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Algos Under ₹50,000
Start trading with algos built for small capital
Fixed RR 1:3 (30% SL)
An extremely high-risk naked-options algo that trades volatility-skew “energy,” going long calls or puts only when stress-imbalances and both alpha signals align—using a strict 30% SL, 90% target, and tightly filtered intraday entries.
Vacuum GRID (35% SL)
Uses the GRID risk management method to execute un-hedged options with deep-SL.
Burst RR 1:2 (25% SL)
Uses the fixed risk-reward method to execute burst un-hedged options.
Burst GRID (30% SL)
Uses the GRID risk management method to execute burst un-hedged options.
Algos Under ₹1,00,000
Algos designed for growing portfolios
SkewHunter
A naked-options “Skew Hunter” algo that hunts extreme IV and volume-OI skew across strikes—entering directional options only when both volatility and flow signals align, with strict intraday risk controls.
SkewHunter TSL
A naked-options “Skew Hunter” algo with a TSL that targets extreme IV and flow skew, taking directional trades only when both signals align—then locking in gains through an adaptive trailing stop-loss.
Curvature Credit Spread Overnight
A credit-spread strategy that behaves like a market fluid-dynamics engineer—reading liquidity flow, viscosity, and curvature across strikes, and profiting when these flow patterns rebalance.
Fixed RR 1:3 (30% SL)
An extremely high-risk naked-options algo that trades volatility-skew “energy,” going long calls or puts only when stress-imbalances and both alpha signals align—using a strict 30% SL, 90% target, and tightly filtered intraday entries.
Algos Under ₹2,00,000
Diversified strategies for mid-size capital
Ratio-Ripple Credit Spread Exit-Early
This algorithmic trading strategy, named "Ratio-Ripple Credit Spread Exit-Early," aims to identify opportunities in the NIFTY 50 index options market by analyzing the relationship between implied volatilities (IV) of out-of-the-money (OTM) and at-the-money (ATM) options. The algorithm calculates a proprietary alpha signal derived from the difference between OTM and ATM implied volatilities and their rate of change, using time-series ranking to normalize the signal. Trades are triggered when the alpha signal exceeds a predefined threshold, indicating a potential mispricing in the options market. A secondary condition has been added that checks the rate of change of delta values. The algorithm factors in market open hours, expiry dates and tested time periods to find trading opportunities. The algorithm implements a credit spread strategy, specifically targeting the execution of credit call spreads or credit put spreads based on the signals generated. Credit spreads profit from a narrowing of the spread between the short and long options, which typically occurs when implied volatility decreases or when the underlying asset price moves in a favorable direction. The trades are executed by shorting a near-the-money (NTM) option and simultaneously buying a further out-of-the-money (OTM) option with the same expiration date and strike type, limiting potential losses. This strategy is typically favorable in sideways or moderately trending markets, where the expectation is for the underlying asset to remain within a defined range, allowing the options to expire worthless or with reduced value, thus generating profit.
Ripple-Return Credit Spread Expiry
The "Ripple-Return Credit Spread Expiry" algorithm is designed to identify and execute credit spread option strategies on the NIFTY 50 index, aiming to profit from the decay of option premiums as they approach their expiry date. The core strategy involves analyzing implied volatility (IV) across different strike prices to determine potential overpricing of options. It leverages technical indicators, specifically comparing the IV of out-of-the-money (OTM) options against at-the-money (ATM) options and their rate of change (delta), using a time-series rank to normalize the alpha signal. By identifying instances where OTM options are relatively overpriced compared to ATM options, the algorithm seeks to sell the overpriced options and simultaneously buy options further out-of-the-money to create a credit spread. The algorithm incorporates risk management techniques such as setting stop-loss and target levels based on a percentage of the margin required and/or spread premium, respectively. This algorithm trades credit spreads on NIFTY 50 index options, specifically targeting weekly expiry options. Credit spreads benefit from sideways or moderately directional market movements where the sold options expire worthless, allowing the trader to keep the premium received. The algorithm enters trades between 10:15 AM and 2:15 PM, avoiding trading on expiry days and outside of defined trading hours to align with backtested timeframes. The strategy aims to capitalize on the time decay of options close to expiry, while limiting potential losses through the purchase of further OTM options in the spread.
SkewHunter
A naked-options “Skew Hunter” algo that hunts extreme IV and volume-OI skew across strikes—entering directional options only when both volatility and flow signals align, with strict intraday risk controls.
SkewHunter TSL
A naked-options “Skew Hunter” algo with a TSL that targets extreme IV and flow skew, taking directional trades only when both signals align—then locking in gains through an adaptive trailing stop-loss.
Algos More Than ₹2,00,000
Advanced algos tailored for large investors
Sahi-Nivesh Short Strangle Overnight
Imagine you're running a small shop and need to decide what to stock for the upcoming week. Instead of guessing, you look at a bunch of information: recent sales data (like past prices), general market trends, and even what's popular on social media (like implied volatility and sentiment). You use all this to figure out if there's a good opportunity to sell something everyone thinks will stay stable – like umbrellas before a predicted sunny week. The goal is to make a small profit if things go as expected, but be ready to quickly cut your losses if the weather suddenly changes. This algorithm does something similar, using market data and indicators to find opportunities where it believes things will stay relatively calm, so it can profit from that stability. This algorithm trades "short strangles" on the NIFTY 50 index, which is like betting that a stock's price won't move much. A short strangle strategy typically works best when the market is expected to be relatively stable. It sells options contracts that will only make money for the buyer if the price of the underlying asset moves a lot. The strategy aims to collect small profits from these options contracts expiring worthless if the market stays within a certain range. It works when the market prediction is stability, or low volatility.
Expiry Short Strangle
Carries the short strangle from one expiry to next, aiming for complete premium decay.
Intraday Short Strangle
Daily strangle algo.
Holonomy's Short Strangles
This Morning Short Strangle algo strategically sells both call and put options on the index in the morning, aiming to capitalize on premium decay or range-bound market conditions.
Similar Categories
Algos Under ₹50,000
Algos Under ₹1,00,000
Algos Under ₹2,00,000
Algos More Than ₹2,00,000
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