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C13: report clear CSV errors in the offline examples

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backtest.py and multi_timeframe.py read OHLCV CSVs with csv.DictReader
and crashed opaquely on malformed input: a missing column raised a bare
KeyError, a non-numeric cell surfaced NumPy's column-less ValueError,
and an empty or all-NaN series hit IndexError deep in summarize.

Validate the header against the required columns up front, catch
non-numeric cells and report the offending row/column, reject a
header-only file distinctly from a headerless one, and guard resample
and summarize against empty input. Every failure mode now raises a
ValueError naming the file, row and column.
This commit is contained in:
kingchenc
2026-05-22 12:29:40 +02:00
parent a8fb0b8181
commit 79d705a746
2 changed files with 84 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
examples/python/backtest.py
+37 -13
View File
@@ -24,6 +24,10 @@ import numpy as np
import wickra as ta
# Columns the OHLCV layout requires; the CSV header must name every one.
REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume")
@dataclass
class History:
timestamp: np.ndarray
@@ -35,24 +39,44 @@ class History:
def read_history(path: str) -> History:
"""Load an OHLCV CSV into typed NumPy columns."""
"""Load an OHLCV CSV into typed NumPy columns.
Raises:
ValueError: if the file has no header, is missing a required column,
holds no data rows, or contains a non-numeric value (the message
pinpoints the offending row and column instead of surfacing an
opaque ``KeyError`` or NumPy ``ValueError``).
"""
rows: List[List[str]] = []
with open(path, newline="") as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(
[
row["timestamp"],
row["open"],
row["high"],
row["low"],
row["close"],
row["volume"],
]
if reader.fieldnames is None:
raise ValueError(f"{path}: CSV has no header row")
missing = [c for c in REQUIRED_COLUMNS if c not in reader.fieldnames]
if missing:
raise ValueError(
f"{path}: CSV is missing required column(s): "
f"{', '.join(missing)}; found: {', '.join(reader.fieldnames)}"
)
for row in reader:
rows.append([row[c] for c in REQUIRED_COLUMNS])
if not rows:
raise ValueError("CSV is empty")
arr = np.array(rows, dtype=np.float64)
raise ValueError(f"{path}: CSV has a header but no data rows")
try:
arr = np.array(rows, dtype=np.float64)
except (TypeError, ValueError) as exc:
# NumPy's own message names neither the row nor the column — locate
# the first non-numeric cell and report it precisely.
for line_no, row in enumerate(rows, start=2):
for col, value in zip(REQUIRED_COLUMNS, row):
try:
float(value)
except (TypeError, ValueError):
raise ValueError(
f"{path}: row {line_no} column '{col}' is not "
f"numeric: {value!r}"
) from exc
raise # could not localise — surface the original error
return History(
timestamp=arr[:, 0].astype(np.int64),
open=arr[:, 1],
examples/python/multi_timeframe.py
+47 -10
View File
@@ -21,17 +21,43 @@ import numpy as np
import wickra as ta
# Columns the OHLCV layout requires; the CSV header must name every one.
REQUIRED_COLUMNS = ("timestamp", "open", "high", "low", "close", "volume")
def read_csv(path: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Read an OHLCV CSV into typed columns."""
"""Read an OHLCV CSV into typed columns.
Raises:
ValueError: if the file has no header, is missing a required column,
holds no data rows, or contains a non-numeric value (the message
names the offending row).
"""
ts, o, h, l, c, v = [], [], [], [], [], []
with open(path, newline="") as f:
for row in csv.DictReader(f):
ts.append(int(row["timestamp"]))
o.append(float(row["open"]))
h.append(float(row["high"]))
l.append(float(row["low"]))
c.append(float(row["close"]))
v.append(float(row["volume"]))
reader = csv.DictReader(f)
if reader.fieldnames is None:
raise ValueError(f"{path}: CSV has no header row")
missing = [col for col in REQUIRED_COLUMNS if col not in reader.fieldnames]
if missing:
raise ValueError(
f"{path}: CSV is missing required column(s): "
f"{', '.join(missing)}; found: {', '.join(reader.fieldnames)}"
)
for line_no, row in enumerate(reader, start=2):
try:
ts.append(int(row["timestamp"]))
o.append(float(row["open"]))
h.append(float(row["high"]))
l.append(float(row["low"]))
c.append(float(row["close"]))
v.append(float(row["volume"]))
except (TypeError, ValueError) as exc:
raise ValueError(
f"{path}: row {line_no} has a non-numeric value: {exc}"
) from exc
if not ts:
raise ValueError(f"{path}: CSV has a header but no data rows")
return (
np.asarray(ts, dtype=np.int64),
np.asarray(o, dtype=np.float64),
@@ -51,7 +77,13 @@ def resample(
v: np.ndarray,
bucket: int,
) -> Tuple[np.ndarray, ...]:
"""Aggregate bar-level columns into coarser buckets of size `bucket`."""
"""Aggregate bar-level columns into coarser buckets of size `bucket`.
Raises:
ValueError: if the input series is empty.
"""
if ts.size == 0:
raise ValueError("resample received an empty series")
bucket_index = (ts // bucket).astype(np.int64)
boundaries = np.diff(bucket_index, prepend=bucket_index[0] - 1) != 0
group_ids = np.cumsum(boundaries) - 1
@@ -76,14 +108,19 @@ def resample(
def summarize(label: str, close: np.ndarray, high: np.ndarray, low: np.ndarray) -> None:
if close.size == 0:
print(f" {label:<10} (empty series — nothing to summarize)")
return
rsi = ta.RSI(14).batch(close)
macd = ta.MACD().batch(close)
adx = ta.ADX(14).batch(high, low, close)
last_macd_hist = macd[~np.isnan(macd[:, 2])][-1, 2] if np.any(~np.isnan(macd[:, 2])) else float("nan")
last_adx = adx[~np.isnan(adx[:, 2])][-1, 2] if np.any(~np.isnan(adx[:, 2])) else float("nan")
valid_rsi = rsi[~np.isnan(rsi)]
last_rsi = valid_rsi[-1] if valid_rsi.size else float("nan")
print(
f" {label:<10} bars={close.size:>5} "
f"last_close={close[-1]:>10.4f} rsi={rsi[~np.isnan(rsi)][-1]:>6.2f} "
f"last_close={close[-1]:>10.4f} rsi={last_rsi:>6.2f} "
f"macd_hist={last_macd_hist:+.4f} adx={last_adx:>6.2f}"
)