Tools for Coded Algo Trading

So you’ve dipped your toes into Python. Now let’s look at how the whole algo trading system works when you’re coding it yourself.

Imagine this as a machine with four parts:

1. Data Input – Get market data (price, volume, indicators)
2. Logic Check – Run your strategy (buy/sell rules)
3. Execution – Place orders if rules are met
4. Monitoring – Keep it running and manage risks

Let’s go through these step by step — desi trader style.

1. Data Input – Where Does the Market Data Come From?

To run any strategy, you need market data. This includes:

• OHLC candles (Open, High, Low, Close)
• Indicators like RSI, EMA, MACD
• Live price feeds (for intraday algos)

How do you get this data in Python?

You use APIs — which are like data delivery boys. They bring fresh candles to your code every few seconds or minutes.

There are public APIs (for learning and testing) and broker APIs (for live trading).

Example code:

import yfinance as yf data = yf.download("RELIANCE.NS", period="1d", interval="5m")

This gives you Reliance’s intraday data in 5-min candles — simple!

2. Logic Check – Apply Your Strategy

Now that you have candles, it’s time to check your conditions. Let’s say:

• Buy when RSI < 30 and Price > 20 EMA
• Sell when RSI > 70 and Price < 20 EMA

You’ll use Python libraries like ta to calculate RSI and EMA, and then use if-else to write the logic.

if rsi < 30 and price > ema_20: place_order("BUY")

Boom. Your brain → logic → code.

3. Execution – Placing Orders via Broker API

Once your logic says “Buy”, your code has to send an order to your broker.

This is done via broker APIs.

What is a Broker API?

A broker API is a set of tools provided by your broker that allows your program to interact with their trading platform. Through it, you can place trades, fetch live market data, check positions, and manage your portfolio—all directly from your code.

How to Get Access:

• Most brokers require you to open a trading account with them.
  
• After that, you can apply for API access from their developer portal or API section.
  
• Many brokers provide Python SDKs or wrappers, making it easier to connect without building everything from scratch.
  
• Access often comes with an API key or authentication token, which you’ll need to keep secure.
  

Here’s how it works in practice:

• You log in securely using tokens or API keys
• You define order type (market, limit, stop loss)
• You send it via a simple line of code like:

broker.place_order("RELIANCE", "BUY", qty=10)

Don’t worry — each broker provides documentation and sample code.

4. Monitoring – Keep an Eye on the Algo

Once your algo is live, you have to:

• Keep it running (via a laptop, cloud, or server)
• Track logs (what trades were placed and why)
• Set alerts for failures or errors
• Add risk controls (e.g., max loss per day, max trades)

You can even get Telegram alerts when your algo trades. Neat, right?

Final Flow Recap:

DATA ➝ STRATEGY ➝ ORDER ➝ MONITORING

That’s the loop your algo runs every minute or second — non-stop.

In the next chapter, we’ll show you an actual sample strategy in code — from data to execution — that you can study and modify for your own use.

Excited? Let’s dive into real code!