GuruSpeak | Puneet Tewani(https://twitter.com/puneet_tewani), an algo trader who deploys 20 intra-day strategies
The former bureaucrat who studied engineering and business administration uses his multi-tasking skills to devise a number of derivative trading strategies
Intra-day trading is exhausting but can still be a satisfying profession provided you are making money. Even though intra-day traders are gradually moving towards algorithmic trading, it still requires close observation. Most traders are happy using one or two strategies as staring at the screen and checking if the trades are taken in line with the strategy can be taxing.
But Puneet Tewani has a different approach and since 2019 trades 20 strategies, mostly intraday, and all are algo-based. Alongside, he runs an algo-development firm and monitors strategies that are deployed by his clients, comprising retail and proprietary funds. He is also an author of a book on algo-trading and trains traders on how to program their strategy.
Multi-tasking comes naturally for Tewani, as he has worked with the Ministry of Corporate Affairs earlier before quitting to do trading full time. Tewani calls himself a wanderer who likes to explore unknown places but always has a mobile to monitor his trades.
In an interview with Moneycontrol, PuneetTewani talks in detail about some of the strategies he deploys and his market journey from the days when he did trading manually to now when he runs multiple algo-trading strategies.
Q: Can you walk us through your early days in the market?
I come from a business family, one which stressed on me getting a good education. I am a graduate in electronics &communication engineering and a post-graduate in business administration.
My introduction to the market was at an early age. When in school, I watched my father regularly watching CNBC and was overwhelmed by the data and charts on the screen. When asked, my father informed me about the market and said “this is the place where we can make fortunes.” Those words stuck with me and I have been hooked to the market.
In 2010, I secretly opened a trading account and used my small savings to trade in the market. In those days I used to trade on recommendations given by the TV experts, but that did not work out well.
I then used screeners to pick up stocks with an intention of holding for a long term, but I used to book profit even if I saw a small gain. I traded small and was elated even if I made Rs 100 in a day. What made me going was the dream to build a large portfolio that could be used to generate steady income. Those small wins gave me the confidence that I would make it big someday. With these small gains, I managed to accumulate a portfolio of Rs 1 lakh in two years.
While pursuing my engineering, I knew I had to learn more about markets, so I joined a post-graduate course in Finance after graduation. (because some may think this was side by side). My interest and confidence increased while pursuing my post-graduation when I got the opportunity to work in the bonds and forex department as a trainee at PowerGrid.
Q: How did you become a full-time trader?
On completion of the post-graduation course, four companies selected me but I preferred to take up a job with a big trading firm in Delhi–Mansukh Securities--as a derivatives analyst.
Soon I was trading derivatives with strategies like ratio spreads and diagonal calendar spreads using straddle premium analysis. A straddle premium analysis helped us to gauge volatility which in turn helped in deciding the strategy to opt for. If the straddle premium kept on increasing, we shorted lesser multiples of ratios.
By ratio spreads I mean, going long on the OTM (out-of-the-money) options and shorting multiple options contracts farther OTM. While diagonal calendar spreads meant going long onto the next expiry options and shorting nearest expiry options of a different strike.
After a brief spell, where I learned the ropes, I decided to try trading on my own. This trading endeavor did not go on expected lines, however, I utilised the time to study and got selected in the Ministry of Corporate Affairs.
I worked with the Ministry of Corporate Affairs where I handled the technical and functional side of daily corporate data filings. I had to compile answers to questions that were asked in the parliament pertaining to the ministry. I was also involved in designing a national CSR portal which is now used nationwide by all the companies.
Even when on the job I continued trading as it was my passion. I kept on doing it meticulously till I became consistently profitable. When I realized I can earn a living out of trading, I decided to quit my job and become a full-time trader.
Rather than trading on my limited capital, I joined the proprietary trading desk of a broking firm to get the benefit of higher leverage. I managed to break even by trading a few scalping/arbitrage strategies in the initial few months. Meanwhile, I understood the importance of psychology management and statistics in trading and decided to fortify my knowledge by pursuing the study of actuarial sciences, to help me shape up the risk management and statistical reasoning behind trading. Calculation of the expectancy of any person's life done by an insurance company has a lot of similarities to option selling.
Q: What made you shift to algo-trading and how have you grown as an algo-trader?
Since 2016, I traded almost every day based on gut feelings/instinct, which I developed by observing the screen. This resulted in unexpected losses at intervals.
In 2019, I started trading Butterfly strategies which is a risk-defined strategy, with a maximum loss amounting to the premium paid. It comprises 3 legs, the first one being the in-the-money (ITM) option which is bought, two at-the-money (ATM) options are sold and a third out-of-the-money (OTM) option is bought. The earnings of my trading were steady though they stagnated after a time.
I needed to go to the next level by increasing my trade size and eliminating emotions from my trading. The solution to this was systematic trading using algorithms. I joined a course to pursue algo-trading and after learning it I started a venture Fox Trading Solutions in 2019 which helps clients build their algorithms and optimise their strategies. We now have more than 500 clients who are using these algorithms to fire more than Rs 25 crore of orders daily.
I also leveraged my capital by joining a proprietary firm in 2020 and have been running a completely automated system since then.
I have authored a book titled 'Step-by-Step Guide to automation using Zerodha's API', which is available on Amazon. It is intended to educate people with zero knowledge about Python. I mentor students on algorithmic trading at a trading-educational platform ‘LearnApp’ and take classes on weekends and help students learn algorithmic trading.
Q: What is the most difficult aspect of trading that you encountered?
While I was trading manually, I made all the possible mistakes. I started with a small capital and tried to achieve phenomenal returns. I did revenge trading on a particular instrument just to recover the losses. Thankfully, I realized the importance of psychology, which is the more important aspect and needs to be developed before deploying a trading strategy.
The key to success in the market lies in perseverance, which means following a strategy with discipline and consistency. I have observed that if anyone even trades a simple moving average strategy with a defined risk and reward ratio, he or she should be able to beat the index return consistently over the long run. The key to success is doing the same thing again and again, even during drawdown phases. That is where algorithmic trading helps.
Q: What was your Eureka moment?
I used to research options back-testing platforms when I learned from Quora about one such platform named StockMock, which changed my perspective about options trading. Backtesting gave me insights into the trading strategies, along with few statistical ratios which helped me in bringing a paradigm shift in my trading strategies.
While back-testing option strategies on StockMock, I found a few strategies with a Calmar of over 6, which theoretically sounded vague then. Calmar ratio is a measure of risk-reward in systematic terms. It is computed by dividing net profit by maximum drawdown (loss at any given point in time). A high ratio implies that it has higher returns on a risk-adjusted basis over the specified timeframe.
This data point fascinated me and pushed me towards algo-trading. I went on combining a few strategies to reduce my drawdown and smoothen my Equity Curve. The bunch of strategies I currently trade on has a cumulative Calmar of 24 with almost 95 percent of a month being profitable.
This and my trading experience blended well and helped me create hundreds of systems of which I run nearly 20 systems daily.
Q: How do you presently trade?
My trading style has changed over the years.
In 2018, I joined a broking firm to trade for myself from the proprietary desk’s fund. I used to trade in Box, Butterflies, Conversion, and Reversion for one year. Conversion is a strategy which takes advantage of arbitrage opportunity between futures and call-put options. This is called put-call parity.
While Box is a four-legged strategy that works well during expiry when spreads between the OTM options tend to become 0. We churn those spreads for a small risk-free profit. I continue to deploy this strategy to date.
In 2019, I moved to another brokerage firm to learn more tools and techniques. The combined exposure of both the firms helped me learn various strategies that I presently trade.
At Fox Trading Solutions, we currently trade a basket of intraday options strategies that help reduce drawdown and increase Calmar.
At the same time, our team of 10+ professionals working on back-testing strategies that are tested on various stringent parameters including Sharpe, Calmar, and different other sets of ratios.
We also perform a Monte Carlo simulation on every strategy before deploying it live. Monte Carlo simulation picks random samples from a set of trades generated via backtest. It gives us the simulations of what could go wrong and right in the worst and the best-case scenario in a trading strategy.
When a strategy becomes operational, we observe it closely. Remember, algorithmic trading is not unsupervised trading and should be performed under proper supervision.
As for my strategies, I trade mainly in Bank Nifty. Here are some of the strategies I trade.
1. Option buying strategy with a Calmar of 5 whose entry depends on the linear regression model.
We buy an ITM option, whether it is a call or put depending on the linear regression slope which is nothing but a straight line combining candles and depicts the angle of that straight line.
As soon as the slope goes above zero, we buy a call option and when the slope moves below 0 we buy the put option and square off our call option. We trail the stop loss based on the previous 3 candle’s low.
2. Synthetic futures strategy with a Calmar of 2.5 based on ADX (Average Directional Movement Index), a risky strategy that utilises only 5 percent of our trading capital.
A synthetic future means, on the long side, we are shorting a PE and going long on CE whereas, on the short side, we go long on PE and short CE of the same strike.
There are 3 parameters in ADX +DI (Directional Indicator), -DI, and ADX line. ADX shows strength and +DI and -DI reveal the trend. Whenever ADX gains 2 basis points, we get into a position based on +DI or -DI. The system creates a synthetic futures position just to reduce the turnover cost.
The profits are trailed based on strength in ADX.
3. A Straddle strategy where we simultaneously sell ATM call and put options. This strategy has a Calmar of 10 and is a time-based entry with per leg stop-loss.
This is one of our favourite and most used strategies, it requires minimal effort. We enter straddles at a particular point in time and place a stop-loss on individual legs. This strategy we trade daily and contributes a significant portion to the gains of strategy mix.
4. I also take positional trade that generates a fixed monthly income. For this strategy, I leverage my portfolio. I have created my portfolio of Nifty Bees (ETFs) in such a way that it matches the current lot size of Bank bees in value terms.
I usually short ATM call every Friday and close them on the following Thursday. The rationale behind this is ATM calls CE trades at a 1.5 premium which can be harnessed. The entire premium can be collected in a flat or negative market and only in a strong bullish market that moves more than 1.5 percent do we incur a loss.
5. I trade the weekly Strangles where deep OTM options on both legs are sold. The option strike selected is two standard deviations away. Now two standard deviations roughly equate to a 2 times straddle premium.
For example, a Bank Nifty Straddle value is 900, 2 standard deviations would be 1800. So, we add and subtract 1800 to the current price and then short options at those strikes.
In case if the option price of any leg doubles, we roll it over to the next expiry with the same premium. We have a notional stop loss in place which is a small percentage of capital deployed.
The most important aspect in my trading, which I religiously follow, is to keep cutting your losses. Do that and the profitable trades can take care of themselves.