Overall Statistics |
Total Trades 838 Average Win 0.68% Average Loss -0.62% Compounding Annual Return 4.183% Drawdown 25.800% Expectancy 0.247 Net Profit 94.122% Sharpe Ratio 0.405 Probabilistic Sharpe Ratio 0.380% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 1.09 Alpha 0.043 Beta -0.044 Annual Standard Deviation 0.097 Annual Variance 0.009 Information Ratio -0.268 Tracking Error 0.207 Treynor Ratio -0.881 Total Fees $638.01 Estimated Strategy Capacity $380.00 |
# https://quantpedia.com/strategies/stock-picking-of-etf-constituents/ # # The investing universe consists of stocks from 9 sector ETFs, the S&P 500 ETF and a small-cap ETF. The first step is to # identify a volume spike on the ETF. The volume spike is considered a day when the volume is at least three standard deviations # away from its mean. For each volume spike accompanied by a negative return, the investor creates an equally-weighted portfolio # of 10% ETF constituents, which have the lowest beta to the ETF. Investor buys these stocks and holds them for 40 days. Potfolio # is equally weighted. import fk_tools import numpy as np from collections import deque from scipy import stats class StockPickingETFConstituents(QCAlgorithm): def Initialize(self): self.SetStartDate(2005, 1, 1) self.SetCash(100000) self.period = 20 # Daily price data. self.data = {} self.symbol = 'OEF' self.holdings_period = 40 # Stock universe. self.symbols = fk_tools.sp100_stocks for symbol in fk_tools.sp100_stocks + [self.symbol]: data = self.AddEquity(symbol, Resolution.Daily) data.SetFeeModel(fk_tools.CustomFeeModel(self)) self.data[symbol] = deque(maxlen = self.period) def OnData(self, data): if self.Portfolio.Invested: self.holdings_period -= 1 if self.holdings_period == 0: self.Liquidate() # Store daily data for universe. for symbol in self.data: if self.Securities.ContainsKey(symbol): price = self.Securities[symbol].Price volume = self.Securities[symbol].Volume if price != 0 and price != 0: self.data[symbol].append([price, volume]) else: # Append latest price as a next one in case there's 0 as price. if len(self.data[symbol]) > 0: last_data = self.data[-1] self.data[symbol].append(last_data) market_closes = None # Market etf data is ready. if self.symbol in self.data and len(self.data[self.symbol]) == self.data[self.symbol].maxlen: market_closes = [x[0] for x in self.data[self.symbol]] volumes = [x[1] for x in self.data[self.symbol]] volume_mean = np.mean(volumes) volume_std = np.std(volumes) recent_volume = volumes[-1] # Volume spike has not occured. if recent_volume <= volume_mean + 3 * volume_std: return # Last day's return was positive. if fk_tools.Return(market_closes[-2:]) >= 0: return else: return market_closes = np.array(market_closes) stock_beta = {} for symbol in self.data: if symbol == self.symbol: continue # Stock data is ready. if (symbol in self.data and len(self.data[symbol]) == self.data[symbol].maxlen): # Beta calc. stock_closes = np.array([x[0] for x in self.data[symbol]]) market_returns = (market_closes[1:] - market_closes[:-1]) / market_closes[:-1] stock_returns = (stock_closes[1:] - stock_closes[:-1]) / stock_closes[:-1] # Manual beta calc. # cov = np.cov(market_returns, stock_returns)[0][1] # market_variance = np.std(market_returns) ** 2 # beta = cov / market_variance beta, alpha, r_value, p_value, std_err = stats.linregress(market_returns, stock_returns) stock_beta[symbol] = beta if len(stock_beta) == 0: return sorted_by_beta = sorted(stock_beta.items(), key = lambda x: x[1], reverse = True) decile = int(len(sorted_by_beta) / 10) long = [x[0] for x in sorted_by_beta[-decile:]] # Trade execution count = len(long) if count == 0: return # Check this. stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in stocks_invested: if symbol.Value not in long: self.Liquidate(symbol) for symbol in long: if self.Securities[symbol].IsTradable and self.Securities[symbol].Price != 0: self.SetHoldings(symbol, 1 / count) if self.holdings_period != 40: self.holdings_period = 40
import numpy as np from scipy.optimize import minimize sp100_stocks = ['AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'] def MonthDiff(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def Return(values): return (values[-1] - values[0]) / values[0] def Volatility(values): values = np.array(values) returns = (values[1:] - values[:-1]) / values[:-1] return np.std(returns) # Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD")) # Quandl free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle" # Quandl short interest data. class QuandlFINRA_ShortVolume(PythonQuandl): def __init__(self): self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible # Quantpedia data # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv) def Reader(self, config, line, date, isLiveMode): data = QuantpediaFutures() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(';') data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data['settle'] = float(split[1]) data.Value = float(split[1]) return data # NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions. # If there's a place for new trade, it will be managed for time of holding period. class TradeManager(): def __init__(self, algorithm, long_size, short_size, holding_period): self.algorithm = algorithm # algorithm to execute orders in. self.long_size = long_size self.short_size = short_size self.weight = 1 / (self.long_size + self.short_size) self.long_len = 0 self.short_len = 0 # Arrays of ManagedSymbols self.symbols = [] self.holding_period = holding_period # Days of holding. # Add stock symbol object def Add(self, symbol, long_flag): # Open new long trade. managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag) if long_flag: # If there's a place for it. if self.long_len < self.long_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, self.weight) self.long_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Open new short trade. else: # If there's a place for it. if self.short_len < self.short_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, - self.weight) self.short_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Decrement holding period and liquidate symbols. def TryLiquidate(self): symbols_to_delete = [] for managed_symbol in self.symbols: managed_symbol.days_to_liquidate -= 1 # Liquidate. if managed_symbol.days_to_liquidate == 0: symbols_to_delete.append(managed_symbol) self.algorithm.Liquidate(managed_symbol.symbol) if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 # Remove symbols from management. for managed_symbol in symbols_to_delete: self.symbols.remove(managed_symbol) def LiquidateTicker(self, ticker): symbol_to_delete = None for managed_symbol in self.symbols: if managed_symbol.symbol.Value == ticker: self.algorithm.Liquidate(managed_symbol.symbol) symbol_to_delete = managed_symbol if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 break if symbol_to_delete: self.symbols.remove(symbol_to_delete) else: self.algorithm.Debug("Ticker is not held in portfolio!") class ManagedSymbol(): def __init__(self, symbol, days_to_liquidate, long_flag): self.symbol = symbol self.days_to_liquidate = days_to_liquidate self.long_flag = long_flag class PortfolioOptimization(object): def __init__(self, df_return, risk_free_rate, num_assets): self.daily_return = df_return self.risk_free_rate = risk_free_rate self.n = num_assets # numbers of risk assets in portfolio self.target_vol = 0.05 def annual_port_return(self, weights): # calculate the annual return of portfolio return np.sum(self.daily_return.mean() * weights) * 252 def annual_port_vol(self, weights): # calculate the annual volatility of portfolio return np.sqrt(np.dot(weights.T, np.dot(self.daily_return.cov() * 252, weights))) def min_func(self, weights): # method 1: maximize sharp ratio return - self.annual_port_return(weights) / self.annual_port_vol(weights) # method 2: maximize the return with target volatility #return - self.annual_port_return(weights) / self.target_vol def opt_portfolio(self): # maximize the sharpe ratio to find the optimal weights cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) bnds = tuple((0, 1) for x in range(2)) + tuple((0, 0.25) for x in range(self.n - 2)) opt = minimize(self.min_func, # object function np.array(self.n * [1. / self.n]), # initial value method='SLSQP', # optimization method bounds=bnds, # bounds for variables constraints=cons) # constraint conditions opt_weights = opt['x'] return opt_weights