认沽-认购价差预测财报公告后的收益
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 认沽-认购价差预测财报公告后的收益 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: 认沽-认购价差预测财报公告后的收益
# 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
Deviations from Put-Call Parity and Earnings Announcement Returns
Volatility Spreads and Earnings Announcement Returns [点击查看论文]
- TRSabancı Üniversitesi
- ?Sabanci University
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1512046
策略概要
: 美国股票市场的每日盈余波动率差轮换
该策略以具有流动性期权的纽约证券交易所（NYSE）、美国证券交易所（AMEX）和纳斯达克（NASDAQ）公司为目标，围绕财报公告进行交易。在公告前一天的收盘时，计算隐含波动率价差，作为匹配的认沽和认购期权之间的加权差异，使用未平仓合约作为权重。根据波动率价差将股票分为五个等级，并通过根据买卖价差将期权对分为三类来考虑流动性。该策略对波动率价差最高的股票做多，对波动率价差最低的股票做空，持仓两天（公告当天及次日）。投资组合等权重，进行每日再平衡，并使用50%的仓位暴露来管理波动性。
II. 策略合理性
研究表明，期权价格可能预示未来股票回报，因为有信息的交易者通常倾向于选择期权市场，在股票市场之前反映信息。如果交易者预期股价上涨（下跌），则对认购（认沽）期权的需求增加，从而使它们的隐含波动率相对于认沽（认购）期权上升。因此，在股价下跌之前，认沽与认购隐含波动率之间的差距扩大，而在股价上涨之前则缩小。这种由有信息交易驱动的认沽-认购平价偏离，尤其在重大信息事件（如财报公告）期间具有预测性，因为此时市场活动加剧，放大了期权市场行为与未来股票价格走势之间的关系。