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Algorithmic trading has gained attention due to its potential for generating superior returns. This paper investigates the effectiveness of deep reinforcement learning (DRL) methods in algorithmic commodities trading. It formulates the commodities trading problem as a continuous, discrete-time stochastic dynamical system. The proposed system employs a novel time-discretization scheme that adapts to market volatility,…
It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of default for the counterparty when traded with brokers or outside clearing houses, or when embedded in o…
We address the modelling of commodities that are supposed to have positive price but, on account of a possible failure in the physical delivery mechanism, may turn out not to. This is done by explicitly incorporating a `delivery liability' option into the contract. As such it is a simple generalisation of the established Black model.
In this paper, we develop a general rough volatility model for commodities that provides an automatic calibration of the initial term structure of the futures prices and an appropriate treatment of the Samuelson effect. After the theoretical analysis of this general model, we focus on the rBergomi and rHeston models and their calibration to market data of vanilla futures options on WTI Crude Oil. Finally, numerical r…
The energy transition has increased the reliance on intermittent energy sources, destabilizing energy markets and causing unprecedented volatility, culminating in the global energy crisis of 2021. In addition to harming producers and consumers, volatile energy markets may jeopardize vital decarbonization efforts. Traders play an important role in stabilizing markets by providing liquidity and reducing volatility. Sev…
This book, which is in Spanish, provides detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes (and trading styles). This includes stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscella…
In this paper, we propose and study a novel continuous-time model, based on the well-known constant elasticity of variance (CEV) model, to describe the asset price process. The basic idea is that the volatility elasticity of the CEV model can not be treated as a constant from the perspective of stochastic analysis. To address this issue, we deduce the price process of assets from the perspective of volatility elastic…
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from the MCX pooled spot prices for 17 commodities. The data was split into training period (January 1st…
The purpose of this paper is to showcase trading strategies that give solutions to three difficult and intriguing problems in business finance, economics and statistics. The paper discusses trading strategies for both commodities and stocks but the main focus is on stock market trading at the New York Stock Exchange. Problem 1: Buy Low and Sell High. The buy low and sell high problem can be summarized like this: supp…
This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity's behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of…
In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, e…
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across d…
Market makers play an essential role in financial markets. A successful market maker should control inventory and adverse selection risks and provide liquidity to the market. As an important methodology in control problems, Reinforcement Learning enjoys the advantage of data-driven and less rigid assumptions, receiving great attention in the market-making field since 2018. However, although the China Commodity market…
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded …
Backwardation is a futures curve that falls with tenor — nearby richer than deferred, usually a tightness / convenience-yield story.
Baltic Dry Index tracks shipping rates for dry bulk commodities and offers a real-economy signal on trade flows, freight conditions, and industrial demand.
Commodity Carry — Return from rolling futures along the curve — core systematic commodity strategy.
Commodity Inventory Financing — Repo-like financing of physical stocks linking curve to rates.
Contango is a futures curve that rises with tenor — deferred contracts richer than nearby, often a storage and rate story.
Copper price is widely used as a proxy for industrial activity, manufacturing demand, and global growth expectations.
Crude Oil Contango — Upward-sloping futures curve implying storage economics and weak spot demand.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
Gold Real Yields Correlation — Gold as non-yielding asset inversely sensitive to real rates and USD.
Natural Gas Storage — Inventory levels driving seasonal price spikes and energy inflation.
A CTA is a manager — often CFTC/NFA registered — that runs client money in futures and options on futures, long and short, across rates, FX, equities, and commodities.
Inside a managed-futures book, overweight backwardated contracts and underweight contango — roll yield as a second family next to price trend.
Shrink size when markets are moving together so that ‘20 commodities’ are not one energy-risk factor wearing 20 tickers.
Which expiry you hold and when you roll is a first-class P&L — not an operations footnote — especially in commodities and VIX.
A program that risks money across the four big futures groups — equity indices, bonds/STIR, FX, and commodities — rather than a single pit.
Dutch disease is the squeeze on tradable non-resource sectors when a resource boom or capital inflow appreciates the real exchange rate and pulls factors into the booming sector.
China credit impulse measures the change in new credit growth relative to GDP and is widely used as a leading indicator for Chinese demand and global cyclical momentum.
China Property Cycle — Developer stress and land sales impacting global commodities and EM growth.
The Hunt brothers’ 1979–80 silver corner drove prices from single digits toward $50 before exchange rule changes and a margin spiral crushed the trade on Silver Thursday.
Terms of Trade Shock — Relative export-import price shifts altering growth and currency paths.
Trade refined-product minus crude (crack) or nearby-versus-deferred calendars — commodity relative value, not a directional oil call.
Long commodity futures with the strongest trailing returns and short the weakest — cross-sectional commodity momentum.
Sort commodities on upside vs downside return asymmetry and hold the preferred tail profile — a moments/tilt book.
Prefer commodity futures with more attractive skewness (or fade lottery-like positive skew) — a moment factor in the curve complex.
Long commodities in backwardation (positive roll yield) and short those in contango — harvest the curve, not the spot headline.
Backwardation is a futures curve that falls with tenor — nearby richer than deferred, usually a tightness / convenience-yield story.
Baltic Dry Index tracks shipping rates for dry bulk commodities and offers a real-economy signal on trade flows, freight conditions, and industrial demand.
China Property Cycle — Developer stress and land sales impacting global commodities and EM growth.
Commodity Carry — Return from rolling futures along the curve — core systematic commodity strategy.
Commodity Inventory Financing — Repo-like financing of physical stocks linking curve to rates.
A CTA is a manager — often CFTC/NFA registered — that runs client money in futures and options on futures, long and short, across rates, FX, equities, and commodities.
Contango is a futures curve that rises with tenor — deferred contracts richer than nearby, often a storage and rate story.
Copper price is widely used as a proxy for industrial activity, manufacturing demand, and global growth expectations.
Crude Oil Contango — Upward-sloping futures curve implying storage economics and weak spot demand.
Shrink size when markets are moving together so that ‘20 commodities’ are not one energy-risk factor wearing 20 tickers.
Which expiry you hold and when you roll is a first-class P&L — not an operations footnote — especially in commodities and VIX.
A program that risks money across the four big futures groups — equity indices, bonds/STIR, FX, and commodities — rather than a single pit.
Gold price reflects demand for a non-yielding reserve asset and is often used as a signal for real yields, macro uncertainty, and confidence in fiat systems.
Gold Real Yields Correlation — Gold as non-yielding asset inversely sensitive to real rates and USD.
Long commodity futures with the strongest trailing returns and short the weakest — cross-sectional commodity momentum.
Natural Gas Storage — Inventory levels driving seasonal price spikes and energy inflation.
Sort commodities on upside vs downside return asymmetry and hold the preferred tail profile — a moments/tilt book.
Prefer commodity futures with more attractive skewness (or fade lottery-like positive skew) — a moment factor in the curve complex.
Long commodities in backwardation (positive roll yield) and short those in contango — harvest the curve, not the spot headline.