Build a Custom Dataset

Create a custom training dataset with EODHD¶

Learn how to create custom crypto training datasets (features and targets) from scratch. This tutorial demonstrates the full pipeline from data download to ML-ready features and modeling.

What you'll learn:

• Download historical crypto data with numerblox and eodhd
• Engineer cross-sectional features with centimators
• Create ranking targets for prediction
• Validate data quality
• Structure datasets for ML

Why build custom datasets? While CrowdCent provides training data, building your own allows you to:

• Experiment with different features and time windows
• Include additional data sources
• Test hypotheses on historical data
• Develop a unique edge in predictions

Get EODHD API Access¶

Special offer for CrowdCent users: Get 10% off all EODHD plans 👉 https://eodhd.com/pricing-special-10?via=crowdcent

Recommended plan: EOD Historical Data - All World ($17.99/mo) which provides 100k calls/day - sufficient for most use cases

In [ ]:

!pip install crowdcent-challenge numerblox eod centimators polars altair vegafusion vl-convert-python

!pip install crowdcent-challenge numerblox eod centimators polars altair vegafusion vl-convert-python

In [ ]:

import os
import polars as pl
from datetime import datetime
import requests

# Set your EODHD API key
EOD_API_KEY = "YOUR_EODHD_API_KEY_HERE"  # Get from https://eodhd.com/cp/dashboard

if EOD_API_KEY == "YOUR_EODHD_API_KEY_HERE":
    print("⚠️ Please set your EODHD API key above")
    print("Get one free at: https://eodhd.com/pricing-special-10?via=crowdcent")

import os import polars as pl from datetime import datetime import requests # Set your EODHD API key EOD_API_KEY = "YOUR_EODHD_API_KEY_HERE" # Get from https://eodhd.com/cp/dashboard if EOD_API_KEY == "YOUR_EODHD_API_KEY_HERE": print("⚠️ Please set your EODHD API key above") print("Get one free at: https://eodhd.com/pricing-special-10?via=crowdcent")

Step 1: Fetch Live Hyperliquid Universe¶

We'll get the current cryptocurrency universe directly from the Hyperliquid API. This includes both active and delisted perpetuals, giving us maximum historical data coverage.

In [3]:

def get_hyperliquid_perpetuals(include_delisted: bool = False):
    """Get perpetual IDs as a polars DataFrame

    Args:
        include_delisted: If True, include delisted perpetuals in the output
    """

    url = "https://api.hyperliquid.xyz/info"
    payload = {"type": "meta"}
    special_map_dict = {
        "POPCAT-USD.CC": "POPCAT28782-USD.CC",
        "VVV-USD.CC": "VVV.CC",
        "BRETT-USD.CC": "BRETT29743-USD.CC",
        "UNIBOT-USD.CC": "UNIBOT27009-USD.CC",
        "ZRO-USD.CC": "ZRO26997-USD.CC",
        "MOVE-USD.CC": "MOVE32452-USD.CC",
        "STG-USD.CC": "STG18934-USD.CC",
        "GOAT-USD.CC": "GOAT33440-USD.CC",
        "PEPE-USD.CC": "PEPE24478-USD.CC",
        "PROMPT-USD.CC": "PROMPT-USD.CC",
        "NIL-USD.CC": "NIL35702-USD.CC",
        "MNT-USD.CC": "MNT27075-USD.CC",
        "ACE-USD.CC": "ACE28674-USD.CC",
        "HYPE-USD.CC": "HYPE32196-USD.CC",
        "IMX-USD.CC": "IMX10603-USD.CC",
        "INIT-USD.CC": "INIT-USD.CC",
        "PURR-USD.CC": "PURR34332-USD.CC",
        "MOODENG-USD.CC": "MOODENG33093-USD.CC",
        "CHILLGUY-USD.CC": "CHILLGUY-USD.CC",
        "FARTCOIN-USD.CC": "FARTCOIN-USD.CC",
        "GRASS-USD.CC": "GRASS32956-USD.CC",
        "GRIFFAIN-USD.CC": "GRIFFAIN-USD.CC",
        "MELANIA-USD.CC": "MELANIA35347-USD.CC",
        "KAITO-USD.CC": "KAITO-USD.CC",
        "SUI-USD.CC": "SUI20947-USD.CC",
        "BERA-USD.CC": "BERA-USD.CC",
        "MEW-USD.CC": "MEW30126-USD.CC",
        "ANIME-USD.CC": "ANIME35319-USD.CC",
        "NEIRO-USD.CC": "NEIRO32521-USD.CC",
        "DOGS-USD.CC": "DOGS32698-USD.CC",
        "STX-USD.CC": "STX4847-USD.CC",
        "S-USD.CC": "S32684-USD.CC",
        "COMP-USD.CC": "COMP5692-USD.CC",
        "TRUMP-USD.CC": "TRUMP-OFFICIAL-USD.CC",
        "BLAST-USD.CC": "BLAST28480-USD.CC",
        "TAO-USD.CC": "TAO22974-USD.CC",
        "SAGA-USD.CC": "SAGA30372-USD.CC",
        "TON-USD.CC": "TON11419-USD.CC",
        "BIO-USD.CC": "BIO.CC",
        "GMX-USD.CC": "GMX11857-USD.CC",
        "NTRN-USD.CC": "NTRN26680-USD.CC",
        "SUPER-USD.CC": "SUPER8290-USD.CC",
        "SCR-USD.CC": "SCR26998-USD.CC",
        "BANANA-USD.CC": "BANANA28066-USD.CC",
        "ME-USD.CC": "ME32197-USD.CC",
        "GMT-USD.CC": "GMT18069-USD.CC",
        "IO-USD.CC": "IO29835-USD.CC",
        "ZK-USD.CC": "ZKSYNC.CC",
        "ALT-USD.CC": "ALT29073-USD.CC",
        "POL-USD.CC": "POL28321-USD.CC",
        "WCT-USD.CC": "WCT33152-USD.CC",
        "XAI-USD.CC": "XAI28933-USD.CC",
        "JUP-USD.CC": "JUP29210-USD.CC",
        "APE-USD.CC": "APE3-USD.CC",
        "SPX-USD.CC": "SPX28081-USD.CC",
        "HYPER-USD.CC": "HYPER36281-USD.CC",
        "IP-USD.CC": "IP-USD.CC",
        "ZORA-USD.CC": "ZORA35931-USD.CC",
        "PEOPLE-USD.CC": "PEOPLE-USD.CC",
        "BABY-USD.CC": "BABY32198-USD.CC",
        "ARB-USD.CC": "ARB11841-USD.CC",
        "UNI-USD.CC": "UNI7083-USD.CC",
        "OMNI-USD.CC": "OMNI30315-USD.CC",
        "SOPH-USD.CC": "SOPHON-USD.CC",
        "NEIROETH-USD.CC": "NEIRO-USD.CC",
        "APT-USD.CC": "APT21794-USD.CC",
        "STRK-USD.CC": "STRK22691-USD.CC",
        "RESOLV-USD.CC": "RESOLV-USD.CC",
        "TST-USD.CC": "TST35647-USD.CC",
        "PUMP-USD.CC": "PUMP29601-USD.CC",
        "WLFI-USD.CC": "WLFI33251-USD.CC",
        "ASTER-USD.CC": "ASTER36341-USD.CC",
        "SKY-USD.CC": "SKY33038-USD.CC",
    }
    response = requests.post(url, json=payload)
    response.raise_for_status()

    data = response.json()
    universe = data.get("universe", [])

    # Extract names of perpetuals based on delisted status
    perpetual_ids = [
        perp["name"]
        for perp in universe
        if include_delisted or not perp.get("isDelisted", False)
    ]

    # Create DataFrame with just id column
    df = pl.DataFrame({"id": perpetual_ids})
    df = df.with_columns(
        (pl.col("id") + "-USD.CC")
        .str.replace("k", "")
        .replace(special_map_dict)
        .alias("eodhd_id")
    )


    return df
        

# Get the Hyperliquid universe
perpetuals_df = get_hyperliquid_perpetuals(include_delisted=True)
print(perpetuals_df)
# Extract ticker lists
eodhd_tickers = perpetuals_df["eodhd_id"].to_list()
id_mapping = dict(perpetuals_df.select("eodhd_id", "id").iter_rows())

print(f"\n📊 Dataset will include {len(eodhd_tickers)} cryptocurrencies")
print(f"Sample tickers: {eodhd_tickers[:10]}")
print(f"Sample IDs: {list(id_mapping.values())[:10]}")

def get_hyperliquid_perpetuals(include_delisted: bool = False): """Get perpetual IDs as a polars DataFrame Args: include_delisted: If True, include delisted perpetuals in the output """ url = "https://api.hyperliquid.xyz/info" payload = {"type": "meta"} special_map_dict = { "POPCAT-USD.CC": "POPCAT28782-USD.CC", "VVV-USD.CC": "VVV.CC", "BRETT-USD.CC": "BRETT29743-USD.CC", "UNIBOT-USD.CC": "UNIBOT27009-USD.CC", "ZRO-USD.CC": "ZRO26997-USD.CC", "MOVE-USD.CC": "MOVE32452-USD.CC", "STG-USD.CC": "STG18934-USD.CC", "GOAT-USD.CC": "GOAT33440-USD.CC", "PEPE-USD.CC": "PEPE24478-USD.CC", "PROMPT-USD.CC": "PROMPT-USD.CC", "NIL-USD.CC": "NIL35702-USD.CC", "MNT-USD.CC": "MNT27075-USD.CC", "ACE-USD.CC": "ACE28674-USD.CC", "HYPE-USD.CC": "HYPE32196-USD.CC", "IMX-USD.CC": "IMX10603-USD.CC", "INIT-USD.CC": "INIT-USD.CC", "PURR-USD.CC": "PURR34332-USD.CC", "MOODENG-USD.CC": "MOODENG33093-USD.CC", "CHILLGUY-USD.CC": "CHILLGUY-USD.CC", "FARTCOIN-USD.CC": "FARTCOIN-USD.CC", "GRASS-USD.CC": "GRASS32956-USD.CC", "GRIFFAIN-USD.CC": "GRIFFAIN-USD.CC", "MELANIA-USD.CC": "MELANIA35347-USD.CC", "KAITO-USD.CC": "KAITO-USD.CC", "SUI-USD.CC": "SUI20947-USD.CC", "BERA-USD.CC": "BERA-USD.CC", "MEW-USD.CC": "MEW30126-USD.CC", "ANIME-USD.CC": "ANIME35319-USD.CC", "NEIRO-USD.CC": "NEIRO32521-USD.CC", "DOGS-USD.CC": "DOGS32698-USD.CC", "STX-USD.CC": "STX4847-USD.CC", "S-USD.CC": "S32684-USD.CC", "COMP-USD.CC": "COMP5692-USD.CC", "TRUMP-USD.CC": "TRUMP-OFFICIAL-USD.CC", "BLAST-USD.CC": "BLAST28480-USD.CC", "TAO-USD.CC": "TAO22974-USD.CC", "SAGA-USD.CC": "SAGA30372-USD.CC", "TON-USD.CC": "TON11419-USD.CC", "BIO-USD.CC": "BIO.CC", "GMX-USD.CC": "GMX11857-USD.CC", "NTRN-USD.CC": "NTRN26680-USD.CC", "SUPER-USD.CC": "SUPER8290-USD.CC", "SCR-USD.CC": "SCR26998-USD.CC", "BANANA-USD.CC": "BANANA28066-USD.CC", "ME-USD.CC": "ME32197-USD.CC", "GMT-USD.CC": "GMT18069-USD.CC", "IO-USD.CC": "IO29835-USD.CC", "ZK-USD.CC": "ZKSYNC.CC", "ALT-USD.CC": "ALT29073-USD.CC", "POL-USD.CC": "POL28321-USD.CC", "WCT-USD.CC": "WCT33152-USD.CC", "XAI-USD.CC": "XAI28933-USD.CC", "JUP-USD.CC": "JUP29210-USD.CC", "APE-USD.CC": "APE3-USD.CC", "SPX-USD.CC": "SPX28081-USD.CC", "HYPER-USD.CC": "HYPER36281-USD.CC", "IP-USD.CC": "IP-USD.CC", "ZORA-USD.CC": "ZORA35931-USD.CC", "PEOPLE-USD.CC": "PEOPLE-USD.CC", "BABY-USD.CC": "BABY32198-USD.CC", "ARB-USD.CC": "ARB11841-USD.CC", "UNI-USD.CC": "UNI7083-USD.CC", "OMNI-USD.CC": "OMNI30315-USD.CC", "SOPH-USD.CC": "SOPHON-USD.CC", "NEIROETH-USD.CC": "NEIRO-USD.CC", "APT-USD.CC": "APT21794-USD.CC", "STRK-USD.CC": "STRK22691-USD.CC", "RESOLV-USD.CC": "RESOLV-USD.CC", "TST-USD.CC": "TST35647-USD.CC", "PUMP-USD.CC": "PUMP29601-USD.CC", "WLFI-USD.CC": "WLFI33251-USD.CC", "ASTER-USD.CC": "ASTER36341-USD.CC", "SKY-USD.CC": "SKY33038-USD.CC", } response = requests.post(url, json=payload) response.raise_for_status() data = response.json() universe = data.get("universe", []) # Extract names of perpetuals based on delisted status perpetual_ids = [ perp["name"] for perp in universe if include_delisted or not perp.get("isDelisted", False) ] # Create DataFrame with just id column df = pl.DataFrame({"id": perpetual_ids}) df = df.with_columns( (pl.col("id") + "-USD.CC") .str.replace("k", "") .replace(special_map_dict) .alias("eodhd_id") ) return df # Get the Hyperliquid universe perpetuals_df = get_hyperliquid_perpetuals(include_delisted=True) print(perpetuals_df) # Extract ticker lists eodhd_tickers = perpetuals_df["eodhd_id"].to_list() id_mapping = dict(perpetuals_df.select("eodhd_id", "id").iter_rows()) print(f"\n📊 Dataset will include {len(eodhd_tickers)} cryptocurrencies") print(f"Sample tickers: {eodhd_tickers[:10]}") print(f"Sample IDs: {list(id_mapping.values())[:10]}")

shape: (216, 2)
┌───────┬──────────────┐
│ id    ┆ eodhd_id     │
│ ---   ┆ ---          │
│ str   ┆ str          │
╞═══════╪══════════════╡
│ BTC   ┆ BTC-USD.CC   │
│ ETH   ┆ ETH-USD.CC   │
│ ATOM  ┆ ATOM-USD.CC  │
│ MATIC ┆ MATIC-USD.CC │
│ DYDX  ┆ DYDX-USD.CC  │
│ …     ┆ …            │
│ HEMI  ┆ HEMI-USD.CC  │
│ APEX  ┆ APEX-USD.CC  │
│ 2Z    ┆ 2Z-USD.CC    │
│ ZEC   ┆ ZEC-USD.CC   │
│ MON   ┆ MON-USD.CC   │
└───────┴──────────────┘

📊 Dataset will include 216 cryptocurrencies
Sample tickers: ['BTC-USD.CC', 'ETH-USD.CC', 'ATOM-USD.CC', 'MATIC-USD.CC', 'DYDX-USD.CC', 'SOL-USD.CC', 'AVAX-USD.CC', 'BNB-USD.CC', 'APE3-USD.CC', 'OP-USD.CC']
Sample IDs: ['BTC', 'ETH', 'ATOM', 'MATIC', 'DYDX', 'SOL', 'AVAX', 'BNB', 'APE', 'OP']

Step 2: Download Historical Data with numerblox¶

In [4]:

from numerblox.download import EODDownloader

# Set date range
start_date = "20200101"
end_date = datetime.now()

# Initialize EOD downloader
eod = EODDownloader(
    directory_path="data",
    key=EOD_API_KEY,
    tickers=eodhd_tickers
)
eod.end_date = end_date

print("Downloading historical data...")
print("This may take a few minutes depending on number of tickers")

# Download data
eod.download_training_data(start=start_date)

# Load the downloaded data
filename = f"data/eod_{start_date}_{end_date.strftime('%Y%m%d')}.parquet"
eod_df = pl.read_parquet(filename)
eod_df = eod_df.with_columns(pl.col("date").str.to_datetime())

# Add clean ID column using Hyperliquid naming
eod_df = eod_df.with_columns(pl.col("ticker").replace(id_mapping).alias("id"))

# Check coverage
requested_tickers = len(eodhd_tickers)
downloaded_tickers = eod_df["ticker"].n_unique()
coverage_pct = (downloaded_tickers / requested_tickers) * 100

print(f"✅ Downloaded {len(eod_df)} rows for {downloaded_tickers} tickers")
print(f"📊 Coverage: {downloaded_tickers}/{requested_tickers} tickers ({coverage_pct:.1f}%)")
print(f"📅 Date range: {eod_df['date'].min()} to {eod_df['date'].max()}")

eod_df.head()

from numerblox.download import EODDownloader # Set date range start_date = "20200101" end_date = datetime.now() # Initialize EOD downloader eod = EODDownloader( directory_path="data", key=EOD_API_KEY, tickers=eodhd_tickers ) eod.end_date = end_date print("Downloading historical data...") print("This may take a few minutes depending on number of tickers") # Download data eod.download_training_data(start=start_date) # Load the downloaded data filename = f"data/eod_{start_date}_{end_date.strftime('%Y%m%d')}.parquet" eod_df = pl.read_parquet(filename) eod_df = eod_df.with_columns(pl.col("date").str.to_datetime()) # Add clean ID column using Hyperliquid naming eod_df = eod_df.with_columns(pl.col("ticker").replace(id_mapping).alias("id")) # Check coverage requested_tickers = len(eodhd_tickers) downloaded_tickers = eod_df["ticker"].n_unique() coverage_pct = (downloaded_tickers / requested_tickers) * 100 print(f"✅ Downloaded {len(eod_df)} rows for {downloaded_tickers} tickers") print(f"📊 Coverage: {downloaded_tickers}/{requested_tickers} tickers ({coverage_pct:.1f}%)") print(f"📅 Date range: {eod_df['date'].min()} to {eod_df['date'].max()}") eod_df.head()

Downloading historical data...
This may take a few minutes depending on number of tickers

EOD price data extraction:   0%|          | 0/216 [00:00<?, ?it/s]

WARNING: Date pull failed on ticker: 'HPOS-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/HPOS-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'FRIEND-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/FRIEND-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'OX-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/OX-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'SHIA-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/SHIA-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'CANTO-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/CANTO-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'NFTI-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/NFTI-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'ALT29073-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/PANDORA-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101WARNING: Date pull failed on ticker: 'PANDORA-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/PANDORA-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101

WARNING: Date pull failed on ticker: 'AI-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/AI-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'TST35647-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/JELLY-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: 'APEX-USD.CC'. Exception: "None of ['date'] are in the columns"
WARNING: Date pull failed on ticker: 'HEMI-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/HEMI-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
WARNING: Date pull failed on ticker: '2Z-USD.CC'. Exception: 404 Client Error: Not Found for url: https://eodhistoricaldata.com/api/eod/2Z-USD.CC?period=d&to=2025-10-08+18%3A02%3A25.499812&fmt=json&api_token=YOUR_API_KEY&from=20200101
✅ Downloaded 219879 rows for 203 tickers
📊 Coverage: 203/216 tickers (94.0%)
📅 Date range: 2020-01-01 00:00:00 to 2025-10-08 00:00:00

Out[4]:

shape: (5, 9)

open	high	low	close	adjusted_close	volume	ticker	date	id
f64	f64	f64	f64	f64	i64	str	datetime[μs]	str
1.796991	1.796991	1.104477	1.331082	1.331082	2314949442	"ARB11841-USD.CC"	2023-03-23 00:00:00	"ARB"
1.325396	1.555872	1.18606	1.272492	1.272492	2537709581	"ARB11841-USD.CC"	2023-03-24 00:00:00	"ARB"
1.272393	1.307232	1.19297	1.224705	1.224705	1294894243	"ARB11841-USD.CC"	2023-03-25 00:00:00	"ARB"
1.224117	1.341853	1.208092	1.283315	1.283315	1059587959	"ARB11841-USD.CC"	2023-03-26 00:00:00	"ARB"
1.282521	1.320275	1.124495	1.162705	1.162705	1014240603	"ARB11841-USD.CC"	2023-03-27 00:00:00	"ARB"

Visualize Raw Price Data¶

Let's look at what the raw downloaded data looks like - this is the "Input" to our feature engineering pipeline.

In [5]:

import altair as alt
alt.data_transformers.enable('vegafusion')

# Select top tickers by data coverage for visualization
top_tickers = (
    eod_df.group_by("id")
    .agg(pl.col("date").count().alias("count"))
    .sort("count", descending=True)
    .head(20)["id"]
    .to_list()
)

# Create visualization of raw prices
viz_df = eod_df.filter(pl.col("id").is_in(top_tickers)).to_pandas()

chart = (
    alt.Chart(viz_df)
    .mark_line(opacity=0.6)
    .encode(
        x=alt.X("date:T", title="Date"),
        y=alt.Y("close:Q", title="Close Price (USD)", scale=alt.Scale(type="log")),
        color=alt.Color("id:N", title="Ticker", legend=alt.Legend(columns=2)),
        tooltip=["id:N", "date:T", "close:Q"]
    )
    .properties(
        width=700,
        height=300,
        title="Input: Raw Stock Prices Over Time (Log Scale)"
    )
)

chart

import altair as alt alt.data_transformers.enable('vegafusion') # Select top tickers by data coverage for visualization top_tickers = ( eod_df.group_by("id") .agg(pl.col("date").count().alias("count")) .sort("count", descending=True) .head(20)["id"] .to_list() ) # Create visualization of raw prices viz_df = eod_df.filter(pl.col("id").is_in(top_tickers)).to_pandas() chart = ( alt.Chart(viz_df) .mark_line(opacity=0.6) .encode( x=alt.X("date:T", title="Date"), y=alt.Y("close:Q", title="Close Price (USD)", scale=alt.Scale(type="log")), color=alt.Color("id:N", title="Ticker", legend=alt.Legend(columns=2)), tooltip=["id:N", "date:T", "close:Q"] ) .properties( width=700, height=300, title="Input: Raw Stock Prices Over Time (Log Scale)" ) ) chart

Out[5]: