The Gap Nobody Talks About

The typical trading bot journey:

  1. Build strategy ✓
  2. Backtest it ✓
  3. Deploy with real money ← HERE BE DRAGONS

What’s missing? The dry run.

A dry run is your bot running in real-time, with real market data, making real decisions — but not placing real orders. Paper trading, but automated.

Why Backtests Aren’t Enough

Your backtest runs on historical data. It has perfect information:

  • Every candle is complete
  • Every price is final
  • There’s no latency
  • There’s no partial fill
  • The exchange never returns an error

Real markets are messier. The dry run catches the mess.

What I Found in Dry Runs

  1. 5-minute candle data lag — My bot fetched candles right at the close, but the exchange API hadn’t updated yet. The bot was making decisions on stale data.

  2. Rate limiting — With 8 coins, scanning every 5 minutes, plus position monitoring — the API calls added up faster than expected.

  3. Order of operations — My backtest checked SL then trailing. My live code checked trailing then SL. Different results on the same data.

  4. RSI warmup — I was fetching 20 candles for RSI calculation. RSI needs ~40 candles to stabilize. My first few signals were based on garbage RSI values.

How to Run a Dry Run

The simplest approach:

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DRY_RUN = True

def place_order(symbol, side, amount, price):
    if DRY_RUN:
        log(f"[DRY] Would {side} {amount} {symbol} at {price}")
        return {'id': 'dry_' + str(time.time()), 'status': 'filled'}
    else:
        return exchange.create_order(...)

Everything else runs exactly the same:

  • Data fetching: real
  • Signal calculation: real
  • Position tracking: simulated
  • Order placement: logged but not executed

The Comparison Tool

After running the dry bot for a few days, compare its decisions against the backtest for the same period:

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python compare_live_bt.py "2026-03-15" "2026-03-18"

This shows:

  • Entry matches: Did the dry bot and backtest enter the same trades?
  • Entry price: Would the entry price have been the same?
  • Exit reason: SL, trailing, time-based — do they agree?
  • PnL: How close are the numbers?

My First Comparison Result

Metric Match Rate
Entry signals 78%
Entry prices 92%
Exit reasons 71%
PnL (±10%) 65%

65% PnL match. Terrible. But this was before fixing the timezone bug, the candle resampling issue, and the trailing stop tick-vs-close problem.

After Fixing Everything

Metric Match Rate
Entry signals 98%
Entry prices 100%
Exit reasons 94%
PnL (±10%) 83%

83% PnL match. The remaining 17% is genuine market slippage and SL timing — things that can’t be simulated.

How Long to Dry Run

Minimum: 1 week. This catches most timing and data bugs.

Better: 2-4 weeks. This covers different market conditions and edge cases like exchange maintenance windows.

When to stop: When your live-backtest match rate stabilizes above 80% and you understand every discrepancy.

The Psychological Benefit

Dry running also prepares you psychologically:

  • You see real drawdowns happening in real-time
  • You experience 5 stop losses in a row
  • You watch a trade go +3% and then reverse to -1%
  • You feel the urge to manually intervene

If you can’t handle watching the dry bot lose, you definitely can’t handle it with real money.

The dry run isn’t just a technical test. It’s a stress test for you.

The Final Checklist

Before going from dry run to live:

  • Match rate above 80% for 1+ week
  • All known discrepancies explained and documented
  • Edge cases tested (restart recovery, network failure)
  • Psychological readiness (survived watching drawdowns)
  • Start with minimum position size for the first live week

The extra week of dry running has never cost me money. Skipping it has.

A dry run is boring. Losing money because you skipped it is exciting in all the wrong ways.