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
Theta-Harvest Credit Spread Expiry
-- 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
-- --
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
-- --
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
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3 months
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1 year
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All time
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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

NiftyHedgedDirectional

The "Theta-Harvest Credit Spread Expiry" algorithm is designed to capitalize on short-term directional movements in the NIFTY 50 index by trading credit spreads, specifically targeting the capture of theta decay as options approach their expiry. It utilizes a combination of technical indicators derived from historical and real-time options chain data to identify potential trading opportunities. The core strategy involves selling a credit spread—simultaneously selling a near-the-money option (either call or put) and buying a further out-of-the-money option of the same type and expiry. This creates a range within which the strategy aims to profit if the index price remains, with the expectation that the premium received from selling the option will decay faster than the cost of the purchased option. The algorithm specifically considers the time until expiry to maximize the theta harvest. The algorithm generates trading signals based on a composite "alpha" factor, calculated from net aggressive option flow, changes in open interest, regime directional energy, and coupling amplification, all normalized using a time-series rank. Long (credit put spread) signals are triggered when "alpha" and a related "alpha3" factor are both high, indicating potential downward price movement. Short (credit call spread) signals are generated when both alpha factors are low, signaling possible upward movement. The algorithm incorporates risk management via a stop-loss percentage based on the margin required for the trade and a profit target which considers the premium received. It also includes checks to avoid trading on the day of expiry and during periods outside of a tested time window (10:15 AM to 2:15 PM). The trade execution logic sends signals to a trading platform, calculates margin requirements, and determines appropriate stop-loss and target levels.

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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