Work on optimizing the model

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