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python-for-finances/portfolios-simulation/portfolios_simulation.ipynb
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feat: add portfolios simulation
2023-08-08 16:00:39 -03:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Libraries Installation"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install yfinance -q\n",
"!pip install pandas -q\n",
"!pip install numpy -q\n",
"!pip install matplotlib -q"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"import yfinance as yf\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup portfolio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"buy_orders = {\n",
" \"VALE3.SA\": 1400,\n",
" \"WEGE3.SA\": 1300,\n",
" \"BPAC11.SA\": 1100,\n",
" \"KNRI11.SA\": 1000,\n",
" \"SMAL11.SA\": 800,\n",
" \"AAPL34.SA\": 700,\n",
" \"IVVB11.SA\": 500,\n",
" \"PETR4.SA\": 1000,\n",
" \"COCA34.SA\": 1000,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"assets = list(buy_orders.keys())"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['VALE3.SA',\n",
" 'WEGE3.SA',\n",
" 'BPAC11.SA',\n",
" 'KNRI11.SA',\n",
" 'SMAL11.SA',\n",
" 'AAPL34.SA',\n",
" 'IVVB11.SA',\n",
" 'PETR4.SA',\n",
" 'COCA34.SA']"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assets"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"total_invested = sum(buy_orders.values())"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8800"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_invested"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import financial assets data"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"start = \"2020-01-01\"\n",
"\n",
"assets_history_price = yf.download(assets, start=start, progress=False)[\"Adj Close\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dummy portfolio simulation"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AAPL34.SA 14.815145\n",
"BPAC11.SA 17.714905\n",
"COCA34.SA 34.120262\n",
"IVVB11.SA 140.600006\n",
"KNRI11.SA 178.510574\n",
"PETR4.SA 12.180102\n",
"SMAL11.SA 139.500000\n",
"VALE3.SA 39.578941\n",
"WEGE3.SA 16.768709\n",
"Name: 2020-01-02 00:00:00, dtype: float64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_line = assets_history_price.iloc[0]\n",
"first_line"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"buy_orders_df = pd.Series(data = buy_orders, index=assets)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"number_of_shares = round(buy_orders_df / first_line, 0)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"net_worth = assets_history_price * number_of_shares"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"net_worth[\"Total net worth\"] = net_worth.sum(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
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" <th>2023-08-02</th>\n",
" <td>2173.280079</td>\n",
" <td>2059.019943</td>\n",
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" <td>3122.339905</td>\n",
" <td>16253.389824</td>\n",
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" <td>2070.179962</td>\n",
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" <td>979.000000</td>\n",
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" <td>1423.320053</td>\n",
" <td>966.799988</td>\n",
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" <td>2364.599915</td>\n",
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" <th>2023-08-07</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>979.960022</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>680.400009</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" AAPL34.SA BPAC11.SA COCA34.SA IVVB11.SA KNRI11.SA \\\n",
"Date \n",
"2020-01-02 696.311793 1098.324097 989.487602 562.400024 1071.063446 \n",
"2020-01-03 697.689195 1101.511440 975.998394 562.799988 1071.063446 \n",
"2020-01-06 701.132250 1099.917828 978.863392 564.799988 1062.370239 \n",
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"2023-08-04 2086.800072 2042.900047 1423.320053 966.799988 972.120026 \n",
"2023-08-07 NaN NaN NaN 979.960022 NaN \n",
"2023-08-08 2061.420029 2055.919991 1438.690018 972.119995 985.019989 \n",
"\n",
" PETR4.SA SMAL11.SA VALE3.SA WEGE3.SA Total net worth \n",
"Date \n",
"2020-01-02 998.768393 837.000000 1385.262947 1307.959316 8946.577619 \n",
"2020-01-03 990.635223 845.399963 1375.064182 1291.256996 8911.418828 \n",
"2020-01-06 1002.347118 834.600037 1366.905117 1294.597252 8905.533220 \n",
"2020-01-07 998.443075 836.399963 1376.848869 1304.618465 8934.035720 \n",
"2020-01-08 992.261967 829.199982 1377.103748 1255.254627 8807.816858 \n",
"... ... ... ... ... ... \n",
"2023-08-02 2503.460056 678.000000 2348.149872 3122.339905 16253.389824 \n",
"2023-08-03 2535.440006 677.880020 2362.850075 3153.540024 16409.770065 \n",
"2023-08-04 2460.000000 683.099991 2364.599915 3186.299881 16185.939972 \n",
"2023-08-07 NaN 680.400009 NaN NaN 1660.360031 \n",
"2023-08-08 2487.059994 679.679993 2357.600021 3156.660095 16194.170124 \n",
"\n",
"[896 rows x 10 columns]"
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},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"net_worth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparison with IBOV"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"ibov = yf.download(\"^BVSP\", start=start, progress=False)[\"Adj Close\"]"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"ibov_df = pd.DataFrame(ibov)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"ibov_df.rename(columns={\"Adj Close\": \"IBOV\"}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"comparison = pd.merge(ibov_df, net_worth, how = \"inner\", on = \"Date\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
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" <td>116247.000000</td>\n",
" <td>708.764479</td>\n",
" <td>1094.519222</td>\n",
" <td>978.378849</td>\n",
" <td>566.200012</td>\n",
" <td>1006.133972</td>\n",
" <td>992.261967</td>\n",
" <td>829.199982</td>\n",
" <td>1377.103748</td>\n",
" <td>1255.254627</td>\n",
" <td>8807.816858</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-01</th>\n",
" <td>121248.000000</td>\n",
" <td>2201.949928</td>\n",
" <td>2095.599953</td>\n",
" <td>1429.699978</td>\n",
" <td>970.599976</td>\n",
" <td>968.339996</td>\n",
" <td>2509.200031</td>\n",
" <td>678.539978</td>\n",
" <td>2386.999893</td>\n",
" <td>3109.080048</td>\n",
" <td>16350.009781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-02</th>\n",
" <td>120859.000000</td>\n",
" <td>2173.280079</td>\n",
" <td>2059.019943</td>\n",
" <td>1445.940018</td>\n",
" <td>960.799988</td>\n",
" <td>962.399963</td>\n",
" <td>2503.460056</td>\n",
" <td>678.000000</td>\n",
" <td>2348.149872</td>\n",
" <td>3122.339905</td>\n",
" <td>16253.389824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-03</th>\n",
" <td>120586.000000</td>\n",
" <td>2201.949928</td>\n",
" <td>2070.179962</td>\n",
" <td>1457.830013</td>\n",
" <td>979.000000</td>\n",
" <td>971.100037</td>\n",
" <td>2535.440006</td>\n",
" <td>677.880020</td>\n",
" <td>2362.850075</td>\n",
" <td>3153.540024</td>\n",
" <td>16409.770065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-04</th>\n",
" <td>119508.000000</td>\n",
" <td>2086.800072</td>\n",
" <td>2042.900047</td>\n",
" <td>1423.320053</td>\n",
" <td>966.799988</td>\n",
" <td>972.120026</td>\n",
" <td>2460.000000</td>\n",
" <td>683.099991</td>\n",
" <td>2364.599915</td>\n",
" <td>3186.299881</td>\n",
" <td>16185.939972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-08</th>\n",
" <td>119163.382812</td>\n",
" <td>2061.420029</td>\n",
" <td>2055.919991</td>\n",
" <td>1438.690018</td>\n",
" <td>972.119995</td>\n",
" <td>985.019989</td>\n",
" <td>2487.059994</td>\n",
" <td>679.679993</td>\n",
" <td>2357.600021</td>\n",
" <td>3156.660095</td>\n",
" <td>16194.170124</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>894 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" IBOV AAPL34.SA BPAC11.SA COCA34.SA IVVB11.SA \\\n",
"Date \n",
"2020-01-02 118573.000000 696.311793 1098.324097 989.487602 562.400024 \n",
"2020-01-03 117707.000000 697.689195 1101.511440 975.998394 562.799988 \n",
"2020-01-06 116878.000000 701.132250 1099.917828 978.863392 564.799988 \n",
"2020-01-07 116662.000000 702.853845 1104.306175 978.863392 565.200012 \n",
"2020-01-08 116247.000000 708.764479 1094.519222 978.378849 566.200012 \n",
"... ... ... ... ... ... \n",
"2023-08-01 121248.000000 2201.949928 2095.599953 1429.699978 970.599976 \n",
"2023-08-02 120859.000000 2173.280079 2059.019943 1445.940018 960.799988 \n",
"2023-08-03 120586.000000 2201.949928 2070.179962 1457.830013 979.000000 \n",
"2023-08-04 119508.000000 2086.800072 2042.900047 1423.320053 966.799988 \n",
"2023-08-08 119163.382812 2061.420029 2055.919991 1438.690018 972.119995 \n",
"\n",
" KNRI11.SA PETR4.SA SMAL11.SA VALE3.SA WEGE3.SA \\\n",
"Date \n",
"2020-01-02 1071.063446 998.768393 837.000000 1385.262947 1307.959316 \n",
"2020-01-03 1071.063446 990.635223 845.399963 1375.064182 1291.256996 \n",
"2020-01-06 1062.370239 1002.347118 834.600037 1366.905117 1294.597252 \n",
"2020-01-07 1066.501923 998.443075 836.399963 1376.848869 1304.618465 \n",
"2020-01-08 1006.133972 992.261967 829.199982 1377.103748 1255.254627 \n",
"... ... ... ... ... ... \n",
"2023-08-01 968.339996 2509.200031 678.539978 2386.999893 3109.080048 \n",
"2023-08-02 962.399963 2503.460056 678.000000 2348.149872 3122.339905 \n",
"2023-08-03 971.100037 2535.440006 677.880020 2362.850075 3153.540024 \n",
"2023-08-04 972.120026 2460.000000 683.099991 2364.599915 3186.299881 \n",
"2023-08-08 985.019989 2487.059994 679.679993 2357.600021 3156.660095 \n",
"\n",
" Total net worth \n",
"Date \n",
"2020-01-02 8946.577619 \n",
"2020-01-03 8911.418828 \n",
"2020-01-06 8905.533220 \n",
"2020-01-07 8934.035720 \n",
"2020-01-08 8807.816858 \n",
"... ... \n",
"2023-08-01 16350.009781 \n",
"2023-08-02 16253.389824 \n",
"2023-08-03 16409.770065 \n",
"2023-08-04 16185.939972 \n",
"2023-08-08 16194.170124 \n",
"\n",
"[894 rows x 11 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data normalization (data scaling)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"comparison = comparison.dropna()\n",
"comparison_scaling = comparison / comparison.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IBOV</th>\n",
" <th>AAPL34.SA</th>\n",
" <th>BPAC11.SA</th>\n",
" <th>COCA34.SA</th>\n",
" <th>IVVB11.SA</th>\n",
" <th>KNRI11.SA</th>\n",
" <th>PETR4.SA</th>\n",
" <th>SMAL11.SA</th>\n",
" <th>VALE3.SA</th>\n",
" <th>WEGE3.SA</th>\n",
" <th>Total net worth</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-01-02</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-03</th>\n",
" <td>0.992696</td>\n",
" <td>1.001978</td>\n",
" <td>1.002902</td>\n",
" <td>0.986367</td>\n",
" <td>1.000711</td>\n",
" <td>1.000000</td>\n",
" <td>0.991857</td>\n",
" <td>1.010036</td>\n",
" <td>0.992638</td>\n",
" <td>0.987230</td>\n",
" <td>0.996070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-06</th>\n",
" <td>0.985705</td>\n",
" <td>1.006923</td>\n",
" <td>1.001451</td>\n",
" <td>0.989263</td>\n",
" <td>1.004267</td>\n",
" <td>0.991884</td>\n",
" <td>1.003583</td>\n",
" <td>0.997133</td>\n",
" <td>0.986748</td>\n",
" <td>0.989784</td>\n",
" <td>0.995412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-07</th>\n",
" <td>0.983883</td>\n",
" <td>1.009395</td>\n",
" <td>1.005447</td>\n",
" <td>0.989263</td>\n",
" <td>1.004979</td>\n",
" <td>0.995741</td>\n",
" <td>0.999674</td>\n",
" <td>0.999283</td>\n",
" <td>0.993926</td>\n",
" <td>0.997446</td>\n",
" <td>0.998598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-01-08</th>\n",
" <td>0.980383</td>\n",
" <td>1.017884</td>\n",
" <td>0.996536</td>\n",
" <td>0.988773</td>\n",
" <td>1.006757</td>\n",
" <td>0.939378</td>\n",
" <td>0.993486</td>\n",
" <td>0.990681</td>\n",
" <td>0.994110</td>\n",
" <td>0.959705</td>\n",
" <td>0.984490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-01</th>\n",
" <td>1.022560</td>\n",
" <td>3.162305</td>\n",
" <td>1.907998</td>\n",
" <td>1.444889</td>\n",
" <td>1.725818</td>\n",
" <td>0.904092</td>\n",
" <td>2.512294</td>\n",
" <td>0.810681</td>\n",
" <td>1.723138</td>\n",
" <td>2.377046</td>\n",
" <td>1.827516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-02</th>\n",
" <td>1.019279</td>\n",
" <td>3.121131</td>\n",
" <td>1.874692</td>\n",
" <td>1.461302</td>\n",
" <td>1.708393</td>\n",
" <td>0.898546</td>\n",
" <td>2.506547</td>\n",
" <td>0.810036</td>\n",
" <td>1.695093</td>\n",
" <td>2.387184</td>\n",
" <td>1.816716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-03</th>\n",
" <td>1.016977</td>\n",
" <td>3.162305</td>\n",
" <td>1.884853</td>\n",
" <td>1.473318</td>\n",
" <td>1.740754</td>\n",
" <td>0.906669</td>\n",
" <td>2.538567</td>\n",
" <td>0.809892</td>\n",
" <td>1.705705</td>\n",
" <td>2.411038</td>\n",
" <td>1.834195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-04</th>\n",
" <td>1.007885</td>\n",
" <td>2.996933</td>\n",
" <td>1.860016</td>\n",
" <td>1.438442</td>\n",
" <td>1.719061</td>\n",
" <td>0.907621</td>\n",
" <td>2.463033</td>\n",
" <td>0.816129</td>\n",
" <td>1.706968</td>\n",
" <td>2.436085</td>\n",
" <td>1.809177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-08-08</th>\n",
" <td>1.004979</td>\n",
" <td>2.960484</td>\n",
" <td>1.871870</td>\n",
" <td>1.453975</td>\n",
" <td>1.728521</td>\n",
" <td>0.919665</td>\n",
" <td>2.490127</td>\n",
" <td>0.812043</td>\n",
" <td>1.701915</td>\n",
" <td>2.413424</td>\n",
" <td>1.810097</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>894 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" IBOV AAPL34.SA BPAC11.SA COCA34.SA IVVB11.SA KNRI11.SA \\\n",
"Date \n",
"2020-01-02 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"2020-01-03 0.992696 1.001978 1.002902 0.986367 1.000711 1.000000 \n",
"2020-01-06 0.985705 1.006923 1.001451 0.989263 1.004267 0.991884 \n",
"2020-01-07 0.983883 1.009395 1.005447 0.989263 1.004979 0.995741 \n",
"2020-01-08 0.980383 1.017884 0.996536 0.988773 1.006757 0.939378 \n",
"... ... ... ... ... ... ... \n",
"2023-08-01 1.022560 3.162305 1.907998 1.444889 1.725818 0.904092 \n",
"2023-08-02 1.019279 3.121131 1.874692 1.461302 1.708393 0.898546 \n",
"2023-08-03 1.016977 3.162305 1.884853 1.473318 1.740754 0.906669 \n",
"2023-08-04 1.007885 2.996933 1.860016 1.438442 1.719061 0.907621 \n",
"2023-08-08 1.004979 2.960484 1.871870 1.453975 1.728521 0.919665 \n",
"\n",
" PETR4.SA SMAL11.SA VALE3.SA WEGE3.SA Total net worth \n",
"Date \n",
"2020-01-02 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"2020-01-03 0.991857 1.010036 0.992638 0.987230 0.996070 \n",
"2020-01-06 1.003583 0.997133 0.986748 0.989784 0.995412 \n",
"2020-01-07 0.999674 0.999283 0.993926 0.997446 0.998598 \n",
"2020-01-08 0.993486 0.990681 0.994110 0.959705 0.984490 \n",
"... ... ... ... ... ... \n",
"2023-08-01 2.512294 0.810681 1.723138 2.377046 1.827516 \n",
"2023-08-02 2.506547 0.810036 1.695093 2.387184 1.816716 \n",
"2023-08-03 2.538567 0.809892 1.705705 2.411038 1.834195 \n",
"2023-08-04 2.463033 0.816129 1.706968 2.436085 1.809177 \n",
"2023-08-08 2.490127 0.812043 1.701915 2.413424 1.810097 \n",
"\n",
"[894 rows x 11 columns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comparison_scaling"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"comparison_scaling.plot();"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"comparison_scaling[[\"IBOV\", \"Total net worth\"]].plot();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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