投资组合

信息

We are currently working on this concept 指南.

The 投资组合 serves as the central hub for managing and tracking all 头寸 across active 策略 for the trading 节点 or backtest. It consolidates position 数据 from multiple 金融工具, providing a unified view of your holdings, risk exposure, and overall 性能. Explore this section to understand how NautilusTrader aggregates and updates 投资组合 state to 支持 effective trading and 风险管理.

投资组合 Statistics

There are a variety of built-in portfolio statistics which are used to analyse a trading portfolios 性能 for both backtests and 实时交易.

The statistics are generally categorized as follows.

PnLs based statistics (per currency)
Returns based statistics
头寸 based statistics
订单 based statistics

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

Custom Statistics

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

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

import pandas as pd
from typing import Any
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()

# Register with the portfolio analyzer
engine.portfolio.analyzer.register_statistic(stat)

:::info
See the `PortfolioAnalyzer` [API Reference](../api_reference/analysis.md#class-portfolioanalyzer) for details on available methods.
:::

提示

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

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

Backtest Analysis

Following a backtest run, a 性能 analysis will be carried out by passing realized PnLs, returns, 头寸 and 订单数据 to each registered statistic in turn, calculating their values (with a default 配置). Any output is then displayed in the tear sheet under the 投资组合性能 heading, grouped as.

Realized PnL statistics (per currency)
Returns statistics (for the entire 投资组合)
General statistics derived from position and order 数据 (for the entire 投资组合)

投资组合 Statistics​

Custom Statistics​

Backtest Analysis​

投资组合 Statistics

Custom Statistics

Backtest Analysis