| Overall Statistics |
|
Total Orders 22 Average Win 15.73% Average Loss -3.41% Compounding Annual Return 9.425% Drawdown 20.200% Expectancy 1.041 Start Equity 7500 End Equity 10204.74 Net Profit 36.063% Sharpe Ratio 0.507 Sortino Ratio 0.65 Probabilistic Sharpe Ratio 16.188% Loss Rate 64% Win Rate 36% Profit-Loss Ratio 4.61 Alpha 0.073 Beta -0.068 Annual Standard Deviation 0.119 Annual Variance 0.014 Information Ratio -0.339 Tracking Error 0.37 Treynor Ratio -0.892 Total Fees $22.00 Estimated Strategy Capacity $140000000.00 Lowest Capacity Asset XLE RGRPZX100F39 Portfolio Turnover 0.91% |
# region imports from AlgorithmImports import * import numpy as np # endregion class BootCampTask(QCAlgorithm): def initialize(self): self.set_start_date(2019, 1, 1) self.set_end_date(2022, 6, 1) self.set_cash(7500) # self.default_order_properties.time_in_force = TimeInForce.GOOD_TIL_CANCELED # Add underlying self.underlying = "XLE" #"TNA" # "IWM" #"UUP", "XLK", "DBA", "USO", "GLD", "DIA", "SPY" # self.add_equity("SPY", Resolution.MINUTE, data_normalization_mode=DataNormalizationMode.RAW) self.add_equity(self.underlying, Resolution.DAILY, data_normalization_mode=DataNormalizationMode.ADJUSTED) self._symbol = self.underlying self.settings.daily_precise_end_time = False self.set_benchmark(self.underlying) # set the benchmark # Build history for indicators self.history[TradeBar](self._symbol, 100, Resolution.DAILY)#, dataNormalizationMode=DataNormalizationMode.SCALED_RAW) # Warm up the close price and trade bar rolling windows with the previous 100-day trade bar data self.rollingwindow = RollingWindow[TradeBar](100) self.Consolidate(self._symbol, Resolution.DAILY, self.CustomBarHandler) # Risk parameters self.target_risk = 0.12 # percentage self.instrument_look_back = 30 # number of days self.instrument_risk_annualizer = 16 #math.sqrt(256/self.instrument_look_back) self.stop_price_instrument_ratio = 0.5 # percentage # Strategy parameters short_look_back = 16 long_look_back = 64 self.short_ma = self.sma(self._symbol, short_look_back) #, Resolution.DAILY) self.long_ma = self.sma(self._symbol, long_look_back) #, Resolution.DAILY) self.set_warm_up(long_look_back) # Warm up for the long ma self.strategy_direction = "" self.new_strategy_direction = "" # Order ticket for our stop order, Datetime when stop order was last hit self.stop_market_ticket = None def on_data(self, data): # Warming up the data and indicator if not self.long_ma.is_ready: return if not self.rollingwindow.is_ready: return # Calculate the trade signal self.cal_trade_signal() # Get current close self.trade_bar_df = self.pandas_converter.get_data_frame[TradeBar](list(self.rollingwindow)[::-1]) self.current_close = self.trade_bar_df.close[-1] # self.debug(self.current_close) # Calculate annualized instrument risk self.cal_annualized_instrument_risk() self.plot("Risks", "Annualized Instrument Risk", self.annalized_instrument_risk) # Get stop adjustment stop_adjustment = self.annalized_instrument_risk*self.stop_price_instrument_ratio # Calculate the stop price self.starting_stop_price_long = self.current_close - stop_adjustment self.starting_stop_price_short = self.current_close + stop_adjustment # Do not trade if the position was liquidated and no signal change happens to trigger an open trade. # if not self.portfolio.invested and self.new_strategy_direction == self.strategy_direction: return #if self.new_strategy_direction == self.strategy_direction: return if not self.portfolio.invested and self.new_strategy_direction != self.strategy_direction: # Calculate the trade size self.cal_trade_size() # Open trade self.open_trade() # Open Trailing-Stop-Loss trade if self.new_strategy_direction == "long": self.stop_price = self.starting_stop_price_long self.open_tsl_trade(-self.size) else: self.stop_price = self.starting_stop_price_short self.open_tsl_trade(self.size) # Start tracking price for trailing stop loss order self.highest_price = self.current_close self.lowest_price = self.current_close elif self.portfolio.invested and self.stop_market_ticket: # update trailing-stop-loss price self.update_tsl_order() #Plot the moving stop price on "Data Chart" with "Stop Price" series name self.plot_data() def on_order_event(self, order_event): if order_event.status != OrderStatus.FILLED: return self.Debug(order_event) def CustomBarHandler(self, bar): self.rollingwindow.add(bar) def Instrument_Risk(self, df) -> float: self.daily_returns = df.close.pct_change() self.risk = self.current_close * self.daily_returns[-self.instrument_look_back-1:-1].std() # offset by -1 for not include the EoD price on the trading day return self.risk def cal_annualized_instrument_risk(self): self.instrument_risk = self.Instrument_Risk(self.trade_bar_df) self.annalized_instrument_risk = self.instrument_risk * self.instrument_risk_annualizer # self.instrument_risk # self.Debug(f"Annualized risk by price points is {round(self.annalized_instrument_risk, 2)}") def cal_trade_size(self): self.target_exposure = self.target_risk*self.portfolio.total_portfolio_value self.Debug(f"Target exposure is {round(self.target_exposure, 2)}.") self.notional_exposure = self.target_exposure/self.annalized_instrument_risk self.size = math.floor(self.notional_exposure) # self.Debug(f"Trade size is {str(self.size)}.") def cal_trade_signal(self): if self.short_ma.current.value > self.long_ma.current.value : self.new_strategy_direction = "long" else: self.new_strategy_direction = "short" if self.new_strategy_direction != self.strategy_direction: self.debug(f"Strategy direction changed to: {self.new_strategy_direction}.") # self.strategy_direction = self.new_strategy_direction def open_trade(self): self.strategy_direction = self.new_strategy_direction if self.strategy_direction == "long": self.market_on_open_order(self.underlying, self.size) else: self.market_on_open_order(self.underlying, -1 * self.size) def open_tsl_trade(self, size): self.stop_market_ticket = self.stop_market_order(self.underlying, size, self.stop_price) def update_tsl_order(self): self.start_stop_price = self.stop_price update_fields = UpdateOrderFields() if self.strategy_direction == "long": if self.current_close > self.highest_price: delta = self.current_close - self.highest_price update_fields.stop_price = self.start_stop_price + delta self.stop_market_ticket.update(update_fields) self.highest_price = self.current_close self.stop_price = update_fields.stop_price else: pass #self.stop_price = self.start_stop_price if self.strategy_direction == "short": if self.current_close < self.lowest_price: delta = self.lowest_price - self.current_close update_fields.stop_price = self.start_stop_price - delta self.stop_market_ticket.update(update_fields) self.lowest_price = self.current_close self.stop_price = update_fields.stop_price else: pass #self.stop_price = self.start_stop_price def plot_data(self): self.plot("Data Chart", "Asset Price", self.current_close) self.plot("Data Chart", "Stop Price", self.stop_price) # self.plot("Data Chart", "Short MA", self.short_ma.current.value) # self.plot("Data Chart", "Long MA", self.long_ma.current.value)
