A quick study of z-scores in varying samples of NVDA stock prices.
I used python to generate these plots.
animation of NVDA zscore scatter plot x , y are in standard deviation units, anything above 3 or below -3 is considered outliers in data science. DeepSeek release was nearly an outlier effect on NVDA value. Image shows last 15 days, 30 days, 45 days, 60 days and 93 days.
Z-Scores for QQQ ETF Tech Index stock, for same time periods above for NVDA. In an index stock such as QQQ one should see a smoother spectrum as it is less susceptible to volatility.
First, we see the last 93 trade days price chart. Then afterwards, we take a look at the 93, 60, 45, 30, 15 sample windows all going backward in time from Feb. 20th, 2025. One major petrubative wave that hit the stock was the release of DeepSeek which had a negative impact temporarily on NVDA value. The question is whether one can see a correlation to the action of the Index ETF for the sector NVDA is in and is a part of the portfolio. Examining the spread in the z-scores to see if one can tell if it is an indicator of up or down motion in relation to the index for NVDA stock prices.
Comparing different plots of Z-scores for the price of stocks for NVDA form Oct 4th, 2024 to February 20, 2025.
NVDA past 93 trading days. open price 124.92, close price 140.11 (gained 13%)
QQQ ETF, of which NVDA is a member of the portfolio, past 93 trading days, open price: 487.32 close price: 537.23 (gained 10.2%)
Z-Scores:
z-score is calcualted as:
93 Days:
NVDA past 93 trading days zscores
QQQ past 93 trading days
I use the terms "prices" as code for NVDA and "trends" for QQQ, the index ETF for tech stocks.
Some Data for Z-scores:
Shape of Z-score plots:
prices max/min, trends max/min: 1.7594241527219399 -2.9115388106292883 1.7722687913449942 -2.0281115555314564
NVDA length and mean of positive and negative:
prices positive len: 51 0.7123723838425315
prices negative len: 43 -0.8449067808364951
prices positive list: [0.14019500216746938, 0.13021735775384247, 0.9441080663512041, 0.9270035330706985, 0.35257629040040717, 0.4737334011373164, 0.6348010895287374, 0.4894125566444479, 0.5934651341008516, 0.3205052904994612, 0.40246451246854575, 1.2149298432925333, 1.6810283751862907, 1.502856153514364, 1.1650416212243906, 1.5969310865571407, 1.30900477633531, 1.3788482872307024, 0.6975177115572552, 0.43667357902955695, 1.4144827315650879, 1.254840420947041, 1.3660198872703233, 0.6932415782371287, 0.16585180208822778, 0.2200161574764928, 0.4523527345366844, 1.147937087943885, 1.136534065756884, 0.7630850891325253, 0.2456729573972512, 0.3169418460660218, 0.03614242471106574, 0.36825544590753456, 0.44665122344318386, 0.40531526801529805, 0.057523091311697735, 0.17440406872848058, 1.052436777127734, 1.7594241527219399, 0.4352482012561788, 0.43097206793605647, 0.08888140232595665, 0.5335992676190859, 1.4230349982053405, 1.4444156648059727, 0.7887418890532837, 0.25137446849075173, 0.32977024602640104, 0.30553882387901676, 0.43097206793605647]
prices negative list: [-1.6826224330881445, -2.0281115555314564, -1.5515748349199896, -1.2967600607041379, -1.3338240278628077, -1.2828610730196344, -1.0088524586680414, -1.4489870686772424, -1.4450159293388134, -1.4225128064210513, -1.2093949952586989, -1.146518622400242, -1.1107783683543844, -1.6137893512220423, -1.3516941548857364, -1.1531371879642918, -1.1478423355130543, -0.8327986146643587, -1.082980392985381, -1.9122866581606137, -1.6753420109676906, -1.7693256419771755, -1.3589745770061903, -0.47407236109295114, -0.032614037970937836, -0.26823497205105246, -1.0704051184136898, -0.8420646064540289, -0.6143859510507724, -0.6335797911865096, -0.5137837544772378, -0.4601733734084476, -0.4072248488960653, -0.22719986555395358, -0.49260434467228414, -0.19874003362854603, -0.10012340672422786, -0.16630906236470946, -0.36751345551177483, -0.4753960742057596, -0.507165188913192]
Trends (QQQ) length and mean for positive/negative:
trends positive len: 53 0.7493882152106839
trends negative len: 41 -0.9687213513699346
trends positive list: [0.05276545780528101, 0.09247685118957147, 0.07262115449743001, 0.011068494751777909, 0.1685903551761238, 0.2731636910880874, 0.6960900306307639, 0.6001208299520626, 0.9092078417931164, 0.6378466536671404, 0.520036186627085, 1.1368864971963693, 0.9105315549059249, 1.1772597471370638, 1.6829181562303424, 1.5260581523624006, 0.2466894288318963, 0.09446242085878412, 0.39163601468454706, 0.670277624930977, 1.1395339234219937, 1.1157070873914194, 0.6497600716824238, 0.18976976498108122, 0.3863411622333134, 0.7808076698505786, 0.16130993305566993, 0.16726664206331537, 0.2619121296292083, 0.022320056210664595, 0.595487834057233, 0.7980159403171032, 1.2421216896647371, 1.316911480538481, 1.117692657060632, 0.0971098470844085, 0.6001208299520626, 0.535258887424397, 0.6821910429462604, 0.631889944659495, 0.3552339040822852, 0.776174673955749, 0.9336965343800949, 1.1157070873914194, 0.6735869077129981, 1.0925421079172493, 1.009148181810243, 1.0296657350587888, 1.5326767179264504, 1.681594443117534, 1.7623409429989234, 1.7722687913449942, 1.6207036399282937]
trends negative list: [-1.6826224330881445, -2.0281115555314564, -1.5515748349199896, -1.2967600607041379, -1.3338240278628077, -1.2828610730196344, -1.0088524586680414, -1.4489870686772424, -1.4450159293388134, -1.4225128064210513, -1.2093949952586989, -1.146518622400242, -1.1107783683543844, -1.6137893512220423, -1.3516941548857364, -1.1531371879642918, -1.1478423355130543, -0.8327986146643587, -1.082980392985381, -1.9122866581606137, -1.6753420109676906, -1.7693256419771755, -1.3589745770061903, -0.47407236109295114, -0.032614037970937836, -0.26823497205105246, -1.0704051184136898, -0.8420646064540289, -0.6143859510507724, -0.6335797911865096, -0.5137837544772378, -0.4601733734084476, -0.4072248488960653, -0.22719986555395358, -0.49260434467228414, -0.19874003362854603, -0.10012340672422786, -0.16630906236470946, -0.36751345551177483, -0.4753960742057596, -0.507165188913192]
zscore silos:
prices 0 to 1: 35 0.4056562939002893
prices 1 to 2: 16 1.3722767337624457
prices 2 to 3: 0 nan
prices 3>: 0 nan
prices 0 to -1: 31 -0.4417795985574785
prices -1 to -2: 8 -1.4510292935080256
prices -2 to 3: 4 -2.7127490308408726
prices <3: 0 nan
trends 0 to 1: 35 -0.27555447552077394
trends 1 to 2: 17 1.344595325715943
trends 2 to 3: 0 nan
trends 3>: 0 nan
trends 0 to -1: 20 -0.41098618215387406
trends -1 to -2: 21 -1.3951694395818528
trends -2 to 3: 1 -2.0790028405616057
trends <3: 0 nan60 DAYS
NVDA past 60 days open: 146.67, close: 140.11
QQQ past 60 days open: 504.98, close: 537.23
"prices" is for NVDA, "trends" is for QQQ
some data for 60 day plots:
shape of plots:
prices max/min, trends max/min: 1.9145807927766163 -2.645347442651133 1.8695586413001115 -1.7542554651846929
prices positive len: 34 0.6792199553321576
prices negative len: 26 -0.8882107108189714
prices positive list: [1.5305282468571573, 0.8737427335456234, 0.04858635771143208, 0.1738208835547299, 0.35888968285649864, 0.4117664826570029, 0.6385801239065363, 1.3176295529235456, 1.3064975950708098, 0.9419259753936441, 0.43681338782566403, 0.5063881244052757, 0.23226366228160591, 0.556481934742594, 0.6330141449801685, 0.5926607977639948, 0.18634433613906048, 0.2531360832554902, 0.36723865124605237, 1.2243994059068675, 1.9145807927766163, 0.6218821871274288, 0.617707702932656, 0.03327991566391562, 0.07919924180646105, 0.2837489673505192, 0.7178953236072966, 1.5861880361208474, 1.6070604570947318, 0.9669728805623052, 0.4423793667520319, 0.5189115769896063, 0.49525616655253607, 0.617707702932656]
prices negative list: [-1.7542554651846929, -1.669273142479056, -1.5853399842512712, -1.2999672462767844, -1.720682201893578, -1.2548531737293467, -0.6725668885240624, -0.506798901024174, -0.11546055078710396, -0.5487654801380664, -0.7900733100429681, -0.318998459489499, -0.6389936252329415, -1.098527666530088, -1.203444114314825, -0.32739177531227037, -0.6841076977803849, -0.6746652174797553, -1.5223901155804267, -1.6934039254695472, -1.7437638204062227, -0.524634697147575, -0.9044322381283226, -0.7858766521315705, -0.0913297677966126, -0.3767025057710995]
trends positive len: 34 0.7207852536147648
trends negative len: 26 -0.9425653316500864
trends positive list: [0.1636172003202925, 0.01148835103241942, 0.5014481621871478, 0.07129072626972463, 0.8623607425666361, 0.5035464911428407, 0.926359775715327, 1.7279214367907145, 1.4792694555408878, 0.12269978568424661, 0.8665574004780336, 0.828787479275527, 0.09017568687097201, 0.29791025348475275, 0.004144199687494526, 0.32518852990878333, 1.0291778945443708, 1.147733480541123, 0.8319349727090662, 0.01148835103241942, 0.14158474628549397, 0.06184824596909498, 0.2905661021398278, 0.5402672478675009, 0.828787479275527, 0.12794560807347868, 0.7920667225508667, 0.6598719983420994, 0.6923960971553621, 1.4897611003193638, 1.7258231078350217, 1.8538211741324033, 1.8695586413001115, 1.6292999758730682]
trends negative list: [-1.7542554651846929, -1.669273142479056, -1.5853399842512712, -1.2999672462767844, -1.720682201893578, -1.2548531737293467, -0.6725668885240624, -0.506798901024174, -0.11546055078710396, -0.5487654801380664, -0.7900733100429681, -0.318998459489499, -0.6389936252329415, -1.098527666530088, -1.203444114314825, -0.32739177531227037, -0.6841076977803849, -0.6746652174797553, -1.5223901155804267, -1.6934039254695472, -1.7437638204062227, -0.524634697147575, -0.9044322381283226, -0.7858766521315705, -0.0913297677966126, -0.3767025057710995]
zscore silos:
prices 0 to 1: 26 0.4857168466015017
prices 1 to 2: 7 1.4753238481642317
prices 2 to 3: 0 nan
prices 3>: 0 nan
prices 0 to -1: 20 -0.42879927835305054
prices -1 to -2: 3 -1.5721141625126993
prices -2 to 3: 4 -2.4158942235473466
prices <3: 0 nan
trends 0 to 1: 23 -0.06220277723223607
trends 1 to 2: 9 1.5519738719284009
trends 2 to 3: 0 nan
trends 3>: 0 nan
trends 0 to -1: 18 -0.4898406138886327
trends -1 to -2: 10 -1.5547354231811998
trends -2 to 3: 0 nan
trends <3: 0 nan45 DAYS
NVDA past 45 days open: 134.25, close: 140.11
QQQ past 45 days open: 530.53, close: 537.23
some data for 45 day plot:
shape of plots:
prices max/min, trends max/min: 2.0056164520976707 -2.335406431884398 1.7445748897642444 -2.004426294606925
zscore means of prices and trends: 4.46309655899313e-15 -1.0288066694859784e-14
NVDA prices length and mean of positive/negative:
prices positive len: 24 0.7357314773777098
prices negative len: 21 -0.8408359741459445
prices positive list: [0.054341899730996124, 0.712714915702643, 0.7855730965445773, 0.7471569648279229, 0.36034625926711406, 0.4239315807291669, 2.943764882851058e-05, 0.5325565048935021, 1.3485681303231487, 2.0056164520976707, 0.7749755429675673, 0.7710014603761928, 0.2146298975832493, 0.2583448060884106, 0.45307485306593986, 0.8663794425692682, 1.6929886215759211, 1.7128590345328127, 1.1034997038548302, 0.13249885736143355, 0.6040899915383078, 0.6769481723802421, 0.6544283710290971, 0.7710014603761928]
prices negative list: [-0.7645562745724367, -1.014925110927081, -0.5261615999565064, -0.06787777341171075, -0.10162313831168997, -0.8581724481659111, -1.334961797397784, -1.443817813204148, -0.5348700812210075, -0.9049805349626544, -0.8951834935400782, -1.7747401012554962, -1.9521754070198687, -2.004426294606925, -0.739519390936971, -1.1335781681560086, -0.19088507127290136, -1.010570870294818, -0.18326515016646283, -0.28994404565668896, -0.048283690566570704, -0.1310142625794062, -0.5860324086500015, -0.0624349726213975]
pos/neg length and mean:
trends positive len: 21 0.8835238047359075
trends negative len: 24 -0.7730833291439385
trends positive list: [0.7659593076650326, 1.5976192684256507, 1.3396305109645679, 0.7039113786554122, 0.6647232129651197, 0.11391177298491237, 0.14221433709456596, 0.8726382031552712, 0.9956455010164618, 0.6679888934393077, 0.10629185187847384, 0.3653691694976192, 0.6647232129651197, 0.6266236074328899, 0.4894650275168725, 0.5232103924168393, 1.3505161125452068, 1.5954421481095253, 1.7282464873932923, 1.7445748897642444, 1.4952946135676752]
trends negative list: [-0.7645562745724367, -1.014925110927081, -0.5261615999565064, -0.06787777341171075, -0.10162313831168997, -0.8581724481659111, -1.334961797397784, -1.443817813204148, -0.5348700812210075, -0.9049805349626544, -0.8951834935400782, -1.7747401012554962, -1.9521754070198687, -2.004426294606925, -0.739519390936971, -1.1335781681560086, -0.19088507127290136, -1.010570870294818, -0.18326515016646283, -0.28994404565668896, -0.048283690566570704, -0.1310142625794062, -0.5860324086500015, -0.0624349726213975]
zscore silos:
prices 0 to 1: 19 0.5159193883045129
prices 1 to 2: 4 1.4639440396810512
prices 2 to 3: 1 2.005079787550808
prices 3>: 0 nan
prices 0 to -1: 14 -0.38097024812529595
prices -1 to -2: 4 -1.454678571075471
prices -2 to 3: 3 -2.1703421886682137
prices <3: 0 nan
trends 0 to 1: 13 0.18729980332171914
trends 1 to 2: 8 1.5002054504426652
trends 2 to 3: 0 nan
trends 3>: 0 nan
trends 0 to -1: 16 -0.42194589789701936
trends -1 to -2: 7 -1.3777869923741477
trends -2 to 3: 1 -2.0051154449596136
trends <3: 0 nan30 DAYS
NVDA past 30 days, during which it took a major dive with the release of DeepSeek close: open: 140.14, close: 140.11
QQQ past 30 days open: 515.18, close: 537.23
Some Data for 30 day plot
shape of zscore plot
prices max/min, trends max/min: 1.7395624418627935 -2.0102455727613866 1.635662150597431 -1.980244163451975
beats_condition, max, min: False True False
avg zscores list: [-0.04900609322786642, -0.043237954955050006, -1.4069222487518587, -1.9069027691119136, -2.1376722944324356, -0.3679613081523311, -1.0756478274354047, 0.34156933192037153, 1.0456770404126563, 2.5158374261919283, 2.6528832614306106, 1.7724244766420925, -2.8159600623456074, -0.7210526618453241, -1.4730439252605447, -1.1233948714886564, -1.765168780240289, -2.622450849103493, -1.7105258315300362, -0.7023407103306647, 0.0587929352193316, -0.500214456769451, 0.6220642834292512, 0.39529360882055975, 0.2241537498518348, 1.531306979740637, 2.2043609778904045, 2.3999373023930133, 2.3948264891602498, 2.2623747818778503]
zscore differential avg: -4.4704980458239636e-15
zscore means of prices and trends: -7.919590908992784e-16 -3.796962744218036e-15
trend count and mean of pos or neg:
prices positive len: 17 0.7172287353811013
prices negative len: 13 -0.9379145001137494
prices positive list: [0.8708241976370331, 0.8671431033818425, 0.35178990765469526, 0.022945487524039902, 0.3922819444618296, 0.06466455574957113, 0.5726555629663304, 0.9554893655064959, 1.7211569705868235, 1.7395624418627935, 1.1751279893997306, 0.06466455574957113, 0.2757139597140209, 0.7125371446636966, 0.780023872675586, 0.7591643385628187, 0.8671431033818425]
prices negative list: [-0.9198302908648995, -0.9103810583368925, -1.758712156406554, -1.9298482566359534, -1.980244163451975, -0.7602432526141607, -1.1403123831849757, -0.23108623104595882, -1.0216720192222633, -0.22373682796862931, -0.326628471051326, -0.09354740202724439, -0.1733409211526078, -0.6122052763421065, -0.10719629345658256]
trend count and mean of pos or neg:
trends positive len: 15 0.8125990002508011
trends negative len: 15 -0.8125990002508086
trends positive list: [0.09018767490616038, 0.7946804556051048, 0.9133208195678171, 0.5972964872423618, 0.055540488970154546, 0.30542019359958456, 0.5941467430663635, 0.5573997276796802, 0.42511047228762966, 0.45765782877296995, 1.2555930200266159, 1.4918238332267078, 1.6199134297174271, 1.635662150597431, 1.395231678496008]
trends negative list: [-0.9198302908648995, -0.9103810583368925, -1.758712156406554, -1.9298482566359534, -1.980244163451975, -0.7602432526141607, -1.1403123831849757, -0.23108623104595882, -1.0216720192222633, -0.22373682796862931, -0.326628471051326, -0.09354740202724439, -0.1733409211526078, -0.6122052763421065, -0.10719629345658256]
zscore silos:
prices 0 to 1: 14 0.5195964415593288
prices 1 to 2: 3 1.5885045560705986
prices 2 to 3: 0 nan
prices 3>: 0 nan
prices 0 to -1: 7 -0.38925651381809206
prices -1 to -2: 5 -1.4606024370377964
prices -2 to 3: 1 -2.0120560681267605
prices <3: 0 nan
trends 0 to 1: 11 0.23112599712881077
trends 1 to 2: 5 1.4620954964259174
trends 2 to 3: 0 nan
trends 3>: 0 nan
trends 0 to -1: 9 -0.4261916020115731
trends -1 to -2: 4 -1.522227807745561
trends -2 to 3: 1 -2.0472318717466145
trends <3: 0 nan15 DAYS
NVDA past 15 days during which price regained momentum and climbed back up. open: 124.65, min on day81 at 116.64, close: 140.11
QQQ past 15 days open: 523.05, close: 537.23.
Some Data for 15 day plot
shape of z-score:
prices max/min, trends max/min: 1.2978590301422221 -1.7892515585278195 1.4875489718437718 -1.7015455028399435
prices positive len: 8 0.7961212804164766
prices negative len: 7 -0.9098528919045411
prices positive list: [0.4368908744960763, 0.3355230641218386, 0.11698986253581116, 0.6633228665008721, 1.1319844313480085, 1.2043900101867528, 1.1820101040002302, 1.2978590301422221]
prices negative list: [-0.9657152036835966, -1.0789198650922653, -1.7015455028399435, -0.7542012310515969, -0.3996918966402359, -0.9850791589245527, -0.04220349219180732, -0.22988490452723273, -0.18370931895265175]
trends positive len: 6 1.056825095650698
trends negative len: 9 -0.7045500637670981
trends positive list: [0.009930233456925725, 0.9483372951340528, 1.2834826743044578, 1.465205946565748, 1.4875489718437718, 1.1464454525992316]
trends negative list: [-0.9657152036835966, -1.0789198650922653, -1.7015455028399435, -0.7542012310515969, -0.3996918966402359, -0.9850791589245527, -0.04220349219180732, -0.22988490452723273, -0.18370931895265175]
zscore silos:
prices 0 to 1: 5 0.3387271345868971
prices 1 to 2: 4 1.13156977020328
prices 2 to 3: 0 nan
prices 3>: 0 nan
prices 0 to -1: 3 -0.4156379102954067
prices -1 to -2: 3 -1.6576670076204636
prices -2 to 3: 0 nan
prices <3: 0 nan
trends 0 to 1: 1 0.584379796953797
trends 1 to 2: 4 1.3465306598267066
trends 2 to 3: 0 nan
trends 3>: 0 nan
trends 0 to -1: 7 -0.3399223000040687
trends -1 to -2: 3 -1.315505544516218
trends -2 to 3: 0 nan
trends <3: 0 nanThe above charts and data are generated in the following code snippets.
This code snippet gets data into Pandas Dataframes from the Alpaca API.
############### INIT CEILLI CLASSES ####################
from classes.stock_list import StockList
from classes.config import Config
from classes.alpaca import Alpaca
from classes.utilities import Utilities
from classes.market_beat import MarketBeat
from classes.profit_loss import ProfitLoss
from classes.plots import Plots
util = Utilities(pd.DataFrame())
conf = Config(api_key=api_key, api_secret=api_secret, api_base_url=api_base_url, algo_version=ALGO_VERSION)
mb = MarketBeat(pd.DataFrame(), api_key=api_key, api_secret=api_secret, api_base_url=api_base_url, algo_version=ALGO_VERSION)
alpa = Alpaca(api_key=api_key, api_secret=api_secret, api_base_url=api_base_url, algo_version=ALGO_VERSION)
stocks = StockList()
plots = Plots(pd.DataFrame())
############## SETTINGS ###################
#CONSTANTS, see setting.toml for conflicts, set here to overide settings.toml file Constants
ALGO_VERSION = conf.algo_version
BASE_CURRENCY = conf.base_currency
############# LOGGING #################
import logging
logging.basicConfig(
filename="logs/charts_"+ALGO_VERSION+".log",
level=logging.INFO,
format="%(asctime)s:%(levelname)s:%(message)s"
)
alpa = Alpaca(api_key=api_key, api_secret=api_secret, api_base_url=api_base_url, algo_version=ALGO_VERSION)
############################### CONFIGS ###################################################
# API Credentials alpaca4 edge
API_KEY = conf.api_key
API_SECRET = conf.api_secret
API_BASE_URL = conf.api_base_url
SECRET_KEY = API_SECRET
#CONSTANTS
TIMEZONE_OFFSET = -4.0 #set in config file, this is deprecated, i think
if DEBUG:
PROCESS_ROWS = 0 #set to low number for debugging, otherwise 1000
else:
PROCESS_ROWS = 1000
########################### DRIVER #######################################
date = DATE
from datetime import date
from datetime import timedelta
import datetime
from datetime import datetime, timezone, timedelta
N_DAYS_AGO = 500
YESTERDAY = 1
#today = datetime.now()
today = date.today()
n_days_ago = today - timedelta(days=N_DAYS_AGO)
one_day_ago = today - timedelta(days=YESTERDAY)
today = date.today()
timezone_offset = -4 # EST is -4, that is 4 hours behind GMT
tzinfo = timezone(timedelta(hours=timezone_offset))
now = datetime.now(tzinfo)
back_time = now - timedelta(minutes=15)
date = back_time.strftime("%Y-%m-%d %H:%M:%S")
start_time = now - timedelta(minutes=45)
start = start_time.strftime("%Y-%m-%d %H:%M:%S")
end = date
beg_date = str(n_days_ago) + ' 00:00:00'
end_date = str(one_day_ago) + ' 23:59:00'
if MODE == 'SCREENER' or MODE == 'HISTORICAL':
try:
#STOCK_LIST = stocks.TECH_AL
STOCK_LIST = ['NVDA', 'MSFT']
STOCK_SET = set(STOCK_LIST) #remove duplicates from list
STOCK_LIST = list(STOCK_SET)
STOCK_LIST = sorted(STOCK_LIST)
symbol_list = STOCK_LIST
index_symbol = stocks.stock_index(ALGO_VERSION)
cnt = 0
for symbol in symbol_list:
print(ALGO_VERSION)
print(symbol)
print(index_symbol)
hundred_dates = alpa.get_calendar(str(n_days_ago), str(one_day_ago))
#get prices for symbol in trading list
symbol_price_data = alpa.stockbars_by_symbol_by_day(symbol, beg_date, end_date)
symbol_price_data = symbol_price_data.reset_index(level=("symbol", "timestamp"))
prices_data = symbol_price_data
#get prices for trend index for symbol above
index_price_data = alpa.stockbars_by_symbol_by_day(index_symbol, beg_date, end_date)
index_price_data = index_price_data.reset_index(level=("symbol", "timestamp")) #alpaca dataframe return has an index of symbol, timestamp format
column_names = index_price_data.columns
trends_data = index_price_data
symbol_prices = symbol_price_data
column_names = prices_data.columns
print(column_names)
prices_data = symbol_prices[['timestamp', 'symbol', 'open', 'close', 'vwap']].copy()
trends_data = trends_data[['timestamp', 'symbol', 'open', 'close', 'vwap']].copy()
prices_data.rename(columns = {'timestamp':'date'}, inplace = True)
trends_data.rename(columns = {'timestamp':'date'}, inplace = True)
#prices_data = prices_data.reset_index()
#trends_data = trends_data.reset_index()
date_stamp = prices_data.iloc[0]['date']
print()
print()
print("Statistical Analysis: ")
prices_arr = np.array(prices_data['close'])
from scipy.stats import skew, kurtosis
# Calculate the skewness
print("Symbol Prices skew: ")
print(skew(prices_data['close'], axis=0, bias=True))
print("Index skew: ")
print(skew(trends_data['close'], axis=0, bias=True))
# Calculate the kurtosis
print("Symbol Prices kurtosis: ")
print(kurtosis(prices_data['close'], axis=0, bias=True))
print("Index kurtosis: ")
print(kurtosis(trends_data['close'], axis=0, bias=True))
print()
print("Covariance between the two: ")
cov_matrix = np.stack((prices_data['close'], trends_data['close']), axis = 0)
print(np.cov(cov_matrix))
print()
print("Correlation between the two: ")
correlations = np.correlate(prices_data['close'], trends_data['close'])
print(correlations)
print()
print()
print("Mean of the Symbol: ")
data_mean = np.mean(prices_data['close'])
data_max = max(prices_data['close'])
data_min = min(prices_data['close'])
print("mean is: " + str(data_mean))
print("max/min is: "+str(max(prices_data['close'])), str(min(prices_data['close'])))
print()
print()
print("Variance of the Symbol: ")
m = sum(prices_data['close']) / len(prices_data['close'])
std_dev = np.std(prices_data['close'])
print("std dev: "+str(std_dev))
import scipy.stats as scipy
zscore_list = scipy.zscore(prices_data['close'])
print("symbol z-scores list: ")
print(zscore_list)
trends_zscore_list = scipy.zscore(trends_data['close'])
print("trends z-scores list: ")
print(trends_zscore_list)
import statistics
# Calculate the variance from a sample of data
data_variance = statistics.variance(prices_data['close'])
print("variance result: "+str(data_variance))
print()
print()
print("Market Beat Metrics: ")
#prices = prices_data.iloc[:lookback_period]
vars, vibe_check = mb.compare_rates(trends_data, prices_data)
vars15, vibe_check15 = mb.compare_rates(trends_data[-15:], prices_data[-15:])
vars30, vibe_check30 = mb.compare_rates(trends_data[-30:], prices_data[-30:])
vars45, vibe_check45 = mb.compare_rates(trends_data[-45:], prices_data[-45:])
vars60, vibe_check60 = mb.compare_rates(trends_data[-60:], prices_data[-60:])
print(vars)
the zscores are put into silos based on standard deviation in a Market Beat Class function, a snippet from that is following, which appends the zscore value to a list based on each silo or bin, I included the logic here because it can be beneficial to be able to sort these ito bins:
if current_idx_z >= 0:
trends_positive.append(current_idx_z)
else:
trends_negative.append(current_idx_z)
if current_price_z >= 0:
prices_positive.append(current_price_z)
else:
prices_negative.append(current_price_z)
if current_price_z > 0 and current_price_z < 1:
prices_0to1.append(current_price_z)
elif current_price_z > 1 and current_price_z < 2 :
prices_1to2.append(current_price_z)
elif current_price_z > 2 and current_price_z < 3:
prices_2to3.append(current_price_z)
elif current_price_z > 3 and current_price_z < 8:
prices_3up.append(current_price_z)
elif current_price_z <= 0 and current_price_z > -1:
prices_0toneg1.append(current_price_z)
elif current_price_z <= 1 and current_price_z > -2:
prices_neg1toneg2.append(current_price_z)
elif current_price_z <= 2 and current_price_z > -3:
prices_neg2toneg3.append(current_price_z)
elif current_price_z <= 3:
prices_neg3.append(current_price_z)
if current_idx_z >= 0 and current_idx_z < 1:
trends_0to1.append(current_price_z)
elif current_idx_z >= 1 and current_idx_z < 2:
trends_1to2.append(current_idx_z)
elif current_idx_z >= 2 and current_idx_z < 3:
trends_2to3.append(current_idx_z)
elif current_idx_z >= 3:
trends_3up.append(current_idx_z)
elif current_idx_z < 0 and current_idx_z > -1:
trends_0toneg1.append(current_idx_z)
elif current_idx_z < 1 and current_idx_z > -2:
trends_neg1toneg2.append(current_idx_z)
elif current_idx_z < 2 and current_idx_z > -3:
trends_neg2toneg3.append(current_idx_z)
elif current_idx_z < -3 and current_idx_z > -8:
trends_neg3.append(current_idx_z)
The graphing part is handled in a Plots Class that is called by this code:
path_15_index = 'plots/stats/zscores/scatter/'+str(today)+'_'+index_symbol+'_15.png'
print(path_15_index)
isFile = os.path.isfile(path_15_index)
if isFile == False:
symbol_zscores_plot = plots.zscores_scatter_by_day(today, index_symbol, trends_data['zscores'][-15:], '15')
else:
print(index_symbol + ' zscores scatter plot file exists for this date')
Then in the plots class I generate the plots:
in the Plots Class:
def zscores_scatter_by_day(self, plot_date, symbol, data, periodicity='all'):
plot_date = str(plot_date)
zscores = data.reset_index(drop = True)
#zscores = zscores.tolist()
print(zscores)
# PLOTTING
import matplotlib.pyplot as plt
zscores_set = set(zscores) #remove duplicates from list
zscores_list = list(zscores_set)
zscores_list = sorted(zscores_list)
print("zscores sorted and unique: ", zscores_list)
import seaborn as sns
sns.displot(zscores_list, color="maroon")
plt.xlabel("zscore", labelpad=14)
plt.ylabel("probability of occurence", labelpad=14)
plt.title("Percent Ratio Z-scores distribution" + plot_date, y=1.015, fontsize=10);
#plt.show()
plt.savefig('plots/stats/zscores/'+symbol+'_'+str(plot_date)+'_'+periodicity+'.png',bbox_inches='tight')
plt.clf()
import matplotlib.pyplot as plt2
x_cnt = 0
color = 'grey'
# https://matplotlib.org/stable/gallery/color/named_colors.html
for i in zscores:
if i < 0 and i > -1:
color = 'orange'
elif i < -1 and i > -2:
color = 'indianred'
elif i < -2 and i > -3:
color = 'firebrick'
elif i < -3 and i > -4:
color = 'maroon'
elif i > 0 and i < 1:
color = 'yellow'
elif i > 1 and i < 2:
color = 'green'
elif i > 2 and i < 3:
color = 'forestgreen'
elif i > 3 and i < 4:
color = 'darkgreen'
elif i > 4 and i < 5:
color = 'darkolivegreen'
elif i > 5:
color = 'black'
print(zscores)
plt2.scatter(i, zscores[x_cnt], c=color)
x_cnt += 1
# depict first scatted plot
#plt.scatter(x, y, c='blue')
print('plots/stats/zscores/scatter/'+str(plot_date)+'_'+symbol+'_'+periodicity+'.png')
plt2.savefig('plots/stats/zscores/scatter/'+str(plot_date)+'_'+symbol+'_'+periodicity+'.png',bbox_inches='tight')
plt2.clf()
# depict illustration
#plt.show()
this function outputs the plots into a directory for safe keeping and reference as needed. The first part of the function generates the zscore bar charts and the second part of the function generates the rainbow spectrum charts of zscores. You have to pass in a dataframe of zscores to be plotted, plus the other apparent variables that are easy to figure out for oneself. You’ll need to include these libraries in your own code for this function to work.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
import seaborn as sns
def zscores_scatter_by_day(self, plot_date, symbol, data, periodicity='all'):
...
...
...I hope this can provide some insights to others on how to plot z-scores and work with pandas.
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