Author: madhu babu

Next week marks a strategic deployment in live algo trading with heavy diversification across natural gas options (14 lots), crude oil options (8 lots), silver futures (2 lots), Nifty index options (7 lots), and stock options (69 lots) across 10 to 14 stocks. Review happens next Saturday to assess profits and losses, but decisions stay data-driven over long-term performance, not weekly swings . Fixed ₹2000-₹3000 target strategies pause in live after booking targets last week but missing bigger gains; forward testing continues with varied SLs and targets .

Deployment Breakdown

Positions span MCX commodities and NSE derivatives for balanced exposure.
This mix reduces correlation risks from past drawdowns in single assets like Nifty and natural gas .

Forward Testing Fixed Targets

Strategies hitting ₹2000-₹3000 daily stay in sandbox mode only, observing SL adjustments post-last week’s live. Live capital flows to uncapped versions for full trend capture, aligning with 5+ years of refining data-backed systems . Review post-next week decides scaling, prioritizing equity curves over short bursts .

Embracing Profits and Losses Alike

Humongous profit weeks excite sharing; loss weeks less so, but both fuel the business—I believe that losses are as  controlled “expenditures” like buying raw materials before profits roll in . controlled losses build resilience for asymmetric wins in algo trading .every one may not share same beliefs about losses this may not be comfortable me sharing them. That’s why generally I avoid sharing losses. But I must accept proudly that I do book losses but controlled losses

Past performance does not guarantee future results. Derivatives trading carries substantial risk of capital loss due to volatility, slippage, and margins. Deploy only risk capital , test in paper mode, and consult SEBI-registered advisors. Not investment advice—trade responsibly.

The stock had already been on my momentum radar for a while. Bosch Ltd has the perfect ingredients for an intraday options play: 
– Wide intraday ranges with strong directional bursts. 
– Respectable option premiums that reward conviction. 
– Clear technical structure that responds well to momentum filters.

Going into today’s session, my Bosch momentum strategy was ready on AlgoTest, configured to fire a 27 JAN 37000 CE breakout trade with proper position sizing and risk controls. The plan was simple: if price moved in line with my momentum rules, the system would enter automatically and manage the leg.

The liquidity twist

The day, however, decided to start with a twist. 

In two of my accounts, the trade simply did not go through. The algo threw up a message that some legs could not be formed because of **liquidity issues** in the selected Bosch options contract. That is the harsh reality of trading stock options in India – even a rock‑solid setup can be blocked by thin depth on the bid–ask. 

For a few minutes it looked like Bosch would remain just another “almost” trade in the log: a good idea, perfectly planned, but never executed.

One fill, four lots

Fortunately, one account slipped through the liquidity filter and actually got the desired fill. The 27 JAN 37000 CALL order was executed for 2 lots, while the other accounts stayed flat. 

From there, Bosch did exactly what a momentum trader hopes for: 
– Price sustained above key breakout levels instead of fizzling out. 
– The call premium started expanding rapidly as the underlying kept pushing higher. 
– Risk remained controlled, as the move was clean rather than whipsaw‑ridden.

Watching the position from my broker’s app, the MTM kept climbing. At the time of writing this blog, the Bosch 27 JAN 37000 CE position in that single account is showing an unrealised profit of around ₹80,000 for 4 lots – a powerful reminder of how one well‑executed idea can pay for a long series of quiet days.

Lessons from the Bosch trade

Days like this are not just about the number on the P&L screen; they are about the lessons the market quietly offers:

Liquidity is part of edge: A strategy is not complete until liquidity is factored in. Robust entries, exits, and risk parameters mean little if the instrument itself does not allow smooth execution. Today’s experience underlined why stock‑option strategies must always respect depth and slippage.

– Missed trades can be blessings: It is easy to feel frustrated when a good setup does not get executed in all accounts. But risk is path‑dependent. The same liquidity issue that blocked entries today could just as easily protect capital on a bad day. In this case, the market was kind and allowed at least one account to participate.

– Consistency beats prediction: There was nothing “magical” about today’s Bosch move. The trade came directly from a predefined momentum framework. No gut feeling, no last‑minute improvisation – just systematic execution. When the system and market conditions aligned, the result showed up in the P&L.

Closing thoughts

For now, Bosch Ltd wears the crown of “show man” of the day in my trading diary. The liquidity drama, the partial execution across accounts, and the strong follow‑through in price action together created a memorable session. 

As this position continues to evolve, one thing is clear: in algorithmic and rule‑based trading, the job is to keep refining the process, respecting liquidity, and showing up every day – because it only takes one clean move like Bosch to make the entire effort feel worthwhile.

Algorithmic trading is fascinating — it gives us the power to automate, optimize, and execute trades across platforms with precision. But with great automation comes the responsibility of double-checking even the smallest details.

Recently, I encountered a situation that perfectly illustrates this. I had deployed one of my live trading strategies on Tradetron. Everything seemed fine — the logic was solid, the backtests were impressive, and I had allocated sufficient funds for execution. Yet, every single day, I was receiving error messages: “Insufficient balance.”

At first glance, this was puzzling. After all, my Dhan account had more than enough funds. It took me a while before I realized what had gone wrong — I had accidentally deployed the strategy with Shoonya as the broker instead of Dhan. Shoonya, of course, had no balance. That simple mismatch was generating daily execution errors.

It might sound like a small mistake, but it was a powerful reminder. In algo trading, even seemingly minor configuration errors can completely derail a system’s performance. The markets may forgive a missed entry, but automation does not forgive oversight.

This experience reinforced one key lesson: always verify your broker mapping and fund allocation before deploying a live strategy. A quick checklist can prevent hours of unnecessary debugging and execution stress. Algo systems are only as reliable as the human discipline behind them.

In short, this “silly” mistake made me ultra-aware of the importance of detail-oriented execution. Automation magnifies efficiency — but it also magnifies human errors. The more advanced our trading setups become, the more humble and careful we must stay as traders.

The first trading day of the year did not come dressed as a gentle mean-reversion day.
It came as one of those rare, asymmetric sessions where markets remind you that returns do not follow neat textbook curves but thick, messy fat tails instead.

On paper, the setup was simple: eight stocks on the radar, six of them ending in the red for the P&L, and only two—DMart and ITC—showing profit. That distribution alone says a lot about how real trading works: most bets grind, a few shine, and a tiny minority pay for the whole day, week, sometimes year.

ITC: When A Defensive Name Misbehaves

ITC is supposed to be the calm, “defensive” counter in most portfolios, the stable leg people hold for comfort.
Yet on this particular day, ITC behaved like a high-beta midcap and tanked around 10%, offering the kind of sharp move that is statistically “rare” but empirically far more frequent in markets with fat-tailed return distributions.

The trade was elegantly one-sided in spirit:

• Buying puts to stay long convexity on the downside.
• Selling calls to monetize elevated volatility and help finance the bearish view.

Capturing nearly 80% of that 10% slide meant the structure was aligned almost perfectly with the path price eventually took. The stock did the violent part; the options  translated that violence into realized P&L.

Taleb, Fat Tails, And Barbell Profits

Nassim Nicholas Taleb’s work is built around a simple but brutal observation: financial markets live closer to “Extremistan” than “Mediocristan”; extreme moves happen more often than Gaussian models admit.
In fat-tailed worlds, the rare event is not an outlier—it is the main character that pays the bills.

The “barbell” Taleb proposes is conceptually binary:

• One side: ultra-safe, low-risk exposures that keep you away from ruin.
• Other side: highly convex, speculative bets that lose small most of the time but make disproportionate gains when tails show up.

On a day like this, the ITC book is the right-side barbell in action: a convex payoff profile positioned for a tail move. The rest of the watchlist, with its small scratches and losses, is the cost of staying in the game long enough to meet that one payoff.

TorntPharma: A Quiet Reminder About Liquidity

Not everything that can theoretically be traded should be traded aggressively.
TorntPharma offered a live reminder: thin liquidity, wider spreads, and the ever-present risk that getting in is easy but getting out is a completely different story.

Illiquidity transforms seemingly attractive option structures into fragile ones.
Slippage and partial fills can eat into the exact convexity that fat-tail thinking is trying to harness. Escaping the day without TorntPharma impacting the book was another, quieter form of profit: the gain of avoiding unnecessary fragility.

The Essence Of A Fat-Tail Trading Day

Looked at through Taleb’s lens, this day is not “lucky” noise; it is the structural logic of a barbell philosophy playing out in miniature.

• Many trades existed, but few mattered.
• Among the few that mattered, one trade—ITC—dominated.
• The payoff came not from predicting the exact magnitude of the fall, but from standing positioned with convexity when the fall happened.

This is what fat-tail profits look like in practice: a handful of outsized winners paying for a sea of mediocrity, wrapped inside a framework that respects risk, avoids ruin, and stays humble before uncertainty—very much in the spirit of Taleb, and a fitting way to open a new trading year.