Forex Spread Analysis: How to Factor It In Backtests

If you have ever run a backtest that looked like a "Holy Grail" only to see it fail in live trading, you have probably been a victim of spread neglect. Most historical data comes as a single price (usually the Mid-price or the Close), but in the real world, you buy at the Ask and sell at the Bid. That gap - the spread - is the silent killer of trading strategies.

To build a realistic model, you must use forex spread data or at least a highly accurate simulation of it. Without factoring in the cost of entry and exit, your "profitable" strategy is likely just a record of how much you would have paid your broker in commissions.

The Reality of Variable Spreads

Spreads are not static. They breathe with the market. During the London-New York overlap, the spread on EUR/USD might be 0.1 pips. But during a major news event or the "witching hour" when New York closes and Sydney opens, that spread can balloon to 10 or 20 pips.

When you look at 25 years of data from historicalforexprices.com, you need to remember that the "volatility" you see in the candles was accompanied by volatility in the spreads. If your bot tries to trade during the Asian session low-liquidity period, the forex spread data will likely show that the costs eat all your potential profits.

Simulating Spreads in Backtests

Since most historical datasets don't include the tick-by-tick bid/ask spread for 25 years (as the file sizes would be astronomical), you have to be smart about how you simulate it. A common mistake is using a "fixed" spread. This is a trap. Instead, you should use a variable spread model based on the ATR (Average True Range) or the time of day.

Here is a simple way to implement a "slippage and spread" penalty in a Python backtest:

import pandas as pd

df = pd.read_csv('forex_data.csv')

# Assume a base spread of 2 pips
base_spread = 0.0002

# Add a penalty for trading during high volatility (news events)
df['ATR'] = df['High'] - df['Low']
df['Realistic_Spread'] = base_spread + (df['ATR'] * 0.1)

# Calculate net profit after spread
df['Raw_Profit'] = df['Close'].diff()
df['Net_Profit'] = df['Raw_Profit'] - df['Realistic_Spread']

print(df[['Raw_Profit', 'Net_Profit']].head())

Why Accurate Data is Critical

If you are trading the 66 currency pairs available at historicalforexprices.com, you will notice that the spread "cost" varies wildly between majors and exotics. Trading USD/MXN is a completely different beast than trading EUR/USD. The forex spread data requirements for an exotic pair are much higher because the cost of business is higher.

By using 25 years of data, you can see how spreads have generally compressed over time as technology improved. However, the "shocks" - like the Swiss Franc floor removal in 2015 - show that spreads can still go to infinity when liquidity vanishes.

Successful traders don't ignore the spread; they embrace it as a core part of their risk management. Before you go live, ensure your backtest accounts for the reality of the market. Use the high-quality data from historicalforexprices.com as your foundation, but always leave room for the broker's cut.

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