The Warmup Period Problem: Why Your Python Backtest Doesn't Match Live Trading

Ashish Agarwal — Thu, 26 Mar 2026 09:36:00 +0000

I ran the same SMA crossover strategy through a pandas backtest and a live-trading simulator. Same rules. Same data. Same time period.

Different results.

Metric	Python/Pandas	Live Simulation
Total Return	13.23%	16.77%
Total Trades	3	2
Max Drawdown	14.50%	13.78%

This isn't a bug in either system. It's a fundamental difference in how they handle indicator warmup periods - and understanding it will change how you write backtests.

The Strategy

Classic moving average crossover on AAPL:

Entry: Buy when 20-day SMA crosses above 50-day SMA
Exit: Sell when 20-day SMA crosses below 50-day SMA
Capital: $10,000
Period: 365 days

Simple enough that implementation differences should be minimal. Yet one found 3 trades, the other found 2.

The Typical Pandas Approach

Here's how most of us write this:

def calculate_moving_averages(df, short_window, long_window):
  df = df.copy()
  df["sma_short"] = df["close"].rolling(window=short_window).mean()
  df["sma_long"] = df["close"].rolling(window=long_window).mean()
  return df

def generate_signals(df):
  df = df.copy()
  df["signal"] = 0
  df.loc[df["sma_short"] > df["sma_long"], "signal"] = 1
  df.loc[df["sma_short"] < df["sma_long"], "signal"] = -1
  df["position"] = df["signal"].diff()
  return df

# Run the backtest
df = calculate_moving_averages(df, 20, 50)
df = generate_signals(df)
df = df.dropna()  # <-- This line is the problem
results = backtest(df)

See that dropna()? It removes the first 49 rows where the 50-day SMA is NaN. Perfectly reasonable.

But here's the hidden assumption: from row 50 onward, your backtest processes every signal as if you were watching the market in real-time from day 1.

What Live Trading Actually Looks Like

Imagine you deploy a trading bot today. What happens?

Bot starts up, loads 50 days of historical data
Calculates the current SMA values
Starts monitoring for new crossovers
Enters trades only when it detects a crossover after it started running

The bot can't act on a crossover that happened last week. It missed it. It has to wait for the next opportunity.

The Trade Pandas Found That Live Missed

Here are the complete trade logs:

Python/Pandas (3 trades):

┌────────────┬────────┬─────────┬────────┬────────────┐
│    Date    │ Action │  Price  │ Shares │   Value    │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2025-06-10 │ BUY    │ $202.67 │ 49     │ $9,930.83  │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2025-06-16 │ SELL   │ $198.42 │ 49     │ $9,722.58  │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2025-07-11 │ BUY    │ $211.16 │ 46     │ $9,713.36  │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2026-01-07 │ SELL   │ $260.33 │ 46     │ $11,975.18 │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2026-02-23 │ BUY    │ $266.18 │ 45     │ $11,978.10 │
├────────────┼────────┼─────────┼────────┼────────────┤
│ 2026-03-18 │ SELL   │ $249.94 │ 45     │ $11,247.30 │
└────────────┴────────┴─────────┴────────┴────────────┘

Live Simulation (2 trades):

┌────────────┬────────────┬─────────────┬────────────┬──────────────────────┐
│   Entry    │    Exit    │ Entry Price │ Exit Price │         P&L          │
├────────────┼────────────┼─────────────┼────────────┼──────────────────────┤
│ 2025-07-10 │ 2026-01-06 │ $211.16     │ $260.33    │ +$2,328.57 (+23.29%) │
├────────────┼────────────┼─────────────┼────────────┼──────────────────────┤
│ 2026-02-22 │ 2026-03-17 │ $266.18     │ $249.94    │ -$610.11 (-6.10%)    │
└────────────┴────────────┴─────────────┴────────────┴──────────────────────┘

The June 2025 trade is completely missing from the live simulation.

Why the June Trade Was Skipped

The crossover that triggered the June 10 entry happened around day 48 - during the warmup period, before the indicators were valid.

~Day 48:

SMA(20) crosses above SMA(50) ← During warmup

Day 50:

Warmup ends, but crossover already happened

2025-06-10:

Pandas enters (sees historical crossover)
Live sim has no position (crossover was during warmup)

2025-06-16:

SMA(20) crosses below SMA(50)
Pandas exits with -$208.25 loss
Live sim has nothing to exit

Pandas processed the historical data and saw "oh, there's a bullish crossover active, let's enter." A live bot that just started running wouldn't see a crossover event - it would
just see that SMA(20) > SMA(50), which isn't an entry signal.

Which Approach Is Correct?

Neither is wrong. They answer different questions:

┌─────────────────┬─────────────────────────────────────────────────────────────────────────────┐
│    Approach     │                             Question It Answers                             │
├─────────────────┼─────────────────────────────────────────────────────────────────────────────┤
│ Pandas backtest │ "How would this strategy have performed with perfect historical knowledge?" │
├─────────────────┼─────────────────────────────────────────────────────────────────────────────┤
│ Live simulation │ "What would happen if I deployed this strategy today?"                      │
└─────────────────┴─────────────────────────────────────────────────────────────────────────────┘

For academic research and strategy development, the pandas approach is standard. For estimating real-world performance, the live simulation approach is more accurate.

How to Fix Your Pandas Backtest

If you want your backtest to match live behavior, you need explicit warmup logic:

def backtest_with_warmup(df, warmup_period, initial_capital=10000.0):
  capital = initial_capital
  shares = 0
  trades = []
  in_warmup = True

  for i, (idx, row) in enumerate(df.iterrows()):
      # Skip warmup period
      if i < warmup_period:
          continue

      # On first non-warmup day, mark warmup complete
      # but DON'T act on existing crossover state
      if in_warmup:
          in_warmup = False
          prev_signal = row["signal"]  # Record current state
          continue

      current_price = row["close"]

      # Only act on CHANGES in signal (new crossovers)
      if row["position"] == 2 and capital > 0:  # New bullish crossover
          shares = capital // current_price
          capital -= shares * current_price
          trades.append({"date": idx, "action": "BUY", "price": current_price})

      elif row["position"] == -2 and shares > 0:  # New bearish crossover
          capital += shares * current_price
          trades.append({"date": idx, "action": "SELL", "price": current_price})
          shares = 0

  return trades, capital + (shares * df.iloc[-1]["close"])

The key insight: don't just drop NaN rows - track whether you've completed warmup and only act on signal changes that occur after.

The Bigger Picture

In this specific case, the missed trade was a loser (-$208.25), so the live simulation outperformed. But this cuts both ways - sometimes warmup will cause you to miss winners.

The real takeaway: expect a few percent variance between backtest and live results. The warmup period, execution delays, slippage, and market impact all create differences between
simulation and reality.

When your backtest shows 50% annual returns and live trading shows 45%, the warmup period might be part of that gap.

I wrote a more detailed breakdown with the full Python code and comparison methodology at: https://quantdock.io/blog/the-warmup-period-problem

Forem: Ashish Agarwal

The Warmup Period Problem: Why Your Python Backtest Doesn't Match Live Trading

The Strategy

The Typical Pandas Approach

The Bigger Picture