Persistence of Abnormal Trading Volume Effect

1from AlgorithmImports import *
2from typing import List, Dict
3from dateutil.relativedelta import relativedelta
4import numpy as np
5# endregion
6
7class PersistenceofAbnormalTradingVolumeEffect(QCAlgorithm):
8
9def Initialize(self) -> None:
10self.SetStartDate(2000, 1, 1)
11self.SetCash(100000)
12
13self.market:Symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
14
15self.long:List[Symbol] = []
16self.short:List[Symbol] = []
17self.long_by_month:Dict[int, Symbol] = {}
18self.short_by_month:Dict[int, Symbol] = {}
19self.symbol_data:Dict[Symbol, SymbolData] = {}
20
21self.monthly_period:int = 18
22self.delete_treshold:int = 2
23self.quantile:int = 5
24self.leverage:int = 5
25
26self.fundamental_count:int = 1000
27self.selection_flag:bool = False
28self.UniverseSettings.Resolution = Resolution.Daily
29self.AddUniverse(self.FundamentalSelectionFunction)
30self.Schedule.On(self.DateRules.MonthStart(self.market), self.TimeRules.AfterMarketOpen(self.market), self.Selection)
31
32def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
33for security in changes.AddedSecurities:
34    security.SetLeverage(self.leverage)
35    security.SetFeeModel(CustomFeeModel())
36
37for security in changes.RemovedSecurities:
38    if security.Symbol in self.symbol_data:
39        self.symbol_data.pop(security.Symbol)
40
41def FundamentalSelectionFunction(self, fundamental:List[Fundamental]) -> List[Symbol]:
42# store volume every day
43for stock in fundamental:
44    symbol:Symbol = stock.Symbol
45
46    if symbol in self.symbol_data:
47        self.symbol_data[symbol].update_daily_volume(stock.Volume)
48
49# monthly selection
50if not self.selection_flag:
51    return Universe.Unchanged
52
53selected:List[Symbol] = [x.Symbol
54    for x in sorted([x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.AdjustedPrice >= 1 and x.MarketCap != 0 and \
55    (x.SecurityReference.ExchangeId == 'NYS') or (x.SecurityReference.ExchangeId == 'NAS') or (x.SecurityReference.ExchangeId == 'ASE')
56    ], 
57    key = lambda x: x.DollarVolume, reverse = True)][:self.fundamental_count]
58
59# selected:List[Fundamental] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.AdjustedPrice >= 1 and x.MarketCap != 0 and \
60#     (x.SecurityReference.ExchangeId == 'NYS') or (x.SecurityReference.ExchangeId == 'NAS') or (x.SecurityReference.ExchangeId == 'ASE')
61# ]        
62# if len(selected) > self.fundamental_count:
63#     selected = [x.Symbol for x in sorted(selected, key=lambda x: x.MarketCap, reverse=True)[:self.fundamental_count]]
64# else:
65#     selected = list(map(lambda x: x.Symbol, selected))
66
67# store monthly data
68for symbol in self.symbol_data:
69    self.symbol_data[symbol].update_monthly_data()
70
71ATV:Dict[Symbol, float] = {}
72for symbol in selected:
73    if symbol not in self.symbol_data:
74        self.symbol_data[symbol] = SymbolData(self.monthly_period)
75
76    if self.symbol_data[symbol].is_ready():
77        ATV[symbol] = self.symbol_data[symbol].ATV()
78
79# sort and divide to upper decile and lower decile
80if len(ATV) >= self.quantile:
81    sorted_volume:List[Symbol] = sorted(ATV, key=ATV.get, reverse=True)
82    quantile:int = len(sorted_volume) // self.quantile
83    self.long_by_month[self.Time.month] = sorted_volume[:quantile]
84    self.short_by_month[self.Time.month] = sorted_volume[-quantile:]
85
86    self.long = symbol_quantile_check(self.Time, self.long_by_month)
87    self.short = symbol_quantile_check(self.Time, self.short_by_month)
88
89    if len(self.long_by_month) > self.delete_treshold:
90        self.long_by_month.pop(list(self.long_by_month.keys())[0])
91        self.short_by_month.pop(list(self.short_by_month.keys())[0])
92    
93return self.long + self.short
94
95def OnData(self, data: Slice) -> None:
96# monthly rebalance
97if not self.selection_flag:
98    return
99self.selection_flag = False
100
101# order execution
102targets:List[PortfolioTarget] = []
103for i, portfolio in enumerate([self.long, self.short]):
104    for symbol in portfolio:
105        if symbol in data and data[symbol]:
106            targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
107
108self.SetHoldings(targets, True)
109
110self.long.clear()
111self.short.clear()
112
113def Selection(self) -> None:
114self.selection_flag = True
115
116def symbol_quantile_check(time:datetime, symbols:Dict[int, List[Symbol]]) -> List[Symbol]:
117t_1 = (time - relativedelta(months=1)).month
118t_2 = (time - relativedelta(months=2)).month
119if t_1 in symbols and t_2 in symbols:
120# fet the symbols for the months
121m_month = set(symbols[time.month])
122m_1_month = set(symbols[t_1])
123m_2_month = set(symbols[t_2])
124
125# symbols in m, m-1 but not in m-2
126missing_symbols = list(m_1_month.intersection(m_month).difference(m_2_month))
127return missing_symbols
128
129return []
130
131# custom fee model
132class CustomFeeModel(FeeModel):
133def GetOrderFee(self, parameters):
134fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
135return OrderFee(CashAmount(fee, "USD"))
136
137class SymbolData():
138def __init__(self, monthly_period:int) -> None:
139self._monthly_period = monthly_period
140
141self._daily_volume_date:List[float] = []
142self._recent_monthly_volume_mean:float = None
143self._monthly_volume_sum:RollingWindow = RollingWindow[float](monthly_period)
144
145def update_daily_volume(self, volume:float) -> None:
146self._daily_volume_date.append(volume)
147
148def update_monthly_data(self) -> None:
149self._recent_monthly_volume_mean = np.mean(list(self._daily_volume_date))
150self._monthly_volume_sum.Add(sum(list(self._daily_volume_date)))
151self._daily_volume_date.clear()
152
153def ATV(self) -> float:
154atv:float = np.log(self._recent_monthly_volume_mean / np.mean(list(self._monthly_volume_sum)[-12:]))
155return atv
156
157def is_ready(self) -> bool:
158return self._monthly_volume_sum.IsReady and self._recent_monthly_volume_mean is not None