Backtest Returns
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Backtest Snapshot

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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
info

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
Bazaar Short Strangle Overnight
-- 1.00
Sahi-Nivesh Short Strangle Overnight
-- --
Chanakya Short Strangle Overnight
-- --
Intraday Short Strangle
-- --
Holonomy's Short Strangles
-- --
Market-Pulse Short Strangle Overnight
-- --
Homecoming Short Strangle Overnight
-- --
Slow-Climb Short Strangle Overnight
-- --
Carry Forward Strangle
-- --
Expiry Short Strangle
-- --
Compressed Strangle
-- --
Flux Strangle
-- --
Premium-zone Strangle
-- --
Theta-zone Strangle
-- --
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Why is less 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.

Backtested Gross Gains (₹)

Backtested Returns Snapshot

PeriodReturns
1 month
3 months
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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.

Overview

NiftySellingNon-directional

Imagine you're a roadside fruit vendor who wants to make a little extra money overnight. You notice that prices for mangoes and bananas tend to be stable, but might fluctuate slightly. So, instead of betting on one specific fruit going up or down, you decide to sell both mangoes *and* bananas at a slightly lower price, hoping they stay within a predictable range. As long as neither fruit price swings wildly up or down, you profit from the difference between your selling price and where you bought the fruit, like a small "premium" for bearing the risk. If mangoes soar in price or bananas become worthless, you might lose money, but you're counting on things staying calm overnight. This algorithm is designed to trade options on the stock market index, Nifty 50. Specifically, it executes what's called a "short strangle" strategy, which involves selling both a call option (the right to buy) and a put option (the right to sell) on the index, both outside of the current market price. This strategy typically works best when the market is expected to be relatively stable, with minimal price fluctuations. The goal is to collect a premium from the sale of these options, profiting if the index price stays within a certain range until the options expire.

This algo is managed by...

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Stratzy

INH000009180 SEBI registered algo provider

Algos in market
Algos in market79
Active since
Active since5 Years
Deployed by
Deployed by12.5K users

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