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# scripts/predict_tomorrow.py
import pandas as pd
import numpy as np
import os
import sys
from datetime import datetime, timedelta
from stable_baselines3 import SAC
# --- Imports ---
try:
# Ensure we can find local scripts
sys.path.append(os.getcwd())
except:
pass
from fetch_market_data import fetch_market_data, ASSETS, FRED_IDS
from llm_analysis_rag import analyze_agent_decision
# --- Configuration ---
MODEL_PATH = "checkpoints/sac_portfolio_model.zip"
WINDOW_SIZE = 30
MACRO_COLS = list(FRED_IDS.values()) # ['Federal Funds Rate', 'CPI', 'VIX']
def get_latest_data_window(window_size=30):
"""
Fetches live data and returns the last 'window_size' rows.
"""
print("--- 🔄 Fetching Real-Time Data for Prediction ---")
# Fetch a buffer to ensure we have enough data after cleaning
lookback_days = window_size + 100
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=lookback_days)).strftime('%Y-%m-%d')
# We don't strictly need to save to a file for prediction, so filename=None
df = fetch_market_data(start_date, end_date, filename=None)
if df is None or len(df) < window_size:
print(f"❌ Not enough data fetched. Got {len(df) if df is not None else 0} rows, needed {window_size}.")
return None
# Return exactly the last N rows (Observation Window)
return df.iloc[-window_size:].copy()
def prepare_observation(data_window):
"""
Normalizes data: Window / First_Row_of_Window
"""
# Extract specific columns to guarantee order
price_data = data_window[ASSETS].values
macro_data = data_window[MACRO_COLS].values
# Normalize
norm_prices = price_data / (price_data[0] + 1e-8)
norm_macro = macro_data / (macro_data[0] + 1e-8)
# Concatenate and flatten for MLP input
obs = np.concatenate([norm_prices, norm_macro], axis=1)
return obs.flatten().astype(np.float32)
def get_allocations(action):
"""Applies Softmax to convert raw action to weights"""
action = np.asarray(action).flatten()
exp_action = np.exp(action)
return exp_action / np.sum(exp_action)
def main():
print(f"🚀 Prediction Job: {datetime.now().strftime('%Y-%m-%d')}")
# 1. Get Data
data_window = get_latest_data_window(WINDOW_SIZE)
if data_window is None: return
# 2. Prepare Obs
obs = prepare_observation(data_window)
# 3. Load MLP Model
if not os.path.exists(MODEL_PATH):
print(f"❌ Model not found at {MODEL_PATH}")
return
print(f"Loading MLP SAC model...")
model = SAC.load(MODEL_PATH)
# 4. Predict
action, _ = model.predict(obs, deterministic=True)
weights = get_allocations(action)
# 5. Format Allocations (THE FIX IS HERE)
allocations = {}
for i, asset in enumerate(ASSETS):
allocations[asset] = float(weights[i]) # Explicit float() cast
allocations['Cash'] = float(weights[-1]) # Explicit float() cast
# 6. Output Results
print("\n" + "="*40)
print(f"🤖 SAC MLP MODEL RECOMMENDATION")
print("="*40)
for asset, weight in allocations.items():
print(f"{asset:<10} : {weight:6.2%}")
print("="*40)
# 7. AI Risk Analyst
print("\n🧠 Running AI Risk Analysis...")
# Now this will work because all numbers are standard floats
analysis = analyze_agent_decision(data_window, allocations)
if isinstance(analysis, dict):
print(f"\nStrategy: {analysis.get('strategy_summary')}")
print(f"Risk Level: {analysis.get('risk_level')}")
print(f"Justification: {analysis.get('justification')}")
if analysis.get('risk_level') == 'High':
print("\n⛔ BLOCKING TRADE: High Risk detected by AI Guardrail.")
else:
print("\n✅ TRADE APPROVED.")
else:
print(analysis)
if __name__ == "__main__":
main() |