“Stock Market is a well, deep you go, deep will be your pocket with money.”
- Aadi Jain
About Bull Brokers:-
The company's aim is to predict the unpredictable or future that cannot be ever thought by a Human Mind.
We can make estimate guesses and inform forecasts based on the information we have in the present and the past of any stock.
We will create our own IPO in Stock Market and also, we will work directly from there by same as increasing our IPO value and investing in other IPO companies.
The core fundamental of our company is to learn and predict statistics by Machine Learning and Artificial Intelligence. Although it will take some time to be highly accurate, and when after a certain time it became accurate and more precise, I can guarantee we can change the whole scenario of the Stock Market. “Good things take time”
Believe me, my company can take stocks up and down like a “bull and bear”, with its Insider Information created by a BOT.
Market Demand:-
We all know the Stock Market of a country is its pride and it can feed every stomach in a country, We just need to understand and learn what it is properly.
Its market demand seems to be infinite because the more you earn the more you crave for.
And that’s the whole vision of my company to make customers earn money.
I want even a lower class family with a saving of 100 rupees should invest and make that 100 to 10000 and goes on.
Relevant Products in the market:-
In Indian Market, there are many stockbrokers or equality funds like Mutual Funds, Zerodha, UpStock, Groww, and many more.
But my aim and functionality are far different from theirs, we will only aim at those IPO’s or Stocks which has future and better results in the past, present and, future. Their IPO increase will be directly proportional to our IPO.
The risk game is not our style to work, we work on Technical Analysis.
Our Technical Analysis will be done through Machine Learning and Developed AI from each interpretation or calculation that our software will do.
Target Customers:-
In general, customer means consumer which has to pay for the product but in my company scenario customer is an earner which will earn through our company.
While humans remain a big part of the trading equation, AI plays an increasingly significant role. According to a recent study by U.K. research firm Coalition, electronic trades account for almost 45 percent of revenues in cash equities trading. And while hedge funds are more reluctant when it comes to automation, many of them use AI-powered analysis to get investment ideas and build portfolios.
National Currencies and Cryptocurrency Datasets:-
1. Historical Stock Market Dataset:- This dataset includes the historical daily prices and volume information for US stocks and ETFs trading on NASDAQ, NYSE, and NYSE MKT. The data was last updated on November 10th, 2017 and the files are all in CSV format.
2. Istanbul Stock Exchange:- With data are taken from imkb.gov.tr and finance.yahoo.com, this dataset was created to test predictive algorithms. The dataset includes info from the Istanbul stock exchange national 100 indexes, S&P 500, and MSCI. Furthermore, it includes the stock market return indexes of Brazil, Germany, Japan, and the UK.
3. News and Stock Data:- Originally prepared for deep learning and NLP class, this dataset was meant to be used for a binary classification task. News and Stock Data includes historical news headlines crawled from Reddit’s world news subreddit from June 8th, 2008 to July 1st, 2016. Additionally, it includes Dow Jones Industrial Average data from August 8th, 2008 to July 1st, 2016.
4. Stock Market from a High Level:- This dataset includes historical stock market data from Dow Jones, NASDAQ, and S&P 500. The data is in a CSV file and includes information from 1977 to 2017.
5. Stock Market Turnover Ratio:-This information comes from the Federal Reserve Bank of St. Louis. The dataset contains data about the total value of shares traded during certain time periods versus the average market capitalization for that period.
6. Uniqlo Stock Price Prediction:- The previous items on this list featured general stock market data. However, this dataset focuses solely on a single company, Uniqlo. One of the largest clothing retailers in Japan, Uniqlo has been around for over five decades. This dataset includes the stock information for the company from 2012 to 2016.
7. Coin Market Cap Dataset:- With the rise of cryptocurrency around the world, more and more people are looking to invest in it. Coin Market Cap is a market analysis website that provides information on thousands of cryptocurrencies. This dataset includes information taken from Coin Market Cap with the following columns: date, symbol, open, high, low, close, volume, and market cap.
8. Currency Exchange Rates:-This dataset includes information about the daily currency exchange rates reported to the International Monetary Fund. Furthermore, the data contains info on 51 currencies from January 1st, 1995 to November 4th, 2018.
9. Daily Prices for All Cryptocurrencies:- This is a large dataset including historical price data for all cryptocurrencies on the market. The data ranges from April 28th, 2013 to November 30th, 2018. Furthermore, it includes the following information: coin names, date, rank, close-ratio, and spread.
10. Free Forex Data:- From Histdata.com, this dataset resource provides free Forex data for multiple currencies. The data is available for the following applications/platforms: General ASCII, Meta Stock, Meta Trader, Microsoft Excel, and Ninja Trader.
Challenges:-
The biggest challenge for our company is the “TRUST Factor” with our customers and how we can make them rely on us. Also, we have to make sure that each and every customer connected with us get a proper response from our side because it’s a matter of their money which is valuable for everyone and we have to make our structures and functionality such that even a single penny of every customer is worth with us.
There is also a problem to make our company grow in its initial phases and funding for our company.
It can only be possible step by step and we will grow slowly by initial fundings and then timely we can grow big.
Risk Analysis:-
Miscalculated risk management can subsequently affect trading companies and individual traders alike. The risk analysis and management is the method through which investors and traders identify, analyze, and measure decisions related to trading. Risk analysis is important because traders are losing millions from years, without any prior knowledge in risk analysis and management. Leveraging data science in predicting subjective risks and taking actions according to future market trends help in making better decisions related to trading. And an important aspect of leveraging risk analysis is to generate a report on the creditworthiness of the customer.
The financial institution's store information about various stakeholders. And the stored information may get affected by originated risks from competitors, markets, trends. With a predictive analytical model for risk analysis, the companies can build advanced strategies that win clients' and customer's trust and increase security for the company.
Data-related Issues:-
The development of a reliable ML-system depends not only on excellent coding — the quality and quantity of the training data play a pivotal role.
1. Lack of Suitable Data
Representative datasets are required to reasonably capture the relationship that may exist between input and output features. If there isn’t enough data, there are options like collecting more data or using external data sources. Another solution is using data augmentation methods to increase the sample size artificially, but these methods often decrease overall quality.
2. Complex Extract, Transform, Load Procedures
One more requirement is that the data must be easy to work with — it must be well-organized, and stored in the proper format in a single place (database, warehouse, cloud). Since this is not the case sometimes, some preparatory activities (e.g., ETL processes) are needed.
3. Unstructured Data Processing
The next cost-effective factor is whether or not the data is structured. It is easier (consequently cheaper) to work with well-structured data. In some cases, data is subject to cleaning, tidying, and conversion to semi-structured data. Moreover, it provides for working with missing, extreme, and unexpected values, dealing with outliers, obvious errors, and so on. In practice, most companies manage unstructured (e.g. free-form text notes), or semi-structured (e.g. XML, email) data. There is a whole class of ML-algorithms created to make use of this kind of data, and typically such projects cost more.
Performance-related Issues:-
The sufficient algorithm performance is another key cost-effective factor, as often a high-quality algorithm requires several rounds of tuning sessions.
- Accuracy Rate Varies
It’s worth noting that the performance rate varies according to the client’s business objective and the cost of wrong predictions. A broker would take advantage of the system that produces 55% of correct predictions ensuring profits. However, a 90.9% accurate system aimed at diagnosing disease, with treatment being lethal to false-positive patients, is by far not satisfactory.
2. Performance of Processing Algorithms
As it is almost impossible to train an accurate ML model from the first try, it is essential to improve quality over many iterations. Accuracy mostly depends on the data and features extracted by the algorithms. If the data is hard to process, feature extraction may take more time than model training, and algorithm performance becomes a bottleneck. To eliminate this issue, our engineers horizontally scale processing algorithms in the cloud. While processing complex data like images or videos, it is critical to use high-performance computer systems powered by expensive hardware. So if the customer wants to accelerate the development and data processing, he pays the server and hardware rent.
How my company will make a Profit and Grow:-
Yes, now the important question arrives that how my company makes a profit, The fundamental of my company profit is not through our customers but by through MNC’s and Multinational companies or our Indian SENSEX and NIFTY.
What we will do is by making a profit for our customers that they will invest through our company and the money they had invested in our company we will then make it invest to other’s IPO and then bit by bit we can take our IPO to a big level.
After each successive growth of our company, our IPO value will increase and from there onwards we can make suitable profits from other equity funds by investing in our IPO.
Milestone and Timeline:-
1. Discovery & Analysis Phase {By 2021}
The purpose of this phase is to conduct a feasibility study and set business and project objectives.
The work on a project starts with analyzing the customer’s business processes, data assets, and current metrics. At this stage, we define success factors (expected metrics improvements), applicable technological stack, timeline, and budget, and reflects them in the corresponding documentation.
2. Data Collection and Preparation Phase {By mid-2022}
The biggest advantage of any AI-based application is the ability to process complex data and extract valuable insights from it. The more data it processes, the more accurate it becomes. The results are strictly dependent on the data quality — if the algorithms or ML models take mediocre data as input, they output disappointing results.
At this phase, we analyze the data the customer provides, evaluate its quality, and decide if it needs additional processing or cleansing. If the customer cannot provide any sample data, engineers collect data from open data sources and the Internet.
3. Prototype Implementation and Evaluation Phase {By end- 2022}
Prototyping is a great technique that allows software professionals to validate requirements and design choices. Prototypes are quick and cheap to produce and flexible to adjust. The risks and costs associated with software implementation are significantly reduced, as the requirements are well-discussed before the development begins.
4. Minimum Viable Product (MVP) {By 2023}
An MVP is a real product with a set of functional features developed based on the prototype findings. The MVP relies on the client’s actual data and is exposed to a small group of real customers as a simplified version of the ultimate product solution.
The feedback is very relevant, as it is a less expensive way to modify the system at this stage than when it is fully developed. According to our experience, it is hardly possible to build an accurate machine learning model from the first try, as always are some details that were not taken into account.
5. Product Release {By mid- 2024}
At the last stage, the product with a complete set of predefined features is developed and then launched into the market. The preceding steps emphasize the requirements elicitation and validation — therefore, the end product is made with minimal risks. The cost of this phase is usually estimated during the previous stages. Also, if the project is successful, the development team helps add extra features to the solution and customize it for specific customer needs.
6. Maintenance and Support
After the successful project release, it is essential to maintain primary metrics and collect feedback. Situation awareness and timely response grants the ability to increase revenue and deliver expectational results. Often as the amounts of data grow, it is crucial to benchmark algorithm performance and quality of the ML model results.
Conclusion:-
I know it all sounds like a little bit of a money-minded start-up but my friend the truth is today without money you cannot live your life.
“I know money cannot buy happiness but neither poverty can buy.”
Today we all are living in a materialistic world that we all need money either it is for our personal, family, food, fun, anything, etc. There is not a single thing you can get for free, even “LOVE” also demands money as “No lady will love a man who cannot afford her a dress in real life rather in overhyped movies.”
“Make money enter your pocket and make yourself a Powerful King or Queen.”
-Aadi Jain
To know about the Stock Market:- Share Market का सम्पूर्ण ज्ञान | Nifty | Share Market | Dr Vivek Bindra
