| Overall Statistics |
|
Total Orders 1898 Average Win 0.62% Average Loss -0.63% Compounding Annual Return -2.163% Drawdown 37.600% Expectancy -0.018 Start Equity 100000 End Equity 86580.12 Net Profit -13.420% Sharpe Ratio -0.124 Sortino Ratio -0.16 Probabilistic Sharpe Ratio 0.064% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.98 Alpha -0.013 Beta -0.042 Annual Standard Deviation 0.134 Annual Variance 0.018 Information Ratio -0.67 Tracking Error 0.172 Treynor Ratio 0.396 Total Fees $9213.19 Estimated Strategy Capacity $1000.00 Lowest Capacity Asset SIF R735QTJ8XC9X Portfolio Turnover 4.05% |
#region imports
from AlgorithmImports import *
from collections import deque
#endregion
# https://quantpedia.com/Screener/Details/66
class CombiningMomentumEffectWithVolume(QCAlgorithm):
def initialize(self):
self.set_start_date(2012, 1, 1)
self.set_end_date(2018, 8, 1)
self.set_cash(100_000)
self.universe_settings.resolution = Resolution.DAILY
self.add_universe(self._coarse_selection_function, self._fine_selection_function)
self._data_dict = {}
# 1/3 of the portfolio is rebalanced every month
self._portfolios = deque(maxlen=3)
self.schedule.on(
self.date_rules.month_start(Symbol.create("SPY", SecurityType.EQUITY, Market.USA)),
self.time_rules.at(0, 0), self._rebalance)
# the lookback period for return calculation
self._lookback = 252
self._filtered_fine = None
self._monthly_rebalance = False
self.set_warmup(self._lookback + 1)
def _coarse_selection_function(self, coarse):
for i in coarse:
if i.symbol not in self._data_dict:
self._data_dict[i.symbol] = SymbolData(i.symbol, self._lookback)
self._data_dict[i.symbol].roc.update(i.end_time, i.adjusted_price)
self._data_dict[i.symbol].volume = i.volume
if self._monthly_rebalance:
# drop stocks which have no fundamental data
return [x.symbol for x in coarse if x.has_fundamental_data]
return []
def _fine_selection_function(self, fine):
if self._monthly_rebalance:
data_ready = {symbol: symbolData for (symbol, symbolData) in self._data_dict.items() if symbolData.roc.is_ready}
if len(data_ready) < 100:
self._filtered_fine = []
else:
sorted_fine = [i for i in fine if i.earning_reports.basic_average_shares.three_months != 0 and i.symbol in data_ready]
sorted_fine_symbols = [i.symbol for i in sorted_fine]
filtered_data = {symbol: symbolData for (symbol, symbolData) in data_ready.items() if symbol in sorted_fine_symbols}
for i in sorted_fine:
if i.symbol in filtered_data and filtered_data[i.symbol].volume != 0:
filtered_data[i.symbol].turnover = i.earning_reports.basic_average_shares.three_months / filtered_data[i.symbol].volume
sorted_by_roc = sorted(filtered_data.values(), key = lambda x: x.roc.current.value, reverse = True)
top_roc = sorted_by_roc[:int(len(sorted_by_roc)*0.2)]
bottom_roc = sorted_by_roc[-int(len(sorted_by_roc)*0.2):]
high_turnover_top_roc = sorted(top_roc, key = lambda x: x.turnover, reverse = True)
high_turnover_bottom_roc = sorted(bottom_roc, key = lambda x: x.turnover, reverse = True)
self._long = [i.symbol for i in high_turnover_top_roc[:int(len(high_turnover_top_roc)*0.01)]]
self._short = [i.symbol for i in high_turnover_bottom_roc[:int(len(high_turnover_bottom_roc)*0.01)]]
self._filtered_fine = self._long + self._short
self._portfolios.append(self._filtered_fine)
else:
self._filtered_fine = []
if not self._filtered_fine:
self._monthly_rebalance = False
return self._filtered_fine
def _rebalance(self):
self._monthly_rebalance = True
def on_data(self, data):
if self._monthly_rebalance and self._filtered_fine:
self._filtered_fine = None
self._monthly_rebalance = False
# 1/3 of the portfolio is rebalanced every month
if len(self._portfolios) == self._portfolios.maxlen:
for i in list(self._portfolios)[0]:
self.liquidate(i)
# stocks are equally weighted and held for 3 months
short = [s for s in self._short if s in data.Bars]
short_weight = 1/len(short)
for i in short:
self.set_holdings(i, -1/3*short_weight)
long_ = [s for s in self._long if s in data.Bars]
long_weight = 1/len(long_)
for i in long_:
self.set_holdings(i, 1/3*long_weight)
class SymbolData:
def __init__(self, symbol, lookback):
self.symbol = symbol
self.roc = RateOfChange(lookback)
self.volume = None