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1import joblib
2import pandas as pd
3import numpy as np
4
5# Load V4 quantum ensemble
6ensemble = joblib.load('trading_model_v4_quantum_daily.pkl')
7
8# Load quantum feature processor
9scalers = joblib.load('quantum_scaler_v4_daily.pkl')
10pca = joblib.load('quantum_pca_v4_daily.pkl')
11
12with open('quantum_features_v4_daily.json', 'r') as f:
13 feature_cols = json.load(f)
14
15# Prepare your data with quantum feature engineering
16# features = quantum_feature_engineer(your_data)[feature_cols]
17# features_scaled = scalers['robust'].transform(features)
18# features_pca = pca.transform(features_scaled)
19# final_features = np.hstack([features_scaled, features_pca])
20
21# Make quantum prediction
22prediction, probability = ensemble.predict_ensemble(final_features)
23
24# prediction: 0 = Down, 1 = Up (quantum state)
25# probability: Quantum probability amplitudexgboost>=1.7.0
lightgbm>=3.3.0
tensorflow>=2.10.0
pandas>=1.5.0
numpy>=1.21.0
scikit-learn>=1.1.0
ta>=0.10.0
yfinance>=0.2.0
joblib>=1.2.0
scipy>=1.7.0
pywavelets>=1.3.0.keras files, try matching the exact TF/Keras version used during training.1import joblib
2import json
3import numpy as np
4from inference_v4 import V4Predictor
5
6# Load tree-only (optional)
7trees = joblib.load('trading_model_v4_quantum_daily.pkl')
8
9# Use the combined predictor which will attempt to load the Keras artifacts
10# Make sure you have tensorflow installed in the same environment
11pred = V4Predictor('daily', use_keras=True, weights={'trees':0.6,'neural':0.4})
12
13# Prepare final features using the provided quantum feature pipeline
14# (See quantum_features_v4_daily.json and the scalers/pca pickles)
15
16# X: numpy array shape (n_samples, n_features)
17proba = pred.predict_proba(X)V4Predictor will look for a folder named models_v4_fresh/trading_model_v4_quantum_daily_keras/ locally. If present it will attempt to load transformer.keras and lstm_attention.keras.git-lfs configured when cloning.run_backtest_with_nn.py in the repository root.