"""Real-time inference for Indian Stock Market Analyzer"""
import torch, json, pickle, numpy as np, pandas as pd, pywt

def wavelet_denoise(s, wavelet="db4"):
    if len(s) < 8: return s
    s = np.array(s, dtype=np.float64)
    m = ~np.isnan(s)
    if m.sum() < 8: return s
    c = s[m]
    level = min(pywt.dwt_max_level(len(c), wavelet), 4)
    if level < 1: return s
    co = pywt.wavedec(c, wavelet, level=level)
    sig = np.median(np.abs(co[-1])) / 0.6745
    th = sig * np.sqrt(2 * np.log(len(c)))
    dn = [co[0]] + [pywt.threshold(x, th, "soft") for x in co[1:]]
    r = s.copy()
    r[m] = pywt.waverec(dn, wavelet)[:len(c)]
    return r

def analyze_stock(model, scaler, ohlcv_df, feature_cols, lookback=30):
    """
    ohlcv_df: DataFrame with columns [open, high, low, close, volume]
              at least `lookback` recent daily rows
    Returns: dict with signal, confidence, predicted_return
    """
    # ... compute features, scale, predict
    pass

print("Usage: Load model, scaler, and call analyze_stock() with daily OHLCV data")