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Fix inconsistent API info on the "Crypto Overview" Page #38

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Fix inconsistent API info on the "Crypto Overview" Page #38

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3205d0b
small updates to filenames, column names, etc to match docs at crypto…
graceful-coder Apr 15, 2026
b01faeb
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graceful-coder Apr 15, 2026
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16 changes: 9 additions & 7 deletions numerai-crypto/crypto-overview.md
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Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,12 @@ You can get started with the the Numerai Data API:
import pandas as pd
from numerapi import CryptoAPI
napi = CryptoAPI()
napi.download_dataset("crypto/v2.0/train_targets.parquet")
training_data = pd.read_parquet("crypto/v2.0/train_targets.parquet")
napi.download_dataset("crypto/v2.0/train.parquet")
training_data = pd.read_parquet("crypto/v2.0/train.parquet")
```

> **Note:** If you are migrating from v1.0 to v2.0, make sure to upgrade numerapi to the latest version: `pip install --upgrade numerapi`

**You will need to acquire distinct and unique crypto market data to generate a high quality signal.** There are a number of other data providers you can also use to get started such as [Messari](https://messari.io/api) and [CoinMarketCap](https://coinmarketcap.com/api)<mark style="background-color:yellow;">.</mark>

If you don't have crypto market data, but are still interested in Numerai, try the [Numerai Tournament](https://numer.ai/) instead to predict the stock market using our data.
Expand All @@ -51,9 +53,9 @@ def generate_training_features(df: pd.DataFrame) -> List[str]:
return ['fake_feature_1']


# Historical targets file contains ["symbol", "date", "target"] columns
napi.download_dataset("crypto/v2.0/train_targets.parquet")
train_df = pd.read_parquet("crypto/v2.0/train_targets.parquet")
# Historical data file contains date, features, and targets columns indexed by symbol
napi.download_dataset("crypto/v2.0/train.parquet")
train_df = pd.read_parquet("crypto/v2.0/train.parquet")

# Add training features for each (symbol, date)
feature_cols = generate_training_features(train_df)
Expand Down Expand Up @@ -92,8 +94,8 @@ def generate_features(df: pd.DataFrame):
napi = NumerAPI("[your api public id]", "[your api secret key]")

# Download latest live universe
napi.download_dataset("crypto/v2.0/live_universe.parquet")
live = pd.read_parquet("crypto/v2.0/live_universe.parquet")
napi.download_dataset("crypto/v2.0/live.parquet")
live = pd.read_parquet("crypto/v2.0/live.parquet")

# Generate features for the live universe
generate_features(live)
Expand Down