Build Your First Trading Bot with Zerodha & Python (2026)

Yeh ek complete walkthrough hai — local setup, code, paper trade, fir live, fir VPS pe deploy. End mein aapka apna bot 24/7 chalega.

Pre-requisites

• Python 3.10+ installed
• Zerodha account + Kite Connect API (₹2k/month)
• Basic Python knowledge
• VPS account (Vultr/DigitalOcean — ₹500/month)

Step 1 — Project setup

mkdir trading-bot && cd trading-bot
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install kiteconnect pandas python-dotenv schedule

Folder structure

trading-bot/
├── .env
├── config.py
├── auth.py
├── strategy.py
├── bot.py
├── logger.py
└── logs/

Step 2 — config & auth

.env

KITE_API_KEY=your_key
KITE_API_SECRET=your_secret
KITE_ACCESS_TOKEN=
SYMBOL=RELIANCE
INSTRUMENT_TOKEN=738561
QUANTITY=10
PAPER_TRADE=true

auth.py

from kiteconnect import KiteConnect
from dotenv import load_dotenv, set_key
import os

load_dotenv()

def get_kite():
    kite = KiteConnect(api_key=os.getenv("KITE_API_KEY"))
    token = os.getenv("KITE_ACCESS_TOKEN")
    if not token:
        print("Visit:", kite.login_url())
        request_token = input("Paste request_token: ")
        data = kite.generate_session(request_token, api_secret=os.getenv("KITE_API_SECRET"))
        token = data["access_token"]
        set_key(".env", "KITE_ACCESS_TOKEN", token)
    kite.set_access_token(token)
    return kite

Step 3 — strategy logic

strategy.py

import pandas as pd

def ma_crossover_signal(df):
    df['ma_fast'] = df['close'].rolling(9).mean()
    df['ma_slow'] = df['close'].rolling(21).mean()

    if len(df) < 22:
        return None

    last = df.iloc[-1]
    prev = df.iloc[-2]

    # Bullish crossover
    if prev['ma_fast'] <= prev['ma_slow'] and last['ma_fast'] > last['ma_slow']:
        return "BUY"
    # Bearish crossover
    if prev['ma_fast'] >= prev['ma_slow'] and last['ma_fast'] < last['ma_slow']:
        return "SELL"
    return None

Step 4 — main bot loop

bot.py

import os, time
import pandas as pd
from datetime import datetime, timedelta
from auth import get_kite
from strategy import ma_crossover_signal
from logger import log

PAPER = os.getenv("PAPER_TRADE", "true") == "true"
SYMBOL = os.getenv("SYMBOL")
TOKEN  = int(os.getenv("INSTRUMENT_TOKEN"))
QTY    = int(os.getenv("QUANTITY"))

def fetch_candles(kite):
    end = datetime.now()
    start = end - timedelta(days=5)
    return kite.historical_data(TOKEN, start, end, "5minute")

def place_order(kite, side):
    if PAPER:
        log(f"[PAPER] {side} {QTY} {SYMBOL}")
        return
    order_id = kite.place_order(
        variety="regular",
        exchange="NSE",
        tradingsymbol=SYMBOL,
        transaction_type=side,
        quantity=QTY,
        product="MIS",
        order_type="MARKET"
    )
    log(f"[LIVE] {side} order: {order_id}")

def main():
    kite = get_kite()
    log("Bot started — paper=" + str(PAPER))
    while True:
        try:
            now = datetime.now()
            # Trade only 9:15-15:15
            if not (9 <= now.hour < 15 or (now.hour == 15 and now.minute < 15)):
                time.sleep(60); continue

            candles = fetch_candles(kite)
            df = pd.DataFrame(candles)
            signal = ma_crossover_signal(df)
            if signal:
                log(f"Signal: {signal}")
                place_order(kite, signal)
            time.sleep(300)  # 5 min
        except Exception as e:
            log(f"ERROR: {e}")
            time.sleep(60)

if __name__ == "__main__":
    main()

Step 5 — logger

logger.py

import os
from datetime import datetime

os.makedirs("logs", exist_ok=True)

def log(msg):
    ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    line = f"[{ts}] {msg}"
    print(line)
    with open(f"logs/{datetime.now().strftime('%Y-%m-%d')}.log", "a") as f:
        f.write(line + "\n")

Step 6 — paper trade test

python bot.py

1-2 hafta paper mode mein chalao. Logs check karo. Strategy theek lag rahi ho toh PAPER_TRADE=false kar do.

Step 7 — VPS deployment

1. Vultr/DigitalOcean pe Ubuntu 22.04 VPS (₹400-600/month)
2. SSH login → git clone aapka repo
3. Same setup repeat karo VPS pe
4. tmux ya systemd service banao taaki bot disconnect ho ke bhi chalta rahe

systemd service example

# /etc/systemd/system/tradingbot.service
[Unit]
Description=Trading Bot
After=network.target

[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu/trading-bot
ExecStart=/home/ubuntu/trading-bot/venv/bin/python bot.py
Restart=always

[Install]
WantedBy=multi-user.target

sudo systemctl enable tradingbot
sudo systemctl start tradingbot
sudo journalctl -u tradingbot -f  # live logs

Step 8 — monitoring

Telegram bot setup karo alerts ke liye:

import requests
def telegram_alert(msg):
    requests.post(
        f"https://api.telegram.org/bot{TOKEN}/sendMessage",
        data={"chat_id": CHAT_ID, "text": msg}
    )

Improvements (homework)

• Stop-loss + target add karo
• Position sizing logic
• Multiple symbols support
• Daily loss limit / killer switch
• Backtesting module

Conclusion

Aapka pehla bot tayar hai. Yeh educational hai — production mein chalane se pehle thorough backtest, paper trade, aur risk management add karna zaroori hai. Custom production-ready bot chahiye? Hum complete system bana ke deten hain.

FAQs

📚 More Algo Trading Guides

›Python Algo Trading in India — A Beginner's Guide (2026)›Zerodha Kite Connect API — Step-by-step Tutorial (2026)›Best Algo Trading Strategies for Nifty & BankNifty (2026)›Python vs VBA — Trading Automation Ke Liye Konsa Best?›Backtesting a Trading Strategy in Python — Step-by-step›SEBI Algo Trading Rules — Simple Hindi-English Guide (2026)

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About Sonali Sinha

Founder, Instacode.in — Software entrepreneur based in Indore. 8+ years building production web apps, algo trading systems, and ecommerce platforms for Indian SMBs and global clients. Writes simple, hype-free guides for Indian developers and traders.

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