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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 "Ratio-Hunter2 Credit Spread Exit-Early" algorithm is designed to identify and execute credit spread options trading strategies on the Nifty 50 index. The algorithm leverages a unique blend of technical analysis, Rate-Distortion theory, and proprietary features derived from options data to generate trading signals. It calculates a composite "alpha" signal, incorporating factors like historical price movements ("Spot Returns"), energy indicators ("H_final", "H_pe"), implied volatility skews ("alpha3"), and option delta values, this is normalized and ranked. The algorithm also manages risk with predefined stop-loss and target percentages, and the ability to scale positions based on available margin. Before execution, it verifies market conditions, checks for active trades, and ensures the data is recent enough for accurate decision-making. All executions are monitored using OpenTelemetry to observe latency and failures. This algorithm specifically focuses on credit spreads, aiming to profit from the time decay of options while limiting potential losses through the spread structure. A credit spread involves selling a near-the-money option and simultaneously buying a further out-of-the-money option of the same type and expiry, which creates a range where the maximum profit is capped at the net premium received, and the maximum loss is the difference between the strike prices less the premium. Credit spreads tend to perform well when the market is stable or experiences low volatility, allowing the options to expire worthless and retaining the initial credit. The "Exit-Early" component suggests the algorithm is proactive in managing these trades, potentially closing positions before expiration to capture profits or minimize losses based on predefined criteria.
This Algo is managed by
Stratzy
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