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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 Delta-Rotation Credit Spread Expiry, aims to capitalize on short-term market inefficiencies by identifying potential credit spread opportunities in Nifty options. The core strategy revolves around analyzing various option chain parameters like implied volatility (IV), option greeks, market energy (Hamiltonian), entropy, and price action to gauge the overall market sentiment and identify potentially mispriced options. This is done by calculating an 'alpha' value, which is derived from a combination of IV, curvature, Hamiltonian, eigenvalues, entropy, and predicted volatility. The algorithm uses this alpha, in conjunction with its momentum and a related "alpha2" value based on spot returns and implied volatility curvature changes, to make informed decisions about initiating credit spreads. The signal generation logic triggers a trade when specific conditions related to the calculated 'alpha' values are met, indicating either a bullish or bearish sentiment. A bullish signal prompts the creation of a credit put spread by selling an at-the-money (ATM) put option and buying an in-the-money (ITM) put option to cap the potential loss. Conversely, a bearish signal triggers a credit call spread by selling an ATM call option and buying an out-of-the-money (OTM) call option. Risk management involves calculating the margin required for the trade and setting a stop-loss percentage based on this margin. Additionally, the algorithm sets a target profit level, aiming for 50% of the maximum potential profit from the spread, with a time-based expiry for the target, and will not trade if a similar trade has not yet closed. It checks the position and does not open a trade if one is open. The algorithm checks the current time to make sure that a trade is valid within testing time. The algorithm will check the expiry date to see if it should trade or not. It will also not trade when the market is closed or outside the testing hours.
This Algo is managed by
Stratzy
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