Scale the data before learning to normalize the output

WebServer/AIPython/ai-predictor.py

@@ -1,5 +1,6 @@
 import os
 import json
+import joblib
 import numpy as np
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 import yfinance as yf
@@ -31,11 +32,21 @@ def Predict():
     # Verify it loaded correctly
     reconstructed_model.summary()
 
+    # Load the scalers
+    feature_scaler = joblib.load(os.path.join(DATA_DIR, "feature_scaler.pkl"))
+    target_scaler = joblib.load(os.path.join(DATA_DIR, "target_scaler.pkl"))
+
+    # Scale the data
+    scaled_data = feature_scaler.transform(df)
+
     # Predict
-    predictions = reconstructed_model.predict(df)
+    scaled_predictions = reconstructed_model.predict(scaled_data)
+
+    # Use the loaded target scaler to get back to % change
+    actual_prediction = target_scaler.inverse_transform(scaled_predictions)
 
     # 'predictions' will be a 2D array, flatten it if you want a simple list
-    flat_predictions = predictions.flatten().tolist()
+    flat_predictions = actual_prediction.flatten().tolist()
 
     print(f"Predicted Target_Close: {flat_predictions}")
 

WebServer/AIPython/ai-trainer.py

@@ -2,6 +2,8 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import datapuller
+import features
+import joblib
 import os
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 from sklearn.model_selection import train_test_split
@@ -20,13 +22,12 @@ def TrainAI(include_pull):
     # Load the dataset
     dataset = pd.read_parquet(os.path.join(DATA_DIR, "stocks.parquet"))
 
-    # Replace Infinity with 0 -> This fixes the AI mental breakdown
-    dataset['Volume_Chg'] = dataset['Volume_Chg'].replace([np.inf, -np.inf], 0)
+    # Create the X, Y vareables
+    X, Y, X_Scaler, Y_Scaler = features.Prepare(dataset)
 
-    # Remove indicators and set the target
-    X = dataset.drop('Target_Close', axis=1)
-    X = dataset.drop('Target_Direction', axis=1)
-    Y = dataset['Target_Close']
+    # Save the scalers for future use
+    joblib.dump(X_Scaler, os.path.join(DATA_DIR, "feature_scaler.pkl"))
+    joblib.dump(Y_Scaler, os.path.join(DATA_DIR, "target_scaler.pkl"))
 
     # Show the datatypes
     print(dataset.dtypes)

WebServer/AIPython/data/stocks.preview.csv

@@ -1,201 +1,201 @@
-Date,Close,High,Low,Open,Volume,Symbol,Spread,Return,Volatility_5,Volatility_20,RSI,Moving_Average_5,Moving_Average_20,Trend_Signal,Dist_From_MA20,BB_Pos,Log_Return,Target_Close,Target_Direction,Volume_Chg
--249955200,0.5309499502182007,0.5340793982846871,0.5267774162512139,0.5267774162512139,170310,0,0.007301982033473209,0.00792023955109511,0.010200076284055561,0.011124590561047922,46.96967860463748,0.5324103951454162,0.5365307688713074,0,-0.010401674939998218,0.3491923187055057,0.007889039088967537,0.0017899616790169048,0,-0.21239195882295814
--249868800,0.5507692694664001,0.5591141834054576,0.534079289296328,0.534079289296328,327226,0,0.025034894109129624,0.037328036738782044,0.019967820772104863,0.014089691406813062,60.241026064686686,0.535331130027771,0.5361135125160217,0,0.02733704077257415,0.9403372979801028,0.03664821166338882,-0.0017867634409282074,0,0.9213551758557923
--249782400,0.5674591064453125,0.5705883627186048,0.5486827692729858,0.5507691304867457,319571,0,0.021905593445619043,0.03030277450861041,0.021312919958663676,0.01570129216082923,74.15762943977647,0.5422156929969788,0.5365307480096817,1,0.05764508101420618,1.3021234797393308,0.02985271487859573,-0.0017885306092033648,0,-0.02339361786655092
--249696000,0.5737179517745972,0.5758041231413228,0.5695454186762002,0.5695454186762002,239200,0,0.006258704465122555,0.011029597125494117,0.020804862968592602,0.014492880446518662,74.4447273302151,0.5499348044395447,0.5379911035299301,1,0.06640787925720693,1.2058768057958824,0.010969214709227714,-0.0017914279751538587,0,-0.25149653754564716
--249436800,0.583105742931366,0.583105742931366,0.5716312364179468,0.5737175968589447,233459,0,0.011474506513419191,0.016363077236350998,0.01269144262539286,0.01465579115332346,74.44469487979632,0.5612004041671753,0.5400773257017135,1,0.07967084560298154,1.1645205402478302,0.01623064480319519,-0.00897690784681826,-1,-0.02400083612040138
--249350400,0.5841494798660278,0.5841494798660278,0.5778907722881246,0.583106393663884,210496,0,0.006258707577903255,0.0017899616790169048,0.014402066056040969,0.014636686967503514,78.16130229319987,0.5718403100967407,0.5422157347202301,1,0.07733775038349733,1.0530096465194634,0.0017883616067049404,-0.014493197689452453,-1,-0.09835988331998335
--249264000,0.583105742931366,0.5862350361185866,0.579976297452283,0.584148827969451,221978,0,0.006258738666303665,-0.0017867634409282074,0.012730182275679593,0.014251035868534318,76.19034661968429,0.5783076047897339,0.54461490213871,1,0.0706753352534093,0.9594435130238214,-0.0017883616067049727,-0.029411523736857625,-1,0.05454735481909401
--249177600,0.582062840461731,0.5841491640300238,0.5810197548235596,0.5831060783918524,116730,0,0.0031294092064642065,-0.0017885306092033648,0.00818306856829537,0.014194081883091866,81.01273879821895,0.5812283515930176,0.5470662295818329,1,0.06397143341611256,0.8916309159931848,-0.0017901319397106616,0.0037878332859868635,0,-0.47413707664723537
--249091200,0.5810201168060303,0.5862357386112195,0.5716319214106673,0.5820632030940569,153088,0,0.01460381720055215,-0.0017914279751538587,0.007871735181274745,0.013123400456746207,79.22115508773804,0.5826887845993042,0.5499869912862778,1,0.05642519916184563,0.840454369029805,-0.0017930345011860928,0.0,0,0.311470915788572
--248832000,0.5758043527603149,0.5799768875226332,0.5737181805616666,0.5799768875226332,110989,0,0.006258706960966642,-0.00897690784681826,0.003932890152491272,0.01345504209786977,76.25005267702875,0.581228506565094,0.5523861706256866,1,0.04239458440478794,0.756134727800381,-0.009017443053621572,-0.014004284031181036,-1,-0.2749986935618729
--248745600,0.5674591064453125,0.5799765122682783,0.5674591064453125,0.5758039802056569,183706,0,0.012517405822965788,-0.014493197689452453,0.005786313777287126,0.013704201772565837,67.85707483252338,0.5778904318809509,0.5538986921310425,1,0.02448175904170924,0.6486828803593688,-0.014599250017904485,0.04030744801722297,1,0.6551730351656471
--248659200,0.5507692694664001,0.5674592496364722,0.547639822038533,0.5674592496364722,179878,0,0.0198194275979392,-0.029411523736857625,0.011450996217928661,0.015341967106059198,58.76285076733484,0.5714231371879578,0.5547853410243988,1,-0.0072389648049875355,0.4549483037218887,-0.029852714878595588,-0.0036906229399022994,0,-0.020837642755272
--248572800,0.5528554916381836,0.5612005586359451,0.5497260819957749,0.5507693200712253,187533,0,0.011474476640170206,0.0037878332859868635,0.012800108143425583,0.01528420834184638,58.7628993511887,0.5655816674232483,0.5555676817893982,1,-0.00488183571527967,0.4690500039017834,0.0037806775097183457,-0.009259100037883772,-1,0.04255662171027019
--248486400,0.5528554916381836,0.5612005586359451,0.5507693200712253,0.5528554916381836,218150,0,0.010431238564719836,0.0,0.013114714087586635,0.014973609906396011,64.04473389891663,0.559948742389679,0.5566629588603973,1,-0.006839807035137313,0.4549873748016093,0.0,-0.0037379547220215414,0,0.16326193256653498
--248227200,0.5451131463050842,0.5503445602901758,0.5451131463050842,0.5503445602901758,197101,0,0.0052314139850915264,-0.014004284031181036,0.013226279754471689,0.015366214628590462,57.34542304831703,0.5538105010986328,0.5572146326303482,0,-0.021717818622491225,0.35359879223509066,-0.014103269248150538,-0.007504493311763172,-1,-0.09648865459546185
--248140800,0.5670852661132812,0.5670852661132812,0.54720600118734,0.54720600118734,174138,0,0.019879264925941276,0.04030744801722297,0.02598125963822912,0.017618276340229062,58.27709679779823,0.5537357330322266,0.5589692175388337,0,0.014519669992173911,0.6020328629271549,0.039516292558868106,0.009451518778709422,1,-0.11650372144230625
--248054400,0.564992368221283,0.5681312535361481,0.5608072005308881,0.5670848565957914,126298,0,0.007324053005260067,-0.0036906229399022994,0.020672341199044233,0.017652040373386763,48.53107685536511,0.5565803527832032,0.5604105561971664,0,0.008175813202391913,0.5597269615909102,-0.003697450091549223,-0.003744953556665709,0,-0.27472464367340843
--247881600,0.5597610473632812,0.5649924986367659,0.5587148029345257,0.5649924986367659,124384,0,0.006277695702240238,-0.009259100037883772,0.021705408834729434,0.01775613753780663,41.58580354537653,0.5579614639282227,0.5617467939853669,0,-0.003534949630949469,0.4725261830496328,-0.009302231952900707,-0.00563918892567894,-1,-0.015154634277660728
--247622400,0.5576686859130859,0.5639463448612618,0.5524372716326951,0.559761175248656,170310,0,0.011509073228566757,-0.0037379547220215414,0.021884028184288463,0.01780863724058096,33.185716233179335,0.5589241027832031,0.5628741085529327,0,-0.009247934059765539,0.423414430062193,-0.0037449583330079967,-0.009452811058468624,-1,0.3692275533830718
--247536000,0.5534836649894714,0.5608076087612449,0.5534836649894714,0.5576688357263903,124384,0,0.007323943771773478,-0.007504493311763172,0.020870980401226993,0.01747155169248724,30.5376731320801,0.5605982065200805,0.5642094045877457,0,-0.019010210590359344,0.32040648908619496,-0.007532793697299797,0.0,0,-0.2696612060360519
--247449600,0.5587149262428284,0.5587149262428284,0.5482520977632833,0.5534835120030559,116730,0,0.01046282847954505,0.009451518778709422,0.007340217948748493,0.017499337651094855,35.30142829844125,0.55892413854599,0.565597653388977,0,-0.012168945724771674,0.3657444433387689,0.009407132633575552,0.007634263329628688,1,-0.06153524569076407
--247363200,0.5566225647926331,0.5639466247973388,0.5524375458558086,0.5587152079201377,139693,0,0.011509078941530193,-0.003744953556665709,0.007341631966794953,0.015519171859193766,34.86049007777312,0.55725017786026,0.5658903181552887,0,-0.016377296209744374,0.3149930396396939,-0.0037519834518120214,0.005681588907118229,1,0.19671892401267876
--247276800,0.5534836649894714,0.5608076087612449,0.5482522493037022,0.5566224380243261,267904,0,0.012555359457542714,-0.00563918892567894,0.0067169490062130265,0.013900319137230157,34.01193675719712,0.555994701385498,0.5651915460824967,0,-0.020714890684717213,0.2716482480331139,-0.005655149181763565,0.0018839145985893868,0,0.9178054734310237
--247017600,0.5482516884803772,0.5555755865720092,0.5482516884803772,0.5534830988147778,202842,0,0.007323898091632031,-0.009452811058468624,0.0074795631358172306,0.013708568924748591,34.00553474509239,0.5541113018989563,0.5639182329177856,0,-0.02778158875329806,0.20296660788575135,-0.009497772442228812,0.015036766308185845,1,-0.24285564978499763
--246931200,0.5482516884803772,0.5482516884803772,0.5440665220245925,0.5482516884803772,216237,0,0.004185166455784706,0.0,0.007191917266587279,0.013014407263728652,37.653778457874566,0.5530649065971375,0.5621755301952363,0,-0.024767783311421376,0.2283570949127671,0.0,0.029629816397279907,1,0.06603661963498686
--246844800,0.5524371862411499,0.5524371862411499,0.5461595282633267,0.5482520172754557,130125,0,0.006277657977823203,0.007634263329628688,0.0064877324386250065,0.013191953324756922,51.27746506521312,0.5518093585968018,0.5605899155139923,0,-0.014543125103075316,0.32844053440833,0.007605269810610924,0.0035969778255016127,0,-0.39822972016814884
--246758400,0.5555759072303772,0.5597610761016905,0.5524371737829663,0.5524371737829663,310003,0,0.007323902318724151,0.005681588907118229,0.007264426639076365,0.01332435134958438,52.05051967224752,0.5516000270843506,0.5592134237289429,0,-0.006504701683142766,0.4147497694722099,0.005665509556288708,0.0035852089736707615,0,1.3823477425552353
--246672000,0.5566225647926331,0.5597614531072659,0.5545300362300648,0.5555762814171928,177965,0,0.005231416877201078,0.0018839145985893868,0.006649468025286538,0.013357493188845056,52.79533434235049,0.5522278070449829,0.557941409945488,0,-0.002363769975388341,0.4642293735442424,0.001882142257092901,-0.0035724011689423074,0,-0.42592491040409286
--246412800,0.564992368221283,0.564992368221283,0.5587146739681039,0.5587146739681039,176051,0,0.006277694253179034,0.015036766308185845,0.005860168875755847,0.0139001312654129,64.61516213847503,0.555575942993164,0.5571400225162506,0,0.014094025537006338,0.7558592592556475,0.014924834801444842,-0.001792145067170181,0,-0.010754923720956322
--246326400,0.581732988357544,0.5827792709854409,0.5670849933787023,0.5670849933787023,325312,0,0.015694277606738583,0.029629816397279907,0.010969799024886371,0.015380716692620477,61.6663976529343,0.5622722029685974,0.5574364542961121,1,0.04358619511548034,1.2144909264863653,0.02919933608528749,-0.0017951580643221776,0,0.8478281861506041
--246240000,0.5838254690170288,0.5859181104830491,0.5827792246605822,0.5827792246605822,340621,0,0.003138885822466908,0.0035969778255016127,0.011504578639640566,0.014971407295703527,65.00000474733271,0.5685498595237732,0.5582547724246979,1,0.04580470755542021,1.1300953231406148,0.0035905241718990183,0.005395671659224721,1,0.04705943832382453
--246153600,0.5859186053276062,0.5869648505676718,0.5848723600875407,0.5848723600875407,137779,0,0.0020924904801310484,0.0035852089736707615,0.011789096653612606,0.01308120248548311,71.92998411909976,0.574618399143219,0.5600122392177582,1,0.04626035699868769,1.0533113995719028,0.0035787974318866895,-0.005366714629196689,-1,-0.5955064426444641
--246067200,0.5838254690170288,0.5869643548394957,0.5838254690170288,0.5859181104830491,135866,0,0.003138885822466908,-0.0035724011689423074,0.013004449582553031,0.013166400812066202,71.9290495769479,0.5800589799880982,0.5615607380867005,1,0.03964794797831961,0.9378121182144027,-0.003578797431886679,0.0017983864522645998,0,-0.013884554249921965
--245808000,0.5827791690826416,0.5859180546057622,0.5817328866376941,0.5838254133393109,162656,0,0.004185167968068115,-0.001792145067170181,0.013434129268375698,0.013192604487356652,75.92483387433842,0.5836163401603699,0.5630569219589233,1,0.03502709291824857,0.868540377709333,-0.0017937528803845801,-0.0017951580643221776,0,0.19717957399202146
--245721600,0.581732988357544,0.5848719126178044,0.581732988357544,0.5827792709854409,225805,0,0.0031389242602604117,-0.0017951580643221776,0.0033536658412486897,0.012649259663219609,71.999161457105,0.5836163401603699,0.5648879140615464,1,0.029820206587323428,0.816654736749348,-0.0017967712915144282,-0.005395056896307926,-1,0.38823652370647266
--245635200,0.5848718285560608,0.5859180728452996,0.5806866604577091,0.5817329047469478,135866,0,0.005231412387590506,0.005395671659224721,0.0038890900581566145,0.009222770895642718,76.46933327376098,0.5838256120681763,0.5657772421836853,1,0.033749300870917986,0.8402792715852764,0.005381167173799264,0.005424321418987299,1,-0.3983038462390115
--245548800,0.581732988357544,0.5859181570574165,0.5785941022855684,0.5848719126178044,195187,0,0.007324054771848054,-0.005366714629196689,0.004090898855021219,0.009280668611764662,76.46941868868463,0.5829884886741639,0.5666142731904984,1,0.026682552633054746,0.7611764697032177,-0.005381167173799135,0.005395671659224721,1,0.43661401675179956
--245462400,0.5827791690826416,0.5838254133393109,0.577547718669626,0.5817328866376941,114816,0,0.0062776946696849745,0.0017983864522645998,0.0040915760173007975,0.008928572480550952,85.10574600354164,0.5827792286872864,0.5677651792764664,1,0.026444013043047665,0.7534539319256531,0.0017967712915142999,0.0,0,-0.411764103142115
--245203200,0.581732988357544,0.5848719126178044,0.5796404994783199,0.5827792709854409,130125,0,0.005231413139484498,-0.0017951580643221776,0.004091841359294374,0.008872629908942295,83.33291794990475,0.5825699925422668,0.5689683943986893,1,0.022434627449464672,0.7138403318184599,-0.0017967712915144282,-0.0017890407572120903,0,0.1333350752508362
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WebServer/AIPython/features.py

@@ -1,3 +1,4 @@
+from sklearn.preprocessing import StandardScaler
 import pandas as pd
 import numpy as np
 
@@ -52,15 +53,27 @@ def MakeFeatures(df, i):
     # This is our training metric of 5 days ahead
     df['Target_Close'] = df['Close'].shift(-5).pct_change()
 
-    # for Up (> 0.5%), -1 for Down (< -0.5%), 0 for Flat
-    df['Target_Direction'] = np.where(df['Target_Close'] > 0.005, 1, np.where(df['Target_Close'] < -0.005, -1, 0))
-
     # Volume Change
     df['Volume_Chg'] = df['Volume'].pct_change()
 
     # Return new df with new features
     return df
 
+def Prepare(df):
+
+    # Remove indicators and set the target
+    X = df.drop('Target_Close', axis=1)
+    Y = df['Target_Close']
+
+    # Scale the features to the same size
+    feature_scaler = StandardScaler()
+    X_scaled = feature_scaler.fit_transform(X)
+
+    # Safe for the Y
+    target_scaler = StandardScaler()
+    y_scaled = target_scaler.fit_transform(Y.values.reshape(-1, 1))
+
+    return X_scaled, y_scaled, feature_scaler, target_scaler
 
 def CleanDF(df):
     # Make date the index so it doesnt influence the training
@@ -69,5 +82,8 @@ def CleanDF(df):
     # Drop rows with null values
     df.dropna(inplace=True)
 
+    # Replace Infinity with 0 -> This fixes the AI mental breakdown
+    df['Volume_Chg'] = df['Volume_Chg'].replace([np.inf, -np.inf], 0)
+
     # Return new df thats cleaned
     return df