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IF you write the same question on Google answer will come to Rakesh Jhunjhunwala, Vijay Kedia, Porinju Veliyath,etc. while someone wrote on Quora also Radhakishan Damani, Ramesh Damani, and Raamdeo Agrawal all are wrong because they are investors not traders.Below are the top successful stock traders in India which I picked from reading many stories of traders.Tasneem MithaiwalaA single mother of two daughters who is pursuing professional courses, Tasneem picked up trading because that was the only thing she could do by sitting at home and taking care of her ailing mother.The first woman successful trader in India is an example of what a strong mind can achieve. An Arts student who doesn’t have any financial knowledge and financial background, Tasneem today trade options successively for a living.While Tasneem looking for opportunities to work from home one of her cousin said that he was doing trading in equity markets and he guided her explore equity trading opportunities. At that time her cousin would give her trades or leads and she does buy and sell according to his suggestions.She was an art student so she won't have any financial background despite that her discipline and focus on trading given more success in trading. She started her ‘trading’ journey with a capital of Rs 5 lakh and by the end of 8 months, She found that the capital had come down to Rs 1.75 lakh. After that, she realizes that for trading need financial knowledge and she understates stock traders' behavior and become successful traders.More than the strategy she trades, Tasneem’s story is about mental strength and discipline and is an inspiration to all those traders who are unable to find their moto.What did we learn from Tasneem Mithaiwala?Never depends blindly on someone else and before doing trading get some financial knowledge and trading knowledge.2. Kirubakaran RajendranKirubakaran Rajendran from Chennai developed Trading Bots which we can say automated trading system which generates trades automatically using a given set of rules to do his trading. His current lifestyle was achieved after years of hard work and learning from every possible mistake one can do and probably more.His work is over by 10 am, after which he sits and reads a journal or a book or watches the TV series – Billions. At the end of the day, the trader goes to the gym. Fridays are compulsorily meant for friends, and he takes about 2-3 road trips with his family and friends in a year.Kirubakaran Rajendran started his job in Infosys with a salary of Rs 15000 per month. A day before Infosys' results he had built a position of Rs. 1.5 lakh in Infosys strangles because of this he could not sleep that day with such a huge position. The next day, results were announced before market hours and when Infosys opened that morning, the value of his strangles had come down to Rs 20,000. He had lost more than 3 months of his salary in a single day. Lessons are never do trades in news base stocks because next day anything can happen we don’t know.This was a big lesson for him in trading career and he understands that rather than running away from the market he started reading and researching what makes a successful trader click. This is when he decided to move from news-based trade to rule-based trade.The second mistake he made in trading he borrowed money to trade in the market. Which most of the new traders doing currently.Advice from Kirubakaran Rajendran for new TradersFor an aspiring trader, he would say always have a clear set of simple rules. Do not trade if you cannot explain in two lines. The more you complicate, the less likely the strategy would work.People generally go for strategies that give higher returns without looking at the drawdowns. For example, if a strategy gives a return of 40 percent per annum with a drawdown of 15 percent, it is certainly better than a strategy giving 90 percent returns but having drawdowns of 40-45 percent. Chose the ones where risk is lower.3. Abhinand BasavarajAbhinand Basavaraj, 29, hails from Mysuru and is almost a hermit in the trading world. A self-taught trader his first trade was at the age of 19. But it was only after seven years of agonising work that he found his mojo.He entered into the stock market at the age of 19 with his cousin who was taken the franchise of broking. He borrowed money from the father to open a Demat account. He started trading the same way other trades do front line stocks like Reliance Industries and HDFC Bank and at that time he was not following any trading strategies and he made a loss. He was one of the traders who lost money in Satyam scam. After this, he turned stock to options for trading and he didn’t tell his father also that he was lost his money. For financial support, he took help from a brother who was a banker but again he made a huge loss and he decided to stop trading for some time. After some time he started learning about technical analysis and understand how trading works and financial knowledge. He borrowed money also for trading with the high cost and he decided to tell all stories to his father. At that time his father one of the major backbone to give the confidence to go forward.After that, he joined his brother real estate startup and continue trading with business. He trades in the Nifty option and specifically more on buy-side. He using many technical strategies like trend line or MACD, etc.Persistence, self-confidence and family support all played an important part in his success. Though he still claims to be an evolving trader, few can claim to have achieved the kind of success that Basavaraj has.Lessons from Abhinand BasavarajFirst learn about stock market than create own strategy and never take loan for trading plus always shared to someone who knows you better.4.Naresh NambisanNambisan is from the old school of trading where he prefers putting in manual effort rather than automating it. Obsessed with charts, he can look at charts for hours without getting bored. And, when he is not looking at them, he likes to read and travel.His wife was a software engineer in Bangalore and he had experience in both India and Gulf so he transferred from the gulf to India for the job. When he came to India he found that getting jobs in India was difficult and Gulf experience not worked in India. During this time he connected with his friends and he came to know about technical analysis so for that he Google a about technical analysis. For more knowledge, he joined the Yahoo group also for technical analysis but on that group, only trading calls are coming to no knowledge he getting from that. In 2008 he was also doing trading and lost 40000 Rs. So from that, he learned Never depends on other no doubt he earned a good amount from that group so to become independent traders he taking out information about technical analysis form Google and YouTube. After some time he understands price chart strategy and he liked it. Today also he working on the same strategy for trading.Currently, Naresh Nambisan living in the village and doing trading.Advice from Naresh NambisanKeep your trading as simple as possible. Do not complicate the charts with too many indicators. Try to eliminate what is not needed. Looking at every data point like open interest, FII and DII buying and selling data do not work.Test the indicators yourself. Improve yourself one step at a time. Take up your system and sharpen it by looking inside the system and correcting it rather than jumping to a new one if the earlier one did not work on a few occasions. Trust your chart. Baaki sab is bakwaas (rest all is rubbish).Lessons from Naresh NambisanNever depends on others and create own strategy5. Madan KumarMadan Kumar born and brought up in a middle-class family when he was in school his teacher insulted him because his family not paid fees. Madan Kumar's mother always an inspiration to his mother knows only education can remove poverty. Madan Kumar completed B.Tech from Madras and moved to the US for post-graduation. He was also got a job in the US which was work from home so he gets a good time to learn about trading.Madan Kumar one of the good things I liked he read many books for the market and then be gone for trading.“He tells his friends who want to be traders that if they do not have a supportive spouse do not be a trader,” he says.Full-time trader and a successful one at that, Madan Kumar has a rags-to-riches tale to tell. And he credits his success to his mother and wife for each of them had a large part to play. His mother because she instilled an inclination towards education and his wife as she supported his family financially when he returned to India from the US.Edit: Thank you so much for 1k UpvotesHariom Panchal CWMIf you like my answer please follow my Quora account for More interesting questions answers like above which will motivate me to give more answers.***********THANKS FOR READING***********If you have any other questions or queries you can send me a message I will happy to reply.Sources: Money control, Google Image

This is how they explain it on their blogPalantir: so what is it you guys do? | PalantirBy Kevin SimlerI often ask candidates if they’re familiar with what we do at Palantir. Most people think they are. “Oh, you’re that data viz. company,” or, worse, “You guys do data mining, right?” At least they’ve heard of us and at least they’re on the right track, but I cringe anyway. We aren’t just a “data visualization” company and we don’t do “data mining.” It’s almost impossible to convey the scope and complexity of what we do in a few short minutes—or to do so without taking the conversation to an eye-glazing level of abstraction.The following is my attempt at describing what we do at a high level without oversimplifying. I hope that after reading this a candidate will ‘get’ what we’re about, or at least understand enough not to apply tiny labels to our expansive vision.The problem: implementing analysisAt Palantir we specialize in analysis.Yes, that’s painfully abstract, and I’ll get to it in a second.In real-world terms, we are building a software platform that enables people to take whatever data is relevant to them and understand it more easily and thoroughly than ever before, using concepts that they already understand. And we are applying this vision, at first, to solving problems in the finance sector and the government intelligence community.The first important thing to note is that we don’t actually do the analysis ourselves. We don’t devise winning trading strategies and we don’t catch terrorists. We write software that enables other people to pull off these feats. These people, experts in their respective fields, are called analysts.So what exactly do analysts do? What is analysis?Analysis is everything necessary to extract insight from information.Let’s break that down a bit.Information is easy: It’s data. It lives in a relational database or as files indexed on a hard drive, and you can easily run queries against it. It comes in two forms, structured and unstructured. And there is a lot of it in the modern world – too much, actually, for current tools to make sense of.Insight is trickier. Insight is something only a person can generate, and understanding this is critical for any organization that wants to do analysis right. Thus the challenge of data analysis is how to bring vast amounts of information into productive contact with human intelligence. In other words, the challenge is how to enable the analyst.From the analyst’s perspective there are five essential features of an analysis platform:First, and most important, the analyst should be in control. In other words, the primary way of interacting with an analysis tool should be human-driven queries. While automated approaches can complement a human-driven approach, there simply is no substitute for human intelligence. Unless you put a person behind the wheel, the system can never be flexible or creative enough to uncover truly original insight. Artificial Intelligence just isn’t there yet.Ability to summarize large data sets. Some of this is what has traditionally been called data mining: the largely automated approach—using machine learning or other statistical techniques—of processing lots of data at once and extracting nuggets that capture something interesting about the data. Unlike Palantir, traditional approaches have focused almost exclusively on this aspect of analysis.Ability to visualize large data sets. Here the analyst wants interesting and informative ways of viewing data graphically, to make it easier for him to digest. The analyst wants more than just a summary of the data; he wants a nuanced view of what’s going oninside these data sets: What’s the overall shape of the distribution? What are the outliers? What are important structures within the data?Ability to iterate rapidly. This means enabling the analyst to ask a question, get the answer, and then quickly ask either a variant on the initial question or a follow-up question that depends on the answer to the initial question. This rapid, iterative process allows the analyst to quickly test out hypotheses and develop theories about what’s going on in the data, and by extension to discover what’s going on in the world.Ability to collaborate with other analysts. Getting a handle on a terabyte of data, especially when it comprises multiple data types, is definitely more than a one-person job. Any organization that’s serious about understanding the world needs a team of analysts that can work together as more than the sum of its parts. This requires the ability for one analyst to effortlessly share the results of his analysis with his colleagues.The Palantir approachThat’s what analysis looks like to the analyst, or rather what it should look like in an ideal world. (Current tools fall far short of this vision.) So what do we do at Palantir in order to make analysis this smooth and easy?You could say that we help summarize large data sets, in the sense that we have to provide the analyst with a rich library of techniques and algorithms. You could also say that we do visualization, in the sense that we have to provide the analyst with a set of interesting and informative ways of visualizing their data. We do both of these things, and we have to be creative and solve hard problems in order to add value in these areas. But we do a lot more than that.Probably the most central hard problem that we address in trying to enable the analyst isdata modeling, the process of figuring out what data types are relevant to a domain, defining what they represent in the world, and deciding how to represent them in the system. At Palantir we make sure our data model (ontology) is both flexible and dynamic, and that it mirrors the concepts people naturally use when reasoning about the domain. This is no small challenge, but we’re already making it a reality. In finance our basic data types include financial instruments, dates, portfolios, indices, and strategies—the same things that financial researchers think about, talk about, and reason with. In the intelligence product our basic data types include people, places, and events (all with associated properties), which is exactly the way we all represent the world in our minds.Data modeling, data summarization, and data visualization are the core disciplines for approaching large data sets. Human-driven queries, rapid iteration, and collaboration are multipliers, taking the power unlocked by the core disciplines to the next level. When these pieces are brought together in a coherent system, the result is in an analysis platform both very generic and very powerful.This is what we mean when we say that we’re changing the way people approach data. Welcome to the future of analysis.

A hueristic is a set of rules, which run somewhat unconsciously.We are always gathering information, and that information helps us form a picture of the world around us. Different pieces of information mean different things to different people.If your life consists of business, and markets, your gamble on risky stocks is not so much a gamble, as an educated risk - your hueristic response is more accurate than a mere guess. An analysis of sorts has taken place, though not necessarily in excel or on paper.Yes, some wealthy people will invest on a whim, but don't mistake that for a blind gamble.