Value Factor After Negative Market Return

1from AlgorithmImports import *
2from numpy import floor
3#endregion
4class ValueFactorAfterNegativeMarketReturn(QCAlgorithm):
5def Initialize(self):
6self.SetStartDate(2000, 1, 1)
7self.SetCash(100000)
8self.coarse_count = 500
9
10self.period = 24 * 21
11self.SetWarmUp(self.period, Resolution.Daily)
12self.quantile = 10
13# Trenching
14self.holding_period = 6
15self.managed_queue = []
16
17self.symbol = self.AddEquity('VTI', Resolution.Daily).Symbol
18self.data = RollingWindow[float](self.period)
19self.selection_flag = False
20
21self.recent_price = {}  # recent stock prices
22
23self.UniverseSettings.Resolution = Resolution.Daily
24self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
25self.settings.daily_precise_end_time = False
26self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
27self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
28
29def OnSecuritiesChanged(self, changes):
30for security in changes.AddedSecurities:
31    security.SetFeeModel(CustomFeeModel())
32    security.SetLeverage(10)
33def CoarseSelectionFunction(self, coarse):
34for stock in coarse:
35    symbol = stock.Symbol
36    # append recent price to market symbol
37    if symbol == self.symbol:
38        self.data.Add(stock.AdjustedPrice)
39    # store monthly stock prices
40    if self.selection_flag:
41        self.recent_price[symbol] = stock.AdjustedPrice
42    
43if not self.selection_flag:
44    return Universe.Unchanged
45selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5],
46    key=lambda x: x.DollarVolume, reverse=True)[:self.coarse_count]
47return [x.Symbol for x in selected]
48def FineSelectionFunction(self, fine):
49fine = [x for x in fine if x.MarketCap != 0 and x.ValuationRatios.PBRatio != 0]
50
51# market data is ready
52if self.data.IsReady:
53    market_ret:float = self.data[0] / self.data[self.period-1] - 1
54    if market_ret >= 0:
55        return Universe.Unchanged
56else:
57    return Universe.Unchanged
58bm_ratio = {}
59market_cap = {}
60for stock in fine:
61    symbol = stock.Symbol
62    
63    market_cap[symbol] = stock.MarketCap
64    bm_ratio[symbol] = 1 / stock.ValuationRatios.PBRatio
65
66long:list = []
67short:list = []
68
69if len(bm_ratio) >= self.quantile:
70    # BM ratio sorting 
71    sorted_by_bm = sorted(bm_ratio.items(), key=lambda x: x[1], reverse=True)
72    decile = int(len(sorted_by_bm) / self.quantile)
73    
74    # Long the highest decile portfolio 
75    # Short the lowest decile portolio
76    long = [x[0] for x in sorted_by_bm[:decile]]
77    short = [x[0] for x in sorted_by_bm[-decile:]]
78
79    # Market cap weighting
80    equity = self.Portfolio.TotalPortfolioValue / self.holding_period
81    
82    weights = {}
83    
84    total_market_cap_long = sum([market_cap[sym] for sym in long if sym in market_cap])
85    for symbol in long:
86        if symbol in market_cap:
87            weights[symbol] = market_cap[symbol] / total_market_cap_long
88    
89    total_market_cap_short = sum([market_cap[sym] for sym in short if sym in market_cap])
90    for symbol in short:
91        if symbol in market_cap:
92            weights[symbol] = -market_cap[symbol] / total_market_cap_short
93    
94    symbol_q = [(symbol, floor((equity*symbol_w) / self.recent_price[symbol])) for symbol,symbol_w in weights.items()]
95    self.managed_queue.append(RebalanceQueueItem(symbol_q))
96        
97return long + short
98def OnData(self, data):
99if not self.selection_flag:
100    return
101self.selection_flag = False
102
103# Trade execution
104remove_item = None
105
106# Rebalance portfolio
107for item in self.managed_queue:
108    if item.holding_period == self.holding_period:
109        for symbol, quantity in item.symbol_q:
110            self.MarketOrder(symbol, -quantity)
111                    
112        remove_item = item
113        
114    elif item.holding_period == 0:
115        open_symbol_q = []
116        
117        for symbol, quantity in item.symbol_q:
118            if symbol in data and data[symbol]:
119                self.MarketOrder(symbol, quantity)
120                open_symbol_q.append((symbol, quantity))
121        
122        # Only opened orders will be closed        
123        item.symbol_q = open_symbol_q
124        
125    item.holding_period += 1
126    
127# We need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue.
128if remove_item:
129    self.managed_queue.remove(remove_item)
130def Selection(self):
131self.selection_flag = True
132class RebalanceQueueItem():
133def __init__(self, symbol_q):
134# symbol/quantity collections
135self.symbol_q = symbol_q  
136self.holding_period = 0
137# Custom fee model.
138class CustomFeeModel(FeeModel):
139def GetOrderFee(self, parameters):
140fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
141return OrderFee(CashAmount(fee, "USD"))