Timing Carry Trade

1import data_tools
2from AlgorithmImports import *
3from collections import deque
4import numpy as np
5class TimingCarryTrade(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2000, 1, 1) 
8self.SetCash(100000)
9
10# Source: https://www.quandl.com/data/OECD-Organisation-for-Economic-Co-operation-and-Development
11self.symbols = {
12                "CME_AD1" : "OECD/KEI_IR3TIB01_AUS_ST_M",   # Australian Dollar Futures, Continuous Contract #1
13                "CME_BP1" : "OECD/KEI_IR3TIB01_GBR_ST_M",   # British Pound Futures, Continuous Contract #1
14                "CME_CD1" : "OECD/KEI_IR3TIB01_CAN_ST_M",   # Canadian Dollar Futures, Continuous Contract #1
15                "CME_EC1" : "OECD/KEI_IR3TIB01_EA19_ST_M",  # Euro FX Futures, Continuous Contract #1
16                "CME_JY1" : "OECD/KEI_IR3TIB01_JPN_ST_M",   # Japanese Yen Futures, Continuous Contract #1
17                "CME_MP1" : "OECD/KEI_IR3TIB01_MEX_ST_M",   # Mexican Peso Futures, Continuous Contract #1
18                "CME_NE1" : "OECD/KEI_IR3TIB01_NZL_ST_M",   # New Zealand Dollar Futures, Continuous Contract #1
19                "CME_SF1" : "SNB/ZIMOMA"                    # Swiss Franc Futures, Continuous Contract #1
20}
21
22# Daily price data.
23self.data = {}
24self.period = 3 * 21
25self.SetWarmUp(self.period)
26# Regression data rolling window.
27self.regression_min_period = 60
28self.regression_data = deque()
29
30self.commodity_index = self.AddEquity('DBC', Resolution.Daily).Symbol    
31self.data[self.commodity_index] = deque(maxlen = self.period)
32
33# Quandl ted spread.
34self.ted_spread = self.AddData(data_tools.QuandlValue, 'FRED/TEDRATE', Resolution.Daily).Symbol
35
36# Last selected long and short symbols used to calculate monthly performace of carry trade.
37self.long = []
38self.short = []
39
40for symbol, rate_symbol in self.symbols.items():
41    self.AddData(data_tools.QuandlValue, rate_symbol, Resolution.Daily)
42    data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
43    data.SetFeeModel(data_tools.CustomFeeModel())
44    data.SetLeverage(5)
45    
46    self.data[symbol] = deque(maxlen = self.period)
47
48self.Schedule.On(self.DateRules.MonthStart(self.commodity_index), self.TimeRules.AfterMarketOpen(self.commodity_index), self.Rebalance)
49def OnData(self, data):
50for symbol in self.data:
51    symbol_obj = self.Symbol(symbol)
52    if symbol_obj in data and data[symbol_obj]:
53        price = data[symbol_obj].Value
54        self.data[symbol].append(price)
55def Rebalance(self):
56# make sure data is still comming in
57if self.Securities[self.commodity_index].GetLastData() and (self.Time.date() - self.Securities[self.commodity_index].GetLastData().Time.date()).days > 5:
58    self.Liquidate()
59    return
60if self.Securities[self.ted_spread].GetLastData() and (self.Time.date() - self.Securities[self.ted_spread].GetLastData().Time.date()).days > 5:
61    self.Liquidate()
62    return
63
64# Regression data.
65carry_trade_performance = None
66delta_crb = None
67delta_vol = None
68ted_spread = None
69
70# Calculate carry trade last month's performance.
71if len(self.long) != 0 and len(self.short) != 0:
72    carry_trade_perf_long = np.sum([data_tools.Return([y for y in self.data[x]][-21:]) for x in self.long if x in self.data and len(self.data[x]) == self.data[x].maxlen])
73    carry_trade_perf_short = np.sum([-1 * data_tools.Return([y for y in self.data[x]][-21:]) for x in self.short if x in self.data and len(self.data[x]) == self.data[x].maxlen])
74    carry_trade_performance = (carry_trade_perf_long + carry_trade_perf_short) / len(self.long + self.short)
75    self.long.clear()
76    self.short.clear()
77    
78# Interbank rate sorting.
79sorted_by_rate = sorted([y for y in self.symbols if self.Securities[y].GetLastData() and (self.Time.date() - self.Securities[y].GetLastData().Time.date()).days <= 5 and \
80                        self.Securities[self.symbols[y]].GetLastData() and (self.Time.date() - self.Securities[self.symbols[y]].GetLastData().Time.date()).days <= 31],   \
81    key = lambda x: self.Securities[self.symbols[x]].Price, reverse = True)
82traded_count = 3
83if len(sorted_by_rate) > traded_count*2:
84    self.long = [x for x in sorted_by_rate[:traded_count]]
85    self.short = [x for x in sorted_by_rate[-traded_count:]]
86
87# Commodity index log change.
88if len(self.data[self.commodity_index]) == self.data[self.commodity_index].maxlen:
89    delta_crb = np.log(self.data[self.commodity_index][-1] / self.data[self.commodity_index][0]) / 3
90    
91# Average volatility.
92# t and t-3 volatility tuples.
93t_t_3_vol = [(data_tools.Volatility([y for y in self.data[x]][-21:]), data_tools.Volatility([y for y in self.data[x]][:21])) for x in self.symbols if x in self.data and len(self.data[x]) == self.data[x].maxlen]
94if len(t_t_3_vol) != 0:
95    avg_t_volatility = np.average([x[0] for x in t_t_3_vol])
96    avg_t_3_volatility = np.average([x[1] for x in t_t_3_vol])
97    if avg_t_volatility != 0 and avg_t_3_volatility != 0:
98        delta_vol = np.log(avg_t_volatility / avg_t_3_volatility) / 3
99# TED spread.
100if self.Securities[self.ted_spread].Price != 0:
101    ted_spread = self.Securities[self.ted_spread].Price
102
103if delta_crb and delta_vol and ted_spread:
104    self.regression_data.append((carry_trade_performance, delta_crb, delta_vol, ted_spread))
105
106# Regression data is ready.
107if len(self.regression_data) >= self.regression_min_period:
108    carry_trade_performances = [float(x[0]) for x in self.regression_data]
109    crb_deltas = [float(x[1]) for x in self.regression_data]
110    vol_deltas = [float(x[2]) for x in self.regression_data]
111    ted_spreads = [float(x[3]) for x in self.regression_data]
112
113    # Regression.
114    x = [crb_deltas[:-1], vol_deltas[:-1], ted_spreads[:-1]]
115    regression_model = data_tools.MultipleLinearRegression(x, carry_trade_performances[1:])
116    # Predicted carry return.
117    alpha = regression_model.params[0]
118    prediction_x = [crb_deltas[-1], vol_deltas[-1], ted_spreads[-1]]
119    betas = np.array(regression_model.params[1:])
120    carry_return_predicted = alpha + sum(np.multiply(betas, prediction_x))
121    
122    if carry_return_predicted > 0:
123        # Trade execution.
124        invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
125        for symbol in invested:
126            if symbol not in self.long + self.short:
127                self.Liquidate(symbol)
128        
129        for symbol in self.long:
130            self.SetHoldings(symbol, 1 / len(self.long))
131        for symbol in self.short:
132            self.SetHoldings(symbol, -1 / len(self.short))