signalnet-sdk 0.1.5

Python SDK for the SignalNet quant signal aggregation platform

SignalNet Python SDK

Official Python client for the SignalNet quant signal aggregation platform.

Installation

pip install signalnet-sdk

Quick Start

from signalnet import SignalNet
import pandas as pd

# Initialize the client
sn = SignalNet(api_key="sn_your_api_key")

# Get the current round
current = sn.get_current_round()
print(f"Round {current.id} — status: {current.status}")

# Download features and build predictions
features = sn.download_features(round_id=current.id)
# ... your model here ...

# Submit predictions
predictions = pd.DataFrame({
    "stock_id": features["stock_id"],
    "signal": your_model.predict(features),
})
result = sn.submit(round_id=current.id, predictions=predictions, stake=500)
print(f"Submission {result.id}: {result.status}")

Configuration

Parameter	Env Variable	Default
api_key	SIGNALNET_API_KEY	—
base_url	SIGNALNET_API_URL	http://localhost:4000/api

# Explicit config
sn = SignalNet(api_key="sn_...", base_url="https://api.signalnet.xyz")

# Or via environment variables
# export SIGNALNET_API_KEY=sn_...
# export SIGNALNET_API_URL=https://api.signalnet.xyz
sn = SignalNet()

Local Data Cache

Downloaded data is automatically cached as Parquet files in ~/.signalnet/data/. Parquet is 5–10× smaller than CSV, preserves dtypes, and loads instantly on subsequent calls.

~/.signalnet/
└── data/
    ├── rounds/
    │   ├── 1/features.parquet
    │   └── 2/features.parquet
    └── training/
        ├── features.parquet
        └── targets.parquet

# First call downloads from API and caches as parquet
features = sn.download_features(round_id=1)
# [SignalNet] Downloading features for round 1... done (12.4 MB → 2.1 MB parquet)

# Second call loads from cache instantly
features = sn.download_features(round_id=1)
# [SignalNet] Using cached features for round 1 (2.1 MB)

# Force re-download
features = sn.download_features(round_id=1, force=True)

# Cache management
print(sn.cache_info())
# {'cache_dir': '/home/user/.signalnet/data', 'total_size': 2148576,
#  'total_size_human': '2.0 MB', 'round_features': [1, 2], 'training_files': ['features']}

sn.clear_cache()

Configure cache location:

sn = SignalNet(api_key="sn_...", cache_dir="/tmp/sn-cache")
# or: export SIGNALNET_CACHE_DIR=/tmp/sn-cache

API Reference

Rounds

get_current_round() → Round

Get the current active round.

get_rounds(limit=None, offset=None, status=None) → list[Round]

List tournament rounds with optional filters.

get_round(round_id) → Round

Get details for a specific round.

get_round_results(round_id) → list[RoundResult]

Get the leaderboard/results for a resolved round.

Tournaments

get_tournaments() → list[Tournament]

List all tournaments.

get_tournament(slug_or_id) → Tournament

Get a tournament by slug (e.g. "genesis") or ID.

Data Downloads

download_features(round_id, *, force=False) → pd.DataFrame

Download feature data for a round. Returns a pandas DataFrame. Cached as Parquet locally.

download_training_data(data_type, *, force=False) → pd.DataFrame

Download training data. data_type must be "features" or "targets". Cached as Parquet locally.

Cache Management

cache_info() → dict

Return cache stats: directory, total size, cached round IDs, and training files.

clear_cache()

Delete all locally cached data files.

Submissions

submit(round_id, predictions, stake=0) → SubmissionResponse

Submit predictions for a round.

• predictions: a DataFrame with columns stock_id and signal
• stake: amount to stake (default 0)

get_my_submission(round_id) → dict

Get your submission for a specific round.

User

get_me() → User

Get the authenticated user's profile.

get_my_scores() → list[ScoreEntry]

Get your score history across rounds.

create_api_key(name=None) → ApiKey

Create a new API key.

list_api_keys() → list[ApiKey]

List all your API keys.

Error Handling

All errors inherit from SignalNetError:

from signalnet import SignalNet, SignalNetError
from signalnet.exceptions import AuthenticationError, NotFoundError, RateLimitError

sn = SignalNet(api_key="sn_...")

try:
    r = sn.get_round(999)
except NotFoundError:
    print("Round not found")
except AuthenticationError:
    print("Bad API key")
except RateLimitError:
    print("Slow down!")
except SignalNetError as e:
    print(f"API error {e.status_code}: {e}")

Exception	HTTP Status
AuthenticationError	401, 403
NotFoundError	404
ValidationError	400, 422
RateLimitError	429
ServerError	5xx

Models

All response models are Pydantic v2 BaseModel subclasses with extra="allow", so new API fields won't break existing code.

• Round — tournament round
• Tournament — tournament info
• RoundResult — leaderboard entry
• SubmissionResponse — submission confirmation
• User — user profile
• ScoreEntry — score history entry
• ApiKey — API key info

License

MIT