Start Algo Trading from Zero
- Amman Kumar
- Mar 1
- 4 min read
Algo trading (Algorithmic Trading) is a way to trade in the stock market using computer programs.
These programs follow pre-set rules to buy and sell stocks automatically, without needing human intervention. It helps traders execute trades faster, remove emotions, and use data-driven decisions.
If you're new to algo trading, this guide will walk you through how to start from scratch, even if you don’t have experience with coding or trading.
Learn a Programming Language
To automate trading, you need to write programs that analyze market trends and execute trades.
The most commonly used language is Python because it is easy to learn and has many tools for trading.
Why Python?
Simple and beginner-friendly
Has ready-made tools (like Pandas, NumPy) for analyzing stock market data
Works well with broker APIs (like Zerodha Kite, Interactive Brokers)
How to Start Learning Python?
Take beginner-friendly courses on Codecademy or Coursera
Practice using YouTube tutorials (like freeCodeCamp)
Learn basic coding concepts like loops, functions, and data handling
Once you are comfortable with Python, the next step is understanding the stock market.
Learn Stock Market Basics
Before writing code for trading, you must understand how trading works. Knowing this will help you create better trading strategies.
Key Stock Market Concepts:
Types of markets – Stocks, Forex, CryptoOrder types – Market orders, Limit orders, Stop-lossTrading indicators – RSI, Moving Averages, MACDTechnical & Fundamental Analysis – How to study stock movements
Where to Learn Stock Market Basics?
Read books like The Intelligent Investor by Benjamin Graham
Watch YouTube channels like Pranjal Kamra, CA Rachana Ranade
Use stock market simulators like Investopedia Stock Simulator
After understanding stock market basics, you can move on to connecting your code with a trading platform.
Connect with a Broker (Broker API Integration)
To execute trades automatically, your algorithm needs to communicate with a broker. This is done using APIs (Application Programming Interfaces) provided by brokers.
Steps to Integrate a Broker API:
Choose a broker that supports API trading (like Zerodha)
Get your API keys from the broker’s website
Use Python libraries like CCXT, Zerodha Kite API, Alpaca API
Write a script to fetch live market data and place trades
Example Code to Fetch Live Stock Prices (Zerodha API)
from kiteconnect import KiteConnect kite = KiteConnect(api_key="your_api_key") data = kite.ltp("NSE:INFY") # Fetch live price of Infosys print(data)Once your broker is connected, you can start testing trading strategies.
Learn and Test Trading Strategies
A trading strategy is a set of rules that decide when to buy and sell stocks. In algo trading, the program follows these rules automatically.
Common Algo Trading Strategies:
Mean Reversion – Buying when prices are low and selling when they rise
Momentum Trading – Buying stocks that are trending up and selling when they slow down
Arbitrage – Profiting from small price differences in different markets
Market Making – Placing buy and sell orders to profit from small price changes
How to Test a Strategy?
Use historical stock data to test how well your strategy would have worked
Try paper trading (using a virtual account with fake money)
Check profit/loss reports to refine the strategy
Once your strategy works well in tests, you can automate it to trade in real-time.
Automate Your Trading Strategy
After testing, it's time to let the computer trade automatically. Your algorithm will:Read live stock prices from the broker Analyze market conditions using your strategy Place buy/sell orders when the conditions are met
Steps to Automate Your Trading Strategy:
Fetch live market data using a broker API
Apply your trading logic (e.g., Buy when RSI < 30)
Place orders automatically based on strategy rules
Monitor the performance and adjust the strategy
Example Code for Automated Trading (Simple Moving Average Strategy)
import pandas as pd from kiteconnect import KiteConnect kite = KiteConnect(api_key="your_api_key") # Fetch historical stock data data = kite.historical_data("NSE:INFY", "2023-01-01", "2023-12-31", "day") df = pd.DataFrame(data) # Calculate moving averages df['SMA_50'] = df['close'].rolling(50).mean() df['SMA_200'] = df['close'].rolling(200).mean() # Generate buy/sell signals df['Signal'] = df['SMA_50'] > df['SMA_200'] print(df.tail()) # Show the latest signals You can now deploy the script on a server so it runs automatically without manual interference.
Final Thoughts
Algo trading gives traders an edge by executing trades faster, reducing emotions, and using data-driven decisions. However, it requires coding skills, stock market knowledge, and testing before going live.
FAQs (Frequently Asked Questions)
Do I need coding knowledge to start algo trading?
Which brokers support algo trading?
Popular brokers include Zerodha Kite (India), Interactive Brokers (Global), and AngelOne.
How much money do I need to start?
It depends on the broker, but you can start with ₹5,000 to ₹50,000 ($100-$500).
Is algo trading risk-free?
No, it has risks like market fluctuations, strategy failure, and technical errors. Always test before using real money.
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