全球资产配置中的动量和趋势跟踪
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 全球资产配置中的动量和趋势跟踪 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: 全球资产配置中的动量和趋势跟踪
# Detected pattern: Momentum rotation
# 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: Hold top 3 by 21-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 21 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 21 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-21 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:3]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
The Trend is Our Friend: Global Asset Allocation Using Trend Following
趋势是我们的朋友：全球资产配置中的风险平价、动量和趋势跟踪 [点击查看论文]
- City, University of London
- ?City University London - Sir John Cass Business School
- Australian National University
- University of York
- ?Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA)
- ?University of York - Department of Economics and Related Studies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2126478
策略概要
该策略投资于跨越五个主要资产类别的约90种期货/交易所交易基金（ETFs）：发达国家股票、新兴市场股票、债券、商品和房地产投资信托（REITs）。每个月，使用十个月信号的趋势跟踪过滤器确定资产类别是否处于上升趋势或下降趋势。如果处于下降趋势，该资产类别的20%配置转移到美国国库券（T-Bills）。如果处于上升趋势，资产类别内的子成分按12个月回报率（由12个月波动率标准化）进行排名，并选择表现最佳的50%。资产类别和子成分均采用等权重。投资组合每月再平衡，以维持策略的配置。
II. 策略合理性
趋势跟踪通常是基于规则的，因此它可以通过机械地止损亏损头寸，同时让盈利头寸继续增长，来帮助克服投资者的行为偏差。这种方法消除了回报分布中的负面肥尾。利用动量效应有助于提高策略的最终表现。