Algorithmic trading 1. Introduction to Algorithmic Trading
Algorithmic trading, often called algo trading or automated trading, is the process of using computer programs to execute trades in financial markets according to a predefined set of rules.
These rules can be based on price, volume, timing, market conditions, or mathematical models. Once set, the algorithm automatically sends orders to the market without manual intervention.
In simple terms:
Instead of sitting in front of a trading screen and clicking “buy” or “sell,” you tell a machine exactly what conditions to look for, and it trades for you.
It’s like giving your brain + discipline to a computer — minus the coffee breaks, panic, and impulsive decisions.
1.1 Why Algorithms?
Humans are prone to:
Emotional bias (fear, greed, hesitation)
Slow reaction times
Fatigue and inconsistency
Computers, in contrast:
Execute instantly (microseconds or nanoseconds)
Follow rules 100% consistently
Handle multiple markets at once
Backtest ideas over years of data within minutes
This explains why algo trading accounts for 70%–80% of trading volume in developed markets like the US and over 50% in Indian markets for certain instruments.
1.2 The Core Components
Every algorithmic trading system consists of:
Strategy Logic – The rules that trigger trades (e.g., moving average crossover, statistical arbitrage).
Programming Interface – The language/platform (Python, C++, Java, MetaTrader MQL, etc.).
Market Data Feed – Real-time price, volume, and order book data.
Execution Engine – Connects to broker/exchange to place orders.
Risk Management Module – Stops, limits, and capital allocation rules.
Performance Tracker – Monitors profit/loss, drawdowns, and execution quality.
2. How Algorithmic Trading Works – Step by Step
Let’s break it down:
Idea Generation
Define a hypothesis: “I think when the 50-day moving average crosses above the 200-day MA, the stock will trend upward.”
Strategy Design
Turn the idea into exact rules: If MA50 > MA200 → Buy; If MA50 < MA200 → Sell.
Coding the Strategy
Program in Python, C++, R, or a broker’s native scripting language.
Backtesting
Run the algorithm on historical data to see how it would have performed.
Paper Trading (Simulation)
Trade in real time with virtual money to test live conditions.
Execution in Live Markets
Deploy with real money, connected to exchange APIs.
Monitoring & Optimization
Adjust based on performance, slippage, and market changes.
2.1 Example of a Simple Algorithm
Pseudocode:
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If Close Price today > 20-day Moving Average:
Buy 10 units
If Close Price today < 20-day Moving Average:
Sell all units
The computer checks the rule every day (or every minute, or millisecond, depending on design).
3. Types of Algorithmic Trading Strategies
Algo trading is not one-size-fits-all. Traders and funds design algorithms based on their objectives, timeframes, and risk appetite.
3.1 Trend-Following Strategies
Logic: “The trend is your friend.”
Tools: Moving Averages, MACD, Donchian Channels.
Example: Buy when short-term average crosses above long-term average.
Pros: Simple, works in trending markets.
Cons: Suffers in sideways/choppy markets.
3.2 Mean Reversion Strategies
Logic: Prices eventually revert to their mean (average).
Tools: Bollinger Bands, RSI, z-score.
Example: If stock falls 2% below its 20-day average, buy expecting a bounce.
Pros: Works well in range-bound markets.
Cons: Can blow up if the “mean” shifts due to fundamental changes.
3.3 Statistical Arbitrage
Logic: Exploit price inefficiencies between correlated assets.
Example: If Reliance and TCS usually move together but Reliance lags by 1%, buy Reliance and short TCS expecting convergence.
Pros: Market-neutral, less affected by overall market trend.
Cons: Requires high-frequency execution and deep statistical modeling.
3.4 Market-Making Algorithms
Logic: Provide liquidity by continuously posting buy and sell quotes.
Goal: Earn the bid-ask spread repeatedly.
Risk: Adverse selection during sharp market moves.
3.5 Momentum Strategies
Logic: Stocks that move strongly in one direction will continue.
Tools: Breakouts, Volume Surges.
Example: Buy when price breaks a 50-day high with high volume.
3.6 High-Frequency Trading (HFT)
Executes in microseconds.
Focuses on ultra-short-term inefficiencies.
Requires co-location servers near exchanges for speed advantage.
3.7 Event-Driven Algorithms
React to corporate actions or news:
Earnings releases
Mergers & acquisitions
Dividend announcements
Often combined with natural language processing (NLP) to scan news feeds.
4. Technologies Behind Algo Trading
4.1 Programming Languages
Python – Most popular for beginners & research.
C++ – Preferred for HFT due to speed.
Java – Stable for large trading systems.
R – Strong in statistical modeling.
4.2 Data
Historical Data – For backtesting.
Real-Time Data – For live execution.
Level 2/Order Book Data – For order flow analysis.
4.3 APIs & Broker Platforms
REST APIs – Easy to use but slightly slower.
WebSocket APIs – Low latency, real-time streaming.
FIX Protocol – Industry standard for institutional trading.
4.4 Infrastructure
Cloud Hosting – AWS, Google Cloud.
Dedicated Servers – For low latency.
Co-location – Servers physically near exchange data centers.
5. Advantages of Algorithmic Trading
Speed – Executes in microseconds.
Accuracy – Removes manual entry errors.
Backtesting – Test before risking real money.
Consistency – No emotional bias.
Multi-Market Trading – Monitor dozens of assets simultaneously.
Scalability – Once built, can trade large portfolios.
6. Risks & Challenges in Algo Trading
6.1 Market Risks
Model Overfitting: Works perfectly on past data but fails live.
Regime Changes: Strategies die when market structure shifts.
6.2 Technical Risks
Connectivity Issues
Data Feed Errors
Exchange Downtime
6.3 Execution Risks
Slippage – Orders filled at worse prices due to latency.
Front Running – Competitors' algorithms act faster.
6.4 Regulatory Risks
Many countries have strict algo trading regulations:
SEBI in India requires pre-approval for certain algos.
SEC & FINRA in the US enforce strict monitoring.
7. Backtesting & Optimization
7.1 Steps for Backtesting
Choose historical data range.
Apply your strategy rules.
Measure key metrics:
CAGR (Compound Annual Growth Rate)
Sharpe Ratio
Max Drawdown
Win/Loss Ratio
7.2 Common Pitfalls
Look-Ahead Bias: Using future data unknowingly.
Survivorship Bias: Ignoring stocks that delisted.
Over-Optimization: Tweaking too much to fit past data.
8. Case Study – Moving Average Crossover Algo
Imagine we test a 50-day vs 200-day MA crossover strategy on NIFTY 50 from 2010–2025.
Capital: ₹10,00,000
Buy Rule: MA50 > MA200 → Buy
Sell Rule: MA50 < MA200 → Sell
Results:
CAGR: 11.2%
Max Drawdown: 18%
Trades: 42 over 15 years
Win Rate: 57%
Conclusion: Low trading frequency, steady returns, low drawdown — suitable for positional traders.
Final Thoughts
Algorithmic trading is not a magic money machine — it’s a discipline that combines mathematics, programming, and market knowledge.
When done right, it can offer speed, precision, and scalability far beyond human capability.
When done wrong, it can cause lightning-fast losses.
The game has evolved from shouting in the trading pit to coding in Python. The traders who adapt, learn, and innovate will keep winning — whether they sit in a Wall Street skyscraper or a small home office in Mumbai.
