Betting Against Correlation in S&P500 Stocks
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Betting Against Correlation in S&P500 Stocks 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: Betting Against Correlation in S&P500 Stocks
# 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)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
The Low-Risk Effect, from Betting Against Beta to Betting Against Correlation
Tommaso Pasetti; Dennis Marco Montagna
- ?Intermonte SIM
- ITUniversity of Pavia
- ?University of Pavia - Department of Economics and Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3995496
Strategy in a nutshell
This strategy invests in AMEX, NYSE, and NASDAQ stocks (CRSP share codes 10 and 11) using the quartile Betting Against Correlation (qBAC) factor. Stocks are first sorted into four quartiles by volatility and then within each quartile into low- and high-correlation portfolios relative to the S&P 500. Long positions are taken in low-correlation stocks and short positions in high-correlation stocks, with correlation-weighted allocations and monthly rebalancing. Risk-adjusted returns are computed per quartile, incorporating inverse correlation weights and beta adjustments, and the qBAC factor is defined as the average of these returns across quartiles.
Economic rationale
CAPM predicts a positive risk-return relationship, but leverage constraints and behavioral biases prevent investors from fully exploiting it. Many overpay for high-beta stocks, despite historical evidence showing low-beta and low-correlated stocks often outperform. The qBAC factor captures this anomaly by going long low-correlated, defensive sectors (Utilities, Healthcare, Consumer Staples) and shorting high-correlated cyclical sectors (Industrials, Tech, Financials). This approach historically outperformed the S&P 500 until 2016, though central bank interventions and tech rallies during the pandemic reduced its relative performance.