接近52周最低点策略

1from AlgorithmImports import *
2from pandas.core.frame import DataFrame
3class Nearnessto52WeekLow(QCAlgorithm):
4def Initialize(self):
5self.SetStartDate(2000, 1, 1)
6self.SetCash(100000)
7market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
8
9self.fundamental_count:int = 3000
10self.fundamental_sorting_key = lambda x: x.MarketCap
11
12self.weight:Dict[Symbol, float] = {}
13self.data:Dict[Symbol, SymbolData] = {}
14
15self.period:int = 52 * 5 + 4*5
16self.quantile:int = 20
17self.leverage:int = 5
18self.min_share_price:float = 5.
19self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
20
21self.selection_flag:bool = False
22self.UniverseSettings.Resolution = Resolution.Daily
23self.AddUniverse(self.FundamentalSelectionFunction)
24self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
25self.Schedule.On(self.DateRules.MonthStart(market), self.TimeRules.AfterMarketOpen(market), self.Selection)
26
27self.settings.daily_precise_end_time = False
28
29def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
30for security in changes.AddedSecurities:
31    security.SetFeeModel(CustomFeeModel())
32    security.SetLeverage(self.leverage)
33def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
34# Update the rolling window every day.
35for stock in fundamental:
36    symbol:Symbol = stock.Symbol
37    if symbol in self.data:
38        # Store daily price.
39        self.data[symbol].update(stock.AdjustedPrice)
40    
41if not self.selection_flag:
42    return Universe.Unchanged
43
44selected:List[Fundamental] = [
45    x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.SecurityReference.ExchangeId in self.exchange_codes and \
46    x.MarketCap != 0 and x.Price >= self.min_share_price
47]
48if len(selected) > self.fundamental_count:
49    selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
50LOW:Dict[Fundamental, float] = {}
51# Warmup price rolling windows.
52for stock in selected:
53    symbol:Symbol = stock.Symbol
54    if symbol not in self.data:
55        self.data[symbol] = SymbolData(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:pd.Series = history.loc[symbol].close
61        for time, close in closes.items():
62            self.data[symbol].update(close)
63    
64    if self.data[symbol].is_ready():
65        LOW[stock] = self.data[symbol].get_latest_price() / self.data[symbol].minimum()
66long:List[Fundamental] = []
67short:List[Fundamental] = []
68if len(LOW) >= self.quantile:
69    # LOW sorting
70    sorted_by_LOW:List[Fundamental] = sorted(LOW, key = LOW.get, reverse = True)
71    quantile:int = int(len(sorted_by_LOW) / self.quantile)
72    long = sorted_by_LOW[-quantile:]
73    short = sorted_by_LOW[:len(sorted_by_LOW) - quantile]
74
75# Market cap weighting.
76for i, portfolio in enumerate([long, short]):
77    mc_sum:float = sum(map(lambda x: x.MarketCap, portfolio))
78    for stock in portfolio:
79        self.weight[stock.Symbol] = ((-1) ** i) * stock.MarketCap / mc_sum
80
81return list(self.weight.keys())
82
83def OnData(self, data: Slice) -> None:
84if not self.selection_flag:
85    return
86self.selection_flag = False
87# Trade execution.
88portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if symbol in data and data[symbol]]
89self.SetHoldings(portfolio, True)
90self.weight.clear()
91def Selection(self) -> None:
92self.selection_flag = True
93
94class SymbolData():
95def __init__(self, period: int):
96self._price:RollingWindow = RollingWindow[float](period)
97
98def update(self, price: float) -> None:
99self._price.Add(price)
100
101def is_ready(self) -> bool:
102return self._price.IsReady
103 
104# Skip last month.
105def minimum(self) -> float:
106return min([x for x in self._price][4*5:])
107
108def get_latest_price(self) -> float:
109return self._price[0]
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"))