Intraday Orderbook Analytics Accelerator

$1490

Intraday electricity-market orderbook analytics: best bid/ask, spread, mid, depth, imbalance, and VWAP at minute resolution. Ships a dependency-free analytics library + offline tests. Fully generic, currency-neutral Databricks Asset Bundle.

📁 16 files🏷 v1.0.0

Production-readyUnit-testedDatabricks Asset Bundle

✓ Instant download✓ Lifetime updates✓ MIT licensed✓ Secure checkout (Stripe)

⚙ Try the Live Demo interactive

Edit an order book and watch best bid/ask, spread, mid, depth, imbalance and VWAP recompute — the accelerator's analytics, live.

⚡ Open Orderbook Explorer

📋 What's Inside 16 files

✓ README.md
✓ LICENSE
✓ manifest.json
✓ databricks.yml
✓ resources/jobs.yml
✓ src/01_top_of_book.sql
✓ src/02_depth_imbalance.sql
✓ src/03_vwap.sql
✓ lib/orderbook.py
✓ tests/test_orderbook.py
✓ conftest.py
✓ guide/01_what-you-get.md
✓ guide/02_getting-started.md
✓ guide/03_architecture.md
✓ guide/04_support.md
✓ guides/orderbook-methodology.md

📁 File Structure 16 files

intraday-orderbook-accelerator/
├── README.md
├── LICENSE
├── manifest.json
├── databricks.yml
├── resources/
│   ├── jobs.yml
├── src/
│   ├── 01_top_of_book.sql
│   ├── 02_depth_imbalance.sql
│   ├── 03_vwap.sql
├── lib/
│   ├── orderbook.py
├── tests/
│   ├── test_orderbook.py
├── conftest.py
├── guide/
│   ├── 01_what-you-get.md
│   ├── 02_getting-started.md
│   ├── 03_architecture.md
│   ├── 04_support.md
├── guides/
│   ├── orderbook-methodology.md

📖 Documentation Preview README excerpt

Intraday Orderbook Analytics Accelerator

Intraday electricity-market **orderbook analytics**: best bid/ask, spread, mid

price, book depth, order-book imbalance, and VWAP — over a configurable source,

at minute resolution. Deploy-ready Databricks Asset Bundle.

Fully generic and currency-neutral: map your orderbook and trade tables and

deploy.

What's inside

- **Databricks Asset Bundle** — a 15-minute analytics refresh.

- **Three SQL steps** — top-of-book (bid/ask/spread/mid), depth + imbalance, and

VWAP from executed trades.

- **A dependency-free analytics library** (`lib/orderbook.py`): best bid/ask,

spread, mid, depth, imbalance, VWAP, and spread-in-bps — with an offline test

suite (6 tests).

Quickstart

```bash

pip install pytest && pytest tests/ -v

databricks bundle deploy -t dev -p <profile>

```

Provide `orderbook_levels(product_id, ts_utc, side, price, volume)` and

`trades(product_id, ts_utc, price, volume)`.

License

MIT — see `LICENSE`.

... preview truncated, see full README in product download.

📄 Code Sample .sql preview

src/01_top_of_book.sql-- Intraday Orderbook: Top of book — best bid/ask, spread, mid per product/minute. -- Mirrors lib/orderbook.best_bid/best_ask/spread/mid_price. CREATE OR REPLACE TABLE IDENTIFIER(:catalog || '.' || :schema || '.f_top_of_book') AS WITH snap AS ( SELECT product_id, DATE_TRUNC('minute', ts_utc) AS minute_utc, side, price, volume FROM IDENTIFIER(:catalog || '.' || :schema || '.orderbook_levels') ) SELECT b.product_id, b.minute_utc, MAX(CASE WHEN b.side = 'bid' THEN b.price END) AS best_bid, MIN(CASE WHEN b.side = 'ask' THEN b.price END) AS best_ask, MIN(CASE WHEN b.side = 'ask' THEN b.price END) - MAX(CASE WHEN b.side = 'bid' THEN b.price END) AS spread, (MIN(CASE WHEN b.side = 'ask' THEN b.price END) + MAX(CASE WHEN b.side = 'bid' THEN b.price END)) / 2 AS mid_price FROM snap b GROUP BY b.product_id, b.minute_utc;

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