Simple Monte Carlo Simulation of Stock Prices with Python

[@maheshhraut]

Amazing work man. No nonsense, just to the point 👏

[@chbrownie18]

Clear straight forward presentation. Perfect. Sadly my browser doesn't want me to bookmark your site for security reasons.

[@otroleonarbe]

great tutorial. One thing that could make this video better will be the font size. I believe it's a little too small, if you're watching on a laptop. IMO. However, keep it up sharing your knowledge to the world :)

[@JoaoVictor-sw9go]

Great tutorial, but I think the nested loops don't actually need a counter because de y variable has the exact same values. The only adjustment you would have to make when removing the break is loop through the number of periods - 1!

[@findpatterns]

Thanks. The video it's self explained the concept really well.

[@goodlack9093]

Great video, definitely super useful! Thank you!

[@divinastrain1286]

As far as I know, the daily earning rate didn’t follow the normal distributin, but the ln(earning rate ) does. Really want a discussion.

[@none4111]

There is something odd about the output until I realized its wrong. -> np.random.normal(0,daily_vol). Instead, it should be np.random.normal(statistics.mean(returns[1:])),daily_vol). Basically, the mean is not 0. On average, prices go up for equity and the normal distribution shifts right. (side note: using normal dist. is simpler but doesn't account for higher moments such as Kurtosis and Skewness) Also as other pointed out "count" is not needed. Using last price is correct since that's the initial starting period of the MC.

[@marku7z]

This was the first thing, i was wondering when watching the Video few seconds ago - Why do we assume the mean to be 0 here?

[@jy5104]

@@marku7z There is something wrong with the last red line as well. It should connect the initial price and the average of the simulation value on the last day.

[@esahin01]

Thank you for the video, it is very informative

[@wilsonkun]

Just learnt how to produce monte carlo using Brownian motion in Python. Looking forward on how to get insights using that graph.

[@RailDevil68]

Nice man, worked fine for me 1st try.. Well, except for the 'google' part, which i replaced by yahoo but nice !

[@DivineZeal]

very great video, lots of possibilities

[@rajkiranboggala]

That was a nice explanation. Thanks

[@manuelpena3988]

I don't know if I am misunderstanding it, but, is the "count" variable really needed? I think the "y" variable has exactly the same value every moment.

[@71javer]

Cheers for this, I been tryin to find out about "share options trading" for a while now, and I think this has helped. Ever heard of - Winoorfa Option Olegroson - (should be on google have a look ) ? Ive heard some super things about it and my friend got amazing success with it.

[@dragomir44]

Hey P4F, have you ever gotten the Google "We're sorry but your computer may be sending automated queries" error? form the prices = web.DataReader line of code?

[@jongcheulkim7284]

Thank you.

[@sornponwichaidit4172]

thanks for the great tutorial. why don't you used log return instead of simple return?

[@TheKenji1681]

What is the difference with log return and return? I am new 😿

[@juinsong7949]

May I know why my final simulation_df only displayed one trial path, even thought my code is identical to yours?

[@jayjaytapp]

Thanks for the great video!! Sorry for being critic, for your next video I'd be useful if the font sizes a bit bigger and if you try to minimize the noise of your keyboard.

[@margy1061]

Great work :)

[@markk364]

great vid - thanks. i see many predictors that fail to utilize stock-price-returns as a factor for their random.normal shock factor. Looking for more.

[@AlvaroLefian]

great tutorial but please move the microphone away from the keyboard the noise is unbearable

[@kurtb3260]

Great informative, concise, vid. Thanks for posting the code. Only had to make a few tweaks to make it work for me. Assuming random price action, the logic is correct.

[@mxcollin95]

Great example! Thanks for posting. 👍

[@nstevens81e]

Why does this video stop playing at 0:21?

[@ZulfiqarAli-ck1kc]

Sir please share the playlist

[@rverm1000]

I tried this with python 3.8 . Got a data_source='Google's is not implemented.

[@atrumluminarium]

Try "yahoo"

[@rverm1000]

Overall it's a good video

[@TheChawamushi]

what does the 0 in the function pd.random.normal represent?

[@euanscott-watson3725]

I believe its because thats how the normal standard deviation works, the formula is that of N(mu, sigma) where mu is the most common value on a bell curve at the peak. So that means that 99% of the values created by the normal function will be between -3*sigma and 3*sigma

[@kurtb3260]

Zero represents the center of the distribution.

[@inglese2996]

4 years later... are you a millionaire yet? I mean... does this modelling have a practical benefit and allow you to trade the market more effectively?

[@francisodonoghue1581]

Does not support 'google' , I think (Line 13)

[@kebman]

You know, given the assumption that price action is random, which it isn't.

[@MatthieuVlogs]

Great video! I'm just now getting started with Python and Apis with excel- so this was a bit over my head but something to aspire to certainly. As a python interface, I see you're using spyder. I'm using one called python 3.6, will I be able to have the same capabilities with 3.6?

[@francisdupuis6213]

Absolutely. I run python 2.7 and 3.5 as separate environments with Anaconda (highly recommended package by the way). But I'm assuming your using idle with Python 3.6, so it should work fine with some minor syntax changes.

[@MatthieuVlogs]

Great thanks! Look forward to learning more with your channel

[@1234dck]

Great video thanks a lot. But it too small. Thanks again.

[@kaplansedat]

font size and mechanical keyboard. oh god mechanical keyboard. developers love band wagon.

[@JR-ly6bx]

What IDE are you using

[@euanscott-watson3725]

Its spyder with an anaconda virtual environment

[@arshavir86]

I am using jupyter notebook and somehow it says "No module named 'pandas_datareader' " How can I fix this issue?

[@ai-cowboy]

open anaconda prompt > copy/paste into first line: conda install -c anaconda pandas-datareaderanaconda.org/anaconda/pandas-datareader

[@NKernytskyy]

Please use LARGER font, please...

[@rverm1000]

Pandas data reader is not implemented

[@Lexluthor2024]

There's a function in python to silence keyboard?

[@oneboy8183]

lol that keyboard tho!

[@jacktrainer4387]

Masterful!

[@qwesx23]

blue switch!

[@michaelkueng8564]

Hi, Thank you for this cool video! I tried to do this, but kept getting this error: File "C:/Users/micha/.spyder-py3/temp.py", line 10 start = dt.datetime(2017, 01, 03) ^ SyntaxError: invalid token Does someone know why?

[@AM-cg3nt]

Can’t lead with 0’s - fix it by putting the date as (2017,1,3)

[@maribelmenese4845]

Bro forgot the "y" in the thumbnail.

[@thunde7226]

I am using Python 3.6.5................I have an error.....................:( some one help...................warnings.warn(UNSTABLE_WARNING, UnstableAPIWarning) UnstableAPIWarning: The Google Finance API has not been stable since late 2017. Requests seem to fail at random. Failure is especially common when bulk downloading.

[@jonswift2455]

a few free sources have stopped working, google stopped awhile ago, yahoo stopped late last year i think.

[@jonswift2455]

change to robinhood or morningstar where google is written

[@Axle_Max]

The code doesn't work, the data sources are deprecated. Thanks for trying but it's broken

[@franklinparker]

you can get the data using the Quandl library import quandl as ql , then ql.get( "WIKI/APPL", start_date = , end_date = )

[@dreamz52]

A lot of issues here

[@wildhostage]

If I could write code like that, I would be so gangsta

[@optimizedpran1247]

sounds like a razer keyboard hahahah

[@divinastrain1286]

Hey man. This tutorial is totally WRONG. This is not Monte Carlo. The mean of your result will be same as lasta_day_price forever. That was misleading…..