Work on optimizing the model

WebServer/AIPython/ai-predictor.py

@@ -20,7 +20,7 @@ def Predict():
         df = features.CleanDF(df)
 
     # Drop our predictor
-    df.drop('Target_Close_Tomorrow', axis=1, inplace=True)
+    df.drop('Target_Close', axis=1, inplace=True)
 
     # Lazy load this so it doesnt interfere with yfinance
     from keras.models import load_model
@@ -37,6 +37,6 @@ def Predict():
     # 'predictions' will be a 2D array, flatten it if you want a simple list
     flat_predictions = predictions.flatten().tolist()
 
-    print(f"Predicted Target_Close_Tomorrow: {flat_predictions}")
+    print(f"Predicted Target_Close: {flat_predictions}")
 
     return json.dumps(flat_predictions)

WebServer/AIPython/ai-trainer.py

@@ -5,7 +5,7 @@ import datapuller
 import os
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 from sklearn.model_selection import train_test_split
-from keras import Sequential, layers, optimizers
+from keras import Sequential, layers, optimizers, losses
 
 def TrainAI():
     # Pull New Data
@@ -17,8 +17,9 @@ def TrainAI():
 
     # Load the dataset
     dataset = pd.read_parquet(os.path.join(DATA_DIR, "stocks.parquet"))
-    X = dataset.drop('Target_Close_Tomorrow', axis=1)
-    Y = dataset['Target_Close_Tomorrow']
+    X = dataset.drop('Target_Close', axis=1)
+    X = dataset.drop('Target_Direction', axis=1)
+    Y = dataset['Target_Close']
 
     # Show the datatypes
     print(dataset.dtypes)
@@ -34,15 +35,15 @@ def TrainAI():
     dnn_linear_model = Sequential([
         layers.Input(shape=(train_features.shape[1],)), # Load the feature count dynamically
         normalizer,
-        layers.Dense(64, activation='relu'),
-        layers.Dense(64, activation='relu'),
+        layers.Dense(64, activation='elu'),
+        layers.Dense(64, activation='elu'),
         layers.Dense(1)
     ])
 
     # Configure the model
     dnn_linear_model.compile(
-        optimizer=optimizers.Adam(learning_rate=0.001),
-        loss='mean_absolute_error'
+        optimizer=optimizers.Adam(learning_rate=0.0001, clipvalue=1.0),
+        loss=losses.Huber()
     )
 
     # Show the summary before training the model
@@ -52,7 +53,7 @@ def TrainAI():
     Training_Data = dnn_linear_model.fit( 
         train_features,
         train_labels,
-        batch_size=1024,
+        batch_size=64,
         epochs=100,
         # Show progress
         verbose=1,
@@ -76,5 +77,7 @@ def TrainAI():
         test_features, test_labels, verbose=0
     )
 
+    print(f"Test Results: {test_results}")
+
     # Save the model
     dnn_linear_model.save(os.path.join(DATA_DIR, "model.keras"))

WebServer/AIPython/data/stocks.preview.csv

@@ -1,201 +1,201 @@
-Date,Close,High,Low,Open,Volume,Symbol,Spread,Return,Volatility_5,Volatility_20,Target_Close_Tomorrow
--249955200,0.5309498310089111,0.5340792783727715,0.526777297978745,0.526777297978745,170310,0,0.007301980394026519,0.007920925618158936,0.01020097087908757,0.011124851514532868,0.03732782059888673
--249868800,0.550769031047821,0.5591139413745083,0.5340790581025554,0.5340790581025554,327226,0,0.025034883271952912,0.03732782059888673,0.019968119940768032,0.014089848206451776,0.030304086275323883
--249782400,0.5674595832824707,0.5705888421852839,0.5486832303323458,0.5507695932992797,319571,0,0.021905611852938156,0.030304086275323883,0.02131353407510427,0.015701564324636316,0.011028222367314378
--249696000,0.5737176537513733,0.5758038240344174,0.5695451228204382,0.5695451228204382,239200,0,0.006258701213979201,0.011028222367314378,0.02080561187040575,0.014493138742101818,0.01636474800748222
--249436800,0.5831063985824585,0.5831063985824585,0.5716318791669666,0.5737182419538933,233459,0,0.011474519415491935,0.01636474800748222,0.012691571008267575,0.01465613912667609,0.0017888352556327813
--249350400,0.5841494798660278,0.5841494798660278,0.5778907722881246,0.583106393663884,210496,0,0.006258707577903255,0.0017888352556327813,0.014402703282386348,0.014637043767007639,-0.00178564103799006
--249264000,0.5831063985824585,0.586235695288294,0.5799769495845895,0.5841494847934009,221978,0,0.006258745703704549,-0.00178564103799006,0.01273075966273603,0.014251384370450933,-0.0017895507897349727
--249177600,0.5820629000663757,0.5841492238483131,0.5810198143213898,0.5831061381033271,116730,0,0.00312940952692331,-0.0017895507897349727,0.008183487480294495,0.014194434631380507,-0.0017914277917073784
--249091200,0.581020176410675,0.586235798750915,0.5716319800522128,0.582063262805708,153088,0,0.014603818698702198,-0.0017914277917073784,0.00787248119327244,0.013123792468612167,-0.00897741985684919
--248832000,0.5758041143417358,0.579976647376367,0.5737179430068917,0.579976647376367,110989,0,0.0062587043694752875,-0.00897741985684919,0.003932797596241202,0.013455459080175522,-0.0144919615046597
--248745600,0.5674595832824707,0.5799769996238405,0.5674595832824707,0.5758044640550312,183706,0,0.012517416341369825,-0.0144919615046597,0.005785936495127997,0.013704502241690689,-0.029412759474609862
--248659200,0.550769031047821,0.5674590039930867,0.5476395849746382,0.5674590039930867,179878,0,0.019819419018448525,-0.029412759474609862,0.011451161187184583,0.01534236299993626,0.003788267808727541
--248572800,0.5528554916381836,0.5612005586359451,0.5497260819957749,0.5507693200712253,187533,0,0.011474476640170206,0.003788267808727541,0.012800574734005627,0.015284604027153705,0.0
--248486400,0.5528554916381836,0.5612005586359451,0.5507693200712253,0.5528554916381836,218150,0,0.010431238564719836,0.0,0.0131151699824541,0.014973992647779379,-0.014004284031181036
--248227200,0.5451131463050842,0.5503445602901758,0.5451131463050842,0.5503445602901758,197101,0,0.0052314139850915264,-0.014004284031181036,0.01322674821459181,0.015366581386836187,0.040306682611882305
--248140800,0.5670848488807678,0.5670848488807678,0.5472055985809793,0.5472055985809793,174138,0,0.019879250299788542,0.040306682611882305,0.025981330391965447,0.017618488315911215,-0.0036896796913634677
--248054400,0.5649924874305725,0.5681313734077197,0.5608073188571375,0.5670849762465808,126298,0,0.00732405455058216,-0.0036896796913634677,0.020671906805434185,0.01765222390442995,-0.009258781595233523
--247881600,0.5597613453865051,0.5649927994452746,0.5587151004007169,0.5649927994452746,124384,0,0.006277699044557661,-0.009258781595233523,0.021704964172990067,0.017756314579735696,-0.0037393370010787574
--247622400,0.5576682090759277,0.5639458626563627,0.5524367992686815,0.5597606966223052,170310,0,0.011509063387681207,-0.0037393370010787574,0.021883680795873956,0.01780883538421884,-0.007504285964694768
--247536000,0.5534833073616028,0.5608072464010841,0.5534833073616028,0.5576684753943159,124384,0,0.007323939039481364,-0.007504285964694768,0.02087060330355825,0.017471664124892053,0.009451632575766089
--247449600,0.5587146282196045,0.5587146282196045,0.5482518053210198,0.5534832167703121,116730,0,0.01046282289858469,0.009451632575766089,0.0073401787635225145,0.017499441367576696,-0.0037450622359893604
--247363200,0.5566222071647644,0.5639462624637895,0.5524371909167985,0.5587148489477538,139693,0,0.01150907154699099,-0.0037450622359893604,0.007341619254532293,0.015519315407075811,-0.005639192548838601
--247276800,0.5534833073616028,0.5608072464010841,0.5482518950560599,0.5566220783683711,267904,0,0.01255535134502428,-0.005639192548838601,0.006717041757863703,0.013900322081773192,-0.009450986435469044
--247017600,0.5482523441314697,0.5555762509817099,0.5482523441314697,0.5534837607220842,202842,0,0.007323906850240203,-0.009450986435469044,0.0074792144921519916,0.013708584847919697,0.0
--246931200,0.5482523441314697,0.5482523441314697,0.5440671726706681,0.5482523441314697,216237,0,0.004185171460801662,0.0,0.0071914891445769135,0.013014298898837685,0.007632949589417404
--246844800,0.5524371266365051,0.5524371266365051,0.5461594693360033,0.5482519581223654,130125,0,0.006277657300501782,0.007632949589417404,0.006486732232712842,0.013191812373258895,0.005682128990067925
--246758400,0.5555761456489563,0.5597613163162833,0.5524374108545964,0.5524374108545964,310003,0,0.00732390546168693,0.005682128990067925,0.007263607062114778,0.013324226683990305,0.00188284094628699
--246672000,0.5566222071647644,0.5597610934626734,0.5545296799466377,0.5555759244615572,177965,0,0.005231413516035732,0.00188284094628699,0.006648482833284379,0.013357353744113301,0.015037632631373699
--246412800,0.5649924874305725,0.5649924874305725,0.5587147918528457,0.5587147918528457,176051,0,0.006277695577726838,0.015037632631373699,0.005860594501200982,0.013900053351495783,0.02962970464925574
--246326400,0.5817330479621887,0.5827793306972883,0.5670850514825064,0.5670850514825064,325312,0,0.015694279214781837,0.02962970464925574,0.010970113918988823,0.015380617774882345,0.0035968749964778723
--246240000,0.5838254690170288,0.5859181104830491,0.5827792246605822,0.5827792246605822,340621,0,0.003138885822466908,0.0035968749964778723,0.011504775802493528,0.014971372991844764,0.00358439222759932
--246153600,0.585918128490448,0.5869643728790495,0.5848718841018464,0.5848718841018464,137779,0,0.0020924887772030276,0.00358439222759932,0.011789472148046317,0.013081005488256562,-0.0035715902472768235
--246067200,0.5838254690170288,0.5869643548394957,0.5838254690170288,0.5859181104830491,135866,0,0.003138885822466908,-0.0035715902472768235,0.013004397390035723,0.013166182069436318,-0.0017916346008756134
--245808000,0.5827794671058655,0.5859183542341581,0.5817331841258672,0.583825711897566,162656,0,0.004185170108290892,-0.0017916346008756134,0.013433901404927364,0.013192376970354362,-0.0017955662523139182
--245721600,0.5817330479621887,0.5848719725440648,0.5817330479621887,0.5827793306972883,225805,0,0.0031389245818760347,-0.0017955662523139182,0.0033531904937791064,0.012649029920169058,0.005396285869235129
--245635200,0.5848722457885742,0.5859184908241768,0.5806870747046312,0.5817333197402338,135866,0,0.005231416119545584,0.005396285869235129,0.0038889003581417717,0.00922263877149125,-0.00536732226394665
--245548800,0.5817330479621887,0.5859182170908755,0.5785941615686013,0.5848719725440648,195187,0,0.00732405552227422,-0.00536732226394665,0.0040911927065744496,0.009280589431861764,0.0017987961099035221
--245462400,0.5827794671058655,0.583825711897566,0.5775480140175763,0.5817331841258672,114816,0,0.006277697879989774,0.0017987961099035221,0.004092022818804176,0.008928511197838702,-0.0017955662523139182
--245203200,0.5817330479621887,0.5848719725440648,0.5796405588685671,0.5827793306972883,130125,0,0.00523141367549762,-0.0017955662523139182,0.004092369012154247,0.008872529517441637,-0.005394851422576563
--245116800,0.5785946846008301,0.5817335738318792,0.5754559099347509,0.5817335738318792,135866,0,0.0062776638971282495,-0.005394851422576563,0.004683497007369334,0.008763573302749999,0.0054241137101420644
--245030400,0.5817330479621887,0.5817330479621887,0.576501634286691,0.5785941615686013,88026,0,0.005231413675497731,0.0054241137101420644,0.004693112582004208,0.008634878904244298,0.005396285869235129
--244944000,0.5848722457885742,0.5859184908241768,0.5827796029641429,0.5827796029641429,166483,0,0.003138887860033912,0.005396285869235129,0.004696028429968295,0.008553220530207835,0.0
--244857600,0.5848722457885742,0.5848722457885742,0.5806870747046312,0.5848722457885742,166483,0,0.004185171083943007,0.0,0.004696648726374026,0.008361136919880502,-0.0017897528547179409
--244598400,0.5838254690170288,0.5848718661266026,0.5806866977594072,0.5848718661266026,116730,0,0.004185168367195402,-0.0017897528547179409,0.004695869099005431,0.007935227499850999,-0.021505332433006896
--244512000,0.5712701082229614,0.5817329335155637,0.563946168706421,0.5817329335155637,176051,0,0.017786764809142652,-0.021505332433006896,0.011102926329956405,0.009658437607174246,-0.010988883720726372
--244425600,0.5649924874305725,0.5712701448200158,0.5649924874305725,0.5712701448200158,168397,0,0.006277657389443303,-0.010988883720726372,0.010590778506735371,0.009989161150624306,0.014815590799072575
--244339200,0.573363184928894,0.5744096212844727,0.5566226854804607,0.5649930306754783,223891,0,0.017786935804012027,0.014815590799072575,0.013510517055868336,0.010406296732222054,0.003649173683535345
--244252800,0.5754554867744446,0.5806869013320748,0.5691778274935834,0.5733628063865068,114816,0,0.011509073838491424,0.003649173683535345,0.013867289359269473,0.010416063172059955,-0.007273157781400474
--243993600,0.5712701082229614,0.5806866891745847,0.5712701082229614,0.5754552765282835,168397,0,0.009416580951623232,-0.007273157781400474,0.0139480435724489,0.010104355996341553,0.01648483846849058
--243907200,0.580687403678894,0.5817336493072214,0.5691783198840102,0.571270811140665,160742,0,0.012555329423211137,0.01648483846849058,0.01247525096689991,0.008400658621466328,-0.01081210857621362
--243820800,0.5744089484214783,0.5859180163766968,0.5744089484214783,0.5806866044932903,116730,0,0.011509067955218555,-0.01081210857621362,0.012424647327587503,0.008683951163698352,0.010930288037241498
--243734400,0.580687403678894,0.5848725771338448,0.5744097389672884,0.5744097389672884,221978,0,0.010462838166556354,0.010930288037241498,0.0116259087453222,0.009026706471258712,-0.0036038651171107494
--243648000,0.5785946846008301,0.5796410828467085,0.5744096644421656,0.5796410828467085,145434,0,0.005231418404542887,-0.0036038651171107494,0.011910626052495876,0.009027304385969591,-0.005425555937402349
--243388800,0.5754554867744446,0.5817331460553057,0.5733628063865068,0.5785942591324323,103334,0,0.008370339668798854,-0.005425555937402349,0.011608998824837517,0.009092885146326108,0.0036367342580034823
--243302400,0.5775482654571533,0.5806871921402967,0.5733630935268192,0.5754557749627721,110989,0,0.007324098613477492,0.0036367342580034823,0.008462551919929836,0.009136098974261254,-0.014492986951440523
--243216000,0.5691778659820557,0.5785942982576547,0.5691778659820557,0.5775480152753778,149261,0,0.009416432275599074,-0.014492986951440523,0.009606357501437823,0.009553913364758332,0.011029277783944691
--243129600,0.5754554867744446,0.5754554867744446,0.5702240722168143,0.5702240722168143,101421,0,0.005231414557630321,0.011029277783944691,0.009639175618165392,0.009885682476104612,0.0
--242784000,0.5754554867744446,0.5806869013320748,0.5733628063865068,0.5754554867744446,141606,0,0.0073240949455679605,0.0,0.009602555863463428,0.009871880139290837,-0.0018186260745073346
--242697600,0.5744089484214783,0.5785939633633764,0.5723162691032887,0.5754551926098839,97594,0,0.0062776942600877295,-0.0018186260745073346,0.009323133533750165,0.009872030218114603,-0.005464469533670413
--242611200,0.5712701082229614,0.5765015208692625,0.5670849399176392,0.5744090321873045,199014,0,0.009416580951623343,-0.005464469533670413,0.00924338553664245,0.009873826836544567,-0.016483795097869236
--242524800,0.5618534088134766,0.5681312172303055,0.5576683941436231,0.5681312172303055,166483,0,0.010462823086682338,-0.016483795097869236,0.009925710815309182,0.010374014917640654,-0.0018611685388680854
--242438400,0.5608077049255371,0.5670853666366164,0.5566225334709676,0.561853950053792,191360,0,0.010462833165648777,-0.0018611685388680854,0.0066518379623553125,0.010239306503671221,-0.05223996874412784
--242179200,0.531511127948761,0.5639458691257686,0.531511127948761,0.5608069850171521,621920,0,0.03243474117700762,-0.05223996874412784,0.021358111193694542,0.015166689907996233,0.025591220279464144
--242092800,0.5451131463050842,0.5482520710727211,0.5273263769440639,0.5315115463204282,359757,0,0.02092569412865719,0.025591220279464144,0.02819467656951823,0.016606896152856442,0.028791377949393793
--242006400,0.5608077049255371,0.5629003479352868,0.5555762501544027,0.5555762501544027,273645,0,0.0073240977808840535,0.028791377949393793,0.03329994240523661,0.01748846908465876,0.01492518912098384
--241920000,0.5691778659820557,0.5765017704813986,0.5576687531770094,0.5608075257472447,199014,0,0.018833017304389243,0.01492518912098384,0.03313927093110399,0.017651261379167997,-0.007353375458937994
--241833600,0.5649924874305725,0.5649924874305725,0.5545296232794107,0.5649924874305725,110989,0,0.010462864151161821,-0.007353375458937994,0.033432123974596675,0.017400510746506333,-0.011111402984343988
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WebServer/AIPython/features.py

@@ -1,4 +1,5 @@
 import pandas as pd
+import numpy as np
 
 def MakeFeatures(df, i):
     # Remove the ticker column
@@ -23,8 +24,39 @@ def MakeFeatures(df, i):
     # Add feature for volitility last 20
     df['Volatility_20'] = df['Return'].transform(lambda x: x.rolling(20).std())
 
-    # This is our training metric
-    df['Target_Close_Tomorrow'] = df['Close'].shift(-1).pct_change()
+    # RSI (Relative Strength Index)
+    delta = df['Close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    df['RSI'] = 100 - (100 / (1 + rs))
+
+    # Moving Average Crossover (Golden/Death Cross logic)
+    df['Moving_Average_5'] = df['Close'].rolling(window=5).mean()
+    df['Moving_Average_20'] = df['Close'].rolling(window=20).mean()
+    # if short term > long term (bullish), else 0
+    df['Trend_Signal'] = (df['Moving_Average_5'] > df['Moving_Average_20']).astype(int)
+
+    # Distance from MA (How overextended are we?)
+    df['Dist_From_MA20'] = (df['Close'] / df['Moving_Average_20']) - 1
+
+    # Bollinger Band Position (Where are we relative to volatility?)
+    std_20 = df['Close'].rolling(20).std()
+    upper_band = df['Moving_Average_20'] + (std_20 * 2)
+    lower_band = df['Moving_Average_20'] - (std_20 * 2)
+    df['BB_Pos'] = (df['Close'] - lower_band) / (upper_band - lower_band)
+
+    # Log Returns (Better for AI than pct_change for statistical normality)
+    df['Log_Return'] = np.log(df['Close'] / df['Close'].shift(1))
+
+    # 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