Koding Books

Professional, free coding tutorials

Momentum Effect in Commodities Futures

Introduction

The Momentum effect in commodities futures is a market anomaly that states that commodities futures that have outperformed in the recent past tend to continue to outperform shortly. This effect has been observed in various commodity markets, including energy, metals, and agriculture.

There are many possible explanations for the momentum effect in commodities futures. One possibility is that it is due to investors’ tendency to extrapolate past trends into the future. Another possibility is that it is due to investors’ herding behaviour, where investors follow the lead of other investors and buy commodities that are already rising in price.

Momentum strategies in commodities futures markets typically involve buying the commodity futures that have outperformed in the recent past and selling the commodity futures that have underperformed. These strategies can be implemented using various technical indicators, such as the rate of change (ROC) and the moving average crossover (MACD).

Momentum strategies in commodities futures markets can be profitable, but it is important to note that they are also risky. Commodities futures markets are volatile, and momentum strategies can lead to large losses if the trend reverses.

Here are some examples of momentum strategies in commodities futures markets:

  • A 12-month momentum strategy would involve buying the commodity futures that have outperformed over the past 12 months and selling the commodity futures that have underperformed over the past 12 months.
  • A 6-month momentum strategy would involve buying the commodity futures that have outperformed over the past 6 months and selling the commodity futures that have underperformed over the past 6 months.
  • A 3-month momentum strategy would involve buying the commodity futures that have outperformed over the past 3 months and selling the commodity futures that have underperformed over the past 3 months.

It is important to note that momentum strategies are not guaranteed to be profitable. However, they can be useful for diversifying a portfolio and generating additional returns.

Calculating the momentum effect

The momentum effect is a statistical anomaly that states that commodities futures that have outperformed in the recent past tend to continue to outperform shortly. This effect has been observed in various commodity markets, including energy, metals, and agriculture.

There are many possible explanations for the momentum effect in commodities futures. One possibility is that it is due to investors’ tendency to extrapolate past trends into the future. This is known as the representativeness heuristic. Another possibility is that it is due to investors’ herding behaviour, where investors follow the lead of other investors and buy commodities that are already rising in price.

The following mathematical equation can be used to calculate the momentum effect in commodities futures:

Momentum Effect = (F1 - F0) / F0

Where:

  • F1 is the price of the commodity futures at the current period
  • F0 is the price of the commodity futures at a previous period

The momentum effect is calculated by subtracting the price of the commodity futures at a previous period from the price of the commodity futures at the current period and then dividing by the price of the commodity futures at the previous period.

The momentum effect can be used to develop trading strategies. For example, a trader could buy commodity futures with a high momentum effect and sell commodity futures with a low momentum effect. This strategy could be profitable if the momentum effect continues in the future.

However, it is important to note that the momentum effect is not guaranteed to continue in the future. Commodity futures markets are volatile, and momentum strategies can lead to large losses if the trend reverses.

Here is an example of how to calculate the momentum effect in commodities futures:

Suppose that the price of a commodity futures contract is $100 today and $90 one month ago. The momentum effect for this commodity futures contract would be calculated as follows:

Momentum Effect = (100 - 90) / 90 = 11.11%

This indicates that the commodity futures contract has outperformed the market by 11.11% over the past month.

Traders can use the momentum effect to develop a variety of trading strategies. For example, a trader could buy commodity futures with a high momentum effect and sell commodity futures with a low momentum effect. This strategy could be profitable if the momentum effect continues in the future.

However, it is important to note that the momentum effect is not guaranteed to continue in the future. Commodity futures markets are volatile, and momentum strategies can lead to large losses if the trend reverses.

It is also important to note that the momentum effect is not a free lunch. There are many risks associated with momentum strategies, including:

  • Herding risk: Momentum strategies can lead to herding behaviour, where investors all follow the same trading signals and buy the same commodity futures contracts. This can create bubbles in the market, which can eventually burst and lead to large losses for investors.
  • Trend reversal risk: Momentum strategies can be profitable if the market trend continues in the future. However, momentum strategies can lead to large losses if the market trend reverses.
  • Liquidity risk: Commodity futures markets can be illiquid, so buying and selling commodity futures contracts at a fair price can be difficult. This can make it difficult to exit a momentum trade if the market trend reverses.

Investors should consider the risks and rewards of momentum strategies before using them in trading.

Implementing the effect in Python

requirements

pip install pandas numpy pytest # Install requirements

Unit tests

import pytest
import numpy as np
import pandas as pd

from momentum_effect import MomentumEffect

def test_calculate_momentum() -> None:
    """
    Tests the `calculate_momentum` method of the `MomentumEffect` class.
    """

    data = pd.DataFrame({'Close': [100, 95, 90]})
    momentum_effect = MomentumEffect(data)

    momentum = momentum_effect.calculate_momentum()

    expected_momentum = pd.Series([-0.05, -0.05555556])
    assert momentum == expected_momentum

def test_identify_momentum_leaders() -> None:
    """
    Tests the `identify_momentum_leaders` method of the `MomentumEffect` class.
    """

    data = pd.DataFrame({'Close': [100, 95, 90, 85, 80]})
    momentum_effect = MomentumEffect(data)

    momentum_leaders = momentum_effect.identify_momentum_leaders(top_n=2)

    expected_momentum_leaders = pd.Series([100, 95])
    assert momentum_leaders == expected_momentum_leaders

Python

from typing import List

import numpy as np
import pandas as pd

class MomentumEffect:
    def __init__(self, data: pd.DataFrame) -> None:
        self.data = data

    def calculate_momentum(self) -> pd.Series:
        """
        Calculates the momentum effect for each commodity futures contract in the data.

        Returns:
            A Pandas Series containing the momentum effect for each commodity futures contract.
        """

        momentum = (self.data['Close'] - self.data['Close'].shift(1)) / self.data['Close'].shift(1)
        return momentum

    def identify_momentum_leaders(self, top_n: int = 10) -> List[str]:
        """
        Identifies the commodity futures contracts with the highest momentum effect.

        Args:
            top_n (int, optional): The number of commodity futures contracts to identify.

        Returns:
            A list of strings containing the commodity futures contracts with the highest momentum effect.
        """

        momentum = self.calculate_momentum()
        momentum_leaders = momentum.sort_values(ascending=False).head(top_n)
        return momentum_leaders.index.tolist()

if __name__ == '__main__':
    # Load the commodity futures data
    data: pd.DataFrame = pd.read_csv('commodity_futures_data.csv', index_col='Date')

    # Create a MomentumEffect object
    momentum_effect: MomentumEffect = MomentumEffect(data)

    # Calculate the momentum effect for each commodity futures contract
    momentum: pd.Series = momentum_effect.calculate_momentum()

    # Identify the commodity futures contracts with the highest momentum effect
    momentum_leaders: List[str] = momentum_effect.identify_momentum_leaders()

    # Print the commodity futures contracts with the highest momentum effect
    print(momentum_leaders)

Conclusion

The momentum effect in commodities futures is a statistical anomaly that states that commodities futures that have outperformed in the recent past tend to continue to outperform shortly. This effect has been observed in various commodity markets, including energy, metals, and agriculture.

There are some possible explanations for the momentum effect in commodities futures. One possibility is that it is due to investors’ tendency to extrapolate past trends into the future. Another possibility is that it is due to investors’ herding behaviour, where investors follow the lead of other investors and buy commodities that are already rising in price.

The momentum effect can be used to develop trading strategies that profit from the tendency of commodities futures contracts with a high momentum effect to continue to outperform the market shortly.

However, it is important to note that the momentum effect is not guaranteed to continue in the future. Commodity futures markets are volatile, and momentum strategies can lead to large losses if the trend reverses.

Overall, the momentum effect is a complex topic with much potential for further research.

Ali Kayani

https://www.linkedin.com/in/ali-kayani-silvercoder007/

Post navigation

Leave a Comment

Leave a Reply

Your email address will not be published. Required fields are marked *

On Balance Volume stock indicator

Capital Asset Pricing Model (CAPM)

The Quest for low latency in finance

Dual Thrust Trading Algorithm