在股票中的动量效应应用违约风险过滤
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 在股票中的动量效应应用违约风险过滤 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: 在股票中的动量效应应用违约风险过滤
# 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
Momentum and Aggregate Default Risk
Momentum and Aggregate Default Risk [点击查看论文]
- Texas A&M University
- ?Texas A&M University - Department of Finance
- University of Toledo
- ?The University of Toledo - Department of Finance
- Case Western Reserve University
- ?Case Western Reserve University - Department of Banking & Finance
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2054707
策略概要
该策略针对AMEX、NYSE和NASDAQ股票，排除那些价格低于1美元的股票、外国股票和ADR（美国存托凭证）。根据Jegadeesh和Titman（1993）的动量方法，股票根据从t-6到t-1月份的累计回报排名，跳过一个月。每月形成动量投资组合，权重相等，并持有六个月。投资者使用穆迪CCC企业债券指数与10年期美国国债之间的利差来计算整体违约溢价。模型的残差估计意外违约冲击。动量投资组合仅在高违约冲击期间持有，通过每月的残差中位数来识别。
II. 策略合理性
研究表明，当整体违约风险意外上升时，企业层面的违约风险变得更加重要，因为高信用风险股票在这些情况下更容易违约，从而导致其表现下降和观察到的动量效应。因此，动量策略的回报是随时间变化的，在高违约冲击时期动量效应更明显，而在低违约冲击时期则较弱。