import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random

def generate_mock_order_book_data(
    mid_price: float = 100.0,
    spread: float = 0.05,
    depth: int = 10,
    qty_range: tuple = (10, 100)
) -> dict:
    """
    Generates mock Level 2 order book data for demonstration.

    Args:
        mid_price (float): The central price around which bids and asks are generated.
        spread (float): The difference between the best ask and best bid.
        depth (int): The number of price levels to generate for bids and asks.
        qty_range (tuple): A tuple (min_qty, max_qty) for random quantities.

    Returns:
        dict: A dictionary containing 'bids' and 'asks' DataFrames.
              Each DataFrame has 'Price' and 'Quantity' columns.
    """
    best_ask = mid_price + spread / 2
    best_bid = mid_price - spread / 2

    # Generate Ask orders
    ask_prices = [best_ask + i * 0.01 for i in range(depth)]
    ask_quantities = [random.randint(qty_range[0], qty_range[1]) for _ in range(depth)]
    asks_df = pd.DataFrame({'Price': ask_prices, 'Quantity': ask_quantities})

    # Generate Bid orders
    bid_prices = [best_bid - i * 0.01 for i in range(depth)]
    bid_quantities = [random.randint(qty_range[0], qty_range[1]) for _ in range(depth)]
    bids_df = pd.DataFrame({'Price': bid_prices, 'Quantity': bid_quantities})

    return {"bids": bids_df, "asks": asks_df}

# Generate and display mock data
order_book = generate_mock_order_book_data(mid_price=1500.00, spread=0.10, depth=15)

print("### Mock Bid Orders (Buyers wanting to buy) ###")
display(order_book['bids'].head())

print("\n### Mock Ask Orders (Sellers wanting to sell) ###")
display(order_book['asks'].head())

def calculate_spread(order_book: dict) -> float:
    """
    Calculates the bid-ask spread from the order book.

    Args:
        order_book (dict): A dictionary containing 'bids' and 'asks' DataFrames.

    Returns:
        float: The bid-ask spread.
    """
    best_bid = order_book['bids']['Price'].max()
    best_ask = order_book['asks']['Price'].min()
    spread = best_ask - best_bid
    return spread

def calculate_mid_price(order_book: dict) -> float:
    """
    Calculates the mid-price from the order book.

    Args:
        order_book (dict): A dictionary containing 'bids' and 'asks' DataFrames.

    Returns:
        float: The mid-price.
    """
    best_bid = order_book['bids']['Price'].max()
    best_ask = order_book['asks']['Price'].min()
    mid_price = (best_bid + best_ask) / 2
    return mid_price

# Demonstrate calculations with mock data
mock_spread = calculate_spread(order_book)
mock_mid_price = calculate_mid_price(order_book)

print(f"Best Bid Price: {order_book['bids']['Price'].max():.2f}")
print(f"Best Ask Price: {order_book['asks']['Price'].min():.2f}")
print(f"Calculated Bid-Ask Spread: {mock_spread:.2f}")
print(f"Calculated Mid-Price: {mock_mid_price:.2f}")

def calculate_order_book_depth(df: pd.DataFrame) -> pd.DataFrame:
    """
    Calculates the cumulative quantity for an order book side (bids or asks).

    Args:
        df (pd.DataFrame): DataFrame representing either bids or asks, with 'Price' and 'Quantity' columns.

    Returns:
        pd.DataFrame: A DataFrame with 'Price', 'Quantity', and 'Cumulative Quantity' columns,
                      sorted appropriately for the order book side.
    """
    df_sorted = df.sort_values(by='Price', ascending=True if 'asks' in df.columns else False)
    df_sorted['Cumulative Quantity'] = df_sorted['Quantity'].cumsum()
    return df_sorted

# Calculate and display depth for bids and asks
bids_with_depth = calculate_order_book_depth(order_book['bids'].sort_values(by='Price', ascending=False).reset_index(drop=True))
asks_with_depth = calculate_order_book_depth(order_book['asks'].sort_values(by='Price', ascending=True).reset_index(drop=True))

print("### Bid Orders with Cumulative Depth ###")
display(bids_with_depth.head())

print("\n### Ask Orders with Cumulative Depth ###")
display(asks_with_depth.head())

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6), sharey=True)

# Plot Bids
ax1.barh(order_book['bids']['Price'], order_book['bids']['Quantity'], color='green', alpha=0.7)
ax1.set_title('Bid Orders (Buyers)', fontsize=14)
ax1.set_xlabel('Quantity', fontsize=12)
ax1.set_ylabel('Price', fontsize=12)
ax1.invert_xaxis() # Bids typically shown decreasing quantity from best price
ax1.tick_params(axis='x', rotation=45)
ax1.grid(axis='x', linestyle='--', alpha=0.6)

# Highlight Best Bid
best_bid_price = order_book['bids']['Price'].max()
ax1.axhline(best_bid_price, color='darkgreen', linestyle='--', linewidth=1.5, label=f'Best Bid: {best_bid_price:.2f}')
ax1.legend()

# Plot Asks
ax2.barh(order_book['asks']['Price'], order_book['asks']['Quantity'], color='red', alpha=0.7)
ax2.set_title('Ask Orders (Sellers)', fontsize=14)
ax2.set_xlabel('Quantity', fontsize=12)
ax2.tick_params(axis='x', rotation=45)
ax2.grid(axis='x', linestyle='--', alpha=0.6)

# Highlight Best Ask
best_ask_price = order_book['asks']['Price'].min()
ax2.axhline(best_ask_price, color='darkred', linestyle='--', linewidth=1.5, label=f'Best Ask: {best_ask_price:.2f}')
ax2.legend()

fig.suptitle(f'Level 2 Order Book Profile (Mid-Price: {mock_mid_price:.2f}, Spread: {mock_spread:.2f})', fontsize=16)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()

plt.figure(figsize=(12, 7))

# Plot Cumulative Bids
plt.plot(bids_with_depth['Price'], bids_with_depth['Cumulative Quantity'], drawstyle='steps-post', color='green', label='Cumulative Bids')
# Plot Cumulative Asks
plt.plot(asks_with_depth['Price'], asks_with_depth['Cumulative Quantity'], drawstyle='steps-post', color='red', label='Cumulative Asks')

plt.axvline(mock_mid_price, color='purple', linestyle=':', label=f'Mid-Price: {mock_mid_price:.2f}')
plt.axvline(order_book['bids']['Price'].max(), color='darkgreen', linestyle='--', label=f'Best Bid: {order_book['bids']['Price'].max():.2f}')
plt.axvline(order_book['asks']['Price'].min(), color='darkred', linestyle='--', label=f'Best Ask: {order_book['asks']['Price'].min():.2f}')

plt.title('Cumulative Order Book Depth', fontsize=16)
plt.xlabel('Price', fontsize=12)
plt.ylabel('Cumulative Quantity', fontsize=12)
plt.legend(fontsize=10)
plt.grid(True, linestyle='--', alpha=0.6)
plt.tight_layout()
plt.show()