Market Timing Using Lumber/Gold Ratio

1import numpy as np
2from AlgorithmImports import *
3class LumberGoldRatio(QCAlgorithm):
4def Initialize(self):
5 self.SetStartDate(2000, 1, 1)
6 self.SetCash(100000)
7 self.etfs = ['IWM', 'IEF']
8 self.symbols = ['CME_LB1', 'CME_GC1']
9 self.data = {}
10 ret_period = 13 * 5
11 self.SetWarmUp(ret_period)
12 
13 for symbol in self.etfs:
14     self.AddEquity(symbol, Resolution.Daily, leverage=4)
15 
16 for symbol in self.symbols:
17     data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily)
18     data.SetLeverage(5)
19     self.data[symbol] = SymbolData(ret_period)
20 
21 self.rebalance_flag: bool = False
22 self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen(self.etfs[0]), self.Rebalance)
23def OnData(self, data):
24 for symbol in self.symbols:
25     if self.securities[symbol].get_last_data() and self.time.date() > QuantpediaFutures.get_last_update_date()[symbol]:
26         self.liquidate()
27         return
28     if symbol in data and data[symbol]:
29         self.data[symbol].update(data[symbol].Value)
30 if not self.rebalance_flag:
31     return
32 self.rebalance_flag = False
33 lumber_data = self.data[self.symbols[0]]
34 gold_data = self.data[self.symbols[1]]
35 if all([data.contains_key(symbol) and data[symbol] for symbol in self.etfs]):
36     if lumber_data.is_ready() and gold_data.is_ready():
37         if lumber_data.performance() > gold_data.performance():
38             if self.Portfolio['IEF'].Invested:
39                 self.Liquidate('IEF')
40             self.SetHoldings('IWM', 1)
41         else:
42             if self.Portfolio['IWM'].Invested:
43                 self.Liquidate('IWM')
44             self.SetHoldings('IEF', 1)
45def Rebalance(self):
46 self.rebalance_flag = True
47class SymbolData:
48def __init__(self, ret_lookback):
49 self.history = RollingWindow[float](ret_lookback)
50 self.price = 0.0
51def is_ready(self):
52 return self.history.IsReady
53 
54def update(self, value):
55 self.price = float(value)
56 self.history.Add(float(value))
57def performance(self):
58 prices = np.array([x for x in self.history])
59 return (prices[-1]-prices[0])/prices[0]
60# Quantpedia data.
61# NOTE: IMPORTANT: Data order must be ascending (datewise)
62class QuantpediaFutures(PythonData):
63_last_update_date:Dict[Symbol, datetime.date] = {}
64@staticmethod
65def get_last_update_date() -> Dict[Symbol, datetime.date]:
66return QuantpediaFutures._last_update_date
67def GetSource(self, config, date, isLiveMode):
68 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
69def Reader(self, config, line, date, isLiveMode):
70 data = QuantpediaFutures()
71 data.Symbol = config.Symbol
72 
73 if not line[0].isdigit(): return None
74 split = line.split(';')
75 
76 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
77 data['back_adjusted'] = float(split[1])
78 data['spliced'] = float(split[2])
79 data.Value = float(split[1])
80 if config.Symbol.Value not in QuantpediaFutures._last_update_date:
81     QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
82 if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
83     QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()
84 return data