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%
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%
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%
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.
