# Strategy Library

## Momentum Effect Combined with Term Structure in Commodities

### Introduction

This algorithm will combine the term structure effect and the momentum effect to exploit the trading signals in commodity futures. The momentum strategy goes long commodity futures with the highest one-month momentum and shorts on futures with the lowest one-month momentum. Signals are based on the historical return. While the portfolio in term structure strategy is created with futures contracts in deep contango and backwardation. It goes long commodities with the highest roll-returns and short commodities with the lowest roll-returns and holds the long-short positions for one month.

### Method

#### Roll Return

The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary `self.chains`

.
First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross-section of futures contracts into 3 portfolios, labeled `Low`

, `Med`

and `High`

. For the formula of the roll-return, please see Term Structure Effect in Commodities.
Contracts fall into `Med`

level are eliminated from the universe. Next we'll calculate the mean return of contracts in `High`

and `Low`

.

roll_return = {} for symbol, chain in self.chains.items(): contracts = sorted(chain, key = lambda x: x.Expiry) expiry_nearest = contracts[0].Expiry price_nearest = float(contracts[0].LastPrice) if contracts[0].LastPrice>0 else 0.5*float(contracts[0].AskPrice+contracts[0].BidPrice) for x in contracts[1:]: roll_return[x] = (price_nearest-float(x.LastPrice))*365 / (x.Expiry-expiry_nearest).days sorted_by_roll_return = sorted(roll_return, key = lambda x: roll_return[x], reverse =True) tertile = floor(1/3*len(sorted_by_roll_return)) high = sorted_by_roll_return[:tertile] low = sorted_by_roll_return[-tertile:]

#### Mean Return

The second filter is the historical mean return. We sort the contracts in the High portfolio into two sub-portfolios (High-Winner and High-Loser) based on the mean return of the commodities over the past one month. High-Winner is thus made of the commodities that have both the highest roll-returns at the time of portfolio construction and the best past performance. Similarly, we sort the commodities in the Low portfolio into two sub-portfolios (Low-Winner and Low-Loser) based on their mean return over the past one months. Low-Loser contains, therefore, commodities that have both the lowest roll-returns at the time of portfolio construction and the worst past performance.

mean_return_high = {} for i in high: hist = self.History(i.Symbol, timedelta(days = 21), Resolution.Minute) if hist.empty: continue hist_close = hist['close'][i.Expiry][i.Symbol.Value] mean_return_high[i] = np.mean(hist_close.pct_change()) high_winners = sorted(mean_return_high, key = lambda x: mean_return_high[x], reverse=True)[:int(len(high)*0.5)] mean_return_low = {} for i in low: hist = self.History(i.Symbol, timedelta(days = 21), Resolution.Minute) if hist.empty: continue hist_close = hist['close'][i.Expiry][i.Symbol.Value] mean_return_low[i] = np.mean(hist_close.pct_change()) low_losers = sorted(mean_return_low, key = lambda x: mean_return_low[x], reverse=True)[-int(len(low)*0.5):]

The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the next month, the strategy liquidates the contracts invested and rebalances the portfolio.

short_weight = 0.5/len(low_losers) for short in low_losers: self.SetHoldings(short.Symbol, -short_weight) long_weight = 0.5/len(high_winners) for long in high_winners: self.SetHoldings(long.Symbol, long_weight)

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