23, pad = 20, color=\"white\");\n",
" plt.ylabel(ylabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel(xlabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=10, color=FOREGROUND)\n",
" plt.yticks(fontsize=10, color=FOREGROUND)\n",
" plt.legend(labels = data.columns, fontsize = 10, loc = legend_loc,\n",
" facecolor = INNER_BACK, labelcolor = FOREGROUND)"
],
"metadata": {
"id": "ncrOobeLFEm9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def correlation_heatmap(df, cmap, annot = True, fmt = '.1g',\n",
" vmin = 0.2, vmax = 0.8,\n",
" title = None, ):\n",
" import seaborn as sns\n",
" correlation_map = df.corr()\n",
" plt.figure(figsize = (13,9), facecolor = OUTTER_BACK)\n",
"\n",
" sns.heatmap(correlation_map, cmap = cmap, annot = annot, vmin = vmin,\n",
" vmax = vmax, linewidth=0.25, linecolor = \"white\", fmt = fmt) \n",
" plt.title(title, fontsize = 23, pad = 20, color=\"white\");\n",
" plt.xticks(fontsize=11, color=\"white\")\n",
" plt.yticks(fontsize=11, color=\"white\")"
],
"metadata": {
"id": "Kpwk2TwIFEkI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def portfolio_returns(weights, portfolio):\n",
" return portfolio.dot(weights)\n",
"\n",
"\n",
"def tracking_error(weights, portfolio, index_data):\n",
" result = portfolio_returns(weights, portfolio).sub(index_data).std() * np.sqrt(250)\n",
" colorprint(f'The annualized tracking error for the portfolio is {(result*100):.2f}%', fontsize = 3)\n",
" return result\n",
" \n",
" \n",
"def tracking_error_general(data, portfolio_list, weights, \n",
" index, start, end, title = None,\n",
" printout=True):\n",
" \n",
" results = data.loc[start:end, portfolio_list].dot(weights).\\\n",
" sub(data.loc[start:end, index]).std() * np.sqrt(250)\n",
" if printout is True:\n",
" highlight(title, 'yellow', 'black', 2)\n",
" highlight(f'Period: {start} to {end}', 'yellow', 'black', 2)\n",
" highlight(f'Overall tracking error: {(results *100): .2f}%', 'yellow', 'black', 2)\n",
" else:\n",
" return results"
],
"metadata": {
"id": "zu7Sy6NrFEhm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def visualize_tracking(data, portfolio_list, weights, index, start, end, \n",
" title=None, xlabel=None, ylabel=None, legend_loc=1, \n",
" cmap = 'rainbow', legend_labels = None, color1='red',\n",
" color2 = 'cyan'):\n",
" track = data.loc[start:end, portfolio_list].dot(weights).add(1).cumprod().mul(100)\n",
" track.name = 'Portfolio'\n",
" index = data.loc[start:end, index].add(1).cumprod().mul(100)\n",
" mpl.rcParams['xtick.color'] = OUTTER_BACK\n",
" mpl.rcParams['ytick.color'] = OUTTER_BACK\n",
" mpl.rcParams['font.family'] = 'monospace'\n",
" fig = plt.subplots(facecolor = OUTTER_BACK, figsize = (13, 7))\n",
" ax = plt.axes();\n",
" track.plot(ax = ax, color = color1)\n",
" index.plot(ax = ax, color = color2)\n",
" plt.legend(fontsize = 15, loc = 2)\n",
" plt.title(title, fontsize = 22, pad = 20)\n",
" plt.style.use(\"ggplot\");\n",
" ax.set_facecolor(INNER_BACK)\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
" plt.tick_params(labelrotation = 40);\n",
" plt.title(title, fontsize = 23, pad = 20, color=\"white\");\n",
" plt.ylabel(ylabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel(xlabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=10, color=FOREGROUND)\n",
" plt.yticks(fontsize=10, color=FOREGROUND)\n",
" plt.legend(labels = legend_labels, fontsize = 11, loc = legend_loc,\n",
" facecolor = INNER_BACK, labelcolor = FOREGROUND)\n",
" plt.show() "
],
"metadata": {
"id": "dS8vZAaZFZR6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def random_portfolios(num_portfolios, total_assets,\n",
" returns_df, stocks_list, seed = 123):\n",
" \n",
" np.random.seed(seed)\n",
" random_error_collection = np.empty(25000)\n",
" random_stocks = np.random.choice(stocks_list, size = total_assets, replace = False)\n",
" for i in range(25000):\n",
" random_numbers = np.random.random(total_assets)\n",
" random_weights = random_numbers / random_numbers.sum()\n",
" random_stock = np.random.choice(stocks_list, size = total_assets, replace = False)\n",
"\n",
" random_error_collection[i] = \\\n",
" tracking_error_general(returns_df,\n",
" random_stocks,\n",
" random_weights,\n",
" 'DJI', '2020', \n",
" '2022', \n",
" printout = False)\n",
" return random_error_collection"
],
"metadata": {
"id": "mzvxuao9FZPs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3Pbhahof_7st"
},
"outputs": [],
"source": [
"def distro_histo(data, num_bins, color='cyan', edgecolor='black',\n",
" title=None, xlabel=None, ylabel=None, legend_loc=None,\n",
" legend_labels=None):\n",
" plt.figure(figsize = (12, 8), facecolor = OUTTER_BACK)\n",
" ax = plt.axes();\n",
" ax.set_facecolor(INNER_BACK)\n",
" plt.hist(data, bins = num_bins, edgecolor = edgecolor, \n",
" linewidth = 0.5, color = \"cyan\")\n",
" plt.title(title, fontsize = 22, pad = 20)\n",
" plt.style.use(\"ggplot\");\n",
" ax.grid(color=FOREGROUND, linestyle=':', linewidth=0.75, alpha = 0.75)\n",
" plt.tick_params(labelrotation = 40);\n",
" plt.title(title, fontsize = 21, pad = 20, color=\"white\");\n",
" plt.ylabel(ylabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xlabel(xlabel, fontsize = 18, color=FOREGROUND);\n",
" plt.xticks(fontsize=10, color=FOREGROUND)\n",
" plt.yticks(fontsize=10, color=FOREGROUND)\n",
" if legend_labels != None:\n",
" plt.legend(labels = legend_labels, fontsize = 13, loc = legend_loc,\n",
" facecolor = INNER_BACK, labelcolor = FOREGROUND)\n",
" plt.show()\n",
"\n",
"\n",
"def compare_random_optimized(random_error_data, optimized_tracking_data, num_portfolios):\n",
" # Retrieving the results of random portfolios and comparing to optimized \n",
" # weight strategy\n",
" highlight(f'The optimized strategy outperformed {num_portfolios:,.0f} \\\n",
" random portfolios and weights {((1 - (random_error_data < optimized_tracking_data).mean()) \\\n",
" * 100):.2f}% of the time.', fontsize = 3)\n",
" highlight(f'{num_portfolios:,.0f} random portfolios & weights tracking error: \\\n",
" {(random_error_data.mean() * 100): .2f}%', fontsize = 3)\n",
" highlight(f'Optimized portfolio & weights tracking error: {(optimized_tracking_data \\\n",
" * 100):.2f}%', fontsize = 3)\n",
" print(\"\")\n",
"\n",
" # Plotting the distribution of tracking error across the random portfolios\n",
" distro_histo(random_error_data, 100, \"cyan\", \"black\",\n",
" title = f'Tracking Errors Across {num_portfolios:,.0f} \\\n",
" Random Portfolios',\n",
" xlabel = \"tracking errors across random portfolios\",\n",
" ylabel = \"count for tracking errors\")"
]
}
]
}
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