| Overall Statistics |
|
Total Orders 453 Average Win 4.66% Average Loss -4.10% Compounding Annual Return 31.448% Drawdown 66.100% Expectancy 0.116 Start Equity 100000.00 End Equity 173016.66 Net Profit 73.017% Sharpe Ratio 0.735 Sortino Ratio 0.801 Probabilistic Sharpe Ratio 27.160% Loss Rate 48% Win Rate 52% Profit-Loss Ratio 1.14 Alpha 0.303 Beta 0.745 Annual Standard Deviation 0.587 Annual Variance 0.345 Information Ratio 0.455 Tracking Error 0.569 Treynor Ratio 0.58 Total Fees $0.00 Estimated Strategy Capacity $340000.00 Lowest Capacity Asset ETHUSD 2XR Portfolio Turnover 60.78% |
from AlgorithmImports import *
from QuantConnect.DataSource import *
class CryptoSlamNFTSalesAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1) # Set Start Date
self.set_end_date(2020, 12, 31) # Set End Date
self.set_cash(100000)
self.ethusd = self.add_crypto("ETHUSD", Resolution.MINUTE).symbol
### Requesting data
self.eth_nft_sales_symbol = self.add_data(CryptoSlamNFTSales, "ETH").symbol
### Historical data
history = self.history(self.eth_nft_sales_symbol, 60, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request for ETH CryptoSlam NFT Sales data")
self.last_avg_sales = None
def on_data(self, slice: Slice) -> None:
### Retrieving data
data = slice.Get(CryptoSlamNFTSales)
if self.eth_nft_sales_symbol in data and data[self.eth_nft_sales_symbol] != None:
current_avg_sales = data[self.eth_nft_sales_symbol].total_price_usd / data[self.eth_nft_sales_symbol].total_transactions
# comparing the average sales changes, we will buy ethereum or hold cash
if self.last_avg_sales != None and current_avg_sales > self.last_avg_sales:
self.set_holdings(self.ethusd, 1)
else:
self.set_holdings(self.ethusd, 0)
self.last_avg_sales = current_avg_sales