Trading the Swing Portfolios and Swing Picks

This website offers various portfolios based on proprietary trading systems, while some systems are for long term holding periods (Trend portfolios), others are trying to capture a short term up swing and the positions are to be held 4-5 days (Swing and All-Weather Portfolios). The goal is not to make 100% on a few big winners but to make many small 1-2% profits 60% to 75% of the time. For example, the US Swing portfolio has a 68% transaction win rate over a 10 years period.

This post will attempt to describe some facets of the Swing Portfolios (and All-Weather Portfolio which trades using similar algorithms as the Swing Portfolios)  in order to help applying trading ideas offered by these portfolios.

The Basics

Trading the ideas to match the performance of my trading systems is very easy:

Entering a position based on a portfolio pending order:

  1. In the evening, write down the BUY pending orders to execute from each portfolios available here.
  2. The next day, execute the orders just before the market closes.

Exiting positions managed by a swing portfolio :

  1. Write down the SELL pending orders to execute from each portfolios
  2. The next day, execute the orders just before the market closes.

That’s it. But learning details on how my system works could help you beat it.

What are we trying to do?

A good stock for this system is identified, then it pulls back for a few days, that’s when we buy it in the hopes or selling it in a few days with a small profit. Here is a typical move that the swing systems  try to capture, the green dot is the buy signal and the red dot is the sell signal, both signals are on the execution day,  communicated the day before:

cva

How to get better returns?

The swing systems presented on this site have only one price per day where they can execute an order, the closing price. A human being has all day long to pick the best time to enter or exit a position, getting a good entry can make a good stock pick become a great one.

Passing on a trade

It is important to note that since the orders are executed at the end of the next trading day, a diligent trader has the opportunity to analyze the current day data to enter earlier or not at all, I’ve intentionally kept my system unconditional on the day of the trade to keep it simpler, it doesn’t mean that you cannot add to it.

For example, if on the day you are supposed to buy the stock, it spikes up above the previous days high, you might want to skip the trade altogether since the short term up move was probably missed:

hpq

One exception to this is if the upswing comes with an above average volume and you think it could be lasting a few days, getting in the trade might be worth it:

mw

Some winners, some losers

If the stock continues to go down in the following days after you bought it it might be because:

  1. The pull back is not over and can continue for a few days before swinging up ending in a winning trade (very frequent).
  2. The move is actually a trend reversal instead of the expected temporary pull-back, in that case, the system has built in stops that will exit when triggered.
  3. The stock is just going sideways interminably (be patient or sell and move on!).

Swing Stock Picks

In addition to the daily portfolio updates, I also post daily Swing and Trend Buy/Short ideas in the “More Stock Picks” section. Trading these ideas is similar to following one of the portfolios but it requires more work on the sell side.

Entering a position based on a swing stock pick:

  1. In the evening, write down the BUY order to execute.
  2. The next day, execute the orders just before the market closes.

Exiting positions entered from a swing stock pick :

  1. As soon as you enter the position, set a real or mental stop, be sure it is not too aggressive and be disciplined when it triggers! I often use a multiple of the ATR value of the stock (3, 4 or 5 depending of volatility) or a fixed %.  And most important, set a target that should trigger a sell, I often use a moving target adjusted daily, if the stock today reaches a 5 to 7 days high, sell during the next day. The swing positions are held on average only 5 days.
  2. Every day review your open positions to see if your stops or your target have been reached, and write down the sell orders to execute the next day.

More ups than downs

Following a trading system can be very hard, you need lots of discipline and overriding it is rarely the best option so do it with caution. After a few loosing trades you should always keep in mind that overall the system should end up with more wins. If not, it’s time to go back to the drawing board!

 

 

 

 

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Portfolio changes and more!

Sorry for the lack of updates in the past weeks, I have been busy. I am in the process of updating the site for some portfolio changes!

The Canadian version of the All Weather Portfolio is being deprecated. Leveraged canadian ETFs have very low volume and I get better results with 3x ETFs which seem to be non-existent in the canadian market.

The US All Weather Portfolio is getting a total makeover, it is now a high risk / reward experimental portfolio with fluctuating position sizing in order to maximize returns.

I have also stopped trading a few leveraged ETFs like Oil and Gas as I couldn’t get the edges I saw in other ETFs such as SPY, you can see the new list of leveraged ETFs traded in the US All Weather  on the Portfolios page.

I am also introducing a new stock picks section which should provide daily interesting trade ideas.

Expect the changes to appear tonight when the current positions page gets updated.

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The Random Stock Portfolio – Part 2: Short Term Edges

In this post, I would like to explore how short term edges can be applied to the random stock portfolio. Many short term edges try to capitalize on buying an oversold security just before it springs back up, you can add more filters to get better quality stocks and less volatility but I will try  to keep the tests as simple as possible.

Note that in the first post the starting capital was 10,000$ for all simulations and the commission cost was 5$. When trading this small with a 5$ transaction cost, short term edges are really hard to realize so I have upped the starting capital and doubled the commission which is in-line with what my broker is charging me. Never forget the costs associated to all transactions when testing a system!

We will start our first simulation run with a starting capital of 100,000$, for the duration of the simulation we randomly buy 10 stocks and sell them 5 days later then we do it again until the simulation is done. The commission per transaction will be set to 10$.

Here are the performance results for 100 portfolios from 1994 to 2014:

Buy Random Sell After 5 days.20140422T215958

This looks pretty random,  about half will have positive returns but nothing really impressive, this is will be our reference data.

Buy low, sell 5 days later

Now let’s try our first short term edge, what if, instead of buying stocks randomly, we buy any 10 stocks when they reach their lowest price in 7 days? That is, only buy stocks whose current price is lower than all previous 7 days and sell them 5 days later:

Buy On 7 days low, Sell after 5 days.20140422T220548

The median compound annual growth rate for our run of 100 portfolios is 35%! And the minimum growth rate is more than 26%! Of course the draw-downs are big but it does demonstrate the effectiveness of that short term edge. Or does it?

Postdictive error

The previous simulation does something wrong, if the price is the lowest in 7 days it buys the stock, see anything wrong here? We buy the stock at the closing price AFTER the markets are closed, this called a postdictive error. I still think the edge is somewhat valid if you consider that most of the time, the closing price is pretty close to the price, say, 10 minutes before the market close, so you could theoretically run your system 10 minutes before the market close and have time to execute the orders. I unfortunately do not have the data to test this assumption. So lets try to fix our error by buying the stock the next day at the close price:

Buy On 7 days low, Sell after 5 days.BUY_NEXTCLOSE.SELL_NEXTCLOSE.20140424T231346

There is still a small edge but it’s not as striking as the first one, moving the order execution a day later had a very noticeable negative effect.  What if we only bought at the next day’s close IF the next day open is down in order to capitalize the pullback and buy at the lowest price? :

Buy On 7 days low, Sell after 5 days.BUY_NEXTCLOSEIFOPENDOWN.SELL_NEXTCLOSE.20140424T231713
Much better! We still don’t have the same edge as when buying but even the lowest performing portfolio made 8% annual growth rate, not bad for randomly selected stocks filtered by a simple oversold signal.

Another idea is to add a Moving Average filter, same test as before but we buy only stocks at their lowest 7 days when the stock price is above their 100 MA:

Buy On 7 days low, Sell after 5 days on MA(100) uptrend.BUY_NEXTCLOSEIFOPENDOWN.SELL_NEXTCLOSE.20140425T222707Again, we improve significantly our result by considering another simple filter before buying the stock.

RSI(2)

Another oversold indicator is the Relative Strength Index with an aggressive value of 2, buy when the RSI is oversold with a value below 15 and sell when it’s overbought with a value of 85, like the last test, we only bought at the next day’s close IF the next day open is down when the price is above the 100 MA :

Buy when RSI(2) below15, Sell after RSI(2) above85  on MA(100) uptrend.BUY_NEXTCLOSEIFOPENDOWN.SELL_NEXTCLOSE.20140425T233639There are many more short term edges that are worth studying, I hope you found the ones presented here interesting. I’m always looking for new edges to test so let me know if you have any to share!

In the next part of the series “The Random Stock Portfolio” I’ll try to look at the performance of longer term edges, stay tuned!

 

 

 

 

 

 

 

 

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The Random Stock Portfolio – Part 1

A few weeks ago I had this idea of building a framework for testing portfolios. These portfolios would buy stocks randomly and I would be able to introduce simple trading rules to them and see their effects on a wide range of securities, for a long period of time. I thought I could post about it the next evening but I was a bit of a fool, this stuff is very interesting and I have only scratched the surface of it, this is the first post of many (I hope) exploring how basic trading rules can affect the random portfolios.

Let’s go back 20 years, and create one hundred portfolios, each portfolio will follow the same rules:

  • Portfolio starting capital is 10000$ and each transaction will cost 5$.
  • Select randomly 10 stocks to buy, each stock must have more than 750000 average daily volume and a minimum stock price of 3$ (I know it’s not totally random but I’m only interested in liquid stocks with decent capitalization at the time they are bought!)
  • The sample includes NYSE and NASDAQ markets from 1994 to 2014.

All images in this post include 3 charts, the top chart contains the combined performance of all the portfolios, the lower left chart shows the distribution of the compounded annual growth rate for all portfolios and the lower left chart include the distribution of the draw downs of each portfolios.

First test, totally (mostly) random: Buy randomly any stock, then every trading day, for each positions, roll a 20 sided dice, if it falls on 1, sell the stock and buy another one randomly (5% chance of selling the stocks every day):

Buy Random Sell 5pct.20140316T200352

More than 81% would have been wiped out (In that context, wiped out means that they went from 10000$ to below 1000$).  Only 10% would have broken even.

What if we just bought 10 stocks randomly in 1994 and never sold them, if a stock we bought is no longer being traded, sell it with the last available price and buy a new one randomly :

Buy Random Sell Never.20140316T200018

More than 50% of the portfolios end up with an annualized return of 8.5%, that means more than half would have seen their 10000$ grow to more than 50000$ in 20 years! Even if there are portfolios having huge draw downs, it does show that “buy and hold” has some merits if you stay invested in ANY stocks. The portfolio with 3.76% annual growth rate is the worse one, slightly doubling its initial 10k investment in 20 years, not bad for randomly selected stocks!

Let’s try to put trailing stops and only sell a stock when the trailing stop is hit, the next 5 images contain the results for 1%, 5%, 8%, 15% and 20% trailing stops on randomly bought stocks:

Buy Random Sell On Trailing 1.00pct.20140318T000215

Buy Random Sell On Trailing 5.00pct.20140318T000719Buy Random Sell On Trailing 8.00pct.20140318T001318

Buy Random Sell On Trailing 15.00pct.20140317T235405Buy Random Sell On Trailing 20.00pct.20140317T235237Many books and traders recommend having a 7% or 8% trailing stop, this test shows that maybe that value is too aggressive,  or at least it cannot be applied to ANY stocks, some volatility values should probably be used to make sure that stocks we take positions in are not too volatile.

Having trailing stops based on a stock percentage value seem to have a negative effect when set to values below 12% and a negligible effect when set above 12%, to the point where I find them a bit useless, if not dangerous.

Let’s try to refine our “sell” strategy and have a stop based on the trailing ATR value of the stock instead of a percentage, if the stock is 10$ with an ATR value of 0.10$, we set our stop at 9.90$, if we sell, we re-buy randomly any stock the same day, every day we adjust our stop to the highest value between the old stop and the new stop generated from today’s ATR value :

Buy Random Sell On Trailing ATR(14) x 1.00.20140316T200716Ouch! That Stop might be too aggressive, I tried many multiple increments and the best value I came up with is a 5 to 8 times the ATR value so for a stock is 10$ with an ATR value of 0.10$, we set our stop at 9.20$ when we want 8 times the ATR value (10$ – 0.10$ *8 = 9.20$) :

Buy Random Sell On Trailing ATR(14) x 8.00.20140319T223309

None of the portfolios have wiped out, all of them broke even, and more than half had annualized returns of more than 9.37%, not bad.  The big 86% draw down was a bit of a let down so I took a look at the actual trades and trailing stop values, using a 8% trailing stop is fine for some stock but for others  it can represent more than the actual stock price! To work around that, I introduced a NATR buying filter which allows for the selection of less volatile stocks. Here are the results when buying only stocks with a NATR value of less than 3%:

Buy Random Sell On Trailing ATR(14) x 8.00 natrMax=3.00pct.20140316T201328

In all runs the NATR value limitation reduced the drawdown significantly with a small negative effect on the total returns.

Stay tuned for part 2 where I’ll try to make the random portfolio even less random by trying different rules for buying and selling as well as some concepts used in my own portfolios, if you have any basic trading ideas you would like to see tested in this framework let me know and I’ll try to add them as well!

Some ideas I would like to try:

  • Buying after consecutive down/up days
  • Buying trending stocks
  • Buying market out-performers
  • Following market wide signals
  • Various common technical signals
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Trend And Swing Portfolio Reset

Why a reset?

Today, on the first trading day of march, I’ve decided to reset the Trend and Swing Portfolios because in the past few months I have made significant changes to the algorithms and I want the back-test results to match the new changes. Here is a brief list of on going changes that were made since November 2013:

  • All Portfolios now behave better in out of sample periods which should make them more consistent in the future.
  • Trend Portfolios now buy on a price pullback like the Swing Portfolios but they stay long until the trailing stop is hit.
  • Both Swing and Trend Portfolios now check for correlation before buying another stock to improve diversification. Note that the parameters are a bit lax so it is still possible to have a many stocks in one sector.
  • Both Swing and Trend Portfolios now have a volatility criteria. If a stock is not volatile enough or too volatile,  it will be ignored.

Looking back

Here is a recap of the first “live” 6 months performance of the Swing and Trend portfolios, I am very happy with the results even if I fully realize we are in a big bull market (up to February 2014 at least!) and there were a few bad trades in the CAD Swing portfolio.

Live Performance from August 23rd 2013 to Febuary 28th 2014:

  • US Trend: 58.4%
  • CAD Trend: 30%
  • US Swing: 25.4%
  • CAD Swing: -4.6%

For the same Period SP 500 returned about 11%.

I have created an archive to preserve that time period here.

The Future

I’m not sure how the stock markets will behave for the rest of 2014 but I think it will be more volatile and less bullish,  but I hope the improvements done to my systems will compensate for it. For example, the annual growth rate of the US Trend Portfolio jumped from 18.9% to more than 27% while reducing the draw down to 20% from 22% for the past 10 years period.

Note that many positions tracked by the Portfolios last week will have disappeared, this is  expected  since the time line of all portfolios will be reset and buying rules were changed.

 

 

 

 

 

 

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A Tool For Building An Index Portfolio

When building the Enhanced Index Portfolios I tried to come up with diversified ETFs with little correlations between each other, I also had to keep in mind the “Enhanced” part of the portfolio which might tip the diversity scale a bit when adjusting the asset allocation each months based on each ETF performance. In order to help me do this I built a simple table of all ETFs with useful technical information in sortable columns such as Yield, Compound Annual Growth Rate… I then started back-testing with traditional index investing first and then the enhanced index investing, I wanted the ETF selection to perform well with both approach. I am happy with the result, the traditional index investing gives a very respectable annual growth rate of 9.2% and the enhanced version 11.96% over the last 10 years.

Weeks after doing that exercise I often re-generated the table for reference because I think it can show ETF information in different interesting ways, so I’ve decided to make it available and I hope you will find it useful too! It will be permanently available on the top of the site by clicking on ETF Tool.

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Are you a Stock Groupie?

First Sighting
When I started this blog with portfolios managed by automated trading systems I thought it would be interesting to post each orders daily on StockTwits and Twitter. And it is, I very much enjoy it, I get some very interesting feedback from other fellow traders. There is one thing I did not anticipate however: The Stock Groupie!

groupie

Identification
The Stock Groupie will usually not manifest himself when I post BUY orders, sometimes I get “likes” or people agreeing with the BUY order, that does not make one a groupie, this is the typical positive fellow trader commenting, it’s all good.

Aggressive Behavior
The Stock Groupie will come out of his lair only when the SELL order is posted. This is when he starts looking for blood; “You are dumb!”, “A traitor!”, “Wrong, so wrong!”, “Time will show you how wrong you are!”, “This stock is the best investment ever what a mistake you are making!”.

groupie2

Characteristics
When that happens, I actually feel sad for the Stock Groupie because I think that being so emotionally linked to a stock can bring mostly loss and despair. Here are some common traits I think most Stock Groupies are afflicted with, if you have more than 3, you should get yourself checked!

  • You have a unhealthy allocation in one stock in particular.
  • Most likely a Bio Pharma or Tech stock.
  • You are convinced this is your 10 bagger stock.
  • You know all posters of all message boards talking about this stock (and you’re a major contributor!).
  • You “feel” it will go up anytime now, a matter of hours or days.
  • You have been holding on a loosing trade for weeks or months.
  • You think random tweets from strangers can influence the stock price in a big negative way
  • You over-think every news that come out about the stock instead of looking at the actual price action.

Cure
The cure is very simple but hard to swallow: Sell the stock and move on!

Let me know if you can think of more Stock Groupie traits, with your help we might heal them all!

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Portfolio format update

Starting today, a revamped daily portfolio update process will be put in place, featuring these changes:

  • The Markets Vane top section is gone, it was not very useful to start with.
  • All updates will now be pushed to a static page named “Current Positions” in the menu below the site logo instead of a new blog post every day to avoid cluttering the “normal” blog posts, I will also remove the automated posts.
  • All updates now feature a pie chart for showing the real allocation of each holdings.

I am still not completely happy with the new look so more changes will probably be done in the coming weeks, as always, feedback is welcome!

 

 

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Alternative Strategies For Re-Balancing Index Based Portfolios

Passive investing

Many investors turn to index investing as a relatively easy and cheap way to invest their savings. Doing so is very easy:

  • Build a portfolio of ETFs or Index Funds.
  • Decide on the proportion each security will get.
  • Re-balance them every year or quarter so that they match your original portfolio model.

This technique is very popular and a lot of interesting articles and blogs exist on the subject presenting many index based portfolios:

Many people think this is optimal because they claim you cannot time the markets and beat the indexes (See efficient-market hypothesis). Thus, re-balancing or buying more of a position that is in a clear downtrend makes sense to them. In my opinion, re-balancing at regular interval in order to keep an arbitrary fixed allocation to each security over a long period of time, while better than no re-balancing at all, is not optimal.

The examples that follows are using Vanguard index funds mostly because the data I have goes way back to the 90s, you should be able to use comparable ETFs to get similar results. First, lets look at a comparison of a very simple portfolio of 60% VFINX (SP 500 index fund) and 40% VBMFX (Bonds) since 1997 while we re-balanced every quarter compared to doing no re-balancing at all.

Note: The performance graphs in this post do not include dividends

Funds Rebalancing Strategy Total Returns Compounded annual growth rate Drawdown
VFINX(60%)
VBMFX(40%)
Static,every quarter 207.45% 6.89% 34.79%
VFINX(60%)
VBMFX(40%)
No rebalancing 188% 6.48% 34.76%

static_and_no_rebalance

What do we see in this table and graph?

  • The 60% stocks and 40% bonds allocation outgrows the SP500 index in the ~15 years period covered by the test.
  • The portfolio using the static re-balancing strategy has slightly better returns compared to no re-balancing at all.
  • The drawdown, at 34% is quite big.

 

How can we improve this strategy?

Let’s add some Enhanced indexing to our strategy. For example, we would like to be out of the markets tracked by our ETFs or Funds when they are in a major down trend to reduce risk and keep our gains, how can we do this?

The re-balancing strategy can be enhanced by applying a filter based on a widely used technical indicator, the 200 days moving average: When one of your security is below its 200 days moving average, sell it and stay in cash, when it gets above the 200 days moving average buy it back. Lets look at VFINX, the red dots mean that you should sell, the green dots mean that you should buy as the asset is back on a uptrend. Of course there will be false signals, but the important, real big trends, are not missed.

vfinx_200_ma

 

Cash is a position

Let’s add this strategy to our original table to see how it performed, every quarter, for each security:

  • Hold using its original allocation, rebalance if needed (VFINX: 60%,  VBMFX : 40%) ONLY if  the asset price is above its 200 days moving average.
  • Sell  using its original allocation ONLY if  the asset price is above its 200 days moving average. So if VFINX is below its 200 days moving average, you should be 60% in cash for that quarter, if both VFINX and VBMFX are below their 200 days moving averages you should be 100% cash.
Funds Rebalancing Strategy Total Returns Compounded annual growth rate Drawdown
VFINX(60%)
VBMFX(40%)
Static,every quarter 207.45% 6.89% 34.79%
VFINX(60%)
VBMFX(40%)
No rebalancing 188% 6.48% 34.76%
VFINX(60%)
VBMFX(40%)
Cash Position When Below MA 203.97% 6.82% 9.95%

cash_position_when_below_ma
As you can see, there are no real improvement in the total return, but did you see the improvement in the drawdown? We went from 34.79% to 9.95%. It means that from 1997 this portfolio would never have lost more than 10% of its value going through the dot com bubble and the 2008 financial crisis.

Buy more winners!

Let’s see if we can improve our “cash when below MA” strategy, what if instead of remaining in cash, we redistributed our cash positions into other winners in our portfolio? For each security:

  • Hold using its original allocation, rebalance if needed (VFINX: 60%,  VBMFX : 40%) ONLY if  the asset price is above its 200 days moving average.
  • Sell using its original allocation ONLY if  the asset price is above its 200 days moving average. So if VFINX is below its 200 days moving average, you should be 60% in cash for that quarter, if both VFINX and VBMFX are below their 200 days moving averages you should be 100% cash.
  • Re-Invest the cash equally between “in market” positions, so if VFINX is below 200 MA and VBMFX above 200 MA, use the 60% cash from VBFINX to buy VBMFX, this means you will be 100% VBMFX.

Of course you might not want to allocate 100% of your portfolio to a mining ETF for example, so later on we’ll see what we can do about that, but for now let’s just stick with VBFINX and VBMFX.

Funds Rebalancing Strategy Total Returns Compounded annual growth rate Drawdown
VFINX(60%)
VBMFX(40%)
Static,every quarter 207.45% 6.89% 34.79%
VFINX(60%)
VBMFX(40%)
No rebalancing 188% 6.48% 34.76%
VFINX(60%)
VBMFX(40%)
Cash Position When Below MA 203.97% 6.82% 9.95%
VFINX(60%)
VBMFX(40%)
Redistributed Position When Below MA 309.47% 8.73% 11.87%

As you can see, we retain a very small drawdown of 11.87 and we improved our compound annual growth rate by almost 2%!
redistribute_when_below_ma

Making it even more interesting

Up to now, all the test cases were using a portfolio of only 2 securities, let’s spice things up a bit and try the same back tests with the following portfolio, the only difference is that we introduce a Max Allocation parameter which limits the absolute maximum allocation each securities in the portfolio can have which helps reduce risk by preventing over allocation in a risky security:

Funds Base Allocation Max Allocation
SP 500 Index VFINX 35% 100%
Total Intl Stock Index VGTSX 20% 100%
REIT Index VGSIX 10% 100%
Precious Metals and Mining VGPMX 5% 15%
Total Bond Market Index VBMFX 30% 100%

Here are the results:

Rebalancing Strategy Total Returns Compounded annual growth rate Drawdown
Static,every quarter 221.87% 7.18% 43.58%
No rebalancing 188.11% 6.48% 46.01%
Cash Position When Below MA 198.25% 6.70% 13.56%
Redistributed Position When Below MA 368.65% 9.60% 15.60%

position5_full

Having a more diversified portfolio seems to favor the Redistribution strategy as well with a very respectable 9.6% compound return.

Commission cost, taxable gains and other caveats

Here is a list of potential issues I can think of with this strategy:

  • Since this strategy means you will have to do a bit more transactions, if you have a small amount invested and/or a greedy broker, you should seriously how the commission fees will affect your returns.
  • Depending in which account you are holding the portfolio, selling securities can have serious tax implications,  be sure to understand what they are.
  • Re-balancing every quarter means that you can miss a big drop in a security by about 4 months, which can be detrimental. But at the same time, it can help you ignore false signals. Many phone apps and websites support alerts based on technical indicators, each security in your portfolio could have an alert when its price goes below 200MA so you can act on it. I ran the same test with a 5 days re-balance interval to see how it would be affected and the CAGR from the last test dropped from 9.6% to 8.69% while the drawdown jumped from  15.60% to 19.89% both negative changes are probably caused by the increase in false signals caused by the smaller re-balancing window. But the strategy is still giving superior results.
  • Anytime you increase the base allocation of a position  in your portfolio you should take every step required to feel comfortable about it, if you cannot sleep because of it, reduce your position or add a stop loss. You can also fall back to the “Cash when below 200MA” strategy, that will at least reduce the drawdowns significantly for a relatively small cost in performance.
  • Dividends, I couldn’t find a reliable source of dividend distribution data yet, I would be very curious to see these graphs updated with the dividends.
  • Other refinements could be done such as taking into account the base allocation when redistributing the cash into the active positions.

 

Conclusion

I think “redistributing when below 200MA” and “cash when below 200MA” strategies are very interesting concepts to add to one’s investor toolbox.  I’ll continue experimenting with these concepts and write a new article about it if I find anything interesting.

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Portfolios review after first “live” month!

It’s been a month already since the start of the daily portfolio posts, moving from back-testing to real-time has been quite a challenge, a few unexpected issues and some  bugs have been fixed in the trading system algorithms, and even more radical updates are coming  for the algorithms soon. But let’s take the first month to look back and see how the portfolios did.

US Portfolios:

  • US Swing Portfolio: +15.88%, biggest winner: DRYS +18.66%, biggest Looser: AVNR -10.64%
  • US Trend Portfolio: +6.73%
  • SPY: +3.5%

 

Canadian Portfolios

  • CAD Swing Portfolio: +0.14%
  • CAD Trend Portfolio: +2.7%
  • XIU.TO: +1.04%

Even if it’s too early to do evaluate these portfolios, I’m satisfied with the US portfolios performance but the CAD ones need a bit more work even if it did not loose any money the CAD Swing Portfolio should be able to beat XIU.TO most months, stay tuned for significant improvments in that area. And finally you can always look at  all the trades done for each portfolios by looking at the portfolios page.

 

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