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Easy algo trading strategies to implement using R ?

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 Nitin Agrawal, .

 Saturday, November 29, 2014

Hi all, I have been able to connect my Interactive Brokers paper trading account to R API IBrokers. I have also been able to put market and limit orders using R API. Can you guys tell me any easy algo trading strategy which I can implement to test? Even if you have used that with any other language ( Java/ C# etc) or it is not much useful now a days then that would also work for me as I have to test something. Thank you. Nitin


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9 comments on article "Easy algo trading strategies to implement using R ?"

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 Salah Elmorry, Founding Partner at Systemathics

 Sunday, November 30, 2014



HI Nitin, try Pair Trading or Turtle Trading models.


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 Jacob Umflat, Quantitative Researcher at WhiteBayTech

 Sunday, November 30, 2014



Why not use moving averages crossover?

Very easy to implement..


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 Anton Pastoriza, Senior Manager at BBVA

 Sunday, November 30, 2014



Try Pair Trading. For the first implementations, consider the usage of liquid assets that are highly correlated (rho>=0.9) intraday. It is easy to backtest.


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 Franck Bardol, Data Scientist & data intelligence evangelist

 Sunday, November 30, 2014



to finish to set up / test everything is ok, start with this "hello word" strategy :

moving average cross (mavg)

(very) simplified pseudo-code :

if fast mavg (7 periods) cross above slow (20 periods) mavg then buy at market

if fast mavg (7 periods) cross below slow (20 periods) mavg then sellshort at market


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 Pablo Torre, Data Solutions Manager @FractalSoft Data Analysis

 Sunday, November 30, 2014



if (price==low):

buy;

elif (price==high):

sell;


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 Vasily Nekrasov, Risk analyst and model developer at Total Energie Gas GmbH

 Monday, December 1, 2014



>Why not use moving averages crossover?


>Very easy to implement..


Yes! They even work(ed) on the German market.


Have a look how I have implemented it in R with help of quantmod:


http://www.yetanotherquant.com/rcode.zip (file 4_3.r)


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 Aatos Heikkinen, Hired gun, data scientist, comp.physics PhD. R&D consulting: BI,econophysics, R, Haskell, C++, JS, NoSQL, AI,innovation.

 Monday, December 1, 2014



Do you think quantmod would work for you? http://www.quantmod.com/examples/charting/


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 Tad Slaff, Co-founder/CEO at Inovance

 Thursday, December 4, 2014



If you are interested in leveraging R's machine-learning capabilities to trade as well, I've done a couple posts that walk you through the process.



Here's a basic example on using a decision tree to help find the parameters: https://www.inovancetech.com/blogML3.html and be sure to check out the backtesting post as well.



Hope that's helpful!


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 Eliad Hochshteadt, Electronic Trading at Bank of America Merrill Lynch

 Thursday, December 4, 2014



Hi Nitin,

What granularity of data do you have in your possession ? intra day, close, tick by tick ?

Assuming you can see tick data, consider something real simple:

- calculate average volatility on a minute by minute of say 21 days --> that would give you a rough estimation for how the spread changes throughout the day (take moving average if the data seems too spiky)

- based on vol estimations, identify high and low points through the day (say on average, low vol is seen around 08:05, as well as 15:26, daily).

- as a start, position the following trades at the points of high volatility:

**passive (say couple of ticks than near touch) buy order.

**passive (say couple of ticks than near touch) sell order

You'll have a good probability for the price to shift twice in your favour; once upwards so someone cross your passive sell order, and another time when the price shift downwards to lift your passive bid.

if you're working on minutely estimations, the IB latency won't kill you.

if you're prices are within the calculated vol spead, there is a good change for the strategy to work.

Keep us update with your preferred choice and best of luck.

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