主动领口策略
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 主动领口策略 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: 主动领口策略
# 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(200); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 200 + 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) < 200: continue
if float(close.iloc[-1]) > float(close.iloc[-200:].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
“放松你的领口：QQQ领口的替代实现”：信用危机与样本外表现的更新 [点击查看论文]
- Providence College
- University of Massachusetts Amherst
- ?University of Massachusetts Amherst - Isenberg School of Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1507991
策略概要
该策略涉及100%纳斯达克指数头寸，以QQQ ETF为例。每个月，投资者卖出1个月期看涨期权，并利用所得权利金购买6个月期看跌期权。看涨期权、看跌期权与QQQ股票的比例，以及期权的实值程度，由三大信号决定：动量、波动率和宏观经济趋势。
动量：纳斯达克-100指数的移动平均交叉（1/50，5/150，1/200 SMA组合）用于识别趋势，买入信号时扩大保护区间，卖出信号时收紧保护区间。
波动性：VIX的日度收盘水平与其移动平均（50、150、250）比较，根据波动性调整看涨期权的写入强度（如果VIX > MA的标准差1倍，则每个头寸写入0.75份看涨期权，如果VIX低于MA，则写入1.25份）。
宏观经济趋势：初请失业金人数和NBER商业周期数据帮助调整期权的实值程度。在经济扩张期间，失业金人数上升时发出看涨信号（ATM看跌期权，OTM看涨期权），而在经济收缩期间，失业金人数上升时会反向调整行权价。
这些信号结合起来将期权的实值程度设置在ATM和5% OTM之间，并根据市场状况进行月度调整。
II. 策略合理性
跨式策略通过交换上行参与和下行保护来调整风险回报结构——该策略将标的资产的回报分布转化为具有更有利特征的新分布。通过使用系统性因素（如动量、波动性和宏观经济形势），该策略提升了风险/回报特征。