Two Portfolios - One Mechanical System - A Trend-following Diary

[Trendnomics]

Hi Trend

Take a look at your X axis on the SMSF graph.
At one point it goes 10/12/14 - 15/5/15 - 28/5/15 and then back to 16/6/14
At another point it goes 1/7/15 - 1/10/15 - 8/1/16 and then back to 3/8/15




The graph doesn't match the closed trade flow in the tables you did provide. Again I'll speculate you may have sorted by buy date. (which is actually not bad information because it highlights the periods of good and bad entries) - but its not what you have described as your chart construction and it doesn't appear to be consistent with the private portfolio construction. To evaluate performance management data you have collect, collate and understand it accurately.

I'm not trying to have a go - its good to see somebody trying to monitor their performance. Just trying to point out something that looks confused to me. But I'm happy to do lack of interest if you prefer.

Hey Craft,

Apologies! Damn Excel filter didn't work, hence some of the closed trades for the SMSF graph, weren't sorted 100% chronologically.

Lets try again:




I appreciate your interest.

 

[craft]

Hey Craft,

Apologies! Damn Excel filter didn't work, hence some of the closed trades for the SMSF graph, weren't sorted 100% chronologically.

Lets try again:

View attachment 66569
View attachment 66570

I appreciate your interest.

Hi trend – looks like you have got it sorted now. Trade paths for the two systems now not that dissimilar.

Any reconsideration of Wysiwyg p.s question now the charts are fixed? What’s the heat on open positions?

Also a detail on the XNT. Whilst the observation that Wysiwyg made regarding there not being much difference between the 200 and 500 is correct – he was contrasting the accumulation indexes for both. The XNT however is constructed by subtracting 30% withholdolding tax before accumulation (ie its designed to show the oversees investor point of view) By its construction it will underperform quite significantly the accumulation indexes over time.

Even the accumulation indexes understate the passive return to a super fund because of supers 15% tax regime and the refund of franking credits.

In short the passive indexation alternative is understated meaningfully overtime by the XNT index especially for the SMSF.

 

[Trendnomics]

A quick update - both portfolio's have been exploding (in a good way):

Private Portfolio: +57.04 % (Since 07-2013)

SMSF Portfolio: +58.89 % (Since 07-2013)

Resources (lithium + gold) contributing the most:

 

[Trendnomics]

Another train-wreck I escaped prior to the crash - as always green line entry, red line exit:

 

[Wysiwyg]

Another train-wreck I escaped prior to the crash - as always green line entry, red line exit:

My vision could be failing me but what happened to year 2015 on your chart?

 

[Trendnomics]

My vision could be failing me but what happened to year 2015 on your chart?

I blame Metastock scaling .

The "2014" on the axis is the end of 2014, right from that lies 2015 to 2016.

 

[Wysiwyg]

I blame Metastock scaling .

The "2014" on the axis is the end of 2014, right from that lies 2015 to 2016.

Okay thanks. You got out at the end of the trend.

 

[Trendnomics]

Portfolios update - the Brexit had little impact - general losses absorbed beautifully by my large positions in gold - minor fluctuations on the six figure profit $$$,$$$.

Private Portfolio: +54.19 % (Since 07-2013)

SMSF Portfolio: +57.30 % (Since 07-2013)

This update's showcase trade - BAP.asx - held in SMSF - 459 day trade +71.5% profit (includes 2x dividends) - as always green vertical line indicates entry:

 

[Trendnomics]

Just want to tie something back to this thread, that was discussed on another thread (HERE):

My system's overall expectancy:

Great thread - related to my pet hate and everyone's stumbling block.

Just comparing expectancy values for different systems/strategies, is like the age old analogy of apples vs. pears. "Expectancy" (per trade) is only one dimension of a profitable system, the other dimension, is "Number-of-Trades" (per time period):

System Expected Profit = Expectancy x Number-of-Trades

Expectancy should be adjusted by the number of trade positions for a system/strategy:

Expectancy = [(Average_win x %_win) - (Average_loss x %_loss)]/(Trade_positions)

The metrics for my Trend-Following System are as follows:

Average_win = 30.77%
Average_loss = -12.13%

%_win = 47.3%
%_loss = 52.7%

Trade_positions = 16

Expectancy = 1.31% /trade
Number-of-Trades = 37 trades/year

System Expected Profit = 48.47% / year

Note that expectancy values, are based on normal-distributed outcomes - approximate reflection of real trading results (quite a bit off for trend-following).

 

[Trendnomics]

An update - my SMSF portfolio has made a new all-time equity high - my private portfolio got hit by CIM.asx (which was sold yesterday):

Private Portfolio: +56.07 % (Since 07-2013)

SMSF Portfolio: +60.59 % (Since 07-2013)

A quick snapshot of current open trades for each portfolio (excludes dividends):

Private:

TWE - (44.13%) - 285 day trade
VOC - (14.34%) - 151 day trade
COH - (15.38%) - 145 day trade
VTG - (51.47%) - 138 day trade
MPL - (0.17%) - 100 day trade
SSM - (5.81%) - 82 day trade
WOR - (2.49%) - 23 day trade
CAT - (-0.73%) - 1 day trade
RRL - (118.92%) - 282 day trade
SAR - (198.69%) - 247 day trade
CTD - (2.23%) - 191 day trade
NAN - (1.67%) - 123 day trade
RFG - (0.75%) - 116 day trade
ORE - (53.44%) - 110 day trade
GMM - (38.02%) - 92 day trade
PGH - (-1.15%) - 12 day trade

SMSF:

BAP - (61.17%) - 466 day trade
TWE - (43.56%) - 285 day trade
RRL - (118.12%) - 282 day trade
SAR - (197.53%) - 247 day trade
SKC - (5.74%) - 191 day trade
ALL - (39.18%) - 183 day trade
VOC - (13.66%) - 151 day trade
COH - (14.61%) - 145 day trade
VTG - (50.8%) - 138 day trade
NAN - (1.1%) - 123 day trade
RFG - (0.2%) - 116 day trade
GMM - (38.3%) - 92 day trade
MPL - (-0.62%) - 89 day trade
SSM - (5.27%) - 82 day trade
FBU - (1.97%) - 23 day trade
CL1 - (-0.21%) - 9 day trade

 

[Wyatt]

Hi Trendnomics,

I am pleased to see you are doing well atm. I notice that you do venture out past the XAO boundary at times, this is something I have been looking into myself, trying to ID the next group of new entrants come the next indices rebalance. I recently bought CL1 and should have jumped into CAT around $2.60 ish, but let it fly right past.

You must get a lot of signals in that large universe, would this not potentially have some bearing on your backtesting accuracy.

Cheers,
Wyatt

 

[Trendnomics]

Hey Wyatt,

My trading universe consists of the following:

• Closing price greater or equal than 50 cents - (Close >= 0.5);
• Product of 100 day closing price moving average and 100 day volume moving average, to be greater or equal than $250,000 - [Mov(CLOSE,100,S)*Mov(VOLUME,100,S) >= 250000].

Yes, this does result in a lot of signals, but as recently discussed on the topic of expectancy, the more trades the better - my focus is to remain 100% invested (given the signals).

I use chronological possibility outcome assessment Monte Carlo back-testing (system generates more trades than available trading capital - numerous combinations possible - i.e. "butterfly-like-effect"):

• Random trade entries (when multiple entries exist on a particular day + insufficient trading capital);
• "Butterfly Effect" trade path generation (i.e. one entry will have a unique exit, which then creates another unique entry, etc.);
• Random price execution.

The software I've used allows for 20,000 Monte Carlo outcomes - the minimum, average and maximum profit outcomes (similar for draw-downs) can then be derived form the results - providing an indication of the range of possible outcomes for the time period.

Example:
View attachment 65802

 

[howardbandy]

Greetings --

Beware, or at least be aware:

1. Monte Carlo is a tool. The way it is being used for this analysis requires that the trades be independent. Applying the technique when the trades are not independent results in an underestimate of risk and overestimate of profit.

2. The universe of issues available today may not be the same as it was for the test period. There are survivor and membership biases to consider.

3. As trading systems are developed, parameters are varied and the "best" system depends on the specific value used. (That value changes over time, but that discussion is about stationarity. The point I am making here is about dimension.) The more values tested, the higher the probability that a good value will be found. Adding indicators and parameter values adds "alternatives" to be tested, from which the best is chosen. When choosing the issue for the next trade, each potential issue is an alternative. Allowing a system to choose a small number of issues from a large population is "optimizing the symbol space."

3A. Optimizing the symbol space is, in itself, worrisome.
3B. The Monte Carlo technique described is inappropriate for analysis of the result.

Best,
Howard

 

[Trendnomics]

Greetings --

Beware, or at least be aware:

1. Monte Carlo is a tool. The way it is being used for this analysis requires that the trades be independent. Applying the technique when the trades are not independent results in an underestimate of risk and overestimate of profit.

2. The universe of issues available today may not be the same as it was for the test period. There are survivor and membership biases to consider.

3. As trading systems are developed, parameters are varied and the "best" system depends on the specific value used. (That value changes over time, but that discussion is about stationarity. The point I am making here is about dimension.) The more values tested, the higher the probability that a good value will be found. Adding indicators and parameter values adds "alternatives" to be tested, from which the best is chosen. When choosing the issue for the next trade, each potential issue is an alternative. Allowing a system to choose a small number of issues from a large population is "optimizing the symbol space."

3A. Optimizing the symbol space is, in itself, worrisome.
3B. The Monte Carlo technique described is inappropriate for analysis of the result.

Best,
Howard

"Monte Carlo" appears to be the magic term to summon you. Thanks for your input - the brush you are using is a little broad for my liking (i.e. treating the back-testing of long-term and short-term strategies in a similar manner).

Business as usual on my end - it's raining $$$$$$$$$$$$$$$$$$ (new equity highs):

Private Portfolio: +62.08 % (Since 07-2013)

SMSF Portfolio: +65.53 % (Since 07-2013)

 

[howardbandy]

Greetings --

Long term holding versus short term holding versus any use of funds at all ...

The metric to compare any use of funds is forecast compound annual gain of the risk-normalized performance. Monte Carlo is the tool of choice for doing that analysis.

If a trader has quantifiable confidence in a system and a procedure in place to detect and respond to changes in stationarity, that is what matters.

Best,
Howard

 

[weird2]

Trendnomics has indicated he uses the software Tradesim, so it may be helpful to put in context the type of Monte Carlo analysis that is performed by that software - which we can term as MC Randomisation when comparing with options available in Amibroker.

If we look at 2 approaches with the focus on the approach taken with Tradesim

1. MC Randomisation - Tradesim or Amibroker
2. MC Bootstrap - Amibroker

From the Tradesim Manual which can be viewed online

Not all Monte Carlo analyzers provide useful results.

The TradeSim Monte Carlo analysis is unique in that it provides a true portfolio Monte Carlo analysis
rather than a Monte Carlo analysis based on time series shuffling or synthesis of trades using random
generators and price distributions.

When thousands of simulations are run in a TradeSim Monte Carlo analysis each simulation represents a real world portfolio-trading scenario, which uses real trades that occurred from the historical trade databases.

Unlike other software TradeSim does not synthesize trades by time series shuffling or price synthesis using questionable price distributions. Looked at it another way when TradeSim runs a 10,000 run Monte Carlo simulation on the top 200 stocks it is like having 10,000 traders trading from the same portfolio or universe of securities, and using the same trading rules. If there are a large number of trades with the same entry dates then you would expect each trader to come up with a different result as each trader would not choose exactly the same trades even though they are obeying the same set of trading rules. Because of this capability we believe our Monte Carlo analysis is unique and stands alone compared to any other offering from any other vendor.

Amibroker can also provide this type of analysis

How about Monte Carlo randomization instead of bootstrap test?

The Monte Carlo randomization is different than bootstrap test because it does not use actual (realized) trade list from the backtest but it attempts to use "all individual returns whenever they are realized or hyphotetical". For example when trading system is generating way more signals than we can actually trade due to limited buying power, then we have to choose which trades we would take and which we would skip. Normally this selection is a part of trading system and in AmiBroker PositionScore variable tells the backtester which positions are preferred and should be traded. In randomization test, instead of using some analytic/deterministic PositionScore, you use random one. If there are more signals to open positions than we could take, this process would lead to randomized trade picks. Now using Optimize() function and random PositionScore we can run thousands of such random picks to produce Monte Carlo randomization test:

step = Optimize( "step", 1, 1, 1000, 1 ); // 1000 backtests
// with random trade picks from the broad universe (make sure you run it on large watch lists)
PositionScore = mtRandom();

Randomization test has one big disadvantage: can not be used in many cases. When system does not produce enough signals each bar there is not much (if any) to choose from. Also, more importantly, MC randomization makes false assumption that all "trading opportunities" (signals) are equal. In many cases they are not. Pretty often our trading system has specific, deterministic way to pick trades from many oppotunities by some sort of ranking/scoring. When system is using a score (rank) as a core component of the system (rotational systems do that) - if you replace analytic score of with random number you are just testing white noise not the system.

I think it is an interesting discussion. I have used Tradesim in the past, Metastock was sadly lacking in portfolio backtesting and it filled that gap nicely. Metastock combined with Tradesim is a powerful coupling. I do use Amibroker now. It is cool that it has the flexibility to perform either the MC Bootstrap approach or the MC Randomisation approach. Although I still have a lot of positive opinion of the Tradesim software.

 

[Trendnomics]

Reached new equity highs this week - bit of volatility in my resource holdings yesterday, but a nice recovery today:

Private Portfolio: +62.46 % (Since 07-2013) ~ +17.6% per year return

SMSF Portfolio: +68.38 % (Since 07-2013) ~ +19.0% per year return

 

[Trendnomics]

Guess which one a robust trend-following strategy is? Hint: The colour of money.

 

[Trendnomics]

I thought I'd share a private message response:

Hi Trendnomics,

I see you have a lot of knowledge on this topic. Do you have any books/courses/ideas recommandations to dive deep into this topic? Up until this moment I bought Amibroker, Premium data and 2 books of Nick Radge - Weekend Trend Trader and Unholy Grails. But I feel something is missing, I don't have confidence in WTT for example. How can I load large amount of cash in a WTT strategy if I don't have enough confidence in it? Will it survive in a 1929 scenario? How a bout a Japan flat market like scenario? Open ended questions for which I need to do more study. Any hints highlt appreciated!

Thank you,
#####

Hey #######,

Thanks for the private message.

The best advice I can give, is to put a lot of time into back-testing. You really need to understand what different system parameters will do. I am self taught, have never read a trading book in my life, but I have spent A LOT of time back-testing and performing statistical analysis.

Once your knowledge is built, you need to design your own system. This will provide you with a fundamental confidence to see it through the hard times. Successful trend-following is all about confidence in your system, the market will test you, if you lack confidence you will abort your system and look for the next best thing. Successful systematic trend-following is like getting married, you have to pick the "right" person (system) and then stick with it through thick and thin. All systematic trend-following systems require a few years (depending on your time-frame), for the positive expectancy to propagate.

A robust un-leveraged trend-following system, will easily survive the 1929 scenario. The system will go into a draw-down, but after the draw-down it will flourish and the subsequent gains would be far more than the draw-down. Remember robust trend-following is "anti-fragile" (i.e. loses a head in volatility but grows two back after). As for a flat Japan-like market, there are always businesses that grow in a "flat" economy. A trend-following system will seek out those high-performing businesses and out-perform the overall market.

 

[Trendnomics]

Portfolio updates: I am killing the pig at the moment - new equity highs - currently like printing money and suffering from reduced motivation in my day job (earning petty cash, compared to daily gains). Normally when it goes this well the next draw-down is just around the corner.

Private Portfolio: +66.09 % (Since 07-2013) ~+18.4% per year return

SMSF Portfolio: +76.01 % (Since 07-2013) ~+20.7% per year return

A quick snapshot of current open trades for each portfolio (excludes dividends):

Private:

TWE - Open (50.73%) - 304 day trade
COH - Open (23.98%) - 164 day trade
VTG - Open (78.41%) - 157 day trade
SSM - Open (15.13%) - 101 day trade
WOR - Open (3.32%) - 42 day trade
CAT - Open (6.5%) - 20 day trade
AAC - Open (-0.16%) - 6 day trade
FMG - Open (0.28%) - 0 day trade
RRL - Open (120.05%) - 301 day trade
SAR - Open (202.29%) - 266 day trade
CTD - Open (17.79%) - 210 day trade
NAN - Open (26.53%) - 142 day trade
RFG - Open (6.27%) - 135 day trade
ORE - Open (41.46%) - 129 day trade
GMM - Open (35.43%) - 111 day trade
PGH - Open (-5.27%) - 31 day trade

SMSF:

BAP - Open (74.66%) - 485 day trade
TWE - Open (50.15%) - 304 day trade
RRL - Open (119.25%) - 301 day trade
SAR - Open (201.12%) - 266 day trade
SKC - Open (13.64%) - 210 day trade
ALL - Open (60.55%) - 202 day trade
COH - Open (23.17%) - 164 day trade
VTG - Open (77.67%) - 157 day trade
NAN - Open (25.9%) - 142 day trade
RFG - Open (5.71%) - 135 day trade
GMM - Open (35.7%) - 111 day trade
SSM - Open (14.57%) - 101 day trade
FBU - Open (10.24%) - 42 day trade
CL1 - Open (1%) - 28 day trade
MND - Open (4.52%) - 2 day trade
FMG - Open (-0.53%) - 0 day trade