Overall Statistics |
Total Trades 1315 Average Win 0.06% Average Loss -0.07% Compounding Annual Return -3.477% Drawdown 17.000% Expectancy -0.349 Net Profit -16.232% Sharpe Ratio -1.024 Probabilistic Sharpe Ratio 0.000% Loss Rate 64% Win Rate 36% Profit-Loss Ratio 0.79 Alpha -0.025 Beta 0.022 Annual Standard Deviation 0.023 Annual Variance 0.001 Information Ratio -0.796 Tracking Error 0.11 Treynor Ratio -1.088 Total Fees $109.25 Estimated Strategy Capacity $43000000.00 Lowest Capacity Asset USDHKD 8G |
#region imports from AlgorithmImports import * from pykalman import KalmanFilter from scipy.optimize import minimize from statsmodels.tsa.vector_ar.vecm import VECM #endregion class KalmanFilterStatisticalArbitrageDemo(QCAlgorithm): def Initialize(self): #1. Required: Five years of backtest history self.SetStartDate(2014, 1, 1) self.SetEndDate(2019, 1, 1) #2. Required: Alpha Streams Models: self.SetBrokerageModel(BrokerageName.AlphaStreams) #3. Required: Significant AUM Capacity self.SetCash(1000000) #4. Required: Benchmark to SPY self.SetBenchmark("SPY") self.assets = ["EURUSD", "GBPUSD", "USDCAD", "USDHKD", "USDJPY"] # Add Equity ------------------------------------------------ for i in range(len(self.assets)): self.AddForex(self.assets[i], Resolution.Minute) # Instantiate our model self.Recalibrate() # Set a variable to indicate the trading bias of the portfolio self.state = 0 # Set Scheduled Event Method For Kalman Filter updating. self.Schedule.On(self.DateRules.WeekStart(), self.TimeRules.At(0, 0), self.Recalibrate) # Set Scheduled Event Method For Kalman Filter updating. self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose("EURUSD"), self.EveryDayBeforeMarketClose) def Recalibrate(self): qb = self history = qb.History(self.assets, 252*2, Resolution.Daily) if history.empty: return # Select the close column and then call the unstack method data = history['close'].unstack(level=0) # Convert into log-price series to eliminate compounding effect log_price = np.log(data) ### Get Cointegration Vectors # Initialize a VECM model following the unit test parameters, then fit to our data. vecm_result = VECM(log_price, k_ar_diff=0, coint_rank=len(self.assets)-1, deterministic='n').fit() # Obtain the Beta attribute. This is the cointegration subspaces' unit vectors. beta = vecm_result.beta # Check the spread of different cointegration subspaces. spread = log_price @ beta ### Optimization of Cointegration Subspaces # We set the weight on each vector is between -1 and 1. While overall sum is 0. x0 = np.array([-1**i/beta.shape[1] for i in range(beta.shape[1])]) bounds = tuple((-1, 1) for i in range(beta.shape[1])) constraints = [{'type':'eq', 'fun':lambda x: np.sum(x)}] # Optimize the Portmanteau statistics opt = minimize(lambda w: ((w.T @ np.cov(spread.T, spread.shift(1).fillna(0).T)[spread.shape[1]:, :spread.shape[1]] @ w)/(w.T @ np.cov(spread.T) @ w))**2, x0=x0, bounds=bounds, constraints=constraints, method="SLSQP") # Normalize the result opt.x = opt.x/np.sum(abs(opt.x)) new_spread = spread @ opt.x ### Kalman Filter # Initialize a Kalman Filter. Using the first 20 data points to optimize its initial state. We assume the market has no regime change so that the transitional matrix and observation matrix is [1]. self.kalmanFilter = KalmanFilter(transition_matrices = [1], observation_matrices = [1], initial_state_mean = new_spread.iloc[:20].mean(), observation_covariance = new_spread.iloc[:20].var(), em_vars=['transition_covariance', 'initial_state_covariance']) self.kalmanFilter = self.kalmanFilter.em(new_spread.iloc[:20], n_iter=5) (filtered_state_means, filtered_state_covariances) = self.kalmanFilter.filter(new_spread.iloc[:20]) # Obtain the current Mean and Covariance Matrix expectations. self.currentMean = filtered_state_means[-1, :] self.currentCov = filtered_state_covariances[-1, :] # Initialize a mean series for spread normalization using the Kalman Filter's results. mean_series = np.array([None]*(new_spread.shape[0]-20)) # Roll over the Kalman Filter to obtain the mean series. for i in range(20, new_spread.shape[0]): (self.currentMean, self.currentCov) = self.kalmanFilter.filter_update(filtered_state_mean = self.currentMean, filtered_state_covariance = self.currentCov, observation = new_spread.iloc[i]) mean_series[i-20] = float(self.currentMean) # Obtain the normalized spread series. normalized_spread = (new_spread.iloc[20:] - mean_series) ### Determine Trading Threshold # Initialize 50 set levels for testing. s0 = np.linspace(0, max(normalized_spread), 50) # Calculate the profit levels using the 50 set levels. f_bar = np.array([None]*50) for i in range(50): f_bar[i] = len(normalized_spread.values[normalized_spread.values > s0[i]]) \ / normalized_spread.shape[0] # Set trading frequency matrix. D = np.zeros((49, 50)) for i in range(D.shape[0]): D[i, i] = 1 D[i, i+1] = -1 # Set level of lambda. l = 1.0 # Obtain the normalized profit level. f_star = np.linalg.inv(np.eye(50) + l * D.T@D) @ f_bar.reshape(-1, 1) s_star = [f_star[i]*s0[i] for i in range(50)] self.threshold = s0[s_star.index(max(s_star))] # Set the trading weight. We would like the portfolio absolute total weight is 1 when trading. trading_weight = beta @ opt.x self.trading_weight = trading_weight / np.sum(abs(trading_weight)) def EveryDayBeforeMarketClose(self): qb = self # Get the real-time log close price for all assets and store in a Series series = pd.Series() for symbol in qb.Securities.Keys: series[symbol] = np.log(qb.Securities[symbol].Close) # Get the spread spread = series @ self.trading_weight # Update the Kalman Filter with the Series (self.currentMean, self.currentCov) = self.kalmanFilter.filter_update(filtered_state_mean = self.currentMean, filtered_state_covariance = self.currentCov, observation = spread) # Obtain the normalized spread. normalized_spread = spread - self.currentMean # ============================== # Mean-reversion if normalized_spread < -self.threshold: orders = [] for i in range(len(self.assets)): orders.append(PortfolioTarget(self.assets[i], self.trading_weight[i])) self.SetHoldings(orders) self.state = 1 elif normalized_spread > self.threshold: orders = [] for i in range(len(self.assets)): orders.append(PortfolioTarget(self.assets[i], -1 * self.trading_weight[i])) self.SetHoldings(orders) self.state = -1 # Out of position if spread recovered elif self.state == 1 and normalized_spread > -self.threshold or self.state == -1 and normalized_spread < self.threshold: self.Liquidate() self.state = 0