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085d910 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | """Round-trip tests for the columnar time-series serializer."""
from __future__ import annotations
import json
from datetime import UTC, datetime
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from TerraFin.data.cache.serializers import (
ColumnarTimeSeriesSerializer,
HistoryChunkSerializer,
)
from TerraFin.data.contracts import HistoryChunk, TimeSeriesDataFrame
def _make_ohlcv_frame(rows: int = 10) -> pd.DataFrame:
idx = pd.date_range("2023-01-02", periods=rows, freq="B")
rng = np.arange(rows, dtype=float) + 100.0
return pd.DataFrame(
{
"Open": rng,
"High": rng + 1,
"Low": rng - 1,
"Close": rng + 0.5,
"Volume": (rng * 1000).astype(float),
},
index=idx,
)
def test_columnar_round_trip(tmp_path: Path) -> None:
serializer = ColumnarTimeSeriesSerializer()
raw = _make_ohlcv_frame(20)
payload = TimeSeriesDataFrame(raw)
artifact_dir = tmp_path / "frame"
serializer.write(artifact_dir, payload)
loaded = serializer.read(artifact_dir)
assert isinstance(loaded, TimeSeriesDataFrame)
assert len(loaded) == len(payload)
pd.testing.assert_series_equal(
loaded["close"].reset_index(drop=True),
payload["close"].reset_index(drop=True),
check_names=False,
)
pd.testing.assert_series_equal(
loaded["volume"].reset_index(drop=True),
payload["volume"].reset_index(drop=True),
check_names=False,
)
def test_columnar_partial_read_recent_and_backfill(tmp_path: Path) -> None:
serializer = ColumnarTimeSeriesSerializer()
rows = pd.date_range("2020-01-01", periods=400, freq="B")
rng = np.arange(len(rows), dtype=float) + 50.0
raw = pd.DataFrame(
{"Open": rng, "High": rng + 1, "Low": rng - 1, "Close": rng + 0.25, "Volume": rng * 100},
index=rows,
)
artifact_dir = tmp_path / "AAPL" / "full"
serializer.write(artifact_dir, raw)
recent, has_older = serializer.read_recent(artifact_dir, "6m", mmap=True)
assert has_older is True
assert not recent.empty
assert len(recent) < len(raw)
backfill, start, end = serializer.read_backfill(artifact_dir, recent["time"].iloc[0].strftime("%Y-%m-%d"))
assert start is not None and end is not None
assert not backfill.empty
assert len(backfill) + len(recent) <= len(raw) + 5
def test_columnar_reads_existing_yfinance_v2_layout(tmp_path: Path) -> None:
"""Manually lay down the legacy on-disk format and ensure the serializer reads it."""
artifact_dir = tmp_path / "yfinance_v2" / "aapl" / "full"
artifact_dir.mkdir(parents=True)
rows = 5
idx = pd.date_range("2024-01-02", periods=rows, freq="B")
times = (pd.DatetimeIndex(idx).view("int64") // 10**9).astype(np.int64)
np.save(artifact_dir / "time_i64.npy", times)
closes = np.array([100.0, 101.0, 102.0, 103.0, 104.0])
np.save(artifact_dir / "open_f64.npy", closes - 0.5)
np.save(artifact_dir / "high_f64.npy", closes + 1)
np.save(artifact_dir / "low_f64.npy", closes - 1)
np.save(artifact_dir / "close_f64.npy", closes)
np.save(artifact_dir / "volume_f64.npy", closes * 1000)
meta = {
"version": 2,
"schema": "ohlcv",
"columns": ["Open", "High", "Low", "Close", "Volume"],
"row_count": rows,
"start_time": idx[0].strftime("%Y-%m-%d"),
"end_time": idx[-1].strftime("%Y-%m-%d"),
"cached_at": datetime.now(UTC).isoformat(),
"is_complete": True,
"has_older": False,
"source": "yfinance",
"index_name": "Date",
}
(artifact_dir / "meta.json").write_text(json.dumps(meta))
serializer = ColumnarTimeSeriesSerializer()
loaded = serializer.read(artifact_dir)
assert isinstance(loaded, TimeSeriesDataFrame)
assert len(loaded) == rows
assert list(loaded["close"]) == pytest.approx(list(closes))
def test_columnar_preserves_name_and_chart_meta(tmp_path: Path) -> None:
serializer = ColumnarTimeSeriesSerializer()
raw = _make_ohlcv_frame(10)
payload = TimeSeriesDataFrame(raw, name="AAPL", chart_meta={"unit": "USD", "kind": "ohlcv"})
artifact_dir = tmp_path / "frame"
serializer.write(artifact_dir, payload)
loaded = serializer.read(artifact_dir)
assert loaded.name == "AAPL"
assert loaded.chart_meta == {"unit": "USD", "kind": "ohlcv"}
def test_columnar_drops_non_serializable_chart_meta(tmp_path: Path) -> None:
serializer = ColumnarTimeSeriesSerializer()
raw = _make_ohlcv_frame(5)
class Weird:
pass
payload = TimeSeriesDataFrame(raw, name="TSLA", chart_meta={"obj": Weird()})
artifact_dir = tmp_path / "frame"
serializer.write(artifact_dir, payload)
loaded = serializer.read(artifact_dir)
# name still preserved
assert loaded.name == "TSLA"
# chart_meta either populated (via default=str) or empty dict; never crash
assert isinstance(loaded.chart_meta, dict)
def test_history_chunk_serializer_round_trip(tmp_path: Path) -> None:
serializer = HistoryChunkSerializer()
frame = TimeSeriesDataFrame(_make_ohlcv_frame(8))
chunk = HistoryChunk(
frame=frame,
loaded_start="2023-01-02",
loaded_end="2023-01-11",
requested_period="3y",
is_complete=False,
has_older=True,
source_version="test",
)
artifact_dir = tmp_path / "chunk"
serializer.write(artifact_dir, chunk)
loaded = serializer.read(artifact_dir)
assert loaded.loaded_start == "2023-01-02"
assert loaded.loaded_end == "2023-01-11"
assert loaded.requested_period == "3y"
assert loaded.is_complete is False
assert loaded.has_older is True
assert loaded.source_version == "test"
assert isinstance(loaded.frame, TimeSeriesDataFrame)
assert len(loaded.frame) == len(frame)
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