I have a similar project where I identify the top 10 stocks with the highest momentum based on a 10-year data review. Then, using the Markowitz efficient frontier, I create portfolios with minimum variance and maximum Sharpe ratio. The I work on the descriptive statistical analysis to visualize everything such as monthly prices, variance etc. After that, I calculate beta and alpha using CAPM, and finally, I run 15 different regression models with 18 independent variables to predict the price-to-earnings ratio. I love this kindda detail stuff. Well done!
This has been a privilege. I worked in finance for a long time but never had the chance to see what goes on behind the scenes at fund management. I was only exposed to traditional portfolio managers who's strategies were thematic in nature and qualitatively driven. I'll probably never use any of these techniques or strategies for personal finance but it really gets you thinking of how things can be approached from a large portfolio perspective. GARCH model is daunting but this inspires me to finally understand what my boss was talking about all those years ago 😅
Garch is actually a fairly simple thing it just sounds scary because of its ugly name, whatever textbook you have just ask chatgpt to explain it in layman terms and build up from there👍
You work for 40yrs to have $1m in your retirement, Meanwhile some people are putting just $10k in a meme coin for just few months and now they are multi millionaires. I pray that anyone who reads this will be successful in life .
I'm new to cryptocurrency and don't understand how it really works. how Can someone know the right approach to investing and making good profits from cryptocurrency investments?
Cynthia Alexandra Jackson is the licensed fiduciary I use. Just research the name. You’d find necessary details to work with a correspondence to set up an appointment..
Thanks for sharing, I just looked her up on the web and I would say she really has an impressive background in investing. I will write her an e-mail shortly.
That's quite remarkable! I'm genuinely interested in benefiting from the guidance of such experienced advisors, especially considering the current state of my struggling portfolio.
Your KMeans clustering is totally useless when you're not normalizing the RSI values. In essence all the other features are just small noise in the clustering and you end up making the wrong conclusion that RSI is the main feature driving the clustering in your data.
Yep, the clustering part was only to make the "algo" more appealing. I thought he would have actually analyze each cluster trying to find useful features...
This example is for intra-day and day-trading as specified at the beginning of the video. After a quick research the best indicator for this kind of trading is the RSi. Due to this the main feature chosen by him is the RSI because it is the best indicator for this kind of trading strategies.
@@TyTy-gm8yb RSI is not the best indicator. It shows basically what has already happened in the past. It is by no means usable in a forecast model. Should you want to have useful data you could try using Money Flow, or simple price action with Volume. Or if possible, you can create a model using real time agression.
the .loc doesn't fix the error encountered at 51:30 with values starting from 2020. It is actually the rolling average calc parameter min_periods=12 that you set that fixes the issue...
Our economy is afflicted by uncertainty, housing troubles, foreclosures, global shifts, and the aftermath of the epidemic, all of which contribute to instability. To restore stability and drive growth, all sectors must urgently address rising inflation, slowing GDP, and trade disruptions
The US dollar is losing value due to inflation, while other currencies are gaining strength, creating uncertainty. Nonetheless, many people remain confident in the Dollar's perceived safety. I'm concerned that my 420K retirement funds may lose value, therefore I'm looking for other financial stability.
With my stressful career, I don't have time for investment analysis. For seven years, a fiduciary has managed my portfolio, responding to market situations, ensuring for effective navigation and informed decisions. Consider taking a similar plan of action.
My CFA, 'Leah Foster Alderman', is a well known figure in her field. I recommend looking into her qualifications further. She has extensive knowledge and is a treasure trove for anyone seeking to manage the financial markets.
Your strategies are simply amazing! I've tried so many different approaches to binary options over the years, but nothing has worked as well as your methods. Thank you for sharing your knowledge and expertise with us - you're truly making a difference in the lives of so many traders.
I am amazed to see algo videos rarely on TH-cam. I appreciate your work. Could you please make a similar kind of video on price action algo trading with Wyckoff multi-timeframe analysis, without using any indicators, and generate trades live through your machine learning or AI models? Many people are eagerly waiting to see these kinds of videos, but no one has ever made any price action trading videos. I request you to do so at the earliest. Thank you, Muni Babu
I'm a former quant. Back in my days we used to write all the algorithms ourselves on c++, including the Greeks to price options, multidimensional volatility surface(48) calculations per trad, etc.. Now days being a "data scientist" and a "quant" its about being just popular on social media.
Hey,since you are a former quant ,I found it really intriguing .I'm currently working on a script for currency analysis and since I'm relatively new to the quant world , I'd love to connect and chat about it .Maybe we could share some insights and experiences .Let me know if you're open to it. Cheers!
If you are concerned about stocks that fall out of tne S&P500 (survivorship bias), you could have just checked the top 150 against those that were always present in the S&P500 and then dropped those that werent from your dataset. (Or set some cut-off where it had to be present at least x% of the time)
Hello Inez! Did you get to make the model he gave functional to the actual market right now? I thought his model wasn't actually functional or did you add a lot more stuff?? I would greatly appreciate a response!
A good future use case might be the production, storage, use and trade of electricity in a household. Solar panels are so cheap, electricity is quite expensive/ volatile due to renewables at times. Electrification/ automatisation of households, variable prizes from providers, self production (photovoltaic cells), storage (e-cars, warm water, iot, refurbished battery cells,...), weather forecast,...
Hi, very good video and good job! But the daily signal volatility definition needs to be more conservative i.e. 1 std and not 1.5 std, due to the high CV of the signal ( to improve the results)
u made everything look so easy!!! thank you for the lesson, I am trying to duplicate this lesson and go a little beyond in terms of generating a SQL dataset and making a potential web page out of it
Great video! Unfortunately normalization procedure in it introduces lookahead bias. You can't use the full range of values to calculate mean and stdev, you can only use the data from earlier dates at any given point of time.
@@loganbishop4619 upon further review, the normalization is applied per security using the entire time series so I think this is an issue raised by @johnsmith-qc8bud ... wouldn't the correct way to do this would be to apply normalization on a rolling basis?
8:08 Workflow Process: 1. Collect and prepare the data, 2. Develop a hypothesis for a strategy, 3. Coding the model, 4. Backtest the strategy 9:19 Unsupervised Learning Project- uses ML strats without a labeled or predefined target variable. Unlike supervised learning because the model is not trained to make predictions, but to extract insights from data 12:45 Sentiment Investing Project - how people feel about stocks can impact stock prices/industries/overall market 14:04 Intraday Strategy Project- intraday approach means to buy/sell financial assets in same trady day to profit from short term price movements. Traders use real time data and risk management to make quick decisions and capitalize on market volatility 2:10:51
Appreciate the detailed breakdown! 🧐 Just a small off-topic question: 😅 I have these words 🤨. (behave today finger ski upon boy assault summer exhaust beauty stereo over). Not sure how to use them, would appreciate help. 🙏
Dear @lachone_ , many many thanks for your efforts and for sharing it with us. I want to ask where the standardization is done in the first project. Don't we need to standardize all the features around 1:22 before we apply Kmeans clustering?
Thanks for sharing! For the 1st project, I wonder how the clustering is different from selecting top RSI stocks in each month given RSI is not normalized? Or is it just for demoing the possibility of using the clustering algo?
Really interesting video. Thanks there are still people with the goal to teach. While I continue with my learning I have bought two bots for ES and NQ just to see how they work and they are giving me nice extra money for the last months. This also gives me the conviction that the authomated strategies could really work fine.
Tested with data until today (feb-22-2024) and a 1:18:00 there were different clusters 3, on dec-31 there was a cluster with rsi = 80 and other where the top rsi was around 60
after doing this for long time, with all sorts of advanced deep learning, math models, and whats not, i can surely say - this is not possible for the simple retail investor to have any alpha (or advantage) over the serious big organizations that do that. It is just not possible, just think that if it was, the biggest AI experts would already do finance and get rich. they aren't, they are making courses like this - because views on youtube pays more than algo trading. It is easy to sell programmers the idea that they are smarter and they can use special coding to make money. they cant.
Can you clarify whether survivorship bias occurs when including data from a company that has gone bankrupt, or when excluding data from a company that has faced bankruptcy?
Doesn't it bother anyone that clustering is done on the ENTIRE dataset? and further asset selection is done month by month using cluster label that was computed using future data?
First 2 hours was ok, mainly about getting some basics about panda, and some clustering idea, though what I miss that if I understand correctly re-balancing/weigthing happens on montly basis, which at 150 security (worst case) it takes big amount of money as most of trading platform has minimum fees, as a consequences probably your extra strategy profit would be eaten by cost. It was fun, but I have doubts that's the way to get rich :)
i am confused. so you use kmeans clustering to select stocks. then you use 1 year of stock prices and efficient frontier to calculate weight of component stocks. you use 18 features to fit a regression model to get the beta coefficients, which basically tells you the weights of the features on each stock. however, you never use the regression model to make predictions. you only use the betas for clustering? so your 'model' does not have forecasting ability? or does it? and where is the bollinger bands and macd used?
Such an amazing video an content. You are magic, this content opens the door to explore different quantum trading strategies assited with artificial intelligence. For those who put destructive opinions , please fck yourselves and criticised when you contribute something. This is an awesome approach for beginners in the algorithm trading and machine learning field. This is the main stone for my project to develop an algorithmic trading strategy using quantitative methods, machine learning and artificial intelligence to create profitable long ansd short entry positions
your quant journy will end soon when you realize it is just not possible, at all, you live in a dream if you think it is. There are just a small number of large organizations who can do this efficiently, it requires much more than a laptop.
I really appreciate your efforts! I need some advice: I have a SafePal wallet with USDT, and I have the seed phrase. (air carpet target dish off jeans toilet sweet piano spoil fruit essay). How should I go about transferring them to Binance?
Can you make for nse(india) trading. Because there is lot of freelancing job opportunity, but I can't find any good material or course to learn the algorithmic trading specifically for nse.
Don't bother with Indian clients on freelancing website for trading. They suck your time and not worth it. And trading on nse is no different than any other market if you get the fundamentals right.
we have error error: subprocess-exited-with-error × Getting requirements to build wheel did not run successfully. │ exit code: 1 ╰─> [15 lines of output] The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
Thanks for sharing the video. Can anyone help me understand the daily signal part in the GARCH modeling? Is the belief that the higher predicted return volatility leads to a higher projected return? Doesn't higher volatility mean higher risk?
Well, good luck making this profitable.... 😄There are exactly zero profitable strategies out in the open like this, for the simple reason that everyone who actually comes up with something profitable will keep is to themselves so as not to lose their edge on the market.
I usually look up your videos for updates! Our government has no idea how people are suffering these days. I feel for people with disabilities not getting the help they deserve. Thank you Mrs SONIA DIXON, imagine investing $12,000 and received $305,500.
success = True except: print(f'Max Sharpe Optimization failed for {start_date}, Continuing with Equal-Weights')
if success==False: weights = pd.DataFrame([1/len(optimization_df.columns) for i in range(len(optimization_df.columns))], index=optimization_df.columns.tolist(), columns=pd.Series(0)).T
Is anyone else having trouble with the following line of code: df['bb_low'] = df.groupby(level=1)['adj close'].transform(lambda x: pandas_ta.bbands(close=np.log1p(x), length=20).iloc[:,0]) Apparently, pandas.tt_bands() returns none, but that doesn't seem to be the case in the video
I changed 'length 20' to 'length 10'. Like this: df['bb_low'] = df.groupby(level=1)['adj close'].transform(lambda x: pandas_ta.bbands(close=np.log1p(x), length=10).iloc[:,0]). It worked, but I don't know why.
I have come across an error normalising the MacD indicator "TypeError: unsupported operand type(s) for -: 'float' and 'NoneType' During handling of the above exception, another exception occurred: TypeError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/pandas/core/computation/expressions.py in _evaluate_standard(op, op_str, a, b) 68 if _TEST_MODE: 69 _store_test_result(False) ---> 70 return op(a, b) 71 72 TypeError: unsupported operand type(s) for -: 'float' and 'NoneType'" Have followed the steps up until this point to the t, and have rewritten it to make sure. Have consulted GPT to see, what the issue is, but I have yet to find a way around it. It's suggestions resulted with a dataframe filled with all MacD values as NaN. Will continue to work towards a solution - thought I 'd post this here in the meantime.
![How to Get a Developer Job - Even in This Economy [Full Course]](http://i.ytimg.com/vi/6nz8GXjxiHg/mqdefault.jpg)
I have a similar project where I identify the top 10 stocks with the highest momentum based on a 10-year data review. Then, using the Markowitz efficient frontier, I create portfolios with minimum variance and maximum Sharpe ratio. The I work on the descriptive statistical analysis to visualize everything such as monthly prices, variance etc. After that, I calculate beta and alpha using CAPM, and finally, I run 15 different regression models with 18 independent variables to predict the price-to-earnings ratio.
I love this kindda detail stuff. Well done!
One thing I like about trading, everyone has an opinion n yet few are making money 💵
well there are some making money, else there won't be big hedge funds
@@nobodyknows3560 the big hedge funds won't share their opinions clearly though, only vaguely
he failed
@@nobodyknows3560Thats why it is increasingly difficult for sole traders as indicators can be manipulated by the big money.
This has been a privilege. I worked in finance for a long time but never had the chance to see what goes on behind the scenes at fund management. I was only exposed to traditional portfolio managers who's strategies were thematic in nature and qualitatively driven. I'll probably never use any of these techniques or strategies for personal finance but it really gets you thinking of how things can be approached from a large portfolio perspective. GARCH model is daunting but this inspires me to finally understand what my boss was talking about all those years ago 😅
Turns out I can't even get the arch package to work. After pip install arch, can't even import it in Jupyter NB. 🤷🏻♂️
GARCH is only daunting if you don't understand it.
Garch is actually a fairly simple thing it just sounds scary because of its ugly name, whatever textbook you have just ask chatgpt to explain it in layman terms and build up from there👍
You work for 40yrs to have $1m in your retirement, Meanwhile some people are putting just $10k in a meme coin for just few months and now they are multi millionaires. I pray that anyone who reads this will be successful in life .
I'm new to cryptocurrency and don't understand how it really works. how Can someone know the right approach to investing and making good profits from cryptocurrency investments?
What opportunities are there in the market, and how do I profit from it?
Cynthia Alexandra Jackson is the licensed fiduciary I use. Just research the name. You’d find necessary details to work with a correspondence to set up an appointment..
Thanks for sharing, I just looked her up on the web and I would say she really has an impressive background in investing. I will write her an e-mail shortly.
That's quite remarkable! I'm genuinely interested in benefiting from the guidance of such experienced advisors, especially considering the current state of my struggling portfolio.
Your KMeans clustering is totally useless when you're not normalizing the RSI values. In essence all the other features are just small noise in the clustering and you end up making the wrong conclusion that RSI is the main feature driving the clustering in your data.
this is correct. upvoted
Yep, the clustering part was only to make the "algo" more appealing. I thought he would have actually analyze each cluster trying to find useful features...
This example is for intra-day and day-trading as specified at the beginning of the video. After a quick research the best indicator for this kind of trading is the RSi. Due to this the main feature chosen by him is the RSI because it is the best indicator for this kind of trading strategies.
Based comment
@@TyTy-gm8yb RSI is not the best indicator. It shows basically what has already happened in the past. It is by no means usable in a forecast model. Should you want to have useful data you could try using Money Flow, or simple price action with Volume. Or if possible, you can create a model using real time agression.
the .loc doesn't fix the error encountered at 51:30 with values starting from 2020. It is actually the rolling average calc parameter min_periods=12 that you set that fixes the issue...
A fellow Bulgarian! Congratulations on your success, Lachezar!!
Thank you mate, cheers!
Bulgarians don't play around, you know you stopped caring what people think when you don't code in dark mode.
Your analysis of market trends is always spot on.
Our economy is afflicted by uncertainty, housing troubles, foreclosures, global shifts, and the aftermath of the epidemic, all of which contribute to instability. To restore stability and drive growth, all sectors must urgently address rising inflation, slowing GDP, and trade disruptions
The US dollar is losing value due to inflation, while other currencies are gaining strength, creating uncertainty. Nonetheless, many people remain confident in the Dollar's perceived safety. I'm concerned that my 420K retirement funds may lose value, therefore I'm looking for other financial stability.
With my stressful career, I don't have time for investment analysis. For seven years, a fiduciary has managed my portfolio, responding to market situations, ensuring for effective navigation and informed decisions. Consider taking a similar plan of action.
Do you mind if I ask you to recommend the coach you employed? It seems you've figured it all out.
My CFA, 'Leah Foster Alderman', is a well known figure in her field. I recommend looking into her qualifications further. She has extensive knowledge and is a treasure trove for anyone seeking to manage the financial markets.
Regards. her handler was simple to locate and appears to be extremely capable and adaptable. We scheduled a phone conversation.
I followed this video and became a billionaire in 9 minutes. Thank you for your excellent instruction.
Your strategies are simply amazing! I've tried so many different approaches to binary options over the years, but nothing has worked as well as your methods. Thank you for sharing your knowledge and expertise with us - you're truly making a difference in the lives of so many traders.
I am amazed to see algo videos rarely on TH-cam. I appreciate your work. Could you please make a similar kind of video on price action algo trading with Wyckoff multi-timeframe analysis, without using any indicators, and generate trades live through your machine learning or AI models?
Many people are eagerly waiting to see these kinds of videos, but no one has ever made any price action trading videos. I request you to do so at the earliest.
Thank you,
Muni Babu
Are you sure successful quant funds really trade Wyckoff, or what's worth - Fibonacci retracements? 😅
@@flexorx what ever they do, price action plays right, that enough for us...😏
I'm a former quant. Back in my days we used to write all the algorithms ourselves on c++, including the Greeks to price options, multidimensional volatility surface(48) calculations per trad, etc.. Now days being a "data scientist" and a "quant" its about being just popular on social media.
Hey,since you are a former quant ,I found it really intriguing .I'm currently working on a script for currency analysis and since I'm relatively new to the quant world , I'd love to connect and chat about it .Maybe we could share some insights and experiences .Let me know if you're open to it. Cheers!
@@thenoblegodplease let me know when he messages you
It's not about beating the market, its about having an unsolvable problem that you can always use to learn against.
Lol. It's about beating the market.
@@over1498 mainly it's about gambling
market inefficiencies*@@over1498
If you are concerned about stocks that fall out of tne S&P500 (survivorship bias), you could have just checked the top 150 against those that were always present in the S&P500 and then dropped those that werent from your dataset. (Or set some cut-off where it had to be present at least x% of the time)
Can you explain more about this?
yyyyeah wow 9 in a row on the demo and 4 in live. Thanks. Been waiting for something that finally works for me. I understand now how it works.
Hello Inez! Did you get to make the model he gave functional to the actual market right now? I thought his model wasn't actually functional or did you add a lot more stuff?? I would greatly appreciate a response!
A good future use case might be the production, storage, use and trade of electricity in a household. Solar panels are so cheap, electricity is quite expensive/ volatile due to renewables at times. Electrification/ automatisation of households, variable prizes from providers, self production (photovoltaic cells), storage (e-cars, warm water, iot, refurbished battery cells,...), weather forecast,...
Hi, very good video and good job! But the daily signal volatility definition needs to be more conservative i.e. 1 std and not 1.5 std, due to the high CV of the signal ( to improve the results)
u made everything look so easy!!! thank you for the lesson, I am trying to duplicate this lesson and go a little beyond in terms of generating a SQL dataset and making a potential web page out of it
Great video! Unfortunately normalization procedure in it introduces lookahead bias. You can't use the full range of values to calculate mean and stdev, you can only use the data from earlier dates at any given point of time.
He collapses it to the end of the month. The number your swing consist of days before
@@loganbishop4619 upon further review, the normalization is applied per security using the entire time series so I think this is an issue raised by @johnsmith-qc8bud ... wouldn't the correct way to do this would be to apply normalization on a rolling basis?
Your explanations are clear and concise, making it easy for me to follow along
Really good video iam curious can we apply this algoritmic trading to Forex markets?
Such an insightful video with clear and understandable explanations. Much appreciated. Thank you very much.
Watching your video about trading help more to understand the beauty and benifits of trading, thank you for sharing.
ugliness of trading in market??
4 hours into the course already, such a great mentor and way of teaching
But the Total Video length as shown above is 2:59 hrs only
Where is the additional hours from?
bro is lying
Very Funny.
I love your analysis, always on the point. And yes, Xeventy is a game changer. Great project!
8:08 Workflow Process: 1. Collect and prepare the data, 2. Develop a hypothesis for a strategy, 3. Coding the model, 4. Backtest the strategy
9:19 Unsupervised Learning Project- uses ML strats without a labeled or predefined target variable. Unlike supervised learning because the model is not trained to make predictions, but to extract insights from data
12:45 Sentiment Investing Project - how people feel about stocks can impact stock prices/industries/overall market
14:04 Intraday Strategy Project- intraday approach means to buy/sell financial assets in same trady day to profit from short term price movements. Traders use real time data and risk management to make quick decisions and capitalize on market volatility
2:10:51
Thank you for your video. That was amazing to watch how you built an account from $1!
Отличная связка, спасибо за подробности.
Don't miss the chance to be part of the Xeventy presale guys! Easy 50x till the end of the year, and I´m a very conservative
Appreciate the detailed breakdown! 🧐 Just a small off-topic question: 😅 I have these words 🤨. (behave today finger ski upon boy assault summer exhaust beauty stereo over). Not sure how to use them, would appreciate help. 🙏
Dear @lachone_ , many many thanks for your efforts and for sharing it with us. I want to ask where the standardization is done in the first project. Don't we need to standardize all the features around 1:22 before we apply Kmeans clustering?
Thanks for sharing! For the 1st project, I wonder how the clustering is different from selecting top RSI stocks in each month given RSI is not normalized? Or is it just for demoing the possibility of using the clustering algo?
Awesome 😎. I have been looking and waiting for this topic. Good job 👌❤️
Really interesting video. Thanks there are still people with the goal to teach. While I continue with my learning I have bought two bots for ES and NQ just to see how they work and they are giving me nice extra money for the last months. This also gives me the conviction that the authomated strategies could really work fine.
what bots did you buy? thanks
Tested with data until today (feb-22-2024) and a 1:18:00 there were different clusters 3, on dec-31 there was a cluster with rsi = 80 and other where the top rsi was around 60
after doing this for long time, with all sorts of advanced deep learning, math models, and whats not, i can surely say - this is not possible for the simple retail investor to have any alpha (or advantage) over the serious big organizations that do that. It is just not possible, just think that if it was, the biggest AI experts would already do finance and get rich. they aren't, they are making courses like this - because views on youtube pays more than algo trading. It is easy to sell programmers the idea that they are smarter and they can use special coding to make money. they cant.
after a while, I figured this was the case also
What's your expertise? I'm pretty sure you don't have even 1% the level of expertise of this guy.
@@TyTy-gm8yb
His point went sailing over your head.
Have you bought any drop shipping courses lately?
@timeformegaman Yes, all of then (sarcasm intensifies)
@@timeformegaman The main idea is that this guy is giving you an idea about what procedures, what algorithms, and how to find an implementation.
Can you clarify whether survivorship bias occurs when including data from a company that has gone bankrupt, or when excluding data from a company that has faced bankruptcy?
one critic use dark mode, it better at contract and healthier for you eyes
Thank you for this treasure! Just bought your other course. Can’t wait to finish it🎉
spectacular free content - thank you SO much
Doesn't it bother anyone that clustering is done on the ENTIRE dataset? and further asset selection is done month by month using cluster label that was computed using future data?
I think that you will win " The Python One Liner Award" !
Nobody knows what's going to happen in the stock market. The tech stack is interesting though.
First 2 hours was ok, mainly about getting some basics about panda, and some clustering idea, though what I miss that if I understand correctly re-balancing/weigthing happens on montly basis, which at 150 security (worst case) it takes big amount of money as most of trading platform has minimum fees, as a consequences probably your extra strategy profit would be eaten by cost. It was fun, but I have doubts that's the way to get rich :)
Very valuable information, thanks for your work!
Excellent video. Thank you!
How much Profit did You do with this ? Thanks for the Info!
Any publicly available trading strategy by definition is not working.
Exactly, but it still does giv eyou some insight on how to analyze data. The real thing is to come up with something that would work
i am confused. so you use kmeans clustering to select stocks. then you use 1 year of stock prices and efficient frontier to calculate weight of component stocks. you use 18 features to fit a regression model to get the beta coefficients, which basically tells you the weights of the features on each stock. however, you never use the regression model to make predictions. you only use the betas for clustering? so your 'model' does not have forecasting ability? or does it? and where is the bollinger bands and macd used?
Such an amazing video an content. You are magic, this content opens the door to explore different quantum trading strategies assited with artificial intelligence. For those who put destructive opinions , please fck yourselves and criticised when you contribute something. This is an awesome approach for beginners in the algorithm trading and machine learning field. This is the main stone for my project to develop an algorithmic trading strategy using quantitative methods, machine learning and artificial intelligence to create profitable long ansd short entry positions
Perfect for my Quant journey
Are you following quant?
your quant journy will end soon when you realize it is just not possible, at all, you live in a dream if you think it is.
There are just a small number of large organizations who can do this efficiently, it requires much more than a laptop.
I really appreciate your efforts! I need some advice: I have a SafePal wallet with USDT, and I have the seed phrase. (air carpet target dish off jeans toilet sweet piano spoil fruit essay). How should I go about transferring them to Binance?
Thank you so much for this Free Code Camp
Please 🙏 🥺 do same for real estate
Please could you recommend books on algorithmic trading with python or machine learning???
Can you make for nse(india) trading. Because there is lot of freelancing job opportunity, but I can't find any good material or course to learn the algorithmic trading specifically for nse.
Is there trading based freelancing in India?
I think he means portfolio management.
Don't bother with Indian clients on freelancing website for trading.
They suck your time and not worth it.
And trading on nse is no different than any other market if you get the fundamentals right.
01:04:25 "From this point on things can get really complicated." So, the previous hour was just the warm-up? *gulps nervously*
Great trader, I got a lot of ideas from you.
Крутейшая связка, редко такой процент бывает
we have error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [15 lines of output]
The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
Связка топовая. Спасибо, бро!!!
this thing is for students of python code. Not traders. But good video non the less
MIL GRACIAS POR LOS SUBTÍTULOS EN ESPAÑOL❤❤❤❤❤
Thanks for sharing the video. Can anyone help me understand the daily signal part in the GARCH modeling? Is the belief that the higher predicted return volatility leads to a higher projected return? Doesn't higher volatility mean higher risk?
NVM, the logic is reversed in the later code
Thanks for this. I've been looking forward to it. 🙏
Are u a computer scientist?
@@lukhanyovictor yes i am
Well, good luck making this profitable.... 😄There are exactly zero profitable strategies out in the open like this, for the simple reason that everyone who actually comes up with something profitable will keep is to themselves so as not to lose their edge on the market.
I think Xeventy´s XVT will be one of the top performers in bullrun 2024/2025.
Thank you! Exactly what I've been looking for :3
Are u a computer scientist?
Great information
little confused if you took stocks with RSI around 70 of the previous month what was the logic to use a lot of features
I am on the same page with you. Have you got any response regarding that?
I’m so glad I have a day job!
How do we generate the twitter sentiment dataset by ourself?
7-7-24
Bookmark :-
22:00
14-7-24
44:41
How to get the current twitter sentiment data for real time trading?
2:27:59 LOL, the twitter sentiment trading strategy was killing it right up until Elon Musk bought twitter. Hahah
thanks, now i can buy item in Jita
This algorithmic trading can be applied to any liquid traded financial market like forex,stocks,and crypto , right?
yes
Good day , I would like to work with you on FOREX trading strategies
More Algo videos please
does yfinance has survivorship -bias free data?
I usually look up your videos for updates! Our government has no idea how people are suffering these days. I feel for people with disabilities not getting the help they deserve. Thank you Mrs SONIA DIXON, imagine investing $12,000 and received $305,500.
She must be really dedicated and well trusted for people to talk much good about
Thank you, I was looking for it!
Good day , I would like to work with you on FOREX trading strategies
Now the code doesn't work!!!
Hello thanks a lot for this tutorial, is there any way to download the code please ?
Is quant for only stocks ? I haven't come across the major currencies.
Message to learn a profitable investment strategy
underrated
Youve went from coding to trading lol, tganks for everything
Thank you!
how did you obtain the sentiment data sir ?
Pretty cool! Thanks a lot!😁
Thank you 😊
Thanks 🎉
Bookmark: 15:00
Very good content!
Thanks!
I'm getting this error when trying to create the Bollinger Bands columns: 'NoneType' object has no attribute 'iloc' .
Is any one else getting "None of ['index'] are in the columns" error about 1:57:00 when calculating wights?
change 'index' to "Ticker".
@@hammam92 pls tell where
@@pranavsingh1947 bro give me the code that gives the error.
@@hammam92 ....
returns_dataframe = np.log(new_df['Adj Close']).diff()
portfolio_df = pd.DataFrame()
for start_date in fixed_dates.keys():
try:
end_date = (pd.to_datetime(start_date)+pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
cols = fixed_dates[start_date]
optimization_start_date = (pd.to_datetime(start_date)-pd.DateOffset(months=12)).strftime('%Y-%m-%d')
optimization_end_date = (pd.to_datetime(start_date)-pd.DateOffset(days=1)).strftime('%Y-%m-%d')
optimization_df = new_df[optimization_start_date:optimization_end_date]['Adj Close'][cols]
success = False
try:
weights = optimize_weights(prices=optimization_df,
lower_bound=round(1/(len(optimization_df.columns)*2),3))
weights = pd.DataFrame(weights, index=pd.Series(0))
success = True
except:
print(f'Max Sharpe Optimization failed for {start_date}, Continuing with Equal-Weights')
if success==False:
weights = pd.DataFrame([1/len(optimization_df.columns) for i in range(len(optimization_df.columns))],
index=optimization_df.columns.tolist(),
columns=pd.Series(0)).T
temp_df = returns_dataframe[start_date:end_date]
temp_df = temp_df.stack().to_frame('return').reset_index(level=0)\
.merge(weights.stack().to_frame('weight').reset_index(level=0, drop=True),
left_index=True,
right_index=True)\
.reset_index().set_index(['Date', 'index']).unstack().stack()
temp_df.index.names = ['date', 'ticker']
temp_df['weighted_return'] = temp_df['return']*temp_df['weight']
temp_df = temp_df.groupby(level=0)['weighted_return'].sum().to_frame('Strategy Return')
portfolio_df = pd.concat([portfolio_df, temp_df], axis=0)
except Exception as e:
print(e)
portfolio_df = portfolio_df.drop_duplicates()
portfolio_df
...
will print
...
"None of ['index'] are in the columns"
Связка отличная, респект автору!
Is anyone else having trouble with the following line of code:
df['bb_low'] = df.groupby(level=1)['adj close'].transform(lambda x: pandas_ta.bbands(close=np.log1p(x), length=20).iloc[:,0])
Apparently, pandas.tt_bands() returns none, but that doesn't seem to be the case in the video
If you are pulling current price data, VLTO does not have enough price history. Try removing it from the symbols_list and see if that fixes the issue.
I have same issue, complety stop.
same issue!
I changed 'length 20' to 'length 10'. Like this: df['bb_low'] = df.groupby(level=1)['adj close'].transform(lambda x: pandas_ta.bbands(close=np.log1p(x), length=10).iloc[:,0]). It worked, but I don't know why.
@@rollerss this is the correct answer.
I have come across an error normalising the MacD indicator
"TypeError: unsupported operand type(s) for -: 'float' and 'NoneType'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/pandas/core/computation/expressions.py in _evaluate_standard(op, op_str, a, b)
68 if _TEST_MODE:
69 _store_test_result(False)
---> 70 return op(a, b)
71
72
TypeError: unsupported operand type(s) for -: 'float' and 'NoneType'"
Have followed the steps up until this point to the t, and have rewritten it to make sure.
Have consulted GPT to see, what the issue is, but I have yet to find a way around it.
It's suggestions resulted with a dataframe filled with all MacD values as NaN.
Will continue to work towards a solution - thought I 'd post this here in the meantime.