拆股后漂移与PEAD异常的结合
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 拆股后漂移与PEAD异常的结合 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: 拆股后漂移与PEAD异常的结合
# 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
Post-Split Drift and Post-Earnings Announcement Drift: One Anomaly or Two?
盈余管理与拆分后漂移 [点击查看论文]
- TWNational Chengchi University
- ?National Chengchi Unversity (NCCU) - Finance
- Deakin University
- ?Deakin University - Deakin Business School
- HKUniversity of Hong Kong
- ?The University of Hong Kong - Faculty of Business and Economics
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329740
策略概要
该策略针对纽约证券交易所（NYSE）、美国证券交易所（AMEX）和纳斯达克（NASDAQ）上市公司，重点关注财报公告和标准化意外盈余（SUE）。每天，筛选过去三个月内SUE处于最高或最低五分位的股票。投资者在财报公布后三天开始建仓，做多SUE位于最高五分位且近期发生拆股的股票，同时做空SUE位于最低五分位且无近期拆股记录的股票。所有头寸均等权重配置，持有期为三个月。该策略结合SUE和拆股信号，以捕捉市场潜在的非有效性。
II. 策略合理性
学术研究表明，股票拆分预示着未来收益的改善，分析师最初低估了拆分公司的收益，并且预测修正缓慢。包括分析师在内的投资者对这些信号反应不足，从而产生了拆分后的漂移效应。通过利用市场对与股票拆分相关的未来收益改善的延迟反应，将这种效应与盈余公告漂移（PEAD）异常相结合可以提高交易回报。这些异常之间的协同作用为系统性交易策略提供了有利可图的机会。