Thanks for your detailed questions. Happy to clarify.
1. Capital Range
Yes, the minimum allocation for Zen Credit Spread Overnight is ₹1 lakh, and the maximum allocation is ₹3.2 lakh.
2 & 3. How many lots will the algo trade?
There isn’t a fixed number of lots. It depends on the margin required for the trade, which varies based on the option premiums at the time of entry.
Generally, 1 lot requires around ₹55,000–₹60,000 in margin.
With an allocation of ₹1 lakh, the algo will typically trade 1 lot. But if the margin requirement is lower on a particular day, it may be able to take 2 lots.
With an allocation of ₹3.2 lakh, the algo usually trades 4–6 lots, depending on the margin requirement and option premiums on that day.
4. Will the algo automatically increase the quantity if my account balance grows?
No. The algo trades based on the capital allocated to it, not the total balance available in your trading account.
5. If my capital grows due to profits, will the algo automatically use the additional capital?
No. Any profits added to your ledger are not automatically considered for position sizing.
6. How can I increase the quantity/allocation then?
If you want the algo to use more capital, you can simply use the “Edit Capital” option for that deployment and increase the allocation. The algo will then size future trades based on the updated allocation.
7. Is there a formula for quantity scaling?
There isn’t a fixed formula. The quantity primarily depends on the margin required for that day’s trade, which varies with option premiums and market conditions. That’s why the number of lots can differ even for the same allocated capital.
Hope this clears it up! If you have any other questions around deployment or capital allocation, feel free to ask.
Since the number of lots depends on the margin requirement, is there any minimum free balance or buffer that should always be maintained above the allocated capital to avoid order rejection due to margin fluctuations?
Also, if the margin requirement suddenly increases after deployment (because of volatility or exchange changes), will the algo automatically reduce the number of lots for the next trade, or will it skip the trade if sufficient margin isn’t available
The minimum capital requirement for every algo is determined after considering the margin requirements of the strategy, with the objective that trades are not skipped due to insufficient margins under normal circumstances.
Since margin requirements can vary with option premiums and exchange regulations, the number of lots is automatically determined based on the allocated capital and the margin required at the time of trade. If margins are higher, the algo may take fewer lots instead of forcing the same quantity.
So, in general, you don’t need to maintain an additional buffer over and above the allocated capital specifically to avoid order rejections. The minimum allocation itself is designed keeping this in mind. However, it’s always a good practice to avoid blocking your available funds with other positions or orders, as that could impact margin availability.
Also, please tell @purab_stratzy the displayed return is a backtest/performance figure, not a guaranteed future return. Real execution can differ because of option liquidity, slippage, brokerage, taxes, spreads, ??? or this is real return after all option liquidity, slippage, brokerage, taxes, spreads
On slippage / real vs. displayed P&L: The displayed performance reflects actual trades taken by the Algo, but your individual execution can still differ from it — this is normal for any live strategy, and is mainly driven by option liquidity, bid-ask spreads, order timing, brokerage, and taxes. Stratzy’s algos execution on DhanHQ runs on Dhan’s infra, which is designed to route and execute orders efficiently and keep slippage as low as practically possible, but no infra can guarantee zero difference between a displayed trade and your fill — especially in less liquid option contracts. So treat the displayed numbers as a real performance record, not a guarantee of what you personally will realize.
On backtest vs. live: To directly answer your second question — no, this is not backtested. The trade data shown on the platform consists of actual LIVE trades executed by the Algo for users, and the history starts from when that Algo went live. Our backtest data itself is proprietary and not published. The one exception is newly launched Algos tagged “Early Access,” for which we do share one year of backtest data, specifically to help users gauge fit before going live with real capital.
Since the displayed performance is based on actual live trades, could you please clarify one more point regarding execution costs?
For Zen Credit Spread Overnight, is there any historical data available showing the approximate difference between:
Algo’s displayed/recorded trade P&L, and
The actual P&L realized by users after their execution/fills, brokerage, STT, taxes and other charges?
For example, if the strategy shows a ₹10,000 profit over a period, is there any typical range users should expect after slippage and all applicable charges?
I understand that slippage cannot be guaranteed and varies with liquidity and bid-ask spreads. I’m mainly trying to estimate a realistic net return after execution costs before deciding the capital allocation.
Also, is the displayed P&L on the platform gross of brokerage/statutory charges, or are any of those charges already deducted from the displayed performance?
Absolutely, I understand what you’re trying to estimate here: essentially, what the actual net P&L could look like after execution and trading costs.
Slippage can vary from user to user and can be both positive and negative, since it is dynamic and depends on factors such as market conditions, liquidity, and the bid-ask spreads . Because of this, taking an average slippage figure across users and comparing it with the Algo’s performance would not give a meaningful estimate of what an individual user can expect.
So, to answer your question directly, we currently don’t provide historical data that compares the Algo’s displayed P&L with the actual realised P&L of users after execution costs.
We also wouldn’t be able to share individual users’ P&L, as that is their personal financial information. However, once you deploy the Algo through Dhan Algos, you can see your own Gross P&L as well as Net P&L, with a bifurcation of brokerage and applicable trading charges. This should give you a much clearer picture of your actual post-cost performance.
One important point regarding the performance displayed on Stratzy: the P&L shown under “Past Trades” is before brokerage and other trading charges. These charges are not deducted from the performance displayed there.
Hope this helps you get a clearer picture as you evaluate the capital allocation.
