内部人士的沉默

1from AlgorithmImports import *
2from collections import deque
3import pandas as pd
4from io import StringIO
5from numpy import floor
6#endregion
7class InsidersSilence(QCAlgorithm):
8def Initialize(self):
9self.SetStartDate(2013, 1, 1)
10self.SetCash(100000)
11self.symbols = [
12                'AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V',
13                'PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ',
14                'MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA',
15                'ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN',
16                'PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX',
17                # 'NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT',
18                # 'GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG',
19                # 'UPS','GILD','CHTR','CAT','MDLZ','GS','USB', 'CI','ANTM','BDX',
20                # 'TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO',
21                # 'D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'
22                ]
23                
24# Create custom universe.
25self.UniverseSettings.Resolution = Resolution.Daily
26self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine))
27
28self.period = 21
29self.holding_period = 12
30self.quantile = 5
31self.max_SI_missing_days = 5
32self.max_missing_insider_days = 3 * 31
33self.trading_activity_period = 6 * 31
34
35self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
36self.managed_queue = []
37
38# Dataframe with insider trades for every stock.
39self.insiders_trading = {}
40
41self.short_interest = {}
42
43for symbol in self.symbols:
44    # Import insiders trading data.
45    csv_string_file = self.Download(f'data.quantpedia.com/backtesting_data/economic/insiders_trading/{symbol}.csv')
46    if csv_string_file == "": continue
47    parser = lambda x: pd.datetime.strptime(x, "%Y-%m-%d")
48    self.insiders_trading[symbol] = pd.read_csv(StringIO(csv_string_file), sep=';', parse_dates=['Tran.Date'], date_parser=parser)
49    # Import short interest daily data.
50    self.AddData(NasdaqCustomColumns, 'FINRA/FNSQ_' + symbol, Resolution.Daily)
51    self.short_interest[symbol] = deque(maxlen = self.period)
52
53self.selection_flag = True
54self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol), self.Selection)
55def OnSecuritiesChanged(self, changes):
56for security in changes.AddedSecurities:
57    security.SetFeeModel(CustomFeeModel())
58    security.SetLeverage(10)
59def SelectCoarse(self, coarse):
60if not self.selection_flag:
61    return Universe.Unchanged
62
63return [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in self.symbols]
64
65def SelectFine(self, fine):
66fine = [x for x in fine if x.Symbol.Value in self.insiders_trading]
67
68short_interest = {}
69
70for stock in fine:
71    symbol = stock.Symbol
72    ticker = symbol.Value
73
74    # Last month's short_interest data is ready.
75    if len(self.short_interest[ticker]) == self.short_interest[ticker].maxlen:
76        if self.Securities['FINRA/FNSQ_' + ticker].GetLastData() and (self.Time.date() - self.Securities['FINRA/FNSQ_' + ticker].GetLastData().Time.date()).days > self.max_SI_missing_days:
77            self.short_interest[ticker].clear()
78            continue
79        # Calculate monthly short interest.
80        short_interest[symbol] = sum([x[0] for x in self.short_interest[ticker]]) / sum([x[1] for x in self.short_interest[ticker]])
81
82if len(short_interest) < self.quantile:
83    return Universe.Unchanged
84    
85# Sorting by short interest.
86sorted_by_short_interest = sorted(short_interest.items(), key = lambda x: x[1], reverse = True)
87quantile = int(len(sorted_by_short_interest) / self.quantile)
88top_by_short_interest = [x[0] for x in sorted_by_short_interest[:quantile]]
89    
90short = []
91    
92# Find stocks which have not been traded 6 months prior to this moment.
93for symbol in top_by_short_interest:
94    ticker = symbol.Value
95    if (self.Time.date() - self.insiders_trading[ticker].iloc[0]['Tran.Date'].date()).days > self.max_missing_insider_days:
96        continue
97    trades = [row['Tran.Date'] for index, row in self.insiders_trading[ticker].iterrows() if row['Symbol'] == ticker and row['Tran.Date'] >= (self.Time - timedelta(days = self.trading_activity_period)) and row['Tran.Date'] <= self.Time]
98    if len(trades) == 0:
99        short.append(symbol)
100
101if len(short) != 0:
102    # append market to short leg
103    short.append(self.symbol)
104    short_w = self.Portfolio.TotalPortfolioValue / self.holding_period / len(short)
105    
106    # symbol/quantity collection
107    short_symbol_q = []
108    
109    for symbol in short:
110        # We need last price for market order trade.
111        history = self.History(symbol, 1, Resolution.Daily)
112        if history.empty:
113            continue
114        closes = history.loc[symbol].close
115        for time, close in closes.iteritems():
116            if symbol == self.symbol:
117                # long market
118                short_symbol_q.append( (self.symbol, floor((self.Portfolio.TotalPortfolioValue / self.holding_period)) / close) )
119            else:
120                short_symbol_q.append( (symbol, -floor(short_w / close)) ) 
121        
122    self.managed_queue.append(RebalanceQueueItem(short_symbol_q))
123
124return short
125
126def OnData(self, data):
127# Store short interest data.
128for symbol in self.symbols:
129    look_up_symbol = 'FINRA/FNSQ_' + symbol
130    if look_up_symbol in data and data[look_up_symbol]:
131        short_vol = data[look_up_symbol].GetProperty("SHORTVOLUME")
132        total_vol = data[look_up_symbol].GetProperty("TOTALVOLUME")
133        
134        if symbol in self.short_interest:
135            self.short_interest[symbol].append((short_vol, total_vol))
136if not self.selection_flag:
137    return
138self.selection_flag = False
139
140remove_item = None
141# Rebalance portfolio
142for item in self.managed_queue:
143    if item.holding_period == self.holding_period:
144                    
145        # Buying short on Liquidate
146        for symbol, quantity in item.short_symbol_q:
147            self.MarketOrder(symbol, -quantity)
148        
149        remove_item = item
150    
151    # Trade execution    
152    if item.holding_period == 0: 
153        open_short_symbol_q = []
154        
155        for symbol, quantity in item.short_symbol_q:
156            if symbol in data and data[symbol]:
157                self.MarketOrder(symbol, quantity)
158                open_short_symbol_q.append((symbol, quantity))
159        
160        # Only opened orders will be closed        
161        item.short_symbol_q = open_short_symbol_q
162        
163    item.holding_period += 1
164    
165# We need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue.
166if remove_item:
167    self.managed_queue.remove(remove_item)
168
169def Selection(self):
170self.selection_flag = True
171
172class RebalanceQueueItem():
173def __init__(self, short_symbol_q):
174# symbol/quantity collections
175self.short_symbol_q = short_symbol_q
176self.holding_period = 0
177
178# Custom fee model
179class CustomFeeModel(FeeModel):
180def GetOrderFee(self, parameters):
181fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
182return OrderFee(CashAmount(fee, "USD"))
183# Quandl short interest data.
184class NasdaqCustomColumns(NasdaqDataLink):
185def __init__(self) -> None:
186self.ValueColumnName = 'shortvolume'    # also 'TOTALVOLUME' is accesible