Algo Trading & Backtesting

1. What Is Algorithmic Trading?

Algorithmic trading (algo trading or automated trading) uses computer programs to execute buy and sell orders based on predefined rules. These rules are written using logic, mathematics, technical indicators, statistical models, or machine learning.

Key characteristics:

Speed: Algorithms execute trades in milliseconds.

Accuracy: Orders are placed exactly as coded, without emotional interference.

Consistency: Strategies run the same way every time.

Scalability: Algorithms can scan hundreds of stocks simultaneously.

Automation: Removes manual effort and human error.

Examples of algo rules:

Buy when the 50-day moving average crosses above the 200-day moving average.

Enter long if RSI < 30 and exit if RSI > 60.

Execute mean reversion when prices deviate from their statistical average.

Place a market-making order when bid-ask spread widens beyond a threshold.

Algo trading is used widely in equities, commodities, forex, crypto, futures, and options markets.

2. Why Algo Trading Matters

Algo trading is not just for institutions anymore. Retail traders now have access to powerful tools like NinjaTrader, TradingView Pine Script, Amibroker AFL, Python (Pandas, NumPy), Zerodha Streak, AlgoBulls, etc.

There are several advantages:

1. Eliminates emotions

Fear, greed, hesitation, revenge trading—algos remove them completely.

2. Enhances speed & efficiency

A computer can process multiple charts at once—no possibility for manual delays.

3. Reduces costs

Efficient execution reduces slippage, spreads, and missed opportunities.

4. Backtesting improves confidence

You know how your strategy performed historically before risking real capital.

5. Suitable for all market styles

Trending, scalping, intraday, swing trading, options strategies—algos cover everything.

3. Core Components of Algo Trading
1. Strategy Logic

The brain of the algorithm. Types include:

Trend-following strategies

Mean reversion models

Breakout systems

Arbitrage models

Options premium-selling/hedging algorithms

Machine learning predictive models

2. Data

The quality of the data determines the quality of your strategy.

Historical data (OHLC, volumes)

Real-time data (market feed)

Fundamental data

Tick/Orderbook data (advanced)

3. Programming Environment

Most common:

Python

TradingView Pine Script

Amibroker AFL

C++ (HFT level)

MetaTrader MQL

Proprietary platforms

4. Execution Engine

A platform that sends orders to the exchange via API.

5. Risk Management Module

Includes:

Stop-loss

Target

Position sizing (fixed lot, % of capital)

Max daily loss

Drawdown limits

Volatility filters

6. Monitoring & Optimization

Live dashboards help track:

Real-time P&L

Slippage

Latency

Execution errors

4. Backtesting – The Heart of Algo Trading

You cannot run an algorithm blindly. You must test it on past data to understand how it behaves. This process is called backtesting.

What Is Backtesting?

Backtesting is the simulation of a trading strategy on historical price data to evaluate its performance. It answers questions like:

Would the strategy have made money?

How much drawdown would it suffer?

What is the risk-reward ratio?

How consistent are returns?

How often does it win?

How Backtesting Works?
Step 1: Define the rules

Example strategy:

Buy when price closes above 20 EMA

Sell when price closes below 20 EMA

Risk 1% of capital per trade

Stop-loss = 1.5%

Target = 3%

Step 2: Select historical data

A minimum of:

2–5 years for intraday

5–10 years for swing

10–15 years for trend models

Step 3: Run the simulation

The software applies your rules on every candle historically.

Step 4: Analyze metrics

Some essential backtesting metrics:

✔ CAGR (Annual Return)

Measures yearly profit.

✔ Win Rate %

How many trades were profitable vs total bets.

✔ Profit Factor

Total gross profit ÷ total gross loss.

PF > 1.5 = Good; PF > 2 = Strong.

✔ Drawdown %

The maximum fall from peak equity.

Lower drawdown = safer strategy.

✔ Sharpe Ratio

Reward/risk ratio based on volatility.

✔ Average trade return

Shows how much each trade earns.

✔ Expectancy

Average win × win rate − average loss × loss rate.

Step 5: Optimize (carefully!)

Adjust parameters to improve performance, but avoid overfitting.

5. Types of Backtesting
1. Historical Backtesting

Runs strategy on past OHLC data.

2. Walk-Forward Testing

Split data into in-sample (training) and out-of-sample (testing).

3. Monte Carlo Simulation

Tests strategy performance across random variations.

4. Paper Trading / Forward Testing

Real-time simulation in live markets without real money.

6. Why Backtesting Can Mislead (Pitfalls)

Backtesting is powerful but dangerous if not done correctly.

1. Overfitting

Your strategy may perform well on history but fail in real markets.

2. Look-Ahead Bias

Using future data unknowingly, giving unrealistic results.

3. Survivorship Bias

Testing only stocks that survived, ignoring delisted ones.

4. Slippage & Transaction Costs

Real-world execution is worse than simulated execution.

5. Market Regime Changes

A strategy profitable during trending phases may fail during sideways markets.

Professional algo traders spend more time fixing biases than writing strategies.

7. Algo Trading Strategies Common in India
1. Trend-Following on NIFTY Futures

EMA crossover, Supertrend, Donchian breakout.

2. Options Selling Strategies

Short Straddle

Short Strangle

Iron Condor

Delta-neutral hedged selling

3. Mean Reversion in Bank Nifty

Price touches lower Bollinger Band → Buy.

4. Intraday Momentum

Breakout of previous day high/low.

5. Arbitrage Models

Cash–futures arbitrage, index arbitrage.

8. Tools & Platforms to Start Algo Trading
Beginner-Friendly

Zerodha Streak

Dhan Options Trader

Angel Algo

TradingView (Pine Script)

Intermediate

Python (using broker APIs)

Amibroker AFL

MetaTrader MQL

Advanced / Professional

QuantConnect

AlgoQuant

C++ HFT engines

Custom low-latency systems

9. Steps to Build a Profitable Algo Trading System
Step 1: Identify a market inefficiency

Find behaviors that occur consistently:

Monday gap filling

Tuesday volatility

Post-2:30 p.m. breakouts

Overnight momentum

Step 2: Create rules

Clear, unambiguous logic.

Step 3: Backtest

Use extensive and high-quality data.

Step 4: Evaluate metrics

Cut poor strategies early.

Step 5: Forward test

Test in real time without money.

Step 6: Deploy small capital

Scale only after long-term stability.

Step 7: Monitor & refine

Markets change → algos must evolve.

Conclusion

Algo trading and backtesting together form a powerful framework for systematic, disciplined, and scalable trading. Instead of relying on emotions or random decisions, traders build clear rules, test them against history, validate them in real-time, and automate execution to gain precision and consistency. With proper design, risk control, and continuous improvement, algorithmic trading can significantly enhance performance in equities, commodities, forex, indices, and options.