股票 · 美国 · 股票，ETF · 每月 Free

卖空利息策略

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学术论文

卖空利息中的好消息

作者Ekkehart Boehmer; Zsuzsa R. Huszár; Bradford D. Jordan

机构

Singapore Management University
Singapore Management University - Lee Kong Chian School of Business
Central European University
National University of Singapore
University of Florida
University of Florida - Department of Finance, Insurance and Real Estate

论文链接https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1405511

导读

投资标的：VNQ、IYR、RWX、SPY、BIL。再平衡：每月。Universe: VNQ, IYR, RWX, SPY, BIL. Parameters: rebalance=monthly.

策略概要

该策略投资于NYSE、AMEX和NASDAQ全部股票，以卖空利息比率为预测变量。按卖空利息比率排序后持有最低百分位股票，组合等权、每月再平衡。

经济逻辑

卖空利息的预测力同样可用高估假说与信息假说解释。Miller（1977）指出，高卖空股票因悲观资金难以做空而被乐观者定价，卖空成本越高、高估越大、随后收益越低。信息假说强调卖空者具备更强分析能力，往往只在公开信息显示估值偏低时开空，例如通过应计项目识别隐藏坏消息。跟随这些知情卖空者的反向或低卖空持仓，可以捕捉被市场忽略的好消息。

入场与出场

入场

每月由 make_on_day 决定目标权重。当前宇宙：VNQ, IYR, RWX, SPY, BIL。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 VNQ, IYR, RWX, SPY, BIL 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

VNQ, IYR, RWX, SPY, BIL

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 卖空利息策略 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 · 43 lines

"""
Quant Buffet interactive lab — 卖空利息策略
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 = ['VNQ', 'IYR', 'RWX', 'SPY', 'BIL']
REBALANCE_ONCE = False

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    state = {"last": None, "done": False}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        available = [s for s in cols if pd.notna(prices.at[dt, s])]
        if not available:
            return
        if REBALANCE_ONCE:
            if state["done"]:
                return
            state["done"] = True
            engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})

    counts = prices[cols].notna().sum(axis=1)
    need = max(1, len(cols) // 2)
    eligible = counts[counts >= need]
    ready = eligible.index.min() if not eligible.empty else None
    return on_day, ready

"""
Quant Buffet interactive lab — 卖空利息策略
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 = ['VNQ', 'IYR', 'RWX', 'SPY', 'BIL']
REBALANCE_ONCE = False

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    state = {"last": None, "done": False}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        available = [s for s in cols if pd.notna(prices.at[dt, s])]
        if not available:
            return
        if REBALANCE_ONCE:
            if state["done"]:
                return
            state["done"] = True
            engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})

counts = prices[cols].notna().sum(axis=1)
    need = max(1, len(cols) // 2)
    eligible = counts[counts >= need]
    ready = eligible.index.min() if not eligible.empty 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

6.75%

Sharpe

0.47

Max DD

-57.95%

Vol

16.96%

Sortino

0.72

Beta

0.77

Up days

62%

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: Custom / hybridAssets: VNQ, IYR, RWX, SPY, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 卖空利息策略
# 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 = ["VNQ", "IYR", "RWX", "SPY", "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: 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。