股票中的均线距离策略

1import numpy as np
2from AlgorithmImports import *
3from typing import List, Dict
4class MovingAveragesDistance(QCAlgorithm):
5def Initialize(self):
6 self.SetStartDate(2000, 1, 1)
7 self.SetCash(100000)
8 self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']	
9 self.fundamental_sorting_key = lambda x: x.DollarVolume
10 self.fundamental_count:int = 500
11 
12 self.min_share_price:int = 5
13 self.leverage:int = 10
14 self.period:int = 200
15 self.month_period:int = 21
16 
17 self.data:Dict[Symbol, SymbolData] = {}
18 
19 self.long:List[Symbol] = []
20 self.short:List[Symbol] = []
21 
22 self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
23 
24 self.selection_flag:bool = False
25 self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
26 self.UniverseSettings.Resolution = Resolution.Daily
27 self.AddUniverse(self.FundamentalSelectionFunction)
28 self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
29 self.settings.daily_precise_end_time = False
30def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
31 for security in changes.AddedSecurities:
32     security.SetFeeModel(CustomFeeModel())
33     security.SetLeverage(self.leverage)
34def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
35 # Update the rolling window every day.
36 for stock in fundamental:
37     symbol = stock.Symbol
38     # Store daily price.
39     if symbol in self.data:
40         self.data[symbol].update(stock.AdjustedPrice)
41 if not self.selection_flag:
42     return Universe.Unchanged
43 selected:List[Fundamental] = [
44     x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.Price > self.min_share_price \
45     and x.SecurityReference.ExchangeId in self.exchange_codes
46 ]
47     
48 if len(selected) > self.fundamental_count:
49     selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
50 MAD:Dict[Symbol, float] = {}
51 # Warmup price rolling windows.
52 for stock in selected:
53     symbol:Symbol = stock.Symbol
54     if symbol not in self.data:
55         self.data[symbol] = SymbolData(symbol, self.period)
56         history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
57         if history.empty:
58             self.Log(f"Not enough data for {symbol} yet")
59             continue
60         closes:Series = history.loc[symbol].close
61         for time, close in closes.items():
62             self.data[symbol].update(close)
63     if not self.data[symbol].is_ready():
64         continue
65     
66     prices:List[float] = self.data[symbol].return_prices()
67     ma21:float = np.average(prices[:self.month_period])
68     ma200:float = np.average(prices)
69     
70     MAD[symbol] = ma21 / ma200
71
72 self.long = [x[0] for x in MAD.items() if x[1] >= 1.2]
73 self.short = [x[0] for x in MAD.items() if x[1] <= 0.8]
74 
75 return self.long + self.short
76def OnData(self, data: Slice) -> None:
77 if not self.selection_flag:
78     return
79 self.selection_flag = False
80 
81# order execution
82 targets:List[PortfolioTarget] = []
83 for i, portfolio in enumerate([self.long, self.short]):
84     for symbol in portfolio:
85         if symbol in data and data[symbol]:
86             targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
87 
88 self.SetHoldings(targets, True)
89 self.long.clear()
90 self.short.clear()
91
92def Selection(self) -> None:
93 self.selection_flag = True
94
95class SymbolData():
96def __init__(self, symbol:Symbol, period:int):
97 self.Symbol:Symbol = symbol
98 self.Prices:RollingWindow = RollingWindow[float](period)
99 
100def update(self, price:float):
101 self.Prices.Add(price)
102 
103def is_ready(self) -> bool:
104 return self.Prices.IsReady
105 
106def return_prices(self) -> List[float]:
107 return [x for x in self.Prices]
108 
109class CustomFeeModel(FeeModel):
110def GetOrderFee(self, parameters):
111 fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
112 return OrderFee(CashAmount(fee, "USD"))