股票中的广告效应
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 股票中的广告效应 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: 股票中的广告效应
# 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
Advertising, Attention, and Stock Returns
Advertising, Attention, and Stock Returns [点击查看论文]
- Boston College
- ?Boston College - Carroll School of Management
- Fordham University
- ?Fordham University - Gabelli School of Business
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1340605
策略概要
该策略针对市值超过2000万美元的NYSE、AMEX和NASDAQ股票。广告变化（ΔAdvt）通过计算从前一年到当年（广告年度）广告支出的对数差异来得出。每年，股票根据ΔAdvt的大小被分为十个分位数。一个零投资组合通过做空第10分位数（广告变化大的股票）并做多第1分位数（广告变化小的股票）来构建。股票在t+1年7月购买，持有六个月，按等权重分配，并基于完整的财务数据进行选择。
II. 策略合理性
学术研究将这一异常现象归因于投资者的有限注意力，这限制了他们评估所有投资和处理大量信息的能力。广告能够吸引注意力，增加买单或投资者信念的异质性，从而在广告年度推动股票价格上涨。然而，随着广告的注意力效应随时间减弱，股价往往会下降，导致未来的负回报。这表明，吸引注意力的广告可以暂时抬高股票价格，但随着广告影响力的减弱，这一效应会反转，从而在股价波动中形成一种可利用的模式，这与投资者行为和注意力动态有关。