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that day.
Backtested Gross Gains
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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Avg Drawdown
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Indicates the average decline the strategy experiences in downturns, revealing how deep its typical losses go.
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.
Frequency of trade
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Indicates how often the Algo trades on average.
Risk
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Indicates the expected volatility of the Algo and is classified into levels like Low, Medium, and High.
Max Drawdown
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Indicates the largest decline the Algo has faced so far, reflecting its most severe historical downturn.
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.
Avg Profit in Trade
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Indicates the average gain the Algo earns on its winning trades.
Avg Loss in Trade
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Indicates the average loss the Algo incurs on its losing trades.
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.
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.
Sharpe Ratio
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Indicates how well an Algo balances risk and return, showing how effectively it manages volatility.
*Metrics/Analytics basis past data. Historical data does not guarantee future results.
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Strategy Overview
This algorithm implements a short straddle strategy that capitalizes on range-bound market conditions and volatility compression, specifically designed to profit from periods of low market volatility and stable price action. The core innovation lies in its use of multi-factor alpha signals that combine open interest dynamics, implied volatility changes, and volatility compression patterns. The algorithm processes 1-minute interval data and calculates implied volatility (IV) for various options (ATM, ITM, and OTM options at different moneyness levels), then computes delta IV changes to capture short-term volatility momentum. The first alpha signal (`alpha`) combines the call-to-put open interest ratio with the inverse of absolute IV changes and volatility, essentially identifying periods when market participants are balanced between calls and puts while volatility is contracting. The trading logic is based on two sophisticated alpha signals that identify optimal conditions for short straddle positions. The first alpha signal (`alpha`) multiplies the call-to-put open interest ratio by factors that measure volatility compression and stability, with higher values indicating favorable conditions for range-bound trading. The second alpha signal (`alpha2`) uses an exponentially weighted mean of volatility ratios, comparing short-term (20-period) to long-term (200-period) IV changes, combined with how close the open interest ratio is to 1:1 balance. The algorithm generates short straddle signals when both alpha values exceed their respective thresholds (0.75 and 0.8), indicating significant volatility compression, balanced open interest, and stable market conditions. The strategy sells both ATM call and put options simultaneously, with sophisticated risk management including a 50-rupee minimum price filter, per-leg loss limits of 5000 rupees, and margin calculations that account for the combined position. The algorithm only executes trades during specific market hours (10:15 AM to 2:00 PM) and includes logic to handle connected trades where both legs are managed as a single straddle position with cross-referenced stop losses.
This Algo is managed by
Stratzy
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