Topic: Goomboo's EMA10/21 Crossover Versus the rebuilder's Flipist Method - page 2. (Read 5831 times)
January 28, 2012, 01:26:57 AM
You need emotional investor and EMA10/21 contrarian controls. I'm not sure how to implement the emotional investor one...
January 28, 2012, 01:25:30 AM
The EMA 10 / 21 Crossover Strategy is:
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
I think most systems based on SMA and EMA crossovers (eg GMMA) give 'buy' or 'sell' signal at the actual point of crossover - ie when (in this case) EMA10==EMA21. The rest of the time they indicate a hold. You shouldn't be changing positions that often.
That is true. I will update the code. There is a way to see how many trades was done but I can't remember right now. I will search for that.
January 28, 2012, 01:18:08 AM
So what do others think of these graphs?
If I am reading the graphs right. The flipist method quickly starts out badly, losing equity in both the short and long trades. It never recovers its equity back.
In the EMA10/21 crossover strategy. It starts with no change in equity, but it starts to lose money because it is bad at predicting when to sell. Its long predictions seem pretty good but even the flipist method recovered pretty well in the December bull market. I would predict anyone that bought during that time did well.
If you look at the cumulative return. The flipist method actually did slightly better. Flipist -0.78 versus EMA10/21 -0.80.
Of course this is assuming I did this correctly.
Comments? Questions? Screams of fury?
If I am reading the graphs right. The flipist method quickly starts out badly, losing equity in both the short and long trades. It never recovers its equity back.
In the EMA10/21 crossover strategy. It starts with no change in equity, but it starts to lose money because it is bad at predicting when to sell. Its long predictions seem pretty good but even the flipist method recovered pretty well in the December bull market. I would predict anyone that bought during that time did well.
If you look at the cumulative return. The flipist method actually did slightly better. Flipist -0.78 versus EMA10/21 -0.80.
Of course this is assuming I did this correctly.
Comments? Questions? Screams of fury?
January 28, 2012, 01:14:42 AM
The EMA 10 / 21 Crossover Strategy is:
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
I think most systems based on SMA and EMA crossovers (eg GMMA) give 'buy' or 'sell' signal at the actual point of crossover - ie when (in this case) EMA10==EMA21. The rest of the time they indicate a hold. You shouldn't be changing positions that often.
Edit: Nice chartage though. What package are you using for the bottom one?
January 28, 2012, 01:06:31 AM
Now for the EMA10-EMA21 crossover.
***EDIT*** Added the results table
****EDIT**** The following graphs title should say EMA10/21, not EMA5/12
***EDIT*** Added the results table
Code:
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
****EDIT**** The following graphs title should say EMA10/21, not EMA5/12
January 28, 2012, 01:03:26 AM
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 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'.
I just used a simple strategy as that is what these two strategies were doing. Blotter along with quantstrat allows for better trading strategies.
I have not heard of the rgp. I will check it out. This was all done with the TTR and quantmod packages.
January 28, 2012, 01:00:19 AM
Flipist Results
**EDIT** Added this results table:
The first graph is the equity change based on the long (green) and short (red) positions.
Main graph. This graph shows the exchange rate at Mtgox at the close of each day, along with the volume and the short and long positions.
Finally, we see a chart giving the cumulative return.
**EDIT** Added this results table:
Code:
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
The first graph is the equity change based on the long (green) and short (red) positions.
Main graph. This graph shows the exchange rate at Mtgox at the close of each day, along with the volume and the short and long positions.
Finally, we see a chart giving the cumulative return.
January 28, 2012, 12:59:26 AM
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 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'.
January 28, 2012, 12:47:13 AM
Awesome.
January 28, 2012, 12:33:40 AM
Build and run! 
January 28, 2012, 12:32:18 AM
EMA5 / EMA21 Crossover Strategy
This code will also input the data, create the indicators, and then create the signals. From the daily return and the buy or sell signals, equity curves are calculated. Last charts are produced to show the performance and draw-down.
Since the EMA21 requires the past 21 days of history before trading can start, the data file used for this contains the last 205 days but trading cannot start until July 23, 2011.
This code will also input the data, create the indicators, and then create the signals. From the daily return and the buy or sell signals, equity curves are calculated. Last charts are produced to show the performance and draw-down.
Since the EMA21 requires the past 21 days of history before trading can start, the data file used for this contains the last 205 days but trading cannot start until July 23, 2011.
Code:
# install.packages(c("quantmod","TTR"))
library(quantmod)
library(TTR)
# Load presorted data
x = last(y,205) #gets the last 205 daily positions from the data
# Calculate the EMA indicators
ema10 <- EMA(Cl(x),10)
ema21 <- EMA(Cl(x),21)
# Create the long (up) and short (dn) signals
sigbuy <- ifelse(ema10 > ema21, 1, 0)
sigsell <- ifelse(ema10 < ema21, -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("EMAcross.png",width=720,height=720)
plot.zoo( cbind(eq_up, eq_dn),
ylab=c("Long","Short"), col=c("green","red"),
main="EMA5-EMA21 Crossover Strategy: 07-23-2011 to 01-22-2012" )
dev.off()
png(filename="emacross.png",width=720,height=720)
plot.zoo( cbind(eq_up, eq_dn), plot.type="single", ylab=c("Long","Short"), col=c("green","red"), main="EMA Crossover Strategy:\n 2011-07-23 through 2012-01-22" )
dev.off()
# Create a chart showing mtgoxUSD
png("EMAcrosschart.png",width=720,height=720)
chartSeries(x, subset="last 184 days", 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-EMAcross.png",height=720,width=720)
charts.PerformanceSummary(ret)
dev.off() January 28, 2012, 12:29:42 AM
Flipist Strategy Methods/code
This code will input the data, create the indicators, and then create the signals. From the daily return and the buy or sell signals, equity curves are calculated. Last charts are produced to show the performance and draw-down.
This code will input the data, create the indicators, and then create the signals. From the daily return and the buy or sell signals, equity curves are calculated. Last charts are produced to show the performance and draw-down.
Code:
# Load data
# install.packages(c("quantmod","TTR"))
library(quantmod)
library(TTR)
x = last(y,184) #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$flip <- rbinom(length(x$.Open),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(filename="flipist.png",width=720,height=720)
plot.zoo( cbind(eq_up, eq_dn), plot.type="single", ylab=c("Long","Short"), col=c("green","red"), main="Flipist Strategy:\n 2011-07-23 through 2012-01-22" )
dev.off()
# Create a chart showing mtgoxUSD
png("flipistchart.png",width=720,height=720)
chartSeries(x, subset="last 184 days", 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-flipist.png",height=720,width=720)
charts.PerformanceSummary(ret)
dev.off()
January 28, 2012, 12:26:42 AM
Methods
Data aquisition
Data was acquired from bitcoincharts.com. The entire data file was downloaded from the Bitcoincharts API ( http://bitcoincharts.com/t/trades.csv?symbol=bcmPPUSD&start=0 ~45mb)
The data was then converted from tick data to Open/High/Low/Close/Adjusted/Volume format. The code for this conversion is:
Data aquisition
Data was acquired from bitcoincharts.com. The entire data file was downloaded from the Bitcoincharts API ( http://bitcoincharts.com/t/trades.csv?symbol=bcmPPUSD&start=0 ~45mb)
The data was then converted from tick data to Open/High/Low/Close/Adjusted/Volume format. The code for this conversion is:
Code:
#install.packages("xts") #install the xts package if you don't already have it. Available on CRAN
require(xts)
ohlc <- function(ttime,tprice,tvolume,fmt)
{
ttime.int <- format(ttime,fmt)
data.frame(time = ttime[tapply(1:length(ttime),ttime.int,function(x) {head(x,1)})],
.Open = tapply(tprice,ttime.int,function(x) {head(x,1)}),
.High = tapply(tprice,ttime.int,max),
.Low = tapply(tprice,ttime.int,min),
.Close = tapply(tprice,ttime.int,function(x) {tail(x,1)}),
.Volume = tapply(tvolume,ttime.int,function(x) {sum(x)}),
.Adjusted = tapply(tprice,ttime.int,function(x) {tail(x,1)}))
}
data <- ohlc(data$time,data$price, data$volume,"%Y%m%d%H") #converts data in CSV to OHLC
data <- xts(data[,-1], order.by=data[,1]) #converts data frame to an XTS object January 28, 2012, 12:25:00 AM
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.
The EMA 10 / 21 Crossover Strategy is:
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
The Flipist Strategy is:
1. Buy when there is heads
2. Sell when there is tails.
Trading dates will be between July 23, 2011 to Jan 22, 2012. A total of 184 days. Money management will be all in and all out. Trades will be done according to the daily close.
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.
The EMA 10 / 21 Crossover Strategy is:
1. Buy when EMA 10 crosses over EMA 21. So EMA 10 > EMA 21, BUY
2. Sell when EMA 10 crosses below EMA 21. So EMA 10 < EMA 21, SELL
The Flipist Strategy is:
1. Buy when there is heads
2. Sell when there is tails.
Trading dates will be between July 23, 2011 to Jan 22, 2012. A total of 184 days. Money management will be all in and all out. Trades will be done according to the daily close.
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