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that day.
Backtested Gross Gains
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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Avg Drawdown
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Indicates the average decline the strategy experiences in downturns, revealing how deep its typical losses go.
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.
Frequency of trade
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Indicates how often the Algo trades on average.
Risk
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Indicates the expected volatility of the Algo and is classified into levels like Low, Medium, and High.
Max Drawdown
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Indicates the largest decline the Algo has faced so far, reflecting its most severe historical downturn.
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.
Avg Profit in Trade
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Indicates the average gain the Algo earns on its winning trades.
Avg Loss in Trade
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Indicates the average loss the Algo incurs on its losing trades.
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.
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.
Sharpe Ratio
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Indicates how well an Algo balances risk and return, showing how effectively it manages volatility.
*Metrics/Analytics basis past data. Historical data does not guarantee future results.
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Strategy Overview
This algorithm is a sophisticated options trading strategy that focuses on volatility skew dynamics and energy-based signals to generate trading opportunities in the Nifty 50 options market. The core innovation lies in its use of "energy" calculations derived from implied volatility skews across different strike prices. The algorithm processes 1-minute interval data and calculates implied volatility (IV) for various options (ATM, ITM, and OTM options at different moneyness levels of 0.25, 0.5, 0.75, and 1.0). It then computes "skew energy" by squaring the differences between OTM and ITM implied volatilities for both calls and puts, creating a measure of market stress or imbalance. The algorithm also incorporates volume momentum indicators by calculating rolling volume ratios and their differences between call and put options, providing additional context for market sentiment. The trading logic is based on two primary alpha signals that combine energy differentials with skew dynamics. The first alpha signal (`alpha`) multiplies the energy difference between call and put options by the difference in their skew patterns, while the second alpha signal (`alpha2`) combines IV delta changes with volume ratio differences. The algorithm generates long signals (buying call options) when the energy differential is negative (indicating put stress), call skew is expanding, and both alpha signals are above their respective thresholds (0.75 and 0.7). Conversely, it generates short signals (buying put options) when the energy differential is positive (indicating call stress), put skew is contracting, and both alpha signals are below their thresholds (0.25 and 0.3). The strategy includes a fixed 1:3 risk-reward ratio with a 30% stop-loss and only executes trades during specific market hours (10:15 AM to 2:15 PM) to avoid high volatility periods, with additional filters to ensure option prices are above 20 rupees for liquidity considerations. It has a fixed RR of 1:3 with SL at 30% and target of 90%. With this algo you could lose upto 20% of your capital in one day, and hence it is an extremely high risk algo,.
This Algo is managed by
Stratzy
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