均值方差套利交易策略
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 均值方差套利交易策略 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: 均值方差套利交易策略
# Detected pattern: Custom / hybrid
# 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: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
def Rebalance(self):
# Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
# 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 Risk and Return of the Carry Trade
套利交易的风险与回报研究 [点击查看论文]
- CHKantonsschule Zürcher Oberland
- ?Zurcher Kantonalbank
- NONorwegian School of Economics
- CHUniversity of Zurich
- ?NHH Norwegian School of Economics
- CHInternational Institute for Management Development
- ?IMD Lausanne
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2184336
策略概要
该策略以美元（USD）为本币，投资于10种外币：瑞士法郎（CHF）、欧元（EUR）、日元（JPY）、英镑（GBP）、澳元（AUD）、加元（CAD）、挪威克朗（NOK）、瑞典克朗（SEK）、新加坡元（SGD）和新西兰元（NZD）。每月，在随机游走假设下，每种货币的预期超额回报计算为与美国无风险利率的利率差。使用过去250个观测值重新估计每日协方差矩阵。投资者应用均值-方差优化来确定每种货币的最佳多头/空头投资组合权重。投资组合每月进行再平衡，利用利率差异和协方差动态来最大化风险调整后的回报。
II. 策略合理性
学术研究表明，无抛补利率平价预测汇率会进行调整以消除套利交易利润，但数据与此相悖。实证证据表明，短期汇率波动是不可预测的，并遵循随机游走。尽管存在汇率风险，但套利交易策略通常平均产生正的预期回报，使投资者受益。