{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9140cf54", "metadata": {}, "outputs": [], "source": [ "import datetime\n", "import pandas as pd\n", "import time\n", "from scipy.stats import linregress" ] }, { "cell_type": "code", "execution_count": 2, "id": "6cb52f60", "metadata": {}, "outputs": [], "source": [ "names=pd.read_csv('names.csv').Symbol.to_list()\n", "names=[x+'.NS' for x in names]\n", "names.append('%5ENSEI')" ] }, { "cell_type": "code", "execution_count": 3, "id": "49a554ff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(names)" ] }, { "cell_type": "code", "execution_count": 4, "id": "e8b24ddb", "metadata": {}, "outputs": [], "source": [ "#t2=int(time.mktime(datetime.date.today().timetuple())+19800)\n", "t2=int(time.mktime((datetime.date.today() - datetime.timedelta(1)).timetuple())+19800)\n", "t1=int(time.mktime((datetime.date.today() - datetime.timedelta(500)).timetuple())+19800)" ] }, { "cell_type": "code", "execution_count": 28, "id": "38e850fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ADANIPORTS success\n", "ASIANPAINT success\n", "AXISBANK success\n", "BAJAJ-AUTO success\n", "BAJFINANCE success\n", "BAJAJFINSV success\n", "BPCL success\n", "BHARTIARTL success\n", "BRITANNIA success\n", "CIPLA success\n", "COALINDIA success\n", "DIVISLAB success\n", "DRREDDY success\n", "EICHERMOT success\n", "GRASIM success\n", "HCLTECH success\n", "HDFCBANK success\n", "HDFCLIFE success\n", "HEROMOTOCO success\n", "HINDALCO success\n", "HINDUNILVR success\n", "HDFC success\n", "ICICIBANK success\n", "ITC success\n", "IOC success\n", "INDUSINDBK success\n", "INFY success\n", "JSWSTEEL success\n", "KOTAKBANK success\n", "LT success\n", "M&M success\n", "MARUTI success\n", "NTPC success\n", "NESTLEIND success\n", "ONGC success\n", "POWERGRID success\n", "RELIANCE success\n", "SBILIFE success\n", "SHREECEM success\n", "SBIN success\n", "SUNPHARMA success\n", "TCS success\n", "TATACONSUM success\n", "TATAMOTORS success\n", "TATASTEEL success\n", "TECHM success\n", "TITAN success\n", "UPL success\n", "ULTRACEMCO success\n", "WIPRO success\n", "NIFTY success\n" ] } ], "source": [ "df= pd.DataFrame()\n", "for name in names:\n", " url='https://query1.finance.yahoo.com/v7/finance/download/' \\\n", " +name+'?period1='+str(t1)+'&period2='+str(t2)+'&interval=1d&events=history&includeAdjustedClose=true'\n", " try:\n", " if name!= '%5ENSEI':\n", " df[name[:-3]]=pd.read_csv(url)['Adj Close']\n", " print(name[:-3]+' success')\n", " else:\n", " df['NIFTY']=pd.read_csv(url)['Adj Close']\n", " print('NIFTY success')\n", " except:\n", " print(name[:-3]+' failed')" ] }, { "cell_type": "code", "execution_count": 99, "id": "9df48f11", "metadata": {}, "outputs": [], "source": [ "df_return= df.div(df.shift())-1" ] }, { "cell_type": "code", "execution_count": 100, "id": "7debe803", "metadata": {}, "outputs": [], "source": [ "df_return.dropna(axis=0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 111, "id": "8a233889", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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12345678910...329330331332333334335336337338
ADANIPORTS-0.001212-0.023190-0.0106280.000000-0.013114-0.032513-0.0045300.019522-0.0038870.009250...0.0733500.0528550.016210-0.032575-0.0053240.0032400.005687-0.007330-0.010267-0.000853
BAJAJ-AUTO-0.008218-0.027728-0.011106-0.000271-0.003394-0.015869-0.0338410.0075030.007150-0.002895...0.0279240.0037320.009312-0.0054480.006740-0.007275-0.003027-0.0140550.0022310.017103
BPCL-0.007991-0.015050-0.017002-0.017406-0.010584-0.039860-0.0328370.030678-0.014824-0.000717...-0.0095410.005716-0.002947-0.008551-0.0074530.008475-0.006170-0.0019270.003968-0.011429
BHARTIARTL0.004048-0.0287760.0100960.001308-0.008023-0.015330-0.0098380.004727-0.0077770.019642...0.0191050.001578-0.005930-0.004381-0.0099240.012009-0.007288-0.0117660.001333-0.005707
BRITANNIA-0.007413-0.007583-0.0008730.002571-0.005031-0.018456-0.0071040.0262450.0120770.020927...-0.0106470.0081920.0005620.0023010.008144-0.005164-0.000763-0.002754-0.002051-0.014755
CIPLA-0.026257-0.012851-0.010113-0.008455-0.002369-0.045347-0.0003730.0580920.052551-0.015749...0.010463-0.000781-0.0044790.001988-0.0071520.0065200.0062170.017185-0.0078600.006379
DIVISLAB-0.007548-0.004103-0.0132670.0123350.006746-0.0230000.0010920.035012-0.0038020.022508...-0.0244170.0075760.014105-0.0149580.006095-0.0003760.0154520.0096770.0119600.006227
DRREDDY-0.006970-0.024268-0.028146-0.003742-0.002699-0.045630-0.0073620.0475470.0419390.004131...-0.000492-0.0003600.004989-0.001695-0.0040950.0059970.0178840.005338-0.0018500.024977
EICHERMOT-0.001407-0.047680-0.023267-0.021409-0.006035-0.0264490.0247590.051433-0.0354560.030204...0.012641-0.0033610.0073190.003826-0.0080080.0051910.007222-0.012899-0.0125500.001554
GRASIM-0.007726-0.035139-0.006208-0.0244310.014300-0.035982-0.0272850.047273-0.015070-0.013343...0.0154640.010738-0.0002340.0016370.0001000.0053040.009125-0.011377-0.0030270.001034
HCLTECH0.003797-0.021215-0.0213390.001974-0.018077-0.0675330.0287270.027924-0.0034510.023619...-0.0113040.006921-0.001935-0.0073460.0172680.006012-0.010395-0.001725-0.0001020.002034
HDFCBANK-0.008230-0.005875-0.007975-0.0008750.000167-0.0181750.0016560.001865-0.0278810.002176...0.0093450.006014-0.0032910.0011460.0139680.005876-0.0044550.000133-0.002763-0.007444
HDFCLIFE-0.009670-0.0221450.015514-0.0218610.017323-0.0398800.0191140.0162310.009051-0.006419...0.0127090.013466-0.0027830.0013950.0025770.008824-0.040981-0.0139330.002659-0.000801
HEROMOTOCO-0.005041-0.025020-0.000023-0.019046-0.015075-0.027722-0.0371880.040193-0.0041370.008576...-0.0078070.0012110.0042140.007327-0.0073080.0110940.001514-0.005486-0.0085060.006856
HINDALCO0.009835-0.057384-0.025691-0.026082-0.009123-0.073953-0.0394480.0637730.008161-0.031756...0.005687-0.010098-0.0001360.0014960.0025810.0185610.017026-0.021057-0.0058780.019755
HINDUNILVR-0.019152-0.0147230.0083290.0011640.008385-0.035566-0.0070350.0033340.0042460.019372...0.0269680.012667-0.009585-0.0039150.003890-0.0166660.0047170.013149-0.0085250.002711
ITC0.003628-0.021451-0.005911-0.015857-0.0052870.000000-0.011389-0.007424-0.0327570.002933...-0.005349-0.0004890.003179-0.005607-0.0034320.008856-0.007071-0.001473-0.0029510.001480
INFY-0.004310-0.0014430.003204-0.019602-0.006260-0.0593300.0127780.0080290.015730-0.005470...0.005350-0.0019960.007698-0.0057550.0372880.009620-0.001525-0.0055670.011356-0.012905
M&M-0.002945-0.022197-0.008281-0.011691-0.018887-0.0738600.0027350.0173470.0180160.009797...-0.029974-0.006014-0.0021240.0087070.0125970.003094-0.000944-0.014997-0.0049900.002250
MARUTI0.000148-0.042500-0.008632-0.0280140.008813-0.0010650.0003260.016054-0.000266-0.002882...-0.001435-0.0085420.0529720.0230270.0127480.016148-0.006896-0.0143160.0037930.009114
NESTLEIND-0.014629-0.0122280.002647-0.0085680.001453-0.0298150.0209840.0154840.0111310.002104...-0.021507-0.001217-0.012046-0.0012470.011769-0.006416-0.0000310.0053180.0019180.000749
ONGC0.010816-0.046206-0.005099-0.017939-0.025574-0.014997-0.0309950.047699-0.005892-0.002155...-0.0387690.0058210.0090950.010651-0.010944-0.0090160.011993-0.024111-0.0142380.009771
POWERGRID0.009333-0.0166450.000269-0.0016120.003228-0.0265490.0110190.0446870.027908-0.005582...-0.0285600.007940-0.003406-0.007691-0.004736-0.0012980.0043320.017899-0.015254-0.006885
RELIANCE-0.011870-0.027592-0.019759-0.017227-0.004131-0.041551-0.0094080.020286-0.002346-0.021311...0.0045130.005212-0.005140-0.009165-0.023511-0.022777-0.0087670.0008630.010944-0.006017
SBILIFE-0.010073-0.013082-0.016801-0.0109300.015707-0.0165130.0042120.006375-0.016392-0.000734...-0.000763-0.0010180.025928-0.0064050.0015990.004989-0.005014-0.0026940.008755-0.001240
SHREECEM-0.005130-0.019171-0.010891-0.0045350.014805-0.0479410.0163670.023919-0.014290-0.008161...-0.0113610.0064020.035915-0.012718-0.0067520.001981-0.011796-0.009008-0.022387-0.016166
SUNPHARMA0.003469-0.017533-0.022496-0.0365130.036830-0.040155-0.0091180.0650880.030238-0.001357...0.0051880.004263-0.006777-0.002325-0.0006760.0112060.0060250.001922-0.0031730.012880
TCS-0.018007-0.0192180.019287-0.015096-0.008523-0.0500360.0005750.0174390.0230820.020089...-0.006164-0.0073390.008585-0.0120560.0344020.002134-0.0130290.0012290.001467-0.001270
TATACONSUM-0.006916-0.029961-0.000271-0.009079-0.006427-0.047206-0.0212340.048701-0.017168-0.002005...-0.0069800.0037170.0190560.001520-0.0005280.0001980.010626-0.009926-0.0048150.002187
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29 rows × 334 columns

\n", "
" ], "text/plain": [ " 1 2 3 4 5 6 \\\n", "ADANIPORTS -0.001212 -0.023190 -0.010628 0.000000 -0.013114 -0.032513 \n", "BAJAJ-AUTO -0.008218 -0.027728 -0.011106 -0.000271 -0.003394 -0.015869 \n", "BPCL -0.007991 -0.015050 -0.017002 -0.017406 -0.010584 -0.039860 \n", "BHARTIARTL 0.004048 -0.028776 0.010096 0.001308 -0.008023 -0.015330 \n", "BRITANNIA -0.007413 -0.007583 -0.000873 0.002571 -0.005031 -0.018456 \n", "CIPLA -0.026257 -0.012851 -0.010113 -0.008455 -0.002369 -0.045347 \n", "DIVISLAB -0.007548 -0.004103 -0.013267 0.012335 0.006746 -0.023000 \n", "DRREDDY -0.006970 -0.024268 -0.028146 -0.003742 -0.002699 -0.045630 \n", "EICHERMOT -0.001407 -0.047680 -0.023267 -0.021409 -0.006035 -0.026449 \n", "GRASIM -0.007726 -0.035139 -0.006208 -0.024431 0.014300 -0.035982 \n", "HCLTECH 0.003797 -0.021215 -0.021339 0.001974 -0.018077 -0.067533 \n", "HDFCBANK -0.008230 -0.005875 -0.007975 -0.000875 0.000167 -0.018175 \n", "HDFCLIFE -0.009670 -0.022145 0.015514 -0.021861 0.017323 -0.039880 \n", "HEROMOTOCO -0.005041 -0.025020 -0.000023 -0.019046 -0.015075 -0.027722 \n", "HINDALCO 0.009835 -0.057384 -0.025691 -0.026082 -0.009123 -0.073953 \n", "HINDUNILVR -0.019152 -0.014723 0.008329 0.001164 0.008385 -0.035566 \n", "ITC 0.003628 -0.021451 -0.005911 -0.015857 -0.005287 0.000000 \n", "INFY -0.004310 -0.001443 0.003204 -0.019602 -0.006260 -0.059330 \n", "M&M -0.002945 -0.022197 -0.008281 -0.011691 -0.018887 -0.073860 \n", "MARUTI 0.000148 -0.042500 -0.008632 -0.028014 0.008813 -0.001065 \n", "NESTLEIND -0.014629 -0.012228 0.002647 -0.008568 0.001453 -0.029815 \n", "ONGC 0.010816 -0.046206 -0.005099 -0.017939 -0.025574 -0.014997 \n", "POWERGRID 0.009333 -0.016645 0.000269 -0.001612 0.003228 -0.026549 \n", "RELIANCE -0.011870 -0.027592 -0.019759 -0.017227 -0.004131 -0.041551 \n", "SBILIFE -0.010073 -0.013082 -0.016801 -0.010930 0.015707 -0.016513 \n", "SHREECEM -0.005130 -0.019171 -0.010891 -0.004535 0.014805 -0.047941 \n", "SUNPHARMA 0.003469 -0.017533 -0.022496 -0.036513 0.036830 -0.040155 \n", "TCS -0.018007 -0.019218 0.019287 -0.015096 -0.008523 -0.050036 \n", "TATACONSUM -0.006916 -0.029961 -0.000271 -0.009079 -0.006427 -0.047206 \n", "\n", " 7 8 9 10 ... 329 330 \\\n", "ADANIPORTS -0.004530 0.019522 -0.003887 0.009250 ... 0.073350 0.052855 \n", "BAJAJ-AUTO -0.033841 0.007503 0.007150 -0.002895 ... 0.027924 0.003732 \n", "BPCL -0.032837 0.030678 -0.014824 -0.000717 ... -0.009541 0.005716 \n", "BHARTIARTL -0.009838 0.004727 -0.007777 0.019642 ... 0.019105 0.001578 \n", "BRITANNIA -0.007104 0.026245 0.012077 0.020927 ... -0.010647 0.008192 \n", "CIPLA -0.000373 0.058092 0.052551 -0.015749 ... 0.010463 -0.000781 \n", "DIVISLAB 0.001092 0.035012 -0.003802 0.022508 ... -0.024417 0.007576 \n", "DRREDDY -0.007362 0.047547 0.041939 0.004131 ... -0.000492 -0.000360 \n", "EICHERMOT 0.024759 0.051433 -0.035456 0.030204 ... 0.012641 -0.003361 \n", "GRASIM -0.027285 0.047273 -0.015070 -0.013343 ... 0.015464 0.010738 \n", "HCLTECH 0.028727 0.027924 -0.003451 0.023619 ... -0.011304 0.006921 \n", "HDFCBANK 0.001656 0.001865 -0.027881 0.002176 ... 0.009345 0.006014 \n", "HDFCLIFE 0.019114 0.016231 0.009051 -0.006419 ... 0.012709 0.013466 \n", "HEROMOTOCO -0.037188 0.040193 -0.004137 0.008576 ... -0.007807 0.001211 \n", "HINDALCO -0.039448 0.063773 0.008161 -0.031756 ... 0.005687 -0.010098 \n", "HINDUNILVR -0.007035 0.003334 0.004246 0.019372 ... 0.026968 0.012667 \n", "ITC -0.011389 -0.007424 -0.032757 0.002933 ... -0.005349 -0.000489 \n", "INFY 0.012778 0.008029 0.015730 -0.005470 ... 0.005350 -0.001996 \n", "M&M 0.002735 0.017347 0.018016 0.009797 ... -0.029974 -0.006014 \n", "MARUTI 0.000326 0.016054 -0.000266 -0.002882 ... -0.001435 -0.008542 \n", "NESTLEIND 0.020984 0.015484 0.011131 0.002104 ... -0.021507 -0.001217 \n", "ONGC -0.030995 0.047699 -0.005892 -0.002155 ... -0.038769 0.005821 \n", "POWERGRID 0.011019 0.044687 0.027908 -0.005582 ... -0.028560 0.007940 \n", "RELIANCE -0.009408 0.020286 -0.002346 -0.021311 ... 0.004513 0.005212 \n", "SBILIFE 0.004212 0.006375 -0.016392 -0.000734 ... -0.000763 -0.001018 \n", "SHREECEM 0.016367 0.023919 -0.014290 -0.008161 ... -0.011361 0.006402 \n", "SUNPHARMA -0.009118 0.065088 0.030238 -0.001357 ... 0.005188 0.004263 \n", "TCS 0.000575 0.017439 0.023082 0.020089 ... -0.006164 -0.007339 \n", "TATACONSUM -0.021234 0.048701 -0.017168 -0.002005 ... -0.006980 0.003717 \n", "\n", " 331 332 333 334 335 336 \\\n", "ADANIPORTS 0.016210 -0.032575 -0.005324 0.003240 0.005687 -0.007330 \n", "BAJAJ-AUTO 0.009312 -0.005448 0.006740 -0.007275 -0.003027 -0.014055 \n", "BPCL -0.002947 -0.008551 -0.007453 0.008475 -0.006170 -0.001927 \n", "BHARTIARTL -0.005930 -0.004381 -0.009924 0.012009 -0.007288 -0.011766 \n", "BRITANNIA 0.000562 0.002301 0.008144 -0.005164 -0.000763 -0.002754 \n", "CIPLA -0.004479 0.001988 -0.007152 0.006520 0.006217 0.017185 \n", "DIVISLAB 0.014105 -0.014958 0.006095 -0.000376 0.015452 0.009677 \n", "DRREDDY 0.004989 -0.001695 -0.004095 0.005997 0.017884 0.005338 \n", "EICHERMOT 0.007319 0.003826 -0.008008 0.005191 0.007222 -0.012899 \n", "GRASIM -0.000234 0.001637 0.000100 0.005304 0.009125 -0.011377 \n", "HCLTECH -0.001935 -0.007346 0.017268 0.006012 -0.010395 -0.001725 \n", "HDFCBANK -0.003291 0.001146 0.013968 0.005876 -0.004455 0.000133 \n", "HDFCLIFE -0.002783 0.001395 0.002577 0.008824 -0.040981 -0.013933 \n", "HEROMOTOCO 0.004214 0.007327 -0.007308 0.011094 0.001514 -0.005486 \n", "HINDALCO -0.000136 0.001496 0.002581 0.018561 0.017026 -0.021057 \n", "HINDUNILVR -0.009585 -0.003915 0.003890 -0.016666 0.004717 0.013149 \n", "ITC 0.003179 -0.005607 -0.003432 0.008856 -0.007071 -0.001473 \n", "INFY 0.007698 -0.005755 0.037288 0.009620 -0.001525 -0.005567 \n", "M&M -0.002124 0.008707 0.012597 0.003094 -0.000944 -0.014997 \n", "MARUTI 0.052972 0.023027 0.012748 0.016148 -0.006896 -0.014316 \n", "NESTLEIND -0.012046 -0.001247 0.011769 -0.006416 -0.000031 0.005318 \n", "ONGC 0.009095 0.010651 -0.010944 -0.009016 0.011993 -0.024111 \n", "POWERGRID -0.003406 -0.007691 -0.004736 -0.001298 0.004332 0.017899 \n", "RELIANCE -0.005140 -0.009165 -0.023511 -0.022777 -0.008767 0.000863 \n", "SBILIFE 0.025928 -0.006405 0.001599 0.004989 -0.005014 -0.002694 \n", "SHREECEM 0.035915 -0.012718 -0.006752 0.001981 -0.011796 -0.009008 \n", "SUNPHARMA -0.006777 -0.002325 -0.000676 0.011206 0.006025 0.001922 \n", "TCS 0.008585 -0.012056 0.034402 0.002134 -0.013029 0.001229 \n", "TATACONSUM 0.019056 0.001520 -0.000528 0.000198 0.010626 -0.009926 \n", "\n", " 337 338 \n", "ADANIPORTS -0.010267 -0.000853 \n", "BAJAJ-AUTO 0.002231 0.017103 \n", "BPCL 0.003968 -0.011429 \n", "BHARTIARTL 0.001333 -0.005707 \n", "BRITANNIA -0.002051 -0.014755 \n", "CIPLA -0.007860 0.006379 \n", "DIVISLAB 0.011960 0.006227 \n", "DRREDDY -0.001850 0.024977 \n", "EICHERMOT -0.012550 0.001554 \n", "GRASIM -0.003027 0.001034 \n", "HCLTECH -0.000102 0.002034 \n", "HDFCBANK -0.002763 -0.007444 \n", "HDFCLIFE 0.002659 -0.000801 \n", "HEROMOTOCO -0.008506 0.006856 \n", "HINDALCO -0.005878 0.019755 \n", "HINDUNILVR -0.008525 0.002711 \n", "ITC -0.002951 0.001480 \n", "INFY 0.011356 -0.012905 \n", "M&M -0.004990 0.002250 \n", "MARUTI 0.003793 0.009114 \n", "NESTLEIND 0.001918 0.000749 \n", "ONGC -0.014238 0.009771 \n", "POWERGRID -0.015254 -0.006885 \n", "RELIANCE 0.010944 -0.006017 \n", "SBILIFE 0.008755 -0.001240 \n", "SHREECEM -0.022387 -0.016166 \n", "SUNPHARMA -0.003173 0.012880 \n", "TCS 0.001467 -0.001270 \n", "TATACONSUM -0.004815 0.002187 \n", "\n", "[29 rows x 334 columns]" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_return" ] }, { "cell_type": "code", "execution_count": 87, "id": "7658fc97", "metadata": {}, "outputs": [], "source": [ "#What Is Beta?\n", "#Beta is a measure of a stock's volatility in relation to the overall market. \n", "#By definition, the market, such as the S&P 500 Index, has a beta of 1.0\n", "\n", "\n", "#A stock that swings more than the market over time has a beta above 1.0. \n", "#If a stock moves less than the market, the stock's beta is less than 1.0. \n", "#High-beta stocks are supposed to be riskier but provide higher return potential; \n", "#low-beta stocks pose less risk but also lower returns." ] }, { "cell_type": "code", "execution_count": 88, "id": "3f20947f", "metadata": {}, "outputs": [], "source": [ "#Alpha refers to excess returns earned on an investment above the benchmark return.\n", "#Active portfolio managers seek to generate alpha in diversified portfolios, \n", "#with diversification intended to eliminate unsystematic risk." ] }, { "cell_type": "code", "execution_count": 89, "id": "aec874dc", "metadata": {}, "outputs": [], "source": [ "stats=pd.DataFrame()\n", "num_days=90\n", "for column in df_return.columns:\n", " slope, intercept,rvalue,pvalue,stderr=linregress(df_return['NIFTY'][:num_days],df_return[column][:num_days])\n", " stats[column]= pd.Series([slope, intercept,rvalue,pvalue,stderr])\n", "stats=stats.T.rename(columns={0:'Beta',1:'Alpha',2:'rvalue',3:'pvalue',4:'stderr'})\n" ] }, { "cell_type": "code", "execution_count": 90, "id": "d44f6efd", "metadata": {}, "outputs": [], "source": [ "stats=stats[(stats.Beta>0) &(stats.Alpha>0)]" ] }, { "cell_type": "code", "execution_count": 91, "id": "8d1c14cb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(stats)" ] }, { "cell_type": "code", "execution_count": 92, "id": "ef140fe9", "metadata": {}, "outputs": [], "source": [ "stats['beta_weights']= stats.Beta.div(sum(stats.Beta))" ] }, { "cell_type": "code", "execution_count": 93, "id": "b47e6b88", "metadata": {}, "outputs": [], "source": [ "stats['alpha_weights']= stats.Alpha.div(sum(stats.Alpha))" ] }, { "cell_type": "code", "execution_count": null, "id": "2acd848a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 94, "id": "8dba0aec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BetaAlpharvaluepvaluestderrbeta_weightsalpha_weights
ADANIPORTS0.8957550.0012800.7111274.049750e-150.0944050.0360810.027759
BAJAJ-AUTO0.8610230.0011010.7572735.804819e-180.0791590.0346820.023887
BPCL0.9957910.0001410.7130103.179843e-150.1043870.0401100.003052
BHARTIARTL0.7678700.0019330.6579931.833635e-120.0936770.0309290.041936
BRITANNIA0.7942360.0031600.7215101.041164e-150.0812500.0319910.068548
CIPLA0.4230740.0047830.3973781.052614e-040.1041480.0170410.103745
DIVISLAB0.5406780.0008020.5917308.160938e-100.0785200.0217780.017398
DRREDDY0.5119750.0027330.5478352.291640e-080.0833430.0206220.059274
EICHERMOT0.8473420.0016750.7181701.622323e-150.0875220.0341300.036323
GRASIM1.1539680.0000340.8311323.787729e-240.0823030.0464810.000738
HCLTECH0.8063420.0007260.7368781.241252e-160.0788580.0324790.015750
HDFCBANK1.0706260.0004400.8639026.003556e-280.0665380.0431240.009551
HDFCLIFE1.1297090.0020560.7743063.535886e-190.0984210.0455040.044600
HEROMOTOCO0.8816820.0038280.6810711.507190e-130.1010460.0355140.083026
HINDALCO1.4137910.0003170.8411263.247921e-250.0969060.0569470.006881
HINDUNILVR0.6740730.0004930.6408791.020894e-110.0860690.0271510.010704
ITC0.6440560.0013520.5552971.344592e-080.1028250.0259420.029328
INFY0.9006690.0010060.7838506.610627e-200.0760580.0362780.021818
M&M0.9861660.0026830.6809601.526249e-130.1130550.0397220.058191
MARUTI1.1592540.0008900.7901802.073142e-200.0958480.0466940.019313
NESTLEIND0.6099980.0010780.6305172.742949e-110.0800480.0245700.023391
ONGC0.9467660.0006150.5912658.476440e-100.1376610.0381350.013335
POWERGRID0.5469620.0006740.6028363.235837e-100.0771700.0220310.014612
RELIANCE1.1350870.0043160.8149251.472509e-220.0860550.0457210.093603
SBILIFE0.9370180.0005770.7678731.046227e-180.0833310.0377430.012512
SHREECEM0.8164410.0006060.7700827.238150e-190.0720990.0328860.013143
SUNPHARMA0.6103990.0029690.5748273.124497e-090.0926260.0245870.064388
TCS0.7909020.0016000.7762602.525346e-190.0684700.0318570.034696
TATACONSUM0.9749100.0022360.7994733.507506e-210.0780870.0392690.048498
\n", "
" ], "text/plain": [ " Beta Alpha rvalue pvalue stderr \\\n", "ADANIPORTS 0.895755 0.001280 0.711127 4.049750e-15 0.094405 \n", "BAJAJ-AUTO 0.861023 0.001101 0.757273 5.804819e-18 0.079159 \n", "BPCL 0.995791 0.000141 0.713010 3.179843e-15 0.104387 \n", "BHARTIARTL 0.767870 0.001933 0.657993 1.833635e-12 0.093677 \n", "BRITANNIA 0.794236 0.003160 0.721510 1.041164e-15 0.081250 \n", "CIPLA 0.423074 0.004783 0.397378 1.052614e-04 0.104148 \n", "DIVISLAB 0.540678 0.000802 0.591730 8.160938e-10 0.078520 \n", "DRREDDY 0.511975 0.002733 0.547835 2.291640e-08 0.083343 \n", "EICHERMOT 0.847342 0.001675 0.718170 1.622323e-15 0.087522 \n", "GRASIM 1.153968 0.000034 0.831132 3.787729e-24 0.082303 \n", "HCLTECH 0.806342 0.000726 0.736878 1.241252e-16 0.078858 \n", "HDFCBANK 1.070626 0.000440 0.863902 6.003556e-28 0.066538 \n", "HDFCLIFE 1.129709 0.002056 0.774306 3.535886e-19 0.098421 \n", "HEROMOTOCO 0.881682 0.003828 0.681071 1.507190e-13 0.101046 \n", "HINDALCO 1.413791 0.000317 0.841126 3.247921e-25 0.096906 \n", "HINDUNILVR 0.674073 0.000493 0.640879 1.020894e-11 0.086069 \n", "ITC 0.644056 0.001352 0.555297 1.344592e-08 0.102825 \n", "INFY 0.900669 0.001006 0.783850 6.610627e-20 0.076058 \n", "M&M 0.986166 0.002683 0.680960 1.526249e-13 0.113055 \n", "MARUTI 1.159254 0.000890 0.790180 2.073142e-20 0.095848 \n", "NESTLEIND 0.609998 0.001078 0.630517 2.742949e-11 0.080048 \n", "ONGC 0.946766 0.000615 0.591265 8.476440e-10 0.137661 \n", "POWERGRID 0.546962 0.000674 0.602836 3.235837e-10 0.077170 \n", "RELIANCE 1.135087 0.004316 0.814925 1.472509e-22 0.086055 \n", "SBILIFE 0.937018 0.000577 0.767873 1.046227e-18 0.083331 \n", "SHREECEM 0.816441 0.000606 0.770082 7.238150e-19 0.072099 \n", "SUNPHARMA 0.610399 0.002969 0.574827 3.124497e-09 0.092626 \n", "TCS 0.790902 0.001600 0.776260 2.525346e-19 0.068470 \n", "TATACONSUM 0.974910 0.002236 0.799473 3.507506e-21 0.078087 \n", "\n", " beta_weights alpha_weights \n", "ADANIPORTS 0.036081 0.027759 \n", "BAJAJ-AUTO 0.034682 0.023887 \n", "BPCL 0.040110 0.003052 \n", "BHARTIARTL 0.030929 0.041936 \n", "BRITANNIA 0.031991 0.068548 \n", "CIPLA 0.017041 0.103745 \n", "DIVISLAB 0.021778 0.017398 \n", "DRREDDY 0.020622 0.059274 \n", "EICHERMOT 0.034130 0.036323 \n", "GRASIM 0.046481 0.000738 \n", "HCLTECH 0.032479 0.015750 \n", "HDFCBANK 0.043124 0.009551 \n", "HDFCLIFE 0.045504 0.044600 \n", "HEROMOTOCO 0.035514 0.083026 \n", "HINDALCO 0.056947 0.006881 \n", "HINDUNILVR 0.027151 0.010704 \n", "ITC 0.025942 0.029328 \n", "INFY 0.036278 0.021818 \n", "M&M 0.039722 0.058191 \n", "MARUTI 0.046694 0.019313 \n", "NESTLEIND 0.024570 0.023391 \n", "ONGC 0.038135 0.013335 \n", "POWERGRID 0.022031 0.014612 \n", "RELIANCE 0.045721 0.093603 \n", "SBILIFE 0.037743 0.012512 \n", "SHREECEM 0.032886 0.013143 \n", "SUNPHARMA 0.024587 0.064388 \n", "TCS 0.031857 0.034696 \n", "TATACONSUM 0.039269 0.048498 " ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats" ] }, { "cell_type": "code", "execution_count": null, "id": "53b7d9c3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 50, "id": "40c5dd85", "metadata": {}, "outputs": [], "source": [ "portfolio_return=pd.DataFrame()" ] }, { "cell_type": "code", "execution_count": null, "id": "960816d4", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 96, "id": "26d99d1a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n" ] }, { "cell_type": "code", "execution_count": 97, "id": "c0e56db5", "metadata": {}, "outputs": [], "source": [ "nifty=df_return.pop('NIFTY')" ] }, { "cell_type": "code", "execution_count": null, "id": "be67c4f2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 104, "id": "62df9fe8", "metadata": {}, "outputs": [], "source": [ "df_return=df_return[stats.T.columns].T" ] }, { "cell_type": "code", "execution_count": null, "id": "a484276c", "metadata": {}, "outputs": [], "source": [ "stats" ] }, { "cell_type": "code", "execution_count": 121, "id": "e7ea6f1a", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "ADANIPORTS -0.001212\n", "BAJAJ-AUTO -0.008218\n", "BPCL -0.007991\n", "BHARTIARTL 0.004048\n", "BRITANNIA -0.007413\n", "Name: 1, dtype: float64" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_return[1].head()" ] }, { "cell_type": "code", "execution_count": 122, "id": "bbe59fa1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ADANIPORTS 0.027759\n", "BAJAJ-AUTO 0.023887\n", "BPCL 0.003052\n", "BHARTIARTL 0.041936\n", "BRITANNIA 0.068548\n", "Name: alpha_weights, dtype: float64" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.alpha_weights.head()" ] }, { "cell_type": "code", "execution_count": 123, "id": "06584e4c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-3.3643908e-05" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0.027759*-0.001212" ] }, { "cell_type": "code", "execution_count": null, "id": "11c894ac", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 124, "id": "be1d50a3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.007182989036520953" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(df_return[1]*stats.alpha_weights)" ] }, { "cell_type": "code", "execution_count": 144, "id": "d005bb25", "metadata": {}, "outputs": [], "source": [ "portfolio_return=pd.DataFrame()\n", "portfolio_return['alpha_return']= df_return.apply(lambda x: sum(x*stats.alpha_weights))\n", "portfolio_return['beta_return']=df_return.apply(lambda x : sum(x*stats.beta_weights))" ] }, { "cell_type": "code", "execution_count": 146, "id": "c2ed1649", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "alpha_return 0.001425\n", "beta_return 0.001311\n", "dtype: float64" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(portfolio_return)" ] }, { "cell_type": "code", "execution_count": 147, "id": "a6e6edee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "alpha_return 0.015755\n", "beta_return 0.017037\n", "dtype: float64" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.std(portfolio_return)" ] }, { "cell_type": "code", "execution_count": null, "id": "31c1f33a", "metadata": {}, "outputs": [], "source": [ "#sharpe ratio= return / std" ] }, { "cell_type": "code", "execution_count": 150, "id": "56f4cf3b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "alpha_return 0.090473\n", "beta_return 0.076961\n", "dtype: float64" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(portfolio_return)/ np.std(portfolio_return)" ] }, { "cell_type": "code", "execution_count": null, "id": "0355a09d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "c3f375e6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "id": "7899a4d2", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'slope'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3079\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3081\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'slope'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_return\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pre\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mstats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'r2_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mR2Score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m\u001b[0m in \u001b[0;36mR2Score\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0my_pre\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdf_return\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'NIFTY'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'slope'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'intercept'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_return\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pre\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mscore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3022\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3023\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3025\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3026\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3080\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3081\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3084\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'slope'" ] } ], "source": [ "from sklearn.metrics import r2_score\n", "def R2Score():\n", " score=[]\n", " for column in df.columns:\n", " y_pre= df_return['NIFTY']*stats['slope'][column]+ stats['intercept'][column]\n", " score.append(r2_score(df_return[column], y_pre))\n", " return score \n", "stats['r2_score']=R2Score()" ] }, { "cell_type": "code", "execution_count": null, "id": "adddebc9", "metadata": {}, "outputs": [], "source": [ "stats.sort_values(by='r2_score', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "58ae0f23", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9c9f04b5", "metadata": {}, "outputs": [], "source": [ "def plot_relations():\n", " import matplotlib.pyplot as plt\n", " import matplotlib as mpl\n", " mpl.rc('figure', max_open_warning = 0)\n", " x=df_return['%5ENSEI']\n", " for column in df_return.columns: #it will plot all 51 graphs \n", " plt.figure()\n", " plt.scatter(x, df_return[column])\n", " plt.plot(x,stats['slope'][column]*x+stats['intercept'][column], color='r')\n", " plt.xlabel('Nifty %Return')\n", " plt.title(column[:-3])\n", "\n", " \n", "#plot_relations()" ] }, { "cell_type": "code", "execution_count": null, "id": "19728789", "metadata": {}, "outputs": [], "source": [ "#if graphs using too much ram use code below\n", "\n", "#plt.close('all')" ] }, { "cell_type": "code", "execution_count": null, "id": "637a22c1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "23cb67db", "metadata": {}, "outputs": [], "source": [ "stats['ratio']= stats.intercept/ stats.slope #to minimize slope and maximize intercept, just maximize this ratio" ] }, { "cell_type": "code", "execution_count": null, "id": "605fb050", "metadata": {}, "outputs": [], "source": [ "stats.sort_values(by='ratio', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "c1d0088a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "ea647559", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bfb20700", "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }