truthtrader-barvault 0.1.1

Cache-first market bar archive: fetch from Polygon (or others), store 1m bars, return any timeframe. No pandas required for callers.

Barvault

Cache-first market bar archive: fetch from Polygon (or other providers), store 1-minute bars in S3 or local, return any timeframe. Callers can use plain Python (datetime, list[dict]) — no pandas required.

• deterministic archive reads (stable ordering + explicit boundary semantics)
• a single golden source stored in the archive: 1-minute bars
• stateless configuration (no module globals, no env lookups at import-time)

Install (Poetry)

poetry install

Quick usage

from __future__ import annotations

from datetime import datetime, timezone

from market_data import ArchiveConfig, MarketDataClient, PolygonConfig

cfg = ArchiveConfig.s3(bucket="public-market-data-truth-trader", prefix="md-archive")
client = MarketDataClient(cfg, polygon=PolygonConfig(api_key="YOUR_POLYGON_KEY"))

records = client.get_bars_records(
    ["AAPL"],
    start=datetime(2025, 1, 2, 14, 30, tzinfo=timezone.utc),
    end=datetime(2025, 1, 2, 15, 30, tzinfo=timezone.utc),
    timeframe="5min",
)
print(records[:2])

Fetch + cache (Polygon) + return any timeframe

The archive’s golden source is always 1-minute bars. MarketDataClient will:

• read what’s already in the archive for [start, end)
• detect missing expected minutes (using the provided calendar)
• fetch missing data from Polygon
• write 1m bars back to the archive (merge + de-dupe)
• return 1m or any derived timeframe (resampled from 1m)

from __future__ import annotations

from market_data import ArchiveConfig, MarketDataClient, PolygonConfig

# Local archive:
# cfg = ArchiveConfig.local(root="/tmp/md-archive")
#
# S3 archive:
cfg = ArchiveConfig.s3(bucket="public-market-data-truth-trader", prefix="md-archive")

client = MarketDataClient(
    cfg,
    polygon=PolygonConfig(api_key="YOUR_POLYGON_KEY"),
)

# Always returns data for [start,end) (start inclusive, end exclusive).
bars_1m = client.get_bars(
    ["AAPL", "MSFT"],
    start="2025-01-02T14:30:00Z",
    end="2025-01-02T15:30:00Z",
    timeframe="1min",
)

# Derived timeframe (resampled from 1m):
bars_5m = client.get_bars(
    ["AAPL"],
    start="2025-01-02T14:30:00Z",
    end="2025-01-02T15:30:00Z",
    timeframe="5min",
)

# If you don't want to work with pandas DataFrames, use plain Python records:
records_5m = client.get_bars_records(
    ["AAPL"],
    start="2025-01-02T14:30:00Z",
    end="2025-01-02T15:30:00Z",
    timeframe="5min",
)

Timeframe notes:

• Use pandas-compatible offset strings like "5min", "1h", "1D".
• Do not use "1m" to mean 1 minute — in pandas "m" means months.

Design constraints

• import market_data must not start servers, read env vars, or require credentials
• all configuration is passed via constructors/args (ArchiveConfig, provider constructors, etc.)
• only 1m is accepted by the writer; higher timeframes are derived from 1m (never written)