How Rule-Based Trading Turned ₹25L Into ₹10CR Without Guesswork : Arjun Tandon EXPLAINS | FWS 98

What is Your Personal Capital Right Now?

Introduction to Trading and Personal Capital

• The conversation begins with a question about personal capital, specifically referencing an initial investment of $25,000.

• The speaker mentions trading activities currently taking place while discussing the topic.

Emotional Detachment in Trading

• A trader's emotional detachment from losing trades is emphasized; one should not become emotionally attached to stocks.

• The importance of maintaining love for family over stocks is highlighted.

Can Full-Time Professionals Succeed in Trading?

Starting Points for New Traders

• It’s advised that full-time professionals should avoid starting with options trading and instead focus on simpler tools.

• Emphasis on using AI tools like ChatGPT to enhance trading skills is discussed.

Algorithmic Trading Insights

Understanding Algorithmic Trading

• The speaker identifies as an algorithmic trader utilizing Python and artificial intelligence for trading decisions.

• Discussion on how algorithms can operate without constant human oversight, allowing traders to be less present at screens.

Risk vs. Returns

• Returns are directly related to risk; higher risks typically lead to higher potential returns.

• Focus on generating profits with minimal risk is presented as a key strategy for retail investors.

Managing Drawdowns and Risks

Strategies for Minimizing Losses

• A strategy involving small drawdowns (3%-4%) while aiming for consistent returns (around 25%) is outlined.

• The philosophy behind this approach emphasizes limiting maximum losses while maximizing upside potential.

Impact of Market Events on Day Trading

Navigating Market Volatility

• Day trading mitigates overnight gaps caused by market news or events, reducing exposure to sudden market shifts.

• Systematic trading strategies help manage risks associated with unpredictable market movements effectively.

The Importance of Data in Systematic Trading

Building Reliable Systems

• Systematic trading relies heavily on historical data analysis, covering various market events over time.

• Confidence in strategies stems from extensive backtesting against past data, minimizing fear during live trades.

Personal Journey into Trading

Transitioning into Full-Time Trading

• The speaker shares their journey beginning in 2017 after quitting their job to pursue trading full-time.

Recommended Resources for Learning

• Suggested reading includes books focused on options trading, particularly one by Sheldon Natenberg considered essential ("the Bible of Options Trading").

Additional Resources

• Mention of "Tasty Trade," a website offering valuable content and resources related to options and futures trading.

Online Learning and Trading Insights

Recommendations for Learning Resources

• The speaker emphasizes the availability of free online resources, particularly on YouTube, where viewers can access entire series related to trading.

• They express no affiliation with these resources but genuinely recommend them based on personal learning experiences.

Journey into Options Trading

• The speaker shares their initial foray into options trading after leaving a job, motivated by the potential for quick financial gains.

• In the first month, they reportedly made around ₹1 lakh, which fueled optimism about rapid growth in earnings.

Lessons from Losses

• A significant loss occurred in the second month when market movements went against their positions, leading to a realization about risk management.

• The speaker stresses the importance of taking losses early to learn valuable lessons and mentions that many beginners overlook stop-loss strategies.

Understanding Market Dynamics

• There is a distinction between theoretical knowledge from books and practical application in trading; not everyone successfully implements learned strategies.

• The speaker notes that if everyone used stop-loss orders effectively, statistics showing high loss rates among traders would differ significantly.

Capital Growth Over Time

• Reflecting on their journey since starting with ₹25 lakhs in 2017, they now manage approximately ₹10 crores through algorithmic trading.

• This growth illustrates how long-term commitment and strategic adjustments can lead to substantial capital increases over time.

Risk Management Strategies

Awareness of Potential Losses

• The speaker discusses understanding potential losses better today compared to earlier experiences when they were unaware of how much they could lose.

• They mention having a hard stop-loss strategy that limits potential losses to about 1% of their capital during adverse market conditions.

Importance of Exit Strategies

• Emphasizing the need for exit strategies, they advise against holding onto losing positions out of hope or emotional attachment.

• Blind hope is identified as detrimental in trading; instead, traders should act decisively when losses occur without waiting unnecessarily.

Cautionary Tales from Market History

Risks Associated with Reputable Stocks

• The discussion includes cautionary tales like Yes Bank's decline from ₹350 to below ₹20, illustrating how even reputable stocks can fail dramatically.

• The speaker warns that one bad trade can wipe out an entire capital investment and urges traders not to rely solely on hope without a solid exit plan.

Demat Accounts and Trading Challenges

Overview of Demat Accounts in India

• There are approximately 20 crore demat accounts in India, with around 3 to 4 crore unique holders. Many of these accounts are duplicates.

• SEBI reports that a significant percentage (93%) of individuals engaging in F&O trading experience negative returns, with only 1-2% actually making money.

The Reality of Part-Time Trading

• It is possible for someone with a full-time job to succeed as a trader, but the majority do not achieve this level of success.

• Among those who trade part-time while holding jobs, very few make consistent profits compared to full-time traders.

Emotional Factors in Trading

• Trading is one of the hardest professions due to emotional influences; greed and fear play significant roles when making trades.

• When stocks move against traders' expectations, emotions can lead them to exit positions prematurely out of fear of losing money.

Costs Associated with Trading

• Traders should open their demat accounts and review the charges incurred from trading activities, including brokerage fees and taxes.

• Most losses attributed to trading are actually due to high transaction costs rather than poor trading decisions.

Importance of Emotional Control

• Despite understanding theoretical concepts like stop-loss orders and risk-reward ratios, many traders struggle to apply these principles consistently due to emotional responses.

• Human emotions often lead traders astray; it only takes one mistake to incur significant financial loss.

Caution Against Reckless Trading Practices

• Traders should avoid risky practices such as buying penny options or engaging in speculative trades without proper knowledge.

• Copying another trader's strategies without understanding can lead to losses; conviction in one's own research is crucial for successful trading.

Understanding Trading Systems and Strategies

The Odds of Making Money in Trading

• The speaker discusses the odds of making money in their trading system, estimating a 60% success rate, meaning profits can be expected on about six out of ten days.

• Emphasizes that even with a good system, individual experiences may vary; one might face consecutive losses before hitting profitable days.

• Highlights the importance of conviction in following a trading strategy, suggesting that without belief in the system, traders are unlikely to succeed.

Diving Deeper into Trading Theory

• The conversation shifts towards systematic trading and its foundational elements such as win rates and risk-reward ratios.

• Systematic trading involves testing various scenarios based on historical data to identify consistently performing patterns.

• Algo trading is introduced as an advanced form where rules dictate when to buy or sell based on specific conditions.

Setting Up an Automated Trading System

• Discusses how algo trading automates the execution process, allowing traders to monitor trades rather than manually placing orders.

• Explains that automation frees up time for traders by using algorithms to execute trades based on pre-set criteria.

• Introduces APIs (Application Programming Interfaces), like Zerodha's Kit API, which facilitate automated trade execution through coding.

Backtesting and Rule Identification

• Stresses the importance of backtesting strategies daily to evaluate what works and what doesn’t in real market conditions.

• Encourages identifying specific rules beyond basic indicators like moving averages for more nuanced strategies.

Advanced Indicators and Their Applications

• Discusses multiple strategies that work under different market conditions, emphasizing adaptability in approach.

• Moving averages are highlighted as effective tools during trending markets; they help determine entry points based on crossovers.

• Introduces additional indicators like Super Trend which provides insights into market direction based on volatility and price movements.

Super Trend and Options Trading Strategies

Understanding Super Trend Tool

• The Super Trend tool can be plotted on any trading chart, regardless of the time frame (daily, hourly, 5-minute, or 1-minute).

• It effectively captures significant trends in markets like silver and gold, allowing traders to identify upward and downward movements.

• The speaker shares personal experiences of successfully capturing entire upward and downward moves using the Super Trend tool.

Options Trading: Time-Based Strategy

• Introduction to a strategy called "Time-Based Straddle," which involves fixed times for trades (e.g., 9:30 AM).

• In this strategy, both call and put options are sold at the same strike price when the market is at a specific level (e.g., Nifty at ₹25,000).

• A stop loss is set for both legs of the trade (call and put), typically around 20%, ensuring risk management.

Managing Trades with Stop Losses

• Trades remain open until the end of the day; if the market moves up or down significantly, one leg may hit its stop loss while the other continues to profit.

• This strategy allows for defined entry points, risk management through stop losses, and potential exit targets based on desired risk-reward ratios.

Risks in Options Selling

• The speaker emphasizes that selling options can be perceived as safer due to higher win rates but warns about potential high losses.

• Unlike buying options where losses are limited to premiums paid, selling options can lead to substantial losses if not managed properly.

Insights from Market Wizards

• Successful traders often achieve high returns; however, there’s no guaranteed safety in trading—risk is inherent in all strategies.

• The discussion highlights that while option sellers might make money quickly during volatile movements (like those seen in gold/silver), they must also manage risks carefully.

• There’s an acknowledgment that nothing is truly safe in markets; traders should always be prepared for unexpected outcomes.

Conclusion: Potential Earnings from Trading

• The best traders have no limits on their earnings potential; they can achieve extraordinary returns compared to average performance metrics.

• Reference made to "Market Wizards" by Jack Schwager as essential reading for understanding successful trading strategies.

Investment Insights and Trading Strategies

The Potential of Returns in Trading

• A trader's journey from an initial investment of approximately $5,000 to a staggering $5 million over a decade highlights the immense potential returns in trading.

• This example serves as inspiration, emphasizing that while such outliers exist, replicating their success is challenging.

Understanding Personal Strengths and Risks

• Emulating successful traders is difficult; individuals must recognize their strengths and risk tolerance before entering the market.

• To achieve significant financial gains, one may need to be prepared for substantial losses along the way.

Setting Realistic Financial Goals

• For someone earning ₹1 lakh per month through traditional employment, transitioning to trading requires understanding realistic capital needs for sustainable income.

• Targeting a 25% return on investments can be simplified to aiming for 2% monthly growth.

Capital Requirements for Desired Income

• To earn ₹1 lakh monthly at a 2% return rate, an estimated capital of ₹50 lakhs would be necessary.

• Achieving a consistent 20% monthly return is highly unlikely; thus, traders must manage expectations regarding potential earnings.

Risk Management in Trading Decisions

• Higher returns necessitate taking greater risks; this often leads traders to make decisions they might not otherwise consider.

• Many inexperienced traders lose money by investing heavily in options without adequate capital or experience.

Recommendations for New Traders

• Young professionals should avoid starting with options trading due to its complexity and volatility; instead, focus on equities and swing trading strategies.

Swing Trading Basics

• Swing trading involves holding positions over days or weeks rather than making rapid trades. It allows busy professionals to engage without constant screen time.

Momentum Trading Introduction

• Momentum trading focuses on stocks showing strong price movements. Mark Minervini is noted as a key figure who popularized this strategy.

Understanding Momentum Trading

Mindset Shift in Trading

• The typical mindset in stock trading is to buy when prices are low, believing they will rise again. This contrasts with momentum trading, where the focus is on buying stocks that are currently rising and breaking their all-time highs.

• In momentum trading, the belief is that once a stock breaks its previous high, it will continue to rise due to positive developments within the company. This approach emphasizes identifying candidates showing upward momentum.

Identifying Strong Candidates

• One effective method for identifying strong candidates in momentum trading is through Relative Strength analysis. This involves comparing a stock's performance against a benchmark index like Nifty over specific time frames (e.g., one month, three months).

• By filtering large-cap stocks based on their performance relative to Nifty, traders can compile lists of top-performing stocks that have outperformed the index over various periods (3 months, 6 months, 1 year). This helps pinpoint where market action is occurring positively.

Utilizing AI in Trading Strategies

• The discussion shifts towards leveraging AI tools like ChatGPT for enhancing trading strategies and decision-making processes. Many users typically utilize AI for basic tasks but can benefit from more advanced applications in trading contexts.

• Traders are encouraged to engage with AI by debating their ideas about market strategies and testing them against AI-generated insights or critiques. For example, questioning the viability of using moving averages as a strategy can lead to deeper understanding and refinement of approaches.

Learning Through Interaction with AI

• Engaging with AI allows traders to challenge their assumptions and learn about potential flaws in their strategies that they might overlook due to limited knowledge or experience. This process encourages critical thinking and adaptation of strategies based on feedback from AI systems.

• A practical application includes creating prompts for ChatGPT that ask it to teach new concepts related to trading daily, which helps build knowledge progressively over time while allowing customization based on individual learning levels (beginner or advanced).

Building Systems Using Coding

• The speaker shares personal experiences of learning coding during challenging times (like COVID) and how this skill has been beneficial for developing automated tools for analysis without needing extensive programming backgrounds. Python was highlighted as an accessible language for traders looking to enhance their analytical capabilities through coding skills.

• Emphasizing the importance of understanding basic coding principles enables traders not only to automate tasks but also critically assess what algorithms are doing behind the scenes—ensuring they remain informed participants rather than passive users of technology.

AI Trading Agents and Their Future

The Concept of AI in Trading

• Discussion begins on the potential of hiring an AI agent to handle both trading strategy and execution, emphasizing that this is not only possible but already being explored by hedge funds in the U.S.

Experimentation with AI Trading

• An experiment involving multiple large language models (LLMs) was conducted where each model was given a capital amount to trade independently, resulting in varied performance across different trades.

Performance Insights from Different Models

• Each LLM took distinct trading positions; for instance, Grok performed well due to its access to Twitter data, which allowed it to analyze recent trends effectively.

Current State of AI Trading

• The conversation highlights that while there may be a high failure rate (around 90%) for AI trading currently, this mirrors human traders' performance. Over time, improvements are expected as technology advances.

Future Predictions for AI in Trading

• It is predicted that AI will evolve to autonomously manage trading strategies based on user inputs, significantly simplifying the process for traders who will only need to provide manual instructions.

Sophistication of Current Models

• Large investment funds are reportedly developing sophisticated autonomous trading models using AI. However, such advancements have yet to be fully realized within Indian markets.

Social Networks for AIs

• A new social network has emerged specifically for AIs where they communicate and collaborate with one another. This platform allows them to build upon shared knowledge rapidly.

Rapid Development Among AIs

• The discussion touches on how these AIs are creating their own systems and methodologies at a much faster pace than humans can comprehend or adapt.

Subscription-Based Trading Agents

• There’s speculation about a future where individuals can create their own trading agents and offer them as subscription services, although high fees might limit widespread adoption initially.

Learning Opportunities with AI

• Emphasis is placed on the importance of curiosity when learning new skills through AI tools. Traditional degrees may become less relevant compared to practical knowledge gained through direct engagement with technology.

Advice for Young People

• Young individuals are encouraged to leverage AI productively rather than merely saving money passively. Finding innovative ways to earn money should be prioritized over traditional investment methods.

Conclusion of Discussion

• The conversation wraps up with gratitude expressed towards Arjun for sharing insights into the future of trading and technology's role in shaping it. Viewers are invited to engage further by leaving questions or suggestions for future topics.