# feature_engineering.py
import numpy as np

def calculate_wallet_concentration(holder_data):
    """
    holder_data: list of (wallet, %_owned)
    Returns: concentration score (0 to 1)
    """
    top10 = sorted(holder_data, key=lambda x: x[1], reverse=True)[:10]
    total_top10 = sum(x[1] for x in top10)
    return total_top10 / 100  # As a ratio

def calculate_volume_acceleration(volume_timeseries):
    """
    volume_timeseries: list of (timestamp, volume)
    Returns: acceleration value
    """
    volumes = [v for _, v in sorted(volume_timeseries)]
    if len(volumes) < 3:
        return 0
    v1, v2, v3 = volumes[-3:]
    accel = ((v3 - v2) - (v2 - v1)) / max(v1, 1)
    return accel

def simulate_slippage(liquidity, order_size):
    """
    liquidity: USD in pool
    order_size: USD value to simulate buy
    Returns: simulated slippage percentage
    """
    if liquidity == 0:
        return 1.0  # 100% slippage
    slippage = min(order_size / liquidity, 1.0)
    return slippage