Overall Statistics
Total Trades
97
Average Win
1.04%
Average Loss
-1.07%
Compounding Annual Return
-77.710%
Drawdown
25.000%
Expectancy
-0.230
Net Profit
-12.271%
Sharpe Ratio
-1.424
Probabilistic Sharpe Ratio
11.731%
Loss Rate
61%
Win Rate
39%
Profit-Loss Ratio
0.97
Alpha
-0.32
Beta
0.895
Annual Standard Deviation
0.524
Annual Variance
0.275
Information Ratio
-0.664
Tracking Error
0.407
Treynor Ratio
-0.835
Total Fees
$541319.47
Estimated Strategy Capacity
$0
Lowest Capacity Asset
VETBTC 18N
 
from AlgorithmImports import *

class CoinAPIDataAlgorithm(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 6, 1)
        self.SetEndDate(2020, 7, 1)
        self.SetCash("BUSD", 100000)
        self.SetCash("BTC", 1000)

        # Kraken accepts both Cash and Margin type account.
        self.SetBrokerageModel(BrokerageName.Binance, AccountType.Margin)

        # 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.Binance, 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")