Is anyone else facing order rejection / reconciliation issues with Dhan Marketplace algos?

Stratzy Algos
1

Hi everyone,

I wanted to check whether anyone else using Dhan Marketplace algos, especially provider-hosted strategies such as Stratzy credit spread overnight algos, has faced similar issues recently.

Over the past few trading sessions, I have noticed a few recurring problems:

  1. Insufficient Funds / RMS rejection even when capital is allocated
    Some legs of credit spread strategies are getting rejected with messages such as “Insufficient Funds” or “This leg is part of a hedge. Close the main position or add funds to continue.”
    This is confusing because these are hedged spread strategies, and capital had already been allocated to the algo.

  2. Hedge-leg sequencing issue during exit
    In some cases, while exiting a hedged position, one leg seems to remain pending and the other leg gets rejected because it is considered part of a hedge. This can leave the user with unexpected open exposure, requiring manual intervention.

  3. Mismatch between broker position view, algo view, and provider platform
    There have been cases where the position appears closed in one view, but still appears open in the algo-wise position view or on the strategy provider’s platform. This creates confusion about the true exchange-level position and whether any live exposure still exists.

  4. Incomplete algo order history
    In one instance, the buy/order entry leg was visible in the algo order history, but the corresponding exit order was missing. Support later mentioned that this was because the position was manually closed, so it did not reflect in the algo history. However, this makes P&L and trade reconciliation difficult, especially when manual closure is required due to earlier algo/order failures.

Dhan support has mentioned that they are investigating the “Insufficient Funds” rejection and P&L mismatch issues. They also explained that in hedge exits, if the first leg remains pending, the second leg may be rejected, and that a retry mechanism is being implemented or used to handle such cases.

I wanted to ask the community:

  • Has anyone else faced similar Insufficient Funds / hedge-leg rejection issues in Dhan Marketplace algos?

  • Have you seen mismatches between Positions by P&L, Positions by Algo, and the strategy provider dashboard?

  • How are you handling reconciliation when positions are manually closed but the algo order history does not fully reflect the exit?

  • Is there any recommended capital buffer, margin setting, or operational practice to avoid these daily hedge-leg failures?

Would appreciate inputs from anyone using similar credit spread / overnight strategies on Dhan Marketplace.

Thanks.

2

I got an insufficient funds error on Thursday. My buy order went through, but my sell order got rejected because of insufficient funds. I sent the details to Stratzy and Dhan, but no one can tell me why it happened.

3

Kb tk solve hoga ye issue?? Is issue ki wajah se koi Big loss ho jaye usse phle kr do

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4

I clearly explained the list of issues in the algo to both Dhan and Stratzy. After 2–3 weeks of regular follow-ups, they informed me that they had introduced a new feature to reduce hedge order failures.

Previously, the system waited for 3 seconds before proceeding. Now, they have implemented a retry mechanism that attempts the hedge order up to 6 times. If the hedge order is executed within these 6 retries, the main leg is placed. However, if the hedge order still fails after all retries, the system proceeds to place the main leg anyway.

While this is an improvement over the previous implementation, it is still not a foolproof solution that traders can rely on with confidence. There remains a possibility of the hedge order failing while the main leg gets executed, exposing the trader to unnecessary risk. As a result, users still need to actively monitor the screen instead of being able to trust the algo to manage execution safely.

5

Hi @MGL_Data2

Thank you for taking the time to share your observations.

I would like to clarify a few points that may help everyone better understand the execution flow.

The scenarios you’ve described are not caused by the Algo strategy itself, but are a consequence of the latest exchange regulations, under which API-based orders are required to be placed only as LIMIT orders. This means that order execution is now dependent on market liquidity and whether the LIMIT order gets filled.

For multi-leg strategies such as Credit Spreads, the long (hedge) leg needs to be executed first so that the broker RMS can unlock the hedge benefit. Only after this can the short leg be executed with the reduced margin requirement.

If the first leg remains pending and the second leg is placed, the broker RMS may reject the second order due to insufficient funds, since the hedge benefit has not yet been released. A similar flow applies during exits, where the short leg needs to be exited successfully before the long leg can be closed. If the first exit order remains pending, the subsequent order may again be rejected as per the broker’s RMS rules stating that it’s a part of the hedge strategy.

To improve the overall execution experience within these regulatory constraints, we have introduced the Retry Mechanism as the first step. Rather than immediately proceeding, the Algo now retries (for exit) pending orders before moving ahead, significantly reducing such scenarios compared to the earlier implementation. To learn more, refer to the post here: Retry Mechanism: Stratzy’s Most Requested Feature Update

That said, we completely understand the expectation that user intervention should be minimal. Improving execution reliability continues to be one of our highest priorities, and we will keep enhancing the order management system while remaining compliant with the latest exchange regulations.

If you notice any difference in P&L, we request you to please reach out to the support team of the platform through which the Algo has been deployed, along with the relevant details. We will be happy to review the specific instance and provide further clarification.

Lastly, the orders displayed under History represent only the orders placed by the Algo. We recommend avoiding manual intervention during the execution process, as this can lead to mismatches between manually placed orders and Algo-managed positions. However, the PnL History of the Algo gets updated even though you intervene manually.

I appreciate your feedback and patience as we continue improving the platform.

6

Hi @Shiv_Kumar

I request you to please write to us at support@stratzy.in and share all the relevant details, including:

  1. Your broker Client ID
  2. The complete broker order book for that trading day (showing all successful, pending, failed, and cancelled orders)
  3. The corresponding order timestamps

These details will help our team investigate the execution flow thoroughly and identify what may have happened in your specific case. Once we have the information, we’ll review it and assist you accordingly.

7

Hi @Manish_Ahuja

Please note that this is not an issue, but a consequence of the latest exchange regulations requiring API-based orders to be placed as LIMIT orders. We’ve explained the execution flow and the reason behind this in the post above. Is anyone else facing order rejection / reconciliation issues with Dhan Marketplace algos? - #5 by jay.gori

If you still need any further clarification or would like us to review a specific instance, please write to us at support@stratzy.in with the relevant details. Our team will be happy to investigate it further and clarify the behaviour accordingly.

8

Thank you for the detailed clarification.

Could you please confirm exactly what data or format you would like me to share for reviewing the P&L difference and execution issues?

I have recorded details of all Algo orders, including cases where Algo orders failed or remained pending and I had to place manual orders. I also have the manual order time, Algo order time, and the corresponding strategy backtest time. In some cases, I have observed a delay between the real order signal and the backtested signal, which appears to have contributed to the P&L difference.

Regarding P&L, the Dhan API team has also accepted that there was a UI bug causing incorrect P&L display. I understand this may have been resolved now, though I have not verified it again yet.

Also, yesterday there was an issue during the exit of Curvature Credit Spread Overnight. The hedge leg was exited, but the main leg order was never placed. We had to manually intervene and exit the main leg. This also subsequently affected Gamma Fluxer Credit Spread Overnight. Dhan team acknowledged that there was an issue and informed affected users after around 30–40 minutes to manually exit their positions. This raises a serious concern regarding execution reliability, especially for overnight strategies where timely and correct exit is critical.

I understand the exchange regulation constraints and the requirement to place API orders as LIMIT orders. However, the concern here is not just about pending orders due to liquidity, but also about cases where the Algo does not place the next required order at all, or where there is a delay compared to the expected strategy execution flow.

Regarding the Retry Mechanism, I would also like to mention that I had suggested a similar retry-based approach to Dhan in writing, as well as during multiple telephonic conversations. I am glad this has now been introduced, but I believe more improvements are still required to make execution more reliable and reduce the need for manual intervention.

Over the last two weeks, I have sent multiple emails to both Dhan and Stratzy regarding various execution issues, P&L mismatches, and possible improvements. I am happy to share all relevant data, but please confirm what specific details are required from my side so that the review can be done properly.

My main request is that these issues should be reviewed not only as individual cases, but also from a system reliability perspective, because manual intervention during Algo execution can create position mismatches and additional risk for users.

9

Hi @MGL_Data2

Thank you for your detailed follow-up.

Based on our current observations, this does not appear to be a widespread behaviour. Since the difference you’ve highlighted is specific to your deployment, we’d like to review the exact trade instances to understand the observed behaviour better. To help us investigate the P&L differences thoroughly, we request you to reach out to the support team of the platform where you have deployed the Algo and share screenshots highlighting the exact difference you observe. This will allow us to compare the execution flow with the displayed P&L and clarify the behaviour accordingly.

Similarly, if you have observed any instances where the Algo order did not proceed for reasons other than a LIMIT order remaining pending, please share the exact trade details, timestamps, and the difference you observed. We’ll be happy to investigate those specific instances in detail.

We understand your expectation that the execution flow should be as seamless as possible. Feedback like this is reviewed carefully by our team and plays an important role in shaping future improvements to the execution framework. We remain committed to continuously enhancing the platform experience while complying with the latest exchange requirements.

With respect to the Retry Mechanism, as mentioned in our announcement, the current implementation is Version 1. This is the first step, and we will continue enhancing it based on real-world observations and user feedback to make the execution flow even more robust.

Since you’ve already been in touch with our support team, we recommend continuing the conversation there. Having all the relevant trade details, account information, and previous communication in one place enables us to investigate more efficiently and avoids duplication or confusion across multiple channels.

We appreciate the time you’ve taken to document your observations and share constructive feedback. It genuinely helps us continue improving the platform.

1 Like
13

@jay.gori @MGL_Data2 i have faced and stopped algo trading…..even if u r trying better order placement. The error in placement of error should be reported immediately. Dhan informed me after 1 hour to square off curvature credit leg. An instant whatsapp notification or app notification on dhan should come. Mannual intervention till it’s not permanently omitted must be strengthened. I don’t understand why do u want feedbacks don’t u have your own money being deployed. All these suggestions should have been implemented from day 1 like common sense. If there is a pot hole on road u should not wait for accident and then user reporting it. If it’s visible and probable u should take care of it on your own. Be proactive rather than reactive

14

Hi @Abhijeet_arora

Your point regarding timely notifications and user communication is valid.

We agree that it’s important, especially when intervention may be required. We’ve noted your feedback regarding instant app and WhatsApp notifications and will evaluate it as part of our ongoing platform improvements.

At Stratzy, our goal has always been to minimise user intervention and deliver the most seamless Algo trading experience possible. At the same time, we also have to operate within the latest exchange regulations governing API-based order execution. We continuously review real-world scenarios and user feedback to identify areas where the platform can be further strengthened while remaining compliant.

Many of the enhancements we’ve introduced, including the recently launched Retry Mechanism (Version 1), are part of this continuous effort. We will continue building on these improvements to make the overall execution experience more robust.

We genuinely appreciate your feedback and the time you’ve taken to share it. It helps us prioritise improvements that create the greatest value for all users.

15

If you’re using DhanHQ, you should switch to Stratzy. I get a notification on my phone whenever an algo enters or exits a trade, so I can check my Dhan account to see if the order went through.