结合国家内动量与价值效应的策略

1from AlgorithmImports import *
2#endregion
3class MomentumCombinedwithValueEffectwithinCountries(QCAlgorithm):
4def Initialize(self):
5self.SetStartDate(2010, 1, 1)
6self.SetCash(100000)
7self.symbols = {
8    'Argentina' : 'ARGT',
9    'Australia' : 'EWA',
10    'Austria' : 'EWO',
11    'Belgium' : 'EWK',
12    'Brazil' : 'EWZ',
13    'Canada' : 'EWC',
14    'Chile' : 'ECH',
15    'China' : 'FXI',
16    'Egypt' : 'EGPT',
17    'France' : 'EWQ',
18    'Germany' : 'EWG',
19    'Hong Kong' : 'EWH',
20    'India' : 'INDA',
21    'Indonesia' : 'EIDO',
22    'Ireland' : 'EIRO',
23    'Israel' : 'EIS',
24    'Italy' : 'EWI',
25    'Japan' : 'EWJ',
26    'Malaysia' : 'EWM',
27    'Mexico' : 'EWW',
28    'Netherlands' : 'EWN',
29    'New Zealand' : 'ENZL',
30    'Norway' : 'NORW',
31    'Philippines' : 'EPHE',
32    'Poland' : 'EPOL',
33    'Russia' : 'ERUS',
34    'Saudi Arabia' : 'KSA',
35    'Singapore' : 'EWS',
36    'South Africa' : 'EZA',
37    'South Korea' : 'EWY',
38    'Spain' : 'EWS',
39    'Sweden' : 'EWD',
40    'Switzerland' : 'EWL',
41    'Taiwan' : 'EWT',
42    'Thailand' : 'THD',
43    'Turkey' : 'TUR',
44    'United Kingdom' : 'EWU',
45    'United States' : 'SPY'
46}
47
48self.data:dict[str, RollingWindow] = {}
49self.period:int = 12 * 21
50self.SetWarmUp(self.period, Resolution.Daily)
51
52for symbol in self.symbols:
53    data = self.AddEquity(self.symbols[symbol], Resolution.Daily)
54    data.SetLeverage(5)
55    
56    self.data[symbol] = RollingWindow[float](self.period)
57self.recent_month:int = -1
58self.max_missing_days:int = 365
59self.quantile:int = 3
60self.country_pb_data:Symbol = self.AddData(CountryPB, 'CountryData').Symbol
61
62def OnData(self, data:Slice) -> None:
63# store daily data
64for symbol, etf in self.symbols.items():
65    etf_symbol:Symbol = self.Symbol(etf)
66    if etf_symbol in data and data[etf_symbol]:
67        self.data[symbol].Add(data[etf_symbol].Value)
68
69# rebalance once a month
70if self.recent_month == self.Time.month:
71    return
72self.recent_month = self.Time.month
73if self.Securities[self.country_pb_data].GetLastData() and (self.Time.date() - self.Securities[self.country_pb_data].GetLastData().Time.date()).days > self.max_missing_days:
74    self.Liquidate()
75    return
76bm_data:dict[str, float] = {}
77performance:dict[str, float] = {}
78country_pb_data = self.Securities[self.country_pb_data].GetLastData()
79if country_pb_data:
80    for symbol in self.symbols:
81        if self.data[symbol].IsReady:
82            pb:float = country_pb_data[symbol]
83            bm_data[symbol] = 1 / pb
84            closes:List[float] = list(self.data[symbol])
85            performance[symbol] = closes[0] / closes[-1] - 1
86long:List[str]= []
87short:List[str] = []
88if len(bm_data) >= self.quantile * 2:
89    sorted_by_bm:List = sorted(bm_data.items(), key = lambda x: x[1], reverse = True)
90    quantile:int = int(len(bm_data) / self.quantile)
91    high_by_bm = [x[0] for x in sorted_by_bm[:quantile]]
92    low_by_bm = [x[0] for x in sorted_by_bm[-quantile:]]
93    
94    high_by_perf:List = sorted(high_by_bm, key = lambda x: performance[x], reverse = True)
95    quantile = int(len(high_by_perf) / self.quantile)
96    long = [x for x in high_by_perf[:quantile]]
97    
98    low_by_perf:List = sorted(low_by_bm, key = lambda x: performance[x], reverse = True)
99    quantile = int(len(low_by_perf) / self.quantile)
100    short = [x for x in low_by_perf[-quantile:]]
101
102invested:List[str] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
103for symbol in invested:
104    if symbol not in long + short:
105        self.Liquidate(symbol)
106long_count:int = len(long)
107short_count:int = len(short)
108
109for symbol in long:
110    traded_symbol:str = self.symbols[symbol]
111    if traded_symbol in data and data[traded_symbol]:
112        self.SetHoldings(traded_symbol, 1 / long_count)
113for symbol in short:
114    traded_symbol:str = self.symbols[symbol]
115    if traded_symbol in data and data[traded_symbol]:
116        self.SetHoldings(traded_symbol, -1 / short_count)
117# Country PB data
118# NOTE: IMPORTANT: Data order must be ascending (date-wise)
119from dateutil.relativedelta import relativedelta
120class CountryPB(PythonData):
121def GetSource(self, config, date, isLiveMode):
122return SubscriptionDataSource("data.quantpedia.com/backtesting_data/economic/country_pb.csv", SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
123def Reader(self, config, line, date, isLiveMode):
124data = CountryPB()
125data.Symbol = config.Symbol
126
127if not line[0].isdigit(): return None
128split = line.split(';')
129
130data.Time = datetime.strptime(split[0], "%Y") + relativedelta(years=1)
131self.symbols = ['Argentina','Australia','Austria','Belgium','Brazil','Canada','Chile','China','Egypt','France','Germany','Hong Kong','India','Indonesia','Ireland','Israel','Italy','Japan','Malaysia','Mexico','Netherlands','New Zealand','Norway','Philippines','Poland','Russia','Saudi Arabia','Singapore','South Africa','South Korea','Spain','Sweden','Switzerland','Taiwan','Thailand','Turkey','United Kingdom','United States']
132index = 1
133for symbol in self.symbols:
134    data[symbol] = float(split[index])
135    index += 1
136    
137data.Value = float(split[1])
138return data