from pt_strategy.trading_strategy import PtResearchStrategy


def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None: 
    # Plot raw price data
    import matplotlib.pyplot as plt
    # Set plotting style
    import seaborn as sns

    pair = strategy.trading_pair_
    SYMBOL_A = pair.symbol_a_
    SYMBOL_B = pair.symbol_b_
    TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"

    plt.style.use('seaborn-v0_8')
    sns.set_palette("husl")
    plt.rcParams['figure.figsize'] = (15, 10)

    # Get column names for the trading pair
    colname_a, colname_b = pair.colnames()
    price_data = strategy.pt_mkt_data_.market_data_df_.copy()

    # Create separate subplots for better visibility
    fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))

    # Plot SYMBOL_A
    price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, 
                        label=f'{SYMBOL_A}', linewidth=1, color='blue')
    price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')
    price_axes[0].set_ylabel(f'{SYMBOL_A} Price')
    price_axes[0].legend()
    price_axes[0].grid(True)

    # Plot SYMBOL_B
    price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, 
                        label=f'{SYMBOL_B}', linewidth=1, color='red')
    price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')
    price_axes[1].set_ylabel(f'{SYMBOL_B} Price')
    price_axes[1].set_xlabel('Time')
    price_axes[1].legend()
    price_axes[1].grid(True)

    plt.tight_layout()
    plt.show()
        

    # Plot individual prices
    fig, axes = plt.subplots(2, 1, figsize=(18, 12))

    # Normalized prices for comparison
    norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]
    norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]

    axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)
    axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)
    axes[0].set_title(f'Normalized Price Comparison (Base = 1.0)  ({TRD_DATE})')
    axes[0].set_ylabel('Normalized Price')
    axes[0].legend()
    axes[0].grid(True)

    # Price ratio
    price_ratio = price_data[colname_a] / price_data[colname_b]
    axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)
    axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')
    axes[1].set_ylabel('Ratio')
    axes[1].set_xlabel('Time')
    axes[1].legend()
    axes[1].grid(True)

    plt.tight_layout()
    plt.show()

    # Print basic statistics
    print(f"\nPrice Statistics:")
    print(f"  {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}")
    print(f"  {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}")
    print(f"  Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}")
    print(f"  Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}")