长期市净率 (PB Ratio) 效应与动量策略结合在股票中的应用
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 长期市净率 (PB Ratio) 效应与动量策略结合在股票中的应用 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: 长期市净率 (PB Ratio) 效应与动量策略结合在股票中的应用
# 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
On the Performance of Cyclically Adjusted Valuation Measures
关于经周期调整估值指标的表现 [点击查看论文]
- ?Alpha Architect
- Villanova University
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329948
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/纳斯达克市场中市值高于纽约证券交易所第40百分位的公司，排除房地产投资信托基金（REITs）、美国存托凭证（ADRs）、封闭式基金和金融公司，并要求有10年的基本面数据。每月，使用当前价格和经通货膨胀调整的过去10年的账面价值计算周期性调整的账面市值比。股票被分为十分位数，并选择最高的十分位数（最高的账面市值比）。这些股票再按过去12-2个月的动量进行划分，动量较高的那一半股票被纳入等权重的投资组合。投资组合每月进行再平衡，结合价值和动量因素以获得系统性的投资机会。
II. 策略合理性
通过在商业周期内平滑价格和账面价值的波动，增加市净率计算的年限可以增强其预测能力。以低市净率（price-to-book ratios）为特征的价值型公司，持续跑赢市场回报，这主要归因于投资者对成长型股票的过度反应，导致价值型股票被低估。在价值型股票中添加动量过滤器有助于识别那些基本面改善和价格上涨的股票，将其与较弱的股票区分开来。这种价值和动量的结合通过捕捉具有积极增长趋势的低估机会，提高了基本价值策略的绩效，为投资决策提供了更有效的方法。