{"id":19343,"date":"2026-11-07T17:42:40","date_gmt":"2026-11-07T17:42:40","guid":{"rendered":"https:\/\/www.simtrade.fr\/blog_simtrade\/?p=19343"},"modified":"2026-11-07T18:06:05","modified_gmt":"2026-11-07T18:06:05","slug":"experience-independent-portfolio-manager-family-funded-equity-derivatives-portfolio","status":"publish","type":"post","link":"https:\/\/www.simtrade.fr\/blog_simtrade\/experience-independent-portfolio-manager-family-funded-equity-derivatives-portfolio\/","title":{"rendered":"My experience as an independent portfolio manager of a family-funded equity and derivatives portfolio"},"content":{"rendered":"\n\"Romil<\/a>\n\n

In this article, Romil SOJITRA<\/a> (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028) shares his experience as an independent portfolio manager of a family-funded equity and derivatives portfolio in the Indian market, which he started with equities in May 2025 and extended to options in May 2026.<\/p>\n\n\n

About the market: Indian equity derivatives<\/h2>\n\n

My portfolio was invested in Indian listed equities and in equity derivatives (futures and options) traded on the National Stock Exchange of India (NSE). NSE was founded in 1992 and introduced fully electronic, screen-based trading in India. It launched futures on the Nifty 50 index in 2000 and options in 2001. Since then, the Indian equity derivatives market has grown enormously. According to the Futures Industry Association (FIA), NSE was the largest derivatives exchange in the world by number of contracts traded for five consecutive years (2019 to 2023), mainly because of very high volumes in options on the Nifty 50 and Nifty Bank indexes with weekly expiries, that is, contracts that expire within seven days or less and that are heavily traded in the last days before expiry.<\/p>\n\n

Logo of the National Stock Exchange of India (NSE).
\n\"Logo
\nSource: the company.<\/p>\n\n

This growth came with risks for individual investors. In September 2024, the Securities and Exchange Board of India (SEBI), the market regulator, published a study showing that 93% of individual traders in equity futures and options made losses between FY22 and FY24 (in India, the fiscal year runs from April to March, so the study covers April 2021 to March 2024). Their aggregate net losses reached \u20b91.81 lakh crore over the three years (1 lakh = 100,000 and 1 crore = 10 million, so 1 lakh crore = \u20b91 trillion), that is about \u20b91.8 trillion or roughly US$21 billion, and most of these losses came from options.<\/p>\n\n

SEBI reacted to these losses. Because most losses were concentrated among small traders buying and selling short-dated index options, its circular of October 1, 2024 (applied from November 20, 2024) made this type of speculation harder: the minimum value of an index derivative contract was raised from \u20b95-10 lakh to \u20b915 lakh, so that each trade requires more capital, and each exchange may now offer weekly expiries on only one benchmark index (the Nifty 50 for NSE), which reduces the number of very short-dated contracts. These rules also mattered for my own sizing: one lot of a Nifty option now represents a notional amount of at least \u20b915 lakh, so I could not size positions as finely as I wanted. I therefore started trading derivatives knowing that most individual participants lose money. My objective was not to “beat the market” quickly, but to build a process that controls risk first.<\/p>\n\n

The portfolio was funded by my family. They gave me capital to manage after I had shown consistent results on a smaller amount. This made me accountable to real “clients”, even though they were family members: I reported my performance to them regularly and explained my decisions and my mistakes.<\/p>\n\n\n

My experience<\/h2>\n\n

I started in May 2025 with Indian equities, investing in stocks and learning how markets work alongside my studies at IIT Kanpur. In May 2026, I moved to a more structured and quantitative approach: I added options trading and began applying the concepts presented in this article (the Black-Scholes-Merton model and the Greeks, the Kelly Criterion, Monte Carlo simulations) to a leveraged equity and derivatives portfolio. This post focuses mainly on this second phase, from May to August 2026 (44 trading sessions), and on the drawdown of March-April 2026 that came just before it. I am responsible for the full investment process: generating ideas, building positions, sizing them, hedging the portfolio, and measuring and reporting performance.<\/p>\n\n

My missions<\/h3>\n\n

Since May 2026, when I added options to the portfolio, my work has covered the five areas below. The first one (relative-value equity positions) continued the equity book I had run since May 2025; the four others were new.<\/p>\n\n

Building relative-value positions.<\/b> I constructed long\/short equity pairs: buying one stock and selling a related stock (for example, two companies in the same sector) to be exposed to their relative performance rather than to the direction of the overall market. When a pair is well built, a large part of the market risk cancels out, and the return depends on the spread between the two stocks.<\/p>\n\n

Running delta-neutral option positions.<\/b> My option positions were mainly short volatility: I sold Nifty index options (for example a call and a put together, as in a short strangle) and kept the delta of the whole position close to zero by adjusting it with Nifty futures or by moving one of the option legs. Each position was therefore a combination of options and of the underlying (through futures), not options alone. Small moves of the index then had little impact, and the profit or loss came from two other sources. The first is time decay (theta): an option loses a little value every day as expiry approaches, and as a seller I earned this decay. The second is volatility. As a seller, I lost money when the volatility implied by option prices rose (negative vega), and above all I earned money when the index actually moved less than the volatility implied by the prices at which I sold the options, and lost money when it moved more (negative gamma). I explain this mechanism with an equation in the section on the Greeks below. I used Sensibull, an Indian options analytics platform, to analyze option chains, Greeks and the payoff of each strategy before entering a position. Figure 1 illustrates this analysis on an example: a short iron condor on the Nifty 50 (selling the 24,650 put and the 25,300 call, and buying the 24,350 put and the 25,600 call as protection), whose maximum loss is capped at the width of a wing (300 points) minus the net premium received (78.85 points).<\/p>\n\n

Figure 1. Illustration: payoff of a short iron condor on the Nifty 50 in the Sensibull strategy builder (expiry of November 9, 2026).
\n\"Analysis
\nSource: Sensibull, screenshot by the author (illustration, November 6, 2026).<\/p>\n\n

Hedging tail risk.<\/b> Short-volatility and leveraged positions can lose a lot of money in a market crash. To protect the portfolio against extreme events, I held a long-dated index put option as a tail-risk hedge. This hedge costs a small premium in normal times but gains value when the market falls sharply. A bought put has positive gamma: its delta becomes more and more negative as the market falls, so its gains accelerate in a crash. It therefore offsets part of the negative gamma of the options I sold.<\/p>\n\n

Sizing positions and modelling risk.<\/b> I used the Kelly Criterion to decide how much capital to allocate to each trade, the Black-Scholes-Merton model to price options and compute their Greeks, and correlation and information-ratio analysis to build the portfolio. I also built Monte Carlo simulations. For position sizing, each session is a random draw: a win with probability p<\/i> = 59.1% that pays b<\/i> = 1.45 times the average loss, or a loss otherwise. I simulated 10,000 paths of 100 sessions for different bet sizes (this model is detailed in my article on the Kelly Criterion). For option positions, I simulated the Nifty 50 with a geometric Brownian motion, the model behind Black-Scholes-Merton, for several levels of annual volatility (for example 10%, 15% and 25%), and revalued the position on each simulated path.<\/p>\n\n

Measuring and reporting performance.<\/b> I kept a structured trading journal and a performance report. I tracked the win rate, the profit factor (gross profits divided by gross losses), the Sharpe, Sortino and Calmar ratios, and the drawdown, for each account and each strategy.<\/p>\n\n

Required skills and knowledge<\/h3>\n\n

Hard skills.<\/b> I needed a solid understanding of derivatives: how futures and options are priced (a futures price follows the cost-of-carry relation F<\/i> = S<\/i> \u00d7 e(r<\/i> \u2212 q<\/i>)T<\/i><\/sup>, where S<\/i> is the spot price, r<\/i> the interest rate, q<\/i> the dividend yield and T<\/i> the maturity, while options are priced with the Black-Scholes-Merton model presented below), what the Greeks measure, and how a combination of options behaves when the price, volatility or time to maturity changes. I also needed statistics and econometrics to measure correlations and evaluate whether a strategy has a real edge or is just lucky. The courses I took at IIT Kanpur in econometrics, financial econometrics and optimization, and my preparation for the CFA Level I exam (which I passed in August 2026), were very useful here.<\/p>\n\n

Soft skills.<\/b> The most important skills were discipline and emotional control. It is easy to follow rules when trades are winning; it is much harder during a drawdown, when the temptation is to increase risk to recover losses quickly. Behavioral finance explains why. In prospect theory, Kahneman and Tversky (1979) show that people feel a loss more strongly than a gain of the same size (roughly twice as strongly) and become risk-seeking when they are below their reference point: after a loss, a trader is tempted to take more risk to get back to break-even. Kahneman (2011) adds that under stress, fast and intuitive thinking takes over from slow and deliberate thinking. This is why I relied on a written process and on my trading journal, not on my feelings, during the drawdown. I also needed honesty and communication skills to report bad periods to my family as clearly as good ones.<\/p>\n\n\n

What I learned<\/h3>\n\n

The most important lesson came from a difficult period. During the market sell-off of March-April 2026, linked to the US-Iran conflict, my portfolio suffered a heavy drawdown, mainly on high-beta stocks in my equity book (Figure 2). I had underestimated how much my positions were exposed to the same market factor, that is, the general movement of the Indian equity market measured by the Nifty 50. High-beta stocks amplify the moves of the index (a stock with a beta of 1.5 tends to fall by about 1.5% when the index falls by 1%), so in a sell-off they all fall together and their correlations rise: positions that looked different were in fact the same bet on the market. I did not try to win everything back at once. I reduced risk, kept following my process, and the portfolio recovered and became profitable over the following two months.<\/p>\n\n

Figure 2. Evolution of the Nifty 50 index in 2026 (daily prices, January to early November).
\n\"Evolution
\nSource: TradingView (data: NSE).<\/p>\n\n

Table 1 summarizes the performance of the portfolio over May-August 2026. A session is a trading day on which I had open positions and recorded a profit or a loss in my journal: there were 44 such sessions over the period. The portfolio delivered a return of 5.79% on deployed capital and 4.05% on total capital (part of the capital stayed in cash as a buffer). Of the 44 sessions, 26 were winners and 18 were losers, which gives a win rate of 59.1% (26\/44). The profit factor (gross profits divided by gross losses) was 2.10, so the average winning session earned about 1.45 times what the average losing session lost (2.10 \u00d7 18\/26). Figure 3 shows the evolution of the portfolio and its drawdown.<\/p>\n\n

Table 1. Performance of the portfolio (May-August 2026).<\/p>\n\n\n
Measure<\/th>Value<\/th>How it is computed<\/th><\/tr>\n
Trading sessions<\/td>44<\/td>Days with open positions<\/td><\/tr>\n
Winning \/ losing sessions<\/td>26 \/ 18<\/td>From the trading journal<\/td><\/tr>\n
Win rate (p<\/i>)<\/td>59.1%<\/td>26 \/ 44<\/td><\/tr>\n
Profit factor<\/td>2.10<\/td>Gross profits \/ gross losses<\/td><\/tr>\n
Payoff ratio (b<\/i>)<\/td>1.45<\/td>Average win \/ average loss = 2.10 \u00d7 18\/26<\/td><\/tr>\n
Return on deployed capital<\/td>5.79%<\/td>Profit \/ capital in positions<\/td><\/tr>\n
Return on total capital<\/td>4.05%<\/td>Profit \/ total capital<\/td><\/tr>\n
Average share of capital deployed<\/td>about 70%<\/td>4.05% \/ 5.79%<\/td><\/tr>\n<\/tbody><\/table>\n\n

Source: trading journal of the author.<\/p>\n\n

Figure 3. Evolution of the portfolio and drawdown (May-August 2026).
\n\"Evolution
\nSource: computation by the author (data: trading journal).<\/p>\n\n

In July-August, a second drawdown appeared. My journal showed that my edge was decaying: the same strategies were producing weaker results. I decided to de-risk rather than force trades. I learned that knowing when to reduce exposure is as important as knowing when to take risk, and that a strategy’s edge is not permanent.<\/p>\n\n

Financial concepts related to my experience<\/h2>\n\n

I present below three financial concepts related to my experience: the Black-Scholes-Merton model and the option Greeks (with an equation that shows how my option positions made or lost money, and alternative ways to bet on volatility), the Kelly Criterion for position sizing, and risk-adjusted performance measures.<\/p>\n\n

The Black-Scholes-Merton model and the option Greeks<\/h3>\n\n

The Black-Scholes-Merton model (Black and Scholes, 1973; Merton, 1973) gives a theoretical price for a European option as a function of the price of the underlying asset, the strike price, the time to maturity, the risk-free interest rate and the volatility of the underlying. Its partial derivatives, called the Greeks, measure how the option price reacts to each of these inputs: delta (price of the underlying), gamma (change in delta), vega (implied volatility), and theta (time). In my delta-neutral strategies, I kept the delta of the whole position close to zero, that is, the sum of the deltas of all the options and futures in the position, each multiplied by its quantity: \u0394position<\/sub> = \u03a3 ni<\/sub><\/i> \u00d7 \u0394i<\/i><\/sub> (a future has a delta of 1 per unit of the index). Small market moves then had little impact. My profit or loss came mainly from theta and gamma, that is, from the passage of time and from the difference between implied and realized volatility, as the equation below shows. Without the model and its Greeks, I could not have measured or controlled these exposures.<\/p>\n\n

The profit and loss of an option position: a second-order approximation<\/h4>\n\n

For a small change dS<\/i> in the price of the underlying asset over a short period dt<\/i>, a second-order Taylor expansion of the value V<\/i> of an option position gives:<\/p>\n\n

dV<\/i> \u2248 \u0394 \u00d7 dS<\/i> + \u00bd \u00d7 \u0393 \u00d7 (dS<\/i>)2<\/sup> + \u0398 \u00d7 dt<\/i> + \u03bd \u00d7 d\u03c3<\/p>\n\n

where \u0394 is the delta, \u0393 the gamma, \u0398 the theta and \u03bd the vega of the position. The first term is linear: it is the first guess given by the delta. The second term is the convexity. It does not depend on the direction of the move because (dS<\/i>)2<\/sup> is always positive: for a bought option (\u0393 > 0, for example the purchase of a put) it is always a gain, and for a sold option (\u0393 < 0, for example the sale of a call) it is always a loss.<\/p>\n\n

For a delta-neutral position (\u0394 = 0) with constant implied volatility, only gamma and theta remain. In the Black-Scholes-Merton model, theta and gamma are linked: when the interest rate is ignored, \u0398 = \u2212\u00bd \u00d7 \u0393 \u00d7 S<\/i>2<\/sup> \u00d7 \u03c3implied<\/sub>2<\/sup>. The profit and loss over one day can then be written as:<\/p>\n\n

dV<\/i> \u2248 \u00bd \u00d7 \u0393 \u00d7 S<\/i>2<\/sup> \u00d7 [ (dS<\/i>\/S<\/i>)2<\/sup> \u2212 \u03c3implied<\/sub>2<\/sup> \u00d7 dt<\/i> ]<\/p>\n\n

The term (dS<\/i>\/S<\/i>)2<\/sup> is the squared return actually realized over the day, while \u03c3implied<\/sub>2<\/sup> \u00d7 dt<\/i> is the squared return implied by option prices. For a seller of options (\u0393 < 0), the day is profitable when the market moves less than implied volatility predicts, and costly when it moves more. Over many days, the seller earns money when realized volatility is below implied volatility. This is the precise meaning of “betting on volatility”, and it explains the returns of my delta-neutral positions. Empirically, implied volatility is on average higher than realized volatility on equity indexes (the volatility risk premium documented by Bakshi and Kapadia, 2003), which is what option sellers try to earn, at the cost of large losses in sudden market moves.<\/p>\n\n

Figure 4 illustrates the convexity with two positions on an index at 25,000 points: the purchase of an at-the-money put (positive gamma) and the sale of an at-the-money call (negative gamma), both with 30 days to expiry, an implied volatility of 15% and an interest rate of 6% (illustrative parameters). The gamma of each option is 0.000368 per point. For a fall of 2,000 points (\u22128%), the bought put gains 1,522 points: the delta alone predicts 892 points, and adding the gamma term (+735 points) gives 1,627 points, much closer to the exact value. For moves of \u00b1500 points (\u00b12%), the second-order approximation is almost exact (269 points for the bought put after a fall of 500 points, against 269 points with the full model). The curve of the bought put bends upward (convex), while the curve of the sold call bends downward (concave): for a rise of 2,000 points, the sold call loses 1,643 points against 1,108 points predicted by the delta alone.<\/p>\n\n

Figure 4. Change in value of a bought put and of a sold call as a function of the price of the underlying asset.
\n\"Change
\nSource: computation by the author (Black-Scholes-Merton model).<\/p>\n\n

Figure 5 applies the second equation to a delta-hedged short at-the-money straddle (one call and one put sold with the same strike) with the same parameters. The position earns about 14.5 points of theta per day but loses \u00bd \u00d7 \u0393 \u00d7 (dS<\/i>)2<\/sup> when the index moves. The break-even daily move is about 198 points, or 0.79% of the index, which is almost exactly the daily move implied by an annual volatility of 15% (15% \/ \u221a365 = 0.79%).<\/p>\n\n

Figure 5. Profit and loss over one day of a delta-hedged short straddle as a function of the daily move of the index.
\n\"Daily
\nSource: computation by the author (Black-Scholes-Merton model).<\/p>\n\n

You can download below the Excel file with the tables and figures of this section (option prices and Greeks, the position as a function of the price of the underlying asset for a bought put and a sold call, the gamma-theta trade-off, and the payoffs of volatility strategies).<\/p>\n\n\n

\"Download<\/a><\/p> \n\n

Alternative strategies to bet on volatility<\/h4>\n\n

Selling options is only one way to take a view on volatility. Table 2 lists the main alternatives. Long-volatility strategies (positive gamma) gain when the market moves a lot and pay theta every day; short-volatility strategies (negative gamma) earn theta and lose in large moves.<\/p>\n\n

Table 2. Strategies to bet on volatility.<\/p>\n\n\n
Strategy<\/th>Position<\/th>Bet<\/th>Gamma<\/th>Maximum loss<\/th><\/tr>\n
Long straddle \/ strangle<\/td>Buy a call and a put (same strike, or out-of-the-money strikes)<\/td>Large move in either direction<\/td>+<\/td>Premiums paid<\/td><\/tr>\n
Delta-hedged long options (gamma scalping)<\/td>Buy options and re-hedge the delta with futures<\/td>Realized volatility above implied volatility<\/td>+<\/td>Premium paid and hedging costs<\/td><\/tr>\n
Long out-of-the-money put (tail hedge)<\/td>Buy a long-dated index put<\/td>Market crash<\/td>+<\/td>Premium paid<\/td><\/tr>\n
Short straddle \/ strangle<\/td>Sell a call and a put<\/td>Calm market, realized volatility below implied<\/td>\u2212<\/td>Unlimited<\/td><\/tr>\n
Iron condor<\/td>Short strangle plus a bought put and a bought call further out of the money<\/td>Index stays within a range<\/td>\u2212<\/td>Width of the wings minus premium received<\/td><\/tr>\n
Calendar spread<\/td>Sell a short-dated option and buy a longer-dated one with the same strike<\/td>Calm market now, stable or rising long-dated implied volatility<\/td>\u2212<\/td>Net premium paid<\/td><\/tr>\n
Volatility index and variance products<\/td>Trade volatility directly (India VIX for NSE, variance swaps)<\/td>Rise or fall of volatility itself<\/td>n\/a<\/td>Depends on the product (variance swaps are mainly traded by institutions)<\/td><\/tr>\n<\/tbody><\/table>\n\n

Source: the author.<\/p>\n\n

Figure 6 compares the payoff at expiry of a long straddle, a short strangle and an iron condor with the same parameters as above. The iron condor is a defined-risk version of the short strangle: with strikes at 24,000, 24,500, 25,500 and 26,000 points, it collects a net premium of 245 points and its maximum loss is capped at 255 points (the 500-point width of a wing minus the premium), whereas the loss of the short strangle has no limit. Defined-risk structures are therefore better suited to a small portfolio, where one large move must not wipe out months of theta.<\/p>\n\n

Figure 6. Payoff at expiry of three volatility strategies.
\n\"Payoff
\nSource: computation by the author (Black-Scholes-Merton model).<\/p>\n\n\n

The Kelly Criterion<\/h3>\n\n

The Kelly Criterion (Kelly, 1956) answers the question: if I have a positive edge, what fraction of my capital should I risk on each trade? It chooses the fraction that maximizes the expected logarithm of wealth, which is the same as maximizing the long-run growth rate of capital. For a trade that wins with probability p<\/i> and pays b<\/i> times the amount risked, the optimal fraction is f<\/i>* = p<\/i> \u2212 (1 \u2212 p<\/i>)\/b<\/i>. Risking more than this lowers long-run growth and greatly increases drawdowns. I used the Kelly Criterion as an upper limit for my position sizes and in practice risked only a fraction of it, because my estimates of p<\/i> and b<\/i> were based on a limited number of trades. I explain this concept in detail in a separate article on the SimTrade blog.<\/p>\n\n\n

Risk-adjusted performance measures<\/h3>\n\n

A return figure alone says nothing about the risk taken to obtain it. The Sharpe ratio (Sharpe, 1966) divides the excess return over the risk-free rate by the volatility of returns. The Sortino ratio (Sortino and Price, 1994) only penalizes downside volatility, which is more relevant for strategies with asymmetric returns such as options strategies. The Calmar ratio compares the annual return to the maximum drawdown, the largest fall from a peak to a trough. I tracked all three measures in my journal because they tell different stories: my drawdown in March-April 2026 hurt my Calmar ratio much more than my Sharpe ratio, which reminded me that volatility is not the only dimension of risk.<\/p>\n\n\n

Why should I be interested in this post?<\/h2>\n\n

If you are a student interested in asset management, trading or derivatives, managing a real portfolio, even a small one, is one of the best ways to understand how theory meets practice. Concepts such as the Greeks, position sizing and drawdowns become very concrete when your own money, or your family’s, is at stake. This experience also shows that a good trading process is mostly about risk management, record-keeping and honest evaluation of performance, and that most individual derivatives traders lose money if they ignore these principles.<\/p>\n\n\n

Related posts on the SimTrade blog<\/h2>\n\n

Professional experiences<\/h3>\n\n

   \u25b6 All posts about Professional experiences<\/a><\/p>\n\n

   \u25b6 Youssef LOURAOUI My experience as a portfolio manager in a central bank<\/a><\/p>\n\n

   \u25b6 William ARRATA My experiences as Fixed Income portfolio manager then Asset Liability Manager at Banque de France<\/a><\/p>\n\n

   \u25b6 Adam MERALLI BALLOU My internship experience in Portfolio Management at Mirabaud<\/a><\/p>\n\n

   \u25b6 Tanguy TONEL My experience as an Investment Specialist at Amundi Asset Management<\/a><\/p>\n\n

   \u25b6 Alberto BORGIA My Experience as a Wealth Management Intern at Nextam Partners<\/a><\/p>\n\n\n

Financial techniques<\/h3>\n\n

   \u25b6 Jayati WALIA Black-Scholes-Merton option pricing model<\/a><\/p>\n\n

   \u25b6 Akshit GUPTA Option Greeks \u2013 Gamma<\/a><\/p>\n\n

   \u25b6 Abel ARAYA Delta Hedging Explained: How Traders Stay Market Neutral<\/a><\/p>\n\n

   \u25b6 Akshit GUPTA Straddle and strangle strategy<\/a><\/p>\n\n

   \u25b6 Akshit GUPTA Protective Put<\/a><\/p>\n\n

   \u25b6 Raphael TRAEN Understanding Correlation in the Financial Landscape: How It Drives Portfolio Diversification<\/a><\/p>\n\n\n

Indian financial markets<\/h3>\n\n

   \u25b6 Nithisha CHALLA The Nifty 50 index<\/a><\/p>\n\n

   \u25b6 Nithisha CHALLA Securities and Exchange Board of India (SEBI)<\/a><\/p>\n\n

   \u25b6 Nithisha CHALLA Top 5 companies by market capitalization in India<\/a><\/p>\n\n\n

Useful resources<\/h2>\n\n

Academic research<\/h3>\n\n

Bakshi, G., Kapadia, N. (2003) Delta-Hedged Gains and the Negative Market Volatility Risk Premium, Review of Financial Studies<\/i>, 16(2), 527\u2013566.<\/p>\n\n

Black, F., Scholes, M. (1973) The Pricing of Options and Corporate Liabilities, Journal of Political Economy<\/i>, 81(3), 637\u2013654.<\/p>\n\n

Gatev, E., Goetzmann, W. N., Rouwenhorst, K. G. (2006) Pairs Trading: Performance of a Relative-Value Arbitrage Rule, Review of Financial Studies<\/i>, 19(3), 797\u2013827.<\/p>\n\n

Hull, J. C. (2021) Options, Futures, and Other Derivatives<\/i>, Eleventh Edition, Harlow, Pearson.<\/p>\n\n

Kahneman, D. (2011) Thinking, Fast and Slow<\/i>, New York, Farrar, Straus and Giroux.<\/p>\n\n

Kahneman, D., Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk, Econometrica<\/i>, 47(2), 263\u2013291.<\/p>\n\n

Kelly, J. L. (1956) A New Interpretation of Information Rate, Bell System Technical Journal<\/i>, 35(4), 917\u2013926.<\/p>\n\n

Merton, R. C. (1973) Theory of Rational Option Pricing, Bell Journal of Economics and Management Science<\/i>, 4(1), 141\u2013183.<\/p>\n\n

Sharpe, W. F. (1966) Mutual Fund Performance, Journal of Business<\/i>, 39(1), 119\u2013138.<\/p>\n\n

Sortino, F. A., Price, L. N. (1994) Performance Measurement in a Downside Risk Framework, Journal of Investing<\/i>, 3(3), 59\u201364.<\/p>\n\n\n

Business resources<\/h3>\n\n

National Stock Exchange of India (NSE)<\/a><\/p>\n\n

SEBI (2024) Updated SEBI study reveals 93% of individual traders incurred losses in equity F&O between FY22 and FY24<\/a><\/p>\n\n

SEBI (2024) Circular of October 1, 2024: Measures to strengthen Equity Index Derivatives framework for increased investor protection and market stability.<\/p>\n\n

Futures Industry Association (FIA)<\/a><\/p>\n\n

CFA Institute<\/a><\/p>\n\n

Sensibull<\/a><\/p>\n\n\n

About the author<\/h2>\n\n

The article was written in October 2026 and revised in November 2026 by Romil SOJITRA<\/a> (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028).<\/p>\n\n

   \u25b6 Discover all posts by Romil SOJITRA<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this article, Romil SOJITRA (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028) shares his experience as an independent portfolio manager of a family-funded equity and derivatives portfolio in the Indian market, which he started with equities in May 2025 and extended to options in May 2026. About the market: Indian equity derivatives My … Read more<\/a><\/p>\n","protected":false},"author":201,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5,18],"tags":[180,268,437],"class_list":["post-19343","post","type-post","status-publish","format-standard","hentry","category-contributors","category-professional-experiences","tag-derivatives","tag-futures","tag-options"],"yoast_head":"\nMy experience as an independent portfolio manager of a family-funded equity and derivatives portfolio - SimTrade blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.simtrade.fr\/blog_simtrade\/experience-independent-portfolio-manager-family-funded-equity-derivatives-portfolio\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"My experience as an independent portfolio manager of a family-funded equity and derivatives portfolio\" \/>\n<meta property=\"og:description\" content=\"In this article, Romil SOJITRA (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028) shares his experience as an independent portfolio manager of a family-funded equity and derivatives portfolio in the Indian market, which he started with equities in May 2025 and extended to options in May 2026. 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