Unemployment Gap Factor in Fixed Income

1import numpy as np
2from AlgorithmImports import *
3import data_tools
4class UnemploymentGapFactorinFixedIncome(QCAlgorithm):
5def Initialize(self):
6self.SetStartDate(2000, 1, 1)
7self.SetCash(100000)
8
9self.symbols = {
10                "ASX_XT1"       : "RBA/H05_GLFSURSA",   # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1 (Australia)
11                "MX_CGB1"       : "UKONS/ZXDZ_M",       # Ten-Year Government of Canada Bond Futures, Continuous Contract #1 (Canada)
12                "EUREX_FGBL1"   : "UKONS/ZXDK_M",       # Euro-Bund (10Y) Futures, Continuous Contract #1 (Germany)
13                "LIFFE_R1"      : "UKONS/YCNO_M",       # Long Gilt Futures, Continuous Contract #1 (U.K.)
14                "CME_TY1"       : "UKONS/ZXDX_M"        # 10 Yr Note Futures, Continuous Contract #1 (USA)
15                }
16# Monthly unemployment data.
17self.data = {}
18self.period = 3 * 12
19
20for symbol in self.symbols:
21    data = self.AddData(data_tools.QuantpediaFutures, symbol, Resolution.Daily)
22    data.SetFeeModel(data_tools.CustomFeeModel())
23    data.SetLeverage(5)
24    
25    unempl_symbol = self.symbols[symbol]
26    if unempl_symbol == 'ASX_XT1':
27        data = self.AddData(data_tools.UnemploymentDataAUD, unempl_symbol, Resolution.Daily)
28    else:
29        data = self.AddData(data_tools.UnemploymentData, unempl_symbol, Resolution.Daily)
30    self.data[symbol] = RollingWindow[float](self.period)
31    
32first_key = [x for x in self.symbols.keys()][0]
33self.Schedule.On(self.DateRules.MonthStart(self.symbols[first_key]), self.TimeRules.At(0, 0), self.Rebalance)
34
35def OnData(self, data):
36# store monthly rates
37for symbol in self.symbols:
38    unempl_symbol = self.symbols[symbol]
39    if unempl_symbol in data and data[unempl_symbol]:
40        unempl_rate = data[unempl_symbol].Value
41        if unempl_rate != 0:
42            self.data[symbol].Add(unempl_rate)
43
44def Rebalance(self):
45# Difference from MA.
46ma_diff = {}
47for symbol in self.symbols:
48    if self.Securities[symbol].GetLastData() and (self.Time.date() - self.Securities[symbol].GetLastData().Time.date()).days < 5:
49        # Unemployment data is ready to calculate MA.
50        if self.data[symbol].IsReady:
51            # Calculate difference from MA.
52            ma = np.mean([x for x in self.data[symbol]])
53            if ma != 0:
54                current_value = self.data[symbol][0]
55                ma_diff[symbol] = current_value - ma
56
57if len(ma_diff) != 0:
58    # Difference weighting.
59    avg_diff = np.mean([x[1] for x in ma_diff.items()])
60    diff_from_avg = { symbol: diff - avg_diff for symbol, diff in ma_diff.items() }
61    
62    total_diff = sum([abs(x[1]) for x in diff_from_avg.items()])
63    weight = { symbol: diff / total_diff for symbol, diff in diff_from_avg.items() }
64
65    for symbol, w in weight.items():
66        self.SetHoldings(symbol, w)
67else:
68    self.Liquidate()