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

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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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 Burst RR 1:2 (25% SL)

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
Fixed RR 1:3 (30% SL)
-- 1.00
SkewHunter
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SkewHunter TSL
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Index Sniper
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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

NiftyBuyingDirectional

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...

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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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