@Jamaal67i
In trading, possessing technical analysis skills is not sufficient on its own; discipline and emotional maturity play crucial roles in achieving success. Embracing the mindset of "time in the market vs. timing the market" proves valuable, especially during market fluctuations.
@tansutazegul8297
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!
@sanethehappypill
One thing I like about trading, everyone has an opinion n yet few are making money 💵
@MrSonglu
Thx a lot for the video. A few critical issues to highlight: 1. RSI is not normalized, which becomes the dominant feature during k-means clustering (euclidean based), and the rest features are just noise 2. When joining betas with technical indicators, the shift makes future month NaN which was later imputed by mean; however, by default the future month should be values shifted from previous month 3. Normalization of features are using std and var produced from entire population, and this will induce look-ahead biases during backtest 4. For monthly resample, technical indicators should be calculated directly from monthly prices, instead of resampled from daily technical indicators These issues are really critical btw. The backtest result could be completely different after implementing fixing.
@sweealamak628
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 😅
@xyzplusxyzis2xyz
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.
@BaileyJames-zv2ddd
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
@ml11566
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
@Tiger-ep6hc
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...
@朱厚駿
Not sure if someone else has this issue, but if you can't download Adjust Close form yf, it may be because that yfinance has adjusted the Close already. Here's how I fix the code ( code is in the bottom ). By adding auto_adjust=False, we can ensure yfinance doesn't adjust the close by default. Hope this helps. df = yf.download(tickers=symbols_list, start=start_date, end=end_date, auto_adjust=False).stack() ps. This is my first time commenting, so pls don't mock me or anything :)
@ShadowMind312
A fellow Bulgarian! Congratulations on your success, Lachezar!!
@dirty_haute
It's not about beating the market, its about having an unsolvable problem that you can always use to learn against.
@sophiophile
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)
@BEAUTIFULDIANAFRANCIS
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 .
@InezPhillips
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.
@munivoltarc
I am amazed to see algo videos rarely on YouTube. 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
@AmantisAnalytics
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.
@Nizav-qu5zt
1:57:47 on here i got no error but the portfolio_df returns 'Adj Close' multiple times and not return any of the dataframe/output other than that. any solution?
@TheAsselmeier
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,...
@camille_ann3