Coinbase Pro × Tardis.dev Documentation — Practical Presentation

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This single‑page presentation (~1000 words) explains how Tardis.dev helps analysts and engineers work with historical and real‑time crypto market data from Coinbase Pro / Coinbase Exchange. You’ll find a concise overview, implementation tips, sample code snippets, best practices, and ten colorful links to official resources for quick reference.

1) Overview

Tardis.dev is a market‑data platform that captures, stores, and serves time‑series and message‑level data from leading crypto exchanges. For Coinbase Pro (now commonly referenced as Coinbase Exchange), Tardis.dev exposes normalized datasets so you can backtest strategies, benchmark execution, audit fills, or power research notebooks. Instead of stitching together ad‑hoc scrapers and fragile archives, you consume consistent, queryable data through APIs and bulk downloads.

Why teams use it

  • Reliability: curated, gap‑aware datasets beat one‑off DIY capture.
  • Uniformity: consistent schemas across venues simplify multi‑exchange research.
  • Speed: ready‑to‑stream archives reduce time to first insight.

Deliverables you can expect

  • Order book snapshots and incremental updates suitable for mid‑/micro‑structure analysis.
  • Trades/agg‑trades for price/volume studies and execution analytics.
  • Metadata on symbols and instruments to keep models aligned.
Who benefits

Quant researchers, data engineers, risk teams, and product analysts who need reproducible results from trustworthy histories.

2) What is Tardis.dev?

Tardis.dev focuses on high‑fidelity historical and streaming market data. It captures raw exchange feeds, normalizes them, and exposes files and endpoints that are simple to consume in Python, JavaScript, or any language that can read JSON/CSV/Parquet. The service is designed for scale, with efficient compression and partitioning so you can pull exactly what you need and nothing more.

Key capabilities

  1. Historical archives: replay order books and trades across long horizons.
  2. Live streaming: subscribe to normalized channels for real‑time apps.
  3. Programmatic access: APIs plus bulk download for pipelines and notebooks.

Normalization advantage

Every venue speaks its own dialect. Tardis.dev smooths those differences—field names, channel semantics, and timestamp precision—so your code is portable and your experiments are reproducible across exchanges.

Security & governance

Commercial archives help with auditability and internal controls by providing consistent, immutable history rather than ad‑hoc captures scattered across drives.

3) Coinbase Pro / Coinbase Exchange context

Coinbase Pro was Coinbase’s professional trading interface; the exchange itself continues as Coinbase Exchange. For data users, the important part is access to market data feeds—trades and order books—regardless of UI branding. Tardis.dev focuses on the data: book updates, trades, and related metadata aligned to instruments like BTC-USD or ETH-USD.

Why Coinbase data matters

  • Deep USD liquidity for BTC and ETH pairs.
  • Institutional adoption and robust market surveillance.
  • Long history of transparent, well‑documented APIs.

Use cases

  • Execution research: simulate child order behavior against historical books.
  • Alphas & signals: microstructure features, imbalance, volatility clustering.
  • Risk: stress tests using volatile periods, latency sensitivity, and gaps.
Tip

When backtesting, align your clock to exchange timestamps and account for maintenance windows to avoid look‑ahead bias.

4) Data coverage & formats

Tardis.dev archives commonly include trades, order book snapshots, and incremental updates. Datasets are typically delivered in compact, time‑partitioned files (e.g., hourly/day partitions) to make selective retrieval fast.

Typical channels

Trades

Tick‑by‑tick executions with price, size, side, and timestamps. Useful for VWAP/TWAP baselines and volatility analysis.

Order books

Snapshots plus deltas. Rebuild books to compute depth, spread, imbalance, and queue dynamics across levels.

Schema awareness

Consult the provider’s schema docs for field names, types, and nullability. Normalized fields reduce glue code and mistakes when you scale to new venues.

5) Getting started

Here’s a minimal flow to pull Coinbase data via Tardis.dev and load it into a research notebook. Replace placeholders with your credentials and desired instruments.

Shell download snippet

# Example: download a day of Coinbase trades for BTC-USD
# (Adjust exchange key/name per provider docs)
export INSTRUMENT="BTC-USD"
export DATE="2024-05-01"
# Pseudo command for illustration; consult docs for exact CLI/API
curl -L "https://api.tardis.dev/download?exchange=coinbase&channel=trades&symbol=${INSTRUMENT}&date=${DATE}" \
  -H "Authorization: Bearer <API_TOKEN>" \
  -o coinbase_trades_${INSTRUMENT}_${DATE}.parquet

Python loader sketch

import pandas as pd
# Parquet/JSON supported — check docs for precise schema fields
trades = pd.read_parquet("coinbase_trades_BTC-USD_2024-05-01.parquet")
trades["notional"] = trades["price"] * trades["size"]
print(trades.head())
Validation checklist
  • Confirm timezone & timestamp precision (ns vs ms).
  • Sanity‑check number of rows vs known active periods.
  • Reconcile sample aggregates against exchange reference data.

6) Example workflows

Order‑book imbalance signal

Rebuild L2 books from snapshot + deltas, compute bid/ask depth within N ticks, and track the ratio over time. Use the signal to predict short‑term drift or to adjust passive quoting.

Pseudocode

# maintain book state
for update in updates:
    apply(update)
    depth_bid = sum_qty(levels=bids[:5])
    depth_ask = sum_qty(levels=asks[:5])
    imbalance = (depth_bid - depth_ask) / (depth_bid + depth_ask)
Result

Feed the feature into a simple logistic regression or gradient‑boosted tree to classify next‑tick direction; validate on out‑of‑sample intervals.

Transaction cost analysis (TCA)

Replay historical trades and books to simulate execution vs VWAP/TWAP baselines. Measure slippage and markout over t+Δ horizons. Use the results to refine slicing, throttle aggressiveness, and tune venue selection.

7) Best practices

  • Partition smartly: Pull only the time ranges and symbols you need.
  • Document schema: Keep a local schema file and update it with provider changes.
  • Rebuild deterministically: Use idempotent book‑replay logic; store checkpoints.
  • Monitor gaps: Log missing intervals and decide whether to impute or drop.
  • Version data: Tag datasets by retrieval date; pin inputs for research reproducibility.

Governance & audit

For regulated reporting, maintain hashes of downloaded files, capture provider metadata (exchange, channel, instrument, time window), and record any local transformations in a processing manifest.

Team workflows

Automate daily pulls with a scheduler, write to object storage, and expose Parquet tables to your compute layer (Spark/DuckDB/Snowflake). Keep notebooks lightweight and reproducible.

Cost control

Cache frequently accessed periods (e.g., crises) and compress aggressively to limit egress and storage costs.

8) Performance & cost awareness

Historical market data can be heavy. Prefer columnar formats for analytics, stream deltas when possible, and keep file sizes within your compute engine’s sweet spot (often 64–512 MB). Parallelize by date and instrument for near‑linear speedups on embarrassingly parallel workloads.

Throughput checklist

  • Exploit vectorized operations (NumPy/Polars) where possible.
  • Avoid Python loops for per‑row work; batch or push to compiled routines.
  • Profile I/O: network, decompression, and parsing often dominate runtime.

Resilience

Use retries with exponential backoff and checksum verification. Log ranges you’ve completed so restarts are painless.

Privacy note

Keep API tokens and credentials in a secure secrets manager; do not hard‑code keys in notebooks.

9) Quick FAQ

Can I mix Coinbase with other exchanges?

Yes—normalization is designed to make multi‑venue analysis straightforward. Keep an eye on symbol naming differences.

How far back does history go?

Consult provider docs for exact retention windows; many venues have multi‑year coverage for trades and books.

Is this suitable for production execution?

Use historical data for research and simulation. For live trading, connect to exchange production APIs and reconcile fills with your broker/custodian.