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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 "Curve-Whisper Credit Spread Overnight" algorithm is designed to identify potential overnight credit spread opportunities in NIFTY options. The core strategy involves analyzing a combination of option chain data, volatility, open interest, and custom indicators to generate trading signals. It uses a proprietary "alpha" signal, derived from option pricing, implied volatility, and open interest data to determine if a credit spread is likely to be profitable. This "alpha" is calculated using a combination of call and put option pressures along with flow resistance, then normalized using a time-series rank. The algorithm then uses the generated signals to enter a credit spread by selling a near-the-money (ATM) option and buying a further out-of-the-money (OTM) option to limit risk. The algorithm generates signals based on the calculated "alpha" values. A low "alpha" suggests a potential long (buy) opportunity on a put spread, while a high "alpha" suggests a potential short (sell) opportunity on a call spread. The algorithm implements risk management primarily through the credit spread structure itself, limiting potential losses to the difference between the strike prices, less the premium received. A stop loss is defined, which is applied at a percentage of margin required. Trades are only executed during specific market hours, between 10:15 and 14:15, and skips trading on the day of NIFTY expiry, ensuring trades align with backtested logic. Lot sizes and quantities are determined programmatically. The algo uses the helper functions to define required values, such as determining strike prices and expiry dates.
This Algo is managed by
Stratzy
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