| Overall Statistics |
|
Total Trades 138 Average Win 0.69% Average Loss -0.36% Compounding Annual Return -98.877% Drawdown 38.200% Expectancy -0.478 Net Profit -33.187% Sharpe Ratio -1.718 Probabilistic Sharpe Ratio 0.496% Loss Rate 82% Win Rate 18% Profit-Loss Ratio 1.96 Alpha -0.271 Beta 2.241 Annual Standard Deviation 0.555 Annual Variance 0.308 Information Ratio -1.353 Tracking Error 0.479 Treynor Ratio -0.425 Total Fees $2389912.48 Estimated Strategy Capacity $17000.00 Lowest Capacity Asset ZRXBTC XJ |
from AlgorithmImports import *
class CoinAPIDataAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 1, 1)
self.SetEndDate(2022, 2, 1)
self.SetCash(100000)
self.SetCash("BTC", 1000)
# Warm up the security with the last known price to avoid conversion error
self.SetSecurityInitializer(lambda security: security.SetMarketPrice(self.GetLastKnownPrice(security)))
self.UniverseSettings.Resolution = Resolution.Daily
# Add universe selection of cryptos based on coarse fundamentals
self.AddUniverse(CryptoCoarseFundamentalUniverse(Market.GDAX, self.UniverseSettings, self.UniverseSelectionFilter))
self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=1), 0.025, None))
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
def UniverseSelectionFilter(self, crypto_coarse):
return [d.Symbol for d in sorted([x for x in crypto_coarse if x.VolumeInUsd], key=lambda x: x.VolumeInUsd, reverse=True)[:5]]
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
# Historical data
history = self.History(security.Symbol, 30, Resolution.Daily)
self.Debug(f"We got {len(history)} items from our history request")