趋势追随与动量结合在商品期货中的应用
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 趋势追随与动量结合在商品期货中的应用 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: 趋势追随与动量结合在商品期货中的应用
# Detected pattern: SMA trend
# 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 where close > SMA(252); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 252 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"].unstack(level=0).iloc[:, 0] if hasattr(hist["close"], "unstack") else hist["close"]
if len(close) < 252: continue
if float(close.iloc[-1]) > float(close.iloc[-252:].mean()):
longs.append(symbol)
weight = 1.0 / len(longs) if longs else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, weight if symbol in longs else 0.0)
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
Trend Following, Risk Parity and Momentum in Commodity Futures
Trend Following, Risk Parity and Momentum in Commodity Futures [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=2126813
策略概要
该策略使用道琼斯-UBS 大宗商品超额回报指数，涵盖 28 种大宗商品，并通过相应的期货进行交易。每月根据过去 12 个月的表现对大宗商品进行四分位排序。投资组合包括表现最好的（赢家）和表现最差的（输家）大宗商品，并采用风险平价方法进行加权，权重与其 60 天波动率成反比。此外，应用趋势跟随过滤器：大宗商品需高于其 6 个月简单移动平均线才能被视为赢家，或低于该均线才能被视为输家。投资者对符合筛选标准的赢家做多，对输家做空，从而构建一个平衡且基于表现的投资组合，同时实现系统性风险管理。
II. 策略合理性
学术研究对趋势跟随策略的历史成功提出了多种解释，包括投资者对新闻的反应不足以及羊群行为。动量效应通常被归因于投资者的非理性行为，因为他们未能完全将新信息纳入交易价格。此外，动量投资者可能利用其他市场参与者的行为偏差（如羊群效应、过度反应、反应不足和确认偏误），以把握可预测的价格趋势并从中获利。