ETF market rotation strategy provides steady positive results and small drawdown

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1590

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Backtest from
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initial capital

Cumulative performance:

Algorithm
Benchmark

Custom data:

Total Returns

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Alpha

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Beta

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Sharpe

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Sortino

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Max Drawdown

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Benchmark Returns

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Volatility

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Returns | 1 Month | 3 Month | 6 Month | 12 Month |

Alpha | 1 Month | 3 Month | 6 Month | 12 Month |

Beta | 1 Month | 3 Month | 6 Month | 12 Month |

Sharpe | 1 Month | 3 Month | 6 Month | 12 Month |

Sortino | 1 Month | 3 Month | 6 Month | 12 Month |

Volatility | 1 Month | 3 Month | 6 Month | 12 Month |

Max Drawdown | 1 Month | 3 Month | 6 Month | 12 Month |

# # USA Market Sector Rotation Strategy # # This strategy rotates between Vanguard market sector ETFs on a monthly # basis. Each month the performance and mean 20-day volitility over # the last 13 weeks are used to rank which ETF should be invested # in for the coming month. import math import pandas def initialize(context): context.stocks = { # 25904: sid(25904), # VFH (Vanguard Financials ETF) 25906: sid(25906), # VHT (Vanguard Health Care ETF) 25905: sid(25905), # VGT (Vanguard Information Technology ETF) # 26667: sid(26667), # VDE (Vanguard Energy ETF) 25902: sid(25902), # VCR (Vanguard Consumer Discretionary ETF) 22445: sid(22445), # IBB (iShares Nasdaq Biotechnology Index Fund) # 39479: sid(39479), # IBB (iShares Nasdaq Biotechnology Index Fund) 22887: sid(22887), # EDV VANGUARD treasury 25899: sid(25899), # VB = Vanguard small cap 25898: sid(25898) # VAW (Vanguard Materials ETF) } # Keep track of the current month. context.currentMonth = None # compare algorithm's performance versus small cap EFT buy and hold set_benchmark(symbol('vb')) # The order ID of the sell order currently being filled context.oid = None # The current stock being held context.currentStock = None # The next stock that needs to get purchased (once the sell order # on the current stock is filled context.nextStock = None # The 3-month lookback period. Calculated based on there being # an average of 21 trading days in a month context.lookback = 63 ''' Gets the minimum and maximum values of an array of values ''' def getMinMax(arr): return min(arr.values()), max(arr.values()) ''' Calculates the n-day historical volatility given a set of n+1 prices. @param period The number of days for which to calculate volatility @param prices An array of price information. Must be of length period + 1. ''' def historicalVolatility(period, prices): # HVdaily = sqrt( sum[1..n](x_t - Xbar)^2 / n - 1) # Start by calculating Xbar = 1/n sum[1..n] (ln(P_t / P_t-1)) r = [] for i in xrange(1, period + 1): r.append(math.log(prices[i] / prices[i-1])) # Find the average of all returns rMean = sum(r) / period; # Determine the difference of each return from the mean, then square d = [] for i in xrange(0, period): d.append(math.pow((r[i] - rMean), 2)) # Take the square root of the sum over the period - 1. Then mulitply # that by the square root of the number of trading days in a year vol = math.sqrt(sum(d) / (period - 1)) * math.sqrt(252/period) return vol ''' Gets the performance and average 20-day volatility of a security over a given period @param prices @param period The time period for which to find ''' def getStockMetrics(prices, period): # Get the prices #prices = data['close_price'][security][-period-1:] start = prices[-period] # First item end = prices[-1] # Last item performance = (end - start) / start # Calculate 20-day volatility for the given period v = [] x = 0 for i in xrange(-period, 0): v.append(historicalVolatility(20, prices[i-21:21+x])) x += 1 volatility = sum(v) / period return performance, volatility ''' Picks the best stock from a group of stocks based on the given data over a specified period using the stocks' performance and volatility @param data The datapanel with data of all the stocks @param stocks A list of stocks to rank @param period The time period over which the stocks will be analyzed ''' def getBestStock(data, stocks, period): best = None performances = {} volatilities = {} # Get performance and volatility for all the stocks for s in stocks: p, v = getStockMetrics(data['price'][s.sid], period) performances[s.sid] = p volatilities[s.sid] = v # Determine min/max of each. NOTE: volatility is switched # since a low volatility should be weighted highly. minP, maxP = getMinMax(performances) maxV, minV = getMinMax(volatilities) # Normalize the performance and volatility values to a range # between [0..1] then rank them based on a 70/30 weighting. for s in stocks: p = (performances[s.sid] - minP) / (maxP - minP) v = (volatilities[s.sid] - minV) / (maxV - minV) rank = p * 0.7 + v * 0.3 #log.info('Rank info for %s: p=%s, v=%s, r=%s' % (s,p,v,rank)) # If the new rank is greater than the old best rank, pick it. if best is None or rank > best[1]: best = s, rank return best[0] ''' Sells all the currently held positions in the context's portfolio ''' def sellHoldings(context): positions = context.portfolio.positions oid = None for p in positions.values(): if (p.amount > 0): #log.debug('ordering %s' % p) oid = order(p.sid, -p.amount) return oid ''' Utilize the batch_transform decorator to accumulate multiple days of data into one datapanel Need the window length to be 20 longer than lookback period to allow for a 20-day volatility calculation ''' @batch_transform(window_length=83) def accumulateData(data): # return price_history = history(bar_count=20, frequency='1d', field='price') return data ''' The main proccessing function. This is called and passed data ''' def handle_data(context, data): # Accumulate data until there is enough days worth of data # to process without having outOfBounds issues. datapanel = accumulateData(data) if datapanel is None: # There is insufficient data accumulated to process return # If there is an order ID, check the status of the order. # If there is an order and it is filled, the next stock # can be purchased. if context.oid is not None: orderObj = get_order(context.oid) if orderObj.filled == orderObj.amount: # Good to buy next holding amount = math.floor((context.portfolio.cash) / data[context.nextStock.sid].price) - 1 log.info('Sell order complete, buying %s of %s (%s of %s)' % \ (amount, context.nextStock, amount*data[context.nextStock.sid].price, context.portfolio.cash)) order(context.nextStock, amount) context.currentStock = context.nextStock context.oid = None context.nextStock = None date = get_datetime() month = date.month if not context.currentMonth: # Set the month initially context.currentMonth = month if context.currentMonth == month: # If the current month is unchanged, nothing further to do return context.currentMonth = month # At this point, a new month has been reached. The stocks # need to be # Ensure stocks are only traded if possible. # (e.g) EDV doesn't start trading until late 2007, without # this, any backtest run before that date would fail. stocks = [] for s in context.stocks.values(): if date > s.security_start_date: stocks.append(s) # Determine which stock should be used for the next month best = getBestStock(datapanel, stocks, context.lookback) if best: if (context.currentStock is not None and context.currentStock == best): # If there is a stock currently held and it is the same as # the new 'best' stock, nothing needs to be done return else: # Otherwise, the current stock needs to be sold and the new # stock bought context.oid = sellHoldings(context) context.nextStock = best # Purchase will not occur until the next call of handle_data # and only when the order has been filled. # If there is no stock currently held, it needs to be bought. # This only happend if context.currentStock is None: amount = math.floor((context.portfolio.cash) / data[context.nextStock.sid].price) - 1 log.info('First purchase, buying %s of %s (%s of %s)' % \ (amount, context.nextStock, amount*data[context.nextStock.sid].price, context.portfolio.cash)) order(context.nextStock, amount) context.currentStock = context.nextStock context.oid = None context.nextStock = None

We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed. We've put everything you need to know here on one page.