Backtest Returns
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Backtest Snapshot

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Backtested Gross Gains (₹)

Backtested Returns Snapshot

PeriodReturns
1 month
3 months
6 months
1 year
All time
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

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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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high 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.

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This algo is a variant of Compressed Strangle

Combine other Algos and compare portfolio stability.

Combine other Algos and compare portfolio stability.

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
Expiry Short Strangle
-- 1.00
Carry Forward Strangle
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Holonomy's Short Strangles
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Intraday Short Strangle
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Compressed Strangle
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Theta-zone Strangle
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Flux Strangle
-- --
Premium-zone Strangle
-- --
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Why is Un-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.

Overview

NiftySellingNon-directional

The Short Strangle algorithm is a sophisticated options trading strategy designed to capitalize on range-bound markets and elevated implied volatility conditions. The strategy employs a multi-factor approach, combining price momentum, open interest dynamics, volume analysis, and volatility metrics to identify optimal entry points. It specifically looks for three key conditions: a low alpha signal (indicating potential mean reversion), high close price gaps (suggesting market inefficiency), and elevated implied volatility rank (indicating premium selling opportunities). When these conditions align, the algorithm executes a short strangle by selling both out-of-the-money call and put options, with strike prices set at 100 points above and below the current ATM strike price. The algorithm incorporates robust risk management protocols, including a stop-loss of around 3% per strangle, which might exceed in case of gap-up or gap-down and margin-based position sizing. It operates exclusively during specific market hours (10:15 AM to 2:15 PM) to ensure optimal liquidity and execution quality, while avoiding new positions after 3:00 PM to accommodate end-of-day position management. The strategy utilizes 1-minute interval data for precise signal generation and maintains a comprehensive database of active trades to prevent overlapping positions. Through its integration with real-time market data, margin calculations, and automated trade execution systems, the algorithm provides a systematic approach to options premium collection while maintaining strict risk controls. It carries the position till expiry if no SL is hit.

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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