期权/股票成交量比率预测股票回报
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 期权/股票成交量比率预测股票回报 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 →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
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_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]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, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest 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.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 期权/股票成交量比率预测股票回报
# 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 = ["SPY", "TLT", "GLD", "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。
学术论文
The Option to Stock Volume Ratio and Future Returns
期权与股票成交量比率与未来回报 [点击查看论文]
- The University of Texas at Austin
- Massachusetts Institute of Technology
- ?Massachusetts Institute of Technology (MIT) - Sloan School of Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1624062
策略概要
该策略目标是具有流动性期权的美国公司，排除CEFs、REITs、ADRs和价格低于1美元的股票。每月，投资者计算O/S比率，即总期权成交量（所有行权价，针对月底后五天开始的30个交易日内到期的期权）除以总股票成交量，进行标准化以考虑代表100股的期权合约。股票按O/S比率排名，投资者卖空高O/S比率的股票，买入低O/S比率的股票。投资组合等权重，每月再平衡，并寻求利用期权和股票交易活动之间的差异。
II. 策略合理性
研究表明，期权/股票成交量比率（O/S）与未来回报之间的负相关关系源于股票市场的卖空成本。这些成本使得期权市场成为交易者对负面消息采取行动的首选场所。资本约束和卖空股票的困难导致知情交易者更多地依赖期权来表达负面信号。因此，较高的相对期权成交量（O/S）表示看跌情绪并预测较低的未来股票回报。这种动态凸显了期权市场在反映股票市场不易交易的负面信息方面的作用。