tests/mock_data.py

import pandas as pd
import numpy as np
import time
from typing import List, Dict

class MockDataGenerator:
    """Generates synthetic L2 Orderbook and Trade data for testing."""
    
    @staticmethod
    def generate_l2_snapshot(num_rows: int = 100, levels: int = 20) -> pd.DataFrame:
        """
        Generates a DataFrame mimicking the L2 Snapshot structure.
        Columns: ts_event, instrument_id, bids, asks
        """
        base_price = 2000.0
        data = []
        
        start_time = time.time() * 1000
        
        for i in range(num_rows):
            ts = start_time + i * 1000 # 1 sec intervals
            
            # Random Walk Price
            noise = np.random.normal(0, 1)
            mid_price = base_price + noise
            base_price = mid_price
            
            # Generate Levels
            bids = []
            asks = []
            
            for l in range(levels):
                spread = (l + 1) * 0.5
                bid_p = mid_price - spread
                ask_p = mid_price + spread
                
                bid_sz = abs(np.random.normal(10, 5)) + 1
                ask_sz = abs(np.random.normal(10, 5)) + 1
                
                bids.append([bid_p, bid_sz])
                asks.append([ask_p, ask_sz])
                
            data.append({
                "ts_event": ts,
                "instrument_id": "ETH-USD",
                "bids": bids, # List of lists format
                "asks": asks
            })
            
        return pd.DataFrame(data)

    @staticmethod
    def generate_trades(num_rows: int = 100) -> pd.DataFrame:
        """
        Generates synthetic trade data.
        Columns: time, coin, px, sz, side
        """
        base_price = 2000.0
        data = []
        start_time = time.time() * 1000
        
        for i in range(num_rows):
            ts = start_time + i * 500
            px = base_price + np.random.normal(0, 1)
            sz = abs(np.random.normal(1, 0.5))
            side = 'B' if np.random.random() > 0.5 else 'A'
            
            data.append({
                "time": ts,
                "coin": "ETH",
                "px": px,
                "sz": sz,
                "side": side
            })
            
        return pd.DataFrame(data)