异常现象中的季节性效应

1from AlgorithmImports import *
2#endregion
3# https://quantpedia.com/strategies/seasonality-effect-in-anomalies/
4#
5# The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, with monthly return data available at CRSP Database. Accounting data (such as firms’ book values) 
6# are obtained from Compustat. The investor takes the long position on three best-performing and short on three worst-performing anomaly portfolios. According to the same-calendar
7# month returns of the portfolios (their formation is outlined in the description of Table 4, page 43) over the period of the last 20 years. Stocks in the portfolios are 
8# value-weighted, the portfolio is to be rebalanced monthly and held for one month.
9#
10# QC implementation:
11#   - Seasonal performance is calculated over 10 year period.
12#   - Investment universe consists of Quantpedia's equity long-short anomalies.
13class SeasonalityEffectinAnomalies(QCAlgorithm):
14def Initialize(self):
15self.SetStartDate(2000, 1, 1)
16self.SetCash(100000)
17# ids with backtest period end year
18self.backtest_to = {}
19
20# daily price data
21self.perf = {}
22self.period = 21
23self.SetWarmUp(self.period)
24# monthly returns
25self.monthly_returns = {}
26self.min_seasonal_period = 10
27
28csv_string_file = self.Download('data.quantpedia.com/backtesting_data/equity/quantpedia_strategies/backtest_end_year.csv')
29lines = csv_string_file.split('\r\n')
30last_id = None
31for line in lines[1:]:
32    split = line.split(';')
33    id = str(split[0])
34    backtest_to = int(split[1])
35    
36    data = self.AddData(QuantpediaEquity, id, Resolution.Daily)
37    data.SetLeverage(10)
38    data.SetFeeModel(CustomFeeModel())
39    
40    self.backtest_to[id] = backtest_to
41    self.perf[id] = self.ROC(id, self.period, Resolution.Daily)
42    self.monthly_returns[id] = []
43    
44    if not last_id:
45        last_id = id
46self.recent_month = -1
47
48def OnData(self, data):
49if self.IsWarmingUp:
50    return
51if self.Time.month == self.recent_month:
52    return
53self.recent_month = self.Time.month
54
55seasonal_return = {}
56
57for id in self.perf:
58    if self.perf[id].IsReady and id in data and data[id]:
59        # store monthly returns
60        perf = self.perf[id].Current.Value
61        self.monthly_returns[id].append((perf, self.Time.month - 1))
62        
63        # calculate seasonal performance of those strategies, which were published last year and sooner
64        if self.backtest_to[id] < self.Time.year:
65            seasonal_monthly_returns = [x[0] for x in self.monthly_returns[id] if x[1] == self.Time.month]
66            
67            # monthly data for at least 10 years is ready
68            if len(seasonal_monthly_returns) >= self.min_seasonal_period:
69                seasonal_return[id] = sum(seasonal_monthly_returns[-self.min_seasonal_period:])
70long = []
71short = []
72
73# seasonal return sorting
74count_traded = 3
75if len(seasonal_return) >= count_traded*2:
76    sorted_by_perf = sorted(seasonal_return.items(), key = lambda x: x[1], reverse = True)
77    long = [x[0] for x in sorted_by_perf[:count_traded]]
78    short = [x[0] for x in sorted_by_perf[-count_traded:]]
79# trade execution
80invested = [x.Key for x in self.Portfolio if x.Value.Invested]
81for symbol in invested:
82    if symbol not in long + short:
83        self.Liquidate(symbol)
84
85long_count = len(long)                
86short_count = len(short)
87
88for symbol in long:
89    self.SetHoldings(symbol, 1 / long_count)
90for symbol in short:
91    self.SetHoldings(symbol, -1 / short_count)
92    
93# Quantpedia strategy equity curve data.
94# NOTE: IMPORTANT: Data order must be ascending (datewise)
95class QuantpediaEquity(PythonData):
96def GetSource(self, config, date, isLiveMode):
97return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/quantpedia_strategies/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
98def Reader(self, config, line, date, isLiveMode):
99data = QuantpediaEquity()
100data.Symbol = config.Symbol
101
102if not line[0].isdigit(): return None
103split = line.split(';')
104
105data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
106data['close'] = float(split[1])
107data.Value = float(split[1])
108return data
109
110# Custom fee model.
111class CustomFeeModel(FeeModel):
112def GetOrderFee(self, parameters):
113fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
114return OrderFee(CashAmount(fee, "USD"))