Algo trading has transformed the way retail traders participate in the markets. By automating rule-based strategies, traders can reduce emotional bias, improve execution speed, and focus on data-backed decision-making. The good news is many popular strategies are simple to understand and can be automated even with limited coding knowledge.

In this blog, we’ll explore five proven algo trading strategies widely used by retail traders in Indian markets. Each strategy includes its core logic, risk management methods, and the type of market environment where it performs best.

1. 9:20 Straddle (Bank Nifty / Nifty Options) 

One of the most popular intraday option-selling strategies is the 9:20 Straddle.

Example Scenario: 

Imagine a trader looking at the market around 9:20 AM. They see that the ATM call and put options on an index like Nifty or Bank Nifty have similar premiums, for example around ₹120 and ₹115, with implied volatility near 13%.

To understand how a straddle works, they think of a scenario where both options are “held” for the day. They watch how the total position behaves if the index stays within a range or starts moving sharply. During the session, they keep an eye on risk limits, such as 25–30% per option or a total MTM limit for the day, and plan to close all positions by about 3:15 PM.

Why it works: 

• Option premiums decay intraday due to theta (time decay).
• On non-trending days, when markets remain range-bound, both call and put lose value, creating profits.

Variants: 

• Re-entry after stop-loss hits.
• Fixed time exit (without SL).

Best for: Nifty & Bank Nifty option traders with moderate risk appetite.

Key Risk: Sudden sharp moves (news events, RBI policy days) can trigger stop-losses quickly.

2. Moving Average Crossover (Trend Following) 

The Moving Average Crossover strategy is a classic trend-following model widely adopted by algo traders.

Example Scenario: 

Imagine a trader looking at a 15-minute chart. They notice the 9-EMA (short-term average) crossing above the 21-EMA (long-term average), with small pullbacks finding support near the faster average.

To understand how a moving average crossover works, the trader observes whether the trend continues or fades. They also consider what would indicate the trend failing, such as the 9-EMA dropping back below the 21-EMA, and use volatility measures like ATR to see how much room the price might move while still being within a “safe” range.

Why it works:

• Captures medium-term trends in trending markets.
• Removes emotions—entries and exits are purely rule-based.

Best for: Nifty, Bank Nifty, and highly liquid stocks.

Key Risk: Generates false signals in sideways markets → mitigated by combining with filters (like ADX > 20).

3. Intraday VWAP Reversal 

VWAP (Volume Weighted Average Price) is considered the fair value benchmark for institutional trading. This strategy looks for price reversals around VWAP.

Example Scenario: 

Imagine the price staying below the VWAP in the morning and then moving back up toward it, with higher volume and participation, which could indicate a potential shift toward the session’s average price. On the other hand, if the price approaches VWAP from below but keeps getting pushed back with selling pressure, it may signal rejection. To understand how this strategy works, the trader might watch a notional band of about 0.5–1% around VWAP and observe how the price behaves as it interacts with this level.

Why it works:

• VWAP is often used as a reference point to gauge the average trading price during the session.
• Price rejection or bounce around this level can indicate a potential intraday reversal.

Best for: High-volume stocks

Key Risk: Works best in high-volume, volatile intraday environments. Loses edge in low-volume sessions.

4. Bollinger Band + RSI Mean Reversion 

This strategy combines Bollinger Bands with RSI (Relative Strength Index) to identify overbought and oversold levels.

Example Scenario: 

Imagine a stock that has been moving sideways. Around midday, the price touches the lower Bollinger Band while the RSI-14 is near or below 30. The trader views this as a short-term move away from the average and watches for signs of stabilization, like smaller candles or slower downward momentum, with the middle band serving as a reference for where the price might normalize. Similarly, if the price reaches the upper band and RSI is above 70, the trader observes whether the upward momentum slows and the price moves back toward the middle band while the range continues.

Why it works:

• Captures quick mean-reversion moves in range-bound markets.
• Combines volatility (Bands) with momentum (RSI) for stronger signals.

Best for: Sideways markets; short-term traders.

Key Risk: Trend days can break bands and hit stop-losses. Must exit quickly.

5. Iron Condor / Short Strangle (with Time Exit)

For option sellers, Iron Condor / Short Strangle is a premium collection strategy designed to benefit from theta decay.

Example Scenario: 

Imagine a relatively calm trading session with moderate volatility. To understand how a market-neutral options strategy works, a trader might consider short positions on both call and put options that are out-of-the-money, for example around one standard deviation from the current price, with a combined illustrative premium of ₹45–₹60. They monitor both individual positions and the overall exposure for large moves, and plan to close all positions by around 3:00 PM to avoid carrying risk overnight.

Why it works:

• If markets stay within a range, both sold options decay in value.
• Theta decay accelerates closer to expiry, providing edge to sellers.

Best for: Experienced traders with capital to manage option writing margins.

Key Risk: Sudden breakouts on either side can cause large losses. Must always use stop-loss.

Final Thoughts 

Algo trading doesn’t mean complex systems—it means discipline + automation. The strategies above are simple, transparent, and have been widely tested by retail traders in India. However, remember:

• No strategy works in all market conditions.
• Proper risk management (stop-loss, position sizing and capital allocation) is non-negotiable.
• Always backtest before deploying live capital.

Ready to Explore Algo Trading? 

With the right strategy and disciplined execution, algo trading can help you trade smarter and more efficiently. To start your journey, open your Demat Account with RMoney and get access to advanced tools, research, and support.

Disclaimer The strategies discussed above are for educational purposes only. Past performance of any strategy does not guarantee future results. Trading in securities and derivatives involves risk of loss, and investors should carefully evaluate their financial position, risk appetite, and consult with a financial advisor before deploying any strategy.

About Author

Raj Kumar

RajKumar holds a PGDM in Finance and a Master’s degree in Economics, with over 11 years of experience in the Indian stock market. At RMoney, he is the driving force behind the firm’s innovative algo trading product, empowering traders to leverage technology effectively. He is also the face behind many of RMoney’s educational videos, making finance and trading concepts easy to understand. Known for his strategic vision, Raj’s inputs in marketing are always appreciated. With a deep passion for markets and education, he continues to bridge the gap between technology, trading, and investor awareness.