Labs/QStat_Tracking Error_Portfolio.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"10PhitbJrFL4tvFtydJvdG9JWq3Ij3F2F","timestamp":1709353707521},{"file_id":"1HcjEtRmZkv5UhFemAYiQGOoJ9AJ6__Oa","timestamp":1709312872250}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pB_Wn-b6ZMV3","outputId":"b34841d9-27c5-475c-933a-d8ea1a2b793a","executionInfo":{"status":"ok","timestamp":1709682201915,"user_tz":300,"elapsed":8352,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: quantstats in /usr/local/lib/python3.10/dist-packages (0.0.62)\n","Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (1.5.3)\n","Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.10/dist-packages (from quantstats) (1.25.2)\n","Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (0.13.1)\n","Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (3.7.1)\n","Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (1.11.4)\n","Requirement already satisfied: tabulate>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (0.9.0)\n","Requirement already satisfied: yfinance>=0.1.70 in /usr/local/lib/python3.10/dist-packages (from quantstats) (0.2.37)\n","Requirement already satisfied: python-dateutil>=2.0 in /usr/local/lib/python3.10/dist-packages (from quantstats) (2.8.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (1.2.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (4.49.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (23.2)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->quantstats) (3.1.1)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.0->quantstats) (2023.4)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.0->quantstats) (1.16.0)\n","Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (2.31.0)\n","Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (0.0.11)\n","Requirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (4.9.4)\n","Requirement already satisfied: appdirs>=1.4.4 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (1.4.4)\n","Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (2.4.0)\n","Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (3.17.1)\n","Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (4.12.3)\n","Requirement already satisfied: html5lib>=1.1 in /usr/local/lib/python3.10/dist-packages (from yfinance>=0.1.70->quantstats) (1.1)\n","Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.11.1->yfinance>=0.1.70->quantstats) (2.5)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from html5lib>=1.1->yfinance>=0.1.70->quantstats) (0.5.1)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance>=0.1.70->quantstats) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance>=0.1.70->quantstats) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance>=0.1.70->quantstats) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.31->yfinance>=0.1.70->quantstats) (2024.2.2)\n"]}],"source":["!pip install quantstats"]},{"cell_type":"code","source":["import quantstats as qs\n","qs.extend_pandas()"],"metadata":{"id":"rtHfVe8TZ1Y6","executionInfo":{"status":"ok","timestamp":1709682201915,"user_tz":300,"elapsed":4,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings(\"ignore\")"],"metadata":{"id":"5zW8Emu27uNr","executionInfo":{"status":"ok","timestamp":1709682201916,"user_tz":300,"elapsed":4,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["stock=qs.utils.download_returns('NVDA',period='5y')"],"metadata":{"id":"T9QzunPi6chi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1709682202743,"user_tz":300,"elapsed":831,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}},"outputId":"4c2466fa-43e0-4f32-860d-90ba31058fdd"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["\r[*********************100%%**********************]  1 of 1 completed\n"]}]},{"cell_type":"code","source":["print(type(stock))"],"metadata":{"id":"OFxIaOe9aNfN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1709682202930,"user_tz":300,"elapsed":194,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}},"outputId":"868d5da0-af0e-4ae0-f7c0-8d7862c99649"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.series.Series'>\n"]}]},{"cell_type":"code","source":["print(stock)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y33ySHZSaqM1","outputId":"cb8d082d-b4fa-4018-c390-df7dda6a0e1d","executionInfo":{"status":"ok","timestamp":1709682202930,"user_tz":300,"elapsed":14,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Date\n","2019-03-06         NaN\n","2019-03-07   -0.018349\n","2019-03-08    0.009246\n","2019-03-11    0.069703\n","2019-03-12    0.008564\n","                ...   \n","2024-02-28   -0.013189\n","2024-02-29    0.018658\n","2024-03-01    0.040032\n","2024-03-04    0.035951\n","2024-03-05    0.008529\n","Name: Close, Length: 1259, dtype: float64\n"]}]},{"cell_type":"code","source":["stock.sharpe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ccjzRvBcdIdh","outputId":"f82f897a-2933-460f-e148-77d97aa4be1f","executionInfo":{"status":"ok","timestamp":1709682202931,"user_tz":300,"elapsed":10,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1.464784442734824"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["qs.plots.snapshot(stock, title='Rendimiento de NVDA', show=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"OW-oYOpfdh4p","outputId":"bfc843aa-157e-4489-f5ff-7b5c4a3453a9","executionInfo":{"status":"ok","timestamp":1709682205020,"user_tz":300,"elapsed":2093,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["qs.reports.html(stock,title='Analisis de retornos de NVDA',output='NVDA_report.html')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ya96-0Kne_QY","outputId":"123b6825-b60d-4292-ba69-152823f09679","executionInfo":{"status":"ok","timestamp":1709682220733,"user_tz":300,"elapsed":15723,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n"]}]},{"cell_type":"code","source":["qs.reports.html(stock,'SPY',title='Analisis de retornos de NVDA vs S&P500',output='NVDA_vs_S&P500_report.html')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7EAp_T_JDAz2","outputId":"26cdd1a8-70e8-4dee-8055-39e5575ebfc3","executionInfo":{"status":"ok","timestamp":1709682237976,"user_tz":300,"elapsed":17255,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["\r[*********************100%%**********************]  1 of 1 completed\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n"]}]},{"cell_type":"code","source":["tickers={'NVDA':0.475,'BRK-B':0.498, 'META':0.027}"],"metadata":{"id":"KFEmQr3wGJ47","executionInfo":{"status":"ok","timestamp":1709682237976,"user_tz":300,"elapsed":12,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["estrategia=portfolio=qs.utils.make_index(tickers,period='5y')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YXm9gl2vGmXz","outputId":"67f78f12-7807-4a22-b89c-ee223c57f584","executionInfo":{"status":"ok","timestamp":1709682238949,"user_tz":300,"elapsed":984,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":20,"outputs":[{"output_type":"stream","name":"stderr","text":["[*********************100%%**********************]  1 of 1 completed\n","[*********************100%%**********************]  1 of 1 completed\n","[*********************100%%**********************]  1 of 1 completed\n"]}]},{"cell_type":"code","source":["qs.reports.html(estrategia,'SPY',title='Analisis de retornos de NVDA, BRK-B and META vs S&P500',output='Portfolio_vs_S&P500_report.html')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jPm5e0iLHOfB","outputId":"612c1e96-686f-4052-ea59-6cb066ee6726","executionInfo":{"status":"ok","timestamp":1709682248843,"user_tz":300,"elapsed":9896,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":["\r[*********************100%%**********************]  1 of 1 completed\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n"]}]},{"cell_type":"code","source":["tickers_1={'NVDA':0.475,'BRK-B':0.498, 'TBX':0.027}"],"metadata":{"id":"Dcqp9fYcH3Mx","executionInfo":{"status":"ok","timestamp":1709682248844,"user_tz":300,"elapsed":6,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["estrategia_TBX_short=qs.utils.make_index(tickers_1,period='3y')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SulIF_dEHr_M","outputId":"13339843-96ab-4e43-d4c9-19c1a8a72454","executionInfo":{"status":"ok","timestamp":1709682249492,"user_tz":300,"elapsed":654,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["[*********************100%%**********************]  1 of 1 completed\n","[*********************100%%**********************]  1 of 1 completed\n","[*********************100%%**********************]  1 of 1 completed\n"]}]},{"cell_type":"code","source":["qs.reports.html(estrategia_TBX_short,'SPY',title='Analisis de retornos de NVDA and BRK-B Short UST vs S&P500',output='Portfolio_shortUST_vs_S&P500_report.html')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"0567561a-4bbd-4440-88a1-122be557ee47","id":"QqHmeQGzIdzl","executionInfo":{"status":"ok","timestamp":1709682259847,"user_tz":300,"elapsed":10356,"user":{"displayName":"Eljaer Edfrance Eusebio Evangelista","userId":"00255645884250107517"}}},"execution_count":24,"outputs":[{"output_type":"stream","name":"stderr","text":["\r[*********************100%%**********************]  1 of 1 completed\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n","WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found.\n"]}]}]}