2025-09-27
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Algo trading might sound complicated at first, but at its core, it follows a clear process. Whether you’re a retail trader experimenting with your first strategy or a hedge fund running advanced AI models, the foundation remains the same: ideas → rules → execution.
In this ultimate guide, we’ll break down how algo trading actually works, step by step. We’ll use simple examples, Indian market context (NSE/BSE, SEBI rules, Zerodha APIs), and practical insights to help you understand the full lifecycle.
Every algo starts with an idea — a hypothesis about how markets behave.
Trend Following: Buy Nifty futures when the 50-day moving average crosses above the 200-day moving average.
Mean Reversion: Buy Infosys if it falls 2% intraday, and sell if it bounces 1%.
Breakout Trading: Buy Reliance if it breaks its 52-week high.
A good strategy must answer:
What market or asset? (Stocks, futures, options, commodities)
What conditions trigger a buy or sell?
How much to buy or sell? (position sizing)
When to exit? (profit/loss targets)
This is the foundation of algo trading.
Once the idea is clear, it needs to be converted into rules a computer understands.
Programming Languages: Python, R, or C++ for full customization.
Broker APIs: Zerodha Kite Connect, Upstox API, Angel One Smart API.
No-Code Platforms: AlgoKart allows drag-and-drop strategy creation for non-coders.
If (50-day Moving Average > 200-day Moving Average) then BUY Nifty
If (50-day Moving Average < 200-day Moving Average) then SELL Nifty
This logic is what the computer executes without hesitation.
Before going live, strategies are tested on historical market data. This is called backtesting.
To check if the idea worked in the past.
To measure profitability, risk, and drawdowns.
To refine rules before risking money.
Win Rate: Percentage of profitable trades.
Sharpe Ratio: Risk-adjusted returns.
Max Drawdown: Largest loss from peak to bottom.
CAGR: Compound Annual Growth Rate.
Testing a moving average crossover strategy on 5 years of Nifty data might reveal:
CAGR: 12%
Max Drawdown: 15%
Win Rate: 55%
This helps traders decide whether to move forward.
Platforms like AlgoKart’s Backtest Lab Pro simplify this step with ready-made tools.
Once tested, the algo is connected to a broker’s API to execute trades in real-time.
Algo scans the market for signals.
Signal conditions met (e.g., price cross above moving average).
Order sent automatically to broker.
Broker routes order to NSE/BSE.
Confirmation returned.
This entire cycle happens in milliseconds.
If your algo is connected to Zerodha’s Kite API, and Infosys drops 2%, the algo will instantly place a buy order in your account.
No algo is “set and forget.” Continuous monitoring is required.
Execution Errors: Missed trades due to server downtime.
Market Conditions: Strategies that worked in 2020 may fail in 2025.
Risk Controls: Exposure, stop losses, portfolio allocation.
Adjusting parameters (e.g., moving averages from 50/200 to 20/100).
Adding filters (e.g., trade only in high-volume stocks).
Diversifying across strategies.
AlgoKart provides real-time dashboards for monitoring and tweaking strategies.
Here’s a simplified view:
Idea: Buy on moving average crossover.
Coding: Write rules in Python or AlgoKart.
Backtesting: Test on 10 years of Nifty data.
Execution: Connect to broker, deploy live.
Monitoring: Track results, adjust parameters.
This cycle repeats — every strategy is continuously improved.
| Stage | Manual Trading | Algo Trading |
|---|---|---|
| Idea | Human thought | Human thought |
| Execution | Slow, manual | Instant, automated |
| Emotions | Fear, greed | None |
| Scale | Few trades | Hundreds |
| Monitoring | Manual | Automated dashboards |
Rahul, a retail trader in Mumbai, creates a simple strategy:
Buy Nifty futures when RSI < 30.
Sell when RSI > 70.
Uses AlgoKart’s no-code builder to set conditions.
Runs a backtest on 5 years of Nifty data.
Finds CAGR = 10%, Max Drawdown = 12%.
Connects with Zerodha API for live execution.
Monitors performance weekly with AlgoKart dashboard.
Result: Rahul automates his strategy, freeing his time and removing emotional bias.
Over-Optimization: Tuning too much to past data.
Technology Failures: Server crashes or poor internet.
Changing Market Regimes: A strategy that worked in bull markets may fail in sideways markets.
Traders must build safeguards like circuit breakers and portfolio diversification.
Q: Do I need coding skills to start algo trading?
No. Platforms like AlgoKart offer no-code solutions.
Q: How much time does it take to create an algo?
Simple strategies can be built in minutes; complex ones take weeks.
Q: Is algo trading profitable?
Yes, but only with good strategies, backtesting, and risk management.
Q: How do I connect my algo to NSE/BSE?
Through broker APIs like Zerodha, Upstox, or Angel One.
Algo trading follows a clear five-step process: Idea → Coding → Backtesting → Execution → Monitoring.
Backtesting is crucial before risking real money.
Execution is instant and error-free when using broker APIs.
Monitoring ensures strategies adapt to new market conditions.
Platforms like AlgoKart make the process accessible to beginners.
Algo trading might seem technical, but once broken into steps, it’s surprisingly straightforward. By following a structured process, traders can remove emotions, trade with discipline, and unlock opportunities previously reserved for hedge funds.
Whether you’re a beginner or an advanced trader, the future of markets is algorithmic — and with AlgoKart, you can be part of it.
👉 Ready to build your first algo? Start today with AlgoKart.