Backtest Returns
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Backtest Snapshot

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Backtested Gross Gains (₹)

Backtested Returns Snapshot

PeriodReturns
1 month
3 months
6 months
1 year
All time
Backtest Best & Worst Holding Periods
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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

Hover to see parameter details.

Click to see parameter details.

Drawdown Icon

Avg Drawdown

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Indicates the average decline the strategy experiences in downturns, revealing how deep its typical losses go.

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Risk Reward Icon

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.

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Win Rate Icon

Avg Trade

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Indicates how often the Algo trades on average.

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high Risk

Risk

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Indicates the expected volatility of the Algo and is classified into levels like Low, Medium, and High.

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Max Drawdown

Max Drawdown

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Indicates the largest decline the Algo has faced so far, reflecting its most severe historical downturn.

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Success Ratio

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.

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Avg Profit

Avg Profit in Trade

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Indicates the average gain the Algo earns on its winning trades.

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Avg Loss

Avg Loss in Trade

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Indicates the average loss the Algo incurs on its losing trades.

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Avg Time to Recovery

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.

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Max Time to Recovery

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.

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Sharpe Ratio

Sharpe Ratio

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Indicates how well an Algo balances risk and return, showing how effectively it manages volatility.

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*Metrics/Analytics basis past data. Historical data does not guarantee future results.

Combine other Algos and compare portfolio stability.

Combine other Algos and compare portfolio stability.

Algo Score
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Algo Score - It's a single number that summarizes an Algo's overall performance by combining returns, risk, volatility, drawdowns, and consistency. A higher score indicates stronger, more stable, and better risk-adjusted performance.

Correlation
Algo Score
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Algo Score - It's a single number that summarizes an Algo's overall performance by combining returns, risk, volatility, drawdowns, and consistency. A higher score indicates stronger, more stable, and better risk-adjusted performance.

Correlation
Crossover Formula Automated
-- 1.00
Curvature Credit Spread Overnight
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Damper Credit Spread
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Carry Forward Strangle
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Zen Credit Spread Overnight
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Holonomy's Short Strangles
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Intraday Short Strangle
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IV-Imbalance Credit Spread Overnight
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Lattice Short Straddles
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SkewHunter
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Alpha Industries Automated
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Expiry Short Strangle
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Convex Credit Spread Overnight
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Mathematician's Credit Spread Overnight
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Chain-Sync Credit Spread Overnight
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Foundation Portfolio Automated
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Fixed RR 1:3 (30% SL)
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Single Lattice Straddle
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Bullion Strategy Automated
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Compressed Strangle
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Quiet Short Straddle
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Balanced Portfolio Automated
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Diversified Stocks
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Dividend Dons Automated
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Wealth Magnet Automated
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Industry Champs Automated
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Single Kurtosis Straddle
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SkewHunter TSL
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High Volatility Stocks
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Vacuum GRID (35% SL)
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Trending Outliers Automated
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Index Sniper
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Value Picker Automated
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Single Rangetrap Straddle
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Hamilton's Credit Spread
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Theta-zone Strangle
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V-Score Credit Spread Overnight
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Flux Strangle
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Single Tightgrip Straddle
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Premium-zone Strangle
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Burst RR 1:2 (25% SL)
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Burst GRID (30% SL)
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Drifting Credit Spread Overnight
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Why is Un-correlation so important in Algotrading?
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At Stratzy, we align strictly with SEBI’s risk management framework by prioritizing uncorrelation in our strategy curation. In algorithmic trading, uncorrelation ensures that our strategies do not react identically to market volatility; if one strategy faces a drawdown due to specific market conditions, others are designed to remain unaffected or perform differently. This statistical diversification acts as a crucial internal hedge, reducing the risk of simultaneous losses and ensuring portfolio stability. By avoiding concentrated risk, we uphold the regulatory mandate to prioritize investor safety and maintain market integrity.

Overview

EquitySwing

The moving average crossover algorithm is probably the most well known and simple quantitative trading algorithm there is. Doesn't work well anymore, since everyone knows of it (Standard alpha decay). However the concept is still valid, the moving averages de-noise the data plus a difference between short and long term moving averages is a lagging indicator of an expected change in the metric. Rather than implementing a moving average crossover on the price, we calculate the short term/ long term moving average ratio of other variables which are derived from the price and volume of the stock as well as from it's analogous instruments in the derivatives segment. The algorithm then recommends the best stocks to hold for the coming month by ranking the 207 FNO stocks on the basis of these MARs, the top 10% stocks are chosen and weighted using anti-volatility measures.

This algo is managed by...

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Stratzy

INH000009180 SEBI registered algo provider

Algos in market
Algos in market43
Active since
Active since5 Years
Deployed by
Deployed by12.5K users

Stratzy is a place where you can get tailored guidance for your portfolio to help you make the right investments. Gain access to battle tested algos, automation of your investments and insights about the market, right in the palm of your hand. Your wealth generation begins here.

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Frequently Asked Questions

How does the Algo work?

Instead of simple price-based moving average crossovers, the Algo uses short/long-term average ratios of price- and volume-derived variables (including derivatives data). It ranks all the F&O stocks and selects the top 10% each month, weighting them with anti-volatility measures.

What is the amount I can start with?

How frequently does the Algo execute trades?

What is DDPI?

What is SIP?

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