Gold to Oil Ratio Predicts Aggregate Stock Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Gold to Oil Ratio Predicts Aggregate Stock Returns 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: Gold to Oil Ratio Predicts Aggregate Stock Returns
# 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
Gold price ratios and aggregate stock returns
Tong Fang
- Shandong University of Finance and Economics
- ?Shandong University - School of Economics
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3950940
Strategy in a nutshell
The strategy dynamically allocates between the S&P 500 index and the one-month Treasury bill using the gold-oil price ratio (GO) as a predictor.
Process:
GO Predictor: Compute the natural log of the gold-to-oil price ratio.
Regression Forecasting: Regress the S&P 500’s excess return (vs. T-bill) on GO using a 240-month rolling window.
Forecasting Returns: At the end of month t, use the regression to forecast the S&P 500 excess return for t+1.
Portfolio Allocation Rule: \text{S&P 500 Allocation} = \frac{1}{\text{risk aversion}} \times \frac{\text{forecasted excess return}}{\text{forecasted variance}}
Variance forecast: 10-year rolling window of past returns.
Risk aversion coefficient = 3.
Allocation bounded between 0% and 150%.
Final Weights: S&P 500 weight determined by rule; remainder allocated to one-month T-bill.
Rebalancing: Monthly updates of regression, forecast, and weights.
Economic rationale
Asset prices reflect both expected cash flows and discount rates (Cochrane, 2011). GO’s predictive ability comes mainly from anticipating aggregate cash flow news.
GO also negatively predicts default spreads, financial stress, and uncertainty, making it a leading indicator of economic conditions.
A higher GO signals stronger economic outlooks, translating into higher expected equity returns