Multi Risk Premia Strategy

1from AlgorithmImports import *
2from pandas.core.frame import DataFrame
3#endregion
4
5class MultiRiskPremiaStrategy(QCAlgorithm):
6
7def Initialize(self):
8 self.SetStartDate(2000, 1, 1)
9 self.SetCash(100000)
10
11 # Carry Quantpedia Strategies:
12 # ID 234 - Carry Factor within Asset Classes - 4 different universes
13
14 # Momentum Quantpedia Strategies:
15 # Commodities - ID 21
16 # Equity Index - ID 15
17 # Equity Single Stocks - ID 14
18 # Bonds - ID 426
19 # FX - ID 8
20
21 # Volatility Risk Strategies:
22 # Commodities - ID 506
23 # Equity Single Stocks - ID 20
24 # FX - ID 507
25
26 # Value Strategies:
27 # Commodities - ID 424
28 # FX - ID 9
29 # Equity Index - ID 26
30 # Bonds - ID 6
31 self.tickers:List[str] = [
32     '234_commodity', '234_equity',
33     '234_fx', '234_bonds',
34     '21', '15',
35     '14', '426',
36     '8', '506',
37     '20', '507',
38     '424', '6',
39     '9', '26',
40 ]
41
42 self.volatility_period:int = 21
43 self.volatility_target:float = 0.1
44 self.leverage_cap:float = 5.
45
46 for ticker in self.tickers:
47     data:Security = self.AddData(QuantpediaEquity, ticker, Resolution.Daily)
48     data.SetLeverage(self.leverage_cap * 3)
49     data.SetFeeModel(CustomFeeModel())
50
51 self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
52
53 self.recent_month:int = -1
54
55def OnData(self, data:Slice) -> None:
56 if self.IsWarmingUp:
57     return
58
59 # quarterly rebalance
60 if self.Time.month == self.recent_month:
61     return
62 self.recent_month = self.Time.month
63
64 if self.Time.month % 3 != 0: return
65 
66 # rebalance
67 _last_update_date:Dict[str, datetime.date] = QuantpediaEquity.get_last_update_date()
68 tickers_to_trade:List[str] = [ticker for ticker in self.tickers if \
69         ticker in _last_update_date and \
70         self.Time.date() < _last_update_date[ticker] and \
71         ticker in data and data[ticker]]
72 
73 # inverse volatility weighting
74 long_count:int = len(tickers_to_trade)
75 price_df:DataFrame = self.History(tickers_to_trade, self.volatility_period, Resolution.Daily).unstack(level=0)
76 
77 if not price_df.empty:
78     price_df = price_df['close']
79     daily_returns:DataFrame = price_df.pct_change().iloc[1:]
80     daily_returns = daily_returns.loc[:, (daily_returns != 0).any(axis=0)]  # drop 0 columns
81     tickers_to_trade = list(map(lambda x: self.Symbol(x).Value, list(daily_returns.columns)))    # updated valid columns
82     std:pd.Series = daily_returns.std()
83     weights:np.ndarray = ((1 / std) / (1 / std).sum()).values
84
85     # volatility target
86     portfolio_vol:float = np.sqrt(np.dot(weights.T, np.dot(daily_returns.cov() * self.volatility_period, weights.T)))
87     leverage:float = min(self.volatility_target / portfolio_vol, self.leverage_cap)
88
89 # trade execution
90 invested:List[str] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
91 for ticker in invested:
92     if ticker not in tickers_to_trade:
93         self.Liquidate(ticker)
94
95 for i, ticker in enumerate(tickers_to_trade):
96     self.SetHoldings(ticker, leverage * weights[i])
97 
98# Quantpedia strategy equity curve data.
99# NOTE: IMPORTANT: Data order must be ascending (datewise)
100class QuantpediaEquity(PythonData):
101def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:
102 return SubscriptionDataSource("data.quantpedia.com/backtesting_data/equity/quantpedia_strategies/911_related/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
103
104_last_update_date:Dict[str, datetime.date] = {}
105
106@staticmethod
107def get_last_update_date() -> Dict[str, datetime.date]:
108return QuantpediaEquity._last_update_date
109
110def Reader(self, config: SubscriptionDataConfig, line: str, date: datetime, isLive: bool) -> BaseData:
111 data:config = QuantpediaEquity()
112 data.Symbol = config.Symbol
113 
114 if not line[0].isdigit(): return None
115 split:List[str] = line.split(';')
116 
117 data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
118 data['close'] = float(split[1])
119 data.Value = float(split[1])
120 
121 # store last update date
122 if config.Symbol.Value not in QuantpediaEquity._last_update_date:
123     QuantpediaEquity._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()
124
125 if data.Time.date() > QuantpediaEquity._last_update_date[config.Symbol.Value]:
126     QuantpediaEquity._last_update_date[config.Symbol.Value] = data.Time.date()
127 
128 return data
129 
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"))