股票中的动量效应结合止损策略
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 股票中的动量效应结合止损策略 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: 股票中的动量效应结合止损策略
# 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
Taming Momentum Crashes: A Simple Stop-Loss Strategy
驯服动量崩溃：一个简单的止损策略 [点击查看论文]
- University of North Carolina at Charlotte
- ?University of North Carolina (UNC) at Charlotte - Finance
- Washington University in St. Louis
- ?Washington University in St. Louis - John M. Olin Business School
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2407199
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公司，排除封闭式基金（CEFs）、房地产投资信托基金（REITs）、美国存托凭证（ADRs）、外国股票、价格低于5美元的股票以及最小规模的十分位数。每月，股票按其6个月的累计回报（跳过一个月）进行排名，并选择最高的十分位数进行投资。投资组合采用价值加权，每只股票设置10%的止损。如果触发止损，则卖出股票，资金以现金形式持有至当月剩余时间。投资组合（包括止损阈值）每月进行再平衡，利用动量的同时降低下行风险。
II. 策略合理性
学术界认为，动量盈利能力源于投资者对过去信息的过度反应，这种反应会在短时间内得到纠正。从业者通常使用预定义的止损技术来有效限制风险，并且这种方法可以通过减轻潜在损失，同时利用该策略固有的盈利能力，来增强动量的风险/回报特征。