FX Pairs (OANDA)

20 major FX pairs sampled daily and at 4-hour bars. Used for currency momentum, carry analyses, and cost-model calibration (spread / impact across majors vs crosses).

Dataset

Source: OANDA REST v20 API (production endpoint).
Coverage: 2011-01-01 → present (daily back-fill); 4-hour bars from 2015 onward.
Pairs: 20 (4 majors, 3 commodity-linked, 13 crosses).
Size on disk: ~22 MB total (~1.5 MB daily parquet, ~8.6 MB 4h parquet, plus per-symbol hive partitions).
Runtime: ~3-5 minutes for a daily refresh; ~10-15 minutes for a 4-hour refresh (OANDA rate-limits at 120 req/min).
API key: OANDA_API_KEY required — free practice account at https://developer.oanda.com/. Production credentials also work.
License / attribution: Data is provided for personal and educational use under the OANDA API terms (https://www.oanda.com/legal/api-terms-of-service). Redistribution of the raw time series is not permitted; derived analytics (returns, features, model outputs) are fine.

Pairs

Group	Pairs
Majors	EUR_USD, GBP_USD, USD_JPY, USD_CHF
Commodity	AUD_USD, USD_CAD, NZD_USD
Crosses	EUR_GBP, EUR_JPY, EUR_CHF, EUR_CAD, EUR_AUD, GBP_JPY, GBP_CHF, GBP_AUD, AUD_JPY, CHF_JPY, CAD_JPY, NZD_JPY, AUD_NZD

Download

uv run python data/fx/market/download.py              # full refresh (daily + 4h)
uv run python data/fx/market/download.py --dry-run    # plan only

Output layout under $ML4T_DATA_PATH/fx/:

daily.parquet                 # consolidated daily bars (loader target for frequency="daily")
4h.parquet                    # consolidated 4-hour bars (loader target for frequency="4h")
fx_dictionary.parquet         # pair metadata (base/quote, group, OANDA instrument code)
ohlcv_daily/symbol=<PAIR>/data.parquet     # hive-partitioned per-symbol daily bars (provider-native)
ohlcv_4h/symbol=<PAIR>/data.parquet        # hive-partitioned per-symbol 4h bars
config.yaml                   # pair list + date range + API endpoint config

The consolidated parquets at the root are what load_fx_pairs() reads. The per-symbol hive partitions are kept for incremental-refresh work and for per-symbol exploratory access.

Loading

from data import load_fx_pairs

df = load_fx_pairs()                              # 4-hour bars (default)
df = load_fx_pairs(frequency="daily")
df = load_fx_pairs(pairs=["EUR_USD", "GBP_USD"])
df = load_fx_pairs(start_date="2020-01-01", end_date="2023-12-31")

Schema (canonical):

Column	Type	Description
`symbol`	String	FX pair (e.g., `EUR_USD`)
`timestamp`	Datetime	Bar timestamp (UTC)
`open`	Float	Opening price (mid)
`high`	Float	High price
`low`	Float	Low price
`close`	Float	Closing price
`volume`	Int	Tick volume

Consumers

Ch2: 12_fx_pairs_eda.py.
Ch7: 01_data_quality_diagnostics.py.
Ch16 validation: validation/weights.py.
Ch18: 01_cost_taxonomy.py, 02_spread_estimation.py, 03_market_impact_calibration.py.
case_studies/fx_pairs/: full pipeline from 01_feasibility_analysis.py through 17_strategy_analysis.py.