Original paper

SSRN-id3360787.pdf1176.6KB

Abstract

Annual rebalancing of the S&P GSCI index provides a novel and strong identification to estimate the shape of supply curves for commodity futures contracts. Using the 24 commodities included in the S&P GSCI for 2004–2017, we show that cumulative abnormal returns (CARs) reach a peak of 59 basis points in the middle of the week following the rebalancing period, but the impact is temporary as it declines to near zero within the next week. The findings provide clear evidence that the supply curve for commodity futures contracts is upward sloping in the short-run but almost flat in the longer-run.

Keywords: commodity futures, index, limits to arbitrage, order flow, rebalancing

Trading rules

Commodity futures from S&P GSCI Index form the investment universe.
Every November, reevaluate the GSCI index weightings.
On the closing of January’s 5th working day, initiate short positions for commodities with reduced index weights.
Allocate portfolio weights based on changes in S&P GSCI Index weights
Cover short positions by the end of the 10th business day

Python code

Backtrader

import backtrader as bt
import datetime

class GSCIStrategy(bt.Strategy):
    def __init__(self):
        self.gsci_weights = {}  # To store the new GSCI index weights

    def next(self):
        date = self.data.datetime.date(0)
        november_month = 11
        january_month = 1
        
        # Determine new GSCI index weights each November
        if date.month == november_month and date.day == 1:
            self.gsci_weights = self.calculate_gsci_weights()  # Implement your calculation logic

        # On January's 5th business day close, short commodities with decreased weights
        if date.month == january_month and self.get_business_day(date) == 5:
            for symbol, data in self.getdatabyname().items():
                new_weight = self.gsci_weights.get(symbol, 0)
                old_weight = self.get_old_weight(symbol)  # Implement your logic to get old weight
                
                if new_weight < old_weight:
                    weight_diff = old_weight - new_weight
                    size = self.calculate_position_size(symbol, weight_diff)
                    self.sell(data=data, size=size)

        # Cover short positions by the end of the 10th business day
        if date.month == january_month and self.get_business_day(date) == 10:
            for symbol, data in self.getdatabyname().items():
                position = self.getpositionbyname(symbol)
                if position.size < 0:
                    self.buy(data=data, size=abs(position.size))

    def get_business_day(self, date):
        # Implement your logic to get the business day of the month
        pass

    def calculate_gsci_weights(self):
        # Implement your logic to calculate new GSCI index weights
        pass

    def get_old_weight(self, symbol):
        # Implement your logic to get old GSCI index weight for a symbol
        pass

    def calculate_position_size(self, symbol, weight_diff):
        # Implement your logic to calculate position size based on weight difference
        pass

Is the Supply Curve for Commodity Futures Contracts Upward Sloping?

Original paper

Abstract

Trading rules

Python code

Backtrader