Backtest Returns
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Backtest Snapshot

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Algo
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Algo Score - It's a single number that summarizes an Algo's overall performance by combining returns, risk, volatility, drawdowns, and consistency. A higher score indicates stronger, more stable, and better risk-adjusted performance.

Score
Correlation
Algo
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Algo Score - It's a single number that summarizes an Algo's overall performance by combining returns, risk, volatility, drawdowns, and consistency. A higher score indicates stronger, more stable, and better risk-adjusted performance.

Score
Correlation
Compressed Strangle
-- 1.00
Carry Forward Strangle
-- --
Holonomy's Short Strangles
-- --
Intraday Short Strangle
-- --
Expiry Short Strangle
-- --
Flux Strangle
-- --
Premium-zone Strangle
-- --
Theta-zone Strangle
-- --
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Why is less correlation so important in Algotrading?
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At Stratzy, we align strictly with SEBI’s risk management framework by prioritizing uncorrelation in our strategy curation. In algorithmic trading, uncorrelation ensures that our strategies do not react identically to market volatility; if one strategy faces a drawdown due to specific market conditions, others are designed to remain unaffected or perform differently. This statistical diversification acts as a crucial internal hedge, reducing the risk of simultaneous losses and ensuring portfolio stability. By avoiding concentrated risk, we uphold the regulatory mandate to prioritize investor safety and maintain market integrity.

Backtested Gross Gains (₹)

Backtested Returns Snapshot

PeriodReturns
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1 year
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Backtest Best & Worst Holding Periods
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This graph compares the Algo's best and worst performance over time, showing how returns can vary depending on when you start using the Algo.

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Performance Summary

Hover to see parameter details.

Click to see parameter details.

Drawdown Icon

Avg Drawdown

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Indicates the average decline the strategy experiences in downturns, revealing how deep its typical losses go.

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Risk Reward Icon

Risk : Reward

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Indicates how much the Algo typically earns for every rupee it risks. E.g., 1:3 means it targets ₹3 in reward for every ₹1 of risk.

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Win Rate Icon

Avg Trade

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Indicates how often the Algo trades on average.

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moderate Risk

Risk

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Indicates the expected volatility of the Algo and is classified into levels like Low, Medium, and High.

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Max Drawdown

Max Drawdown

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Indicates the largest decline the Algo has faced so far, reflecting its most severe historical downturn.

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Success Ratio

Success Ratio

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Indicates the percentage of trades that end in profit. E.g., 70% means 7 out of 10 trades are winners.

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Avg Profit

Avg Profit in Trade

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Indicates the average gain the Algo earns on its winning trades.

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Avg Loss

Avg Loss in Trade

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Indicates the average loss the Algo incurs on its losing trades.

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Avg Time to Recovery

Avg Time to Recovery

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Indicates the average number of days the Algo took to bounce back after experiencing its average drawdown.

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Max Time to Recovery

Max Time to Recovery

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Indicates the number of days the Algo took in the past to recover from its worst drawdown to date.

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Sharpe Ratio

Sharpe Ratio

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Indicates how well an Algo balances risk and return, showing how effectively it manages volatility.

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*Metrics/Analytics basis past data. Historical data does not guarantee future results.

Overview

NiftySellingNon-directional

This algorithm implements a short strangle strategy that capitalizes on correlation compression in the options market, specifically designed to profit from periods of low market correlation between different strike prices and the underlying asset. The core innovation lies in its use of correlation-based signals derived from the relationship between various options strikes (ATM, ITM, and OTM options at different moneyness levels) and the Nifty 50 spot price. The algorithm calculates rolling correlations over a 375-minute window (approximately 6.25 hours) for each option contract, then combines these correlations into composite measures that capture the overall correlation structure of the options market. It also incorporates volatility dynamics by comparing recent volatility against historical maximum volatility to identify periods of relative market calm. The trading logic is based on two primary alpha signals that identify optimal conditions for short strangle positions. The first alpha signal (`alpha`) aggregates the absolute correlations across ATM, OTM, and ITM options, with the negative sign indicating that lower correlations (more compression) generate higher alpha values. The second alpha signal (`alpha7`) combines correlation differentials between ITM and OTM options with volatility compression, measuring how much the recent volatility has compressed relative to its historical maximum. The algorithm generates short strangle signals when both alpha values exceed 0.8, indicating significant correlation compression and volatility contraction. The strategy sells both call and put options at strikes 100 points away from ATM (creating a wider strangle), with a 2% risk allocation SL and only executes trades during specific market hours (10:15 AM to 2:00 PM) to avoid high volatility periods at market open and close. It carry forwards the Strangle overnight and squares it off at 3pm the next day.

This algo is managed by...

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Stratzy

INH000009180 SEBI registered algo provider

Algos in market
Algos in market43
Active since
Active since5 Years
Deployed by
Deployed by12.5K users

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