in this video I will be looking at how you can get more out of a seasonality
based trading strategy including filters stop losses profit targets out-of-sample
testing and analyzing drawdown this is Mark from Tradinformed.com and welcome
to this video on how to get more profitable trades using seasonality part
2 this video is the second part in a two-part series looking at how
seasonality and in particular trading on specific days of the month can boost
your trading strategy whether you are a day trader a swing trader even a
position trader anybody who is trying to time the financial markets in the first
part of this video series I introduced the concept of trading on specific days
of the month and if you haven't seen this video there is a link on the screen
and below this video I showed that using the S&P 500 we can look for the patterns
that occur during a typical trading month and take advantage of that by only
trading specific days you can see on the screen the seasonality pattern that I am
interested in in the middle of the month there is a hump where days are more
likely to be profitable and again we see a similar thing towards the end of the
month that goes into the start of each new month so I have taken this
information and I have put it into a backtest model you can see on the screen
at the moment the backtest model that I have used now this is a Tradinformed
backtest model and it is built in Excel and it is a way to test all sorts of
different trading strategies different markets and timeframes and you can use
it to test your trading strategies help you hit your trading goals just simply
to make your trade easier if you want more information
about this backtest model there is a link on the screen or down below this
video now the trading strategy that we're looking at today as I have said is
based on trading days now I mean trading days of the month starting from the
first day of the month and counting upwards excluding weekends and holidays
so when I said we are trading the end of the month we are trading here based on
entering on day 19 and we are exiting on day 3
we're also entering and trading that middle of the month hump and so we're
entering on day 8 and exiting on day 12 now all these inputs are variables but I am
leaving them in this situation because as you can see I am testing up until
2015 now why I'm doing this is so we can have
some out-of-sample data we can refine optimize the model how we want it this
time period and I am using the last three years worth of data as my
out-of-sample test what I said is we're going to look at different scenarios so
the first thing I want to look at is some stop losses you can see I've set
these values to a hundred which means in this case that they're not being used
but I can easily change these and you can see as I scroll through a profit
target using an ATR multiplier I like to use the ATR because it adjusts to the
market it adjusts not just to the size of the market the type of market but
also the volatility that is occurring at any particular time so you can see here
with reference to the results that we're not getting a lot of value out of a
profit target I tend to prefer using profit targets in
my own trading so I may well use one around three or four we can see that
over the time period of this analysis it's not really helped or improved the
results we can do the same thing with a stop loss now this is a simple stop loss
we can set this to one time's the ATR and you can see immediately visually how
it affects the capital graph and also a maximum drawdown and also CAGR which
stands for compound annual growth rate now if I was to push out the stop-loss
we can see that the capital graph returns to something pretty close to
what it was without any stop-loss but there's one other variability that I
would like to show you that I've put into this model and this is to use what
I'm referring to as a close stop-loss this is instead of exiting at the market
when we hit our stop loss level this is exiting at the end of that day so we
haven't got a hard stop loss in the market but if our stop loss level is hit
we will exit at the end of that day I've clicked that on as a scenario and you
can see here we have made a small improvement to the trading strategy if I
go through here you see we've got slightly lower drawdown and our compound
annual growth rate net profit has stayed the same so this is one option that is
interesting to note in the development of this particular trading strategy okay
so the next thing I want to look at is a number of other scenarios the first
thing here is trading the next day open many traders are not able to trade at
the close which is my preferred time of day to trade on a daily timeframe but
they would like to open in the next day so if I click this we can have a look
let's check the right one we can have a look what this strategy would look like
if instead of creating the clothes we trade the following open and we see
we're not making a lot of difference to the strategy let's be fair and set
that back to 100 here we're not making a lot of difference whether we switch this
on or off fact we can see a very small increase in net profit but probably
nothing significant at all so this strategy can work equally well trading
the close or the open which is a useful feature for some traders the next filter
scenario I would like to have a look at is one that is hugely popular including
famously used by Paul Tudor Jones hedge fund manager and that is the 200 period
simple moving average so I check this box we can see how the strategy performs
above the 200 period average and somewhat surprisingly to me at least
again it is not making a big amount of difference in the strategy one thing we
are getting is slightly lower drawdown and that is very very important
we ideally want trading strategies with low drawdown because even if we have
lower profit we are more likely to stick with those strategies when times get
tough we've got a good percentage of winning trades and slightly lower
compound annual growth rate
another one one of my favorite filters the linear regression in this case to
200 period linear regression and this line is pointing upwards then we're
going to take a trade and again you can see we've got similar enough results
slightly higher percentage of winning trades similar net profits and growth
rates draw down similar and finally we can have a play around with fast EMA
greater than slow EMA so what I've got set up here is two different EMA's we've
got the 50 and the 200 and you can see that the classic golden cross filter
here has produced a decent enough strategy improvement on the strategy
although lower net profit but we can quite easily test this further we can do
a very small or very fast EMA against a very slow one we've put this down to 25
we could say the 12 26 the common MACD numbers and you can see each of them
have an effect on the strategy on the net profit and the capital graph but
interestingly to my mind none of them are making a huge
difference so it comes down to personal preference now if you want to compare
any of these strategies together we can easily do so so if we want to have a
look at now let's have a look at the 200 period SMA if I click on here up linear
regression click on here again go to strategy review you see we have the two
strategies here so we have the 200
SMA and we have the 200 linear regression and we can compare them
directly and we can see here there isn't an awful lot to compare both of these
scenarios are very similar so I'm going to go back to the results again I said
at the start I'm going to look at out-of-sample testing and how I'm going
to do that first of all let's get back to our original scenario these original
days were selected based on data up until 2015
so I'm going to look at the past three years so I'm going to change this date
here to 2015 and I've changed this date to 2019 and here we have the most recent
period of data you can see this slightly more clearly if I go into the annual
analysis tab you can see what we've got here we have most 2015 and we can
compare it to the market here that is buy-and-hold
we're comparing it to 2015 was a tough year 0.2 percent for the
market practically flat strategy returned a bit better than that the
recovery year here interestingly was not matched by the strategy but the booming
low volatility 2017 which saw the market go up 20% was more than matched by the
strategy and more importantly to my mind when the going got tough in 2018
volatility returns the VIX spooked the market we only have relatively small 2.3
percent and compared to many hedge funds many big traders and investors and is a
pretty reasonable return for that year and we can see in the uptick we have had
so far in 2019 we have captured a decent part of it
so let's go back here again to the Result
now we don't want to optimize the strategy of using our out-of-sample data
we want to optimize using our in sample which is our earlier data and then just
to see the results in our new data but for the purposes of this video we'll
assume we have this strategy optimized we're not going to change it again I'm
just going to stick this on here so we can have a look at all the data and how
the strategy has performed since 2000 up to 2019 and we can see that the looking
at the capital graph at least with no scenarios no clever filters we've
maintained it we had the run up here which was 2017 the volatility of 2018
and we've done pretty well compared to the market now remember this is not a
clever trading strategy this is just opening and closing on specific days of
the month I hope you enjoyed this video on using seasonality to improve your
trading strategies on how we can use statistical quantitative data to give us
an edge in the markets you want to see more videos like this please hit the
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the financial markets please go to
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