{ "cells": [ { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "ename": "FileNotFoundError", "evalue": "File downloaded_etf/VWCE-MI-IE00BK5BQT80-italy-vanguard_ftse_all_world_ucits_etf_usd_accumulation-2020_01_14-2022_12_30.csv.json does not exist", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[70], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m file_name2 \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mSEGA-MI-IE00B4WXJJ64-italy-ishares_core_€_govt_bond_ucits_etf_eur_(dist)-2010_01_18-2022_12_30.csv.json\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 8\u001b[0m filename3 \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mCSSPX-MI-IE00B5BMR087-italy-ishares_core_s&p_500_ucits_etf_usd_(acc)-2010_05_27-2022_12_30.csv.json\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m----> 9\u001b[0m df1 \u001b[39m=\u001b[39m po\u001b[39m.\u001b[39;49mfile_to_dataframe(\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m{\u001b[39;49;00mdirectory\u001b[39m}\u001b[39;49;00m\u001b[39m/\u001b[39;49m\u001b[39m{\u001b[39;49;00mfile_name1\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 10\u001b[0m df2 \u001b[39m=\u001b[39m po\u001b[39m.\u001b[39mfile_to_dataframe(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mdirectory\u001b[39m}\u001b[39;00m\u001b[39m/\u001b[39m\u001b[39m{\u001b[39;00mfile_name2\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 11\u001b[0m df3 \u001b[39m=\u001b[39m po\u001b[39m.\u001b[39mfile_to_dataframe(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mdirectory\u001b[39m}\u001b[39;00m\u001b[39m/\u001b[39m\u001b[39m{\u001b[39;00mfilename3\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", "File \u001b[0;32m~/eodhistoricaldata/portfolio_optimizer.py:75\u001b[0m, in \u001b[0;36mfile_to_dataframe\u001b[0;34m(filename, date_column_name, price_column_name)\u001b[0m\n\u001b[1;32m 70\u001b[0m df\u001b[39m.\u001b[39mset_index(date_column_name, inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 72\u001b[0m \u001b[39mreturn\u001b[39;00m df[[price_column_name]]\n\u001b[0;32m---> 75\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdistribute_money_by_ticker_weights\u001b[39m(tickers_weights: Dict[\u001b[39mstr\u001b[39m, \u001b[39mfloat\u001b[39m], money:\u001b[39mint\u001b[39m, tickers_prices: Dict[\u001b[39mstr\u001b[39m, \u001b[39mfloat\u001b[39m],):\n\u001b[1;32m 76\u001b[0m \u001b[39m\"\"\"distribute money by weights\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[39m returning for every ticker the amount of money and the amount of shares\u001b[39;00m\n\u001b[1;32m 78\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m 79\u001b[0m \u001b[39mreturn\u001b[39;00m {\n\u001b[1;32m 80\u001b[0m ticker: (money\u001b[39m*\u001b[39mweight, money\u001b[39m*\u001b[39mweight\u001b[39m/\u001b[39mtickers_prices[ticker])\n\u001b[1;32m 81\u001b[0m \u001b[39mfor\u001b[39;00m ticker, weight \u001b[39min\u001b[39;00m tickers_weights\u001b[39m.\u001b[39mitems()\n\u001b[1;32m 82\u001b[0m }\n", "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.._deprecate_kwarg..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[39m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m 326\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 327\u001b[0m msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 328\u001b[0m \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m 329\u001b[0m stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/json/_json.py:733\u001b[0m, in \u001b[0;36mread_json\u001b[0;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, encoding_errors, lines, chunksize, compression, nrows, storage_options)\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[39mif\u001b[39;00m convert_axes \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m orient \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtable\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 731\u001b[0m convert_axes \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 733\u001b[0m json_reader \u001b[39m=\u001b[39m JsonReader(\n\u001b[1;32m 734\u001b[0m path_or_buf,\n\u001b[1;32m 735\u001b[0m orient\u001b[39m=\u001b[39;49morient,\n\u001b[1;32m 736\u001b[0m typ\u001b[39m=\u001b[39;49mtyp,\n\u001b[1;32m 737\u001b[0m dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m 738\u001b[0m convert_axes\u001b[39m=\u001b[39;49mconvert_axes,\n\u001b[1;32m 739\u001b[0m convert_dates\u001b[39m=\u001b[39;49mconvert_dates,\n\u001b[1;32m 740\u001b[0m keep_default_dates\u001b[39m=\u001b[39;49mkeep_default_dates,\n\u001b[1;32m 741\u001b[0m numpy\u001b[39m=\u001b[39;49mnumpy,\n\u001b[1;32m 742\u001b[0m precise_float\u001b[39m=\u001b[39;49mprecise_float,\n\u001b[1;32m 743\u001b[0m date_unit\u001b[39m=\u001b[39;49mdate_unit,\n\u001b[1;32m 744\u001b[0m encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[1;32m 745\u001b[0m lines\u001b[39m=\u001b[39;49mlines,\n\u001b[1;32m 746\u001b[0m chunksize\u001b[39m=\u001b[39;49mchunksize,\n\u001b[1;32m 747\u001b[0m compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m 748\u001b[0m nrows\u001b[39m=\u001b[39;49mnrows,\n\u001b[1;32m 749\u001b[0m storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m 750\u001b[0m encoding_errors\u001b[39m=\u001b[39;49mencoding_errors,\n\u001b[1;32m 751\u001b[0m )\n\u001b[1;32m 753\u001b[0m \u001b[39mif\u001b[39;00m chunksize:\n\u001b[1;32m 754\u001b[0m \u001b[39mreturn\u001b[39;00m json_reader\n", "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/json/_json.py:818\u001b[0m, in \u001b[0;36mJsonReader.__init__\u001b[0;34m(self, filepath_or_buffer, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options, encoding_errors)\u001b[0m\n\u001b[1;32m 815\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlines:\n\u001b[1;32m 816\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mnrows can only be passed if lines=True\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 818\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_data_from_filepath(filepath_or_buffer)\n\u001b[1;32m 819\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_preprocess_data(data)\n", "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/json/_json.py:874\u001b[0m, in \u001b[0;36mJsonReader._get_data_from_filepath\u001b[0;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[1;32m 866\u001b[0m filepath_or_buffer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n\u001b[1;32m 867\u001b[0m \u001b[39melif\u001b[39;00m (\n\u001b[1;32m 868\u001b[0m \u001b[39misinstance\u001b[39m(filepath_or_buffer, \u001b[39mstr\u001b[39m)\n\u001b[1;32m 869\u001b[0m \u001b[39mand\u001b[39;00m filepath_or_buffer\u001b[39m.\u001b[39mlower()\u001b[39m.\u001b[39mendswith(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m file_exists(filepath_or_buffer)\n\u001b[1;32m 873\u001b[0m ):\n\u001b[0;32m--> 874\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mFile \u001b[39m\u001b[39m{\u001b[39;00mfilepath_or_buffer\u001b[39m}\u001b[39;00m\u001b[39m does not exist\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 876\u001b[0m \u001b[39mreturn\u001b[39;00m filepath_or_buffer\n", "\u001b[0;31mFileNotFoundError\u001b[0m: File downloaded_etf/VWCE-MI-IE00BK5BQT80-italy-vanguard_ftse_all_world_ucits_etf_usd_accumulation-2020_01_14-2022_12_30.csv.json does not exist" ] } ], "source": [ "import pandas as pd\n", "import portfolio_optimizer as po\n", "import requests\n", "\n", "directory = \"downloaded_etf\"\n", "file_name1 = \"VWCE-MI-IE00BK5BQT80-italy-vanguard_ftse_all_world_ucits_etf_usd_accumulation-2020_01_14-2022_12_30.csv.json\"\n", "file_name2 = \"SEGA-MI-IE00B4WXJJ64-italy-ishares_core_€_govt_bond_ucits_etf_eur_(dist)-2010_01_18-2022_12_30.csv.json\"\n", "filename3 = \"CSSPX-MI-IE00B5BMR087-italy-ishares_core_s&p_500_ucits_etf_usd_(acc)-2010_05_27-2022_12_30.csv.json\"\n", "df1 = po.file_to_dataframe(f\"{directory}/{file_name1}\")\n", "df2 = po.file_to_dataframe(f\"{directory}/{file_name2}\")\n", "df3 = po.file_to_dataframe(f\"{directory}/{filename3}\")\n", "\n", "# rename columns\n", "df1.rename(columns={\"adjusted_close\": \"VWCE\"}, inplace=True)\n", "df2.rename(columns={\"adjusted_close\": \"SEGA\"}, inplace=True)\n", "df3.rename(columns={\"adjusted_close\": \"CSSPX\"}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "( VWCE SEGA CSSPX\n", " VWCE 0.024949 0.004248 0.027240\n", " SEGA 0.004248 0.008362 0.004645\n", " CSSPX 0.027240 0.004645 0.031857,\n", " VWCE -0.013707\n", " SEGA -0.117323\n", " CSSPX 0.007130\n", " Name: 2022-12-30 00:00:00, dtype: float64)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.concat([df1, df2, df3], axis=1)\n", "optimizer = po.PortfolioOptimizer(\n", " data, True\n", ")\n", "\n", "optimizer.sigma, optimizer.mu" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "maxdata = optimizer.maximum_return()\n", "mindata = optimizer.minimum_risk()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "min risk: 0.08763370433800144, max risk: 0.17848613699023425\n" ] } ], "source": [ "minrisk = float(mindata[1]['risk'])\n", "maxrisk = float(maxdata[1]['risk'])\n", "\n", "print(f\"min risk: {minrisk}, max risk: {maxrisk}\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "interval = (maxrisk - minrisk) / 6" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('VWCE', 0.3443), ('SEGA', 0.5182), ('CSSPX', 0.1376)])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interval1 = minrisk + interval\n", "interval2 = minrisk + interval * 2\n", "interval3 = minrisk + interval * 3\n", "interval4 = minrisk + interval * 4\n", "interval5 = minrisk + interval * 5\n", "interval6 = minrisk + interval * 6\n", "\n", "optimizer.efficient_risk(interval1)[0]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('VWCE', 0.1447), ('SEGA', 0.1807), ('CSSPX', 0.6745)])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optimizer.efficient_risk(interval4)[0]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "soldi = 5321\n", "\n", "def get_portfolio_value(weights, prices):\n", " return [weights[ticker] * prices[ticker] for ticker in weights] \n", "\n", "port = get_portfolio_value(optimizer.efficient_risk(interval1)[0], data.iloc[-1])\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VWCE: 1832.0203 (20)\n", "SEGA: 2757.3422 (26)\n", "CSSPX: 732.1696 (2)\n" ] } ], "source": [ "for w in optimizer.efficient_risk(interval1)[0]:\n", " soldi_per_ticker = soldi * optimizer.efficient_risk(interval1)[0][w]\n", " quote_per_ticker = int(round((soldi_per_ticker / data.iloc[-1][w]),0))\n", " print(f\"{w}: {soldi_per_ticker} ({quote_per_ticker})\")" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "indb = \"\"\"IE00B58HMN42 CCEUAS MI\n", "IE00B579F325 SGLD MI\n", "IE00BKM4GZ66 EIMI MI\n", "LU1781541179 LCWD MI\n", "IE00BP3QZB59 IWVL MI\n", "IE00B4L5Y983 SWDA MI\n", "IE00BP46NG52 EMH5 MI\n", "IE00BF3N7102 IHYE MI\n", "IE00B9M6RS56 EMBE MI\n", "LU0478205379 XBLC MI\n", "LU0290358224 XEIN MI\n", "LU1437016204 GGOV PA\n", "LU1048317025 CBUSE MI\n", "IE00BDZVH966 IBC5 XETRA\n", "LU1407890976 U10H MI\n", "LU1650487413 EM13 MI\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "for row in indb.splitlines():\n", " isin, ticker, market = row.split()\n", " \n", " resp = requests.get(f\"http://localhost:1323/download_etf?ticker={ticker}&exchange={market}\")\n", " resp.text" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['CBUSE{}MI{}LU1048317025{}italy{}UBS Barclays US Liquid Corporates UCITS hedged to EUR A-acc{}2014-06-20{}2023-01-05.json',\n", " 'CCEUAS{}MI{}IE00B58HMN42{}italy{}UBS (Irl) Fund Solutions plc – CMCI Composite SF UCITS ETF (hedged to EUR) A-acc{}2013-05-28{}2023-01-05.json',\n", " 'EIMI{}MI{}IE00BKM4GZ66{}italy{}iShares Core MSCI Emerging Markets IMI UCITS{}2014-07-24{}2023-01-05.json',\n", " 'EM13{}MI{}LU1650487413{}italy{}Lyxor Euromts 1-3Y Inv Grd (DR) ETF CEUR{}2010-01-01{}2023-01-05.json',\n", " 'EMBE{}MI{}IE00B9M6RS56{}italy{}iShares J.P. Morgan Emerging Markets Bond EUR Hedged UCITS{}2015-08-05{}2023-01-05.json',\n", " 'EMH5{}MI{}IE00BP46NG52{}italy{}SPDR BofA Merrill Lynch 0-5 Emerging Markets Government Bond UCITS{}2016-02-23{}2023-01-05.json',\n", " 'GGOV{}PA{}LU1437016204{}france{}Amundi Index Solutions - Amundi Index J.P. Morgan GBI Global Govies{}2017-02-15{}2023-01-05.json',\n", " 'IBC5{}XETRA{}IE00BDZVH966{}germany{}iShares $ TIPS UCITS ETF EUR Hedged (Acc) EUR{}2018-03-29{}2023-01-05.json',\n", " 'IHYE{}MI{}IE00BF3N7102{}italy{}iShares $ High Yield Corp Bond UCITS ETF EUR Hedged (Dist){}2021-06-01{}2023-01-05.json',\n", " 'IWVL{}MI{}IE00BP3QZB59{}italy{}iShares MSCI World Value Factor UCITS{}2015-02-20{}2023-01-05.json',\n", " 'LCWD{}MI{}LU1781541179{}italy{}Lyxor Core MSCI World (DR) UCITS ETF{}2021-02-19{}2023-01-05.json',\n", " 'SGLD{}MI{}IE00B579F325{}italy{}Invesco Physical Gold ETC{}2014-11-28{}2023-01-05.json',\n", " 'SWDA{}MI{}IE00B4L5Y983{}italy{}iShares Core MSCI World UCITS ETF USD (Acc){}2010-02-01{}2023-01-05.json',\n", " 'U10H{}MI{}LU1407890976{}italy{}Lyxor iBoxx $ Treasuries 10+ DR UCITS Monthly Hedged D-EUR{}2017-05-15{}2023-01-05.json',\n", " 'XBLC{}MI{}LU0478205379{}italy{}Xtrackers II EUR Corporate Bond UCITS ETF 1C{}2010-06-25{}2023-01-05.json',\n", " 'XEIN{}MI{}LU0290358224{}italy{}Xtrackers II Eurozone Inflation-Linked Bond UCITS ETF 1C{}2010-01-04{}2023-01-05.json']" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resp = requests.get(\"http://localhost:1323/available_etfs_as_filename\")\n", "resp.json()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" } } }, "nbformat": 4, "nbformat_minor": 2 }