Tag Archives: good

Early days look good for NEW forex algo trading bot

This looks good for now but we have seen this picture before last week the new Kraken bot. I show in this video how positions and entrys get established. It is very similar to the Kraken bot with more forex trading opportunities. it is a nice balance to have but this is all explained in the video below

All market data is from a Oanda practice account.

This bot was developed in 6 hours with the Python Infrastructure course listed here

Python Algo Trading Infrastructure with Crypto Currency

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Results of new Kraken crypto currency markets bot looks good

I show pretty well all you to need to know about this NEW bot which was created within 24 hours. It seems to be also profitable even in down crypto currency markets like today. I am pretty excited to test over the next few days to see how it handles profit wise when the crypto currency markets come back.

 

I cannot stress I was able to build this bot within 24 hours with profit on a NEW exchange. Seriously, why are you not taking advantage of these huge massive crypto moves!

 

Python Algo Trading Infrastructure with Crypto Currency

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Where did the least-square come from? Good for machine learning?

Where did the least-square come from?

Math is key sometimes to math. Guess what? I find the simpler, the better. If you know your difference between an simple moving average or a weighted average, you should be ok. You don’t need the complicated math as far as I can see. The complicated quant can be important for processes like machine learning, it can take you much longer to find the result.

Check out this article

https://towardsdatascience.com/where-did-the-least-square-come-from-3f1abc7f7caf

 

Use market data different from your broker or mt4 or tradingview

I got this question via Facebook Messenger:

Hi Bryan, I have read https://www.youtube.com/watch?v=A07CBHXq75A I was wondering, are there any codes on github where one could execute an indicator from MT4 or Tradingview via Java, or instead of executing orders, receiving buy sell signals via sms from an MT4 or tradingview indicator, via java, on an sms? How would you construct such indicator?

My answer is in the video but in a nutshell: always use the market data of where you trade or submit orders.

Everything described is in this future.

Also, in this video I addressed the changing situation of brokers due to their Python API support. One example is how Dukascopy seems to become irrelevant since more people want machine learning in their trading. It seems they seek Python for this. You can find all these answers towards the end of the video.

 

I teach this in my Python course

https://quantlabs.net/academy/new-course-building-python-algo-trading-system-with-bitcoin-an-crypto-currency-focus/

Thanks Bryan

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Good C++ books to learn from

Good C++ books to learn from

Here are some suggested by some Quant Elite members

https://www.amazon.com/C-Plain-English-Brian-Overland/dp/1558284729

http://www.cprogramming.com/c++book/jumping_into_c++_sample_toc.pdf

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NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Good book to learn C++ programming for trading

Good book to learn C++ programming for trading

This was recommended by Quant Elite member so check it out

http://www.goodreads.com/book/show/17881534-jumping-into-c

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NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Good Bad Ugly on Google Tensor for Deep Learning and Machine Learning

Good Bad Ugly on Google Tensor for Deep Learning and Machine Learning

Some bad and ugly but I think this still needs to mature

http://www.kdnuggets.com/2016/05/good-bad-ugly-tensorflow.html

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NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

Strategy out of an academic paper. 16.5 % return at 2.36 sharpe, max drawdown 6.9% through 1999-2010. Is this good, medium or bad?

Strategy out of an academic paper. 16.5 % return at 2.36 sharpe, max drawdown 6.9% through 1999-2010. Is this good, medium or bad? Equity curve, year by year statistics and relative performance here

https://docs.google.com/file/d/0B3NaiGhKcKu0NTVmNjA4YmItYjA5Zi00ZWQ3LWJiY2MtZDlmNDEzN2IzNWI5/edit?hl=en_US&authkey=CNbSt6QC&pli=1

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Looks ok, but really these 2 pages does not give much information.

Hss this been verified by independent auditors, or is this backtest results? if this is backtest results, then I have seen a lot like it.
You say this is an academic paper. Is this present and open source?

Nice graph though 🙂

 

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The strategy (implemented out of sample) above is based on the two papers below

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1340879http://www.futuresmag.com/Issues/2011/May-2011/Pages/Capturing-backwardation.aspx?page=1

Adding the dynamic S&P strategy to the dynamic commodity strategies results in a significant reduction in volatility.

 

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You are right the 2 pages give much information. What else would you like to know?

These are backtest results or you could say paper traded results for a while ago..

 

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I skimmed through the above article and would say that your results seem pretty realistic. I missed the part in the paper where you have a sharpe of 2.36 not sure if that was for all ten years or your best year. Looks to me like for an active strategy applied to a particular commodity the sharpes are between 1 an 1.5. Incidentally I have an intermediate strategy for ETFs that produces somewhat similar results ie active management does better than long only posns and sharpes in the same range as you. What happens if you toss out your most volatile commodities from the mix and instead of having ten use say the least volatile 5-7. Just curious I am going to review your paper more closer it looks pretty good.

 

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sorry i did not read the google docs paper first so now i see where the 2.36 comes from
(a) how were the weights chosen
(b) if you are shorting assets do you adjust the sharpes in any way to account for the short position ie is sharpe still appropriate measure for a long/short basket as opposed to long only or would you use an adjusted sharpe.

Initially I skimmmed your other papers

 

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Thanks for your interest
(a). The strategy is a combination of 3 equally weighted strategies (2 for commodities, 1 – S&P 500). Weights are chosen on equal weighting principles rather using mean-variance optimization on historical data.

(b). Our sharpes are simply calculated as the ratio of (Mean return – risk free) and (sigma of return). I see your point about sharpe for shorts, because return (or more specifically ROI in this case) would change for a short position.

Other statistics of our strategy is that for the 11 year period, it the maximum weekly loss was -2.39 percent and there were only 4 occasions when return was less than -2%. And also only 25 weeks when return was greater than 2%.

 

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the papers you posted were very interesting. Thank you for posting them.

 

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For some reason I can’t view\download the pdf from Google Docs. Any way, to be concrete, On a simple scale of bad, fair, good then 6.9% is good, 16.5% is fair and 2.36 sharpe is also fair (that is, if you’re after setting up your own hedge fund).

 

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Thanks for the reply. Wonder why arent you able to access the google docs document. I can email it you if you like?

 

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The results are too good to be true. The Devraj Basu and Alexander Stremme paper uses data from 1993-2007 to find a suitable model and the uses 1993-1998 to estimate the predictive model and then used the 1999-2007 as out of sample data. It is like not having any out of sample data at all. I wonder what was the performance during 2008-2011.
From my experience strategies that uses linear regressions would show performance with long periods of really good and really bad performances.

 

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The performance of the backtest over 2008-2010 is given in the document attached to the comment. The Sharpe ratios were 2008: 1.80, 2009:4.71 2010:2.02. For 2011 the strategy was up around 5% until the end of June. THe statement that the Basu and Stremme paper used data from 1993-2007 to find an appropriate model is not entirely accurate. An unconditionally efficient portfolio strategy was chosen ahead of time and the in-sample period was used for parameter estimation via the predictive regression and the fitted model was then evaluated out of sample.In effect it could have been run in real time.

 

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I wasn’t clear enough. I would like to see the performance of the strategy only for SP500 during 2008-2011. You are right my statement is not entirely accurate but I see an issue related to variable selection (Why COT+VIX ?) using the entire sample.

 

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the email with links to this article was dumped into spam (i use gmail) because it said it has text frequently used by spamers. i see what they are talking about with those cheap air jordan ads.

 

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How large of leverage do you use?
thx..

 

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which platform u r using for doing back testing and how robust is ur platform..

 

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Thanks for sharing the papers. I was wondering how did you back test this system? Did you back tested from 1999 – 2010 or year by year ( 1999- 2000, 2000 – 2001, 2001- 2002,etc), You may get completely different results.

 

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Quant analytics: Are there any good exit strategies than traditional profit target / stop loss, trailing stops.. ?

Are there any good exit strategies than traditional profit target / stop loss, trailing stops.. ?

Of course normal would be just to take guidance or opposite of from your enter strategy but that is not always possible if you need reasonable stop losses (money management) and so on.

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But.. it is complicated enough to set your stop loss and profit target.

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That’s true, and if You set up some break even points it seems to have more psychological meaning than a real monetary value. And quite often those auto trail parameters need to be quite far a way, of course this is a strategy dependent.

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Chart support and resistance levels are good exits.

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Probably, at least one of my best strategy for FDAX is sort of pivot robot but maybe that can be enhanced with local intraday support/resistance levels and even utilize a new SwingProfile indicator (http://pvoodoo.com/Indicators.html). Thanks, let’s see.

 

 

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!