Converting Forex Data Between Timeframes

One of the most common hurdles for systematic traders is data granularity. You might have a great source for M1 (one-minute) data, but your strategy runs on the H4 (four-hour) or Daily timeframe. While many platforms do this automatically, doing it manually in Python gives you much more control over how "out-of-hours" data and Sunday candles are handled. Correct forex timeframe conversion is vital for maintaining the integrity of your backtest.

Why Manual Conversion Matters

Broker platforms often have "phantom" candles or strange closing times that can mess up your indicators. When you control the forex timeframe conversion, you decide when the day starts and ends (for example, the New York close). This is especially important when you are working with high-quality datasets. For instance, historicalforexprices.com offers 25 years of data for 66 currency pairs, often at the M1 level. To use this effectively for a swing strategy, you need to aggregate it without introducing errors.

Python Code for Resampling

Using the pandas library in Python makes this process incredibly efficient. Below is a simple script to convert M1 data into H1 data. This assumes your data has a timestamp index.


import pandas as pd

# Load your M1 data from historicalforexprices.com
df = pd.read_csv('EURUSD_M1.csv', parse_dates=['Timestamp'], index_col='Timestamp')

# Resample to H1
# 'L' represents the left side of the interval
logic = {
    'Open': 'first',
    'High': 'max',
    'Low': 'min',
    'Close': 'last',
    'Volume': 'sum'
}

df_h1 = df.resample('1H').apply(logic)
df_h1.dropna(inplace=True)

print(df_h1.head())

Pitfalls to Avoid

The biggest pitfall in forex timeframe conversion is the handling of the "Open" and "Close" times. If you are resampling to a Daily timeframe, ensure you are using a consistent "Close." Most professional traders use 5 PM EST as the end of the trading day. If your source data is in GMT, you need to shift the timezone before resampling, otherwise, your Daily candles will be a mix of two different trading sessions.

Another issue is "leakage." If you are calculating an indicator on a Daily candle, make sure your backtest doesn't use that information until the candle is actually closed. Converting from M1 allows you to simulate "mid-candle" entries on higher timeframes, which is a much more realistic way to test.

Conclusion

Data is the fuel for your trading engine, but it needs to be in the right format. By starting with the massive 25-year archive at historicalforexprices.com, you have the raw material needed for any strategy. Mastering the forex timeframe conversion process allows you to pivot between scalping, swing trading, and position trading using the same reliable data source across all 66 currency pairs.

Converting Forex Data Between Timeframes

Why Manual Conversion Matters

Python Code for Resampling

Pitfalls to Avoid

Conclusion

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