Swinging For The Fenches

  • Ruban Phukan

Get ready for the Future of Trading!

Welcome to Researchfin.ai

The wait is finally over, and we are now opening doors to our latest and most remarkable improvement of the Researchfin.ai app. A lot has changed since we announced the private beta in December 2020. We have received some incredible early feedback from advisors and early testers, which helped us improve the app significantly. A huge thanks to them that we are now ready to provide access to a much-improved version of the app to everyone who has signed up for beta access. You will be receiving the details soon - please lookout for an email from support@researchfin.ai and add this email address to your contact to make sure that your email provider doesn’t accidentally miscategorize this as either promotion or spam.

We want to thank everyone who has signed up for the beta access and has patiently waited for the access. Your trust and support motivate us, and we are committed to keep improving our app to continue to deliver you the best value. We eagerly await all feedback and suggestions. So please have them coming to hello@researchfin.ai.

If you already haven't signed up on your website, no worries. You can download the app directly from App Store or Play Store and signup directly from there.

Why we created Researchfin.ai?

We started Researchfin.ai to help ourselves become better traders. When we began trading, we went through years of significant learning curve and challenges that every trader has to go through. The big challenges are:

· How to scan for the best trade-setups from the thousands of stocks regularly - i.e., identify which stocks to trade and the best entry points for those stocks?

· How to identify the right exit points, i.e., the proper profit-taking and stop-loss targets for every trade?

· How to analyze the true reward and risk probabilities for a trade setup for a given stock in a certain market condition?

· Following a systematic approach to trading consistently without letting emotions or biases interfere.

Expert traders have developed the market experience needed to get the intuition to navigate around those challenges for many years and even decades. While there is no shortcut to learning and experience in trading as with any other business domain, but we wanted to explore whether Artificial Intelligence (AI) can help accelerate that learning to provide support to traders to make better and more informed trading decisions. We are pleased and proud of the product that our team has created - after two years of extensive research, rigorous development, and countless testing - that solves the above challenges.

Our team comes with strong experience across financial and technology domains. Our financial domain experience comes from working with organizations like the New York Stock Exchange (NYSE), JPMorgan Chase, Charles Schwab, Aditya Birla Sun Life Mutual Fund, and GE Capital. We have technology domain experience working with Yahoo, IBM, Progress Software, Naspers Group, Boeing, among others.

We are also serial entrepreneurs who co-founded DataRPM, a pioneer in Enterprise AI, where we solved tough problems of the Industrial domain. At DataRPM, we created an AI platform that could automatically learn the patterns of "normal behavior" of complex industrial machines from massive time-series data, detected anomalies in them, and helped the field engineers prevent catastrophic failures from happening. It helped the domain experts automatically scan for micro patterns and glitches that indicated early on a high probability of a machine failure in the future. This was otherwise very difficult to detect from the data manually. Big industrial companies across the globe used DataRPM's AI for predictive maintenance of large industrial machinery, manufacturing plants, connected cars, jet engines, among many other such use cases. At Researchfin.ai, we used this experience of building a product for the industrial time-series analysis using AI and applied it to building a product for financial time-series analysis using AI.

We have combined our decades of finance and technology expertise to create an incredible financial AI product that can learn patterns and anomalies of the stock market to help traders accelerate their research for the best trade setups in a systematic and scalable manner. We are confident that this will help both new traders and experienced traders augment and complement their research. We know that it has improved our trading manifold.

Does that mean that we have created an AI that can predict the stock market?

Anyone who has traded the stock market for some time knows that the market is inherently not predictable – anything can happen in the market at any time. Anyone who claims to predict the market either doesn’t understand the market well enough or hasn’t been in the market long enough.

However, certain patterns repeat in the market, and stocks tend to show similar characteristics under similar regimes. Understanding the expected behavior of stocks in different regimes can help one trade successfully even in the unpredictable environment of the stock market. There regimes at the macro-level like the broader market characteristics and macro-economic data, but more importantly there are micro-level regimes that are unique to a specific group of stocks – by sector, industry group, and market cap - taking price actions, fundamental factors, and corporate actions along with external factors like news, social media action, etc. It is the analysis of the micro-level regimes combined with macro-level regimes that hold the key to successful trading.

While analyzing the macro regimes is relatively easier and can be done manually but identifying and analyzing the characteristics of the micro regimes to determine the normal behavior of stocks and detecting anomalies are all tough problems - especially when done manually. It requires analysis of a huge amount of historical data, for thousands of stocks, from multiple data sources, which results in billions of data points causing serious scalability challenges! Finding micro patterns in such a massive set of data is just like finding needles in a haystack. But this is the kind of analysis problem where AI excels and scales up really well.

Our AI Scanner processes massive data, identifies micro-patterns, detects anomalies, and does it in an incredibly short time, daily. From there, it identifies the best stocks creating opportunities to trade on a daily basis, confirms the opportunities by mapping to breakouts from common chart patterns and some of our proprietary chart patterns. It scans for significant breakouts from these chart patterns and those close-to-break out from their respective pivotal points. To determine the significance of the breakout, the AI analyzes the historical performance of the same or similar stocks after the breakout from the same patterns under the same regime (micro and macro). The scanner filters in only those breakouts that provide the best risk-reward scenario for traders based on this analysis. For every breakout, the app shows the expectation of the return (Average), the ideal profit-taking target (Up) or the stop-loss target (Down), and the probabilities of the stock reaching those targets within a definite time. It provides traders with all the necessary decision-making points to identify whether that trade fits their trading style and risk tolerance.

It is important to understand how statistical probabilities work. While one or two trades may not generate a positive return but when traded systematically with risk management and consistently over time, the results will start to converge towards the expected values. And experienced traders know this well. Even if your trades are correct, say only 50% of the time, but as long as the losses are much smaller than the wins, one will always be successful. We have written about this in detail before. Researchfin.ai provides traders with all the necessary information needed to manage the risk and rewards of their trades well.

[ Please note that Researchfin.ai is not an investment advisor or financial advisor. As such, the breakouts and close-to-breakouts opportunities identified by the AI should not be considered as investment advice. Also, past performance in the market is not indicative of future results. So, one should exercise caution and use judgment when making any trades. Also, it is always best to consult an investment/financial or tax advisor. ]

Can AI replace the human trader?

Not really. Quite on the contrary, the goal of AI is to augment the research and decision-making process of the traders by automating the complex and time-consuming analysis and the learning process that traders otherwise must painfully endure and, worse, one that they cannot scale beyond a certain point. AI helps traders scale their analysis to a whole new level. But, when it comes to trade selection and execution, many factors come into play, like personal experience, preferences, trading style, risk tolerance, and other environmental factors unique to every trader. So, a trader needs to manage their trades before and after entering into any trade. The AI can only provide insights for decision-making, but ultimately it is the trader’s decision to make.

What is our goal with Researchfin.ai?

Democratizing AI-enabled trading and creating an even trading field for retail traders!

For a long time, advanced technology like AI has been exclusively used by hedge funds and institutions as a technology edge. Most retail traders had to rely solely on their manual analysis skills. It created an uneven trading field of machine vs. human. We want to change that with Researchfin.ai by enabling the machine (AI) and human (retail traders) to work together for achieving greater success with trading.

Trading in the modern day is increasingly becoming a technology-defined business, and AI is becoming an indispensable part of this technology. Anyone trading without AI will be at a serious disadvantage in the future. Trading is a business, and there are many examples in history of once-successful businesses that have disappeared because of their inability to change and adapt to new technologies early on. Our goal with Researchfin.ai is to make advanced AI technology easily accessible and affordable to retail traders to future-proof their trading business.

Another big technology shift is the migration from desktop to mobile. Research shows that there is a big growth in investment app installs on mobile in recent years. It is only going to grow in the coming years. So it should be possible to do research and analysis for trade setups on the mobile device as it gives one the ability to do that anytime and anywhere on the move without being restricted to a static workstation. We have taken a mobile-first approach for our app to ensure that we make everyone ready for this new future of mobile trading and research.

Welcome to Researchfin.ai. This launch is a small step for us but a giant leap for retail trading, or so we hope! But we want to hear from you if we have met this goal and how we can make this even better for you. Would you please let us know your thoughts?

If you are hearing about us the first time, you can learn more at: https://researchfin.ai

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The blog by Researchfin Team on Swing Investing, Risk Management, Active Portfolio Management, Market Analysis, Technology, AI and Machine Learning, and our wonderful journey in helping Retail Investors take back control of their portfolios