Forex Mean Reversion: Testing with Historical Data

The market spends about 70% of its time in a range. While trend following gets all the glory in the movies, many professional bank traders make their living through mean reversion. The idea is simple: price eventually returns to its average. However, the execution is difficult. To build a profitable system, you need robust forex mean reversion data to determine what "average" actually means and how far price can deviate before it snaps back.

Without at least 25 years of data, like the archive provided by historicalforexprices.com, you might mistake a multi-year trend for a simple deviation. You need to see how "the mean" moves through different economic regimes to avoid the trap of catching a falling knife.

Selecting the Right Pairs

Not all pairs are created equal for mean reversion. High-volatility pairs like GBP/JPY are often too "trendy" and can stay overbought for weeks. On the other hand, crosses like EUR/GBP or AUD/NZD are famous for their range-bound behavior. When you look at the 66 currency pairs available at historicalforexprices.com, you should look for pairs with high "stationary" characteristics. These are the pairs where mean reversion strategies thrive.

Optimal Lookback Periods

The biggest question in mean reversion is: "How far back should I look?" Is the 200-day moving average the mean, or is it the 20-period Bollinger Band? By testing your strategy against historicalforexprices.com's deep archives, you can run an optimization to find the sweet spot. Be careful, though - over-optimization is the enemy. You want a lookback period that has worked across the early 2000s, the 2008 crisis, and the post-pandemic era.

Code Example: Simple Mean Reversion Test

Here is a basic way to think about mean reversion in Python using a standard deviation stretch (Bollinger Band style):

import pandas as pd

def mean_reversion_signal(df, window=20, std_devs=2):
    df['ma'] = df['close'].rolling(window=window).mean()
    df['std'] = df['close'].rolling(window=window).std()
    df['upper'] = df['ma'] + (df['std'] * std_devs)
    df['lower'] = df['ma'] - (df['std'] * std_devs)
    
    # Sell if price is above upper band
    df['signal'] = 0
    df.loc[df['close'] > df['upper'], 'signal'] = -1
    # Buy if price is below lower band
    df.loc[df['close'] < df['lower'], 'signal'] = 1
    return df

The Danger of "New Normals"

One of the reasons you need 25 years of data is to recognize when the mean has fundamentally shifted. A currency pair might trade in a specific range for five years and then, due to a central bank policy change, shift to a completely new level. If your forex mean reversion data is too short-term, you will keep trying to buy the old "lows" while the market is establishing a new baseline. Having access to historicalforexprices.com ensures you have the context to see these big structural moves.

Mean reversion is a game of statistics and discipline. By using high-quality data and respecting the power of the trend, you can carve out a very profitable niche in the forex markets.

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