| Overall Statistics |
|
Total Orders 49 Average Win 19.17% Average Loss -2.18% Compounding Annual Return 10.936% Drawdown 19.700% Expectancy 5.924 Start Equity 100000 End Equity 1331827.89 Net Profit 1231.828% Sharpe Ratio 0.517 Sortino Ratio 0.536 Probabilistic Sharpe Ratio 3.003% Loss Rate 29% Win Rate 71% Profit-Loss Ratio 8.78 Alpha 0 Beta 0 Annual Standard Deviation 0.114 Annual Variance 0.013 Information Ratio 0.712 Tracking Error 0.114 Treynor Ratio 0 Total Fees $1017.61 Estimated Strategy Capacity $840000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 0.54% |
# region imports
from AlgorithmImports import *
from dateutil.relativedelta import relativedelta
# endregion
class RecessionSignal(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2000, 1, 1)
self.set_cash(100_000)
self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.MARGIN)
self.settings.minimum_order_margin_portfolio_percentage = 0
self.settings.daily_precise_end_time = False
period: int = 10 * 21
self._market: Symbol = self.add_equity("SPY", Resolution.MINUTE).symbol
self._sma: SimpleMovingAverage = self.SMA(self._market, period, Resolution.DAILY)
# safe asset
self._safe_asset: Symbol = self.add_equity("IEF", Resolution.MINUTE).symbol
self.set_warm_up(period, Resolution.DAILY)
self._indpro: Symbol = self.add_data(YOYData, 'INDPRO_YOY', Resolution.DAILY).symbol
self._rrsfs: Symbol = self.add_data(YOYData, 'RRSFS_YOY', Resolution.DAILY).symbol
self._rebalance_flag: bool = False
self.schedule.on(self.date_rules.month_end(self._market), self.time_rules.before_market_close(self._market, 1), self._selection)
def on_data(self, slice: Slice) -> None:
if not self._rebalance_flag:
return
self._rebalance_flag = False
if self.is_warming_up: return
if not self._sma.is_ready: return
if not slice.contains_key(self._market) or not slice.contains_key(self._safe_asset): return
traded_asset: Symbol = self._market
# check end of custom datasets
last_update_date: datetime.date = YOYData.get_last_update_date()
if not all(self.securities[symbol].get_last_data() and symbol.value in last_update_date and self.time.date() < last_update_date[symbol.value] \
for symbol in [self._indpro, self._rrsfs]):
# signal no recession in case there's an end of custom data
pass
else:
# signal recession
if any(self.securities[symbol].price < 0. for symbol in [self._indpro, self._rrsfs]):
if slice[self._market].value <= self._sma.current.value:
traded_asset = self._safe_asset
if not self.portfolio[traded_asset].invested:
self.set_holdings(traded_asset, 1., True)
def _selection(self) -> None:
self._rebalance_flag = True
class YOYData(PythonData):
_last_update_date: Dict[str, datetime.date] = {}
def GetSource(self, config: SubscriptionDataConfig, date: datetime, isLiveMode: bool) -> SubscriptionDataSource:
return SubscriptionDataSource(f'data.quantpedia.com/backtesting_data/economic/{config.symbol.value}.csv', SubscriptionTransportMedium.REMOTE_FILE, FileFormat.CSV)
@staticmethod
def get_last_update_date() -> Dict[Symbol, datetime.date]:
return YOYData._last_update_date
def Reader(self, config: SubscriptionDataConfig, line: str, date: datetime, is_live_mode: bool) -> BaseData:
data: YOYData = YOYData()
data.symbol = config.symbol
if not line[0].isdigit(): return None
split = line.split(';')
# Parse the CSV file's columns into the custom data class
data.time = datetime.strptime(split[0], "%Y-%m-%d") + relativedelta(months=1)
data.value = float(split[1])
if config.symbol.value not in YOYData._last_update_date:
YOYData._last_update_date[config.symbol.value] = datetime(1,1,1).date()
if data.time.date() > YOYData._last_update_date[config.symbol.value]:
YOYData._last_update_date[config.symbol.value] = data.time.date()
return data