Value and Profitability in Chinese Equities

1from AlgorithmImports import *
2import data_tools
3# endregion
4
5class ValueAndProfitabilityInChineseEquities(QCAlgorithm):
6
7def Initialize(self):
8self.SetStartDate(2010, 1, 1)
9self.SetCash(100000)
10
11# https://www.tradingview.com/markets/stocks-hong-kong/market-movers-large-cap/
12self.tickers:list[str] = [
13    '0700', '3690', '1299', '9618', '9633', '0388', '2388', '1024', '1810', '0016', 
14    '0011', '0066', '1876', '0688', '1109', '0267', '2020', '2269', '0960', '0001',
15    '1113', '0002', '0981', '0669', '0027', '2057', '2015', '2328', '0003', '0788',
16    '2319', '0020', '0012',
17    '0001', '0004', '0006', '0017', '0019', '0083', '0101', '0135', '0144', '0151', 
18    '0175', '0241', '0268', '0270', '0288', '0291', '0316', '0322', '0384', '0489',
19    '0586', '0656', '0762', '0836', '0868', '0881', '0883', '0916', '0968', '0992', 
20    '1038', '1044', '1093', '1099', '1177', '1179', '1193', '1209', '1299', '1308', 
21    '1359', '1378', '1821', '1913', '1928', '1929', '1972', '1997', '2007', '2066', 
22    '2313', '2331', '2382', '2618', '2638', '2688', '3311', '3323', '3692', '3799', 
23    '3800', '6098', '6186', '6618', '6823', '6862', '6969', '9889', '9961',
24]
25
26self.quantile:int = 3
27self.leverage:int = 5
28self.max_missing_statement_days:int = 3*30
29
30# fundamental symbols
31self.income_statement_symbols:dict[Symbol] = {}
32self.balance_statement_symbols:dict[Symbol] = {}
33
34for ticker in self.tickers:
35    # price data
36    data = self.AddData(data_tools.HongKongStocks, ticker, Resolution.Daily)
37    data.SetFeeModel(data_tools.CustomFeeModel())
38    data.SetLeverage(self.leverage)
39
40    # fundamental symbols
41    self.income_statement_symbols[data.Symbol] = self.AddData(data_tools.HongKongIncomeStatement, ticker, Resolution.Daily).Symbol
42    self.balance_statement_symbols[data.Symbol] = self.AddData(data_tools.HongKongBalanceSheetStatement, ticker, Resolution.Daily).Symbol
43
44self.recent_month:int = -1
45
46def OnData(self, data: Slice) -> None:
47measure_dict:dict[str, list] = {} # dict of lists with measure values
48market_cap:dict[str, float] = {}
49rebalance_flag:bool = False
50
51for symbol in self.income_statement_symbols:
52    income_symbol:Symbol = self.income_statement_symbols[symbol]
53    balance_symbol:Symbol = self.balance_statement_symbols[symbol]
54
55    if self.Time.month == self.recent_month:
56        break
57
58    if data.ContainsKey(symbol):
59        if self.Securities[income_symbol].GetLastData() and (self.Time.date() - self.Securities[income_symbol].GetLastData().Time.date()).days <= self.max_missing_statement_days and \
60            self.Securities[balance_symbol].GetLastData() and (self.Time.date() - self.Securities[balance_symbol].GetLastData().Time.date()).days <= self.max_missing_statement_days:
61
62            bs_data:dict[str, str] = self.Securities[balance_symbol].GetLastData().GetProperty('statement')
63            is_data:dict[str, str] = self.Securities[income_symbol].GetLastData().GetProperty('statement')
64            
65            # valid price data
66            if data[symbol].Value != 0. and bs_data and is_data:
67                # valuation and profitability measures
68                price:float = data[symbol].Value
69
70                market_equity:float = float(is_data['weightedAverageShsOutDil']) * price # market cap
71                total_assets:float = float(bs_data['totalAssets'])
72
73                if market_equity != 0 and total_assets != 0:
74                    # gross-profit-to-market-equity
75                    # gross profit = Revenue - COGS
76                    revenue:float = float(is_data['revenue'])
77                    gross_profit:float = revenue - float(is_data['costOfRevenue'])
78                    gross_profit_to_market_equity:float = gross_profit / market_equity
79                    
80                    # operating-profit-to-market-equity
81                    # Operating Profit = Revenue - Cost of Goods Sold (COGS) - Operating Expenses - Depreciation & Amortization
82                    COGS:float = float(is_data['operatingExpenses'])
83                    operating_profit:float = gross_profit - COGS - float(is_data['depreciationAndAmortization'])
84                    operating_profit_to_market_equity:float = operating_profit / market_equity 
85
86                    # net-profit-to-market-equity
87                    # Net Profit = Total Revenue - Total Expenses
88                    net_profit:float = revenue - COGS
89                    net_profit_to_market_equity:float = net_profit / market_equity
90
91                    # gross-profit-to-total-assets
92                    gross_profit_to_total_assets:float = gross_profit / total_assets
93                    
94                    # Book Value = Total Assets - Total Liabilities
95                    book_value:float = total_assets - float(bs_data['totalLiabilities'])
96                    
97                    # operating-profit-to-book-equity
98                    operating_profit_to_book_equity:float = operating_profit / book_value
99                    
100                    # net-profit-to-book-equity
101                    net_profit_to_book_equity:float = net_profit / book_value
102
103                    measures:list[float] = [
104                        gross_profit_to_market_equity,
105                        operating_profit_to_market_equity,
106                        net_profit_to_market_equity,
107                        gross_profit_to_total_assets,
108                        operating_profit_to_book_equity,
109                        net_profit_to_book_equity
110                        ]
111                    
112                    # NOTE for the sake of predictable backtest, let's require measures different from 0
113                    if all(x != 0. for x in measures):
114                        # store measure values
115                        measure_dict[symbol] = measures
116                        market_cap[symbol] = market_equity
117
118# rebalance monthly
119if self.Time.month == self.recent_month:
120    return
121self.recent_month = self.Time.month
122
123weights:dict[Symbol, float] = {}
124
125if len(measure_dict) >= self.quantile:
126    measurement_count:int = 6
127    avg_rank:dict[str, int] = {}
128
129    # rank stocks by every measure
130    for i in range(measurement_count):
131        i_measures:dict = { symbol : x[1] for symbol, x in measure_dict.items() }
132        sorted_by_measure:list = [x[0] for x in sorted(i_measures.items(), key=lambda x: x[1], reverse=False)]
133        for i, symbol in enumerate(sorted_by_measure):
134            # add current rank to total rank first
135            if symbol not in avg_rank:
136                avg_rank[symbol] = 0
137            avg_rank[symbol] += i+1
138
139    # calculate average rank
140    avg_rank = { symbol : x / measurement_count for symbol, x in avg_rank.items() }
141
142    # sort by average rank
143    sorted_by_avg_rank:list = sorted(avg_rank.items(), key=lambda x:x[1], reverse=True)
144    quantile:int = int(len(sorted_by_avg_rank) / self.quantile)
145    long:list[str] = [x[0] for x in sorted_by_avg_rank[:quantile]]
146    short:list[str] = [x[0] for x in sorted_by_avg_rank[-quantile:]]
147
148    # market cap weighting
149    total_mc_long:float = sum([market_cap[x] for x in long])
150    total_mc_short:float = sum([market_cap[x] for x in short])
151    
152    for symbol in long:
153        weights[symbol] = market_cap[symbol] / total_mc_long
154    for symbol in short:
155        weights[symbol] = -market_cap[symbol] / total_mc_short
156
157# trade execution
158invested = [x.Key for x in self.Portfolio if x.Value.Invested]
159for symbol in invested:
160    if symbol not in weights:
161        self.Liquidate(symbol)
162        
163for symbol, w in weights.items():
164    self.SetHoldings(symbol, w)