Trading Volume in Cryptocurrency Markets and Reversals

1from AlgorithmImports import *
2class TradingVolumeInCryptocurrencyMarketsAndReversals(QCAlgorithm):
3def Initialize(self):
4self.SetStartDate(2015, 1, 1)
5self.SetCash(100000)
6
7self.cryptos = [
8    "BTCUSD", # Bitcoin
9    "ETHUSD", # Ethereum
10    "XRPUSD", # XRP
11    # "BCHUSD", # Bitcoin cash
12    "LTCUSD", # Litecoin
13    "BSVUSD", # Bitcoin SV
14    "EOSUSD", # EOS
15    "XMRUSD", # Monero
16    "TRXUSD", # Tron
17    "XTZUSD", # Tezos
18    "XLMUSD", # Stellar
19    "NEOUSD", # Neo
20    "DAIUSD", # Dai
21    "ZECUSD", # Zcash
22    "VETUSD", # VeChain
23    "ETCUSD", # Ethereum Classic
24    "MKRUSD", # Maker
25    "OMGUSD", # OMG Network
26    # "DGBUSD", # Dogecoin
27    # "BATUSD", # Basic Attention Token
28    # "ZRXUSD", # Ox
29]
30
31self.data = {}
32self.period = 61
33self.traded_percentage = 0.1
34self.quantile = 3
35
36self.SetBrokerageModel(BrokerageName.Bitfinex)
37
38for crypto in self.cryptos:
39    # GDAX is coinmarket, but it doesn't support this many cryptos, so we choose Bitfinex
40    data = self.AddCrypto(crypto, Resolution.Minute, Market.Bitfinex)
41    data.SetFeeModel(CustomFeeModel())
42    data.SetLeverage(10)
43    
44    self.data[crypto] = SymbolData(crypto, self.period)
45
46self.last_day = -1
47
48def OnData(self, data):
49performance = {}
50volume_shock = {}
51
52if self.last_day == self.Time.day: return
53self.last_day = self.Time.day
54
55for crypto in self.cryptos:
56    if crypto in data.Bars and data[crypto]:
57        # Volume can be taken only from TradeBar and data[crypto] returns QuoteBar by default
58        price = data.Bars[crypto].Value
59        volume = data.Bars[crypto].Volume
60        self.data[crypto].update(price, volume)
61        
62        if self.data[crypto].is_ready():
63            result_volume_shock = self.data[crypto].volume_shock()
64            if result_volume_shock:
65                performance[crypto] = self.data[crypto].performance()
66                volume_shock[crypto] = result_volume_shock
67if len(performance) < self.quantile:
68    self.Liquidate()
69    return
70sorted_by_performance = [x[0] for x in sorted(performance.items(), key=lambda item: item[1])]
71sorted_by_volume_shock = [x[0] for x in sorted(volume_shock.items(), key=lambda item: item[1])]
72
73quantile = int(len(sorted_by_performance) / self.quantile)
74lowest_performance = sorted_by_performance[:quantile]
75lowest_volume_shock = sorted_by_volume_shock[:quantile]
76
77highest_performance = sorted_by_performance[-quantile:]
78
79long = [x for x in lowest_performance if x in lowest_volume_shock]
80short = [x for x in highest_performance if x in lowest_volume_shock]
81
82# Trade execution
83invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
84for symbol in invested:
85    if symbol not in long + short:
86        self.Liquidate(symbol)
87
88long_length = len(long)
89short_length = len(short)
90
91for crypto in long:
92    if crypto in data and data[crypto]:
93        self.SetHoldings(crypto, self.traded_percentage / long_length)
94    
95for crypto in short:
96    if crypto in data and data[crypto]:
97        self.SetHoldings(crypto, -self.traded_percentage / short_length)
98
99class SymbolData():
100def __init__(self, symbol, period):
101self.Symbol = symbol
102self.Closes = RollingWindow[float](period)
103self.Volumes = RollingWindow[float](period)
104    
105def update(self, close, volume):
106self.Closes.Add(close)
107self.Volumes.Add(volume)
108    
109def is_ready(self):
110return self.Closes.IsReady and self.Volumes.IsReady
111
112def performance(self):
113closes = [x for x in self.Closes]
114return closes[0] / closes[-1] - 1
115
116# Log deviation, 10 base log of current day volume - 10 base log of sum of 30 days volumes before current day devided by 30    
117def volume_shock(self):
118volumes = [x for x in self.Volumes]
119current_day_volume = volumes[0]
120avg_volume = sum(volumes[1:]) / len(volumes[1:]) # sum of 30 days volumes before current day devided by 30
121if (current_day_volume <= 0):
122    return None
123else:
124    return np.log(volumes[0]) - np.log(avg_volume)
125# Custom fee model.
126class CustomFeeModel(FeeModel):
127def GetOrderFee(self, parameters):
128fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
129return OrderFee(CashAmount(fee, "USD"))