谷歌搜索量结合新闻报道范围预测股票回报
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 谷歌搜索量结合新闻报道范围预测股票回报 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: 谷歌搜索量结合新闻报道范围预测股票回报
# 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
Media and Google: The Impact of Supply and Demand for Information on Stock Returns
信息供求对股票回报的影响 [点击查看论文]
- KRSungkyunkwan University
- ?Sungkyunkwan University (SKK) Graduate School of Business
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2180409
策略概要
该策略的目标是纽约证券交易所、美国证券交易所和纳斯达克的股价高于5美元的股票，排除封闭式基金、房地产投资信托基金、单位信托、美国存托凭证和外国股票。信息供应通过Factiva的新闻文章进行分析，分为三种情况：没有新闻报道、新闻报道增加（当前文章高于12个月移动平均线）和新闻报道减少（当前文章低于平均线）。信息需求通过谷歌趋势进行评估，搜索量也类似地分类：没有搜索量、搜索量增加和搜索量减少。
新闻报道和搜索量都增加的公司月份表明信息供应和需求增加。相反，新闻报道和搜索量都减少表明兴趣减弱。每个月，投资者构建一个等权重投资组合，做多供应和需求增加的股票，做空供应和需求减少的股票。这种双重方法利用信息动态的变化来捕捉市场无效性并优化回报。通过将投资决策与新闻和搜索活动的变化保持一致，该策略旨在利用信息流增加的预测能力，同时降低与市场关注度降低相关的风险。投资组合每月进行系统性再平衡。
II. 策略合理性
研究强调了“吸引注意力”的假设，即个人投资者专注于吸引注意力的股票，从而产生积极的价格压力。仅信息需求的增加无法确定投资者是对公司新闻还是情绪做出反应，这两者对回报的预测不同。新闻驱动的事件通常会导致价格动量，而情绪驱动的事件会导致价格反转。如果谷歌搜索量的增加与重要的公司新闻一致，那么注意力预示着正回报。否则，随着情绪消退，注意力可能预示着价格反转。该研究得出结论，只有在新闻报道增加的情况下，搜索量的增加才能预测正回报。