Overall Statistics |

Total Trades 2149 Average Win 0.33% Average Loss -0.32% Compounding Annual Return 2.827% Drawdown 31.000% Expectancy 0.098 Net Profit 38.460% Sharpe Ratio 0.321 Loss Rate 46% Win Rate 54% Profit-Loss Ratio 1.02 Alpha 0.032 Beta 0 Annual Standard Deviation 0.1 Annual Variance 0.01 Information Ratio -0.295 Tracking Error 0.217 Treynor Ratio -98.982 Total Fees $0.00 |

from QuantConnect.Python import PythonQuandl from datetime import timedelta import numpy as np import pandas as pd class ImprovedCommodityMomentumTrading(QCAlgorithm): ''' Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations This paper proposed 3 modifications to the basic time-series momentum strategies in order to reduce portfolio turnover and improve portfolio performance. 1. Volatility Estimator: Yang and Zhang (2000) range-based estimator, which replaces the traditional estimator (standard deviation of past daily returns) 2. Trading Rules: Trading positions takes a continuum of values between -1 and +1 to reflect the statistical strength of price trend, which replaces the traditional trading rules (binary +1 or -1 based on the sign of historical mean return) 3. Pairwise Correlations: Incorporate signed pairwise correlations in the weighing scheme of portfolio construction Reference: [1] Baltas, Nick and Kosowski, Robert, "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations", May 8, 2017. URL: https://pdfs.semanticscholar.org/a2e9/df201d4b4774fda84a961cc804f2450988c5.pdf [2] Yang, Dennis, and Qiang Zhang, "DriftâIndependent Volatility Estimation Based on High, Low, Open, and Close Prices", The Journal of Business, vol. 73, no. 3, 2000, pp. 477–492. URL: www.jstor.org/stable/10.1086/209650.''' def Initialize(self): self.SetStartDate(2008,1, 1) self.SetEndDate(2019, 9, 1) self.SetCash(25000) tickers = ["CHRIS/CME_S1", # Soybean Futures, Continuous Contract #1 "CHRIS/CME_W1", # Wheat Futures, Continuous Contract #1 "CHRIS/CME_SM1", # Soybean Meal Futures, Continuous Contract #1 "CHRIS/CME_BO1", # Soybean Oil Futures, Continuous Contract #1 "CHRIS/CME_C1", # Corn Futures, Continuous Contract #1 "CHRIS/CME_O1", # Oats Futures, Continuous Contract #1 "CHRIS/CME_LC1", # Live Cattle Futures, Continuous Contract #1 "CHRIS/CME_FC1", # Feeder Cattle Futures, Continuous Contract #1 "CHRIS/CME_LN1", # Lean Hog Futures, Continuous Contract #1 "CHRIS/CME_GC1", # Gold Futures, Continuous Contract #1 "CHRIS/CME_SI1", # Silver Futures, Continuous Contract #1 "CHRIS/CME_PL1", # Platinum Futures, Continuous Contract #1 "CHRIS/ICE_B1", # Brent Crude Futures, Continuous Contract "CHRIS/ICE_O1", # Heating Oil Futures, Continuous Contract #1 "CHRIS/ICE_M1", # UK Natural Gas Futures, Continuous Contract #1 "CHRIS/ICE_CT1", # Cotton No. 2 Futures, Continuous Contract "CHRIS/ICE_OJ1", # Orange Juice Futures, Continuous Contract "CHRIS/ICE_KC1", # Coffee C Futures, Continuous Contract "CHRIS/ICE_CC1", # Cocoa Futures, Continuous Contract "CHRIS/ICE_G1", # Gas Oil Futures, Continuous Contract "CHRIS/ICE_RS1"] # Canola Futures, Continuous Contract for ticker in tickers: data = self.AddData(QuandlFutures, ticker, Resolution.Daily) data.SetLeverage(3) # Leverage was set to 3 for each of the futures contract self.OneYear = 365 # time period for trading rule calculation self.OneMonth = 30 # time period for YZ volatility estimator self.ThreeMonths = 90 # time period for pairwise correlation calculation # Last trading date tracker to achieve rebalancing the portfolio every month self.nextRebalance = self.Time # Set portfolio target level of volatility, set to 12% self.portfolio_target_sigma = 0.12 def OnData(self, data): ''' Monthly rebalance at the beginning of each month. Portfolio weights for each constituents are calculated based on Baltas and Kosowski weights. ''' # skip if less than 30 days passed since the last trading date if self.Time < self.nextRebalance: return '''Monthly Rebalance Execution''' # dataframe that contains the historical data for all securities history = self.History(self.Securities.Keys, self.OneYear, Resolution.Daily) history.replace(0, np.nan, inplace = True) # Get the security symbols are are in the history dataframe available_symbols = list(set(history.index.get_level_values(level = 0))) # Liquidate symbols that are not in the history dataframe anymore for security in self.Securities.Keys: if security.Value not in available_symbols: self.Liquidate(security, 'Not found in history request') # Get the trade signals and YZ volatility for all securities trade_signals = self.GetTradingSignal(history) volatility = self.GetYZVolatility(history, available_symbols) # Get the correlation factor CF_rho_bar = self.GetCorrelationFactor(history, trade_signals, available_symbols) #Rebalance the portfolio according to Baltas and Kosowski suggested weights N_assets = len(available_symbols) for symbol, signal, vol in zip(available_symbols, trade_signals, volatility): # Baltas and Kosowski weights (Equation 19 in [1]) weight = (signal*self.portfolio_target_sigma*CF_rho_bar)/(N_assets*vol) self.SetHoldings(symbol, weight) # Set next rebalance time self.nextRebalance = Expiry.EndOfMonth(self.Time) def GetCorrelationFactor(self, history, trade_signals, available_symbols): ''' Calculate the Correlation Factor, which is a function of the average pairwise correlation of all portfolio contituents - the calculation is based on past three month pairwise correlation - Notations: rho_bar - average pairwise correlation of all portfolio constituents CF_rho_bar - the correlation factor as a function of rho_bar''' # Get the past three month simple daily returns for all securities settle = history.settle.unstack(level = 0) past_three_month_returns = settle.pct_change().loc[settle.index[-1]-timedelta(self.ThreeMonths):] # Get number of assets N_assets = len(available_symbols) # Get the pairwise signed correlation matrix for all assets correlation_matrix = past_three_month_returns.corr() # Calculate rho_bar summation = 0 for i in range(N_assets-1): for temp in range(N_assets - 1 - i): j = i + temp + 1 x_i = trade_signals[i] x_j = trade_signals[j] rho_i_j = correlation_matrix.iloc[i,j] summation += x_i * x_j * rho_i_j # Equation 14 in [1] rho_bar = (2 * summation) / (N_assets * (N_assets - 1)) # Calculate the correlation factor (CF_rho_bar) # Equation 18 in [1] return np.sqrt(N_assets / (1 + (N_assets - 1) * rho_bar)) def GetTradingSignal(self, history): ''' TREND Trading Signal - Uses the t-statistics of historical daily log-returns to reflect the strength of price movement trend - TREND Signal Conditions: t-stat > 1 => TREND Signal = 1 t-stat < 1 => TREND Signal = -1 -1 < t-stat < 1 => TREND Signal = t-stat ''' settle = history.settle.unstack(level = 0) # daily futures log-returns based on close-to-close log_returns = np.log(settle/settle.shift(1)).dropna() # Calculate the t-statistics as # (mean-0)/(stdev/sqrt(n)), where n is sample size mean = np.mean(log_returns) std = np.std(log_returns) n = len(log_returns) t_stat = mean/(std/np.sqrt(n)) # cap holding at 1 and -1 return np.clip(t_stat, a_max=1, a_min=-1) def GetYZVolatility(self, history, available_symbols): ''' Yang and Zhang 'Drift-Independent Volatility Estimation' Formula: sigma_YZ^2 = sigma_OJ^2 + self.k * sigma_SD^2 + (1-self.k)*sigma_RS^2 (Equation 20 in [1]) where, sigma_OJ - (Overnight Jump Volitility estimator) sigma_SD - (Standard Volitility estimator) sigma_RS - (Rogers and Satchell Range Volatility estimator)''' YZ_volatility = [] time_index = history.loc[available_symbols[0]].index today = time_index[-1] #Calculate YZ volatility for each security and append to list for ticker in available_symbols: past_month_ohlc = history.loc[ticker].loc[today-timedelta(self.OneMonth):today] open, high, low, close = past_month_ohlc.open, past_month_ohlc.high, past_month_ohlc.low, past_month_ohlc.settle estimation_period = past_month_ohlc.shape[0] # Calculate constant parameter k for Yang and Zhang volatility estimator # using the formula found in Yang and Zhang (2000) k = 0.34 / (1.34 + (estimation_period + 1) / (estimation_period - 1)) # sigma_OJ (overnight jump => stdev of close-to-open log returns) open_to_close_log_returns = np.log(open/close.shift(1)) open_to_close_log_returns = open_to_close_log_returns[np.isfinite(open_to_close_log_returns)] sigma_OJ = np.std(open_to_close_log_returns) # sigma_SD (standard deviation of close-to-close log returns) close_to_close_log_returns = np.log(close/close.shift(1)) close_to_close_log_returns = close_to_close_log_returns[np.isfinite(close_to_close_log_returns)] sigma_SD = np.std(close_to_close_log_returns) # sigma_RS (Rogers and Satchell (1991)) h = np.log(high/open) l = np.log(low/open) c = np.log(close/open) sigma_RS_daily = (h * (h - c) + l * (l - c))**0.5 sigma_RS_daily = sigma_RS_daily[np.isfinite(sigma_RS_daily)] sigma_RS = np.mean(sigma_RS_daily) # daily Yang and Zhang volatility sigma_YZ = np.sqrt(sigma_OJ**2 + k * sigma_SD**2 + (1 - k) * sigma_RS**2) # append annualized volatility to the list YZ_volatility.append(sigma_YZ*np.sqrt(252)) return YZ_volatility class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "Settle"