跨资产类别的波动率投资

1from collections import deque
2import numpy as np
3
4class VolatilityInvestingAcrossAssetClasses(QCAlgorithm):
5
6def Initialize(self):
7self.SetStartDate(2015, 1, 1)
8self.SetCash(100000)
9
10self.symbols = ['SPY', 'IWM', 'EUE', 'CNKY', 'EEM', 'EWZ', 'HSI', 'USO', 'GLD', 'SLV', 'EURUSD', 'GBPUSD', 'JPYUSD', 'IEF']
11
12# Daily price data.
13self.data = {}
14self.period = 21
15self.SetWarmUp(self.period)
16
17for symbol in self.symbols:
18    data = self.AddEquity(symbol, Resolution.Daily)
19    data.SetLeverage(5)
20    
21    data_symbol = data.Symbol
22    option = self.AddOption(data_symbol, Resolution.Minute)
23    self.data[symbol] = deque(maxlen = self.period)
24
25self.last_day = -1
26
27self.invested_etf = []
28
29def OnData(self, slice):
30# Check once a day.
31if self.Time.day == self.last_day:
32    return
33self.last_day = self.Time.day
34
35# Store underlying daily price data.
36for symbol in self.symbols:
37    if symbol in slice and slice[symbol]:
38        price = slice[symbol].Value
39        self.data[symbol].append(price)
40
41if self.IsWarmingUp: return
42
43weight_ratio = sum([1 / Volatility(self.data[symbol]) for symbol in self.data if len(self.data[symbol]) == self.data[symbol].maxlen])
44if weight_ratio == 0: return
45
46invested = [x.Key for x in self.Portfolio if x.Value.Invested]
47if len(invested) <= len(self.invested_etf):
48    # Only ETF's or nothing are invested in.
49    symbols_to_remove = []
50    for symbol in self.invested_etf:
51        # Liquidate etf holdings and underlying symbol for options.
52        self.Liquidate(symbol)
53        symbols_to_remove.append(symbol)
54    for symbol in symbols_to_remove:
55        self.invested_etf.remove(symbol)
56        
57    for i in slice.OptionChains:
58        chains = i.Value
59
60        calls = list(filter(lambda x: x.Right == OptionRight.Call, chains))
61        puts = list(filter(lambda x: x.Right == OptionRight.Put, chains))
62    
63        if not calls or not puts: continue
64    
65        symbol = chains.Underlying.Symbol.Value
66        if len(self.data[symbol]) != self.data[symbol].maxlen: continue
67        
68        underlying_price = chains.Underlying.Price
69        expiries = [i.Expiry for i in puts]
70        
71        # Determine expiration date nearly one month.
72        expiry = min(expiries, key=lambda x: abs((x.date()-self.Time.date()).days-30))
73        strikes = [i.Strike for i in puts]
74    
75        # Determine at-the-money strike.
76        strike = min(strikes, key=lambda x: abs(x-underlying_price))
77        atm_call = [i for i in calls if i.Expiry == expiry and i.Strike == strike][0]
78        atm_put = [i for i in puts if i.Expiry == expiry and i.Strike == strike][0]
79
80        if atm_call and atm_put:
81            etf_weight = (1 / Volatility(self.data[symbol])) / weight_ratio
82            self.SetHoldings(symbol, etf_weight)
83            self.invested_etf.append(symbol)
84            
85            self.Securities[atm_call.Symbol].MarginModel = BuyingPowerModel(5)
86            self.Securities[atm_put.Symbol].MarginModel = BuyingPowerModel(5)
87            
88            # Sell at-the-money straddle.
89            self.Sell(atm_call.Symbol, 1)
90            self.Sell(atm_put.Symbol, 1)
91
92def Volatility(values):
93values = np.array(values)
94returns = (values[1:] - values[:-1]) / values[:-1]
95return np.std(returns)