#Picgif lite code#
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#Picgif lite free#

However, you won’t be able to host images or videos hence, ensure that you download GIFs once they are prepared. The app has the file-size limit of 30 MB. You can capture up to 400 photos and videos to create GIFs. You can even split and merge multiple GIFs in one frame. It allows you to go with basic edits like crop, resize, optimize and more.

#Picgif lite free#

I used a free app for the Mac called PicGIF lite to generate the final animated gifs.Unlike above listed Mac apps, Ezgif is an online GIF converter.

#Picgif lite code#

The following code was used to produce a sequence of PNG image files that I later used to create the animated gifs above. ProfileName ) xlabel ( 'Age' ) ylabel ( 'Portfolio Value' ) for s in 1 : iterations predictions = global df_pred = DataFrame ( Age = Float64, MC_Price = Float64, MC_Balance = Float64, Deterministic_Balance = Float64 ) last_price = 1 for i in 0 : yrs_to_days ( N ) i = 0 ? mc_price = 1 : mc_price = last_price * exp ( drift + pdr_std * norminvccdf ( rand ())) push! ( df_pred, ) push! ( predictions, original_principle_sum * mc_price ) last_price = mc_price end s = 1 ? predictions_all = predictions : predictions_all = hcat ( predictions_all, predictions ) plot ( df_pred, df_pred, color = '#B8BFC5', label = 'Monte Carlo Iteration' ) end if show_Q #Show quantile predictions df_Q = DataFrame ( Age = Float64, Q1 = Float64, Q5 = Float64, Q9 = Float64 ) for i in 1 : yrs_to_days ( N ) push! ( df_Q, , 0.1 ), quantile ( predictions_all, 0.5 ), quantile ( predictions_all, 0.9 )]) end plot ( df_Q, df_Q, color = 'r', label = '10th Percentile' ) plot ( df_Q, df_Q, color = 'b', label = '50th Percentile' ) plot ( df_Q, df_Q, color = 'g', label = '90th Percentile' ) else plot ( df_pred, df_pred, color = 'b', label = 'Deterministic Prediction' ) end end we start by setting up the known variables and add a few useful functions at the same time.įunction montecarlo ( rp, N, iterations, show_Q ) # rp = the index of the risk profile to use # N = Number of years forward to project # iterations - no of times to iterate and produce a simulation # show_Q - if True, so quantile lines growth = interest_rate ( rp ) # Periodic Daily Return (PDR) pdr = log ( deterministic_predict ( 1, growth, frequency, 2 * days ) / deterministic_predict ( 1, growth, frequency, 1 * days )) pdr_std = sigma ( rp ) * sqrt ( days ) pdr_var = pdr_std ^ 2 drift = pdr - ( pdr_var / 2 ) predictions_all = axis () title ( df. Let’s take a closer look at the code used. These gifs were generated with the functions below. We can see that as we take more risk the simulations become more volatile. The animated gifs below show 20 simulations per risk profile. Here are some animated gifs showing 20 simulations per risk profile. Additional parameters are built into the function to repeat the GBM simulations over-and-over to generate what is known as a Monte Carlo experiment. The function below uses Geometric Brownian motion (GBM) to simulate randomised returns based on the given risk profiles. Price fluctuations are generally more volatile for stocks that have the potential to earn more income for the investor. The reality with real share portfolios is that unit prices fluctuate up and down on a daily basis. For more information see this article on Periodic Compounding.īy using the Moneysmart Compound Interest Calculator as a second sanity check we can see our deterministic function is working. This prediction assumes no additional contributions and perfect market conditions. T is the overall length of time the interest is applied (expressed using the same time units as r, usually years). The following function makes a deterministic prediction of the future portfolio value based on the following parameters:. The elongated bell curve shape means there is scope to earn much higher returns at the expense of possible negative returns.įor more information on Probability Distributions click here Deterministic Prediction Function You can observe that the probability of achieving the mean total growth of 7.27% is the lowest. The last and most risky investment profile is ’Very Aggressive’. You can observe that probability of achieving the mean total growth of 4.2% is the highest and most of the bell curve area is in the positive growth area. The first and least risky investment profile is ‘Defensive’. The vertical dotted line shows the boundary of positive growth (i.e. For rp in 1 : length ( df ) plot_rp ( rp ) end