We had previously raised a community question titled:
“How do you decide when to pause or stop an algo strategy?”
The earlier discussion is available here:
From the beginning, we understood that all subscribed algos would not continue performing in the future exactly as they had performed historically. Market regimes change, and an algo that performed well in one period may enter a drawdown or stop working effectively in another period.
For this reason, we had already contacted both Dhan and Stratzy asking for long-term risk-management guidance:
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When should an algo be started?
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When should its allocation be reduced?
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When should it be paused or stopped?
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When should it be restarted?
Since we did not receive a clearly defined start, stop, and restart framework for each algo, we developed our own internal system to evaluate performance and rebalance our portfolio on a daily or weekly basis.
Updated live performance
We currently have 17 algos subscribed through Dhan/Stratzy. The updated cumulative performance available in our current report for 14 algos, including trades through 5 August 2026, is as follows:
| Algo | Cumulative P&L | Trades | Win Rate |
|---|---|---|---|
| Zen Credit Spread Overnight | ₹1,32,931.96 | 25 | 64.00% |
| Ratio-Return Credit Spread Exit-Early | ₹64,175.73 | 13 | 69.23% |
| Ratio-Fluxer Credit Spread Expiry | ₹25,820.49 | 2 | 100.00% |
| Settle-Down 40% TSL | ₹25,141.83 | 13 | 38.46% |
| Ripple-Return Credit Spread Expiry | ₹19,399.84 | 3 | 66.67% |
| Ratio-Ripple Credit Spread Exit-Early | ₹5,230.48 | 4 | 50.00% |
| Gamma-Fluxer Credit Spread Overnight | ₹765.99 | 18 | 44.44% |
| Wise-Move 25% TSL | -₹1,928.50 | 6 | 50.00% |
| Damper Credit Spread | -₹2,107.95 | 42 | 54.76% |
| Mathematician’s Credit Spread Overnight | -₹21,865.45 | 25 | 48.00% |
| Convex Credit Spread Overnight | -₹55,053.30 | 27 | 44.44% |
| Curvature Credit Spread Overnight | -₹59,137.55 | 26 | 46.15% |
| Fixed RR 1:3 (30% SL) | -₹72,930.09 | 15 | 26.67% |
| SkewHunter | -₹2,79,048.47 | 40 | 30.00% |
The updated performance summary is:
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Profit from profitable algos: ₹2,73,466.30
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Gross booked loss from loss-making algos: ₹4,92,071.30 -
Net booked loss: ₹2,18,605.00
In our live experience, the option-buying algos have not performed well in the current market regime. We therefore need to reduce or pause such algos and redirect capital toward strategies that are better suited to the current regime.
If we cannot rebalance and continue deploying underperforming algos, the risk of additional losses increases. Rebalancing is therefore an important drawdown-control measure, not just a method of improving returns.
The capital-efficiency problem
The total required allocation for our 17 subscribed algos is approximately ₹54 lakh.
However, our analysis of historical trades and simultaneous positions indicates that the actual capital requirement at any one time is generally only around ₹20 lakh. Even after maintaining an additional safety buffer, the requirement remains significantly below ₹54 lakh.
This means approximately ₹34 lakh, or about 63% of the total capital, may remain unutilized.
Dhan has explained that the allocated amount must be maintained separately for every deployed algo and has suggested reducing the allocation of individual algos manually.
However, this does not solve the main problem.
Whenever we:
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Add a new algo
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Reactivate a paused algo
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Modify an existing allocation
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Shift capital between algos
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Rebalance our daily or weekly active portfolio
the platform again asks us to maintain the entire cumulative allocated capital.
There is another point that requires clarification.
Full capital is required at the time of subscription or allocation modification. However, after the algo is deployed, capital can be withdrawn from the Dhan ledger. If adequate margin is not available when an order is generated, the order will be rejected by RMS.
We understand and accept that orders should fail when sufficient margin is unavailable. We are not asking for any order to be executed without adequate margin.
Our question is:
If margin is already checked at the time of every order, why should the full nominal capital of all subscribed algos be maintained merely to subscribe, reactivate, or rebalance them?
Our order history can also be checked. Except for one or two incidents when we were initially new to the platform, our orders have generally not failed due to insufficient funds. We actively monitor utilization and add capital whenever required.
We have developed our own internal platform that evaluates algo performance and selects which algos should be active each day. However, the current Dhan allocation mechanism prevents us from implementing this risk-management process efficiently.
Possible solutions could include:
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Order-level margin validation instead of full upfront allocation
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The ability to pause and reactivate algos without maintaining their full nominal allocation
This is not merely a convenience issue. It directly affects capital efficiency and our ability to control drawdowns.
We request both Dhan and Stratzy to clarify whether any portfolio-level or dynamic capital-allocation solution is planned. Without such a solution, users managing multiple algos may have to consider other platforms that provide better capital efficiency and portfolio-level rebalancing.
Has anyone else faced this issue while managing multiple subscribed algos?