Portfolio Statistics

There are a variety of built-in portfolio statistics which are used to analyse a trading portfolios performance for both backtests and live trading.

The statistics are generally categorized as follows.

• PnLs based statistics (per currency)
• Returns based statistics
• Positions based statistics
• Orders based statistics

It's also possible to call a traders PortfolioAnalyzer and calculate statistics at any arbitrary time, including during a backtest, or live trading session.

Custom Statistics

Custom portfolio statistics can be defined by inheriting from the PortfolioStatistic base class, and implementing any of the calculate_ methods.

For example, the following is the implementation for the built-in WinRate statistic:

from nautilus_trader.analysis.statistic import PortfolioStatistic


class WinRate(PortfolioStatistic):
    """
    Calculates the win rate from a realized PnLs series.
    """

    def calculate_from_realized_pnls(self, realized_pnls: pd.Series) -> Any | None:
        # Preconditions
        if realized_pnls is None or realized_pnls.empty:
            return 0.0

        # Calculate statistic
        winners = [x for x in realized_pnls if x > 0.0]
        losers = [x for x in realized_pnls if x <= 0.0]

        return len(winners) / float(max(1, (len(winners) + len(losers))))

These statistics can then be registered with a traders PortfolioAnalyzer.

stat = WinRate()

engine.portfolio.analyzer.register_statistic(stat)

tip

Ensure your statistic is robust to degenerate inputs such as None, empty series, or insufficient data.

The expectation is that you would then return None, NaN or a reasonable default.

Backtest Analysis

Following a backtest run a performance analysis will be carried out by passing realized PnLs, returns, positions and orders data to each registered statistic in turn, calculating their values (with a default configuration). Any output is then displayed in the tear sheet under the Portfolio Performance heading, grouped as.

• Realized PnL statistics (per currency)
• Returns statistics (for the entire portfolio)
• General statistics derived from position and order data (for the entire portfolio)