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 Index Sniper

Combine other Algos and compare portfolio stability.

Combine other Algos and compare portfolio stability.

Algo Score
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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.

Correlation
Algo Score
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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.

Correlation
SkewHunter
-- 1.00
Fixed RR 1:3 (30% SL)
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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 is a sophisticated options trading algorithm called "Skew Hunter" that focuses on exploiting volatility skew in the NIFTY options market. The algorithm monitors both ATM (At-The-Money) and OTM (Out-of-The-Money) options across different strike prices, calculating various metrics including implied volatility (IV), volume ratios, and open interest changes. It uses two main alpha signals: the first alpha combines volume ratios and open interest changes for OTM calls and ITM puts, while the second alpha measures the IV skew between OTM and ITM options for both calls and puts. The trading logic is triggered when both alpha signals align in extreme regions (alpha > 0.75 and alpha2 > 0.8 for long calls, or alpha < 0.25 and alpha2 < 0.2 for long puts). The algorithm only trades during specific market hours (10:15 AM to 2:15 PM) and implements strict risk management rules: it won't enter trades if the option price is below ₹20, sets a 40% stop-loss from the entry price, and automatically squares off positions at the end of the trading day. The strategy is implemented in two versions: a regular version and a trailing stop-loss (TSL) version, both of which are managed through a database system that prevents multiple active trades from running simultaneously.

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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