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Why Debugging Matters in Algo Trading

A single logic error or API misfire in a trading bot can mean serious losses. Whether you’re backtesting or live trading, robust debugging practices are critical for safety, performance, and confidence in your automation.

Here are the best practices I follow when debugging my trading bots.

1. Log Everything — Smartly

Instead of print statements scattered everywhere, use Python’s built-in logging module:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

logger.info("Bot started")
logger.debug(f"Current position: {self.position}")
logger.error("API failed to fetch price")

Set different log levels for backtest vs. live modes.

2. Reproduce Bugs with Backtesting

Never debug live trades first. If something breaks, isolate the issue in backtesting mode where you can run hundreds of scenarios quickly.

  • Feed in the same historical data
  • Simulate the exact conditions that caused the issue
  • Log order decisions and strategy logic step-by-step

3. Use Dry-Run Mode Before Going Live

Most platforms (like Freqtrade, CCXT-based setups) offer a “dry-run” mode that simulates real trading without executing orders. This helps:

  • Validate strategy signals
  • Test order generation logic
  • Catch config issues before money is involved

4. Write Unit Tests for Strategy Components

Break your logic into functions and test them:

def is_bullish_crossover(sma_short, sma_long):
    return sma_short[-1] > sma_long[-1] and sma_short[-2] <= sma_long[-2]

Use pytest or unittest to cover logic that shouldn’t fail.

5. Log Order Flow and Exchange Responses

Especially when using live APIs like CCXT:

  • Log order IDs, amounts, and responses
  • Monitor rate limits and error codes
  • Handle exceptions gracefully
try:
    order = exchange.create_market_buy_order('BTC/USDT', 0.01)
    logger.info(f"Order executed: {order['id']}")
except Exception as e:
    logger.exception("Order failed")

6. Visualize Before You Deploy

Use Matplotlib or Plotly to plot:

  • Entry/exit points
  • Indicator behavior
  • Equity curve

This makes spotting anomalies much easier than reading logs alone.

7. Use Assertions for Sanity Checks

If your code assumes something must always be true — make it explicit:

assert self.data.close[0] > 0, "Price must be positive"

This avoids silent logic errors.

8. Keep Separate Configs for Backtest and Live

Avoid cross-contamination between simulation and real-money settings:

  • Use YAML or JSON for configurations
  • Explicitly load different configs based on mode

Final Thoughts

Debugging is a skill that separates reckless traders from systematic ones. The more you invest in building transparent, testable, and logged systems, the more reliable your trading automation becomes.

Want to explore open source trading frameworks built with debuggability in mind? Start with our handpicked reviews.