内部人士的沉默
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Quant Buffet 原生回测 IDEEdit and run Quant Buffet Python for 内部人士的沉默 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: 内部人士的沉默
# 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
The Sound of Silence: What Do We Know When Insiders Do Not Trade?
沉默的声音：当内部人士不交易时，我们知道什么？[点击查看论文]
- Cornell University
- ?T. Rowe Price Group (United States)
- ?Cornell University - Samuel Curtis Johnson Graduate School of Management
- ?T. Rowe Price Group
- California State University, Chico
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2167998
策略概要
该策略专注于纽约证券交易所（NYSE）、美国股票交易所（Amex）和纳斯达克（Nasdaq）股票，每月根据前一个月的空头仓位将股票分为五个等级。在空头仓位最高的五分位内，识别出过去六个月内没有内幕交易活动的股票。投资者对这些股票进行做空，并持仓一年，同时在股市上建立多头仓位。该投资组合采用等权重，并每月进行再平衡，旨在利用在高空头仓位股票中缺乏内幕人士信心的情况。
II. 策略合理性
研究确定了两种解释内部交易模式的假设。诉讼风险假设认为，对于面临较高诉讼风险的公司，内部人士的沉默与较弱的负面未来回报相关。第二个假设认为，内部人士的投资组合约束推动了结果，因为他们不能卖空，必须保留股份以维持控制权，或者持有不可出售的受限股份。大量的内部人士卖出，随后交易减少，向市场发出潜在的坏信息信号，反映了内部人士由于约束或法律顾虑而不愿交易，最终影响市场认知和未来股票表现。