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 website contains the full set of endpoints and data offered by Coin Metrics.

• The Coin Metrics Product Documentation gives detailed, conceptual explanations of the data that Coin Metrics offers.

• The API Spec contains a full list of functions.

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sales_demo_order_book_updates.ipynb

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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.

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:

# 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:

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:

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.

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):

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

<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.

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

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

<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:

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

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()

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.