Building a Forex Data Pipeline in Python

A forex data pipeline is not just a folder of downloaded candles. A useful pipeline makes every step repeatable: ingestion, schema validation, timestamp checks, known-gap reporting, storage, and downstream backtest preparation.

Use Parquet as the local research layer

CSV is easy to inspect, but large multi-year forex datasets are easier to query when the columns are typed and compressed. Parquet works directly with pandas, Polars, DuckDB, R, and many warehouse tools, so you can load only the columns or date ranges you need.

import pandas as pd

df = pd.read_parquet('EURUSD_M1.parquet')
print(df.dtypes)
print(df['timestamp'].min(), df['timestamp'].max())

Validate before transformation

Do not resample or backtest before checking the raw bars. At minimum, verify duplicate timestamps, nulls, monotonic ordering, and invalid OHLC rows. Also separate normal weekend market closures from true source gaps.

def validate_ohlcv(df):
    checks = {
        'duplicate_timestamps': int(df.duplicated('timestamp').sum()),
        'null_cells': int(df.isna().sum().sum()),
        'bad_high': int((df['high'] < df[['open', 'close', 'low']].max(axis=1)).sum()),
        'bad_low': int((df['low'] > df[['open', 'close', 'high']].min(axis=1)).sum()),
    }
    return checks

print(validate_ohlcv(df))

Track coverage explicitly

A naive minute-by-minute expected range will flag every weekend as missing. That is not useful. Your pipeline should understand market sessions, source-backed gaps, and the exact date ranges that are safe for each pair. Store those coverage facts next to the data so your backtester can avoid ranges that are not defensible.

Query with DuckDB when files get large

DuckDB can query Parquet files directly, which is useful when you want summaries without loading every row into memory.

import duckdb

summary = duckdb.sql("""
    select date_trunc('day', timestamp) as day,
           count(*) as bars,
           min(low) as low,
           max(high) as high
    from read_parquet('EURUSD_M1.parquet')
    group by 1
    order by 1
""").df()

Keep conversion as a separate audited step

If you need CSV, MT4, or MT5 files, generate them after validating the source Parquet. Platform conversion has its own failure modes: timezone offsets, broker suffixes, row limits, duplicate bars, and import settings. Treat conversion as an output artifact that needs QA, not as a simple save-as operation.

Where HistoricalFX fits

HistoricalFX is positioned as the prepared source-data layer: audited Parquet files, release coverage, and a sample-first workflow. Use the free sample to test your loader, review release coverage, and request a data audit if your pipeline needs broker-specific or platform-specific validation.