共动量策略
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 共动量策略 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: 共动量策略
# 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
Co-Momentum: Inferring Arbitrage Capital from Return Correlations
从收益相关性推断套利活 [点击查看论文]
- London School of Economics and Political Science
- Centre for Economic Policy Research
- ?Centre for Economic Policy Research (CEPR)
- ?London School of Economics & Political Science (LSE)
- ?London School of Economics
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2023989
策略概要
该策略专注于纽约证券交易所、美国证券交易所和纳斯达克的股票，排除那些价格低于5美元或位于纽约证券交易所规模最小的十分位数的股票。每月，股票按其前12个月的回报（不包括最后一个月）进行排名，并分为十分位数。对于每个十分位数，计算52周回报的成对偏相关，控制Fama-French三个因子以消除共同风险因子效应。计算输家十分位数的平均相关性（ComomL）。仅当当月的ComomL处于历史最低五分之一时，才实施经典的动量策略（做多赢家，做空输家），以确保最佳交易条件。
II. 策略合理性
学术研究表明，价格动量的表现因投资的资本而异。当动量策略中的资本较低时，动量反映的是反应不足，导致短期价格持续而没有长期反转。相反，高套利资本会产生过度反应，即价格超调并最终在长期内反转。此外，拥挤的动量策略容易出现突然崩溃，如果套利者因追加保证金或业绩不佳而被强制撤回资本，从而引发快速平仓和重大的市场影响。这些动态凸显了监控动量策略中的资本流动以有效管理风险和回报的重要性。