Cross-Sectional Seasonalities in International Government Bond Returns

1from AlgorithmImports import *
2import data_tools
3from collections import deque
4class SeasonalitiesBondReturns(QCAlgorithm):
5def Initialize(self):
6self.SetStartDate(2000, 1, 1)
7self.SetCash(100000)
8
9self.symbols = {
10    "ASX_XT1",       # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1 (Australia)
11    "MX_CGB1",       # Ten-Year Government of Canada Bond Futures, Continuous Contract #1 (Canada)
12    "EUREX_FOAT1",   # Euro-OAT Futures, Continuous Contract #1 (France)
13    "EUREX_FGBL1",   # Euro-Bund (10Y) Futures, Continuous Contract #1 (Germany)
14    "LIFFE_R1",      # Long Gilt Futures, Continuous Contract #1 (U.K.)
15    "EUREX_FBTP1",   # Long-Term Euro-BTP Futures, Continuous Contract #1 (Italy)
16    "CME_TY1",       # 10 Yr Note Futures, Continuous Contract #1 (USA)
17    "SGX_JB1"        # SGX 10-Year Mini Japanese Government Bond Futures, Continuous Contract #1 (Japan)
18}
19# daily price data
20self.data = {}
21
22# monthly returns
23self.monthly_return = {}
24
25self.daily_period = 21
26self.monthly_period = 20 * 12
27self.traded_count = 1
28for symbol in self.symbols:
29    # Bond future data.
30    data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
31    data.SetFeeModel(data_tools.CustomFeeModel())
32    data.SetLeverage(5)
33    
34    self.data[symbol] = RollingWindow[float](self.daily_period)
35    self.monthly_return[symbol] = deque(maxlen=self.monthly_period)
36
37self.settings.minimum_order_margin_portfolio_percentage = 0.
38self.settings.daily_precise_end_time = False
39self.rebalance_flag: bool = False
40self.Schedule.On(self.DateRules.MonthEnd('ASX_XT1'), self.TimeRules.At(0, 0), self.Rebalance)
41
42def OnData(self, data):
43# store monthly future returns
44for symbol in self.symbols:
45    if symbol in data and data[symbol]:
46        price = data[symbol].Value
47        self.data[symbol].Add(price)
48
49if not self.rebalance_flag:
50    return
51self.rebalance_flag = False
52curr_month = self.Time.month
53SAME = {}
54
55# store monthly returns
56for symbol in self.symbols:
57    if self.Securities[symbol].GetLastData() and self.Time.date() < data_tools.QuantpediaFutures.get_last_update_date()[symbol]:
58        if self.data[symbol].IsReady:
59            monthly_ret = self.data[symbol][0] / self.data[symbol][self.daily_period - 1] - 1
60            self.monthly_return[symbol].append((monthly_ret, curr_month))
61            
62            # monthly returns are ready
63            if len(self.monthly_return[symbol]) >= self.monthly_period / 2:
64                next_month = curr_month+1 if curr_month < 12 else 1
65                same_month_returns = [x[0] for x in self.monthly_return[symbol] if x[1] == next_month]
66                SAME[symbol] = np.mean(same_month_returns)
67    else:
68        self.liquidate(symbol)
69        continue
70
71long = []
72short = []
73if len(SAME) >= self.traded_count * 2:
74    # decile = int(len(SAME) / self.quantile)
75    # count = decile
76
77    # sorting by SAME
78    sorted_by_SAME = sorted(SAME.items(), key = lambda x: x[1], reverse = True)
79    long = [x[0] for x in sorted_by_SAME[:self.traded_count]]
80    short = [x[0] for x in sorted_by_SAME[-self.traded_count:]]
81# order execution
82targets: List[PortfolioTarget] = []
83for i, portfolio in enumerate([long, short]):
84    for symbol in portfolio:
85        if symbol in data and data[symbol]:
86            targets.append(PortfolioTarget(symbol, ((-1) ** i) / self.traded_count))
87
88self.SetHoldings(targets, True)
89def Rebalance(self):
90self.rebalance_flag = True