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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, named "Vega-Shift Credit Spread Expiry," aims to capitalize on short-term opportunities in NIFTY options trading, specifically focusing on credit spreads close to expiry. The core strategy revolves around identifying moments when the implied volatility, reflected in option prices, is likely to shift, thus creating favorable conditions for a credit spread. It utilizes historical option chain data, technical indicators, and proprietary calculated signals, `alpha` and `alpha9`, to predict these shifts. The algorithm analyzes minute-by-minute data, combining price action with volume and open interest changes to pinpoint potential overbought or oversold conditions in specific option strikes. Based on these signals, it aims to execute credit spreads, selling a near-the-money option and buying a further out-of-the-money option to limit risk, profiting from the premium difference if the underlying asset stays within a predicted range. The signal generation is driven by comparing the `alpha` and `alpha9` values to predefined thresholds (0.7 and 0.3). When `alpha` and `alpha9` are both above 0.7, a credit put spread is initiated; conversely, when both fall below 0.3, a credit call spread is initiated. Risk management is implemented through defining stop-loss and target profit levels calculated as percentages of the initial margin required for the trade. The algorithm dynamically calculates margin requirements and sets stop-loss and target levels based on a percentage of this margin. It considers market open/close times, skips trades on NIFTY expiry dates (can be disabled), and operates only within a specified intraday time window (10:15 AM to 2:15 PM) to align with backtested performance, ensuring trades are executed during optimal market conditions.
This Algo is managed by
Stratzy
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