On-Chain Cashflows and the Cross-Section of Cryptocurrency Returns

1from AlgorithmImports import *
2from io import StringIO
3from pandas.core.frame import DataFrame
4from typing import List, Dict
5import pandas as pd
6# endregion
7
8class OnChainCashflowsandtheCrossSectionofCryptocurrencyReturns(QCAlgorithm):
9
10def Initialize(self):
11self.SetStartDate(2015, 1, 1)
12self.SetCash(100000)
13
14self.leverage:int = 2
15self.quantile:int = 3
16self.traded_percentage:float = 0.1
17self.rebalance_hour:int = 3 # rebalance at 03:00
18
19self.crypto_tickers:Dict[str, str] = { 
20    'Bitcoin': 'BTCUSD', 'Ethereum': 'ETHUSD', 'Solana': 'SOLUSD', 'Cardano': 'ADAUSD', 'XRP': 'XRPUSD', 
21    'Polkadot': 'DOTUSD', 'Dogecoin': 'DOGEUSD', 'Terra': 'LUNAUSD', 'Avalanche': 'AVAXUSD', 'Uniswap': 'UNIUSD', 
22    'Chainlink': 'LINKUSD', 'Litecoin': 'LTCUSD', 'Bitcoin Cash ABC': 'BCHABCUSD', 'Bitcoin SV': 'BSVUSD', 
23    'Filecoin': 'FILUSD', 'Stellar': 'XLMUSD', 'Tezos': 'XTZUSD', 'NEO': 'NEOUSD', 'Cosmos Hub': 'ATOMUSD', 
24    'IOTA': 'IOTAUSD', 'Ethereum Classic': 'ETCUSD', 'Dash': 'DASHUSD', 'Elrond': 'EGLDUSD', 'Aave': 'AAVEUSD', 
25    'Enjin Coin': 'ENJUSD', 'EOS': 'EOSUSD', 'MakerDAO': 'MKRUSD', 'Decentraland': 'MANAUSD', 'Synthetix': 'SNXUSD', 
26    'FTX Token': 'FTTUSD', 'OMG Network': 'OMGUSD', 'SushiSwap': 'SUSHIUSD', 'yearn.finance': 'YFIUSD', 
27    'Wrapped Bitcoin': 'WBTCUSD', 'Monero': 'XMRUSD', 'Zcash': 'ZECUSD', '0x': 'ZRXUSD', 'XRP (Avalanche C-Chain)': 'XRAUSD', 
28    'Ampleforth': 'AMPLUSD', 'The Graph': 'GRTUSD', 'DigiByte': 'DGBUSD','1inch': '1INCHUSD', 'Tron': 'TRXUSD'
29    }
30
31# source: https://tokenterminal.com/terminal/metrics/fees
32crypto_fees:str = self.Download('data.quantpedia.com/backtesting_data/crypto/crypto_fees/crypto_fees.csv')
33self.crypto_fees_df:DataFrame = pd.read_csv(StringIO(crypto_fees), delimiter=';')
34self.crypto_fees_df['date'] = pd.to_datetime(self.crypto_fees_df['date']).dt.date
35self.crypto_fees_df.set_index('date', inplace=True)
36self.last_date:DateTime = self.crypto_fees_df.index[-1]
37
38# data subscription
39for ticker in list(self.crypto_tickers.values()):
40    data = self.AddCrypto(ticker, Resolution.Hour, Market.Bitfinex)
41    data.SetLeverage(self.leverage)
42    data.SetFeeModel(CustomFeeModel())
43
44self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
45
46def OnData(self, data: Slice) -> None:
47# rebalance at specific hour
48if not self.Time.hour == self.rebalance_hour:
49    return
50
51# check on custom data date
52if self.Time.date() > self.last_date:
53    self.Liquidate()
54    return
55
56selected_crypto_fees:DataFrame = self.crypto_fees_df[[col for col in self.crypto_fees_df.columns if self.crypto_tickers[col] in data and data[self.crypto_tickers[col]]]]
57
58if not self.Time.date() in selected_crypto_fees.index:
59    return
60
61# sort selected crypto fees
62sorted_current_fees:List[str] =  list(map(lambda x: self.crypto_tickers[x], list(selected_crypto_fees.loc[selected_crypto_fees.index < self.Time.date()].iloc[-1].sort_values(ascending=False).dropna().index)))
63
64long:List[str] = []
65short:List[str] = []
66
67if len(sorted_current_fees) >= self.quantile:
68    quantile:int = len(sorted_current_fees) // self.quantile
69    long = list(sorted_current_fees)[:quantile]
70    short = list(sorted_current_fees)[-quantile:]
71
72# trade execution
73stocks_invested:List[Symbol] = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
74for ticker in stocks_invested:
75    if ticker not in long + short:
76        self.Liquidate(ticker)
77
78for ticker in long:
79    if ticker in data and data[ticker]:
80        self.SetHoldings(ticker, 1/len(long) * self.traded_percentage)
81
82for ticker in short:
83    if ticker in data and data[ticker]:
84        self.SetHoldings(ticker, -1/len(short) * self.traded_percentage)
85
86# custom fee model
87class CustomFeeModel(FeeModel):
88def GetOrderFee(self, parameters):
89fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
90return OrderFee(CashAmount(fee, "USD"))