notebook+run indexer

Elaborated_Data/Not_Normalized.csv

@@ -1,91 +1,91 @@
 ,Ticker,Valuation,Financial Health,Estimated Growth,Past Performance
-0,AAPL,2.8478,0.3300066233140655,8.02%,1.55
-1,ABBV,0.0,0.4558265192176074,-4.17%,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,3.8395,0.19448645675787157,13.27%,9.74
-6,AMGN,1.2905,0.6942628494138864,1.72%,3.81
-7,AMZN,2.2453,0.3043878047624995,-278.70%,-42.47
-8,APD,1.7767,0.3320219871311414,9.38%,1.68
-9,AVGO,1.1592,0.5382865599649199,8.30%,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,0.593,0.23549113544599592,2.75%,23.16
-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,4.0654,0.02071438551860043,4.53%,2.09
-20,COST,3.6281,0.1367131402584969,8.77%,0.74
-21,CRM,1.3911,0.142520409918158,19.56%,15.89
-22,CSCO,2.0002,0.08606670836366619,7.39%,2.59
-23,CSX,2.6633,0.43208447851873283,8.19%,5.95
-24,CVS,1.364,0.3195504115656207,4.00%,6.24
-25,CVX,2.7809,0.0905633873865484,-7.68%,7.46
-26,D,10.427,0.45626394493235223,5.60%,2.08
-27,DE,1.1147,0.5907551831779312,13.36%,6.62
-28,DHR,3.3251,0.23320790216368767,2.81%,12.23
-29,DIS,1.0753,0.23683722383309413,21.87%,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,1.7394,0.6470819260073836,2.05%,1.67
-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,3.3782,2.0,15.96%,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,3.4208,0.23381232255815113,8.50%,35.35
-47,LLY,1.8737,0.3552019261516468,24.38%,-2.53
-48,LMT,3.2504,0.28559920910988246,10.89%,0.18
-49,LOW,1.2378,0.8692687837466825,7.48%,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,2.4965,0.29880808209573584,1.04%,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,-14.94%,19.04
-68,PG,4.3277,0.30530408590666747,5.38%,1.23
-69,PM,2.4533,0.7592813406380922,7.40%,4.23
-70,PYPL,0.5571,0.1394518218093805,15.76%,7.28
-71,SCHW,1.6392,0.06864791979295796,9.80%,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,0.814,0.3610930009587728,-7.51%,-2.33
-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,3.6062,0.26062525249639873,5.36%,11.79
-89,XOM,1.809,0.11220696806192149,-10.74%,10.51
+0,AAPL,2.8478,3.0302422113761804,8.02%,2.056000450056732
+1,ABBV,0.0,2.193817072592499,-4.17%,1.3643231826828999
+2,ABT,22.469,4.2017385414314745,-2.70%,16.221505969455265
+3,ACN,2.9116,14.875260752606902,9.00%,3.125994103956993
+4,ADBE,1.568,5.863371465573063,13.98%,2.3571434347409626
+5,AMAT,3.8395,5.141746200070696,13.27%,9.899482013689063
+6,AMGN,1.2905,1.4403766539491842,1.72%,3.728574353574352
+7,AMZN,2.2453,3.2852827358844294,-278.70%,-61.06442577030811
+8,APD,1.7767,3.0118487291777516,9.38%,1.664735813911232
+9,AVGO,1.1592,1.8577465505829642,8.30%,2.490510115467356
+10,BA,0.0,2.461537072809662,93.80%,-2379.667762144398
+11,BAC,4.1895,9.383988814376815,3.36%,6.887973814803082
+12,BDX,2.4258,2.984745390693591,9.85%,5.584638849547876
+13,BIDU,0.593,4.246444343249283,2.75%,25.709916173234124
+14,BMY,0.0,2.394681364456072,3.75%,6.609816410785568
+15,CAT,1.4198,2.258464279928722,12.87%,16.690231474594547
+16,CCI,5.0871,1.3694508731327582,-4.38%,1.1434621925693333
+17,CHTR,0.362,1.4786877883476912,16.84%,-1.5543025412690668
+18,CMCSA,0.8061,2.715595658044864,7.42%,9.086076525100912
+19,CME,4.0654,48.27562947025644,4.53%,2.0929396865062535
+20,COST,3.6281,7.31458584090163,8.77%,0.7022840372888224
+21,CRM,1.3911,7.016538898353208,19.56%,15.038469229787918
+22,CSCO,2.0002,11.618894448415535,7.39%,2.555664961483716
+23,CSX,2.6633,2.3143622363575496,8.19%,6.066607849171981
+24,CVS,1.364,3.1293966892439657,4.00%,6.1332987993061785
+25,CVX,2.7809,11.041989802476541,-7.68%,6.798682483159102
+26,D,10.427,2.1917138338604962,5.60%,2.0196249611605026
+27,DE,1.1147,1.692748584312983,13.36%,6.716190927540026
+28,DHR,3.3251,4.288019362646228,2.81%,12.150539916741248
+29,DIS,1.0753,4.222309246053011,21.87%,-1.2474504662004655
+30,DUK,3.0382,2.321933548864566,5.80%,0.5643998761633824
+31,EXC,2.5916,2.3196678154317443,6.30%,2.2139092791266686
+32,FDX,1.3726,2.252021634110481,4.78%,1.1865562472240336
+33,FIS,0.7185,3.052068758744753,2.05%,2.1715993414909844
+34,GE,3.1891,7.335622853574773,25.50%,47.37235586600727
+35,GILD,0.5725,2.451602678394548,2.25%,9.464019290494036
+36,GOOGL,1.1345,13.945161533816425,17.61%,-5.584976190004754
+37,GS,2.0757,5.026143790849673,1.11%,-1.5345378932072622
+38,HD,1.7394,1.5453993687788299,2.05%,1.5688534735078083
+39,HON,2.2588,3.125091326583864,7.80%,3.8168087284000825
+40,IBM,2.0869,2.1667585446527013,6.62%,2.9896273699006493
+41,INTC,6.2729,3.6859347960137647,6.02%,-24.38244047619048
+42,ISRG,3.3782,2.0,15.96%,0.7370798319327688
+43,JNJ,4.3545,4.7247283088328,4.34%,4.547893454504981
+44,JPM,3.2773,7.754062578305718,-4.33%,10.185285013321064
+45,KO,3.5034,2.297264150943396,5.97%,4.635027985074622
+46,LIN,3.4208,4.276934547584811,8.50%,38.40029639869726
+47,LLY,1.8737,2.8153000487195237,24.38%,-2.9632885310978514
+48,LMT,3.2504,3.5014102564102565,10.89%,-5.74650013495231
+49,LOW,1.2378,1.1503921671842923,7.48%,4.905642116132294
+50,LRCX,1.7507,3.8445377840670143,0.01%,11.094549855781523
+51,MA,1.5338,2.501027749229188,20.29%,4.704107759087078
+52,MCD,3.6629,1.0846838602500923,8.57%,6.426164395995392
+53,MCO,2.0425,1.859163824681066,12.21%,8.336941598338818
+54,MDT,2.4965,3.3466296928327646,1.04%,0.5633648257910022
+55,MMC,2.2618,2.2934383552499833,9.73%,2.9515360255345033
+56,MMM,4.4859,2.8900943105467545,1.64%,6.8086477749591365
+57,MO,8.8684,1.4505849450506165,3.92%,0.029059829059829123
+58,MRK,2.3138,3.5567430191261282,8.17%,7.36063643958381
+59,MSFT,2.3007,6.280162585506097,12.54%,2.6823350568434843
+60,NEE,2.5982,2.340807746209708,8.80%,9.489300857329233
+61,NFLX,1.635,3.4279910104004063,21.72%,-4.562647278945063
+62,NKE,2.0436,3.047429571860576,8.56%,22.508955039525688
+63,NSC,2.9593,2.6133573525487055,5.47%,4.472463705675281
+64,NVDA,3.5519,3.473808519611978,21.20%,-15.268424271929499
+65,ORCL,1.9008,1.4335972813715132,9.06%,3.2198702287752097
+66,PEP,3.3521,2.227643833648574,7.80%,5.771889848077926
+67,PFE,1.1173,5.655434470891884,-14.94%,19.18648204573236
+68,PG,4.3277,3.27542291820393,5.38%,1.2290646511957997
+69,PM,2.4533,1.3170348676810764,7.40%,4.409467807344407
+70,PYPL,0.5571,7.170935359789849,15.76%,7.584126145172658
+71,SCHW,1.6392,14.567083795342944,9.80%,3.672161172161172
+72,SO,3.4609,2.225532617671346,7.30%,11.369532987398081
+73,SPG,13.079,1.3077838185627797,8.60%,10.158696263885645
+74,SPGI,2.2617,5.0718783220214245,12.70%,2.897283881535927
+75,T,4.4541,2.5713964989704743,-0.64%,7.124651651741004
+76,TGT,0.814,2.7693696564176093,-7.51%,-5.513897780623133
+77,TMO,3.8993,2.684800181524236,8.57%,4.9700752857065496
+78,TMUS,0.3603,1.91016004871443,65.36%,194.507371007371
+79,TSLA,1.6016,32.44880418535127,10.66%,9.50925181013677
+80,TXN,3.0209,2.8856409243531504,10.00%,7.605414978999398
+81,UNH,1.5703,4.018799229330765,13.04%,4.837717955752949
+82,UNP,2.7512,1.8838849701573521,9.01%,1.6435455625996247
+83,UPS,2.4608,2.734949801098693,3.62%,2.4446950249488704
+84,USB,1.0216,6.898271330367974,3.84%,2.214847717184163
+85,V,1.4973,3.8085077951002226,14.65%,7.597283768499744
+86,VZ,6.652,2.118037603808605,-0.26%,0.3920014094432702
+87,WFC,0.7586,7.412967191018301,5.68%,-7.061137169371726
+88,WMT,3.6062,3.836926738378192,5.36%,11.876073092960503
+89,XOM,1.809,8.912102494812528,-10.74%,10.179827374721386

Elaborated_Data/employees_over_time.csv

@@ -1,91 +1,91 @@
 ,Ticker,Employees_over_time
-0,AAPL,"[37376.653472053375, 46285.99144811846, 51441.24426379464, 61558.083471658676, 58907.38311263634, 67601.33974642337, 78133.03145013178, 90447.36485241847, 105018.28649449686, 129688.03180312812, 156569.24938667504, 164000]"
-1,ABBV,"[27248.874340193106, 27552.39237111908, 25329.378309300493, 24229.460715240515, 27009.567208570257, 31059.673341866426, 32257.759396694128, 30011.387247618415, 34143.59094205851, 33799.28937126673, 41578.389196673495, 50000]"
-2,ABT,"[91007.48710343671, 86868.75171395317, 93817.1623331741, 105047.67638738238, 98564.62584384151, 118992.1314737311, 135266.01670105054, 127526.1467181144, 116294.33295714317, 113003.84886759392, 116747.70043441781, 115000]"
-3,ACN,"[243273.94377881405, 290087.4969981179, 343925.2594186647, 341669.11975675786, 415821.9435065691, 481734.41578104754, 494311.2717074493, 469703.9365311923, 514855.4322787341, 524893.7554318984, 647268.9157036883, 738000]"
-4,ADBE,"[17726.403646993465, 16567.650711758204, 18329.439842507712, 17340.05259916847, 20007.941004025153, 24406.195004350684, 23106.685262763673, 22719.59564365903, 26638.135896237327, 24223.033279166484, 28386.396411308244, 29239]"
-5,AMAT,"[11981.734185195062, 13554.654056730837, 14226.9991651384, 14649.703856998272, 15910.02708403942, 19339.90217030812, 20382.435942489305, 24008.273741077763, 27364.093480959342, 27813.028775570885, 27246.88500846486, 33000]"
-6,AMGN,"[10128.930224553129, 12259.313225113365, 14387.302760224953, 15567.900191961, 16467.23382061571, 19453.4467901448, 21491.82329180788, 23962.02404326488, 22887.500003764275, 23410.18751537769, 25766.316337053584, 25200]"
-7,AMZN,"[1262912.700243943, 1201928.3308559773, 1245970.8252602476, 1181847.028490609, 1304561.9453141615, 1221615.039806315, 1261468.8997341848, 1299701.5003163582, 1406278.9801054702, 1461105.4451448843, 1681676.2771212864, 1541000]"
-8,APD,"[7786.644490555564, 8857.082988287897, 9142.43000132444, 9670.049447077987, 9383.460374810173, 11279.410992426756, 12126.890985622158, 14021.881230481304, 15650.694773264693, 17324.9765337677, 20724.13476817378, 19710]"
-9,AVGO,"[8292.303379505296, 9819.104351514392, 10010.38157624642, 11523.899876911342, 12794.741767102136, 13139.15890579595, 15748.514650245996, 16830.007498527593, 18627.96619490903, 16936.287326630933, 18370.444985137205, 20000]"
-10,BA,"[95930.45791529941, 102854.87471190048, 111500.18298496939, 124266.94742913038, 120500.43106732905, 110828.71716376691, 108315.1011590143, 135351.89227980666, 136226.13845666437, 131793.21645080685, 136842.59862416761, 156000]"
-11,BAC,"[109788.55207052034, 100822.67315052185, 112679.34756263994, 126059.94359930868, 128148.08183462192, 142422.5097087328, 163307.8831649143, 155174.35421865247, 183644.4188782105, 167386.41336481116, 197693.8344233226, 217000]"
-12,BDX,"[29320.57293755517, 34711.29483135434, 38803.2808813335, 37266.95045358426, 39953.20304823652, 48584.93230304006, 55858.004003931506, 65406.57409695402, 75329.7420659041, 78508.08283057253, 82852.82237290371, 77000]"
-13,BIDU,"[31527.199089740803, 32974.14767678813, 34544.316410037405, 35833.33884363531, 37082.49558120737, 41817.77285198103, 38933.65737774911, 36642.213356244996, 41136.74614891428, 48793.42258773984, 45132.0985318714, 41300]"
-14,BMY,"[18730.039952272895, 17863.59870351158, 19378.840730049244, 21660.17089125763, 23964.999146304566, 26770.617054708448, 30628.253008807995, 28512.44370457592, 31690.359642550673, 38376.76432314398, 37309.05310145302, 34300]"
+0,AAPL,"[130018.38468363398, 141076.8005355685, 155784.846765916, 144068.2496395892, 147585.78791850968, 144161.53938975144, 135335.386838129, 157892.8911702549, 172435.29424948394, 171232.72613010253, 174697.1108963188, 164000]"
+1,ABBV,"[17742.51514057005, 20262.647494095814, 25219.441061761645, 31521.595984653097, 31713.15695487243, 33785.84875669344, 39268.23094048308, 45271.03896761534, 44887.94176390959, 41265.84616889874, 41242.22438033375, 50000]"
+2,ABT,"[67993.18650616032, 72007.02125533295, 67778.78644229041, 67126.66372420955, 61223.54767586611, 70968.13651285366, 73542.49598563302, 85846.91467915138, 99437.3078972557, 94139.12963708657, 112032.22052793686, 115000]"
+3,ACN,"[468063.99883928464, 484000.93672438036, 498498.8244353208, 552398.5551404932, 618395.4454875157, 669536.2120281755, 611778.078074895, 642367.7485116324, 633282.4660061151, 674525.8724125386, 700888.3295343312, 738000]"
+4,ADBE,"[13574.048766528136, 15644.07909414357, 18184.873403062422, 21931.914992237726, 24491.99871748578, 30309.98321910754, 28692.65148298806, 27166.96568650849, 28874.528480726145, 27309.574081785835, 30914.643445037855, 29239]"
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Elaborated_Data/eps_quarterly_bar_chart.csv

@@ -3,10 +3,10 @@
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 2,XOM,3.4,2022-12-31
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 10,WFC,0.61,2022-12-31
@@ -51,10 +51,10 @@
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@@ -172,9 +172,9 @@
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@@ -247,10 +247,10 @@
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 340,ADBE,3.35,2022-05-31
 341,ADBE,3.4,2022-08-31
 342,ADBE,3.6,2022-11-30

Elaborated_Data/normalized_data.csv

@@ -1,91 +1,91 @@
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+14,14,BMY,200.0,56.28224546262015,90.05,157.90196295822074
+15,15,CAT,163.40118417419154,53.55509305520508,134.18,172.25676107573844
+16,16,CCI,51.60759610039482,34.440086227734014,53.29,148.10332803419888
+17,17,CHTR,197.12970389804275,36.91713403869733,150.4,142.7540896298299
+18,18,CMCSA,186.53241729625944,62.508078075905985,108.35,161.86981714996048
+19,19,CME,70.51225582214956,199.74429209968443,93.92,149.90683400928697
+20,20,COST,81.21604015295037,127.11342100388643,115.0,147.25110958932757
+21,21,CRM,164.6066568792097,124.0530554424684,159.84,170.22383180857588
+22,22,CSCO,138.45301784981535,159.75805206019515,108.2,150.7704959575312
+23,23,CSX,111.84871270542078,54.680411171067874,112.16,156.99213866747704
+24,24,CVS,165.73828672281633,70.13952258804798,91.29,157.1046042887805
+25,25,CVX,107.56926163193683,156.4232925934618,41.41,158.21497021304387
+26,26,D,15.290217372440877,52.19987452348332,99.27,149.76907243749045
+27,27,DE,175.73738275715903,41.664205111032715,136.33,158.07846825349745
+28,28,DHR,89.74740588127347,89.32801962827195,85.42,166.37597832539674
+29,29,DIS,177.2304186526414,88.31988255963364,166.75,143.37893353895083
+30,30,DUK,98.73477290665436,54.83216775931689,100.27,146.9829257987685
+31,31,EXC,114.53011384059165,54.786770860233815,102.77,150.1335905537568
+32,32,FDX,165.3799074709708,53.42483316963083,95.17,148.18609108925386
+33,33,FIS,189.15029196529755,68.74632805013584,81.72,150.05435892868684
+34,34,GE,93.89495615810021,127.32471174786522,175.64,193.286805536025
+35,35,GILD,192.9724525123606,57.406746396225685,82.69,162.4495882302495
+36,36,GOOGL,174.97659262783273,170.80828094983508,153.22,134.17890356570058
+37,37,GS,135.25159681247422,100.04605564885955,77.22,142.7944607078797
+38,38,HD,149.68177206214273,38.41161739915208,81.72,148.91653404471012
+39,39,HON,127.63986325220993,70.06234435281297,110.23,153.07304044835178
+40,40,IBM,134.77945084892974,51.689998571464315,104.36,151.57130951191266
+41,41,INTC,37.22910058041551,79.73972351151389,101.37,88.66691736326142
+42,42,ISRG,88.1824052503914,48.2374138477517,147.05,147.3186494389415
+43,43,JNJ,64.37383689552667,95.80075746770112,92.98,154.37317560440758
+44,44,JPM,91.18219867928472,131.40245610974392,53.49,163.537162577464
+45,45,KO,84.61107192396322,54.33711955549583,101.12,154.5264281178425
+46,46,LIN,86.94865586807126,89.15859465222107,113.68,189.68237376568726
+47,47,LLY,143.87624697553244,64.38690253392383,173.14,139.83271260181596
+48,48,LMT,92.00055125094745,76.63804579578257,125.16,133.82177570962426
+49,49,LOW,170.9052945742457,29.35877594139251,108.64,155.00006617324732
+50,50,LRCX,149.19248955823937,82.34328938517204,72.08,164.87313020839653
+51,51,MA,158.55479153795613,58.37601717453873,162.13,154.64766745442583
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+53,53,MCO,136.65551753769535,45.25899888764812,131.24,160.7004656036106
+54,54,MDT,118.16829492456176,73.97472665545837,76.89,146.98090933965918
+55,55,MMC,127.51723231747657,54.26019510471775,119.66,151.50137376086525
+56,56,MMM,61.806185203921274,65.77944876236245,79.75,158.23143800332244
+57,57,MO,20.53657072101916,36.28343931065285,90.89,145.93346615682916
+58,58,MRK,125.4034643977795,77.57644064971683,112.06,159.13614755399942
+59,59,MSFT,125.93383004616338,115.92956749228225,132.72,151.0051655360135
+60,60,NEE,114.28104697519642,55.20978559802842,115.15,162.488127712038
+61,61,NFLX,154.19903240107166,75.38180697731005,166.33,136.41612493077184
+62,62,NKE,136.60890150159412,68.6622715229345,113.97,178.50756887948205
+63,63,NSC,101.36586853042037,60.55447099893274,98.62,154.24021598490785
+64,64,NVDA,83.27167524546657,76.1672591641748,164.85,111.35389139840007
+65,65,ORCL,142.70944266140097,35.89918874950595,116.42,151.9925730188873
+66,66,PEP,88.94786061768049,52.9309039494008,110.23,156.49257362489402
+67,67,PFE,175.63787032294806,108.36004105657459,22.44,175.10691161959272
+68,68,PG,64.91401871892884,72.73023037988067,98.17,148.26764524720696
+69,69,PM,119.85073876884272,33.238182006680745,108.25,154.1289647966488
+70,70,PYPL,193.33300975831932,125.65411783574227,146.26,159.49828096645865
+71,71,SCHW,154.01759201257377,173.20916630274402,119.99,152.81278211387055
+72,72,SO,85.80487600994968,52.88804944862483,107.75,165.2694958898281
+73,73,SPG,9.996654397458014,33.02515044863611,114.17,163.49750846485017
+74,74,SPGI,127.52131883874166,100.67491493719751,133.43,151.40164828366832
+75,75,T,62.4154643001036,59.7446556316317,69.11,158.7511615621489
+76,76,TGT,186.2853419515832,63.524603362870124,41.98,134.33574576253847
+77,77,TMO,74.36621243620229,61.92252488534686,114.02,155.1123218446322
+78,78,TMUS,197.15621675792158,46.34416253371296,199.49,199.99556645491742
+79,79,TSLA,155.64037394861813,197.72863178235542,124.08,162.51851966807016
+80,80,TXN,99.30575999707457,65.69693584902097,120.95,159.5326513042247
+81,81,UNH,156.98803331417588,85.13895735772505,134.93,154.8815129323568
+82,82,UNP,108.63594543845102,45.78600115998577,116.17,149.05845315328233
+83,83,UPS,119.5573457818638,62.87481139800505,89.41,150.56429143343914
+84,84,USB,179.21741918122777,122.80336075731796,90.49,150.13534696610725
+85,85,V,160.11517917436944,81.75682714969174,141.79,159.51952621990839
+86,86,VZ,33.803385687015506,50.689478058528096,70.84,146.64638813716547
+87,87,WFC,187.98025116241868,128.0962877459593,99.67,130.87949394012657
+88,88,WMT,81.80078636041182,82.21964676130035,98.07,165.9903705253845
+89,89,XOM,146.6696098368374,141.5338927985445,32.26,163.5290258578648

backend/api/assets_debts.py

@@ -10,12 +10,18 @@ def compare_balance_sheets(ticker_list: list):
 
     for i, ticker in enumerate(ticker_list):
         assets_debt = pd.read_csv(ROOT_PATH + r'/Companies_Data/' + ticker + '_Data/' + ticker + '_balance_sheet_4Y+4Q.csv')
-        selected_data = assets_debt[['TotalAssets', 'TotalDebt', 'CurrentAssets', 'CurrentDebt']]
+        selected_data = assets_debt
 
-        df_ret.loc[ticker, 'total_assets'] = selected_data.iloc[0]['TotalAssets']
-        df_ret.loc[ticker, 'current_assets'] = selected_data.iloc[0]['TotalDebt']
-        df_ret.loc[ticker, 'total_debt'] = selected_data.iloc[0]['CurrentAssets']
-        df_ret.loc[ticker, 'current_debt'] = selected_data.iloc[0]['CurrentDebt']
+        def get_or_na(column: str): 
+            row = selected_data.iloc[-2]
+            if column in row and not np.isnan(row[column]):
+                return row[column]
+            return 0
+
+        df_ret.loc[ticker, 'total_assets'] = get_or_na('TotalAssets')
+        df_ret.loc[ticker, 'current_assets'] = get_or_na('CurrentAssets')
+        df_ret.loc[ticker, 'total_debt'] = get_or_na('TotalDebt')
+        df_ret.loc[ticker, 'current_debt'] = get_or_na('CurrentDebt')
 
     df_ret.loc[:, 'old_assets'] = df_ret['total_assets'].copy() - df_ret['current_assets'].copy()
     df_ret.loc[:, 'old_debt'] = df_ret['total_debt'].copy() - df_ret['current_debt'].copy()

backend/api/eps.py

@@ -3,15 +3,56 @@ import pandas as pd
 import numpy as np
 from scraper.top100_extractor import programming_crime_list
 from typing import Optional
+from dateutil.parser import isoparse
 
 DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_quarterly_bar_chart.csv')
 
+def ticker_quarter_list(df_ticker: pd.Series) -> list[int]:
+    dfu = df_ticker.unique()
+    months = set([isoparse(date).month for date in dfu])
+    return list(sorted(months))
+
 
 def get_eps(tickers: list[str]) -> list[dict]:
     df = pd.read_csv(DF_EPS_PATH)
     ticker_series = pd.Series(tickers)
+    qmap = dict([ \
+        [ticker, ticker_quarter_list(df.loc[df.symbol == ticker, 'quarter'])] \
+        for ticker in tickers \
+    ])
+
     df = df.loc[df.symbol.isin(ticker_series), :] \
         .rename(columns={"epsActual": "quarterlyEps", "symbol": "ticker"}) \
         .reset_index(drop=True)
+
+    def get_quart(x):
+        date = isoparse(x.quarter)
+        quarter = qmap[x.ticker].index(date.month) + 1
+        return f"{date.year}-Q{quarter}"
+
+    df['quarter'] = df.apply(get_quart, axis=1)
     df = df.pivot(index='quarter', columns='ticker', values='quarterlyEps').reset_index(drop=False)
-    return df.replace({ np.nan: None }).to_dict('records')
+    return df.replace({ np.nan: None }).to_dict('records')
+
+
+def get_eps_old(tickers: list[str]) -> list[dict]:
+    eps_df = pd.read_csv('Elaborated_Data/eps_quarterly_bar_chart.csv', index_col=[0])
+
+    s_tickers = pd.Series(tickers)
+    eps_df = eps_df.loc[eps_df['symbol'].isin(s_tickers), :]
+
+    dates = eps_df['quarter'].to_list()
+
+    for i, date in enumerate(dates):
+        eps_df[date] = eps_df['epsActual'].copy().apply(lambda x: int(eval(str(x))[i]))
+
+    eps_df = pd.melt(eps_df, id_vars=['symbol'], value_vars=['quarter'], var_name='quarter').reset_index(drop=True)
+    print(eps_df)
+    eps_df = eps_df.pivot(index='value', columns='symbol', values='value').reset_index(drop=False)
+    exit(1)
+    for ticker in tickers:
+        filepath = f'Companies_Data/{ticker}_Data/{ticker}earnings.csv'
+        eps_quarterly = pd.read_csv(filepath)
+        ticker_df = eps_quarterly.loc[eps_quarterly['symbol'] == ticker]
+        ticker_df['ticker'] = ticker
+        combined_df = combined_df._append(ticker_df)

backend/api/past_performance.py

@@ -3,6 +3,8 @@ import pandas as pd
 import numpy as np
 from scraper.top100_extractor import programming_crime_list
 from typing import Optional
+from backend.api.eps import ticker_quarter_list
+from dateutil.parser import isoparse
 
 DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_comparison.csv')
 
@@ -10,8 +12,23 @@ DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborat
 def get_eps_comp(tickers: list[str]) -> list[dict]:
     df = pd.read_csv(DF_EPS_PATH)
     ticker_series = pd.Series(tickers)
+
     df = df.loc[df['Ticker'].isin(ticker_series), :] \
         .rename(columns={"epsDifferential": "quarterlyDifferential", "Ticker": "ticker"}) \
         .reset_index(drop=True)
+
+    qmap = dict([ \
+        [ticker, ticker_quarter_list(df.loc[df.ticker == ticker, 'quarter'])] \
+        for ticker in tickers \
+    ])
+
+    def get_quart(x):
+        date = isoparse(x.quarter)
+        quarter = qmap[x.ticker].index(date.month) + 1
+        return f"{date.year}-Q{quarter}"
+
+    df['quarter'] = df.apply(get_quart, axis=1)
+    
+
     df = df.pivot(index='quarter', columns='ticker', values='quarterlyDifferential').reset_index(drop=False)
     return df.replace({ np.nan: None }).to_dict('records')

indexer/indexer.py

@@ -44,7 +44,7 @@ def get_financial_health(ticker: str):
     
     # Create financial health column
     try:
-        balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets'] 
+        balance_sheet['financial_health'] = balance_sheet['TotalAssets'] / balance_sheet['TotalDebt']
     except KeyError:
         return 2.0
 
@@ -83,9 +83,11 @@ def employees_over_time(ticker: str):
 
 def past_performance_earnings(ticker: str):
 
-    earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv', index_col=[0]) # Read earnings csv
+    earnings = pd.read_csv(f'Elaborated_Data/eps_comparison.csv', index_col=[0]) # Read earnings csv
     
-    performance_index = round((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2) #Performance 
+    selected_rows = earnings[earnings['Ticker'] == ticker] # Select rows with ticker
+    # performance_index = round(((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2) #Performance 
+    performance_index = selected_rows['epsDifferential'].mean()
 
     return performance_index
 
@@ -95,20 +97,20 @@ def normalizer():
     ''' Normalize the dataframe columns to a range between 0 and 200'''
 
     not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv') # Read Not_normalized .csv
-    
-    v_values = (200/(1+math.e**( 0.5*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
+    # v_values = (200/(1+math.e**( 0.1*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
+    v_values = (200/(1+(1/9*not_normalized['Valuation']**2))) # VALUATION STAT
     not_normalized['Valuation'] = v_values
 
-    fh_values= (200/(1+math.e**( 0.5*(not_normalized['Financial Health'].mean()+not_normalized['Financial Health'])))) #FINANCIAL HEALTH STAT
+    # fh_values= (200/(1+math.e**( -0.1*(-not_normalized['Financial Health'].mean()+not_normalized['Financial Health'])))) #FINANCIAL HEALTH STAT
+    fh_values = (200-200*math.e**(-0.138*not_normalized['Financial Health'])) #FINANCIAL HEALTH STAT
     not_normalized['Financial Health'] = fh_values 
-
     not_normalized['Estimated Growth'] = not_normalized['Estimated Growth'].str.strip("%").astype("float")
-    eg_values= (200/(1+math.e**( 0.1*(not_normalized['Estimated Growth'].mean()-not_normalized['Estimated Growth'])))) #ESTIMATED GROWTH STAT
+    eg_values= (200/(1+math.e**( -0.1*(-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
 
-    pf_values = (200/(1+math.e**( 0.1*(not_normalized['Past Performance'].mean()-not_normalized['Past Performance'])))) #PAST PERFORMANCE
+    pf_values = (200/(1+math.e**( -0.05*(-not_normalized['Past Performance'].mean()+not_normalized['Past Performance'])))) #PAST PERFORMANCE
     not_normalized['Past Performance'] = pf_values
     
     # Create normalized dataframe for main page

presentation_notebook.ipynb

@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipywidgets as widgets\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import pandas as pd \n",
+    "from IPython.display import Image, display\n",
+    "\n",
+    "#Newest_York = r'Newest_York.png'\n",
+    "\n",
+    "#display(Image(filename=Newest_York, width=700, height=500))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>I + II) Function for getting <b style=\"color:DodgerBlue;\">Estimated Growth & Past Performance </b> Scores --> <b>Sigmoid fucntion</b></h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6694062359364cc2ab6544bc4c5c8130",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=5.746777777777778, description='x', max=200.0, min=-200.0, step=1.0), …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_function(x)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
+    "\n",
+    "\n",
+    "average_score = not_normalized['Estimated Growth'].str.rstrip('%').astype(float).mean() #STARTING POINT (Analyt's yearly estimated growth (%) for next 5 years)\n",
+    "\n",
+    "slope = 0.1\n",
+    "\n",
+    "def plot_function(x):\n",
+    "    y = (200 / (1 + math.e**(-slope * (-average_score + x))))\n",
+    "    plt.plot(x_values, y_values)\n",
+    "    plt.scatter(x, y, color='red', label=f'x={x}')\n",
+    "    plt.xlabel('Estimated Growth (yearly in %)')\n",
+    "    plt.ylabel('Stockingly Score')\n",
+    "    plt.title('Estimated growth Score = sigmoid(Estimated growth %)')\n",
+    "    plt.legend()\n",
+    "    plt.grid(False)  \n",
+    "    plt.axhline(0, color='black')  \n",
+    "    plt.axvline(0, color='black')  \n",
+    "\n",
+    "    plt.axhline(y, color='green')  \n",
+    "\n",
+    "    plt.xlim(-200, 200)  \n",
+    "    plt.ylim(-10, 250)  \n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "# Generate x values from -100 to 100\n",
+    "x_values = np.linspace(-200, 200, 200)\n",
+    "y_values = (200 / (1 + np.exp(-slope * (-average_score + x_values))))\n",
+    "\n",
+    "widgets.interact(plot_function, x=widgets.FloatSlider(min=-200, max=200, step=1, value=average_score))\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>III) Function for getting <b style=\"color:DodgerBlue;\">Financial health --> </b> score: <b>reverse exponential</b></h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5306c1272eb748aeb69bdd401c9262ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=4.78006822089688, description='x', max=50.0, step=0.5), Output()), _do…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_function(x)>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../group-1')\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "import numpy as np \n",
+    "\n",
+    "not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
+    "\n",
+    "\n",
+    "average_score = not_normalized['Financial Health'].mean() #STARTING POINT (Mean Assets/debts)\n",
+    "\n",
+    "\n",
+    "slope = 0.1\n",
+    "\n",
+    "def plot_function(x):\n",
+    "    y = 200 -  200*math.e**(-0.138 * (x))\n",
+    "    plt.plot(x_values, y_values)\n",
+    "    plt.scatter(x, y, color='red', label=f'x={x}')\n",
+    "    plt.xlabel('Total Assets/Total Debt')\n",
+    "    plt.ylabel('Stockingly Score')\n",
+    "    plt.title('Financial Health Score = ReverseExp(TOT Assets/TOT Debts)')\n",
+    "    plt.legend()\n",
+    "    plt.grid(False)  \n",
+    "    plt.axhline(0, color='black')  \n",
+    "    plt.axvline(0, color='black')  \n",
+    "    plt.axhline(y, color='blue')  \n",
+    "    plt.xlim(0, 100)  \n",
+    "    plt.ylim(-10, 250)  \n",
+    "    plt.show()\n",
+    "\n",
+    "x_values = np.linspace(-200, 200, 200)\n",
+    "y_values = 200 -  200*math.e**(-0.138 * (x_values))\n",
+    "\n",
+    "\n",
+    "widgets.interact(plot_function, x=widgets.FloatSlider(min=0, max=50, step=0.5, value=average_score))\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2> IV) Function for getting <b style=\"color:DodgerBlue;\">valuation$ score</b> from PEG: --><b>Custom Fractal</b></h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e249d428e5a4413969ea198ea92763e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=2.8344899999999997, description='x', max=30.0), Output()), _dom_classe…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_function(x)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../group-1')\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "import numpy as np \n",
+    "\n",
+    "not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')\n",
+    "\n",
+    "average_score = not_normalized['Valuation'].mean() #STARTING POINT (MEAN PEG RATIO)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def plot_function(x):\n",
+    "    y = (200/(1+(1/9*x**2)))\n",
+    "    plt.plot(x_values, y_values)\n",
+    "    plt.scatter(x, y, color='red', label=f'x={x}')\n",
+    "    plt.xlabel('PEG value')\n",
+    "    plt.ylabel('Stockingly Score Valuation')\n",
+    "    plt.title('Valuation Score = FractalPolynomial(PEG)')\n",
+    "    plt.legend()\n",
+    "    plt.grid(False)  \n",
+    "    plt.axhline(0, color='black')  \n",
+    "    plt.axvline(0, color='black')  \n",
+    "    plt.axhline(y, color='blue')  \n",
+    "    plt.xlim(0, 100)  \n",
+    "    plt.ylim(-10, 250)  \n",
+    "    plt.show()\n",
+    "\n",
+    "x_values = np.linspace(0, 200, 2000)\n",
+    "\n",
+    "y_values = (200/(1+(1/9*x_values**2)))\n",
+    "\n",
+    "widgets.interact(plot_function, x=widgets.FloatSlider(min=0, max=30, step=0.1, value=average_score))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "analyticvisual",
+   "language": "python",
+   "name": "visualanalytics"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -7,3 +7,4 @@ flask==2.3.2
 itsdangerous==2.1.2
 Flask_Cors==3.0.10
 six==1.16.0
+python-dateutil==2.8.2

stockingly-frontend/src/api/index.ts

@@ -41,7 +41,6 @@ export interface BalanceSheet {
 export interface Eps {
     [ticker: string]: string | number; // really just number
     quarter: string; 
-
 }
 
 export interface EpsComp {

stockingly-frontend/src/components/BalanceSheet.vue

@@ -108,6 +108,7 @@ const options = computed(() => {
           enabled: true,
           text: 'Amount in USD',
         },
+        min: 0,
         type: 'number',
         position: 'left',
         label: {

stockingly-frontend/src/components/EmployeesChart.vue

@@ -80,6 +80,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         gridStyle: [{
           lineDash: [Infinity]
         }, {

stockingly-frontend/src/components/EpsChart.vue

@@ -13,14 +13,13 @@
 <script setup lang="ts">
 import { Eps, getEps } from '@/api';
 import { defineLoader } from '@/api/loader';
-import { DateTime } from 'luxon';
 import { roundTo } from 'round-to';
 import { onMounted, computed } from 'vue';
 import { AgChartsVue } from 'ag-charts-vue3';
 
 const renderer = (params: any) => ({
   title: params.title,
-  content: DateTime.fromMillis(params.xValue).toFormat('yyyy-MM') + ': ' + roundTo(params.yValue, 4),
+  content: params.xValue + ': ' + roundTo(params.yValue, 4),
 });
 
 const props = defineProps<{
@@ -37,7 +36,7 @@ const options = computed(() => {
 
   return {
     theme: 'ag-material',
-    data: epsData.data?.map(e => ({ ...e, quarter: Date.parse(e.quarter) })),
+    data: epsData.data ?? [],
     series: props.tickers.map((t: string) => ({
       xKey: 'quarter',
       type: 'column',
@@ -58,9 +57,6 @@ const options = computed(() => {
         label: {
           autoRotate: true,
           autoRotateAngle: 335,
-          formatter: (params: any) => {
-            return DateTime.fromMillis(parseInt(params.value)).toFormat('yyyy-MM');
-          }
         },
         gridStyle: [{
           lineDash: [Infinity]
@@ -75,6 +71,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         gridStyle: [{
           lineDash: [Infinity]
         }, {

stockingly-frontend/src/components/EpsComparisonChart.vue

@@ -13,7 +13,6 @@
 <script setup lang="ts">
 import { EpsComp, getEpsComp } from '@/api';
 import { defineLoader } from '@/api/loader';
-import { DateTime } from 'luxon';
 import { AgAxisLabelFormatterParams } from 'ag-charts-community';
 import { roundTo } from 'round-to';
 import { onMounted, computed } from 'vue';
@@ -21,7 +20,7 @@ import { AgChartsVue } from 'ag-charts-vue3';
 
 const renderer = (params: any) => ({
   title: params.title,
-  content: DateTime.fromMillis(params.xValue).toFormat('yyyy-MM') + ': ' + roundTo(params.yValue, 4) + '%',
+  content: params.xValue + ': ' + roundTo(params.yValue, 4) + '%',
 });
 
 const props = defineProps<{
@@ -38,7 +37,7 @@ const options = computed(() => {
 
   return {
     theme: 'ag-material',
-    data: epsCompData.data?.map(e => ({ ...e, quarter: Date.parse(e.quarter) })),
+    data: epsCompData.data?.map(e => ({ ...e })),
     series: props.tickers.map((t: string) => ({
       xKey: 'quarter',
       type: 'line',
@@ -60,11 +59,7 @@ const options = computed(() => {
         type: 'category',
         position: 'bottom',
         label: {
-          autoRotate: true,
-          autoRotateAngle: 335,
-          formatter: (params: any) => {
-            return DateTime.fromMillis(parseInt(params.value)).toFormat('yyyy-MM');
-          }
+          rotation: 315,
         },
         gridStyle: [
           {
@@ -84,6 +79,8 @@ const options = computed(() => {
             return params.value + '%';
           }
         },
+        min: Math.min(0, ...epsCompData.data?.flatMap(e => Object.values(e)
+          .filter((e: string | number): e is number => typeof e === 'number')) ?? []),
         gridStyle: [{
           lineDash: [Infinity],
         }, {

stockingly-frontend/src/components/StockPrice.vue

@@ -111,7 +111,9 @@ const maxPrice = computed(() => {
     if (Date.parse(d.date) > maxDate) {
       maxDate = Date.parse(d.date);
       for (const ticker in maxVal) {
-        maxVal[ticker] = d[ticker] as number;
+        if (typeof d[ticker] === 'number' && !isNaN(d[ticker] as number)) {
+          maxVal[ticker] = d[ticker] as number;
+        }
       }
     }
   }
@@ -186,6 +188,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         label: {
           format: '$~s',
           formatter: (params: AgAxisLabelFormatterParams) =>

stockingly-frontend/src/views/Home.vue

@@ -116,37 +116,56 @@ const sortSelected = ref<number>(sortItems.length - 1);
 
 const loading = ref(true);
 const companies = ref<Company[]>([]);
-const filteredCompanies = ref<(Company & { score?: number })[]>([]);
+const tags = ref<string[]>([]);
+const tickers = ref<string[]>([]);
+
+type CompanyWithScore = Company & { score?: number }
+
+const filteredCompanies = ref<CompanyWithScore[]>([]);
 const selected = reactive<string[]>([]);
 const searchText = ref("");
 const router = useRouter();
 let fuse: Fuse<Company> | undefined = undefined;
 
-const sortCompanies = () => {
+const sortCompanies = (companiesToSort: CompanyWithScore[]) => {
   const sorter = sortItems[sortSelected.value]!;
   if (sorter.sortBy !== undefined) {
     const key: keyof Company = sorter.sortBy;
-    filteredCompanies.value = filteredCompanies.value.sort(naturalOrder(c => c[key]))
+    filteredCompanies.value = companiesToSort.sort(naturalOrder(c => c[key]))
+  } else {
+    filteredCompanies.value = companiesToSort.sort(naturalOrder(c => c.score ?? c.ticker));
   }
 }
 
 const search = debounce(() => setTimeout(() => {
-  filteredCompanies.value = fuse!.search(searchText.value).map(e => ({ ...e.item, score: e.score }))
-  sortCompanies();
+  let companiesToSort: CompanyWithScore[];
+  if (tags.value.indexOf(searchText.value) !== -1) {
+    companiesToSort = companies.value.filter(e => e.tags.indexOf(searchText.value) !== -1);
+  } else {
+    const ticker = searchText.value.trim().toUpperCase();
+    if (tickers.value.indexOf(ticker) !== -1) {
+      companiesToSort = [companies.value.find(c => c.ticker == ticker)!];
+    } else {
+      companiesToSort = fuse!.search(searchText.value).map(e => ({ ...e.item, score: e.score }));
+    }
+  }
+  sortCompanies(companiesToSort);
 }, 0), 1000);
 
-watch(sortSelected, () => sortCompanies());
+const searchAll = debounce(() => {
+  sortCompanies(companies.value);
+}, 1000);
+
+watch(sortSelected, () => {
+  sortCompanies(filteredCompanies.value)
+});
 
 watch(searchText, () => {
   if (!fuse) return;
+  loading.value = true;
   if (searchText.value === '') {
-    filteredCompanies.value = companies.value;
-    sortCompanies();
-    loading.value = false;
-    return;
-  }
-  else {
-    loading.value = true;
+    searchAll();
+  } else {
     search();
   }
 });
@@ -171,8 +190,16 @@ watch(filteredCompanies, () => loading.value = false);
 
 getCompanies().then(cs => {
   companies.value = cs;
-  const myIndex = Fuse.createIndex(['ceo', 'company name', 'description', 'sector', 'tags', 'ticker', 'website'], cs);
-  fuse = new Fuse(cs, { threshold: 0.5, ignoreLocation: true }, myIndex);
+  tags.value = cs.flatMap(c => c.tags);
+  tickers.value = cs.map(c => c.ticker);
+  const myIndex = Fuse.createIndex([
+    { name: 'company name', weight: 2 },
+    'sector',
+    'tags',
+    { name: 'ticker', weight: 10 },
+    'website'
+  ], cs);
+  fuse = new Fuse(cs, { threshold: 0.6 }, myIndex);
   filteredCompanies.value = cs;
 });
 </script>