已实现偏度预测股票回报
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 已实现偏度预测股票回报 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: 已实现偏度预测股票回报
# 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
Do Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns?
已实现偏度是否预测股票回报的横截面？[点击查看论文]
- CAWilfrid Laurier University
- CAUniversity of Toronto
- DKCopenhagen Business School
- DKAarhus University
- ?Aarhus University - CREATES
- ?University of Toronto - Rotman School of Management
- University of Houston
- ?University of Houston - C.T. Bauer College of Business
- MXInstituto Tecnológico Autónomo de México
- ?Instituto Tecnológico Autónomo de México (ITAM) - Department of Business Administration
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1898735
策略概要
投资范围包括价格高于5美元的纽约证券交易所、美国证券交易所和纳斯达克股票，重点关注规模最大的五分之一。盘中5分钟报价计算对数回报，并从该数据中得出偏度，汇总为每周实现的偏度。负偏度表示左偏回报分布，而正偏度表示右偏分布。每周，股票根据实现的偏度被分为十分位数。投资者买入最高十分位数的股票，卖出最低十分位数的股票，创建一个每周再平衡的价值加权投资组合。该策略利用盘中数据来利用偏度驱动的回报异常，以获得系统性的交易机会。
II. 策略合理性
研究得出的结论是，在累积前景理论或使用异质投资者偏好偏度理论的情况下，偏度越大的资产回报越低。