From bb1c508c998aee5a03996aba0ffa05f3270af88d Mon Sep 17 00:00:00 2001
From: derek holloway <dholloway543@gmail.com>
Date: Wed, 18 Feb 2026 15:17:10 -0800
Subject: [PATCH] Work on optimizing the model

---
 Todo.txt                                   |   9 +-
 WebServer/AIPython/ai-predictor.py         |   4 +-
 WebServer/AIPython/ai-trainer.py           |  19 +-
 WebServer/AIPython/data/model.keras        | Bin 88136 -> 92792 bytes
 WebServer/AIPython/data/stocks.preview.csv | 402 ++++++++++-----------
 WebServer/AIPython/features.py             |  36 +-
 6 files changed, 250 insertions(+), 220 deletions(-)

diff --git a/Todo.txt b/Todo.txt
index 5faa27dc..e23016d4 100644
--- a/Todo.txt
+++ b/Todo.txt
@@ -1,7 +1,2 @@
-Raw prices are useless to the AI because $500 today isn't the same as $500 in 1990. Use relative indicators:
-    RSI (Relative Strength Index): Is it overbought?
-    Moving Average Crossovers: Is the trend up?
-    Distance from MA: (Close / Moving_Average_20) - 1
-    Volume Change: df['Volume'].pct_change()
-
-Fix AI returning the Current market price as the prediction
\ No newline at end of file
+Fix AI returning the Current market price as the prediction
+During training it keeps crashing with NAN
\ No newline at end of file
diff --git a/WebServer/AIPython/ai-predictor.py b/WebServer/AIPython/ai-predictor.py
index df229552..5573d52a 100644
--- a/WebServer/AIPython/ai-predictor.py
+++ b/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)
\ No newline at end of file
diff --git a/WebServer/AIPython/ai-trainer.py b/WebServer/AIPython/ai-trainer.py
index c2d9e76e..ebc90d83 100644
--- a/WebServer/AIPython/ai-trainer.py
+++ b/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"))
\ No newline at end of file
diff --git a/WebServer/AIPython/data/model.keras b/WebServer/AIPython/data/model.keras
index 99df110e102201d53dd4e9b4eeaa5123a1926c21..e8013ca106103f38994751572e92e405111dac03 100644
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diff --git a/WebServer/AIPython/data/stocks.preview.csv b/WebServer/AIPython/data/stocks.preview.csv
index d7be2b20..8e783f28 100644
--- a/WebServer/AIPython/data/stocks.preview.csv
+++ b/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
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diff --git a/WebServer/AIPython/features.py b/WebServer/AIPython/features.py
index e454e2d3..bf16789d 100644
--- a/WebServer/AIPython/features.py
+++ b/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