Chapter 14
FX Pairs — Exploratory Data Analysis
NotebookPython 3 (ipykernel)24 cells
FX Pairs — Exploratory Data Analysis
Docker image: ml4t
Purpose
Profile the OANDA 20-pair, 4-hour FX dataset that anchors the FX case study. FX is OTC: there is no central tape, so quotes and reported volumes are venue-specific. The notebook surveys coverage, quote conventions, OHLC integrity, and the 4h→daily aggregation used by downstream chapters.
Learning Objectives
- Load and inspect the 4-hour OHLC + indicative-volume panel for 20 pairs.
- Distinguish direct (USD-quoted), indirect (USD-base), and cross pairs.
- Read FX volume as an OANDA indicator, not an authoritative tape.
- Aggregate 4h bars to UTC-day daily bars and read the gap-pattern signal.
Book reference
Chapter 2, §2.2 (asset-class market data — foreign exchange). The FX case
study built on this dataset lives in case_studies/fx_pairs/.
Prerequisites
- OANDA 4h FX parquet files materialized under
ML4T_DATA_PATH. - Loader
data.load_fx_pairs.
In [1]python · cell 2
python
"""FX Pairs — Exploratory data analysis of OANDA currency pair data."""
import plotly.graph_objects as go
import polars as pl
from data import load_fx_pairs
from utils.data_quality import check_ohlc_invariants, per_asset_stats
from utils.style import COLORSIn [2]python · cell 3
python
# Production defaults — Papermill injects overrides for CI
# (No tunable knobs: this notebook EDAs the full 20-pair universe via the
# canonical load_fx_pairs() API; there is no MAX_SYMBOLS / START_DATE knob to expose.)1. Load and Inspect
In [3]python · cell 5
python
fx_4h = load_fx_pairs(frequency="4h")
print("=== FX Dataset ===")
print(f"Shape: {fx_4h.shape}")
print(f"Columns: {fx_4h.columns}")
print(f"Date range: {fx_4h['timestamp'].min()} to {fx_4h['timestamp'].max()}")Output
=== FX Dataset === Shape: (478640, 7) Columns: ['timestamp', 'symbol', 'open', 'high', 'low', 'close', 'volume'] Date range: 2011-01-02 14:00:00 to 2025-12-31 18:00:00
Volume Disclaimer
Important: FX is an OTC market. Volume figures are indicative estimates from OANDA, not authoritative exchange data. Do not interpret FX volume the same way as equity volume.
In [4]python · cell 7
python
fx_4h.head()Output
shape: (5, 7) ┌─────────────────────┬─────────┬────────┬────────┬────────┬────────┬────────┐ │ timestamp ┆ symbol ┆ open ┆ high ┆ low ┆ close ┆ volume │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ datetime[ms] ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i64 │ ╞═════════════════════╪═════════╪════════╪════════╪════════╪════════╪════════╡ │ 2011-01-02 18:00:00 ┆ AUD_JPY ┆ 83.0 ┆ 83.332 ┆ 82.8 ┆ 82.875 ┆ 986 │ │ 2011-01-02 22:00:00 ┆ AUD_JPY ┆ 82.877 ┆ 83.015 ┆ 82.698 ┆ 82.796 ┆ 4378 │ │ 2011-01-03 02:00:00 ┆ AUD_JPY ┆ 82.8 ┆ 82.98 ┆ 82.782 ┆ 82.924 ┆ 1934 │ │ 2011-01-03 06:00:00 ┆ AUD_JPY ┆ 82.926 ┆ 83.03 ┆ 82.826 ┆ 83.03 ┆ 3949 │ │ 2011-01-03 10:00:00 ┆ AUD_JPY ┆ 83.032 ┆ 83.158 ┆ 82.986 ┆ 83.01 ┆ 5296 │ └─────────────────────┴─────────┴────────┴────────┴────────┴────────┴────────┘
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shape: (5, 7)
| timestamp | symbol | open | high | low | close | volume |
|---|---|---|---|---|---|---|
| datetime[ms] | str | f64 | f64 | f64 | f64 | i64 |
| 2011-01-02 18:00:00 | "AUD_JPY" | 83.0 | 83.332 | 82.8 | 82.875 | 986 |
| 2011-01-02 22:00:00 | "AUD_JPY" | 82.877 | 83.015 | 82.698 | 82.796 | 4378 |
| 2011-01-03 02:00:00 | "AUD_JPY" | 82.8 | 82.98 | 82.782 | 82.924 | 1934 |
| 2011-01-03 06:00:00 | "AUD_JPY" | 82.926 | 83.03 | 82.826 | 83.03 | 3949 |
| 2011-01-03 10:00:00 | "AUD_JPY" | 83.032 | 83.158 | 82.986 | 83.01 | 5296 |
In [5]python · cell 8
python
# Available pairs
pairs = fx_4h["symbol"].unique().sort().to_list()
print(f"\nCurrency pairs ({len(pairs)}):")
for pair in pairs:
print(f" {pair}")Output
Currency pairs (20): AUD_JPY AUD_NZD AUD_USD CAD_JPY CHF_JPY EUR_AUD EUR_CAD EUR_CHF EUR_GBP EUR_JPY EUR_USD GBP_AUD GBP_CHF GBP_JPY GBP_USD NZD_JPY NZD_USD USD_CAD USD_CHF USD_JPY
Symbol Normalization
The data uses underscore format (EUR_USD). The canonical format for this dataset is
concatenated (EURUSD). Here's how to convert:
In [6]python · cell 10
python
# Normalize symbols: EUR_USD → EURUSD (canonical format)
# The raw file uses underscores; we normalize to concatenated format for downstream joins
fx = fx_4h.with_columns(pl.col("symbol").str.replace("_", "").alias("symbol"))
print("Symbol normalization example:")
print(" Raw format: EUR_USD, USD_JPY, GBP_USD")
print(" Canonical: EURUSD, USDJPY, GBPUSD")
print(f"\nNormalized pairs: {fx['symbol'].unique().sort().to_list()[:6]} ...")Output
Symbol normalization example: Raw format: EUR_USD, USD_JPY, GBP_USD Canonical: EURUSD, USDJPY, GBPUSD Normalized pairs: ['AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'CHFJPY', 'EURAUD'] ...
2. Coverage Summary
In [7]python · cell 12
python
# Per-pair statistics (using normalized symbols)
pairs = fx["symbol"].unique().sort().to_list()
pair_stats = per_asset_stats(
fx,
time_col="timestamp",
asset_col="symbol",
price_col="close",
volume_col="volume",
)
pair_stats.sort("avg_volume", descending=True)Output
shape: (20, 6) ┌────────┬───────┬─────────────────────┬─────────────────────┬────────────┬──────────────┐ │ symbol ┆ rows ┆ start ┆ end ┆ avg_price ┆ avg_volume │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ u32 ┆ datetime[ms] ┆ datetime[ms] ┆ f64 ┆ f64 │ ╞════════╪═══════╪═════════════════════╪═════════════════════╪════════════╪══════════════╡ │ GBPAUD ┆ 23945 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 1.795271 ┆ 27719.096262 │ │ GBPJPY ┆ 23924 ┆ 2011-01-02 18:00:00 ┆ 2025-12-31 18:00:00 ┆ 156.656959 ┆ 22954.817673 │ │ EURCAD ┆ 23921 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 1.44887 ┆ 21140.860081 │ │ GBPCHF ┆ 23924 ┆ 2011-01-02 18:00:00 ┆ 2025-12-31 18:00:00 ┆ 1.302496 ┆ 18534.499415 │ │ CHFJPY ┆ 23919 ┆ 2011-01-02 18:00:00 ┆ 2025-12-31 18:00:00 ┆ 122.492732 ┆ 18296.437686 │ │ … ┆ … ┆ … ┆ … ┆ … ┆ … │ │ NZDUSD ┆ 23943 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 0.702921 ┆ 7312.247797 │ │ EURCHF ┆ 23925 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 1.099216 ┆ 6110.44698 │ │ EURGBP ┆ 23922 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 0.846666 ┆ 5989.497952 │ │ AUDUSD ┆ 23941 ┆ 2011-01-02 18:00:00 ┆ 2025-12-31 18:00:00 ┆ 0.789923 ┆ 5563.880832 │ │ USDCHF ┆ 23927 ┆ 2011-01-02 14:00:00 ┆ 2025-12-31 18:00:00 ┆ 0.93156 ┆ 5375.534835 │ └────────┴───────┴─────────────────────┴─────────────────────┴────────────┴──────────────┘
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shape: (20, 6)
| symbol | rows | start | end | avg_price | avg_volume |
|---|---|---|---|---|---|
| str | u32 | datetime[ms] | datetime[ms] | f64 | f64 |
| "GBPAUD" | 23945 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 1.795271 | 27719.096262 |
| "GBPJPY" | 23924 | 2011-01-02 18:00:00 | 2025-12-31 18:00:00 | 156.656959 | 22954.817673 |
| "EURCAD" | 23921 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 1.44887 | 21140.860081 |
| "GBPCHF" | 23924 | 2011-01-02 18:00:00 | 2025-12-31 18:00:00 | 1.302496 | 18534.499415 |
| "CHFJPY" | 23919 | 2011-01-02 18:00:00 | 2025-12-31 18:00:00 | 122.492732 | 18296.437686 |
| … | … | … | … | … | … |
| "NZDUSD" | 23943 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 0.702921 | 7312.247797 |
| "EURCHF" | 23925 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 1.099216 | 6110.44698 |
| "EURGBP" | 23922 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 0.846666 | 5989.497952 |
| "AUDUSD" | 23941 | 2011-01-02 18:00:00 | 2025-12-31 18:00:00 | 0.789923 | 5563.880832 |
| "USDCHF" | 23927 | 2011-01-02 14:00:00 | 2025-12-31 18:00:00 | 0.93156 | 5375.534835 |
Liquidity is venue-specific
Ranking pairs by average indicative volume shows something that looks wrong at first: the global majors (EUR/USD, USD/JPY) are not at the top. That is correct — this is OANDA's retail flow, not the interbank tape. The largest interbank markets are simply not visible to a single retail venue. Read this as a relative liquidity indicator on this venue, nothing more.
In [8]python · cell 14
python
vol_rank = (
fx.group_by("symbol")
.agg(pl.col("volume").mean().alias("avg_volume"))
.sort("avg_volume", descending=True)
)
fig = go.Figure(
go.Bar(
x=vol_rank["avg_volume"].to_list(),
y=vol_rank["symbol"].to_list(),
orientation="h",
marker_color=COLORS["slate"],
)
)
fig.update_layout(
title="Average indicative volume per 4h bar, by pair (OANDA retail flow)",
xaxis_title="Indicative volume / 4h",
yaxis=dict(autorange="reversed"),
height=520,
)
fig.show()Output
3. Quote Conventions
FX pairs follow BASE/QUOTE convention: the price is units of QUOTE per one unit of BASE.
| Pair | Interpretation | USD Strength |
|---|---|---|
| EUR/USD = 1.10 | 1 EUR costs 1.10 USD | Down = USD stronger |
| USD/JPY = 150 | 1 USD costs 150 JPY | Up = USD stronger |
| EUR/GBP = 0.86 | 1 EUR costs 0.86 GBP | Cross rate (no USD) |
Rather than hand-label a subset, classify all pairs by rule: Direct if USD is the quote (price = USD per base, so invert for a USD-strength composite), Indirect if USD is the base (price already reads as USD strength), and Cross if USD does not appear.
In [9]python · cell 16
python
def classify_pair(sym: str) -> tuple[str, str]:
"""Classify a canonical FX symbol (e.g. 'EURUSD') by USD role."""
base, quote = sym[:3], sym[3:]
if quote == "USD":
return "Direct", "invert for USD strength"
if base == "USD":
return "Indirect", "direct USD strength"
return "Cross", "no USD"
quote_conventions = pl.DataFrame(
[
{
"symbol": p,
"convention": classify_pair(p)[0],
"meaning": f"{p[3:]} per {p[:3]}",
"usd_strength": classify_pair(p)[1],
}
for p in pairs
]
).sort("convention", "symbol")
print(f"Pairs classified: {quote_conventions.height} of {len(pairs)}")
print(quote_conventions["convention"].value_counts().sort("convention"))
quote_conventionsOutput
Pairs classified: 20 of 20 shape: (3, 2) ┌────────────┬───────┐ │ convention ┆ count │ │ --- ┆ --- │ │ str ┆ u32 │ ╞════════════╪═══════╡ │ Cross ┆ 13 │ │ Direct ┆ 4 │ │ Indirect ┆ 3 │ └────────────┴───────┘
shape: (20, 4) ┌────────┬────────────┬─────────────┬─────────────────────────┐ │ symbol ┆ convention ┆ meaning ┆ usd_strength │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ str ┆ str ┆ str │ ╞════════╪════════════╪═════════════╪═════════════════════════╡ │ AUDJPY ┆ Cross ┆ JPY per AUD ┆ no USD │ │ AUDNZD ┆ Cross ┆ NZD per AUD ┆ no USD │ │ CADJPY ┆ Cross ┆ JPY per CAD ┆ no USD │ │ CHFJPY ┆ Cross ┆ JPY per CHF ┆ no USD │ │ EURAUD ┆ Cross ┆ AUD per EUR ┆ no USD │ │ … ┆ … ┆ … ┆ … │ │ GBPUSD ┆ Direct ┆ USD per GBP ┆ invert for USD strength │ │ NZDUSD ┆ Direct ┆ USD per NZD ┆ invert for USD strength │ │ USDCAD ┆ Indirect ┆ CAD per USD ┆ direct USD strength │ │ USDCHF ┆ Indirect ┆ CHF per USD ┆ direct USD strength │ │ USDJPY ┆ Indirect ┆ JPY per USD ┆ direct USD strength │ └────────┴────────────┴─────────────┴─────────────────────────┘
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shape: (20, 4)
| symbol | convention | meaning | usd_strength |
|---|---|---|---|
| str | str | str | str |
| "AUDJPY" | "Cross" | "JPY per AUD" | "no USD" |
| "AUDNZD" | "Cross" | "NZD per AUD" | "no USD" |
| "CADJPY" | "Cross" | "JPY per CAD" | "no USD" |
| "CHFJPY" | "Cross" | "JPY per CHF" | "no USD" |
| "EURAUD" | "Cross" | "AUD per EUR" | "no USD" |
| … | … | … | … |
| "GBPUSD" | "Direct" | "USD per GBP" | "invert for USD strength" |
| "NZDUSD" | "Direct" | "USD per NZD" | "invert for USD strength" |
| "USDCAD" | "Indirect" | "CAD per USD" | "direct USD strength" |
| "USDCHF" | "Indirect" | "CHF per USD" | "direct USD strength" |
| "USDJPY" | "Indirect" | "JPY per USD" | "direct USD strength" |
4. Data Quality
In [10]python · cell 18
python
# OHLC invariants
invariants = check_ohlc_invariants(fx)
invariantsOutput
shape: (6, 4) ┌─────────────────────┬───────────┬─────────────────┬────────────┐ │ check ┆ valid_pct ┆ applicable_rows ┆ total_rows │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ i64 ┆ i64 │ ╞═════════════════════╪═══════════╪═════════════════╪════════════╡ │ high_gte_low ┆ 100.0 ┆ 478640 ┆ 478640 │ │ high_gte_open ┆ 100.0 ┆ 478640 ┆ 478640 │ │ high_gte_close ┆ 100.0 ┆ 478640 ┆ 478640 │ │ low_lte_open ┆ 100.0 ┆ 478640 ┆ 478640 │ │ low_lte_close ┆ 100.0 ┆ 478640 ┆ 478640 │ │ volume_non_negative ┆ 100.0 ┆ 478640 ┆ 478640 │ └─────────────────────┴───────────┴─────────────────┴────────────┘
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shape: (6, 4)
| check | valid_pct | applicable_rows | total_rows |
|---|---|---|---|
| str | f64 | i64 | i64 |
| "high_gte_low" | 100.0 | 478640 | 478640 |
| "high_gte_open" | 100.0 | 478640 | 478640 |
| "high_gte_close" | 100.0 | 478640 | 478640 |
| "low_lte_open" | 100.0 | 478640 | 478640 |
| "low_lte_close" | 100.0 | 478640 | 478640 |
| "volume_non_negative" | 100.0 | 478640 | 478640 |
In [11]python · cell 19
python
# Check for weekend gaps (expected in FX, which trades 24/5)
eurusd = fx.filter(pl.col("symbol") == "EURUSD").sort("timestamp")
eurusd_gaps = eurusd.with_columns(
pl.col("timestamp").diff().dt.total_hours().alias("hours_since_prev")
)
large_gaps = eurusd_gaps.filter(pl.col("hours_since_prev") > 24)
print(f"\nGaps > 24 hours (EURUSD): {len(large_gaps)} (should be weekends only)")Output
Gaps > 24 hours (EURUSD): 747 (should be weekends only)
The bar-to-bar gap distribution is the calendar made visible: a dominant spike at the normal 4-hour cadence, and a second cluster around the weekend close (Friday evening → Sunday evening). There is nothing in between — FX trades 24/5, not 24/7, and the ~750 weekend gaps line up with ~15 years of weekends.
In [12]python · cell 21
python
gap_hours = eurusd_gaps.drop_nulls("hours_since_prev")["hours_since_prev"].to_list()
fig = go.Figure()
fig.add_trace(
go.Histogram(
x=gap_hours,
xbins=dict(start=0, end=72, size=2),
marker_color=COLORS["slate"],
)
)
fig.add_vline(x=4, line_color=COLORS["amber"], line_width=1)
fig.add_annotation(
x=4,
y=1,
xref="x",
yref="paper",
text="4h cadence",
showarrow=False,
xanchor="left",
yanchor="top",
font=dict(color=COLORS["amber"]),
)
fig.update_layout(
title="EURUSD hours between bars (spike at 4h, weekend cluster ~48–65h)",
xaxis_title="Hours since previous bar",
yaxis_title="Bars",
yaxis_type="log",
height=420,
)
fig.show()Output
5. Daily Aggregation
Aggregate 4-hour bars to daily for consistency with other datasets.
Note: This uses UTC midnight boundaries. FX daily bars are conventionally defined by a session cutoff (often 5pm New York). For production, align to your broker's convention. This simple calendar-day aggregation is sufficient for exploration.
In [13]python · cell 23
python
# Daily aggregation (must be sorted for group_by_dynamic)
fx_daily = (
fx.sort("symbol", "timestamp")
.group_by_dynamic("timestamp", every="1d", group_by="symbol")
.agg(
[
pl.col("open").first(),
pl.col("high").max(),
pl.col("low").min(),
pl.col("close").last(),
pl.col("volume").sum(),
]
)
)
print(f"Daily aggregation (UTC boundaries) — shape: {fx_daily.shape}")
fx_daily.filter(pl.col("symbol") == "EURUSD").tail(5)Output
Daily aggregation (UTC boundaries) — shape: (94642, 7)
shape: (5, 7) ┌────────┬─────────────────────┬─────────┬─────────┬─────────┬─────────┬────────┐ │ symbol ┆ timestamp ┆ open ┆ high ┆ low ┆ close ┆ volume │ │ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ datetime[ms] ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i64 │ ╞════════╪═════════════════════╪═════════╪═════════╪═════════╪═════════╪════════╡ │ EURUSD ┆ 2025-12-26 00:00:00 ┆ 1.17873 ┆ 1.17968 ┆ 1.17616 ┆ 1.1771 ┆ 99014 │ │ EURUSD ┆ 2025-12-28 00:00:00 ┆ 1.1768 ┆ 1.17864 ┆ 1.17654 ┆ 1.17842 ┆ 10811 │ │ EURUSD ┆ 2025-12-29 00:00:00 ┆ 1.1784 ┆ 1.17891 ┆ 1.17494 ┆ 1.17751 ┆ 110397 │ │ EURUSD ┆ 2025-12-30 00:00:00 ┆ 1.1775 ┆ 1.17798 ┆ 1.17419 ┆ 1.17426 ┆ 83060 │ │ EURUSD ┆ 2025-12-31 00:00:00 ┆ 1.17424 ┆ 1.17595 ┆ 1.172 ┆ 1.17459 ┆ 92505 │ └────────┴─────────────────────┴─────────┴─────────┴─────────┴─────────┴────────┘
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text-align: right;
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}
shape: (5, 7)
| symbol | timestamp | open | high | low | close | volume |
|---|---|---|---|---|---|---|
| str | datetime[ms] | f64 | f64 | f64 | f64 | i64 |
| "EURUSD" | 2025-12-26 00:00:00 | 1.17873 | 1.17968 | 1.17616 | 1.1771 | 99014 |
| "EURUSD" | 2025-12-28 00:00:00 | 1.1768 | 1.17864 | 1.17654 | 1.17842 | 10811 |
| "EURUSD" | 2025-12-29 00:00:00 | 1.1784 | 1.17891 | 1.17494 | 1.17751 | 110397 |
| "EURUSD" | 2025-12-30 00:00:00 | 1.1775 | 1.17798 | 1.17419 | 1.17426 | 83060 |
| "EURUSD" | 2025-12-31 00:00:00 | 1.17424 | 1.17595 | 1.172 | 1.17459 | 92505 |
Key Takeaways
Profile of the OANDA 4h FX panel that anchors the FX case study.
Quantitative Findings
- Panel scale: 478,640 4h observations across 20 currency pairs spanning 2011-01-02 → 2025-12-31. Each pair has ~23,920–23,950 4h bars.
- Liquidity tiers (by indicative OANDA volume): GBPAUD, GBPJPY, EURCAD, GBPCHF and CHFJPY top the table at 18k–28k contracts/4h; the bottom of the universe (NZDUSD, EURCHF, EURGBP, AUDUSD, USDCHF) sits at 5k–7k. These rankings are OANDA-specific — interbank-market liquidity for EURUSD/USDJPY is the largest globally but is not visible to a single retail venue.
- OHLC integrity: 100% of 4h bars satisfy all six invariants (high ≥ low/open/close, low ≤ open/close, volume ≥ 0).
- Session gaps: 747 EURUSD inter-bar gaps exceed 24 h, matching the ~780 weekend closes over the ~15-year span — confirming the 24/5 calendar.
- Daily roll-up: UTC-boundary aggregation produces 94,642 daily rows across the panel (≈4,732 trading days × 20 pairs).
Implications for Practitioners
- Volume: Treat as a relative liquidity indicator across pairs on this venue, not as an interbank tape.
- Quote inversion: USD strength composites must invert direct pairs (EUR/USD, GBP/USD, AUD/USD, NZD/USD); USD-base and cross pairs do not need inversion.
- Daily session convention: UTC-day aggregation is convenient for joins
with the equity/crypto panels but is not a tradable session boundary;
broker-specific 5pm-NY cutoffs are wired in
case_studies/fx_pairs/downstream.
Next: 13_data_quality_framework profiles the cross-asset DQ checks
that consume this panel and the others built up so far.