Geopolitical Risk and Commodities
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Geopolitical Risk and Commodities 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: Geopolitical Risk and Commodities
# Detected pattern: Momentum rotation
# 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: Hold top 2 by 126-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 126 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 126 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:2]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
Strategy in a nutshell
The investment universe consists of 29 commodity futures contracts across four categories: agriculture, energy, livestock, and metals. Data is sourced from the Chicago Mercantile Exchange (CME).
The historical geopolitical risk index (GPRH) is constructed using the proportion of articles on geopolitical risks from The New York Times, The Chicago Tribune, and The Washington Post, via ProQuest Historical Newspapers, dating back to 1900. The GPRH data is sourced from Caldara and Iacoviello.
To estimate the GPRH beta, a rolling regression is run:
Independent variable: Monthly excess return of each commodity.
Dependent variables: Change in GPRH (month t – t-1), average factor (excess return of a long position in all commodity futures), carry factor, and commodity-momentum factor.
Estimation window: 60 months with at least 24 observations.
Each month, commodities are sorted into three portfolios based on their previous month’s GPRH beta:
Low: 5 contracts with lowest GPRH beta.
High: 5 contracts with highest GPRH beta.
Medium: All other contracts.
The strategy is long high-beta portfolios and short low-beta portfolios, equally weighted and rebalanced monthly.
Economic rationale
The strategy exploits the geopolitical risk premium, rooted in uncertainty-driven investment behavior.
Investors recognize the impact of geopolitical events (e.g., wars, terrorist attacks) on economic uncertainty and asset returns. As a result:
Risk-averse investors demand higher expected returns for assets positively correlated with geopolitical risk.
Assets negatively correlated with geopolitical risk are viewed as safer and can command higher prices.
This aligns with preference-based theory and the intertemporal CAPM model (Merton, 1973): as uncertainty rises, investors prefer higher-yielding assets to hedge future investment and consumption possibilities. The strategy captures returns arising from these risk-adjusted preferences.