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Backtested Gross Gains (₹)

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Performance Summary

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

This algo is a variant of Zen Credit Spread Overnight

Combine other Algos and compare portfolio stability.

Algo Score Correlation

Mathematician's Credit Spread Overnight

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Curvature Credit Spread Overnight

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Damper Credit Spread

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Convex Credit Spread Overnight

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IV-Imbalance Credit Spread Overnight

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Chain-Sync Credit Spread Overnight

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V-Score Credit Spread Overnight

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Why is Un-correlation so important in Algotrading?

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

NiftyHedgedDirectional

The algorithm implements the mathematician Lyapunov-based approach to measure market stability and predictability by calculating a "lyapunov" exponent from price divergence patterns. It computes the logarithmic divergence between consecutive option prices, which captures how quickly small changes in the system amplify over time - a core concept in Lyapunov stability theory. When the Lyapunov exponent is high, the system is more chaotic and less predictable; when it's low, the system is more stable and potentially more predictable. Application in Trading Strategy: The Lyapunov exponent is then transformed into a trading signal through a composite calculation that combines market stability with predictability measures. The algorithm uses a hyperbolic tangent function to normalize the Lyapunov exponent, creating an inverse relationship where values closer to zero indicate higher stability. This stability measure is then multiplied by the R-squared value from regression analysis to create a composite alpha signal that combines both market predictability and stability. The algorithm uses this alpha signal to determine when to enter credit spread trades, with higher alpha values (indicating stable, predictable conditions) triggering long positions and lower values triggering short positions. This approach essentially uses Lyapunov theory to identify periods when the market is in a stable, predictable state suitable for systematic trading strategies.

This algo is managed by...

Stratzy

INH000009180 SEBI registered algo provider

Algos in market43

Active since5 Years

Deployed by12.5K users

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