Example Notebooks

Use this page when you want a fast notebook setup and then a curated runnable polaris-data example. The full notebook sources live in the polaris-data/notebooks repository. Keep this docs page as the map, and use the repository when you want the full runnable analysis.

Start a notebook quickly

If you do not already have Jupyter running, start in a terminal:

python3 -m pip install --upgrade jupyterlab ipykernel polaris-data pandas matplotlib
jupyter lab

If you already have a notebook open, install the basics in a cell:

%pip install polaris-data pandas matplotlib

Most Polaris notebooks follow the same first pattern:

Confirm the exact source and market with catalog(...).
Fetch ohlcv(...) or trades(...).
Load the response into pandas.
Plot or summarize the result.

import pandas as pd
from polaris_data import PolarisClient

source = "hyperliquid"
market = "BTC"

with PolarisClient() as client:
    catalog_result = client.catalog(source=source, market=market)
    bars = client.ohlcv(source=source, market=market, interval="1m")

markets_df = pd.DataFrame(catalog_result["markets"])
bars_df = pd.DataFrame(bars)
bars_df["time"] = pd.to_datetime(bars_df["timestamp"], unit="us", utc=True)

display(markets_df[["market", "start", "end"]])
display(bars_df.head())

Use Market Coverage first when you need a high-level view of supported venues, then confirm the exact identifier in Catalog before you fetch data.

Repository overview

The current example repo is intentionally small and curated:

hyperliquid_btc_trade_analysis.ipynb: single-market walkthrough for bars, trades, and trade-flow visualization

Hyperliquid BTC trade analysis

Open hyperliquid_btc_trade_analysis.ipynb when you want the most direct end-to-end SDK example. This notebook shows how to:

confirm the exact market with catalog(...)
fetch a bounded OHLCV window and the matching raw trades
normalize both responses into pandas DataFrames
summarize buy and sell flow
overlay trade points on the close series and chart signed flow per minute

Short excerpt:

from polaris_data import PolarisClient
import pandas as pd

source = "hyperliquid"
market = "BTC"

with PolarisClient() as client:
    bars = client.ohlcv(
        source=source,
        market=market,
        interval="1m",
    )
    trades = client.trades(
        source=source,
        market=market,
    )

bars_df = pd.DataFrame(bars)
trades_df = pd.json_normalize(trades)

Use this notebook when you want to understand the core polaris-data workflow quickly without adding multi-market logic.

Choosing the right notebook

Start with the Hyperliquid example if you want the fastest path to understanding catalog(...), ohlcv(...), and trades(...).
Stay in the docs and read Python SDK if you want method signatures and usage patterns before opening a notebook.

Next steps

Browse the full notebook repo at polaris-data/notebooks.
Read Python SDK for the core client methods.
Read Catalog before hardcoding source and market IDs.
Read OHLCV and Trades for the normalized response shapes behind the notebook examples.

Getting Started

SDKs

Data Schemas

Reference

Resources

Example Notebooks

Start a notebook quickly

Repository overview

Hyperliquid BTC trade analysis

Choosing the right notebook

Next steps

​Start a notebook quickly

​Repository overview

​Hyperliquid BTC trade analysis

​Choosing the right notebook

​Next steps

Start a notebook quickly

Repository overview

Hyperliquid BTC trade analysis

Choosing the right notebook

Next steps