Closed-End Fund Mean Reversion Trading

1from AlgorithmImports import *
2#endregion
3class ClosedEndFundMeanReversionTrading(QCAlgorithm):
4def Initialize(self):
5 self.SetStartDate(2000, 1, 1)
6 self.SetCash(100000)
7 
8 self.symbol_count: int = 100     # due to QC limitation, maximum amount of 100 individual custom data series can be loaded
9 self.quantile: int = 5
10 self.leverage: int = 3
11 self.CEF_data: Dict[Symbol, CEFData] = {}  # CEF NAV and price storage
12 
13 # load csv with CEF tickers
14 # source: https://stockanalysis.com/list/closed-end-funds/
15 csv_string_file: str = self.Download('data.quantpedia.com/backtesting_data/equity/CEFs/CEFs.csv')
16 line: str = csv_string_file.split('\r\n')
17 line_split: List[str] = line[0].split(';')
18 
19 for ticker in line_split[:self.symbol_count]:
20     stock_symbol: Symbol = self.AddEquity(ticker, Resolution.Daily).Symbol
21     # subscribe to QuantpediaCEF with csv name
22     cef_symbol: Symbol = self.AddData(QuantpediaCEF, ticker, Resolution.Daily).Symbol
23     # create object for each subscribed symbol
24     self.CEF_data[stock_symbol] = CEFData(cef_symbol)
25 
26 self.recent_month: int = -1
27 self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
28 
29def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
30 for security in changes.AddedSecurities:
31     security.SetFeeModel(CustomFeeModel())
32     security.SetLeverage(self.leverage)
33				
34def OnData(self, slice: Slice) -> None:
35 rebalance_flag: bool = False
36 discount: Dict[Symbol, float] = {}
37 last_update_date: Dict[str, datetime.date] = QuantpediaCEF.get_last_update_date()
38 # update NAV values for each CEF
39 for stock_symbol, CEF_data in self.CEF_data.items():
40     cef_symbol: Symbol = CEF_data.get_CEF_symbol()
41     if cef_symbol in slice and slice[cef_symbol]:
42         # update CEF's NAV
43         nav: float = slice[cef_symbol].Value
44         CEF_data.update_NAV(nav)
45     if stock_symbol in slice and slice[stock_symbol]:
46         # update CEF stock price
47         stock_price: float = slice[stock_symbol].Value
48         CEF_data.update_price(stock_price)
49         if self.recent_month != self.Time.month:
50             rebalance_flag = True
51         
52         # calculate discount
53         if rebalance_flag:
54             if CEF_data.is_ready() and stock_symbol.Value in last_update_date and self.Time.date() < last_update_date[stock_symbol.Value]:
55                 discount[stock_symbol] = CEF_data.discount()
56 
57 # rebalance monthly
58 if not rebalance_flag:
59     return
60 self.recent_month = self.Time.month
61         
62 if len(discount) < self.quantile:
63     self.Liquidate()
64     return
65 
66 quintile: int = int(len(discount) / self.quantile)
67 sorted_by_discount: List[Symbol] = sorted(discount, key=discount.get)
68 
69 # long funds with the biggest discounts and short funds with the biggest premium  
70 long: List[Symbol] = sorted_by_discount[:quintile]
71 short: List[Symbol] = sorted_by_discount[-quintile:]
72 
73 # trade execution
74 invested: List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested and x.Key not in long + short]
75 for symbol in invested:
76     self.Liquidate(symbol)
77 
78 for i, portfolio in enumerate([long, short]):
79     for symbol in portfolio:
80         if symbol in slice and slice[symbol]:
81             self.SetHoldings(symbol, ((-1) ** i) / len(portfolio))
82class CEFData():
83def __init__(self, cef_symbol: Symbol) -> None:
84 self._cef_symbol: Symbol = cef_symbol
85 self._NAV: float = -1
86 self._price: float = -1
87
88def get_CEF_symbol(self) -> Symbol:
89 return self._cef_symbol
90def update_NAV(self, nav: float) -> None:
91 self._NAV = nav
92 
93def update_price(self, price: float) -> None:
94 self._price = price
95 
96def is_ready(self) -> bool:
97 return self._NAV != -1 and self._price != -1
98 
99def discount(self) -> float:
100 # Difference between log market price and log NAV, which is the price premium in relative terms. 
101 # In this framework, discounts are negative premiums.
102 return np.log(self._price) - np.log(self._NAV)
103 
104# Quantpedia data
105# NOTE: IMPORTANT: Data order must be ascending (datewise)
106# NOTE: IMPORTANT: Name of the csv file has to be upper case
107class QuantpediaCEF(PythonData):
108_last_update_date:Dict[str, datetime.date] = {}
109@staticmethod
110def get_last_update_date() -> Dict[str, datetime.date]:
111return QuantpediaCEF._last_update_date
112# Source: https://finance.yahoo.com/quote/XGDLX?p=XGDLX
113def GetSource(self, config, date, isLiveMode):
114 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/CEFs/X{0}X.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
115def Reader(self, config, line, date, isLiveMode):
116 data = QuantpediaCEF()
117 data.Symbol = config.Symbol
118 
119 if not line[0].isdigit(): return None
120 split = line.split(';')
121 
122 data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
123 data.Value = float(split[1])
124 # store last update date
125 if config.Symbol.Value not in QuantpediaCEF._last_update_date:
126     QuantpediaCEF._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
127 if data.Time.date() > QuantpediaCEF._last_update_date[config.Symbol.Value]:
128     QuantpediaCEF._last_update_date[config.Symbol.Value] = data.Time.date()
129 return data
130# Custom fee model
131class CustomFeeModel(FeeModel):
132def GetOrderFee(self, parameters):
133 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
134 return OrderFee(CashAmount(fee, "USD"))