封闭式基金均值回归交易
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 封闭式基金均值回归交易 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: 封闭式基金均值回归交易
# 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
Exploiting Closed-End Fund Discounts: The Market May Be Much More Inefficient than You Thought
利用封闭式基金折价：市场可能比你想象的更加低效 [点击查看论文]
- Federal Deposit Insurance Corporation
- ?OCC
- ?(Federal Deposit Insurance Corporation)
- Oklahoma State University
- ?Oklahoma State University - Stillwater - Spears School of Business
- NLRutgers Sexual and Reproductive Health and Rights
- Rutgers, The State University of New Jersey
- ?Rutgers University, Newark - School of Business - Department of Finance & Economics
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2468061
策略概要
该策略专注于具有足够流动性的封闭式基金（CEFs）。一种简单的方法是将CEFs按上个月的折价分为五等分，做多折价最大的基金，做空溢价最大的基金，每月进行再平衡。更高级的版本采用回归模型（第6页，等式4a和4b），该模型结合了过去的折价和折价的月度变化，以预测下个月的业绩。投资者做多预测业绩最高的基金，做空预测业绩最低的基金，每月重新平衡投资组合，以根据预测指标优化回报。
II. 策略合理性
有许多研究论文解释了封闭式基金（CEF）折价/溢价的原因（投资者情绪效应、交易摩擦、代理成本和管理技能）。研究表明，封闭式基金领域存在多种套利限制；因此，系统且耐心的策略应该能够从中提取不相关的回报。