Link to article: https://www.fxbeginner.net/best-stock-trading-apps-in-germany/
Finding the best stock market app as a German investor can be a difficult process. Most especially getting the ones that accept German stock traders and would let you trade stocks on your iOS or android device. Don’t worry, we’ve got you covered!
We’ve listed the top best stock investment apps that would let you as a German stock trader, trade Growth stocks, Tech stocks, Small-cap, Mid-cap and Large-cap stocks.
This is the recording of the 1st Cross-Meetup-Group Virtual Event. General slides are found under https://hilpisch.com/virtual_meetup_01.pdf.
Dr. Richard L. Peterson & Anthony Luciani (MarketPsych Indices):
Creating Market Forecasts with News and Social Media Data using Jupyter Notebooks
Dr. Yves Hilpisch (The Python Quants | The AI Machine):
Reinforcement Learning: From Playing Games to Trading Stocks
The event is co-organized by The Python Quants and Refinitiv.
Art of Finding Great Long Term Stocks: https://youtu.be/FcDMdlMOCc4
In this video, we look at what is a better investment, stocks or ETFs.
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Investing Basics Playlist
Investing Books I like:
The Intelligent Investor – https://amzn.to/2PVhfEL
Common Stocks & Uncommon Profits – https://amzn.to/2DAV8h9
Understanding Options – https://amzn.to/2T9gFSp
Little Book of Common Sense Investing – https://amzn.to/2DfFGG2
How to Value Exchange-Traded Funds – https://amzn.to/2PWSkRg
A Great Book on Building Wealth – https://amzn.to/2T8AKZ1
Dale Carnegie – https://amzn.to/2DDAk8w
Effective Speaking – https://amzn.to/2DBncAT
Audible Membership I Use (Audio Books): https://amzn.to/2LCorAY
Equipment I Use:
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Laptop – https://amzn.to/2T4xA8Z
DISCLAIMER: I am not a financial advisor. These videos are for educational purposes only. Investing of any kind involves risk. Your investments are solely your responsibility and we do not provide personalized investment advice. It is crucial that you conduct your own research. I am merely sharing my opinion with no guarantee of gains or losses on investments. Please consult your financial or tax professional prior to making an investment.
This Time Series Analysis (Part-2) in R tutorial will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text.
Link to Time Series Analysis Part-1: https://www.youtube.com/watch?v=gj4L2isnOf8
You can also go through the slides here: https://goo.gl/9GGwHG
A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.
Below topics are explained in this “ Time Series in R Tutorial “ –
1. Introduction to ARIMA model
2. Auto-correlation & partial auto-correlation
3. Use case – Forecast the sales of air-tickets using ARIMA
4. Model validating using Ljung-Box test
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Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth .53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is 8,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Time-Series-Analysis-Y5T3ZEMZZKs&utm_medium=Tutorials&utm_source=youtube
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Build a Artificial Neural Network (ANN) with Long-Short Term Memory (LSTM) to predict value which can be impacted by multiple different features.
In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let’s say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc.
Link to github notebook: https://github.com/vb100/multivariate-lstm/blob/master/LSTM_model_stocks.ipynb
The video has 3 parts:
– Part 01. Data pre-processing (4:11)
| Step 01: Read data.
| Step 02: Data pre-processing (shaping and transformations).
– Part 02. Create a LSTM model and train it. (10:39)
| Step 03: Building-up the LSTM based Neural Network.
| Step 04: Start training.
– Part 03. Make future predictions. (13:50)
| Step 05: Make predictions for future date.
| Step 06: Visualize the predictions.
In this tutorial I used Tensorflow 1.15.0 and Keras 2.3.1
Download data from: https://finance.yahoo.com/quote/GOOG/history (check 1:59 in video).
This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned.
My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset).
Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects. TIme Series forecasting with LSTM is the good choice if you want to manipulate with multiple different data features and see which ones has impact to predictions and which ones do not.
If you are interested how to run Tensorboard on this LSTM Keras model, check this tutorial: https://youtu.be/-9-Hy5dWKLE
Sorry for video quality. There were some unexpected issues with resolution. Video Rating: / 5
In this video tutorial, Philip Mugglestone shows how to predict potential outliers using a one-class Support Vector Machine (SVM) model via the predictive analysis library with HANA 2.0 SPS 01.
One-class SVM is an unsupervised algorithm that learns a decision function for outlier detection: classifying new data as similar or different to the training set. In one-class SVM scenario, f(∙) refers to decision function, and there is no TARGET needed since it is unsupervised.
To access the code snippets used in the video series please visit https://github.com/saphanaacademy/PAL
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Thank you for watching. Video by the SAP HANA Academy. Video Rating: / 5
Please find the jupyter/colab notebook files in the blog slides here: https://dphi.tech/data-science-bootcamp-day-12-session-on-data-preprocessing-exploratory-data-analysis/
This is the 5th session of the bootcamp, the session is designed for people who already have some knowledge of Data Preprocessing & Exploratory Data Analysis.
In the session, the speaker talks about what is Data Preprocessing & Exploratory Data Analysis. In addition to why do we need them & what are their uses, the speaker also talks about different techniques.
The session speaker is Ayon Roy. Ayon is a passionate Data Scientist, a speaker, & a problem solver. He has had multiple stints in field of Data Science through various internships. Ayon is currently pursuing engineering in Computer Science from Guru Gobind Singh Indraprastha University.
We encourage you to ask all your doubts. We’ll be responding to each & every query 🙂 Video Rating: / 5
What if I had 00 to invest in the stock market? What if I was just starting out, all over again, and wanted to invest my first 00 in stocks? Today’s video discusses this very hypothetical question, from a dividend growth investing perspective. Learn how I would personally invest five thousand dollars for passive income and cash flow.
In particular, I would:
(1) Buy three stocks, with roughly equal investments in each. Focus on three sectors: consumer non-cyclical food and beverage, consumer non-cyclical household products, and healthcare (pharmaceutical, medical devices, and household products). I love these sectors for the long-term because everyone needs these items in both good and bad economies.
(2) Leverage dividend reinvestment plans (or DRIPs) for at least two of the three stocks. Open a low-cost brokerage account with dividend reinvestment for the third stock, if a no fee (or low fee) DRIP did not exist.
(3) Make lump sum investments, however stagger my investments one month at a time. It would take three total months to deploy my 00 in the stock market.
(4) Reinvest all dividends, compounding my passive income portfolio over time. (Eventually, I would not reinvest dividends and would live off the cash flow. In the short and medium-term, however, I would reinvest dividends to fuel portfolio growth.)
(5) Make periodic, ongoing investments. Since I would own three stocks, I would allocate capital to the one that has the most favorable valuation at the time of my purchase.
(6) Focus on blue chip stocks with international exposure (including India and Africa).
(7) Strive to purchase stocks with a long history of 7% year-over-year dividend growth.
(8) Focus on stocks with payout ratios in the 50% range.
(9) Focus on stocks with a starting yield in the 3% range (although anywhere from 2.0%-3.25% should do just fine).
Let’s assume for minute that I don’t care about capital appreciation and these three stocks go nowhere over the next 30 years. Let’s also assume I don’t reinvest dividends (although that is not true). From a conservative modeling standpoint, my yield on cost would be 23% after 30 years. Meaning: I would receive ,150 in dividend income each year for the rest of my life. That’s a nice stream of cash flow for my 00 initial investment. And, that’s a really conservative model (in my opinion).
Today’s video highlights the power and beauty of dividend growth investing. Starting with just 00 is a solid foundation and a great way to begin one’s dividend stock journey.
Please note that today’s video builds on my last video about investing your first 00:
If you’re researching stock brokers for dividend growth investing, you may want to check out this recent video:
Disclaimer: I’m not a licensed investment advisor, and today’s video is just for entertainment and fun. This video is NOT investment advice. Please talk to your licensed investment advisor before making any financial decisions. Video Rating: / 5