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Pietro Rodolfo Masera 2023-05-17 10:39:32 +02:00
commit 5b30fceeda
6 changed files with 128 additions and 127 deletions
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3
.gitignore vendored
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@ -161,3 +161,6 @@ cython_debug/
.env/ .env/
**/.DS_Store **/.DS_Store
# Debugger
sandbox.py

91
Elaborated_Data/Not_Normalized.csv
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@ -1,91 +0,0 @@
,Ticker,Valuation,Financial Health,Estimated Growth,Past Performance
0,AAPL,2.8478,0.3300066233140655,8.02%,1.55
1,ABBV,0.0,0.4558265192176074,-4.20%,1.47
2,ABT,22.469,0.23799672210430156,-2.70%,16.58
3,ACN,2.9116,0.06722571231732856,9.00%,3.05
4,ADBE,1.568,0.17055034051168783,13.98%,2.39
5,AMAT,3.3254,0.20233198612253658,12.98%,6.8
6,AMGN,1.2905,0.6942628494138864,1.72%,3.81
7,AMZN,2.2453,0.3043878047624995,-278.70%,-42.47
8,APD,1.739,0.30713303133498127,9.38%,1.68
9,AVGO,1.0479,0.5382865599649199,8.30%,2.43
10,BA,0.0,0.4062502291946284,93.80%,987.5
11,BAC,4.1895,0.10656449190006939,3.36%,7.07
12,BDX,2.4258,0.3350369526050667,9.85%,5.53
13,BIDU,5.8388,0.2336580786908559,0.85%,31.9
14,BMY,0.0,0.4175920917257984,3.75%,6.57
15,CAT,1.4198,0.4427787540795467,12.87%,16.75
16,CCI,5.0871,0.7302196958058074,-4.38%,1.05
17,CHTR,0.362,0.6762752812866708,16.84%,-2.77
18,CMCSA,0.8061,0.36824333439977797,7.42%,9.12
19,CME,4.0654,0.02071438551860043,4.38%,2.09
20,COST,3.6281,0.1367131402584969,9.27%,0.74
21,CRM,1.3911,0.142520409918158,19.56%,15.89
22,CSCO,1.923,0.09272746243739566,7.32%,2.08
23,CSX,2.6633,0.43208447851873283,8.19%,5.95
24,CVS,1.4292,0.3195504115656207,4.00%,6.24
25,CVX,2.7809,0.0905633873865484,-7.68%,7.46
26,D,10.427,0.45626394493235223,5.60%,2.08
27,DE,1.143,0.5900895001091465,13.70%,3.41
28,DHR,3.3251,0.23320790216368767,2.81%,12.23
29,DIS,1.0565,0.2393431754764402,21.87%,2.8
30,DUK,3.0382,0.43067554645954603,5.80%,-0.19
31,EXC,2.5916,0.43109620840856333,6.30%,3.11
32,FDX,1.3726,0.4440454677936462,4.78%,-3.7
33,FIS,0.7185,0.327646615802744,2.05%,1.73
34,GE,3.1891,0.1363210759278175,25.50%,25.71
35,GILD,0.5725,0.40789643803736503,2.25%,9.03
36,GOOGL,1.1345,0.0717094597703327,17.61%,-6.07
37,GS,2.0757,0.19895968790637192,1.11%,0.86
38,HD,1.7271,0.658826607364772,2.52%,4.12
39,HON,2.2588,0.3199906484311073,7.80%,3.59
40,IBM,2.0869,0.4615188907263707,6.62%,2.02
41,INTC,6.2729,0.27130159792340114,6.02%,-12.15
42,ISRG,3.3782,NoDebt,15.96%,0.63
43,JNJ,4.3545,0.21165238181643523,4.34%,4.52
44,JPM,3.2773,0.12896465432169654,-4.33%,10.71
45,KO,3.5034,0.43530039834093054,5.97%,5.0
46,LIN,3.4208,0.23381232255815113,10.41%,7.12
47,LLY,1.8737,0.3552019261516468,23.67%,-2.53
48,LMT,3.2504,0.28559920910988246,10.89%,0.18
49,LOW,1.1925,0.8692687837466825,7.63%,4.73
50,LRCX,1.7507,0.2601092917188427,0.01%,10.79
51,MA,1.5338,0.3998356276967331,20.29%,4.6
52,MCD,3.6629,0.9219276110270812,8.57%,6.31
53,MCO,2.0425,0.5378762144167403,12.21%,8.25
54,MDT,2.4965,0.29880808209573584,1.58%,0.38
55,MMC,2.2618,0.4360265440363234,9.73%,2.76
56,MMM,4.4859,0.3460094697777588,1.64%,5.13
57,MO,8.8684,0.6893770705479824,3.92%,0.0
58,MRK,2.3138,0.28115610113594725,8.17%,7.5
59,MSFT,2.3007,0.15923154637873335,12.54%,2.63
60,NEE,2.5982,0.42720296086648885,8.80%,9.85
61,NFLX,1.635,0.2917160508782066,21.72%,10.98
62,NKE,2.0436,0.32814540136836057,8.56%,19.24
63,NSC,2.9593,0.3826495442824683,5.47%,4.54
64,NVDA,3.5519,0.28786848623184885,21.20%,-17.57
65,ORCL,1.9008,0.6975459656587145,9.06%,3.73
66,PEP,3.3521,0.44890479568366976,7.80%,5.74
67,PFE,1.1173,0.17682107451636622,-14.72%,19.04
68,PG,4.3277,0.30530408590666747,5.38%,1.23
69,PM,2.4533,0.7592813406380922,7.40%,4.23
70,PYPL,0.5571,0.1394518218093805,16.48%,7.28
71,SCHW,1.6392,0.06864791979295796,10.27%,3.54
72,SO,3.4609,0.4493306420493336,7.30%,9.94
73,SPG,13.079,0.7646523728202831,8.60%,10.74
74,SPGI,2.2617,0.1971656133109764,12.70%,2.88
75,T,4.4541,0.3888937394137295,-0.64%,7.17
76,TGT,0.8275,0.3520577481953689,-7.51%,-17.78
77,TMO,3.8993,0.372467197701198,8.57%,4.94
78,TMUS,0.3603,0.5235163412997864,65.36%,6.97
79,TSLA,1.6016,0.030817776651733787,10.66%,7.84
80,TXN,3.0209,0.3465434633812457,10.00%,7.62
81,UNH,1.5703,0.24883054438291166,13.04%,4.74
82,UNP,2.7512,0.5308179723502304,9.01%,1.69
83,UPS,2.4608,0.3656374239842635,3.62%,2.54
84,USB,1.0216,0.1449638542916892,3.84%,2.21
85,V,1.4973,0.26257002842072025,14.65%,7.49
86,VZ,6.652,0.47213514915968613,-0.26%,0.4
87,WFC,0.7586,0.13489874893977946,5.68%,-6.79
88,WMT,3.6193,0.24202631265480146,5.09%,5.9
89,XOM,1.809,0.11220696806192149,-10.74%,10.51
1 Ticker Valuation Financial Health Estimated Growth Past Performance
Ticker Valuation Financial Health Estimated Growth Past Performance
0 AAPL 2.8478 0.3300066233140655 8.02% 1.55
1 ABBV 0.0 0.4558265192176074 -4.20% 1.47
2 ABT 22.469 0.23799672210430156 -2.70% 16.58
3 ACN 2.9116 0.06722571231732856 9.00% 3.05
4 ADBE 1.568 0.17055034051168783 13.98% 2.39
5 AMAT 3.3254 0.20233198612253658 12.98% 6.8
6 AMGN 1.2905 0.6942628494138864 1.72% 3.81
7 AMZN 2.2453 0.3043878047624995 -278.70% -42.47
8 APD 1.739 0.30713303133498127 9.38% 1.68
9 AVGO 1.0479 0.5382865599649199 8.30% 2.43
10 BA 0.0 0.4062502291946284 93.80% 987.5
11 BAC 4.1895 0.10656449190006939 3.36% 7.07
12 BDX 2.4258 0.3350369526050667 9.85% 5.53
13 BIDU 5.8388 0.2336580786908559 0.85% 31.9
14 BMY 0.0 0.4175920917257984 3.75% 6.57
15 CAT 1.4198 0.4427787540795467 12.87% 16.75
16 CCI 5.0871 0.7302196958058074 -4.38% 1.05
17 CHTR 0.362 0.6762752812866708 16.84% -2.77
18 CMCSA 0.8061 0.36824333439977797 7.42% 9.12
19 CME 4.0654 0.02071438551860043 4.38% 2.09
20 COST 3.6281 0.1367131402584969 9.27% 0.74
21 CRM 1.3911 0.142520409918158 19.56% 15.89
22 CSCO 1.923 0.09272746243739566 7.32% 2.08
23 CSX 2.6633 0.43208447851873283 8.19% 5.95
24 CVS 1.4292 0.3195504115656207 4.00% 6.24
25 CVX 2.7809 0.0905633873865484 -7.68% 7.46
26 D 10.427 0.45626394493235223 5.60% 2.08
27 DE 1.143 0.5900895001091465 13.70% 3.41
28 DHR 3.3251 0.23320790216368767 2.81% 12.23
29 DIS 1.0565 0.2393431754764402 21.87% 2.8
30 DUK 3.0382 0.43067554645954603 5.80% -0.19
31 EXC 2.5916 0.43109620840856333 6.30% 3.11
32 FDX 1.3726 0.4440454677936462 4.78% -3.7
33 FIS 0.7185 0.327646615802744 2.05% 1.73
34 GE 3.1891 0.1363210759278175 25.50% 25.71
35 GILD 0.5725 0.40789643803736503 2.25% 9.03
36 GOOGL 1.1345 0.0717094597703327 17.61% -6.07
37 GS 2.0757 0.19895968790637192 1.11% 0.86
38 HD 1.7271 0.658826607364772 2.52% 4.12
39 HON 2.2588 0.3199906484311073 7.80% 3.59
40 IBM 2.0869 0.4615188907263707 6.62% 2.02
41 INTC 6.2729 0.27130159792340114 6.02% -12.15
42 ISRG 3.3782 NoDebt 15.96% 0.63
43 JNJ 4.3545 0.21165238181643523 4.34% 4.52
44 JPM 3.2773 0.12896465432169654 -4.33% 10.71
45 KO 3.5034 0.43530039834093054 5.97% 5.0
46 LIN 3.4208 0.23381232255815113 10.41% 7.12
47 LLY 1.8737 0.3552019261516468 23.67% -2.53
48 LMT 3.2504 0.28559920910988246 10.89% 0.18
49 LOW 1.1925 0.8692687837466825 7.63% 4.73
50 LRCX 1.7507 0.2601092917188427 0.01% 10.79
51 MA 1.5338 0.3998356276967331 20.29% 4.6
52 MCD 3.6629 0.9219276110270812 8.57% 6.31
53 MCO 2.0425 0.5378762144167403 12.21% 8.25
54 MDT 2.4965 0.29880808209573584 1.58% 0.38
55 MMC 2.2618 0.4360265440363234 9.73% 2.76
56 MMM 4.4859 0.3460094697777588 1.64% 5.13
57 MO 8.8684 0.6893770705479824 3.92% 0.0
58 MRK 2.3138 0.28115610113594725 8.17% 7.5
59 MSFT 2.3007 0.15923154637873335 12.54% 2.63
60 NEE 2.5982 0.42720296086648885 8.80% 9.85
61 NFLX 1.635 0.2917160508782066 21.72% 10.98
62 NKE 2.0436 0.32814540136836057 8.56% 19.24
63 NSC 2.9593 0.3826495442824683 5.47% 4.54
64 NVDA 3.5519 0.28786848623184885 21.20% -17.57
65 ORCL 1.9008 0.6975459656587145 9.06% 3.73
66 PEP 3.3521 0.44890479568366976 7.80% 5.74
67 PFE 1.1173 0.17682107451636622 -14.72% 19.04
68 PG 4.3277 0.30530408590666747 5.38% 1.23
69 PM 2.4533 0.7592813406380922 7.40% 4.23
70 PYPL 0.5571 0.1394518218093805 16.48% 7.28
71 SCHW 1.6392 0.06864791979295796 10.27% 3.54
72 SO 3.4609 0.4493306420493336 7.30% 9.94
73 SPG 13.079 0.7646523728202831 8.60% 10.74
74 SPGI 2.2617 0.1971656133109764 12.70% 2.88
75 T 4.4541 0.3888937394137295 -0.64% 7.17
76 TGT 0.8275 0.3520577481953689 -7.51% -17.78
77 TMO 3.8993 0.372467197701198 8.57% 4.94
78 TMUS 0.3603 0.5235163412997864 65.36% 6.97
79 TSLA 1.6016 0.030817776651733787 10.66% 7.84
80 TXN 3.0209 0.3465434633812457 10.00% 7.62
81 UNH 1.5703 0.24883054438291166 13.04% 4.74
82 UNP 2.7512 0.5308179723502304 9.01% 1.69
83 UPS 2.4608 0.3656374239842635 3.62% 2.54
84 USB 1.0216 0.1449638542916892 3.84% 2.21
85 V 1.4973 0.26257002842072025 14.65% 7.49
86 VZ 6.652 0.47213514915968613 -0.26% 0.4
87 WFC 0.7586 0.13489874893977946 5.68% -6.79
88 WMT 3.6193 0.24202631265480146 5.09% 5.9
89 XOM 1.809 0.11220696806192149 -10.74% 10.51

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Elaborated_Data/normalized_data.csv Normal file
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@ -0,0 +1,91 @@
,Unnamed: 0,Ticker,Valuation,Financial Health,Estimated Growth,Past Performance
0,0,AAPL,100.37236494563554,71.91074584729277,108.98,50.22864143559244
1,1,ABBV,128.0757006911408,99.32777907526436,62.12,49.98829507192271
2,2,ABT,3.903272785624139,51.86114637294232,67.37,105.48825740982531
3,3,ACN,99.73436729144395,14.648951782564373,112.86,54.87552258889583
4,4,ADBE,113.09474091365172,37.16410921556946,131.72,52.79831233698156
5,5,AMAT,95.59921096703175,44.08955155117806,128.07,67.58636122420656
6,6,AMGN,115.8115875705338,151.28471911882195,83.95,57.3294184458553
7,7,AMZN,106.38865357852976,66.32822652913868,0.0,1.962719193567985
8,8,APD,111.41045537738816,66.9264305541503,114.35,50.62083916878424
9,9,AVGO,118.16743710104177,117.29639732052325,110.09,52.92275822659215
10,10,BA,128.0757006911408,88.52475955980067,199.83,200.0
11,11,BAC,87.02885672660139,23.22114634068666,90.4,68.55624922628604
12,12,BDX,104.5891409798187,73.00688969901516,116.18,63.11751643656517
13,13,BIDU,71.29243525267314,50.91572570018997,80.58,158.348982708326
14,14,BMY,128.0757006911408,90.99623054339271,91.95,66.76549150803656
15,15,CAT,114.54840127334644,96.48458001066467,127.67,106.16594582416573
16,16,CCI,78.32852868831253,159.11996683716484,61.5,48.73910491310664
17,17,CHTR,124.70829183516719,147.3651025153211,141.6,38.36719142424548
18,18,CMCSA,120.49495151602524,80.24279199020978,106.6,76.12367142225216
19,19,CME,88.25088121502138,4.513809139501069,94.45,51.87096942525953
20,20,COST,92.58301243995469,29.79074718072496,113.92,47.83075748481232
21,21,CRM,114.82920872764136,31.05619175989376,150.18,102.73309694796748
22,22,CSCO,109.59079683339692,20.205961072647767,106.2,51.8402409569602
23,23,CSX,102.21700333279603,94.15422274646298,109.66,64.57789456959388
24,24,CVS,114.45637828205703,69.63226434890085,92.94,65.59648607467646
25,25,CVX,101.04132902481982,19.734394019195516,50.86,69.96882496581931
26,26,D,36.235424407706844,99.42309719065304,99.33,51.8402409569602
27,27,DE,117.24626163879934,128.5846194338981,130.7,56.0297272881746
28,28,DHR,95.60220516090067,50.817629093805174,88.22,88.14972071019906
29,29,DIS,118.0842626059964,52.154547957634,156.77,54.08274821363356
30,30,DUK,98.46848642408442,93.84720661991736,100.13,45.17595002844379
31,31,EXC,102.93352474374487,93.93887179378456,102.13,55.06686019467603
32,32,FDX,115.01009064759145,96.7606057674847,96.05,36.11299438295461
33,33,FIS,121.33263038562899,71.3964837435809,85.24,50.772222440029985
34,34,GE,96.96030338780842,29.70531362743399,165.8,139.7046496026774
35,35,GILD,122.72176329769937,88.88347995308483,86.02,75.78448727890905
36,36,GOOGL,117.32872134581163,15.625991638002597,144.12,30.837600812615126
37,37,GS,108.07574163031246,43.35470423960425,81.58,48.18099359602783
38,38,HD,111.52789001410734,143.5629147193028,87.08,58.34898433068543
39,39,HON,106.25419306449868,69.72819503365103,108.11,56.61235803598746
40,40,IBM,107.96446204894477,100.5681865456486,103.41,51.65611882252366
41,41,INTC,67.36065467705929,59.118511199295,101.01,20.15714828519607
42,42,ISRG,95.07236085735104,435.81395503602187,138.65,47.51124468536676
43,43,JNJ,85.41022920558748,46.12053080610741,94.29,59.680172857534586
44,44,JPM,96.07937735460422,28.10229802989597,61.67,82.20525436144348
45,45,KO,93.82423614969966,94.85499411485834,100.81,61.30038430228592
46,46,LIN,94.64748701325152,50.949336515112954,118.36,68.7365882072592
47,47,LLY,110.07902724894022,77.40097813628108,161.45,38.96605042928049
48,48,LMT,96.3479915485429,62.23406043866885,120.21,46.219501051665546
49,49,LOW,116.76557817038395,189.41973331699705,107.43,60.385988930203325
50,50,LRCX,111.29496333398598,56.6796295828036,77.37,82.5152969561821
51,51,MA,113.43072478606045,87.1269731354118,152.33,59.94849611082308
52,52,MCD,92.23701754912624,200.89445920931172,111.16,65.8435826070515
53,53,MCO,108.40548678487258,117.20698016238146,125.21,72.87040339212528
54,54,MDT,103.8834127204269,65.11236602743546,83.4,46.79055844390791
55,55,MMC,106.22430984950167,95.01322632857912,115.72,53.95657532168604
56,56,MMM,84.126733647578,75.39787775188097,83.64,61.74339347884984
57,57,MO,46.4135318774777,150.22007381333142,92.62,45.709732413314406
58,58,MRK,105.7061613994012,61.26587620928245,109.58,70.11446487776
59,59,MSFT,105.83672502534945,34.69766499690875,126.44,53.54779756728987
60,60,NEE,102.86758027329674,93.09050598916167,112.07,78.8957471199218
61,61,NFLX,112.43566117364969,63.566962940360305,156.36,83.25302875018251
62,62,NKE,108.3945644013568,71.50517259861401,111.12,115.9952333583064
63,63,NSC,99.25738031854726,83.38200564323695,98.81,59.747188952170255
64,64,NVDA,93.34123440057793,62.728551757467315,154.92,13.545606179950594
65,65,ORCL,109.81070761854856,152.00013305657274,113.09,57.06804115086576
66,66,PEP,95.33276000587897,97.8194872207687,108.11,63.84548798636756
67,67,PFE,117.49550657549727,38.53054590934835,32.52,115.2147190477393
68,68,PG,85.67262649041187,66.52789058382105,98.45,49.271869110498486
69,69,PM,104.3146861910115,165.45270202426994,106.52,58.713318161304116
70,70,PYPL,122.86776140601104,30.387524999862347,140.41,69.31518433773357
71,71,SCHW,112.39430852805086,14.958860714982293,117.82,56.45014875933075
72,72,SO,94.2477238002292,97.91228211519756,106.12,79.23997313802575
73,73,SPG,23.037945514747708,166.62308741324316,111.28,82.32147928830356
74,74,SPGI,106.2253059746783,42.96376286707977,127.04,54.335650344289206
75,75,T,84.43687680647686,84.74265933132276,74.92,68.91715284950327
76,76,TGT,120.28985079459592,76.7158398210498,51.38,13.334987110686992
77,77,TMO,89.89200425040458,81.16320127567148,111.16,61.096517889077184
78,78,TMUS,124.7242533151007,114.0778636139239,198.31,68.19625231090778
79,79,TSLA,112.76435826134933,6.715408564004417,119.32,71.35797462546317
80,80,TXN,98.64145025650339,75.51423868403076,116.77,70.55221850582774
81,81,UNH,113.07213461934721,54.22191184064154,128.29,60.41971719335949
82,82,UNP,101.33828676184909,115.6689399670778,112.9,50.6510919364983
83,83,UPS,104.23982340635455,79.67494592788235,91.43,53.26595116675375
84,84,USB,118.42163459170818,31.588635338063334,92.3,52.24063320906951
85,85,V,113.78896354941334,57.21584127997737,134.11,70.07804181463166
86,86,VZ,64.01650111199801,102.88154333340245,76.35,46.8479328972033
87,87,WFC,120.94955349309633,29.395378652428313,99.65,29.36497090944773
88,88,WMT,92.67053402685255,52.73922227043689,97.29,64.40311313622539
89,89,XOM,110.71902830024783,24.45068126683328,42.14,81.43170431167135
1 Unnamed: 0 Ticker Valuation Financial Health Estimated Growth Past Performance
2 0 0 AAPL 100.37236494563554 71.91074584729277 108.98 50.22864143559244
3 1 1 ABBV 128.0757006911408 99.32777907526436 62.12 49.98829507192271
4 2 2 ABT 3.903272785624139 51.86114637294232 67.37 105.48825740982531
5 3 3 ACN 99.73436729144395 14.648951782564373 112.86 54.87552258889583
6 4 4 ADBE 113.09474091365172 37.16410921556946 131.72 52.79831233698156
7 5 5 AMAT 95.59921096703175 44.08955155117806 128.07 67.58636122420656
8 6 6 AMGN 115.8115875705338 151.28471911882195 83.95 57.3294184458553
9 7 7 AMZN 106.38865357852976 66.32822652913868 0.0 1.962719193567985
10 8 8 APD 111.41045537738816 66.9264305541503 114.35 50.62083916878424
11 9 9 AVGO 118.16743710104177 117.29639732052325 110.09 52.92275822659215
12 10 10 BA 128.0757006911408 88.52475955980067 199.83 200.0
13 11 11 BAC 87.02885672660139 23.22114634068666 90.4 68.55624922628604
14 12 12 BDX 104.5891409798187 73.00688969901516 116.18 63.11751643656517
15 13 13 BIDU 71.29243525267314 50.91572570018997 80.58 158.348982708326
16 14 14 BMY 128.0757006911408 90.99623054339271 91.95 66.76549150803656
17 15 15 CAT 114.54840127334644 96.48458001066467 127.67 106.16594582416573
18 16 16 CCI 78.32852868831253 159.11996683716484 61.5 48.73910491310664
19 17 17 CHTR 124.70829183516719 147.3651025153211 141.6 38.36719142424548
20 18 18 CMCSA 120.49495151602524 80.24279199020978 106.6 76.12367142225216
21 19 19 CME 88.25088121502138 4.513809139501069 94.45 51.87096942525953
22 20 20 COST 92.58301243995469 29.79074718072496 113.92 47.83075748481232
23 21 21 CRM 114.82920872764136 31.05619175989376 150.18 102.73309694796748
24 22 22 CSCO 109.59079683339692 20.205961072647767 106.2 51.8402409569602
25 23 23 CSX 102.21700333279603 94.15422274646298 109.66 64.57789456959388
26 24 24 CVS 114.45637828205703 69.63226434890085 92.94 65.59648607467646
27 25 25 CVX 101.04132902481982 19.734394019195516 50.86 69.96882496581931
28 26 26 D 36.235424407706844 99.42309719065304 99.33 51.8402409569602
29 27 27 DE 117.24626163879934 128.5846194338981 130.7 56.0297272881746
30 28 28 DHR 95.60220516090067 50.817629093805174 88.22 88.14972071019906
31 29 29 DIS 118.0842626059964 52.154547957634 156.77 54.08274821363356
32 30 30 DUK 98.46848642408442 93.84720661991736 100.13 45.17595002844379
33 31 31 EXC 102.93352474374487 93.93887179378456 102.13 55.06686019467603
34 32 32 FDX 115.01009064759145 96.7606057674847 96.05 36.11299438295461
35 33 33 FIS 121.33263038562899 71.3964837435809 85.24 50.772222440029985
36 34 34 GE 96.96030338780842 29.70531362743399 165.8 139.7046496026774
37 35 35 GILD 122.72176329769937 88.88347995308483 86.02 75.78448727890905
38 36 36 GOOGL 117.32872134581163 15.625991638002597 144.12 30.837600812615126
39 37 37 GS 108.07574163031246 43.35470423960425 81.58 48.18099359602783
40 38 38 HD 111.52789001410734 143.5629147193028 87.08 58.34898433068543
41 39 39 HON 106.25419306449868 69.72819503365103 108.11 56.61235803598746
42 40 40 IBM 107.96446204894477 100.5681865456486 103.41 51.65611882252366
43 41 41 INTC 67.36065467705929 59.118511199295 101.01 20.15714828519607
44 42 42 ISRG 95.07236085735104 435.81395503602187 138.65 47.51124468536676
45 43 43 JNJ 85.41022920558748 46.12053080610741 94.29 59.680172857534586
46 44 44 JPM 96.07937735460422 28.10229802989597 61.67 82.20525436144348
47 45 45 KO 93.82423614969966 94.85499411485834 100.81 61.30038430228592
48 46 46 LIN 94.64748701325152 50.949336515112954 118.36 68.7365882072592
49 47 47 LLY 110.07902724894022 77.40097813628108 161.45 38.96605042928049
50 48 48 LMT 96.3479915485429 62.23406043866885 120.21 46.219501051665546
51 49 49 LOW 116.76557817038395 189.41973331699705 107.43 60.385988930203325
52 50 50 LRCX 111.29496333398598 56.6796295828036 77.37 82.5152969561821
53 51 51 MA 113.43072478606045 87.1269731354118 152.33 59.94849611082308
54 52 52 MCD 92.23701754912624 200.89445920931172 111.16 65.8435826070515
55 53 53 MCO 108.40548678487258 117.20698016238146 125.21 72.87040339212528
56 54 54 MDT 103.8834127204269 65.11236602743546 83.4 46.79055844390791
57 55 55 MMC 106.22430984950167 95.01322632857912 115.72 53.95657532168604
58 56 56 MMM 84.126733647578 75.39787775188097 83.64 61.74339347884984
59 57 57 MO 46.4135318774777 150.22007381333142 92.62 45.709732413314406
60 58 58 MRK 105.7061613994012 61.26587620928245 109.58 70.11446487776
61 59 59 MSFT 105.83672502534945 34.69766499690875 126.44 53.54779756728987
62 60 60 NEE 102.86758027329674 93.09050598916167 112.07 78.8957471199218
63 61 61 NFLX 112.43566117364969 63.566962940360305 156.36 83.25302875018251
64 62 62 NKE 108.3945644013568 71.50517259861401 111.12 115.9952333583064
65 63 63 NSC 99.25738031854726 83.38200564323695 98.81 59.747188952170255
66 64 64 NVDA 93.34123440057793 62.728551757467315 154.92 13.545606179950594
67 65 65 ORCL 109.81070761854856 152.00013305657274 113.09 57.06804115086576
68 66 66 PEP 95.33276000587897 97.8194872207687 108.11 63.84548798636756
69 67 67 PFE 117.49550657549727 38.53054590934835 32.52 115.2147190477393
70 68 68 PG 85.67262649041187 66.52789058382105 98.45 49.271869110498486
71 69 69 PM 104.3146861910115 165.45270202426994 106.52 58.713318161304116
72 70 70 PYPL 122.86776140601104 30.387524999862347 140.41 69.31518433773357
73 71 71 SCHW 112.39430852805086 14.958860714982293 117.82 56.45014875933075
74 72 72 SO 94.2477238002292 97.91228211519756 106.12 79.23997313802575
75 73 73 SPG 23.037945514747708 166.62308741324316 111.28 82.32147928830356
76 74 74 SPGI 106.2253059746783 42.96376286707977 127.04 54.335650344289206
77 75 75 T 84.43687680647686 84.74265933132276 74.92 68.91715284950327
78 76 76 TGT 120.28985079459592 76.7158398210498 51.38 13.334987110686992
79 77 77 TMO 89.89200425040458 81.16320127567148 111.16 61.096517889077184
80 78 78 TMUS 124.7242533151007 114.0778636139239 198.31 68.19625231090778
81 79 79 TSLA 112.76435826134933 6.715408564004417 119.32 71.35797462546317
82 80 80 TXN 98.64145025650339 75.51423868403076 116.77 70.55221850582774
83 81 81 UNH 113.07213461934721 54.22191184064154 128.29 60.41971719335949
84 82 82 UNP 101.33828676184909 115.6689399670778 112.9 50.6510919364983
85 83 83 UPS 104.23982340635455 79.67494592788235 91.43 53.26595116675375
86 84 84 USB 118.42163459170818 31.588635338063334 92.3 52.24063320906951
87 85 85 V 113.78896354941334 57.21584127997737 134.11 70.07804181463166
88 86 86 VZ 64.01650111199801 102.88154333340245 76.35 46.8479328972033
89 87 87 WFC 120.94955349309633 29.395378652428313 99.65 29.36497090944773
90 88 88 WMT 92.67053402685255 52.73922227043689 97.29 64.40311313622539
91 89 89 XOM 110.71902830024783 24.45068126683328 42.14 81.43170431167135

49
indexer/indexer.py
View file

@ -1,11 +1,14 @@
import sys import sys
sys.path.append('../group-1') sys.path.append('../VISUAL-AN-PROJECT')
import math
import pandas as pd import pandas as pd
import os
from scraper.top100_extractor import programming_crime_list from scraper.top100_extractor import programming_crime_list
import numpy as np
from sklearn import preprocessing from sklearn import preprocessing
pd.set_option('display.max_rows', 500) pd.set_option('display.max_rows', 500)
def get_peg(ticker: str): def get_peg(ticker: str):
@ -25,7 +28,7 @@ def get_peg(ticker: str):
# Take first value (the last peg ratio) # Take first value (the last peg ratio)
# If it does not exist, it returns 0 # If it does not exist, it returns 0
print(ticker)
try: try:
if len(current_ratios['PegRatio']) > 0: if len(current_ratios['PegRatio']) > 0:
peg_ratio = current_ratios['PegRatio'].iloc[:1] peg_ratio = current_ratios['PegRatio'].iloc[:1]
@ -53,7 +56,7 @@ def get_financial_health(ticker: str):
try: try:
balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets'] balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets']
except KeyError: except KeyError:
return "NoDebt" return 2.0
# Get financial health # Get financial health
financial_health = balance_sheet['financial_health'].iloc[:1] financial_health = balance_sheet['financial_health'].iloc[:1]
@ -76,24 +79,32 @@ def past_performance_earnings(ticker: str):
return performance_index return performance_index
def normalizer(): def normalizer():
''' Normalize the dataframe columns to a range between 0 and 200'''
# Read Not_normalized .csv # Read Not_normalized .csv
not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv') not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')
# Takes values for Valuation and compute normalization # Elaborate Valuation column
v_low, v_up = not_normalized['Valuation'].min(), not_normalized['Valuation'].max() v_values = (200/(1+math.e**( 0.2*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
# v_values = (100 - 0) * ((not_normalized['Valuation'] - v_low) / v_up - v_low) + 0
v_values = 240 / not_normalized['Valuation']
not_normalized['Valuation'] = v_values not_normalized['Valuation'] = v_values
# # Takes values for financial health and compute normalization # Elaborate Financial health column
# fh_low, fh_up = not_normalized['Financial Health'],min(), not_normalized['Financial Health'].max() fh_values= (80/not_normalized['Financial Health'].mean())*not_normalized['Financial Health'] #FINANCIAL HEALTH STAT
# fh_values = (100 - 0) * ((not_normalized['Financial Health'] - fh_low) / fh_up - fh_low) + 0 not_normalized['Financial Health'] = fh_values
# not_normalized['Financial Health'] = fh_values
# eg_low, eg_up = not_normalized['Estimated Growth'],min(), not_normalized['Estimated Growth'].max() # Elaborate Estimated Growth column
# eg_values = (100 - 0) * ((not_normalized['Financial Health'] - fh_low) / fh_up - fh_low) + 0 not_normalized['Estimated Growth'] = not_normalized['Estimated Growth'].str.strip("%").astype("float")
eg_values= (200/(1+math.e**( 0.08*(not_normalized['Estimated Growth'].mean()-not_normalized['Estimated Growth'])))) #ESTIMATED GROWTH STAT
for i in range(len(eg_values)):
eg_values[i] = float(round(eg_values[i],2))
not_normalized['Estimated Growth']= eg_values
print(not_normalized) # Elaborate Past Performance Column
pf_values = (200/(1+math.e**( 0.08*(not_normalized['Past Performance'].mean()-not_normalized['Past Performance'])))) #PAST PERFORMANCE
not_normalized['Past Performance'] = pf_values
# Create normalized dataframe for main page
not_normalized.to_csv(r'Elaborated_Data/normalized_data.csv')
def create_df(companies_list): def create_df(companies_list):
# Dictionary # Dictionary
@ -120,7 +131,11 @@ def create_df(companies_list):
df.to_csv("Elaborated_Data/Not_Normalized.csv") df.to_csv("Elaborated_Data/Not_Normalized.csv")
def main(): def main():
# create_df(programming_crime_list)
if not os.path.exists(r"Elaborated_Data"):
os.mkdir(r"Elaborated_Data")
create_df(programming_crime_list)
normalizer() normalizer()
# print(get_peg('GOOGL')) # < 1 ( GREEN); > 1 (RED); = 1 (ORANGE) # print(get_peg('GOOGL')) # < 1 ( GREEN); > 1 (RED); = 1 (ORANGE)
# print(get_financial_health('GOOGL')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE) # print(get_financial_health('GOOGL')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)

10
scraper/marketcap.py
View file

@ -1,10 +0,0 @@
from yahooquery import Ticker
stock_symbol = 'AAPL' # Replace with your desired stock symbol
ticker = Ticker(stock_symbol)
summary = ticker.summary_detail
market_cap = summary[stock_symbol.upper()]['marketCap']
print("Market Cap:", market_cap)

7
scraper/retriever.py
View file

@ -23,19 +23,12 @@ def get_market_cap(ticker1):
return market_cap return market_cap
<<<<<<< HEAD
=======
def get_earnings(ticker): def get_earnings(ticker):
aapl = Ticker(ticker) aapl = Ticker(ticker)
performance = aapl.earning_history performance = aapl.earning_history
return performance return performance
>>>>>>> 87c225170b8caaa835e939886712744ade00a165
def get_analyst_estimates(ticker): def get_analyst_estimates(ticker):
estimates = si.get_analysts_info(ticker) estimates = si.get_analysts_info(ticker)
next_5_years_estimates = estimates["Growth Estimates"].iloc[4].dropna() next_5_years_estimates = estimates["Growth Estimates"].iloc[4].dropna()