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When it comes to the world of investing, most people don't know where to start. Fortunately, great investors of the past and present can provide us with guidance
Absolutely amazing video, I have finally realised that as a beginner in the financial investment market, you can achieve close to nothing yourself because you still have a lot to learn. Trading with a professional broker is more profitable and my advice for beginners is to always take advantage of that.
Instead of timing the market, you should try to diversify your portfolio in order to get a dollar-cost average when it’s time to retire. Keep in mind that you don't need a ton of money to invest. Investing in small amounts can build long-term wealth too!
Reading about people grabbing multi-figures monthly as income in investments even in this crazy days in the market,any pointers on how to make substantial progress in earnings?would be appreciated
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I recreated this exact model and then checked to see how often the simple up/down movement of the price was correct (never mind the actual change amount), it couldn't reliably get better than 50% so it's basically the same thing as randomly guessing a slight up or down movement. So this was a cute demonstration of the concept but you'll definitely lose your ass if you tried to trade on anything like this haha. You need a vastly more complex model to start consistently getting higher than 50% and even then if there are large market corrections, they will definitely break your model. So Something like this might give you some minor assistance in day trading but you wont get rich one it. But anyways, we certainly appreciate the explanation on how general LSTM model functions
I am glad to see someone tried it while using only past data. The conclusion you make is what I would concur with just by simple reasoning. There should be a need for another factor at least to hope to get some sort of accuracy. Unless there is a repeated pattern to be found somewhere due to a factor we are unaware of. Like patterns in roulette due to flaws making the results not quite random. Something that has surely been caught onto, and is probably now verified for at casinos.
Actually this model will accurately tell you the range of values that the stock has high probability of trading in. Example if model prediction is 180 with rmse of 5 then there is 65% chance that it will close between 175 - 185 range, and there is 95% chance that it will close between 170 - 190 range given that stock returns are randomly distributed and it follows standard normal distribution. If someone knows the range maybe they can wait for the stock to hit either high or low before taking trade or they can deploy option strategies based on the information they got from the model.
How can use multiple data as a input to classify which stock to pick ? from high to low. let's say I input 10 datasets at once & it classifies top 3. (just for project purpose)
Just saw the video, thanks for the effort! One question though, isn't it leaking data when you do the scaling before splitting data into train/test sets?
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The flaw nobody seems to have realized is that it’s predicting 1 day in the future GIVEN the last 60 days of actual values. So each prediction on the graph is actually using the validation data from yesterday + the NN prediction for today. Anyone can now see why it tracks the chart so accurately. I could get the same chart with a random number generator. Caveat emptor.
I see your point: if you seeded any number within 1 standard deviation form the previous day to predict today's price, then zooming out to a large timeframe you could get a very similar chart. You brought up a good point and seem to be knowledgeable on this topic, so what would you do to implement a better forecast model ? Suggestions are appreciated, thanks!
@@diegotrazzi It's not possible. Assuming a sufficiently efficient market, if it were possible to forecast asset prices then people would take advantage and trade on such information. Price actions would then disappear.
@@weimondo NOT TRUE that's a total fallacy - the whole insane and inane premise that you cannot predict the future to find for example - a cycle in the market that no one knows about it and make a profit off of it b/c everyone will eventually learn it and use it thus incorporating into the market therefore it is not longer a predictor - this whole premise is so absurb as to make even the term "laughing stock" blush - all you have to do is NOT tell someone else what you discovered and the prediction holds - take for example the Scandavian Finance professor in the 90s that discovered an 80% accurate super cycles that was so freakishly exact like to 1 or 2 day out of every 2 months ish in finding the total and absolute highs and lows of almost everything that occured every 2-3 months - but being a brainwashed bonehead - tells the whole world about it - it was then put onto every Media in the world like Nightly Business Report - the euro continents finance channel and all the finance channels in the USA - it stilll took 2 MONTHS to have it incorporated by rest of the world but in mean time predicted every major and sometimes even minor turns in the USA stockmarket (never mind every other stockmarket index on the planet ) - now despite the fact that that is MORE than enough to make a few billion dollars and retire - keep in mind had he not been a brainwashed bonehead and had he kept this info secret it would never have been known and never incorporated into the stockmarket at all.... is this not common sense - FEEL FREE TO NOT REPLY BACK GEEZZZZ
I do everything the same and I got that df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2024-02-16') df TypeError: string indices must be integers, just in the begginer 😭 help please
get an answer from the comment below, hope it helps. "import yfinance as yf and change the line of code to df = yf.download('AAPL', start='2012-01-01', end=end='2019-12-17'). It seems like yahoo changed their api and these changes broke compatibility with pandas datareader" , and thank you @lucaartz
What is the possibility of making decent returns for short term investing? I've been reading success stories of people that make a total return of upto $75,000 monthly profit from their investments and I'd really love to know how to go about investing to make huge 5figure returns monthly
Even with the right technique and assets some investors would still make more than others, as an investor, you should’ve known that by now, nothing beats experience and that’s final, personally I had to reach out to a market analyst for guidance which is how I was able to grow my account close to a million, withdraw my profit right before the correction and now I’m buying again.
Okay here's the deal,.... The Yahoo finance API has likely changed since this video was first published. For all of those introductory lines of code importing all those different packages you only need a single line now as follows: import yfinance as yf
40:03 It is meaningless and misleading to plot predictions (especially zoomed out) unless you do out-of-sample forecasting. Many people are just obsessed with plotting forecast curves and comparing them with actual curves. It has got to stop! You can get a better fit by predicting the next value to be the current value. Out-of-sample forecasting is when the model is forced to predict N steps successively while not being provided with the true values of the previous time steps. Over time, errors would accumulate and the prediction curve would sway away from the actual curve. Only if you are doing out-of-sample forecasting, it makes sense to plot.
Hi! I agree with you. I'm new to this so I'd like to know if you see a way to deal with this problem so you can better predict the time series. Thanks!
so the caveat is that machine learning is amazing but it is not objective. That means while it can crunch the numbers it can only crunch the features you give it. If a ML model is given data to predict a pandemic based on particular behaviour then it would. You would then to add this model to another model that is predicting price and its volatility contingent on vis major or what some may call natural disasters or ' act of God'. This exercise is not a replacement for objectivity it is an aid to it and it only returns what you feed it. i hope this is helpful to aid your unedrsanding
This video does not teach anything useful. There's tons of material on the internet teaching how to use 'just' LSTM, Keras, Pandas, data loaders... This video would have much more value if the guy had presented some graph of how the network looks and what it actaully does. May a comparison with classic pure NN, and where the backpropagation comes in, and what recurrent means etc etc.
Thanks for your opinion. This video may not help you but their are others in the world that this video can help. There's tons of material online for what you would've like to see from this video as well but I will take your comment into consideration.
Really nice tutorial, however if anyone could tell me why did we keep the range from 60 specifically for the xtrain and ytrain timeseries datasets, it would be a huge help
When you undertake this project in Python notebook as the presenter has done Gemini AI will complete the code for you by suggesting the lines of code. To accept what is suggested, just press the tab key on your keyboard. Through running the code can and getting an explanation from the Gemini AI regarding the errors, I determined that the API had changed.
Data Science 101 : Never scale your data set with a test or validation set. You can't scale the entire dataset which introduces significant leakage to your model.
@@daspittin9954 Looking through the code, I see that he did a fit_transform on the entire dataset, then used that same scaler model to transform the test set back; he should have only used the training set when he fit the original scaler model; not both training and test, its akin to someone writing an exam, and giving them hints as to what the answers are (when estimating the predictions), but not providing the actual answers
@@surengrigorian7888 This is the general approach, which you'll have to implement after the step where he implemented the lag structure > scaler.fit(X_train) # scaler model fit to training set only > X_train_scaled= scaler.transform(X_train) > # some code .... > X_test_scaled = scaler.transform(X_test)
I see how this is an unsound practice, but scaling the input data should only make the training a bit more simple. In the end you should get the same result, up to a factor for the weights in the first layer. Plus the difference in scaling is rather low, so it is really not a big deal here.
For anyone criticising the effectiveness of this algorithm...you are missing the point. This is a very good FREE tutorial on applying LSTM & RNN to real world datasets using python, for learning purposes only. It's a standard and basic learning topic for Neural Networks. To criticise this would be same as saying Man City should win UCL every year based on their FIFA20 stats...
I'm getting error in this line. Does anyone know what is the issue behind it ? Command: df = web.DataReader('AAPL', data_source='yahoo', start="2012-01-01", end="2019-12-17") Error: TypeError: string indices must be integers
import yfinance as yf and change the line of code to df = yf.download('AAPL', start='2012-01-01', end=end='2019-12-17'). It seems like yahoo changed their api and these changes broke compatibility with pandas datareader
@@nczioox1116 Yes - but the video shows the common missconception about stock price prediction with LSTMs. This model just adds random noise of small amplitude to previous values. It cannot predict anything, its structure is just unsuitable to do so.
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@@RoboticusMusic This RNN yields an error of ~16 dollars. If you always predict tomorrow's closing price as yesterday's closing price from 2001 to 2020. You get 0.71 dollar mean absolute error. abs(df.close.diff(1)).mean() == 0.7417740650899958
@@shaw7598 If yesterday's close is $100 and today's close is $101, you're saying predicting that tomorrows close is $100 is accurate on average of how many ticks?
Well don't get too excited guys. Because every model predicts a price very close to the closing price. If you zoom the chart, you can see that yellow prediction is next to red ones. So this is not going to help you to make money. It's almost similar to shifting price one day to the future. Stop trying to predict. YOU CANNOT FORECAST THE FUTURE PRICES!
this is nice as a thought experiment, but you'll loose your money if you use this for real. There is no edge or competitive advantage to be found by using this model.
First of all , thanks for a wonderful session. One question about scaling though. Shouldn't the MinMaxScaler be used to fit_transform the training data and then use the "fitted" scaler to the test data ?
Great ,but there is one small thing you missed which may introduce bias into your model. To avoid leakage from the test set into the training set, it is important to split the data before applying any preprocessing steps. This is because the preprocessing steps can introduce information from the test set into the training set, leading to overly optimistic performance estimates and poor generalization performance on unseen data. so you have to split the data before scaling
I HAVE BEEN MAKING LOSSES TRADING MYSELF...I THOUGHT TRADING ON DEMO ACCOUNT IS JUST LIKE TRADING THE REAL MARKET... CAN ANYONE HELP ME OUT OR AT LEAST ADVICE ME ON WHAT TO DO?
Hi Everybody. I'm here in 27-Dec-2020. Just went thru all the steps from this video. It did not go as planned. All I got was a blue and a red line (Train, Val) - no prediction line, with a good distance between each other. Ok, this video is from an year ago, but nothing in the code tells me it would not work in Dec-2020. I apologize if this was answered already in the comments. But it seems I'm missing something here. Any assistance would be very welcomed! Thanks!
Your graph makes it seem like you predicted the last 2 years from previous data when at each point you had the past 60 days. Very misleading. From what I can tell your model is useless.
Any guy who has studied computational finance, know that you cant predict stock prices/security prices on previous data. Otherwise, the biggest HFTs would be utilizing machine learning by running a regression on previous data and they would be able to exploit this arbitrage forever and violate the no-risk arbitrage principle.
Can confirm. When I started Machine learning I started with exactly this approach and couldn’t get any useful predictions on any stocks. Back tested all my models in real trading Programms (with no real money) and kept generating losses. I tried out so many different models and amounts of data but nothing worked. I’m currently working with a friend on collecting news data and use a sentiment analysis in combination with stocks to predict them.
I think the markets are moving solely based on the psychology of the investors. therefore, technical analysis is the only valid approach. for example, it does not really matter how good a company is doing, if investors are paying attention, understanding their future. AI is making things a lot easier, by looking into investors' behavior. thanks for the great insight.
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
It would be interesting to see how this compares with a basic strategy, like just predicting todays close will be the same as yesterdays close. I imagine the LSTM doesn't perform as well
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
@@RogerCarelli Because the author is not telling you the full truth. The model is pretty much as useless as a random guess. You can't use it neither for day trading nor for swing not for any kind of trading or investing. It's useless because it makes an assumption that the next day's return will be the same as the previous. So, it made a prediction for tomorrow. What happens tomorrow? Whatever it was right or wrong, it will take the real return and use it to predict the next day, making the same assumption. And that's for an entire year. In the end, predicted values walking around real ones, showing correct return approximately 50/50. But if you zoom out, show graphs for the entire year, they're pretty close. That creates an illusion that the model works. That's why so harsh.
@@arsenyturin I think you got confused , he is not showing how to create a trading bot, and not showing a forward feed model. This is a very well made example on building simple models, using simple time series.
let's say you need data for the next 30 days, just change the Y to 30, so instead of predicting for 1 day the model will predict for 30 days. This is an alternative and effective solution I guess..
Sir, how can i remove the 60 data read limitation ??? Because i want to make this read from .csv file and read and learn from the whole data on csv file i have given
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
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This isn't for coin prediction. It's for "stocks" (AAPL in particular). Stocks historically attract investors "based on their past price performances", if not on their specific ratios. These past price data is vulnerable to very little or no manipulation due to strict regulations by the governments. So there's no point in predicting coin prices when there's NO REGULATION and zillions of tons of manipulation going on thru the so-called pump channels, etc. Stay away from coins unless you're part of a community that do those pumps because it's merely a gambling.
Good job, but I think you only wrote simple moving average using RNN. can you expand the dates and see what prices it predicts? THANK YOU for your tutorial!
What if I want to predict the for the date that is not in my train dataset i.e I would use my whole dataset as training dataset and would create a module 2 predict the future price?
I think this model is good for practice but not for real use. 1st issue is the scaling, you should scale based on the training set, not the full dataset, 2nd issue is the test dataset, the prediction works if you know the previous 60 days of the data for all the testing set. It would be good to see how is the performance of predicting next days (1+ days) just using a single vector of previous 60 days.
Hello, I am plotting the graph of train, val, predictions. But my graph shows that the val, prediction are on the left tail(starting point of the graph) not the right tail. why is it? from the video, the data is in ascending order but my data is descending order.
so the last prediction is a way to far. if yo just use a naive method, like the next day is equal to the previous you get better results :) , but it was about using Python and not getting results, right. thanx for sharing, i find forecasting with neural networks confusing because of all extra small steps and endless parameters. is there any way to predict more than 1 day ahead? i believe more simple methods will give beter results and can be easier followed, ,so if you know any , let me know.
Nice job. However, there is a small error in your equation for RMSE. You need to take the mean of the squared residuals rather than the square of the mean of the residuals.
w michaux can I send you some code to take a look at and maybe give me some pointers on what i can do better. my code runs im just not getting the results i intended and do not know where or why im stuck
So, as a piece of general information to everyone, the process is alright but the model is not so powerful. Try to tweak this model and you would get a very good result.
everytime i run the same parameters (date range, same stock) within few min intervals it gives different prediction, can someone help me to understand why
Knowing nothing about price movements, you just show a neural net 60 days of price 1 time, and then ask it: "oh oracle, what will price be the next day". It really needs more work than that. This is exactly what I was afraid of when deep learning was made more available. Just fire a DL at everything you don't grasp. Waste some electricity while you're at it.
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
Super disappointing video - Never use MinMax scaling for this purpose, data should be standardized but not MinMax scaled. Also, he implemented the scaling in the wrong part of the code causing data leakage... AND there is no dropout layer in the model. The whole video is like a first year computer science student attempts to do a data science project. Do not waste your time with this video, there are others much better than this one with fewer views.
#plot the data train = data[:training_data_len] valid = data[training_data_len:] valid['Predictions'] = predictions it says 'data' is not defined what to do ?
if i change it to dataset, it gives me this error ---> 24 valid['Predictions'] = predictions IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
@@shogunkicksbutt127 Like the current top comment says, just always predicting the price from the day before would have much smaller error than what he is doing. So this is more than useless.
Hi I'm a beginner of data science (know nothing) and I saw this video last night. It may be kind of stupid but I cannot even get the stoke quote following your steps.... it seems that datareader can no longer be used on yahoo. I googled several answers and methods yet i still cannot succeed....So i wonder if you could help me out about how to get the data thx.
It’s very important that the programmer should deeply understand the market as well to prevent the creation of BS like presented here! These results only seem to be close to the actual price, but in reality it’s 95% noisy!
This isn't accurate at all, you're literally just retracing the line. This is not predictive at all and a fundamentally flawed attempt. There are dozens of videos, medium articles, and blog posts using this same flawed method and they're all incorrect.
i dont understand anything... but the voice is calming down... and it sounds interesting 😆👍 Thanks for the video. Maybe i will understand it in 2 Years or so 🤓
Great video! Can someone explain to me why the input layer has 50 neurons and we pass 60 close prices each time, please? I thought that the number of neurons had to match with the number of inputs that we were passing. Thanks in advance :)
With 3/1 risk reward you only need 50% on the model to make 10% gains on every ten trades. Training the model on only the closing prices is the wrong approach. You need open low high close and volume to correctly see the flow of money. And you only need the model to predict a day or two ahead. Thats it :-)
Thank you so much for this tutorial, it was very helpful and I learned a great deal :) I have a question, if I want to predict more days should I change the number of neurons? Could you clarify this, please?
You will need to use for loop on the last cell he made for predicting next day value. The for loop will help to execute same lines of code for how many days you want to predict in future.
It's an univariate time series and you are making a single day ahead forecast. However, that is not very useful from the business perspective. Try to incorporate other variables like high and low prices to train the model.
Hello, Thanks a lot for this interesting instruction about stock price prediction. I am working on the prediction related to battery performance deterioration, so I have a few questions on the prediction part of the prediction algorithm. To my knowledge, the prediction part you proposed in the video can only make one step ahead prediction, right? Though the first algorithm you create a NumPy array contains test close stock price (many rows), but in your pred function, you iterate the prediction only based on the x_test, without the predicted close stock values. Is it possible to overcome this to help make a real prediction that can predict the stock price for a longer period, like 10 days, one month? Thank you very much.
This video is simply to demonstrate the implementation of LSTM. There are many factors to consider while making real predictions. Imagine if this was effecient way then wont all the LSTM algorithm makers be making huge profit 🤍
I am kinda new to machine learning and I never made any projects, but the fact that the result matched almost perfectly made me a bit suspicious about the method you use here. My first observation would be that the usage of scale would bound all result between the training dataset maximum and minimum, so there is no way that you could get a higher price as a prediction than the highest number found in the training dataset. My second obesrvation is about the actual training dataset. According to my knowledge testing dataset should not be included in the training data. I guess this is the reason it got the prediction so perfectly. I would suggest creating a dataset by actual % returns day by day or week by week. Maybe setting up some technical indicators. Volatility data, VIX, SP500 prices or anything should help predicting better the price in my opinion. However as commented by others already, pricemovements are not easy to predict and entire businesses are built to figure it out and still fail to beat the market. Thank you for the video! It was helpful to understand better how ML works in practice :)
lol ...are u serious?? Are you aware of computing automation? Do you know what software is for? You should go and just watch trading analysts videos. This one is for software PROGRAMMERS and clearly you are not one of them.
Cool video. But LSTM is not working so well here. Baseline test: if you simply predict the next day's stock price to be THE SAME as this day, you get an RMSE of 2.25 or so.
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The video was great, saved me a lot of time trying to figure it out myself. Also learn how to use the python syntax. On a side note, back of the envelope calculations. dec 17,19 aapl = 70.1, dec 17, 09 appl = 6.85. (70.1-6.85)/6.85 = 9.233 or 923% return over 10 years, 923%/10yr = 9.23%/year, 9.23%/365 days/yr = 0.0253%/day. If we use the previous day closing as an estimate plus the average daily change then, 70.1*(1.000253) = 70.118, accounting for the 1 to 4 stock split the price on the 18th is estimated at 70.1177 x 4 = 280.47, act close 279.74, vs AI 263.66. Would have been interesting to see which one would be better est, previous Close +% or AI, with the standard error.
Hello great Video really helpful for understanding the concepts of LSTM models and stock price prediction 😊! I just have one question how can we add a dropout function in order to reduce overfitting and improve the performance of the model ?
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
I wouldn't put much store in the model created to be honest for reasons others have raised about scaling but as a 'get you going' video into the perilous world of trading using ML then it's an honest attempt - well done.
Hey you seem like an expert . Can you please guide me as I have been really wanting to get into trading and finance using machine learning. I would really appreciate it if you could give me resources that could get me industry ready to create complex algorithms that can help me out.
Most newbies fail simply because they don't understand how the market works in general or in particular how the market relates to stock or currency pair they entering. If a retail trades doesn't grasp what the market makers are doing and when they are doing it, the greatest strategy in the world will fall. For new traders the markets are like entering an F1 race before you've passed your drivers license test. I am a beginner I never believe I made $30,000 in just a week from trading and with the market. an expert financial analysis and he made me-learn to read and understand the language of price action. He guides me with the exact time frame to trade and now I just received my first withdrawals of $30k in my bank account today I'm very happy, my advice is for you to contact him he will guide you perfectly well, and thank me later, I guess this is a good way to show my heartfelt appreciation for literally breaking the chain of my financial debit when I needed it most, you can contact him on his email (elvishercules48@gmail.com)
