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
Gamma-Fluxer Credit Spread Overnight
-- 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
-- --
Warp-Drive Credit Spread Exit-Early
-- --
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
-- --
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 Gamma-Fluxer Credit Spread Overnight algorithm aims to profit from overnight price movements in NIFTY options by implementing credit spread strategies. The core strategy involves analyzing technical indicators and implied volatility skews to identify potential overbought or oversold conditions in the options market. The algorithm calculates various features, including realized volatility, implied volatility skews (both put and call), and relative price changes in the NIFTY spot price. These features are combined to generate alpha signals that represent the potential edge in the options market. The strategy leverages historical data, including options prices and implied volatilities at different strike prices, to construct a model capable of identifying profitable credit spread opportunities. The algorithm generates trading signals based on two alpha factors ("alpha" and "alpha2") derived from implied volatility skews and realized volatility. A credit put spread is initiated when both alpha factors exceed a certain threshold (0.75 and 0.7), involving selling a put option and buying another put option with a lower strike price to cap potential losses. Conversely, a credit call spread is implemented when both alpha factors fall below specified thresholds (0.25 and 0.3), consisting of selling a call option and buying another call option with a higher strike price. Risk management involves setting a stop-loss based on a percentage of the margin required for the trade and potentially setting a target profit level, calculated as a percentage of the spread premium. The algorithm considers factors like market open status, trading hours, and expiry dates to avoid trading during unfavorable conditions.

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
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Deployed by12.5K users

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