FXデータ品質、気配値の慣行、セッション基準の日足

# 12_fx_pairs_eda.py


```py
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# %% [markdown]
# # 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, and the trading
# day is a convention rather than an event. The notebook surveys coverage, quote conventions,
# OHLC integrity, the gap structure of a 24/5 calendar, and the daily aggregation downstream
# chapters use.
#
# ## Learning Objectives
# - Load and inspect the 4-hour OHLC and indicative-volume panel for 20 pairs.
# - Distinguish direct (USD-quoted), indirect (USD-base) and cross pairs.
# - Read FX volume as an OANDA indicator rather than an authoritative tape.
# - Recover the session grid the file is stamped on, and aggregate to daily bars on it.
#
# ## 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`.

# %%
"""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 COLORS, show_plotly_with_alt

# %% [markdown]
# ### Declared parameters
#
# `SESSION_TIMEZONE` and `SESSION_ROLLOVER_HOUR` are the two that carry an argument rather than
# a preference. Section 5 shows that this file's bars are stamped on a grid anchored to 5PM in
# New York, which is the rollover the interbank market treats as the start of a new value date,
# and the daily aggregation is built on that boundary rather than on UTC midnight. Both are
# declared here so the whole convention is visible in one place and CI can override it.
#
# `MAX_PAIRS` passes straight to the loader, so a CI run can narrow the universe without any
# cell downstream knowing the difference.

# %% tags=["parameters"]
FREQUENCY = "4h"
MAX_PAIRS = 0  # 0 loads the full 20-pair universe

SESSION_TIMEZONE = "America/New_York"
SESSION_ROLLOVER_HOUR = 17  # 5PM New York starts the next value date

DEMO_PAIR = "EURUSD"
LONG_GAP_HOURS = 24  # a gap this long or longer is not an ordinary bar-to-bar step
GAP_HIST_MAX_HOURS = 80
GAP_HIST_BIN_HOURS = 2

# %% [markdown]
# ## 1. Load and Inspect

# %%
fx_4h = load_fx_pairs(frequency=FREQUENCY, max_symbols=MAX_PAIRS)

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()}")

# %% [markdown]
# ### The volume column is not traded volume
#
# FX is an OTC market with no consolidated tape, so nothing in this file can report what a
# currency traded. What the column does report is narrower than that and worth naming
# exactly: `data/fx/README.md` records it as **tick volume**, a count of how many times the
# venue updated its quote inside the bar. It is a count of updates, not a sum of sizes.
#
# That makes it useless for anything sized in currency and still informative about activity,
# because a venue reprices when something moves. Section 2 shows how far the two come apart.

# %%
fx_4h.head()

# %% [markdown]
# ### Symbol normalization
#
# The file writes pairs with an underscore (`EUR_USD`). The canonical form used everywhere
# downstream is concatenated (`EURUSD`), so the join keys line up.

# %%
fx = fx_4h.with_columns(pl.col("symbol").str.replace_all("_", "").alias("symbol"))
pairs = fx["symbol"].unique().sort().to_list()

print(f"Currency pairs ({len(pairs)}): {', '.join(pairs)}")

# %% [markdown]
# ## 2. Coverage Summary

# %%
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)

# %% [markdown]
# ### The ranking is a ranking of quote updates
#
# Ranking pairs by average tick volume puts the global majors well down the table, and the
# next cell prints exactly where they land rather than leaving that as an impression.
#
# It is tempting to read this as a liquidity ranking and it is not one. The column counts one
# venue's quote updates, so the ordering reflects how often that venue repriced each pair,
# and a pair can be repriced often for reasons that have nothing to do with how much of it
# trades anywhere. What this file supports is the negative claim, which is the useful one: a
# ranking built from this column is not a ranking of market size, and the majors sitting mid
# table is the proof. Explaining *why* the ordering comes out as it does would need trade
# data this file does not carry.

# %%
vol_rank = (
    fx.group_by("symbol")
    .agg(pl.col("volume").mean().alias("avg_volume"))
    .sort("avg_volume", descending=True)
    .with_row_index("rank", offset=1)
)

_majors = ["EURUSD", "USDJPY", "GBPUSD"]
print(f"Rank by average indicative volume, of {vol_rank.height}:")
for row in vol_rank.filter(pl.col("symbol").is_in(_majors)).iter_rows(named=True):
    print(f"  {row['symbol']}: rank {row['rank']}, {row['avg_volume']:,.0f} per bar")
print(f"  top of the table: {vol_rank['symbol'][0]}, {vol_rank['avg_volume'][0]:,.0f} per bar")

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",
    xaxis_title="Indicative volume per 4h bar",
    yaxis=dict(autorange="reversed"),
    height=520,
)
show_plotly_with_alt(
    fig,
    "A horizontal bar chart of twenty currency pairs ranked by average indicative volume per "
    "four-hour bar. GBPAUD is the longest bar at roughly twenty-eight thousand and the bars "
    "shorten steadily down to USDCHF at roughly five thousand. USDJPY, GBPUSD and EURUSD sit "
    "in the lower half of the ranking rather than near the top.",
)

# %% [markdown]
# ## 3. Quote Conventions
#
# FX pairs follow a **BASE/QUOTE** convention: the price is the number of QUOTE units that buy
# one unit of BASE. EURUSD is dollars per euro, so it falls when the dollar strengthens. USDJPY
# is yen per dollar, so it rises when the dollar strengthens. EURGBP names no dollar at all.
#
# The direction of the dollar therefore depends on where the dollar sits in the symbol, and any
# composite built across pairs has to invert one group before averaging. Rather than hand-label
# a subset, classify every pair by rule: *Direct* if USD is the quote currency, *Indirect* if
# USD is the base, *Cross* if USD does not appear.


# %%
def classify_pair(sym: str) -> tuple[str, str]:
    """Classify a canonical FX symbol (e.g. 'EURUSD') by the role the dollar plays in it."""
    base, quote = sym[:3], sym[3:]
    if quote == "USD":
        return "Direct", "invert for USD strength"
    if base == "USD":
        return "Indirect", "reads as USD strength already"
    return "Cross", "no USD leg"


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_conventions

# %% [markdown]
# ## 4. Data Quality
#
# ### A percentage cannot show you one bad bar
#
# `check_ohlc_invariants` reports the share of rows satisfying each invariant. On a panel this
# size a single violation moves that share by less than the display rounds away, so a column
# of hundreds is consistent with a clean file and with a handful of broken bars. The count is
# printed beside it, because a count of zero is a different statement from a percentage that
# rounds to a hundred.

# %%
invariants = check_ohlc_invariants(fx)

_conditions = {
    "high_gte_low": pl.col("high") >= pl.col("low"),
    "high_gte_open": pl.col("high") >= pl.col("open"),
    "high_gte_close": pl.col("high") >= pl.col("close"),
    "low_lte_open": pl.col("low") <= pl.col("open"),
    "low_lte_close": pl.col("low") <= pl.col("close"),
    "volume_non_negative": pl.col("volume") >= 0,
}
breaches = pl.DataFrame(
    {
        "check": list(_conditions),
        "breaches": [fx.filter(~cond).height for cond in _conditions.values()],
    }
)
print(f"Rows checked: {fx.height:,}")
invariants.join(breaches, on="check", how="left")

# %% [markdown]
# ### The gap between bars is the calendar
#
# FX trades continuously from Sunday evening to Friday evening, so the interval between
# consecutive bars is not always the bar length. The check runs over every pair rather than a
# reference one: a gap is a property of the file, and picking the most liquid pair to test it
# on samples the row least likely to show it.
#
# Three cases are separated rather than two. A step of exactly one bar length is the ordinary
# case. A Friday-to-Sunday step is the weekend close, which is the calendar working as intended
# and not a hole. Everything else is neither, and the next cell shows what those turn out to be.

# %%
BAR_HOURS = int(FREQUENCY.rstrip("h"))

stepped = (
    fx.sort("symbol", "timestamp")
    .with_columns(
        pl.col("timestamp").diff().dt.total_hours().over("symbol").alias("gap_hours"),
        pl.col("timestamp").shift(1).over("symbol").alias("previous_timestamp"),
    )
    .drop_nulls("gap_hours")
)

_is_weekend = (pl.col("previous_timestamp").dt.weekday() == 5) & (
    pl.col("timestamp").dt.weekday() == 7
)
stepped = stepped.with_columns(
    pl.when(pl.col("gap_hours") == BAR_HOURS)
    .then(pl.lit("one bar"))
    .when(_is_weekend)
    .then(pl.lit("weekend close"))
    .otherwise(pl.lit("neither"))
    .alias("step_kind")
)

print(f"Intervals between consecutive bars: {stepped.height:,}")
print(stepped.group_by("step_kind").len().sort("len", descending=True))

# %%
_neither = stepped.filter(pl.col("step_kind") == "neither").with_columns(
    pl.col("previous_timestamp").dt.date().alias("date"),
    pl.col("previous_timestamp").dt.strftime("%m-%d").alias("month_day"),
)
print(f"Steps that are neither one bar nor a weekend: {_neither.height:,}")

_per_date = _neither.group_by("date").agg(pl.col("symbol").n_unique().alias("pairs"))
_universe = fx["symbol"].n_unique()
_universe_wide = _per_date.filter(pl.col("pairs") == _universe)
_wide_steps = _neither.join(_universe_wide.select("date"), on="date").height
print(
    f"Distinct dates they start from: {_per_date.height}, of which "
    f"{_universe_wide.height} take out all {_universe} pairs at once. Those dates account "
    f"for {_wide_steps:,} of the {_neither.height:,} steps "
    f"({100 * _wide_steps / _neither.height:.0f}%)."
)

print("\nBy day of the year:")
print(
    _neither.group_by("month_day")
    .agg(pl.len().alias("steps"), pl.col("date").n_unique().alias("years"))
    .sort("steps", descending=True)
    .head(8)
)

# %% [markdown]
# The third group is the holiday calendar. Christmas Eve and New Year's Eve dominate it, each
# recurring across most years of the sample, and the days around them fill in much of the rest.
#
# The distinction that matters is between a closure and a fault, and the pair count makes it.
# Roughly half these dates take the entire universe out at once, and because those are the
# recurring ones they carry the large majority of the steps. A download failure would have to
# knock out twenty independently quoted instruments simultaneously and pick December 24th to
# do it on. The remaining dates hit a subset of pairs and are the residue worth treating as
# possible faults, which is a far smaller thing to investigate than every long gap in the file.
#
# This is why "weekends only" is the wrong summary even though it is nearly right by count. The
# leftover is small, systematic, and predictable from a calendar, and a pipeline that treats
# every long gap as a weekend will read the year-end holidays as missing data every year.

# %%
gap_hours = stepped.filter(pl.col("gap_hours") <= GAP_HIST_MAX_HOURS)["gap_hours"].to_list()

fig = go.Figure()
fig.add_trace(
    go.Histogram(
        x=gap_hours,
        xbins=dict(start=0, end=GAP_HIST_MAX_HOURS, size=GAP_HIST_BIN_HOURS),
        marker_color=COLORS["slate"],
    )
)
fig.add_vline(x=BAR_HOURS, line_color=COLORS["amber"], line_width=1)
fig.add_annotation(
    x=BAR_HOURS,
    y=1,
    xref="x",
    yref="paper",
    xshift=8,
    yshift=-6,
    text="one bar",
    showarrow=False,
    xanchor="left",
    yanchor="top",
    font=dict(color=COLORS["amber"]),
)
fig.update_layout(
    title="Hours between consecutive bars, all pairs",
    xaxis_title="Hours since previous bar",
    yaxis_title="Intervals (log scale)",
    yaxis_type="log",
    height=420,
)
show_plotly_with_alt(
    fig,
    "A histogram of the hours between consecutive bars, counted on a logarithmic axis. A "
    "single bar at four hours towers over everything else at more than a hundred thousand "
    "intervals. A second cluster spans roughly forty-four to fifty-four hours and peaks near "
    "twelve thousand. Between and beyond those two, isolated bars of ten to several hundred "
    "intervals appear at scattered values from eight hours out to seventy-six.",
)

# %% [markdown]
# ## 5. The Session Grid, and Daily Aggregation On It
#
# Aggregating to daily bars needs a day boundary, and the obvious one is UTC midnight. Before
# taking it, it is worth asking what grid the timestamps are already on, because the file
# answers that question directly.

# %%
_stamps = fx.select("timestamp").unique().sort("timestamp")
_utc_hours = sorted(_stamps.select(pl.col("timestamp").dt.hour().unique()).to_series().to_list())
_local = _stamps.with_columns(
    pl.col("timestamp")
    .dt.replace_time_zone("UTC")
    .dt.convert_time_zone(SESSION_TIMEZONE)
    .alias("local")
)
_local_hours = sorted(_local.select(pl.col("local").dt.hour().unique()).to_series().to_list())

print(f"Distinct hours-of-day the bars are stamped on, in UTC: {_utc_hours}")
print(f"Same timestamps in {SESSION_TIMEZONE}:                  {_local_hours}")

# %% [markdown]
# Twelve hours in UTC, six in New York. The bars are not on a UTC grid at all: they are on a
# six-slot local grid that includes 5PM New York, and daylight saving moves the whole grid by
# an hour twice a year, which is what splits each slot into two UTC hours.
#
# That settles the day boundary. The file is already stamped against the rollover the interbank
# market uses, so a session runs from one 5PM New York to the next, and a bar printed after 5PM
# counts toward the following trading day. The case study's
# [`01_feasibility_analysis`](../case_studies/fx_pairs/01_feasibility_analysis.ipynb) uses a
# session calendar for exactly this, declared in its `setup.yaml` as
# `decision.session_calendar`.
#
# Both aggregations are built below, because the cost of the convenient one is worth seeing.

# %%
sessioned = fx.with_columns(
    pl.col("timestamp")
    .dt.replace_time_zone("UTC")
    .dt.convert_time_zone(SESSION_TIMEZONE)
    .alias("local_time")
).with_columns(
    (pl.col("local_time") + pl.duration(hours=24 - SESSION_ROLLOVER_HOUR))
    .dt.date()
    .alias("session")
)

_agg = [
    pl.col("open").first(),
    pl.col("high").max(),
    pl.col("low").min(),
    pl.col("close").last(),
    pl.col("volume").sum(),
    pl.len().alias("bars"),
]

session_daily = (
    sessioned.sort("symbol", "local_time")
    .group_by("symbol", "session")
    .agg(_agg)
    .sort("symbol", "session")
)
utc_daily = (
    fx.sort("symbol", "timestamp")
    .group_by_dynamic("timestamp", every="1d", group_by="symbol")
    .agg(_agg)
)

_bars_per_day = 24 // BAR_HOURS
for label, frame in [("UTC calendar day", utc_daily), ("5PM New York session", session_daily)]:
    _full = frame.filter(pl.col("bars") == _bars_per_day).height
    print(
        f"{label:22s} {frame.height:,} daily rows, "
        f"{_full:,} of them complete ({100 * _full / frame.height:.1f}%), "
        f"{frame.filter(pl.col('bars') == 1).height:,} containing a single 4h bar"
    )

# %% [markdown]
# The UTC day manufactures thousands of one-bar days. The session opens on Sunday evening in
# New York, which is already Sunday night or Monday morning in UTC depending on the season, so
# the UTC Sunday collects one bar and the UTC Monday collects the rest. Nothing is missing;
# the boundary is simply in the wrong place, and it cuts the same session twice a week.
#
# The two conventions also disagree about what the day closed at, on days both of them cover.

# %%
_compare = utc_daily.with_columns(pl.col("timestamp").dt.date().alias("day")).join(
    session_daily.rename({"session": "day"}), on=["symbol", "day"], suffix="_session"
)
_diff = (pl.col("close") - pl.col("close_session")).abs()
_differs = _compare.filter(_diff > 0)

print(f"Days both conventions cover: {_compare.height:,}")
print(
    f"  days where the close differs: {_differs.height:,} ({100 * _differs.height / _compare.height:.1f}%)"
)
print(
    "  size of that difference, in basis points: mean "
    f"{_compare.select((_diff / pl.col('close_session') * 1e4).mean()).item():.1f}, "
    f"max {_compare.select((_diff / pl.col('close_session') * 1e4).max()).item():.0f}"
)

session_daily.filter(pl.col("symbol") == DEMO_PAIR).tail(5)

# %% [markdown]
# A daily close that is wrong by a handful of basis points on most days is not a rounding
# concern. It is the whole size of a daily FX move on a quiet pair, so a label built on the UTC
# close and a label built on the session close are different labels, not two estimates of one.
#
# The general form: **an aggregation boundary is a modelling choice, and the data usually tells
# you which one it was built for.** Twelve UTC hours and six local ones is the file saying so.

# %% [markdown]
# ## Key Takeaways
#
# 1. **The volume column counts quote updates, not traded size.** The repository's own schema
#    calls it tick volume, and the ranking it produces puts the global majors mid table. That
#    is enough to establish what the column is not - a measure of market size - and not
#    enough to explain the ordering, which would take trade data this file does not carry.
#
# 2. **The dollar's direction depends on where the dollar sits in the symbol.** Direct pairs
#    quote dollars per unit and have to be inverted before entering a dollar-strength
#    composite; indirect pairs already read that way; crosses have no dollar leg. Every pair is
#    classified by rule above rather than a subset by hand.
#
# 3. **A share of rows passing is not a count of rows failing.** On a panel of this size one
#    broken bar cannot move the reported percentage far enough to see, so the invariant table
#    carries breach counts beside it.
#
# 4. **Long gaps are the calendar, and the calendar has three parts, not two.** Almost every
#    step is one bar; most of the rest is the Friday-to-Sunday close; what remains is the
#    holiday calendar, concentrated on the days around Christmas and New Year and hitting the
#    whole universe on the same dates. Treating every long gap as a weekend misreads the
#    year-end holidays as missing data, every year.
#
# 5. **The file is stamped on a New York session grid, not a UTC one.** The timestamps occupy
#    twelve hours-of-day in UTC and six in New York, because daylight saving moves the grid.
#    Aggregating on UTC midnight cuts each session in the wrong place, produces thousands of
#    one-bar days, and disagrees with the session close on most days by an amount comparable to
#    a day's move. The daily bars above are built on the 5PM New York rollover the file already
#    uses and the case study declares.
#
# **Next**: `13_data_quality_framework` profiles the cross-asset data-quality checks that
# consume this panel and the others built up so far.

```