polaris-data 0.8.6

Python SDK for the Polaris market data API

polaris-py

Python SDK for the Polaris API, optimized for notebook workflows and trading scripts. Documentation can be found at https://polaris.supply/docs

Install

Install the published SDK from PyPI:

pip install polaris-data

If you use uv, install it into a project with:

uv add polaris-data

Or install it into the active environment with:

uv pip install polaris-data

Quickstart

from polaris_data import PolarisClient

with PolarisClient(api_key="polaris_key_your_key") as client:
    row_count = sum(
        1
        for _ in client.replay(
            source="binance",
            market="BTC-USDT",
            from_="2024-01-01T00:00:00Z",
            to="2024-01-01T01:00:00Z",
        )
    )
    print(f"Replayed {row_count} rows")

If api_key is omitted, the client reads POLARIS_API_KEY from the environment.

PolarisClient API

PolarisClient is the main sync client for the SDK:

PolarisClient(
    api_key=None,
    base_url="https://api.polaris.supply",
    timeout=30.0,
    dataset_root=None,
)

Use it to inspect available data and query historical market data.

Discovery

Method	Returns	Use case
health()	API health/status payload	Connectivity checks and startup validation
catalog(source=None, market=None, q=None)	Source/market metadata, including normalized instrument fields	Discover supported datasets, markets, instrument metadata, and time coverage

Access patterns

Method	Returns	Use case
replay(source=..., market=..., from_=None, to=None, standard=True, allow_gaps=False, parallel=False)	Iterator of historical events	Backfills, notebooks, and replay-style processing without materializing everything up front
raw(source=..., market=..., from_=None, to=None, limit=1000)	List of raw source payloads	Inspect exchange-native payloads and compare raw vs standardized schemas

Standardized Data Schemas

Method	Returns	Use case
events(source=..., market=..., from_=None, to=None, allow_gaps=False)	List of standardized historical events	General-purpose historical analysis when you want the normalized event stream in memory
trades(source=..., market=..., from_=None, to=None, allow_gaps=False)	List of standardized trade events	Trade-level analytics, execution studies, and derived bar calculations
l2_snapshots(source=..., market=..., from_=None, to=None, allow_gaps=False)	List of standardized orderbook snapshot rows	Order book reconstruction and microstructure analysis
funding_rates(source=..., market=..., from_=None, to=None, allow_gaps=False)	List of funding-rate point series rows	Perpetual funding studies and carry modeling
mark_prices(source=..., market=..., from_=None, to=None, allow_gaps=False)	List of mark-price point series rows	Basis analysis, mark tracking, and liquidation-related research
ohlcv(source=..., market=..., from_=None, to=None, interval=..., format=None, allow_gaps=False)	Aggregated OHLCV bars	Charting, bar-based strategies, and downstream TA workflows
volume(source=..., market=..., from_=None, to=None, interval=..., allow_gaps=False)	Bucketed trade volume series	Volume profiling and participation analysis
vwap(source=..., market=..., from_=None, to=None, interval=..., allow_gaps=False)	Bucketed VWAP series	Execution benchmarking and price smoothing
volatility(source=..., market=..., from_=None, to=None, interval=..., method="log_returns", allow_gaps=False)	Bucketed realized volatility series	Risk modeling and intraperiod volatility analysis
bbo(source=..., market=..., from_=None, to=None, allow_gaps=False)	Best bid/offer quote series	Spread tracking, quote analytics, and top-of-book monitoring
depth_metrics(source=..., market=..., from_=None, to=None, depth_pct=0.01, slippage_notional=10000.0, allow_gaps=False)	Derived depth, spread, imbalance, and slippage metrics	Liquidity analysis and market impact estimation

For parameter details, response shapes, and end-to-end examples, see the Python SDK docs.

Local dataset storage

Standardized snapshots are stored under the shared Polaris app-data root so the Python SDK and CLI can reuse the same files. Legacy materialized day files are also recognized when present.

Default roots:

• macOS: ~/Library/Application Support/polaris
• Linux: $XDG_DATA_HOME/polaris or ~/.local/share/polaris
• Windows: %APPDATA%\\polaris

Within that root, the SDK uses the same layout as the CLI:

<root>/
  data/
  daily/
  tmp/
  cache/
  locks/

Standardized snapshot downloads are stored under:

<root>/data/<tier>/<source>/<market>/<YYYY-MM-DD>/<opaque-key>.jsonl.zst

The opaque key is the flat upstream snapshot identifier, for example:

standard-aster-ASTERUSDT-2026-06-01-00

which is stored on disk as:

<root>/data/standard/aster/ASTERUSDT/2026-06-01/standard-aster-ASTERUSDT-2026-06-01-00.jsonl.zst

Compatible materialized day files, when present, are stored under:

<root>/daily/<source>/<market>/<YYYY-MM-DD>.jsonl.zst

Pass dataset_root=... to PolarisClient(...) to override the root explicitly. POLARIS_ROOT overrides the shared root globally. POLARIS_DATASET_DOWNLOAD_DIR is still accepted as a deprecated compatibility override.

Snapshot-first replay

For standardized historical data, replay(...), events(...), trades(...), vwap(...), volatility(...), bbo(...), depth_metrics(...), l2_snapshots(...), volume(...), and default/tradingview ohlcv(...) now prefer /snapshots plus daily bulk /download?source=...&market=...&date=...&mode=json manifests, and reuse local snapshot files when they already exist:

from polaris_data import PolarisClient

with PolarisClient(api_key="polaris_key_your_key") as client:
    for row in client.replay(
        source="binance",
        market="BTC-USDT",
        from_="2024-01-01T00:00:00Z",
        to="2024-01-01T01:00:00Z",
    ):
        print(row)

If the requested standardized range cannot be satisfied from available standardized snapshots, replay(...), events(...), trades(...), vwap(...), volatility(...), bbo(...), depth_metrics(...), l2_snapshots(...), volume(...), and ohlcv(...) raise by default instead of falling back. Pass allow_gaps=True on standardized methods to return only covered data and receive a warning with the missing intervals.

Error handling

from polaris_data import PolarisClient, RateLimitedError, UnauthorizedError

client = PolarisClient()

try:
    client.replay(
        source="binance",
        market="BTC-USDT",
        from_="2024-01-01T00:00:00Z",
        to="2024-01-01T01:00:00Z",
    )
except UnauthorizedError:
    print("API key is required")
except RateLimitedError as err:
    print(f"Rate limited. Reset at: {err.reset_at}")

Tests

uv run pytest