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Author Topic: Goomboo's EMA10/21 Crossover Versus the rebuilder's Flipist Method  (Read 5225 times)
stochastic
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January 28, 2012, 08:24:03 AM
 #21

Now for the Flipist strategy.  This is the same strategy as before but now we can flip every hour if needed.

First the code.  I added in everything to this code, from making the signals, making the charts, and outputting the statistics.

Code:
############################################
#Backtesting Flipist Method
############################################
    # We will need the quantmod package for charting and pulling
    # data and the TTR package to calculate RSI(2).
    # You can install packages via: install.packages("packageName")
    # install.packages(c("quantmod","TTR"))
    library(quantmod)
    library(TTR)

    # Load data
    x = last(y,4415) #gets the last 184 daily positions from the data (starts at July 23, 2011)

    # Calculate the random indicator
    set.seed(43) #include this to reproduce my results
    x$flip <- rbinom(4415,1,0.5) #50% probability that 1 (heads/buy) will show and 50% probability that 0 (tails/sell) will show

    # Create the buy (1) and sell (-1) signals
    sigbuy <- ifelse(x$flip == 1, 1, 0)
    sigsell <- ifelse(x$flip == 0, -1, 0)

    # Lag signals to align with days in market,
    # not days signals were generated
    sigbuy <- lag(sigbuy,1) # Note k=1 implies a move *forward*
    sigsell <- lag(sigsell,1) # Note k=1 implies a move *forward*

    # Replace missing signals with no position
    # (generally just at beginning of series)
    sigbuy[is.na(sigbuy)] <- 0
    sigsell[is.na(sigsell)] <- 0

    # Combine both signals into one vector
    sig <- sigbuy + sigsell
    # Calculate Close-to-Close returns
    ret <- ROC(Cl(x))
    ret[1] <- 0

    # Calculate equity curves
    eq_up <- exp(cumsum(ret*sigbuy))
    eq_dn <- exp(cumsum(ret*sigsell*-1))
    eq_all <- exp(cumsum(ret*sig))

    # Equity Chart
    png("flipist.png",width=720,height=720)
    plot.zoo( cbind(eq_up, eq_dn),
    ylab=c("Long","Short"), col=c("green","red"),
    main="Flipist Strategy: 07-23-2011 to 01-22-2012" )
    dev.off()

    # Create a chart showing mtgoxUSD
    png("flipistchart.png",width=720,height=720)
    chartSeries(x, subset="last 4415 hours", type="line")

    # Add the total equity line
    addTA(eq_all)
    dev.off()

    # Evaluate the Strategy

    # install.packages("PerformanceAnalytics")
    require(PerformanceAnalytics)
   
    # chart equity curve, daily performance, and drawdowns
    png("performance.png",height=720,width=720)
    charts.PerformanceSummary(ret)
    dev.off()

    # This function gives us some standard summary
    # statistics for our trades.
    tradeStats <- function(signals, returns) {
    # Inputs:
    # signals : trading signals
    # returns : returns corresponding to signals

    # Combine data and convert to data.frame
    sysRet <- signals * returns * 100
    posRet <- sysRet > 0 # Positive rule returns
    negRet <- sysRet < 0 # Negative rule returns
    dat <- cbind(signals,posRet*100,sysRet[posRet],sysRet[negRet],1)
    dat <- as.data.frame(dat)

    # Aggreate data for summary statistics
    means <- aggregate(dat[,2:4], by=list(dat[,1]), mean, na.rm=TRUE)
    medians <- aggregate(dat[,3:4], by=list(dat[,1]), median, na.rm=TRUE)
    sums <- aggregate(dat[,5], by=list(dat[,1]), sum)

    colnames(means) <- c("Signal","% Win","Mean Win","Mean Loss")
    colnames(medians) <- c("Signal","Median Win","Median Loss")
    colnames(sums) <- c("Signal","# Trades")

    all <- merge(sums,means)
    all <- merge(all,medians)

    wl <- cbind( abs(all[,"Mean Win"]/all[,"Mean Loss"]),
    abs(all[,"Median Win"]/all[,"Median Loss"]) )
    colnames(wl) <- c("Mean W/L","Median W/L")

    all <- cbind(all,wl)
    return(all)
    }

    print(tradeStats(sig,ret))

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January 28, 2012, 08:35:20 AM
 #22

As expected with a random method with a greater sample size (number of trades) it is almost even between buy and sell signal.

Code:
 
-1 = short
1 = long
Signal # Trades    % Win Mean Win Mean Loss Median Win Median Loss Mean W/L Median W/L
1     -1     2208 50.00000 1.123302 -1.034769  0.6283153  -0.6485819 1.085558  0.9687524
2      0        1  0.00000      NaN       NaN         NA          NA      NaN         NA
3      1     2206 49.50136 1.063690 -1.027253  0.6050835  -0.5874410 1.035470  1.0300328









Introducing constraints to the economy only serves to limit what can be economical.
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January 28, 2012, 08:41:33 AM
 #23

You need emotional investor and EMA10/21 contrarian controls. I'm not sure how to implement the emotional investor one...

How would you do the controls then?

Introducing constraints to the economy only serves to limit what can be economical.
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January 28, 2012, 09:16:23 AM
 #24

***EDIT*** Calculated Expectancy

Quote
Expectancy = (Probability of Win * Average Win) – (Probability of Loss * Average Loss)

Flipist Daily Flip

Code:
 (1) buy / (-1) sell
   Signal # Trades  % Win   Mean Win Mean Loss Median Win Median Loss Mean W/L Median W/L
1     -1       93   52.68817 7.132123 -6.507944   3.636423   -4.839239 1.095910  0.7514452
2      0        1    0.00000      NaN       NaN         NA          NA      NaN         NA
3      1       90   46.66667 4.551028 -4.431499   2.366660   -2.242254 1.026973  1.0554826


Flipist - Hourly Flip
Code:
(1) buy / (-1) sell
    Signal # Trades  % Win   Mean Win Mean Loss Median Win Median Loss Mean W/L Median W/L
1     -1     2208   50.00000 1.123302 -1.034769  0.6283153  -0.6485819 1.085558  0.9687524
2      0        1    0.00000      NaN       NaN         NA          NA      NaN         NA
3      1     2206   49.50136 1.063690 -1.027253  0.6050835  -0.5874410 1.035470  1.0300328

Expectancy = -4.0892

EMA10/21 Crossover using Daily data.  There are 18 less trades when looking at the

Code:
 EMA10/21, buy (1) when EMA10 > EMA21, sell (-1) when EMA10 < EMA21
    Signal # Trades  % Win   Mean Win  Mean Loss  Median Win Median Loss Mean W/L Median W/L
1     -1      132    56.81818 6.341372 -5.713935 2.840616 -4.183966  1.109808 0.678929
2      0       21    0.00000      NaN       NaN       NA        NA       NaN       NA
3      1       52    57.69231  5.068756 -3.911978 3.199519 -1.747152  1.295702 1.831277

EMA10/21 Crossover using Hourly data.
Code:
EMA10/21 Buy (1) when EMA10 crosses over EMA21, sell (-1) when EMA10 crosses below EMA21
    Signal # Trades    % Win   Mean Win Mean Loss Median Win Median Loss  Mean W/L Median W/L
1     -1       83    51.80723 0.8337976 -0.8799018  0.4516124  -0.5958119 0.9476030  0.7579782
2      0     4249     0.00000       NaN        NaN         NA          NA       NaN         NA
3      1       83    46.98795 0.6240971 -1.4239146  0.4776158  -0.5503347 0.4382968  0.8678642

Expectancy = -45.00281

Introducing constraints to the economy only serves to limit what can be economical.
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January 28, 2012, 09:41:07 AM
 #25

Using modified code from here and here.  I ran some backtesting in R to compare the EMA 5 EMA 21 Crossover and a random buy/sell strategy based on a coin flip.

Why did I do this?  I was making my own trading strategies but got stuck on making one for bullish/bearish divergence and I thought some practice may help me find out what I am doing wrong.

Why not generate your own? The R package rgp lets you evolve GAs. I've got some that consistently give me better than 'buy and hold'.

Quote
In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.

Wow, thanks for the recommendation.  I am an evolutionary biologist (or trying to be) and this makes a lot of sense.  How have you applied GP to trading strategies?

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January 28, 2012, 10:08:11 AM
 #26

Very nice work.

Also try expanding the time frame to daily & weekly scales.
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January 28, 2012, 10:22:31 AM
 #27

Sure - although tbh I've only been using rgp for a couple of weeks, before that I was using eureqa but I didn't find it flexible enough (it's really just for symbolic regression).

rgp is well documented but if you don't have a lot of experience with GAs (i don't) then the tutorials at rsymbolic.org are a bit incomplete. Still, I have plenty of algorithms generated. Some overfit and just buy below a certain level and sell above it. It all comes down to your fitness function and i think mine leaves a bit to be desired.

Some of the useful trading algo's I've got are of the longlag-shortlag=0 variety, just like the EMA10 EMA21 variety.

I'll be happy to share code I've done so far - I'm sure you can improve on it.

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January 28, 2012, 10:39:18 AM
 #28

Sure - although tbh I've only been using rgp for a couple of weeks, before that I was using eureqa but I didn't find it flexible enough (it's really just for symbolic regression).

rgp is well documented but if you don't have a lot of experience with GAs (i don't) then the tutorials at rsymbolic.org are a bit incomplete. Still, I have plenty of algorithms generated. Some overfit and just buy below a certain level and sell above it. It all comes down to your fitness function and i think mine leaves a bit to be desired.

Some of the useful trading algo's I've got are of the longlag-shortlag=0 variety, just like the EMA10 EMA21 variety.

I'll be happy to share code I've done so far - I'm sure you can improve on it.

Thanks, if you can put some here or pm to me I will take a look at it.  I requested some books on the subject.

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January 28, 2012, 10:49:06 AM
 #29

Very nice work.

Also try expanding the time frame to daily & weekly scales.

I already have the daily scale up.  I personally think bitcoin is too volatile to really hold it long term in large quantities, so holding a position for large amounts for weeks or months would be unrealistic if you are using margin.

Introducing constraints to the economy only serves to limit what can be economical.
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January 28, 2012, 11:03:51 AM
 #30

I already have the daily scale up.  I personally think bitcoin is too volatile to really hold it long term in large quantities, so holding a position for large amounts for weeks or months would be unrealistic if you are using margin.

Right now and for some time to come, yes. You'd have to manage a balanced position by scaling it instead of going all in each time.

With Bitcoin, I use weekly data as an overall trending and reversal guide, then daily/hourly to spot scaling opportunities. It'd be more complex using multiple temporal dimensions, but even building on the simple EMA10/21 technique that way would be far more robust - even somewhat predictive.

The greater the time-frame, the harder it is to reverse the trend.
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January 28, 2012, 11:21:42 AM
 #31

Just popped in to say "nice work!"

I don't have the time to look through the whole thread right now, but I'll be sure to do it when I can.

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January 28, 2012, 02:47:58 PM
 #32





It says EMA5/21 on the title of the chart?


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January 28, 2012, 07:18:40 PM
 #33





It says EMA5/21 on the title of the chart?




Thanks for catching that.  It should be EMA10

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January 28, 2012, 07:31:04 PM
 #34

I already have the daily scale up.  I personally think bitcoin is too volatile to really hold it long term in large quantities, so holding a position for large amounts for weeks or months would be unrealistic if you are using margin.

Right now and for some time to come, yes. You'd have to manage a balanced position by scaling it instead of going all in each time.

With Bitcoin, I use weekly data as an overall trending and reversal guide, then daily/hourly to spot scaling opportunities. It'd be more complex using multiple temporal dimensions, but even building on the simple EMA10/21 technique that way would be far more robust - even somewhat predictive.

The greater the time-frame, the harder it is to reverse the trend.

Next time I will add in scaling.  Anyone that wants to jump ahead check out here.  R has some other tools such as blotter that can keep track of positions.

Introducing constraints to the economy only serves to limit what can be economical.
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