| Overall Statistics |
|
Total Orders 63 Average Win 3.77% Average Loss -2.21% Compounding Annual Return 20.242% Drawdown 47.100% Expectancy 0.701 Start Equity 100000 End Equity 190674.56 Net Profit 90.675% Sharpe Ratio 0.54 Sortino Ratio 0.852 Probabilistic Sharpe Ratio 11.517% Loss Rate 37% Win Rate 63% Profit-Loss Ratio 1.70 Alpha 0.146 Beta 0.269 Annual Standard Deviation 0.309 Annual Variance 0.095 Information Ratio 0.278 Tracking Error 0.318 Treynor Ratio 0.621 Total Fees $430.14 Estimated Strategy Capacity $6000.00 Lowest Capacity Asset PBIB TM78KLLUMAG5 Portfolio Turnover 0.51% |
#region imports
from AlgorithmImports import *
#endregion
class PriceEarningsAnamoly(QCAlgorithm):
def initialize(self):
self.set_start_date(2016, 1, 1)
self.set_end_date(2019, 7, 1)
self.set_cash(100000)
self.set_benchmark("SPY")
self.universe_settings.resolution = Resolution.DAILY
self._symbols = []
# record the year that have passed since the algorithm starts
self._year = -1
self._num_coarse_stocks = 200
self._num_stocks_in_portfolio = 10
self.add_universe(self._coarse_selection_function, self._fine_selection_function)
def _coarse_selection_function(self, coarse):
if self.time.year == self._year:
return self._symbols
# drop stocks which have no fundamental data or have low price
coarse_with_fundamental = [x for x in coarse if x.has_fundamental_data and x.price > 5]
sorted_by_dollar_volume = sorted(coarse_with_fundamental, key=lambda x: x.dollar_volume, reverse=False)
return [i.symbol for i in sorted_by_dollar_volume[:self._num_coarse_stocks]]
def _fine_selection_function(self, fine):
if self.time.year == self._year:
return self._symbols
self._year = self.time.year
fine = [x for x in fine if x.valuation_ratios.pe_ratio > 0]
sorted_pe_ratio = sorted(fine, key=lambda x: x.valuation_ratios.pe_ratio)
self._symbols = [i.symbol for i in sorted_pe_ratio[:self._num_stocks_in_portfolio]]
return self._symbols
def on_securities_changed(self, change):
# liquidate securities that removed from the universe
for security in change.removed_securities:
if self.portfolio[security.symbol].invested:
self.liquidate(security.symbol)
count = len(change.added_securities)
# evenly invest on securities that newly added to the universe
for security in change.added_securities:
symbol = security.symbol
if not self.securities[symbol].price:
continue
self.set_holdings(symbol, 1.0/count)