wehub-resource-sync
86 lines
3.7 KiB
Markdown
86 lines
3.7 KiB
Markdown
# FX Pairs (OANDA)
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20 major FX pairs sampled daily and at 4-hour bars. Used for currency
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momentum, carry analyses, and cost-model calibration (spread / impact
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across majors vs crosses).
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## Dataset
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- **Source**: OANDA REST v20 API (production endpoint).
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- **Coverage**: 2011-01-01 → present (daily back-fill); 4-hour bars from
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2015 onward.
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- **Pairs**: 20 (4 majors, 3 commodity-linked, 13 crosses).
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- **Size on disk**: ~22 MB total (~1.5 MB daily parquet, ~8.6 MB 4h
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parquet, plus per-symbol hive partitions).
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- **Runtime**: ~3-5 minutes for a daily refresh; ~10-15 minutes for a
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4-hour refresh (OANDA rate-limits at 120 req/min).
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- **API key**: `OANDA_API_KEY` required — free practice account at
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https://developer.oanda.com/. Production credentials also work.
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- **License / attribution**: Data is provided for personal and
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educational use under the OANDA API terms
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(https://www.oanda.com/legal/api-terms-of-service). Redistribution of
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the raw time series is not permitted; derived analytics (returns,
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features, model outputs) are fine.
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## Pairs
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| Group | Pairs |
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| --------- | ---------------------------------------------------------------------------------------------------- |
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| Majors | EUR_USD, GBP_USD, USD_JPY, USD_CHF |
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| Commodity | AUD_USD, USD_CAD, NZD_USD |
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| 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 |
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## Download
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```bash
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uv run python data/fx/market/download.py # full refresh (daily + 4h)
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uv run python data/fx/market/download.py --dry-run # plan only
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```
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Output layout under `$ML4T_DATA_PATH/fx/`:
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```
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daily.parquet # consolidated daily bars (loader target for frequency="daily")
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4h.parquet # consolidated 4-hour bars (loader target for frequency="4h")
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fx_dictionary.parquet # pair metadata (base/quote, group, OANDA instrument code)
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ohlcv_daily/symbol=<PAIR>/data.parquet # hive-partitioned per-symbol daily bars (provider-native)
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ohlcv_4h/symbol=<PAIR>/data.parquet # hive-partitioned per-symbol 4h bars
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config.yaml # pair list + date range + API endpoint config
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```
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The consolidated parquets at the root are what `load_fx_pairs()` reads.
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The per-symbol hive partitions are kept for incremental-refresh work and
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for per-symbol exploratory access.
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## Loading
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```python
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from data import load_fx_pairs
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df = load_fx_pairs() # 4-hour bars (default)
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df = load_fx_pairs(frequency="daily")
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df = load_fx_pairs(pairs=["EUR_USD", "GBP_USD"])
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df = load_fx_pairs(start_date="2020-01-01", end_date="2023-12-31")
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```
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Schema (canonical):
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| Column | Type | Description |
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| ----------- | -------- | --------------------------- |
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| `symbol` | String | FX pair (e.g., `EUR_USD`) |
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| `timestamp` | Datetime | Bar timestamp (UTC) |
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| `open` | Float | Opening price (mid) |
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| `high` | Float | High price |
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| `low` | Float | Low price |
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| `close` | Float | Closing price |
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| `volume` | Int | Tick volume |
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## Consumers
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- **Ch2**: `12_fx_pairs_eda.py`.
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- **Ch7**: `01_data_quality_diagnostics.py`.
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- **Ch16 validation**: `validation/weights.py`.
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- **Ch18**: `01_cost_taxonomy.py`, `02_spread_estimation.py`, `03_market_impact_calibration.py`.
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- **`case_studies/fx_pairs/`**: full pipeline from `01_feasibility_analysis.py` through
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`17_strategy_analysis.py`.
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