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交易VIX交易所交易基金（VIX ETFs）

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学术论文

Easy Volatility Investing

作者简单波动率投资 [点击查看论文]

机构

Double-Digit Numerics

论文链接http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2255327

策略概要

该策略投资于4种波动率交易所交易票据（ETNs）（XIV、VXX、ZIV、VXZ），每日根据其83天动量对其进行排名。投资者持有过去83天表现最高的正表现交易所交易票据（ETN），前提是该表现为正。如果没有任何交易所交易票据（ETN）具有正表现，则投资组合保持未分配状态。交易信号每日检查，投资组合相应地进行再平衡，以确保与基于动量的策略保持一致。这种方法旨在通过优先考虑表现最佳的资产，同时避免那些具有负动量的资产，来捕捉波动率交易所交易票据（ETNs）的趋势，从而确保一个纪律性和动态性的投资过程。

II. 策略合理性

波动率溢价由时变投资者效用理论解释，该理论指出，投资者重视在危机期间表现良好的策略。长期波动率敞口提供危机保护，但带有负风险溢价。相反，做空波动率提供正风险溢价，但由于其高偏度，使投资者在危机期间面临极端损失。动量过滤器策略通过根据当前趋势在多空波动率敞口之间动态切换来解决这个问题，使投资者能够在危机期间获得波动率溢价，同时减轻严重负回报的风险。

入场与出场

入场

每月计算 252 日收益。只持有收益为正的标的（SVXY, BIL），等权。

出场

滚动收益转负则卖出该标的；全部为负则空仓，避免逆势持仓。

所需行情数据

需要 SVXY, BIL 至少 252 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

SVXY, BIL

频率

每日 · 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 252 根交易日 K 线（约 12 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 交易VIX交易所交易基金（VIX ETFs） 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 · 50 lines

"""
Quant Buffet interactive lab — 交易vix交易所交易基金（vix-etfs）
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 = ['SVXY', 'BIL']
LOOKBACK = 252
CASH = 'BIL'

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    if CASH not in cols and CASH in prices.columns:
        cols = cols + [CASH]
    risky = [c for c in cols if c != CASH]
    rets = prices[cols].pct_change(LOOKBACK)
    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
        scores = {
            s: float(rets.at[dt, s])
            for s in risky
            if s in rets.columns and pd.notna(rets.at[dt, s])
        }
        if not scores:
            engine.set_target_weights(dt, {CASH: 1.0} if CASH in cols else {})
            return
        best = max(scores, key=scores.get)
        if scores[best] > 0:
            engine.set_target_weights(dt, {best: 1.0})
        elif CASH in cols:
            engine.set_target_weights(dt, {CASH: 1.0})
        else:
            engine.set_target_weights(dt, {})

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

"""
Quant Buffet interactive lab — 交易vix交易所交易基金（vix-etfs）
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 = ['SVXY', 'BIL']
LOOKBACK = 252
CASH = 'BIL'

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    if CASH not in cols and CASH in prices.columns:
        cols = cols + [CASH]
    risky = [c for c in cols if c != CASH]
    rets = prices[cols].pct_change(LOOKBACK)
    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
        scores = {
            s: float(rets.at[dt, s])
            for s in risky
            if s in rets.columns and pd.notna(rets.at[dt, s])
        }
        if not scores:
            engine.set_target_weights(dt, {CASH: 1.0} if CASH in cols else {})
            return
        best = max(scores, key=scores.get)
        if scores[best] > 0:
            engine.set_target_weights(dt, {best: 1.0})
        elif CASH in cols:
            engine.set_target_weights(dt, {CASH: 1.0})
        else:
            engine.set_target_weights(dt, {})

ready = rets.dropna(how="all").index.min() if rets.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

-5.22%

Sharpe

0.16

Max DD

-94.50%

Vol

39.98%

Sortino

0.19

Beta

0.60

Up days

55%

Run the backtest to populate charts.

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: Absolute momentumAssets: SVXY, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 交易VIX交易所交易基金（VIX ETFs）
# Detected pattern: Absolute momentum
# 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 = ["SVXY", "BIL"]
        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: Long assets with positive 252-day return; equal-weight; monthly.

    def Rebalance(self):
        # Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
        # 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。