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Make slightly more memory efficient

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This commit is contained in:
Derek Holloway
2026-02-26 21:39:57 -08:00
parent 7567496e1c
commit ff67f21858
1 changed files with 51 additions and 57 deletions
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WebServer/AIPython/features.py
+50 -56
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@@ -1,14 +1,46 @@
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import numpy as np
def MakeFeatures(df, i):
# Remove the ticker column
df.columns = df.columns.get_level_values(0)
def MakeFeatures(df):
# Convert all F64 to F32 to save ram
df = df.astype({col: 'float32' for col in df.select_dtypes('float64').columns})
# Make sure Date is a number object
df = df.reset_index()
df['Date'] = pd.to_numeric(pd.to_datetime(df['Date']))
# Candle Wick's
df['Spread'] = (df['High'] - df['Low']) / df['Close'] # in percentage of price
candle_top = df[['Open', 'Close']].max(axis=1)
df['Body_Size'] = (df['Close'] - df['Open']).abs() / (df['High'] - df['Low'])
df['Upper_Shadow'] = (df['High'] - candle_top) / (df['High'] - df['Low'])
# Remove Unused Symbols to save ram
df.drop('Open', axis=1, inplace=True)
df.drop('High', axis=1, inplace=True)
df.drop('Low', axis=1, inplace=True)
# Is volume 2x higher than the 20-day average?
df['Vol_Intensity'] = df['Volume'] / df['Volume'].rolling(20).mean()
# Volume Change
df['Volume_Chg'] = df['Volume'].pct_change()
# Remove Unused Symbols to save ram
df.drop('Volume', axis=1, inplace=True)
# Moving Average Crossover (Golden/Death Cross logic)
df['Moving_Average_5'] = df['Close'].rolling(window=20).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)
# Remove Unused Symbols to save ram
df.drop('Moving_Average_5', axis=1, inplace=True)
df.drop('Moving_Average_20', axis=1, inplace=True)
# Add feature for Returns
df['Return'] = df['Close'].pct_change()
@@ -27,31 +59,6 @@ def MakeFeatures(df, i):
rs = gain / loss
df['RSI'] = 100 - (100 / (1 + rs))
# Moving Average Crossover (Golden/Death Cross logic)
df['Moving_Average_5'] = df['Close'].rolling(window=20).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)
# Candle Wick's
df['Spread'] = (df['High'] - df['Low']) / df['Close'] # in percentage of price
candle_top = df[['Open', 'Close']].max(axis=1)
df['Body_Size'] = (df['Close'] - df['Open']).abs() / (df['High'] - df['Low'])
df['Upper_Shadow'] = (df['High'] - candle_top) / (df['High'] - df['Low'])
# Is volume 2x higher than the 20-day average?
df['Vol_Intensity'] = df['Volume'] / df['Volume'].rolling(20).mean()
# Volume Change
df['Volume_Chg'] = df['Volume'].pct_change()
# Return lagged
for lag in range(1, 4):
df[f'Return_Lag_{lag}'] = df['Return'].shift(lag)
@@ -61,14 +68,17 @@ def MakeFeatures(df, i):
df['Target_Close'] = np.log(df['Close'].shift(-5) / df['Close'])
df['Target_Close'] = np.log(df['Close'].shift(-5) / df['Close']) / df['Volatility_20']
# Remove noise data from the model to really focus on percent changes
df.drop('Open', axis=1)
df.drop('High', axis=1)
df.drop('Low', axis=1)
df.drop('Close', axis=1)
df.drop('Volume', axis=1)
df.drop('Moving_Average_5', axis=1)
df.drop('Moving_Average_20', axis=1)
# Remove Unused Symbols to save ram
df.drop('Close', axis=1, inplace=True)
# Drop rows with null values
df.dropna(inplace=True)
# Replace Infinity with 0 -> This fixes the AI mental breakdown
df = df.replace([np.inf, -np.inf], 0)
# 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 with new features
return df
@@ -90,19 +100,3 @@ def Prepare(df):
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
df.set_index('Date', inplace=True)
# Drop rows with null values
df.dropna(inplace=True)
# Replace Infinity with 0 -> This fixes the AI mental breakdown
df = df.replace([np.inf, -np.inf], 0)
# 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