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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
The Equinox Credit Spread Overnight algorithm is designed to identify and capitalize on short-term volatility imbalances in the NIFTY 50 index. It leverages a unique combination of technical indicators, including customized volatility and volume skew, alongside proprietary measures derived from option chain data, such as gravitational force ratios and orbital anomaly sums of implied volatility differences, to generate trading signals. By calculating these sophisticated features, the algorithm aims to detect moments of extreme bearish sentiment coupled with market over-pessimism, or vice versa, indicating potential opportunities for overnight option trades. The algorithm uses a time-series rank normalization on its raw alpha to ensure the signal is robust and adapts to changing market conditions. Finally the signal is calculated for the latest row in dataframe which is compared against calibrated condition that can determine a trade. This algorithm trades a Credit Spread, specifically designed to profit from a neutral to bullish or bearish outlook on the NIFTY 50 index. A credit spread involves selling a near-the-money (NTM) option and buying a further out-of-the-money (OTM) option in the same expiry, creating a range within which the strategy is profitable. The algorithm looks for opportunities where implied volatility is expected to decrease, or when the market is likely to stay within a defined range. The Credit Put Spread is entered when the algorithm identifies a potential short term upside signal and the Credit Call Spread is entered when the algorithm identifies potential short term downside signal, allowing the algorithm to capture premium.
This Algo is managed by
Stratzy
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