Huge Drawdown in Credit Spreads and Option Buying

[Rohit1]

When every strategy fails, you start questioning yourself.

Started from last two months, I’ve deployed strategies, Credit Spreads, Zen, Curvature, Damper, Imbalance, Delta Rotation, Skew, and Fixed RR.

I diversified across different stratergies expecting consistency. Instead, I’ve experienced one of the toughest drawdowns of my trading journey.

Performance:

• Month 1: -₹50,000

• Month 2: -₹2,90,000

• Total Drawdown: ₹3.4 lakh on ₹30 lakh capital.

The most painful part isn’t the loss—it’s seeing every strategy struggle at the same time. Credit spreads, option buying… nothing seems to have an edge. Most trading days have ended with losses of nearly ₹60,000.

I’m looking for perspective to traders who have survived deep drawdowns:

Did you continue trading, reduce your risk, or step aside until market conditions changed?

I’d genuinely appreciate insights from those who’ve lived through phases like this.

[jay.gori]

Hi @Rohit1

Thank you for sharing your experience so openly.

I understand that going through a drawdown of this magnitude can be difficult, especially when multiple Algos in your portfolio are underperforming simultaneously. One point worth highlighting is that diversification is not simply about deploying multiple Algos. During certain market conditions, strategies that are otherwise uncorrelated may temporarily experience drawdowns at the same time.

This is one of the reasons we encourage users to evaluate an Algo not only on recent performance but also on its historical drawdowns, worst phases, and risk metrics before including it in a portfolio.

Since markets are inherently uncertain, no strategy can be expected to perform consistently across every market phase, and past performance should not be interpreted as an assurance of future returns.

Two months is generally not sufficient to evaluate the long-term characteristics of a quantitative strategy, particularly those designed to operate across different market cycles. Such strategies can experience extended drawdown phases before market conditions become favourable again. This is why we encourage users to evaluate Algos over a longer horizon while ensuring that the portfolio aligns with their own risk appetite and investment objectives.

For anyone building an Algo portfolio, we also recommend referring to our community post on portfolio construction, which discusses how to approach diversification from a risk-management perspective rather than simply increasing the number of deployed Algos. Which algo trading strategy should you deploy? A cheat sheet — and the philosophy behind it

We appreciate you sharing your experience, and we’re sure the discussion here will help other community members better understand the importance of portfolio construction and risk management.

[Mohit_Pandit]

Thanks for the valuable information Jay. Just to add to Rohit’s concerns, here I am putting what I have observed as pain area that inflated the losses in credit spreads this month.

I ended this month in minor losses compared to good profits in previous months but this month could have also ended in minor profits or cost to cost provided algos could have handled some of the situations in better manner like:

• One significant reason for the unnecessary losses was the re-entries triggered in spread algos like Damper and Curvature. As soon as the SL is hit, these algos sits in opposite direction without checking the conditions like price stretches and standard deviation. When it is clearly visible that market will take a break after this sprint to go back to its base level before making the further movement, these algos make re entries at almost the highest stretch point of price and ultimately cause 2 SLs in same day. One is normal SL due to opposite direction trade and another one due to re entry at inflated level.

If we could either stop these re-entries or at least put a check to re entry after the price stretch from base like vwap or ema is normalised, we could stop unnecessary SLs or make it bit difficult to be hit and thus overall performance will increase.

• Second observation is, there should be a re entry time threshold specially in intraday algos like Damper as many a times they trigger re entry quite late in the day like around 1PM and there is not enough time to achieve anything or cover up for the previous loss and ultimately it ends in further loss or unnecessary trade charges.

So the point is, in intraday spread algos, if SL is hit early in the day only then re entry should be considered or else no re entry should be triggered.

Finally, I do not know if that’s viable and possible to integrate in your current fabric of automation but if users can be given option to enable/ disable reentry or select the time threshold for re entry that could give better control to them to manage their algo risks. Like I am fine with one SL per algo but 2 SL in same algo in same day is too much and this is what has happened to many spread algos this month.

I have tried to chalk down what I observed in last 3 months of my algo running experience and while I am mostly happy with their performance, I just wanted to highlight the points that were unwanted and needs to be looked upon.

Thanks!

Mohit

[Vishal_M]

@jay.gori @Mohit_Pandit,

@Mohit_Pandit

am fine with one SL per algo but 2 SL in same algo in same day is too much and this is what has happened to many spread algos this month.

I have observed this exact same behavior closely over the past three months. Because of this, I want to reiterate the need for the previously requested “Single Trade Per Session” Toggle for Algorithms toggle for algorithms.

Stratzy needs to prioritize giving users more hands-on control to prevent users leaving the algo platform. Here is why this needs to happen quickly:

• Need for Customization: The platform must start offering more options for user preferences, tweaks, and overall customization capabilities.

• Experience-Based Overrides: Based on their own observations, users will naturally want the ability to override obvious algorithmic executions when it conflicts with their personal understanding of the market.

• Retention Urgency: If these safeguards aren’t implemented soon, users experiencing deep drawdowns will be forced to abandon algo trading entirely.

[jay.gori]

Hi @Mohit_Pandit @Vishal_M

Thank you for taking the time to share your observations.

I’d like to highlight one important point. Suggestions around re-entries or multiple stop-losses are naturally influenced by the outcome of recent trades. If the same re-entry had resulted in a profitable trade, it’s quite possible that the discussion would have been different. This is why strategy decisions cannot be evaluated based on a few recent outcomes alone.

It’s important to recognise that this is a common form of hindsight bias and outcome bias, where strategy rules are judged based on the outcome of recent trades rather than on their long-term statistical edge across different market conditions.

The Algo is currently functioning exactly as designed, based on its predefined entry, exit, and re-entry criteria. Every trade, including a re-entry, is taken only when the strategy conditions are satisfied. A losing sequence does not by itself indicate that the strategy logic is incorrect or that the Algo requires optimisation.

Our philosophy is to build robust strategies that can perform across different market environments rather than continuously modifying them in response to recent drawdowns. Frequent changes based on short-term performance can lead to curve-fitting, where a strategy appears better for recent data but becomes less reliable over the long run.

If you personally prefer not to take re-entry trades during a particular phase, you may pause the Algo after the first trade is completed. This can be done from the PnL section by turning off the toggle available at the top-right corner under the Automated Algos tab. This gives you control over your deployment without changing the underlying strategy for all users.

Finally, it’s important to remember that every Algo on the platform is designed with a specific risk profile. Drawdown phases, including periods where multiple losses occur consecutively, are a normal part of systematic trading. For this reason, we always encourage users to evaluate historical drawdowns, worst phases, and risk metrics before deploying an Algo and to ensure that the selected strategies align with their own risk appetite and investment horizon.

I sincerely appreciate your observations and the time you’ve taken to document them. Your suggestions have been noted, and we’ll continue evaluating the Algo based on its long-term performance and behaviour across different market conditions.

[Manish_Sawhney]

Hi, if you are not happy with trades which algo took on a particular day, then manual exit why to change logic of an algos? Users have deployed as they are working professionals & can’t watch/trade markets. If you don’t like any thing then pause the algo till you understand the things.

[Girish_Nair]

Hi @Rohit1

This is a valid concern and I underwent the same issues in the beginning of my trading career.

One way I have managed to lower the risk is to start the month with a non-directional strategy (delta neutral, batman, Iron condor, strangle etc.) with a large enough span where the day today changes in the premiums (or P&L) is minimum. Yes there can be volatility spikes on some days but on monthly strategies they usually iron out after a few sessions. This provides portfolio stability and peace of mind which is most important for a professional trader.

This then acts as the foundational strategy upon which I deploy credit spreads after confirming their correlation metrics available on the platform. Its also important to remember that simply choosing an algo doesn’t mean that they would be correct all the time, so I check on the algo trades the moment they are triggered and ensure that not all algos are trigerred on the same direction. This may sound counter intuitive to the algo philosophy as we are supposed to leave them alone and let them do their job. But the key issue is that Algos do not know our ‘Risk tolerance or appetite’ as we have ‘chosen’ them and not the other way around.

So if all the credit spreads are in the same direction then I trim the positions to prevent large losses (I am okay to sacrifice some profit on this account). However, If two or more algos run on different directions, I leave them untouched as the algo logic will ensure preemptive exits on loss making trades and retain the profitable trades as long as they are valid. This way I have managed to avoid deep drawdowns, maintain a smooth returns curve and my sanity. I am not saying this is the best strategy, but this has worked for me.

All the best!

[MGL_Data2]

To evaluate the strategy on long horizon, we must have the data. But we are only getting roughly 12months of the data.

[Purab]

Thank you for your query.

The trade data that you see on the platform is not backtest data. It consists of actual LIVE trades executed by the Algos for users, which is why the history begins from the time the respective Algo was made LIVE.

Please note that our backtest data is proprietary and is not shared publicly. At Stratzy, we intentionally focus on showcasing the actual LIVE trades executed by the Algos, as we believe real-world performance offers users a more transparent and practical perspective than backtest results alone.

That said, for newly launched Algos (those carrying the Early Access tag), we do provide one year of backtest data. This is intended to help users make a more informed decision on whether the Algo aligns with their risk appetite and investment approach before deploying it.

[MGL_Data2]

@Purab @jay.gori

Thank you for the clarification. However, my request for longer historical data arises directly from your earlier response:

“Two months is generally not sufficient to evaluate the long-term characteristics of a quantitative strategy, particularly those designed to operate across different market cycles.”

I completely agree with this statement. But if subscribers are shown only around 12 months of live-trade history—and the longer backtest data is not shared—how are they expected to evaluate an Algo’s long-term characteristics, historical drawdowns, worst phases, and behaviour across different market cycles?

I am not asking Stratzy to disclose the proprietary logic, parameters, source code, or intellectual property behind the strategies. I am requesting sufficient historical performance information, such as an equity curve, monthly returns, maximum drawdown, longest drawdown duration, recovery period, number of trades, and performance during different market regimes.

Ideally, the data should cover several years and include different conditions, such as:

• The COVID-19 crash and subsequent recovery

• The monetary-tightening and rising-interest-rate cycle

• Strong bull markets

• Sideways and low-volatility periods

• High-volatility and gap-driven markets

Without such data, a subscriber cannot reasonably determine whether the current drawdown is historically normal, approaching the backtested maximum drawdown, or has already exceeded the strategy’s expected risk parameters.

This concern becomes more important because several Algos appear to have crossed, or come close to crossing, their displayed backtested risk metrics and are experiencing longer or deeper drawdowns than subscribers may have expected.

Index Sniper is one example. Subscribers who deployed it around September 2025 made their decision based on the performance and risk information available at that time. It has subsequently experienced a drawdown close to the initially indicated capital requirement and has still not recovered from its peak. In such a situation, simply stating that the Algo is “Very High Risk” does not help subscribers determine whether the current drawdown remains within its historically tested range.

There also appears to be a contradiction in the current position:

1. Subscribers are advised not to judge an Algo based on a few months of performance.

2. Subscribers are encouraged to assess historical drawdowns and performance across market cycles.

3. However, the historical data necessary to conduct that assessment is not made available.

If the complete backtest is proprietary, Stratzy could still share a standardized, non-proprietary risk report for each Algo without revealing its strategy logic. At a minimum, this could include:

• Backtest period and live-trading period

• Year-wise and month-wise returns

• Maximum and average drawdown

• Longest drawdown and recovery duration

• Worst month and worst sequence of losses

• Results before and after estimated brokerage, taxes, and slippage

• Comparison of current live drawdown with the backtested maximum drawdown

• Performance across major market regimes

This would allow subscribers to make informed decisions based on evidence rather than relying on a limited recent performance window.

Could Stratzy please explain how subscribers are expected to evaluate the long-term suitability and risk of an Algo when only approximately 12 months of live history is available and the longer historical performance data is withheld?

[nijinwilson]

reduce position size. put capital into swing trading.