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firmai/synthetic-portfolio-opimization.ipynb

Created April 28, 2023 18:23
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Synthetic Portfolio Opimization.ipynb
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synthetic-portfolio-opimization.ipynb
{
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"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNT6ORUl6OPM0dY9jYjJt12",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
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"name": "python"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/firmai/0cd368120b6cedfd9bca30cc41e4dcf5/synthetic-portfolio-opimization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip install PyPortfolioOpt"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jhVV5CsnD-Iy",
"outputId": "8e25719c-3a71-4cf3-ad55-fe2299a626ba"
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"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting PyPortfolioOpt\n",
" Downloading pyportfolioopt-1.5.4-py3-none-any.whl (61 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.9/61.9 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: cvxpy<2.0.0,>=1.1.10 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.3.1)\n",
"Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.10.1)\n",
"Requirement already satisfied: numpy<2.0.0,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.22.4)\n",
"Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from PyPortfolioOpt) (1.5.3)\n",
"Requirement already satisfied: setuptools>65.5.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (67.7.2)\n",
"Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.0.12)\n",
"Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.6.2.post8)\n",
"Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (3.2.3)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2022.7.1)\n",
"Requirement already satisfied: qdldl in /usr/local/lib/python3.10/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.1.7)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.19->PyPortfolioOpt) (1.16.0)\n",
"Installing collected packages: PyPortfolioOpt\n",
"Successfully installed PyPortfolioOpt-1.5.4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"from pypfopt import EfficientSemivariance\n",
"from pypfopt import risk_models\n",
"from pypfopt import expected_returns\n",
"import warnings\n",
"\n",
"# ignore all warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# Create fake returns data\n",
"np.random.seed(42)\n",
"dates = pd.date_range(start='2015-01-01', end='2020-12-31', freq='M')\n",
"strategies = ['president_tweet', 'media_stock', 'profitable_stock', 'famous_hf', 'meme_stock',\n",
" 'google_trends', 'warren_buffet', 'stock_twist', 'bankruptcy', 'momentum']\n",
"returns_data = pd.DataFrame(np.random.normal(loc=0.01, scale=0.05, size=(len(dates), len(strategies))),\n",
" index=dates, columns=strategies)\n",
"\n",
"# Rescale the returns data\n",
"returns_data = returns_data * 100\n",
"\n",
"# Perform mean-variance portfolio optimization on a monthly basis\n",
"monthly_allocations = []\n",
"\n",
"for i in range(11, len(returns_data)):\n",
" data = returns_data.iloc[i-11:i, :]\n",
" mu = expected_returns.mean_historical_return(data)\n",
" Sigma = risk_models.sample_cov(data)\n",
"\n",
" ef = EfficientSemivariance(mu, Sigma)\n",
" weights = ef.min_semivariance()\n",
" cleaned_weights = ef.clean_weights()\n",
" monthly_allocations.append(cleaned_weights)\n",
"\n",
"allocations_df = pd.DataFrame(monthly_allocations, index=returns_data.index[11:])\n",
"\n",
"# Plot the allocation changes over time using Plotly\n",
"fig = go.Figure()\n",
"\n",
"for strategy in strategies:\n",
" fig.add_trace(go.Scatter(x=allocations_df.index, y=allocations_df[strategy], mode='lines', name=strategy))\n",
"\n",
"fig.update_layout(title='Portfolio Allocations Over Time',\n",
" xaxis_title='Time',\n",
" yaxis_title='Allocation',\n",
" legend_title='Strategies')\n",
"\n",
"fig.show()"
],
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"height": 542
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"id": "SUgRCdjjEcVL",
"outputId": "4c1ba6c1-047c-410f-a80d-eeedd2ec5a20"
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"execution_count": 22,
"outputs": [
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"source": [
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"\n",
"def stacked_area_chart(allocations_df):\n",
" fig = go.Figure()\n",
"\n",
" for strategy in strategies:\n",
" fig.add_trace(go.Scatter(x=allocations_df.index, y=allocations_df[strategy],\n",
" stackgroup='one', mode='none', name=strategy))\n",
"\n",
" fig.update_layout(title='Stacked Area Chart of Portfolio Allocations',\n",
" xaxis_title='Time',\n",
" yaxis_title='Allocation',\n",
" legend_title='Strategies')\n",
" fig.show()\n",
"\n",
"def heatmap_chart(allocations_df):\n",
" fig = px.imshow(allocations_df.T, labels=dict(x=\"Date\", y=\"Strategy\", color=\"Allocation\"),\n",
" x=allocations_df.index, y=strategies,\n",
" title='Heatmap of Portfolio Allocations')\n",
" fig.update_xaxes(title='Time')\n",
" fig.update_yaxes(title='Strategies')\n",
" fig.show()\n",
"\n",
"def stacked_bar_chart(allocations_df):\n",
" fig = go.Figure()\n",
"\n",
" for strategy in strategies:\n",
" fig.add_trace(go.Bar(x=allocations_df.index, y=allocations_df[strategy],\n",
" name=strategy))\n",
"\n",
" fig.update_layout(title='Stacked Bar Chart of Portfolio Allocations',\n",
" xaxis_title='Time',\n",
" yaxis_title='Allocation',\n",
" legend_title='Strategies',\n",
" barmode='stack')\n",
" fig.show()\n",
"\n",
"\n",
"def sunburst_chart(allocations_df):\n",
" data = allocations_df.reset_index().melt(id_vars='index', var_name='Strategy', value_name='Allocation')\n",
" data.columns = ['Date', 'Strategy', 'Allocation']\n",
"\n",
" fig = px.sunburst(data, path=['Date', 'Strategy'], values='Allocation',\n",
" title='Sunburst Chart of Portfolio Allocations')\n",
" fig.show()\n"
],
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"id": "qHoufLpxFUVg"
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"execution_count": 23,
"outputs": []
},
{
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"source": [
"stacked_area_chart(allocations_df)\n",
"heatmap_chart(allocations_df)\n",
"stacked_bar_chart(allocations_df)\n",
"sunburst_chart(allocations_df)"
],
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{
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" president_tweet media_stock profitable_stock famous_hf \\\n",
"2015-12-31 0.05178 0.00857 0.04309 0.04344 \n",
"2016-01-31 0.03899 0.00161 0.02122 0.17317 \n",
"2016-02-29 0.05020 0.00313 0.02574 0.23948 \n",
"2016-03-31 0.02113 0.01218 0.01550 0.25077 \n",
"2016-04-30 0.00967 0.01031 0.10837 0.22083 \n",
"\n",
" meme_stock google_trends warren_buffet stock_twist bankruptcy \\\n",
"2015-12-31 0.00532 0.07035 0.35165 0.10866 0.19132 \n",
"2016-01-31 0.11050 0.07640 0.05359 0.04238 0.41986 \n",
"2016-02-29 0.00267 0.04853 0.06066 0.09009 0.41783 \n",
"2016-03-31 0.00934 0.00161 0.02430 0.00863 0.59802 \n",
"2016-04-30 0.00576 0.02444 0.05393 0.00159 0.49627 \n",
"\n",
" momentum \n",
"2015-12-31 0.12584 \n",
"2016-01-31 0.06227 \n",
"2016-02-29 0.06166 \n",
"2016-03-31 0.05852 \n",
"2016-04-30 0.06883 "
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" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-2045a75a-1700-4acd-94d8-d0009ada0c17 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-2045a75a-1700-4acd-94d8-d0009ada0c17');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "_8_SHJbEGhx6"
},
"execution_count": null,
"outputs": []
}
]
}
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