Discover Algos by Investment

Fixed RR 1:3 (30% SL)

by Stratzy

NiftyBuyingDirectional

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.

Min. Amount: ₹45,000

Results:

IIIIIIIIIII% Backtest

Deploy

Vacuum GRID (35% SL)

by Stratzy

NiftyBuyingDirectional

Uses the GRID risk management method to execute un-hedged options with deep-SL.

Min. Amount: ₹50,000

Results:

IIIIIIIIIII% Backtest

Deploy

Dividend Dons Automated

by Stratzy

EquityLong-Term

A stock picking algo of dividend giving stocks.

Min. Amount: ₹30,000

Results:

IIIIIIIIIII% Backtest

Deploy

Alpha Industries Automated

by Stratzy

EquityLong-Term

Invest in top sectoral ETFs.

Min. Amount: ₹35,000

Results:

IIIIIIIIIII% Backtest

Deploy

SkewHunter

by Stratzy

NiftyBuyingDirectional

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.

Min. Amount: ₹1,00,000

Results:

IIIIIIIIIII% Backtest

Deploy

Fixed RR 1:3 (30% SL)

by Stratzy

NiftyBuyingDirectional

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.

Min. Amount: ₹45,000

Results:

IIIIIIIIIII% Backtest

Deploy

Settle-Down 40% TSL

by Stratzy

NiftyBuyingDirectional

Imagine a savvy shopper at a bustling farmers market. They're not just grabbing the first apple they see; instead, they carefully scan the stalls, looking at the volume of shoppers around each vendor and comparing the quality and prices of the produce. This shopper is particularly interested in finding unusual deals where there are lots of sellers with fewer buyers, suggesting prices might be about to move. They’re not afraid to buy something cheap, but they set a firm stop-loss: if the apple starts to rot (the price drops too much), they quickly toss it to avoid a bigger loss. They also have a clever trick—if the apple starts to ripen nicely (price rises), they’ll adjust how quickly it has to rot (stop loss trails the price) before they toss it, locking in some profit. This algorithm trades options on the Nifty 50, a major Indian stock market index. It's looking for potential imbalances in the options market by comparing the trading volumes of different types of options (calls and puts, both in-the-money and out-of-the-money) to gauge market sentiment. It uses some complex calculations based on options data to determine if it should buy a call option (betting the market will go up) or a put option (betting the market will go down). Before acting, it makes sure it's the right time of day and not close to the expiry date of the options. If it decides to trade, it buys a single option and sets a stop-loss level to limit potential losses. It may also move the stop-loss as the price of the option changes, and takes the intiative to stop the trade to minimize losses.

Min. Amount: ₹1,00,000

Results:

IIIIIIIIIII% Backtest

Deploy

Curvature Credit Spread Overnight

by Stratzy

NiftyHedgedDirectional

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.

Min. Amount: ₹1,00,000

Results:

IIIIIIIIIII% Backtest

Deploy

Ratio-Ripple Credit Spread Exit-Early

by Stratzy

NiftyHedgedDirectional

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.

Min. Amount: ₹1,20,000

Results:

IIIIIIIIIII% Backtest

Deploy

Ripple-Return Credit Spread Expiry

by Stratzy

NiftyHedgedDirectional

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.

Min. Amount: ₹1,20,000

Results:

IIIIIIIIIII% Backtest

Deploy

SkewHunter

by Stratzy

NiftyBuyingDirectional

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.

Min. Amount: ₹1,00,000

Results:

IIIIIIIIIII% Backtest

Deploy

Fixed RR 1:3 (30% SL)

by Stratzy

NiftyBuyingDirectional

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.

Min. Amount: ₹45,000

Results:

IIIIIIIIIII% Backtest

Deploy

Chanakya Short Strangle Overnight

by Stratzy

NiftySellingNon-directional

Imagine you are a farmer deciding when to harvest your crops. You look at various factors like weather patterns, the plant's growth stage, and even the overall market demand for your produce. Instead of just guessing, you use a checklist and some historical data to see if all the conditions are right: is the crop mature enough? Is the weather stable for a few days? Are there signals that prices might rise soon? If everything lines up according to your plan, you harvest; otherwise, you wait and check again later. This algorithm similarly assesses market conditions and executes a trading strategy only when specific criteria are met, aiming to capitalize on those moments. This trading algorithm focuses on "short strangles" on the NIFTY 50 index options, a strategy that benefits when the market is expected to remain relatively stable. A short strangle involves simultaneously selling a call option (betting the price won't go much higher) and a put option (betting the price won't go much lower) at strike prices outside the current market price. This strategy can be profitable as long as the price of the underlying asset doesn't move beyond those strike prices before the options expire, allowing the trader to collect the premium from selling the options.

Min. Amount: ₹2,50,000

Results:

IIIIIIIIIII% Backtest

Deploy

Sahi-Nivesh Short Strangle Overnight

by Stratzy

NiftySellingNon-directional

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.

Min. Amount: ₹2,50,000

Results:

IIIIIIIIIII% Backtest

Deploy

Homecoming Short Strangle Overnight

by Stratzy

NiftySellingNon-directional

Imagine you're a shopkeeper predicting foot traffic in your store. This algorithm is similar: it analyzes past market data (like historical prices and volatility) to estimate how much an index will move overnight. Based on this estimate, it decides to "rent out" a range of prices *above* and *below* where the index is currently trading. If the price stays within that range, the algorithm keeps the "rent" (profit). It's like betting that not many customers will come into your store, and then profiting because you were right. This algorithm trades "short strangles" on the NIFTY 50 index options. Short strangles are typically chosen when the market is expected to be relatively stable, with low volatility, overnight. The idea is that the prices of the options sold will decline as time passes, and the algorithm profits from this decay, as long as the underlying asset's price doesn't move beyond a certain range.

Min. Amount: ₹2,50,000

Results:

IIIIIIIIIII% Backtest

Deploy

Bazaar Short Strangle Overnight

by Stratzy

NiftySellingNon-directional

Imagine you're a roadside fruit vendor who wants to make a little extra money overnight. You notice that prices for mangoes and bananas tend to be stable, but might fluctuate slightly. So, instead of betting on one specific fruit going up or down, you decide to sell both mangoes *and* bananas at a slightly lower price, hoping they stay within a predictable range. As long as neither fruit price swings wildly up or down, you profit from the difference between your selling price and where you bought the fruit, like a small "premium" for bearing the risk. If mangoes soar in price or bananas become worthless, you might lose money, but you're counting on things staying calm overnight. This algorithm is designed to trade options on the stock market index, Nifty 50. Specifically, it executes what's called a "short strangle" strategy, which involves selling both a call option (the right to buy) and a put option (the right to sell) on the index, both outside of the current market price. This strategy typically works best when the market is expected to be relatively stable, with minimal price fluctuations. The goal is to collect a premium from the sale of these options, profiting if the index price stays within a certain range until the options expire.

Min. Amount: ₹2,50,000

Results:

IIIIIIIIIII% Backtest

Deploy