## Averaged Input Assumptions and Momentum

Today I want to share another interesting idea contributed by Pierre Chretien. Pierre suggested using **Averaged** Input Assumptions and Momentum to create **reasonably quiet** strategy. The **averaging** techniques are used to avoid over-fitting any particular frequency.

To create **Averaged** Input Assumptions we combine returns over different look-back periods, giving more weight to the recent returns, to form overall Input Assumptions.

create.ia.averaged <- function(lookbacks, n.lag) { lookbacks = lookbacks n.lag = n.lag function(hist.returns, index=1:ncol(hist.returns), hist.all) { nperiods = nrow(hist.returns) temp = c() for (n.lookback in lookbacks) temp = rbind(temp, hist.returns[(nperiods - n.lookback - n.lag + 1):(nperiods - n.lag), ]) create.ia(temp, index, hist.all) } }

To create **Averaged** Momentum we take a look-back weighted avaerage of momentums computed over different look-back periods.

momentum.averaged <- function(prices, lookbacks = c(20,60,120,250) , # length of momentum look back n.lag = 3 ) { momentum = 0 * prices for (n.lookback in lookbacks) { part.mom = mlag(prices, n.lag) / mlag(prices, n.lookback + n.lag) - 1 momentum = momentum + 252 / n.lookback * part.mom } momentum / len(lookbacks) }

Next let’s compare using historical Input Assumptions vs **Averaged** Input Assumptions and Momentum vs **Averaged** Momentum. I will consider Absolute Momentum (not cross sectional), for more information about relative and absolute momentum, please see

Absolute Momentum: A Simple Rule-Based Strategy and Universal Trend-Following Overlay, Gary Antonacci, 2013

Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach by W.J. Keller and H. S. Van Putten 2012

############################################################################### # Load Systematic Investor Toolbox (SIT) # https://systematicinvestor.wordpress.com/systematic-investor-toolbox/ ############################################################################### setInternet2(TRUE) con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb')) source(con) close(con) #***************************************************************** # Load historical data #****************************************************************** load.packages('quantmod') # 10 funds tickers = spl('Us.Eq = VTI + VTSMX, Eurpoe.Eq = IEV + FIEUX, Japan.Eq = EWJ + FJPNX, Emer.Eq = EEM + VEIEX, Re = RWX + VNQ + VGSIX, Com = DBC + QRAAX, Gold = GLD + SCGDX, Long.Tr = TLT + VUSTX, Mid.Tr = IEF + VFITX, Short.Tr = SHY + VFISX') start.date = 1998 dates = paste(start.date,'::',sep='') data <- new.env() getSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T) for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T) bt.prep(data, align='keep.all', dates=paste(start.date-2,':12::',sep=''), fill.gaps = T) #***************************************************************** # Setup #****************************************************************** prices = data$prices n = ncol(prices) nperiods = nrow(prices) periodicity = 'months' period.ends = endpoints(prices, periodicity) period.ends = period.ends[period.ends > 0] max.product.exposure = 0.6 #***************************************************************** # Input Assumptions #****************************************************************** lookback.len = 40 create.ia.fn = create.ia # input assumptions are averaged on 20, 40, 60 days using 1 day lag ia.array = c(20,40,60) avg.create.ia.fn = create.ia.averaged(ia.array, 1) #***************************************************************** # Momentum #****************************************************************** universe = prices > 0 mom.lookback.len = 120 momentum = prices / mlag(prices, mom.lookback.len) - 1 mom.universe = ifna(momentum > 0, F) # momentum is averaged on 20,60,120,250 days using 3 day lag mom.array = c(20,60,120,250) avg.momentum = momentum.averaged(prices, mom.array, 3) avgmom.universe = ifna(avg.momentum > 0, F) #***************************************************************** # Algos #****************************************************************** min.risk.fns = list( EW = equal.weight.portfolio, MV = min.var.portfolio, MCE = min.corr.excel.portfolio, MV.RSO = rso.portfolio(min.var.portfolio, 3, 100, const.ub = max.product.exposure), MCE.RSO = rso.portfolio(min.corr.excel.portfolio, 3, 100, const.ub = max.product.exposure) ) #***************************************************************** # Code Strategies #****************************************************************** make.strategy.custom <- function(name, create.ia.fn, lookback.len, universe, env) { obj = portfolio.allocation.helper(data$prices, periodicity = periodicity, universe = universe, lookback.len = lookback.len, create.ia.fn = create.ia.fn, const.ub = max.product.exposure, min.risk.fns = min.risk.fns, adjust2positive.definite = F ) env[[name]] = create.strategies(obj, data, prefix=paste(name,'.',sep=''))$models } models <- new.env() make.strategy.custom('ia.none' , create.ia.fn , lookback.len, universe , models) make.strategy.custom('ia.mom' , create.ia.fn , lookback.len, mom.universe , models) make.strategy.custom('ia.avg_mom' , create.ia.fn , lookback.len, avgmom.universe, models) make.strategy.custom('avg_ia.none' , avg.create.ia.fn, 252 , universe , models) make.strategy.custom('avg_ia.mom' , avg.create.ia.fn, 252 , mom.universe , models) make.strategy.custom('avg_ia.avg_mom' , avg.create.ia.fn, 252 , avgmom.universe, models) #***************************************************************** # Create Report #***************************************************************** strategy.snapshot.custom <- function(models, n = 0, title = NULL) { if (n > 0) models = models[ as.vector(matrix(1:len(models),ncol=n, byrow=T)) ] layout(1:3) plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = title) mtext('Cumulative Performance', side = 2, line = 1) plotbt.strategy.sidebyside(models) barplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', T) } # basic vs basic + momentum => momentum filter has better results models.final = c(models$ia.none, models$ia.mom) strategy.snapshot.custom(models.final, len(min.risk.fns), 'Momentum Filter') # basic vs basic + avg ia => averaged ia reduce turnover models.final = c(models$ia.none, models$avg_ia.none) strategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged Input Assumptions') # basic + momentum vs basic + avg.momentum => mixed results for averaged momentum models.final = c(models$ia.mom, models$ia.avg_mom) strategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged Momentum') # basic + momentum vs avg ia + avg.momentum models.final = c(models$ia.mom, models$avg_ia.avg_mom) strategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged vs Base')

Above, I compared results for the following 4 cases:

1. Adding Momentum filter: all algos perfrom better

2. Input Assumptions vs **Averaged** Input Assumptions: returns are very similar, but using **Averaged** Input Assumptions helps reduce portfolio turnover.

3. Momentum vs **Averaged** Momentum: returns are very similar, but using **Averaged** Momentum increases portfolio turnover.

4. historical Input Assumptions + Momentum vs **Averaged** Input Assumptions + **Averaged** Momentum: results are mixed, no consistent advantage of using **Averaged** methods

Overall, the **Averaged** methods is a very interesting idea and I hope you will experiemtn with it and share your findings, like Pierre. Pierre, again thank you very much for sharing.

The full source code and example for the bt.averaged.test() function is available in bt.test.r at github.

As already mentioned by tstudent earlier in the comments on this blog, transaction costs can make a huge difference in the final result of a strategy. Therefore, it is important that you make your own tests taking into consideration the real costs/taxes you are paying on transactions, as well as the slipping costs you are facing when trading. Your conclusions regarding the pertinence of averaging momentum/ia might be different. But what is clear is that momentum filtering is an interesting tool to reduce drawdowns.

Hi,

I was hoping that the strategies described in the Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach by W.J. Keller and H. S. Van Putten 2012 paper can be implemented on this website. I think they have very interesting findings around absolute momentum, volatility momentum and correlation momentum.

Thanks

Sanjay, the Flexible Asset Allocation strategy was already discussed and implemented by Michael Guan at

http://systematicedge.wordpress.com/2013/02/26/flexible-asset-allocation/

Thanks so much for the link.

Pierre, Michael was so kind to add percentage commission, so now it’s very easy to make this analysis and penalize turnover in an efficent way. If it’s not clear for you how to add percentage commission ask, I’m very glad to help.

Michael has done an exelent job with his SIT.

The only question that remains unresolved is: how much data-mining\data-snooping exists in the backtest of our strategies?

Which are the most interesting ideas (in letterature or in practitioners world) for test if our backtest performance are just the byproduct of a very long lucky period?