| Overall Statistics |
|
Total Orders 411 Average Win 0.76% Average Loss -0.35% Compounding Annual Return -18.759% Drawdown 77.300% Expectancy -0.843 Start Equity 100000 End Equity 29151.14 Net Profit -70.849% Sharpe Ratio -0.771 Sortino Ratio -0.915 Probabilistic Sharpe Ratio 0.000% Loss Rate 95% Win Rate 5% Profit-Loss Ratio 2.14 Alpha -0.039 Beta -0.952 Annual Standard Deviation 0.187 Annual Variance 0.035 Information Ratio -0.753 Tracking Error 0.338 Treynor Ratio 0.152 Total Fees $423.69 Estimated Strategy Capacity $690000.00 Lowest Capacity Asset XLI RGRPZX100F39 Portfolio Turnover 0.56% |
from AlgorithmImports import *
class EODHDEconomicEventsAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2019, 1, 1)
# Use industrial sector ETF as a vehicle to trade.
self.equity_symbol = self.add_equity("XLI").symbol
# Request US PMI economic event data to generate trade signals.
ticker = EODHD.Events.UnitedStates.MARKIT_MANUFACTURING_PURCHASING_MANAGERS_INDEX
self.dataset_symbol = self.add_data(EODHDEconomicEvents, ticker).symbol
def on_data(self, slice):
# Trade based on the updated economic events.
if self.dataset_symbol in slice.get(EODHDEconomicEvents):
# Use the Manufacturing Index to generate trade signals on manufacturing industry vehicles.
# Make sure previous and estimate are available to estimate the direction of the industry.
event = slice[self.dataset_symbol].data[0]
if event.previous and event.estimate:
# If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
if event.previous > event.estimate:
self.set_holdings(self.equity_symbol, 1)
# Otherwise, it is expected manufacturing ETF prices will drop.
else:
self.set_holdings(self.equity_symbol, -1)