def calculate_recent_performance(df, games=10):
    """
    Calculate the team's recent performance metrics for home and away teams efficiently, including current winning
    and losing streaks.
    :param df: DataFrame containing the game data.
    :param games: Number of recent games to calculate metrics for.
    :return: DataFrame with calculated metrics for both home and away teams.
    """
    # Prepare DataFrame for rolling calculations
    # Calculate for home games
    home_df = df.copy()
    home_df['home_wins'] = home_df['home_win']
    home_df['home_goals_scored'] = home_df['score_home']
    home_df['home_goals_conceded'] = home_df['score_away']

    home_df.sort_values(['home_team', 'game_date'], inplace=True)

    # Rolling calculate win rate, goals scored, and goals conceded for home team
    home_df['home_recent_win_rate'] = home_df.groupby('home_team')['home_wins'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())
    home_df['home_avg_goals_scored'] = home_df.groupby('home_team')['home_goals_scored'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())
    home_df['home_avg_goals_conceded'] = home_df.groupby('home_team')['home_goals_conceded'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())

    # Calculate current winning and losing streak for home teams
    home_df['home_winning_streak'] = home_df.groupby('home_team')['home_wins'].transform(lambda x: x.groupby((x != x.shift()).cumsum()).cumcount() + 1)
    home_df['home_losing_streak'] = home_df.groupby('home_team')['home_wins'].transform(lambda x: (1 - x).groupby((x != x.shift()).cumsum()).cumcount() + 1)

    # Calculate for away games
    away_df = df.copy()
    away_df['away_wins'] = away_df['home_win'].apply(lambda x: 1 if x == 0 else 0)  # Invert home_win for away team perspective
    away_df['away_goals_scored'] = away_df['score_away']
    away_df['away_goals_conceded'] = away_df['score_home']

    away_df.sort_values(['away_team', 'game_date'], inplace=True)

    # Rolling calculate win rate, goals scored, and goals conceded for away team
    away_df['away_recent_win_rate'] = away_df.groupby('away_team')['away_wins'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())
    away_df['away_avg_goals_scored'] = away_df.groupby('away_team')['away_goals_scored'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())
    away_df['away_avg_goals_conceded'] = away_df.groupby('away_team')['away_goals_conceded'].transform(lambda x: x.rolling(window=games, min_periods=1).mean())

    # Calculate current winning and losing streak for away teams
    away_df['away_winning_streak'] = away_df.groupby('away_team')['away_wins'].transform(lambda x: x.groupby((x != x.shift()).cumsum()).cumcount() + 1)
    away_df['away_losing_streak'] = away_df.groupby('away_team')['away_wins'].transform(lambda x: (1 - x).groupby((x != x.shift()).cumsum()).cumcount() + 1)

    # Merge the metrics back to the original dataframe
    df = df.merge(home_df[['game_date', 'home_team', 'home_recent_win_rate', 'home_avg_goals_scored', 'home_avg_goals_conceded', 'home_winning_streak', 'home_losing_streak']], on=['game_date', 'home_team'], how='left')
    df = df.merge(away_df[['game_date', 'away_team', 'away_recent_win_rate', 'away_avg_goals_scored', 'away_avg_goals_conceded', 'away_winning_streak', 'away_losing_streak']], on=['game_date', 'away_team'], how='left')

    return df