> For the complete documentation index, see [llms.txt](https://gitbook-docs.coinmetrics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gitbook-docs.coinmetrics.io/tutorials-and-examples/tutorials/order-book-updates.md).

# Reconstructing the Order Book from Snapshots and Updates

An order book is the outstanding bids and asks for a market, by price level. Coin Metrics serves it **three ways** — the three delivery schemas:

1. **Historical snapshots** — periodic point-in-time state of the whole book.
2. **Historical updates + snapshots** — the full event-driven stream of level changes; reconstruct the exact book at any timestamp (`dataset=updates`).
3. **Real-time streaming** — a live websocket that opens with a snapshot, then streams updates.

All three share **one** `[price, size]` message shape (a `size` of `0` removes a level), so the **same reconstruction code works across all of them**. This notebook is fully self-contained — every cell uses the Coin Metrics Python client directly — and it makes the point concrete by **streaming a live sample, then replaying the exact same time window from the historical `updates` dataset**.

### Resources

This notebook demonstrates basic functionality offered by the Coin Metrics Python API Client and Market Data Feed.

Coin Metrics offers a vast assortment of data for hundreds of cryptoassets. The Python API Client allows for easy access to this data using Python without needing to create your own wrappers using `requests` and other such libraries.

To understand the data that Coin Metrics offers, feel free to peruse the resources below.

* The [Coin Metrics API v4](https://docs.coinmetrics.io/api/v4) website contains the full set of endpoints and data offered by Coin Metrics.
* The [Coin Metrics Product Documentation](https://docs.coinmetrics.io/info) gives detailed, conceptual explanations of the data that Coin Metrics offers.
* The [API Spec](https://docs.coinmetrics.io/python-api-client/reference) contains a full list of functions.

### File Download

Download the notebook to run it yourself:

{% file src="/files/r0t0kDnfV65Dt95dBYKY" %}

## Setup

The only dependency is the Coin Metrics Python API client (`pip install coinmetrics-api-client`). The API key is read from the environment — never hardcode it.

```python
import os, json, time
from datetime import timedelta
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
from IPython.display import Image, display
from coinmetrics.api_client import CoinMetricsClient

client = CoinMetricsClient(os.environ["CM_API_KEY"])
MARKET = 'coinbase-btc-usd-spot'
DEPTH_PCT = 0.001   # depth band around the mid (0.001 = 0.1%)
```

## Schema 1 — Historical snapshots

The simplest shape: a periodic **snapshot** of the whole book. Coin Metrics stores three snapshot products — the top **100** levels and all levels **within 10% of mid** every **10s**, and the **full book hourly**. We pull the latest few (top-100) and show the top of the most recent one:

```python
# Latest few snapshots (default "snapshots" dataset), returned as a DataFrame.
# limit_per_market + paging_from use format="json" (json_stream always streams from start).
snaps = client.get_market_orderbooks(
    markets=[MARKET],
    limit_per_market=3,
    depth_limit="100",
    paging_from="end",
    format="json",
).to_dataframe()

latest = snaps.sort_values("time").iloc[-1]   # most recent snapshot
print(f"latest snapshot: {latest['time']}")
pd.DataFrame({
    "bid_price": [l["price"] for l in latest["bids"][:5]],
    "bid_size":  [l["size"]  for l in latest["bids"][:5]],
    "ask_price": [l["price"] for l in latest["asks"][:5]],
    "ask_size":  [l["size"]  for l in latest["asks"][:5]],
})
```

```
latest snapshot: 2026-07-07 05:31:59.984346+00:00
```

|   | bid\_price | bid\_size  | ask\_price | ask\_size  |
| - | ---------- | ---------- | ---------- | ---------- |
| 0 | 63033.89   | 0.36320277 | 63033.9    | 0.09268223 |
| 1 | 63033.88   | 0.03       | 63035.48   | 0.05552568 |
| 2 | 63033.86   | 0.03       | 63036.42   | 0.00022309 |
| 3 | 63033.8    | 0.00079322 | 63037.05   | 0.01784664 |
| 4 | 63032.01   | 0.11820631 | 63037.61   | 0.0694071  |

## Reconstructing the book

Both the update stream and the websocket deliver the same messages: a `snapshot` is the full state; an `update` carries absolute `[price, size]` levels where a `size` of `0` removes the level. To maintain the book, treat **every** `snapshot` as a full reset (a redundant-stream failover can emit an out-of-band snapshot), then apply updates in time order. That's the whole algorithm:

```python
def new_book():
    return {"bids": {}, "asks": {}}   # price -> size, per side

def apply_message(book, msg):
    if msg.get("type") == "snapshot":       # full state replacement
        book["bids"].clear()
        book["asks"].clear()
    for side in ("bids", "asks"):           # snapshot or update: apply levels
        levels = msg.get(side)
        if levels is None:
            continue
        for level in levels:
            price, size = float(level["price"]), float(level["size"])
            if size == 0:
                book[side].pop(price, None)  # level removed
            else:
                book[side][price] = size

def best_bid_ask(book):
    bid = max(book["bids"]) if book["bids"] else None
    ask = min(book["asks"]) if book["asks"] else None
    return bid, ask
```

And a small helper to **animate** the top of the book as messages are applied. The *same* function works on live websocket messages and on historical `updates` rows — because they are the same message shape. Levels are drawn by rank (best bid/ask at the center, deeper levels outward) so the bars stay readable and never overlap:

```python
GREEN, CORAL, DARK, TEXT, MUTED = "#22CAAD", "#F96167", "#10121A", "#C9D2DC", "#8E97A8"

def animate_book(messages, out_path, title, top_n=12, max_frames=100, fps=12):
    book, frames = new_book(), []
    for msg in messages:
        apply_message(book, msg)
        if not book["bids"] or not book["asks"]:
            continue
        bids = sorted(book["bids"], reverse=True)[:top_n]
        asks = sorted(book["asks"])[:top_n]
        frames.append(([(p, book["bids"][p]) for p in bids],
                       [(p, book["asks"][p]) for p in asks]))
    if len(frames) > max_frames:
        frames = frames[:: max(1, len(frames) // max_frames)]

    ymax = max((s for b, a in frames for _, s in b + a), default=1.0) * 1.1
    fig, ax = plt.subplots(figsize=(7, 4))
    fig.patch.set_facecolor(DARK)

    def draw(i):
        ax.clear(); ax.set_facecolor(DARK)
        bids, asks = frames[i]
        ax.bar([-(j + 0.5) for j in range(len(bids))], [s for _, s in bids], width=0.9, color=GREEN)
        ax.bar([(j + 0.5) for j in range(len(asks))], [s for _, s in asks], width=0.9, color=CORAL)
        ax.axvline(0, color=MUTED, ls="--", lw=0.8, alpha=0.6)
        ax.text(-0.5, ymax * 0.99, f"bid {bids[0][0]:,.2f}", color=GREEN, fontsize=7.5, ha="right", va="top")
        ax.text(0.5, ymax * 0.99, f"ask {asks[0][0]:,.2f}", color=CORAL, fontsize=7.5, ha="left", va="top")
        ax.text(-(len(bids) - 0.5), ymax * 0.88, f"{bids[-1][0]:,.2f}", color=MUTED, fontsize=6, ha="center", va="top")
        ax.text((len(asks) - 0.5), ymax * 0.88, f"{asks[-1][0]:,.2f}", color=MUTED, fontsize=6, ha="center", va="top")
        ax.set_xlim(-(top_n + 0.5), top_n + 0.5); ax.set_ylim(0, ymax); ax.set_xticks([])
        ax.set_title(f"{title} · frame {i + 1}/{len(frames)}", color=TEXT, fontsize=10)
        ax.set_xlabel("best bid/ask at center · deeper levels outward   (bids ←  |  → asks)", color=TEXT, fontsize=8)
        ax.set_ylabel("size", color=TEXT, fontsize=8)
        ax.tick_params(colors=TEXT, labelsize=7)
        for sp in ("top", "right"): ax.spines[sp].set_visible(False)
        for sp in ("left", "bottom"): ax.spines[sp].set_color(MUTED)

    anim = FuncAnimation(fig, draw, frames=len(frames), interval=1000 / fps)
    anim.save(out_path, writer=PillowWriter(fps=fps), savefig_kwargs={"facecolor": DARK})
    plt.close(fig)
    return out_path
```

## Schema 3 — Real-time streaming

The websocket feed opens with a `snapshot`, then sends `update` messages as the book changes (with occasional re-sync snapshots) — **the production path** for a live book. We subscribe at **`depth_limit="full_book"`** (the stream defaults to top-100) so the live feed matches the historical `updates` dataset, which is full-book — making the two directly comparable. We take a small live sample and **record the time window it spans**, so we can replay exactly that window from history next.

> This cell is *live*: its exact messages (and window) differ every run — unlike the historical replay below, which is reproducible.

```python
def stream_sample(market, n, max_seconds=120):
    """Consume `n` websocket messages, then close. Full-book to match the updates dataset."""
    stream = client.get_stream_market_orderbooks(markets=[market], depth_limit="full_book")
    messages = []
    start = time.time()
    def on_message(ws, message):
        messages.append(json.loads(message))
        if len(messages) >= n or time.time() - start > max_seconds:
            ws.close()
    stream.run(on_message=on_message, reconnect=False)
    return messages

live_msgs = stream_sample(MARKET, n=800)
times = sorted(m["time"] for m in live_msgs)
# The window we just watched, truncated to whole seconds (the messages carry
# nanosecond timestamps; .parallel() chunking needs a second-precision start/end).
win_start, win_end = times[0][:19] + "Z", times[-1][:19] + "Z"
live_changes = sum(len(m.get("asks") or []) + len(m.get("bids") or []) for m in live_msgs)
print(f"{len(live_msgs):,} live messages, {live_changes:,} level-changes")
print(f"streamed window: {win_start} -> {win_end}")
live_msgs[0]
```

```
800 live messages, 85,120 level-changes
streamed window: 2026-07-07T05:31:59Z -> 2026-07-07T05:32:12Z





{'market': 'coinbase-btc-usd-spot',
 'time': '2026-07-07T05:31:59.984346000Z',
 'coin_metrics_id': 'FxT65kMNSy6PL0celq9B7AAAAACEbj6G',
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  ...],
 'type': 'snapshot',
 'collect_time': '2026-07-07T05:31:59.996317000Z',
 'cm_sequence_id': '0'}
```

Animate the live book as those messages are applied (green bids, coral asks):

```python
display(Image(animate_book(live_msgs, "live_book.gif", f"Live — {MARKET}")))
```

<figure><img src="/files/pGFBumzE3LJ9zvTYRPEG" alt=""><figcaption></figcaption></figure>

```
<IPython.core.display.Image object>
```

## Schema 2 — Historical updates + snapshots (replay the window we just streamed)

`dataset=updates` returns the **identical** message shape (snapshot + updates) for a **historical** window, and the sequence is **reproducible** — the same market and time range return the same rows on every query. Instead of a hard-coded window, we pass the **`win_start` / `win_end` we just captured from the live stream** — so we replay the very window we watched live. Following the product docs, we fetch it **in parallel** and return a **DataFrame** (`.parallel(time_increment=…).to_dataframe()`), then reconstruct with the **same `apply_message` / `animate_book`** code. `start_with_snapshot=True` prepends a snapshot so we can initialise before the first update.

> **Row count vs live-message count.** Both feeds carry the **full book** (we subscribed the websocket at `depth_limit="full_book"` to match). The websocket **bundles many level changes into each message**, while historical `granularity="raw"` emits them as **finer, individual rows** — so there are more rows than live messages, but the **total book activity (level-changes) is comparable**. Same book, same schema, same code — just different message framing.

> Historical updates settle after a short persistence lag, so the final seconds of a just-streamed window may still be landing; the leading snapshot keeps the replay well-formed regardless.

```python
df = client.get_market_orderbooks(
    markets=[MARKET],
    dataset="updates",
    start_with_snapshot=True,   # leading snapshot to initialise the book
    depth_limit="full_book",
    granularity="raw",
    format="json_stream",
    paging_from="start",
    start_time=win_start,       # the window we streamed live above
    end_time=win_end,
).parallel(time_increment=timedelta(minutes=1)).to_dataframe()

rows = df.to_dict("records")   # message dicts for reconstruction / animation
hist_changes = int(df["asks"].map(len).sum() + df["bids"].map(len).sum())
print(f"historical (raw): {len(df):,} rows, {hist_changes:,} level-changes")
print(f"live (websocket): {len(live_msgs):,} messages, {live_changes:,} level-changes")
df.head()
```

Exporting to dataframe type: 0%| | 0/1 \[00:00\<?, ?it/s]

Exporting to dataframe type: 100%|██████████| 1/1 \[00:08<00:00, 8.02s/it]

Exporting to dataframe type: 100%|██████████| 1/1 \[00:08<00:00, 8.02s/it]

```
historical (raw): 52,974 rows, 97,459 level-changes
live (websocket): 800 messages, 85,120 level-changes
```

|   | market                | time                             | coin\_metrics\_id         | asks                                               | bids                                               | type     | database\_time                      | collect\_time                    |
| - | --------------------- | -------------------------------- | ------------------------- | -------------------------------------------------- | -------------------------------------------------- | -------- | ----------------------------------- | -------------------------------- |
| 0 | coinbase-btc-usd-spot | 2026-07-07 05:30:09.985257+00:00 | AAEDAAZV\_q83wulCVEMtVVNE | \[{'price': '62893.29', 'size': '0.00141817'}, ... | \[{'price': '62893.28', 'size': '1.40248756'}, ... | snapshot | 2026-07-07 05:30:10.204346194+00:00 | 2026-07-07 05:30:09.999438+00:00 |
| 1 | coinbase-btc-usd-spot | 2026-07-07 05:30:10.013888+00:00 | AAEDAAZV\_q84MsBCVEMtVVNE | \[]                                                | \[{'price': '62871.98', 'size': '0.540278'}]       | update   | 2026-07-07 05:30:10.204398185+00:00 | 2026-07-07 05:30:10.018256+00:00 |
| 2 | coinbase-btc-usd-spot | 2026-07-07 05:30:10.016763+00:00 | AAEDAAZV\_q84PftCVEMtVVNE | \[]                                                | \[{'price': '62872.11', 'size': '0.48858106'}]     | update   | 2026-07-07 05:30:10.204419996+00:00 | 2026-07-07 05:30:10.024894+00:00 |
| 3 | coinbase-btc-usd-spot | 2026-07-07 05:30:10.016875+00:00 | AAEDAAZV\_q84PmtCVEMtVVNE | \[{'price': '62925.66', 'size': '0.94122215'}, ... | \[]                                                | update   | 2026-07-07 05:30:10.204430666+00:00 | 2026-07-07 05:30:10.024923+00:00 |
| 4 | coinbase-btc-usd-spot | 2026-07-07 05:30:10.018792+00:00 | AAEDAAZV\_q84RehCVEMtVVNE | \[{'price': '62925.67', 'size': '0'}]              | \[]                                                | update   | 2026-07-07 05:30:10.204440166+00:00 | 2026-07-07 05:30:10.024948+00:00 |

```python
display(Image(animate_book(rows[:600], "historical_book.gif", "Historical replay — same window")))
```

<figure><img src="/files/NDJJYPIKK36zuSTaoRJ1" alt=""><figcaption></figcaption></figure>

```
<IPython.core.display.Image object>
```

## Reconstruct & measure

Replaying the updates in time order gives the level-1 series (best bid/ask, spread) and depth near the mid — the raw material for TCA, event studies, and backtests. The **same** `apply_message` that drove the animations builds the metrics here:

```python
book, series = new_book(), []
for row in sorted(rows, key=lambda r: r["time"]):
    apply_message(book, row)
    bid, ask = best_bid_ask(book)
    if bid is None or ask is None:
        continue
    mid = (bid + ask) / 2
    lo, hi = mid * (1 - DEPTH_PCT), mid * (1 + DEPTH_PCT)
    series.append({
        "time": row["time"],
        "spread_bps": (ask - bid) / mid * 1e4,
        "bid_depth": sum(s for p, s in book["bids"].items() if p >= lo),
        "ask_depth": sum(s for p, s in book["asks"].items() if p <= hi),
    })

metrics = pd.DataFrame(series)
metrics["time"] = pd.to_datetime(metrics["time"])
metrics[["spread_bps", "bid_depth", "ask_depth"]].describe()
```

|       | spread\_bps  | bid\_depth   | ask\_depth   |
| ----- | ------------ | ------------ | ------------ |
| count | 52974.000000 | 52974.000000 | 52974.000000 |
| mean  | 0.037895     | 13.749850    | 14.006826    |
| std   | 0.110120     | 2.348151     | 2.138078     |
| min   | 0.001587     | 6.192858     | 7.618362     |
| 25%   | 0.001588     | 12.175648    | 12.528570    |
| 50%   | 0.001589     | 13.971209    | 14.032747    |
| 75%   | 0.001590     | 15.600170    | 15.622603    |
| max   | 1.188795     | 20.020468    | 22.517797    |

```python
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
ax1.plot(metrics["time"], metrics["spread_bps"], color=GREEN, lw=1)
ax1.set_title("Bid-ask spread (bps)"); ax1.set_ylabel("bps")
ax2.plot(metrics["time"], metrics["bid_depth"], color=GREEN, lw=1, label="bid")
ax2.plot(metrics["time"], metrics["ask_depth"], color=CORAL, lw=1, label="ask")
ax2.set_title(f"Depth within {DEPTH_PCT * 100:g}% of mid"); ax2.set_ylabel("size"); ax2.legend()
fig.autofmt_xdate()
plt.tight_layout(); plt.show()
```

<figure><img src="/files/SyZ5zIDTwYsDpbSDqXk5" alt=""><figcaption></figcaption></figure>

## Takeaways

* **Three schemas, one data model.** Historical snapshots, historical updates, and the real-time stream all share the same `[price, size]` message shape.
* **One code path, live or historical.** The same `apply_message` reconstructs the book from websocket messages and from `dataset=updates` rows — we even replayed the exact window we had just streamed live.
* Treat **every** `snapshot` as a full reset (not just the first) — a redundant-stream failover can emit an out-of-band snapshot.
* Historical `dataset=updates` is **reproducible**, so research and production share the exact same book-building logic.


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://gitbook-docs.coinmetrics.io/tutorials-and-examples/tutorials/order-book-updates.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.