My Journey into Algorithmic Trading

Introduction

As a software engineer, I've always been fascinated by the intersection of technology and finance. This blog post documents my journey into algorithmic trading in the Indian markets.

Why Algo Trading?

Trading manually requires constant attention and is prone to emotional decisions. Algorithmic trading solves both these problems:

• Emotion-free execution - The algorithm follows rules, not feelings
• Speed - Computers can analyze and execute faster than humans
• Backtesting - You can test strategies on historical data before risking real money
• Scalability - Once built, the system can run 24/7

My Tech Stack

Here's what I use to build and deploy my trading algorithms:

# Example: Simple Moving Average Crossover Strategy
import pandas as pd
import numpy as np

def sma_crossover(data, short_window=20, long_window=50):
    signals = pd.DataFrame(index=data.index)
    signals['signal'] = 0.0
    
    # Short moving average
    signals['short_mavg'] = data['close'].rolling(window=short_window).mean()
    
    # Long moving average
    signals['long_mavg'] = data['close'].rolling(window=long_window).mean()
    
    # Generate signals
    signals['signal'][short_window:] = np.where(
        signals['short_mavg'][short_window:] > signals['long_mavg'][short_window:], 
        1.0, 0.0
    )
    
    return signals

Key Learnings

1. Risk Management is Everything - Never risk more than 2% of your capital on a single trade
2. Backtesting ≠ Live Performance - Slippage and latency matter in real markets
3. Keep It Simple - Complex strategies aren't always better
4. Continuous Learning - Markets evolve, so should your strategies

What's Next?

I'm currently working on:

• Machine learning-based signal generation
• Options strategies automation
• Real-time sentiment analysis integration

"The stock market is a device for transferring money from the impatient to the patient." - Warren Buffett

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This is not financial advice. Trading involves risk and you should only trade with money you can afford to lose.