Commodity Futures Roll-Return Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Commodity Futures Roll-Return Strategy 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: Commodity Futures Roll-Return Strategy
# Detected pattern: Custom / hybrid
# 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: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
def Rebalance(self):
# Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
# 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
Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals
Ana-Marı́a Fuertes; Joëlle Miffre; Georgios Rallis
- City, University of London
- ?Bayes Business School, City, University of London
- Audencia Business School
- ?City University of London - Sir John Cass Business School
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1127213
Strategy in a nutshell
Each month, this straightforward strategy invests in the top 20% of commodities showing the highest roll-returns, while short selling the bottom 20% with the lowest roll-returns, maintaining these positions for a month. Positions within each quintile are allocated equally to ensure balanced exposure. The approach encompasses the entire spectrum of commodity futures contracts as its investment universe, aiming to capitalize on discrepancies in roll-returns among different commodities.
Economic rationale
Keynes (1930) and Cootner (1960) suggest commodity futures prices are influenced by hedgers' net positions, with risk transferred from producers/consumers to speculators seeking profit from price changes. When short hedgers outweigh long hedgers, futures prices today are likely biased lower than at maturity. Practically, term-structure strategies offer appealing features like lower drawdowns, higher run-ups, and better rolling returns compared to benchmarks, with rewarding risk-adjusted returns. These strategies' returns mirror S&P GSCI fluctuations but are independent of the S&P 500, enhancing equity portfolio diversification. Erb and Harvey note that while individual commodity futures typically yield zero excess return and exhibit low correlation, a diversified, rebalanced futures portfolio could achieve equity-like returns, with term structure and strategy selection driving above-average outcomes. Durr and Voegeli's analysis emphasizes the term structure's structural properties, highlighting the consistent explanatory power of principal components, particularly the level factor, suggesting investment opportunities through term structure insights.