24-Hour Reversal in Cryptocurrencies

1from AlgorithmImports import *
2from data_tools import CustomFeeModel, SymbolData
3# endregion
4
5class HourReversalInCryptocurrencies(QCAlgorithm):
6
7def Initialize(self):
8self.SetStartDate(2016, 1, 1)
9self.SetCash(100000)
10
11self.cryptos:list[str] = [
12    #"ANTUSD", # Aragon
13    #"BATUSD", # Basic Attention Token
14    "BTCUSD", # Bitcoin
15    #"DAIUSD", # Dai
16    #"DGBUSD", # Dogecoin
17    #"EOSUSD", # EOS
18    #"ETCUSD", # Ethereum Classic
19    "ETHUSD", # Ethereum
20    #"FUNUSD", # FUN Token
21    "LTCUSD", # Litecoin
22    #"MKRUSD", # Maker
23    #"NEOUSD", # Neo
24    #"OMGUSD", # OMG Network
25    #"SNTUSD", # Status
26    #"TRXUSD", # Tron
27    #"XLMUSD", # Stellar
28    #"XMRUSD", # Monero
29    "XRPUSD", # XRP
30    #"XTZUSD", # Tezos
31    #"XVGUSD", # Verge
32    #"ZECUSD", # Zcash
33    #"ZRXUSD"  # Ox
34]
35
36self.data:dict[Symbol, SymbolData] = {}
37
38self.lag_period:int = 24 + 1
39self.max_missing_hours:int = 0
40
41self.leg_count:int = 2
42
43self.leverage:int = 5
44self.portfolio_percentage:float = 0.1
45
46self.SetBrokerageModel(BrokerageName.Bitfinex)
47
48for crypto in self.cryptos:
49    # GDAX is coinmarket, but it doesn't support this many cryptos, so we choose Bitfinex
50    data = self.AddCrypto(crypto, Resolution.Hour, Market.Bitfinex)
51    data.SetFeeModel(CustomFeeModel())
52    data.SetLeverage(self.leverage)
53    
54    self.data[data.Symbol] = SymbolData(self.lag_period)
55
56def OnData(self, data):
57curr_time:datetime = self.Time
58
59performance:dict[Symbol, float] = {}
60
61for symbol, symbol_data in self.data.items():
62    if symbol in data and data[symbol]:
63        price:float = data.Bars[symbol].Value
64
65        if not symbol_data.data_still_coming(curr_time, self.max_missing_hours):
66            symbol_data.reset_data()
67
68        if symbol_data.prev_price_ready():
69            symbol_data.calculate_perf(price)
70
71            if symbol_data.performances_ready():
72                performance[symbol] = symbol_data.get_first_perf()
73
74        symbol_data.update_price(curr_time, price)
75
76if len(performance) < (2 * self.leg_count):
77    self.Liquidate()
78    return
79
80sorted_by_perf:list[Symbol] = [x[0] for x in sorted(performance.items(), key=lambda item: item[1])]
81long_leg:list[Symbol] = sorted_by_perf[:self.leg_count]
82short_leg:list[Symbol] = sorted_by_perf[-self.leg_count:]
83
84# trade execution
85invested:list[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
86for symbol in invested:
87    if symbol not in long_leg + short_leg:
88        self.Liquidate(symbol)
89
90for symbol in long_leg:
91    q:float = self.CalculateOrderQuantity(symbol, (1 / self.leg_count) * self.portfolio_percentage)
92    if q > self.Securities[symbol].SymbolProperties.MinimumOrderSize:
93        self.MarketOrder(symbol, q)
94
95for symbol in short_leg:
96    q:float = self.CalculateOrderQuantity(symbol, (1 / self.leg_count) * self.portfolio_percentage)
97    if q > self.Securities[symbol].SymbolProperties.MinimumOrderSize:
98        self.MarketOrder(symbol, -q)