liberal-alpha 0.1.21

Liberal Alpha Python SDK for interacting with gRPC-based backend

Liberal Alpha Python SDK (Historical HTTP APIs)

This SDK provides historical upload, historical download, and real-time subscription APIs (HTTP + WebSocket).

Install

pip install liberal_alpha
# Optional: override default API base (default is https://api.librealpha.com)
export LIBALPHA_API_BASE="https://api.librealpha.com"

# Upload auth (X-API-Key)
export LIBALPHA_API_KEY="YOUR_API_KEY"

# Download auth (used to obtain JWT via /api/users/auth)
export LIBALPHA_PRIVATE_KEY="0xYOUR_PRIVATE_KEY"

Optional upload tuning:

export LIBALPHA_UPLOAD_BATCH_ID="12345"      # optional batch id (int)
export LIBALPHA_UPLOAD_CHUNK_SIZE="1048576"  # default 1MB
export LIBALPHA_UPLOAD_RESUME="1"            # 1=enable resume, 0=disable

Initialize Client

from liberal_alpha.client import LiberalAlphaClient

# api_base defaults to https://api.librealpha.com
# can be overridden by env LIBALPHA_API_BASE or by passing api_base=...
client = LiberalAlphaClient(
    api_key="YOUR_API_KEY",            # optional if using env LIBALPHA_API_KEY
    private_key="0xYOUR_PRIVATE_KEY",  # optional if using env LIBALPHA_PRIVATE_KEY
)

Historical Upload API (Python)

def upload_data(record_id: int, df: pandas.DataFrame) -> bool:
    pass

DataFrame Format

Your DataFrame must contain these columns:

-record_id (int)

-symbol (str)

-data (dict) — e.g. {"open": 50000.0, "close": 51000.0}

-timestamp (int) — milliseconds since epoch
(seconds are also accepted and will be auto-converted to milliseconds)

Example

import pandas as pd
from liberal_alpha.client import LiberalAlphaClient

client = LiberalAlphaClient(
    api_key="YOUR_API_KEY",
    # api_base defaults to https://api.librealpha.com
)

sample_data = []
for i in range(1000):
    sample_data.append({
        "record_id": 4,
        "symbol": "BNfBTC",
        "data": {"open": 50000.0 + i, "close": 51000.0 + i},
        "timestamp": 1733299200000 + i * 1000,
    })

df = pd.DataFrame(sample_data)

# Optional batch_id: set via env because public API has only 2 args
# export LIBALPHA_UPLOAD_BATCH_ID=12345
ok = client.upload_data(record_id=4, df=df)
print("Upload ok:", ok)

Notes:

-Upload uses X-API-Key authentication.

-Upload is chunked and supports resume if enabled (default enabled).

## Historical Upload API (Python) - protobuf stream (/api/entries/history/upload)

This is a **historical backfill** uploader that sends protobuf `DataEntry` messages in a length-prefixed stream:

- Request body format: `[4-byte big-endian length][DataEntry][4-byte length][DataEntry]...`
- Backend groups entries **by minute**; this SDK implementation will also **bucket by minute** and send **1 minute per request**.
- Auth: `X-API-Key` (required).
- Data is carried per-symbol in `symbol_values`:
  - For non-encrypted records: `symbol_values[i].values.items = [float, ...]`
  - For encrypted records: `symbol_values[i].encrypted_payload = <bytes>` (AES-256-GCM)

Public function:

```python
def upload_data(
    record_id: int,
    df: pd.DataFrame,
    api_key: str | None = None,
    private_key: str | None = None,
) -> None:
    pass

DataFrame format:

• Required columns:
  • symbol (str): symbol_id string (e.g. "1", "3"). If you pass non-numeric symbol names, the SDK will best-effort map them to symbol_id via /api/records/user-records (target_symbols).
  • timestamp (datetime64 or unix timestamp; will be normalized to UTC and uploaded in microseconds)
• Feature columns (choose ONE approach):
  • Provide a features column containing a list of floats (or a string like "[1.0, nan, 2.0]"), OR
  • Provide user-defined float columns matching the record schema; the SDK will fetch feature order from /api/records/{id}.

Upload packing behavior:

• 1 request = 1 minute (backend requirement)
• Within that minute, the SDK aggregates rows by exact timestamp into 1 DataEntry.
• Each aggregated DataEntry carries multiple symbols in symbol_values: [{symbol_id, values.items}, {symbol_id, values.items}, ...]

Example (upload one minute from a previously-downloaded CSV):

import pandas as pd
from liberal_alpha import upload_data

df = pd.read_csv("history_1h_record12_BTC.csv")

# pick the first minute only (backend requires 1 minute per upload)
first_minute = int(df["timestamp"].iloc[0] // 60_000_000)
df1 = df[df["timestamp"].apply(lambda x: int(x // 60_000_000) == first_minute)].copy()
df1 = df1[["symbol", "timestamp", "features"]]

upload_data(
    record_id=12,
    df=df1,
    api_key="YOUR_API_KEY",
    private_key="YOUR_RECORD_PRIVATE_KEY",  # only needed if record is encrypted
)

Historical Download API (Python)

def download_data( record_id: int, symbols: list[str], dates: list[int], tz_info: datetime.tzinfo | str = "Asia/Singapore" ) -> pandas.DataFrame: pass

Historical File Download API (Python) - protobuf (length-prefixed)

def download_history_data(
    record_id: int,
    symbol: str,
    start: datetime | str | int,
    end: datetime | str | int,
    private_key: str | None = None,
) -> pandas.DataFrame:
    pass

Parameters:

• record_id: the record id to download
• symbol: symbol_id string (e.g. "1", "3")
• start/end: datetime | ISO string | unix timestamp (sec/ms/us)
• private_key: optional, record private key for decrypting symbol_values[].encrypted_payload

Notes:

• Backend /api/entries/download-links enforces end-start <= 24h (microseconds). The SDK automatically splits long ranges into multiple 24h windows and merges results locally.
• Returned timestamps are in microseconds.
• DataEntry uses repeated SymbolValues symbol_values; each row corresponds to one symbol_id.
• When data is encrypted (symbol_values[].encrypted_payload present), pass private_key to decrypt.

Example

from liberal_alpha.client import LiberalAlphaClient
import datetime as dt

client = LiberalAlphaClient(
    private_key="0xYOUR_PRIVATE_KEY",  # or api_key="YOUR_API_KEY"
    api_base="https://api.librealpha.com",
)

start = dt.datetime(2026, 1, 1, 0, 0, 0, tzinfo=dt.timezone.utc)
end   = dt.datetime(2026, 1, 3, 0, 0, 0, tzinfo=dt.timezone.utc)  # >24h is OK (SDK will split)

df = client.download_history_data(
    record_id=2,
    symbol="1",
    start=start,
    end=end,
    private_key="YOUR_RECORD_PRIVATE_KEY",  # for decrypting encrypted data
)

print(df.head())
print("rows:", len(df))

Real-time Subscribe API (Python) - WebSocket ws/data

from typing import List, Callable

def subscribe_data(
    record_id: int,
    *,
    symbols: List[str],
    on_data_fn: Callable[[str, pd.DataFrame], None],
    private_key: str | None = None,
) -> None:
    """Subscribes to live data for a record into a DataFrame (schema matches publish).

    Args:
        record_id: Backend record identifier.
        symbols: Symbols to filter by (required).
        on_data_fn: Callback function invoked upon receiving new data. The
            function takes two arguments: the symbol (str) and a DataFrame
            containing the new data for that symbol.
        private_key: Optional, record private key for decrypting encrypted_payload.
    """

Parameters:

• record_id: must be in user's subscriptions (from /api/subscriptions)
• symbols: symbol_id strings to filter by (required). Only rows with symbol_id in this list are passed to on_data_fn.
• on_data_fn: callback invoked upon receiving new data. Takes (symbol: str, df: DataFrame).
• private_key: optional, record private key for decrypting symbol_values[].encrypted_payload; required if data is encrypted.

Notes:

• Connects to ws/data, sends { wallet_address, record_id } on open.
• If data is encrypted (symbol_values[].encrypted_payload has value), pass private_key or set LIBALPHA_RECORD_PRIVATE_KEY.
• DataFrame schema matches download_history_data (entry_id, record_id, symbol, features, timestamps).
• Blocking; run in a daemon thread for long-lived subscription.

Example

import threading
import pandas as pd
from liberal_alpha import LiberalAlphaClient

client = LiberalAlphaClient(api_key="YOUR_API_KEY", api_base="https://api.librealpha.com")

def on_data_fn(symbol: str, df: pd.DataFrame):
    print(f"[{symbol}] entry_id={df['entry_id'].iloc[0]} features_len={len(df['features'].iloc[0])}")

t = threading.Thread(
    target=client.subscribe_data,
    kwargs={"record_id": 2, "symbols": ["1", "3"], "on_data_fn": on_data_fn, "private_key": "YOUR_RECORD_KEY"},
    daemon=True,
)
t.start()

Historical Download API (download_data) Parameters

-record_id: the record id to download

-symbols: list of symbols, e.g. ["BTCUSDT", "ETHUSDT"]

If you pass [], the SDK will automatically fetch all symbols (if supported by backend).

-dates: list of local dates in YYYYMMDD format, e.g. [20251214, 20251215]

If you pass [], no date filter is applied.

-tz_info: controls how local_date is computed

"Asia/Singapore" (IANA tz string)

numeric offsets like 8, -4, or strings like "+8", "-4"

8 means UTC+8 (SGT/HKT)

-4 means UTC-4 (NYC during DST)

Example

from liberal_alpha.client import LiberalAlphaClient

client = LiberalAlphaClient( private_key="0xYOUR_PRIVATE_KEY", # api_base defaults to https://api.librealpha.com )

df = client.download_data( record_id=24, symbols=[], # empty => auto fetch all symbols dates=[], # empty => no date filter tz_info="Asia/Singapore" # or tz_info=8 )

print(df.head()) print("rows:", len(df))