# Exploring Availability of Market Data for CME (Derivatives) {% embed url="" %} {% embed url="" %} ### Instructions The notebook linked above shows how to explore data from CME (Chicago Mercantile Exchange), one of the big derivatives exchanges. Options on derivatives are probably the most complicated market instruments available on Coin Metrics, so this may be particularly interesting! This notebook linked above is in [Marimo](https://marimo.io/) format. The code below is more readable, but does not preserve the cell breaks. We recommend using Marimo as new, modern Jupyter environment. ```python from coinmetrics.api_client import CoinMetricsClient import os import marimo as mo from datetime import datetime, timedelta, time, timezone import matplotlib.pyplot as plt import pandas as pd client = CoinMetricsClient(os.getenv('CM_API_KEY')) end_time = datetime.now(tz=timezone.utc) start_time = end_time - timedelta(days=60) cme = client.reference_data_markets(exchange='cme').to_dataframe() cme cme.loc[(cme.pair=='btc-usd') & (cme.expiration >= end_time)].groupby(['expiration', 'pair', 'type', 'contract_size'], observed=True).agg({ 'strike': ['count', 'min', 'max'], 'market': ['count', 'first'] }) my_markets = cme.loc[(cme.symbol.apply(len)==5) & (cme.expiration >= end_time) & (cme.size_asset.isin(['btc', 'eth'])) ].sort_values(['expiration', 'size_asset']).loc[:, ['expiration', 'symbol', 'market']] my_markets.set_index('symbol') client.catalog_market_candles_v2(markets=list(my_markets.market)).to_dataframe().set_index(['market', 'frequency']) df = client.get_market_candles(markets=['cme-BTCM5-future', ], start_time=start_time, frequency='1d').to_dataframe() # 'cme-BTCK5-future', 'cme-BTCM5-future', # Ensure we have a datetime index or 'time' column if not isinstance(df.index, pd.DatetimeIndex): if 'time' in df.columns: _df_time = pd.to_datetime(df['time']) else: raise ValueError("No datetime index or 'time' column found.") else: _df_time = df.index # Cumulative sum of volume per day, starting at midnight _df = df.copy() _df['__date'] = _df_time.dt.date _df['vol_current_day_'] = _df.groupby(['market','__date'])['volume'].cumsum() _df = _df.drop(columns='__date') _df import plotly.graph_objs as go candles_90 = df.sort_index().iloc[-90:].copy() # If the index is not a DatetimeIndex, reset and use time column if available if not isinstance(candles_90.index, pd.DatetimeIndex): if 'time' in candles_90.columns: candles_90['time'] = pd.to_datetime(candles_90['time']) else: # fallback to numeric index as x axis candles_90['time'] = candles_90.index # Candle chart fig = go.Figure() fig.add_trace(go.Candlestick( x=candles_90.get('time', candles_90.index), open=candles_90['price_open'], high=candles_90['price_high'], low=candles_90['price_low'], close=candles_90['price_close'], name="Price", increasing_line_color='green', decreasing_line_color='red', )) fig.add_trace(go.Bar( x=candles_90.get('time', candles_90.index), y=candles_90['volume'], name='Volume', marker_color='steelblue', opacity=0.35, yaxis='y2', )) # Layout for dual y-axis fig.update_layout( title="CME Candle Chart with Volume (BTCM5=June 2025 epxiry)", yaxis_title="Price", xaxis_title="Time", yaxis2=dict(title='Volume', overlaying='y', side='right', showgrid=False), xaxis_rangeslider_visible=False, height=500, legend=dict(orientation='h', yanchor="bottom", y=1.02, xanchor="right", x=1) ) fig client.catalog_market_contract_prices_v2(exchange='cme').to_dataframe() client.catalog_market_open_interest_v2(exchange='cme').to_dataframe() client.catalog_market_implied_volatility_v2(exchange='cme').to_dataframe() client.catalog_market_funding_rates_v2(exchange='cme').to_dataframe() client.catalog_market_greeks_v2(exchange='cme').to_dataframe() ob = client.catalog_market_orderbooks_v2(markets=list(my_markets.market), ).to_list() ob[0] unpack = [] for m in ob: for de in m['depths']: unpack.append( { "market": m['market'], "depth": de['depth'], "min_time": de['min_time'], "max_time": de['max_time'], } ) pd.DataFrame(unpack) EAM = client.catalog_exchange_asset_metrics_v2().to_dataframe() EAM.loc[(EAM.metric.str.contains('future')) & (EAM.exchange_asset.str.startswith('cme-'))] EAM.loc[(EAM.metric.str.contains('option')) & (EAM.exchange_asset.str.startswith('cme-'))] import plotly.express as px # Prepare data metric_name = "volume_reported_option_notional_usd_1d" exchange_asset = "cme-usd" freq = "1d" series_px = client.get_exchange_asset_metrics( metrics=[metric_name], frequency=freq, exchange_assets=[exchange_asset], ).to_dataframe().reset_index() series_px['time'] = pd.to_datetime(series_px['time']) # Find top 3 top3 = series_px.nlargest(3, metric_name) # Plot fig_px = px.line( series_px, x='time', y=metric_name, title="CME Reported Option Notional Volume (Top 3 Labeled)", labels={ "time": "Time", metric_name: "Option Notional Volume (USD)" }, markers=True ) # Add explicit text labels for top 3 values for _, row in top3.iterrows(): fig_px.add_annotation( x=row['time'], y=row[metric_name], text=f"{row['time'].date().isoformat()}/{row[metric_name]/1e6:,.0f} mUSD", showarrow=True, arrowhead=2, ay=-40, font=dict(color="crimson", size=13) ) fig_px.update_traces(line_color="indigo", marker=dict(size=7)) fig_px.update_layout(yaxis_title="Option Notional Volume (USD)", xaxis_title="Time") fig_px ``` --- # Agent Instructions: 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. 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