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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
Imagine a savvy shopper at a bustling farmer's market, but with a very specific agenda. This shopper isn't browsing leisurely. Instead, they're intensely focused on a few select stalls selling unique, handcrafted goods. They've done their homework, tracking the stall owners' recent pricing and sales patterns. They're looking for a rare combination: a product that's both unusually cheap compared to its past prices, and from a stall owner whose overall sales are showing strong potential. If they spot this sweet spot, they'll quickly buy a small quantity, always keeping a close eye on the price in case they need to quickly cut their losses. This algorithm trades NIFTY options, specifically aiming to buy "naked" call or put options. It's constantly crunching data, looking for a specific blend of signals derived from option prices, implied volatility, and trading volume. The algorithm waits for a potent combination of factors to align—basically, it wants to see a call or put option that looks unusually cheap based on its own volatility metrics, and also sees a pattern suggestive of overall buying or selling strength. If this combination shows up, the algorithm buys a single lot, setting a stop-loss order to limit potential losses. It's designed to be selective and risk-conscious, only entering trades when specific conditions are met and quickly exiting if things go south.
This Algo is managed by
Stratzy
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