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2

I'm going to separate your question in two. The key thing you're asking is that how does Return.rebalancing treat your different frequencied and number of asset return and weight objects. Data munging: It subsets the first ncol(weight) columns of R (as ncol(edhec) > ncol(weights) ncol R is now 11. Checks if the first date in R is less than the first date ...


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I found out that the upper time series is the result of a call > tail(Return.rebalancing(edhec,weights)) portfolio.returns 2009-03-31 0.005082048 2009-04-30 0.022982981 2009-05-31 0.037432398 2009-06-30 0.011107189 2009-07-31 0.025580507 2009-08-31 0.017983519 (by optical comparison. ;-) ) A glance ...


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The GBM is a continuous model, so using large integer time steps naturally leaves large discretization error (which vanishes when you increase the number of steps). Use small time step 0.001: paths(j + 1,i) = paths(j,i) * exp((mu - vol^2/2)*0.001 + vol * 0.001^0.5*shocks_ant(j,i)); Then the mean is almost exactly 100 as expected.


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appearantly your sampling variance is too large. I reimplemented your example in R. What I first saw is, that the mean got worse if I took more time steps (you take $300$). Your volatility is $0.3$ which is $30\%$ per year and you sample $300$ years. What you should do is the following: define a variable nbr_steps_peryear choose the number of years then ...


2

You should look at confidence interval. Normally, your confidence interval size is proportional to the standard deviation, looking something like: with probability $p$ your value will be in the interval: $$[\bar{S} - k*StdDev, \bar{S} + k*StdDev]$$ Then, getting back to your simulation, we can say that your time step is very big (1 year) and you simulate ...


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You're confusing the language that you will use for your analysis with the language you will use to execute your results. The modern approach for APIs (for all industries/apps) is to use and HTTP based REST API that is exposed to the outside world, most likely with something like JSON. This approach works because it can be used by a large variety of ...


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You have typo "vol^2", but it should be "vol". Its $$\sqrt{\sigma^2T}=\sigma\sqrt{T}$$


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I work with time series intensively, and I am experienced in Java and scripting languages such as MATLAB and R. I strongly suggest that you should cook up your own implementations in Java, and stop hunting for and relying on any off-the-shelf implementations. They are not reliable. One should be able to write std, corr, cov, ma, etc., easily by hand. Coding ...


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You might find TA-Lib useful: TA-Lib is widely used by trading software developers requiring to perform technical analysis of financial market data. Includes 200 indicators such as ADX, MACD, RSI, Stochastic, Bollinger Bands etc... (more info) Candlestick pattern recognition Open-source API for C/C++, Java, Perl, Python and 100% Managed .NET ...


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If you're a student, the first place I would suggest you seek guidance would be from your professors. They could at least steer you in a direction of developing something that could be meaningful and appropriate for someone with your background (whatever that may be, your professors probably would know). If you're looking for something open source, then I ...



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