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不可知风险平价：驯服已知与未知的未知

学术论文

不可知风险平价：驯服已知与未知的未知

作者Raphael Benichou; Yves Lempérière; Emmanuel Sérié; Julien Kockelkoren; Philip Seager; Jean-Philippe Bouchaud; Marc Potters

论文链接https://arxiv.org/abs/1610.08818v1

导读

投资标的：SPY、EFA、EEM、VNQ、DBC、GLD、TLT、IEF、HYG。再平衡：每月。Universe: SPY, EFA, EEM, VNQ, DBC, GLD, TLT, IEF, HYG. Parameters: rebalance=monthly.

策略概要

该策略投资于 SPY、EFA、EEM、VNQ、DBC、GLD、TLT、IEF、HYG，按 63 日波动率的倒数分配权重，使风险贡献大致相等，并按月再平衡。论文仅凭对称性构造特征风险平价，使协方差矩阵各主成分实现风险相等，并在最不可知情形下得到 Agnostic Risk Parity，以减少过度对冲带来的未知未知风险。

经济逻辑

相关证据来自“不可知风险平价：驯服已知与未知的未知”：Markowitz 样本外分散化常失败；当未来收益指示被假定为不相关且等方差时，不可知风险平价在趋势跟踪等技术策略上表现稳健。

入场与出场

入场

每月由 make_on_day 决定目标权重。当前宇宙：SPY, EFA, EEM, VNQ, DBC, GLD, TLT, IEF, HYG。请对照实验室代码中的 engine.set_target_weights。

出场

权重被设为空或该标的权重为 0 即视为出场。引擎按 5 bps 佣金 + 2 bps 滑点执行。

所需行情数据

需要 SPY, EFA, EEM, VNQ, DBC, GLD (+3 more) 至少 60 根复权收盘价，才能形成第一个有效信号。ETF 仅交易日有行情。实验室通过 backtest.data.load_daily_prices 读取 Yahoo Finance。

标的

SPY, EFA, EEM, VNQ, DBC, GLD, TLT, IEF, HYG

频率

每月 · 日频复权收盘价；信号按月再平衡（用当月最后一个有效交易日）

所需历史

每个标的至少 60 根交易日 K 线（约 3 个交易月），外加数根缓冲才能发出第一个信号。

字段

复权收盘价（Adj Close / auto_adjust Close）
交易日日期索引

凭证

回测与实验室：无需密钥
实盘下单才需要券商 / 交易所 API

API 与数据源

Quant Buffet 数据 API

实验室用 load_daily_prices 拉取 ASSETS 的复权收盘价面板，磁盘缓存后回测可复现。

提供方: backtest.data.load_daily_prices
鉴权: 回测无需密钥
接口: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /zh/docs/data-api

Yahoo Finance（yfinance）

美股 / ETF 复权 OHLCV，免费研究接口，有速率限制。

提供方: yfinance → Yahoo Finance
鉴权: 公开历史无需密钥
接口: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet 原生回测 IDE

Edit and run Quant Buffet Python for 不可知风险平价：驯服已知与未知的未知 in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →

IDE · 40 lines

"""
Quant Buffet interactive lab — 不可知风险平价：驯服已知与未知的未知
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['SPY', 'EFA', 'EEM', 'VNQ', 'DBC', 'GLD', 'TLT', 'IEF', 'HYG']
VOL_LOOKBACK = 63

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change()
    vol = rets.rolling(VOL_LOOKBACK, min_periods=VOL_LOOKBACK).std() * np.sqrt(252)
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        inv = {
            s: 1.0 / float(vol.at[dt, s])
            for s in cols
            if pd.notna(vol.at[dt, s]) and vol.at[dt, s] > 1e-8
        }
        total = sum(inv.values())
        weights = {k: v / total for k, v in inv.items()} if total > 0 else {}
        engine.set_target_weights(dt, weights)

    ready = vol.dropna(how="all").index.min() if vol.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — 不可知风险平价：驯服已知与未知的未知
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['SPY', 'EFA', 'EEM', 'VNQ', 'DBC', 'GLD', 'TLT', 'IEF', 'HYG']
VOL_LOOKBACK = 63

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change()
    vol = rets.rolling(VOL_LOOKBACK, min_periods=VOL_LOOKBACK).std() * np.sqrt(252)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        inv = {
            s: 1.0 / float(vol.at[dt, s])
            for s in cols
            if pd.notna(vol.at[dt, s]) and vol.at[dt, s] > 1e-8
        }
        total = sum(inv.values())
        weights = {k: v / total for k, v in inv.items()} if total > 0 else {}
        engine.set_target_weights(dt, weights)

ready = vol.dropna(how="all").index.min() if vol.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(200, min_periods=200).mean()
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        weights = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, weights)

    ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

4.43%

Sharpe

0.47

Max DD

-44.85%

Vol

10.47%

Sortino

0.72

Beta

0.42

Stored preview only. Run backtest to simulate every daily session from Yahoo Finance adjusted closes.

Run the backtest to load orders, then mark them on the equity chart.

Equity curve (indexed = 100)

Accent = strategy · dashed grey = buy-and-hold benchmark. Hover or tap any point for its date and values.

Drawdown

Metrics bar chart

Monthly returns

Order records

Run the backtest to load every order fill alongside performance.

Export to your platform

Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Custom / hybridAssets: SPY, EFA, EEM, VNQ, DBC, GLD, TLT, IEF, HYG

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 不可知风险平价：驯服已知与未知的未知
# Detected pattern: Custom / hybrid
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.

from AlgorithmImports import *


class QuantBuffetExport(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(100000)
        tickers = ["SPY", "EFA", "EEM", "VNQ", "DBC", "GLD", "TLT", "IEF", "HYG"]
        self.symbols = []
        for t in tickers:
            if "-" in t:  # crypto proxy e.g. BTC-USD
                self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
            else:
                self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
        self.Schedule.On(
            self.DateRules.MonthStart(self.symbols[0]),
            self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
            self.Rebalance,
        )
        # Logic: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().

    def Rebalance(self):
        # Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
        # Default: equal-weight. Port your make_on_day weights here via SetHoldings.
        w = 1.0 / len(self.symbols) if self.symbols else 0.0
        for symbol in self.symbols:
            self.SetHoldings(symbol, w)

导出代码使用目标平台的原生类与库。请在第三方 IDE 中安装依赖后运行；实盘前请自行验证。