2025-09-27
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One of the most exciting aspects of algorithmic trading is the wide variety of strategies available. From simple moving average crossovers to advanced AI-driven models, algo strategies allow traders to approach markets with structure and discipline. In this ultimate guide, we’ll explore the most common types of algo trading strategies, explain how they work, and provide practical examples from both Indian and global markets.
At the heart of every algorithm lies a strategy a set of rules that determines when to buy, when to sell, and how much risk to take. Without a strategy, even the most powerful technology is useless. Strategies provide the logic that drives algorithmic trading.
These are among the most popular algo strategies worldwide.
“The trend is your friend.”
Buy when prices are rising, sell when prices are falling.
Moving Average Crossover: Buy Nifty when the 50-day moving average crosses above the 200-day moving average.
Breakout Strategy: Buy Reliance when it breaks above its 52-week high.
Trends in markets often persist due to momentum, institutional flows, and herd behavior.
Many retail traders in India use simple moving average systems on NSE stocks and Nifty futures.
These strategies assume that prices eventually return to their average levels.
“What goes up must come down.”
Buy when prices fall sharply, expecting a rebound.
Bollinger Bands: Buy TCS when price touches the lower band, sell when it hits the upper band.
Relative Strength Index (RSI): Buy Infosys if RSI falls below 30 (oversold), sell when it rises above 70 (overbought).
Markets often overreact to news, leading to short-term mispricing.
Arbitrage involves exploiting price differences between markets or instruments.
Cash and Futures Arbitrage: If HDFC Bank’s stock trades at ₹1,600 in cash market and ₹1,620 in futures, buy cash and sell futures simultaneously.
Exchange Arbitrage: If Infosys trades at different prices on NSE and BSE, buy low and sell high.
Inefficiencies exist across markets, though opportunities are small and require speed.
HFT firms in India heavily use arbitrage between NSE and BSE. Retail traders can also benefit, though competition is intense.
Market makers continuously quote buy and sell prices, profiting from the bid-ask spread.
Place a buy order slightly below market price.
Place a sell order slightly above market price.
Capture the spread.
On Infosys, bid ₹1,495, offer ₹1,505. If both execute, you earn the ₹10 spread.
Provides liquidity to markets while generating small but frequent profits.
These rely on mathematics and probability.
Use statistical models to find mispricings.
Trade based on probabilities, not just price action.
Pairs Trading: If Tata Steel and JSW Steel usually move together but diverge, buy one and short the other.
Regression Models: Predict Nifty movements based on correlations with global indices.
Markets have hidden relationships that can be exploited mathematically.
HFT involves executing thousands of trades in microseconds.
Use ultra-fast connections and co-located servers.
Profit from tiny inefficiencies before others notice.
HFT is dominated by institutions in India, as it requires significant infrastructure.
The most advanced category.
Algorithms learn from new data and adapt automatically.
Use neural networks, decision trees, or reinforcement learning.
Predict stock price direction using news sentiment.
Dynamic portfolio rebalancing based on AI forecasts.
AI uncovers patterns humans can’t see and adapts faster to new market conditions.
Every trader is unique. Custom strategies may include:
Seasonal patterns (e.g., festive season stock rallies).
Event-driven trading (Budget announcements, RBI policy changes).
Proprietary formulas developed by individual traders.
Platforms like AlgoKart empower traders to create, test, and deploy such strategies easily.
| Strategy Type | Risk Level | Skill Needed | Common Use |
|---|---|---|---|
| Trend Following | Medium | Low | Retail + Institutions |
| Mean Reversion | Medium | Low | Swing/Intraday Traders |
| Arbitrage | Low | High (Speed) | HFT Firms |
| Market Making | Low | High | Brokers, Institutions |
| Statistical Models | Medium | Medium-High | Quant Traders |
| AI & ML | High | Very High | Hedge Funds |
Trend Following: May fail in sideways markets.
Mean Reversion: Can suffer in strong trending markets.
Arbitrage: Margins are small, competition is fierce.
AI Models: Risk of overfitting and data bias.
Every strategy has pros and cons the key is diversification and risk control.
An Indian trader builds two strategies:
Trend Following: Moving average crossover on Nifty futures.
Mean Reversion: RSI-based strategy on Infosys.
By running both together, the trader reduces risk when one fails, the other often succeeds.
Q: Which strategy is best for beginners?
Trend following and mean reversion are easiest to start with.
Q: Do I need advanced math for algo trading?
Not for basic strategies. But statistical and AI strategies require strong math/programming.
Q: Can I copy institutional strategies?
Not exactly. Institutions use scale and speed retail traders can’t match. Focus on retail-friendly strategies.
Algo strategies vary from simple moving averages to complex AI models.
Beginners should start with trend following or mean reversion.
Arbitrage and HFT are usually institutional domains.
Statistical and AI strategies require advanced skills but offer huge potential.
Platforms like AlgoKart make it possible for retail traders to access strategies once limited to Wall Street.
The beauty of algo trading lies in its diversity. There’s no single “best” strategy the right one depends on your risk tolerance, skill level, and goals. By understanding different approaches, traders can build a balanced portfolio of strategies.
With AlgoKart, you don’t just learn about these strategies — you can build, test, and deploy them instantly. Whether you’re a beginner starting with simple moving averages or an advanced quant experimenting with AI, AlgoKart provides the tools to turn ideas into action.
👉 Ready to test your first strategy? Explore AlgoKart’s marketplace and backtesting lab today!