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
Warp-Drive Credit Spread Exit-Early
-- 1.00
Damper Credit Spread
-- --
Zen Credit Spread Overnight
-- --
Curvature Credit Spread Overnight
-- --
Ratio-Ripple Credit Spread Exit-Early
-- --
Ratio-Fluxer Credit Spread Expiry
-- --
Ripple-Return Credit Spread Expiry
-- --
Theta-Harvest Credit Spread Expiry
-- --
Delta-Ripple Credit Spread Overnight
-- --
Mathematician's Credit Spread Overnight
-- --
IV-Imbalance Credit Spread Overnight
-- --
Convex Credit Spread Overnight
-- --
Delta-Leverage Credit Spread Overnight
-- --
Wave-Return Credit Spread Overnight
-- --
Delta-Rotation Credit Spread Expiry
-- --
E-QUEUE Credit Spread Overnight
-- --
Ratio-Hunter2 Credit Spread Exit-Early
-- --
Delta-Shift Credit Spread Expiry
-- --
Chain-Sync Credit Spread Overnight
-- --
Ratio-Return Credit Spread Exit-Early
-- --
Rolling-Carry Credit Spread Exit-Early
-- --
Gamma-Fluxer Credit Spread Overnight
-- --
Curve-Whisper Credit Spread Overnight
-- --
V-Score Credit Spread Overnight
-- --
Vega-Shift Credit Spread Expiry
-- --
Ratio-Weave Credit Spread Expiry
-- --
Theta-Flux Credit Spread Overnight
-- --
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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
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

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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

NiftyHedgedDirectional

The "Warp-Drive Credit Spread Exit-Early" algorithm is designed to identify and capitalize on short-term anomalies in NIFTY 50 options pricing by implementing a credit spread strategy. The core methodology involves analyzing a combination of volatility and option chain data to identify potential mispricings. Key calculations involve comparing implied volatility (IV) across different strikes (ITM & OTM) and the Call-Put Volume ratio to understand bullish or bearish sentiments. The algorithm then normalizes the raw alpha through rolling time-series rank to generate a trading signal. Based on a series of conditions applied to these indicators, the algorithm determines whether to initiate a credit call spread or credit put spread, profiting from the decay of option premiums, if the underlying asset remains within a projected range. An 'exit-early' logic with target exits also makes for risk management This algorithm specifically trades **credit spreads** on the NIFTY 50 index options. Credit spreads are options strategies designed to profit from sideways market movements or when the price of the underlying asset remains within a defined range. Favorable conditions for this strategy typically arise when the market exhibits low volatility and a clear directional bias is absent. The algorithm aims to profit from the theta decay (time decay) of the options it sells while limiting potential losses by purchasing further out-of-the-money options. This risk management approach helps capitalize on opportunities in range-bound markets.

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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