Convenience Yield Risk Factor Predicts Commodity Futures Returns

1from AlgorithmImports import *
2import data_tools
3import numpy as np
4from typing import List, Dict, Tuple
5# endregion
6
7class ConvenienceYieldRiskFactorPredictsCommodityFuturesReturns(QCAlgorithm):
8
9def Initialize(self):
10self.SetStartDate(2008, 1, 1)
11self.SetCash(100000)
12
13self.period:int = 12
14self.filter_period:int = 182
15
16self.tickers:dict[str, str] = {
17    "CME_S1"  : Futures.Grains.Soybeans,
18    "CME_W1"  : Futures.Grains.Wheat,
19    "CME_SM1" : Futures.Grains.SoybeanMeal,
20    "CME_BO1" : Futures.Grains.SoybeanOil,
21    "CME_C1"  : Futures.Grains.Corn,
22    "CME_O1"  : Futures.Grains.Oats,
23    "CME_LC1" : Futures.Meats.LiveCattle,
24    "CME_FC1" : Futures.Meats.FeederCattle,
25    "CME_LN1" : Futures.Meats.LeanHogs,
26    "CME_GC1" : Futures.Metals.Gold,
27    "CME_SI1" : Futures.Metals.Silver,
28    "CME_PL1" : Futures.Metals.Platinum,
29    "CME_HG1" : Futures.Metals.Copper,
30    "CME_LB1" : Futures.Forestry.RandomLengthLumber,
31    "CME_NG1" : Futures.Energies.NaturalGas,
32    "CME_PA1" : Futures.Metals.Palladium,
33    "CME_CU1" : Futures.Energies.ChicagoEthanolPlatts,
34    "CME_DA1" : Futures.Dairy.ClassIIIMilk,
35    "ICE_CC1" : Futures.Softs.Cocoa,
36    "ICE_CT1" : Futures.Softs.Cotton2,
37    "ICE_KC1" : Futures.Softs.Coffee,
38    "ICE_O1"  : Futures.Energies.HeatingOil,
39    "ICE_OJ1" : Futures.Softs.OrangeJuice,
40    "ICE_SB1" : Futures.Softs.Sugar11CME,
41}
42
43self.leverage:int = 2
44self.data:Dict[Symbol, Tuple[float, float]] = {}
45self.cyr_signal:Dict[Symbol, RollingWindow] = {}
46
47self.futures_data:dict[Symbol, data_tools.FuturesData] = {}
48
49for qp_ticker, qc_ticker in self.tickers.items():
50    # subscribe Quantpedia data
51    security:Security = self.AddData(data_tools.QuantpediaFutures, qp_ticker, Resolution.Daily)
52    security.SetFeeModel(data_tools.CustomFeeModel())
53    security.SetLeverage(self.leverage)
54
55    qp_symbol:Symbol = security.Symbol
56
57    # QC futures
58    future:Future = self.AddFuture(qc_ticker, Resolution.Daily, dataNormalizationMode=DataNormalizationMode.Raw)
59    future.SetFilter(0, self.filter_period)
60    future_symbol:str = future.Symbol
61    self.futures_data[future_symbol] = qp_symbol
62
63self.recent_month:int = -1
64
65def OnData(self, data:Slice):
66qp_custom_data_last_update_date:Dict[Symbol, datetime.date] = data_tools.QuantpediaFutures._last_update_date
67
68if all([self.Securities[x].GetLastData() for x in list(self.futures_data.keys())]) and any([self.Time.date() >= qc_custom_data_last_update_date[x] for x in qc_custom_data_last_update_date]):
69    self.Liquidate()
70    return
71
72# save daily data 
73for contract_symbol, chain in data.FutureChains.items():
74    if len([i for i in chain]) >= 3:
75        sorted_by_date:List[Symbol] = sorted(chain, key=lambda x: x.Expiry)
76
77        first_convenience_yield:float = (365 * (sorted_by_date[0].LastPrice - sorted_by_date[1].LastPrice)) / ((sorted_by_date[1].Expiry - self.Time).days - (sorted_by_date[0].Expiry - self.Time).days)
78        second_convenience_yield:float = (365 * (sorted_by_date[1].LastPrice - sorted_by_date[2].LastPrice)) / ((sorted_by_date[2].Expiry - self.Time).days - (sorted_by_date[1].Expiry - self.Time).days)
79
80        if contract_symbol not in self.data:
81            self.data[contract_symbol] = []
82        self.data[contract_symbol].append((first_convenience_yield, second_convenience_yield))
83
84# monthly rebalance
85if self.Time.month == self.recent_month:
86    return
87self.recent_month = self.Time.month
88
89if len(self.data) == 0:
90    self.Liquidate()
91    return
92
93# save convenience yield risk values
94for contract_symbol, cyr_values in self.data.items():
95    if len(cyr_values) != 0:
96        first_std:float = np.std([i[0] for i in cyr_values])
97        second_std:float = np.std([i[1] for i in cyr_values])
98
99        if contract_symbol not in self.cyr_signal:
100            self.cyr_signal[contract_symbol] = RollingWindow[float](self.period)
101        
102        self.cyr_signal[contract_symbol].Add(first_std - second_std)
103
104self.data.clear()
105
106if len(self.cyr_signal) == 0:
107    self.Liquidate()
108    return
109
110cyr_mean:Dict[Symbol, float] = {}
111
112# mean of 12 months convenience yield risk values
113for contract_symbol, cyr_signals in self.cyr_signal.items():
114    if cyr_signals.IsReady:
115        if contract_symbol not in cyr_mean:
116            cyr_mean[contract_symbol] = np.mean(list(cyr_signals))
117
118# sort and divide
119if len(cyr_mean) != 0:
120    sorted_cyr:List[Symbol] = sorted(cyr_mean.items(), key=lambda x:x[1])
121    CYR_median:float = np.median([i[1] for i in sorted_cyr])
122    high:List[Symbol] = [symbol for symbol, cyr_value in sorted_cyr if cyr_value > CYR_median]
123    low:List[Symbol] = [symbol for symbol, cyr_value in sorted_cyr if cyr_value <= CYR_median]
124
125    # trade execution
126    invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
127    for symbol in invested:
128        if symbol not in high + low:
129            self.Liquidate(symbol)
130
131    for symbol in high:
132        if self.futures_data[symbol] in data and data[self.futures_data[symbol]]:
133            self.SetHoldings(self.futures_data[symbol], 1 / len(high))
134    
135    for symbol in low:
136        if self.futures_data[symbol] in data and data[self.futures_data[symbol]]:
137            self.SetHoldings(self.futures_data[symbol], -1 / len(low))