股票的日内动量
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 股票的日内动量 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: 股票的日内动量
# 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
Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return
市场日内动量 [点击查看论文]
- George Mason University
- University of North Carolina at Charlotte
- ?University of North Carolina (UNC) at Charlotte - Finance
- Rutgers, The State University of New Jersey
- Washington University in St. Louis
- ?Washington University in St. Louis - John M. Olin Business School
- ?Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2440866
策略概要
投资者使用第一个和第十二个半小时的回报作为市场择时信号。如果两个信号都为正，则在最后一个半小时建立多头头寸；如果两个信号都为负，则建立空头头寸。如果信号不同，投资者保持中性。头寸在收盘时清算，确保没有隔夜风险。该策略可以使用ETF或期货执行，利用日内市场择时来利用短期趋势，同时保持纪律严明的系统性方法。
II. 策略合理性
学术研究对这种效应提供了两种解释。首先，日内交易者可能在第一个半小时回报强劲后做空，预期价格反转。当他们在收盘前平仓时，一些人会等到最后一个半小时，从而影响价格走势。其次，知情交易者会在高交易量时期策略性地择时交易。阿德马蒂和普费德勒（1988）以及霍拉（2006）的理论表明，最优策略包括在交易日的开始和结束时进行快速交易，在交易日中期进行较慢的交易，这与交易量和信息动态一致。这两种行为都导致了特定日内时段内可预测的价格模式。