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I. Introduction: Why I Built My Own Bot

Crypto automation sounds exciting: a bot that trades while you sleep, catches profitable trends, and never gets tired. But when I started exploring the space, most bots I found were either overhyped, under-documented, or flat-out broken.

So I decided to build my own bot from scratch—one that works in real time, with full control from the command line, supports backtesting, paper trading, and can deploy to a $5 VPS.

This post covers my journey from zero to real-time execution bot. If you’re a Python dev or a trader curious about bot building, this one’s for you.

II. Planning the Bot: Goals and Features

Before writing a single line of code, I outlined the core requirements:

  • Real-time strategy execution using Binance API
  • CLI-based interface for strategy selection and control
  • Strategy modularity (plug-in based system)
  • Support for backtesting and paper/live modes
  • Logging of trades and errors
  • Lightweight for VPS deployment

III. Tech Stack

  • Python 3.10+
  • Binance API (python-binance)
  • TA-Lib / pandas-ta for technical indicators
  • pandas / NumPy for data processing
  • SQLite for trade logs (optional)
  • argparse or Typer for CLI

IV. Architecture Overview

crypto_bot/
├── strategies/
│   ├── rsi_strategy.py
│   ├── macd_strategy.py
├── core/
│   ├── trader.py
│   ├── logger.py
├── config/
│   └── settings.json
├── cli.py
├── bot.py
└── logs/
  • Strategies: Each strategy is a Python class
  • Trader: Manages data fetching and trade execution
  • Logger: Handles both file and optional DB logging
  • CLI: Entry point to control bot behavior

V. Real-Time Trading Loop

Here’s a simplified example of how the main bot loop works:

while True:
    df = fetch_ohlcv(symbol, interval)
    signal = strategy.evaluate(df)
    if signal == "BUY":
        trader.buy(symbol, quantity)
    elif signal == "SELL":
        trader.sell(symbol, quantity)
    sleep(next_candle_time())

Key points:

  • fetch_ohlcv() pulls the latest OHLCV data
  • Strategy evaluates the last N candles
  • Orders are placed if a valid signal is returned

VI. Modular Strategy Example (RSI)

class RSIStrategy:
    def __init__(self, period=14, overbought=70, oversold=30):
        self.period = period
        self.overbought = overbought
        self.oversold = oversold

    def evaluate(self, df):
        df['rsi'] = ta.rsi(df['close'], length=self.period)
        if df['rsi'].iloc[-1] < self.oversold:
            return "BUY"
        elif df['rsi'].iloc[-1] > self.overbought:
            return "SELL"
        return "HOLD"

You can add more strategies by following this simple class pattern.

VII. CLI Control (argparse or Typer)

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--symbol', type=str, required=True)
parser.add_argument('--strategy', type=str, required=True)
parser.add_argument('--mode', choices=['paper', 'live'], default='paper')
args = parser.parse_args()

bot = TradingBot(symbol=args.symbol, strategy=args.strategy, mode=args.mode)
bot.run()

This allows you to run the bot with:

python cli.py --symbol BTCUSDT --strategy rsi --mode paper

VIII. Backtesting and Paper Trading

The bot supports:

  • Historical simulation from CSV or Binance fetch
  • Paper trading logs trades without execution
  • Seamless switch to live once tested

Example paper trade log:

[2025-04-23 14:22:01] BUY BTCUSDT at 62410.5 [RSI: 29.7]

IX. Logging and Trade History

All trades and errors are logged. You can choose between:

  • Simple log file per session
  • SQLite database logging for analytics and visualization

Sample log output:

[INFO] RSI crossed below 30. Triggering BUY signal.
[EXECUTE] Simulated BUY 0.01 BTC at 61,420.20

X. Deployment to VPS (Lightweight)

I deployed this bot on a $5/month DigitalOcean droplet:

  1. Install Python + pip + virtualenv
  2. Clone repo and install requirements
  3. Use screen or tmux to run persistently
  4. Monitor logs via tail -f logs/session.log

That’s it—bot runs 24/7 with minimal resource usage.

XI. Lessons Learned

  • Always test with paper mode first
  • Real-time systems need real-time error handling
  • Modular design makes everything easier
  • Logging saves hours of debugging
  • Don’t over-optimize your strategy—simplicity wins

XII. Build It Yourself

Want the full guide with 250+ pages of hands-on instruction?

📘 Download the Full PDF Guide
💻 Get the Complete Python Bot Repository

XIII. Final Thoughts

This bot was more than a side project—it’s now part of my toolkit for long-term trading automation. If you’re a dev looking to break into crypto bots, I highly recommend building your own.

Let me know if you try it or want help modifying the code!

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