cpz-ai 1.1.3

CPZ — Python SDK for Strategy Lab, Risk, Backtesting, and Execution (multi-broker)

CPZ AI — Python SDK

Install

pip install cpz-ai

60-second Quickstart (Sync)

Trading

import cpz
from cpz.execution.models import OrderSubmitRequest
from cpz.execution.enums import OrderSide, OrderType, TimeInForce

client = cpz.clients.sync.CPZClient()
client.execution.use_broker("alpaca", env="paper")

order = client.execution.submit_order(OrderSubmitRequest(
    symbol="AAPL",
    side=OrderSide.BUY,
    qty=10,
    type=OrderType.MARKET,
    time_in_force=TimeInForce.DAY,
))
print(order.id, order.status)

Execution Architecture

CPZClient.execution  -->  BrokerRouter  -->  AlpacaAdapter
                              |               ^
                              +---- future brokers (IBKR, Tradier, ...)

Configuration (.env)

Key	Description	Example	Required
CPZ_ENV	SDK environment	dev	No
CPZ_LOG_LEVEL	Log level	INFO	No
CPZ_REQUEST_TIMEOUT_SECONDS	Default request timeout	30	No
CPZ_AI_URL	CPZ AI API Endpoint	https://api.cpz-lab.com/cpz-ai/v1	No
CPZ_AI_API_KEY	CPZ AI API Key	sb_publishable_...	Yes (for platform access)
CPZ_AI_SECRET_KEY	CPZ AI Secret Key	sb_secret_...	Yes (for platform access)
CPZ_AI_USER_ID	CPZ AI User ID	user123	Yes (for user-specific access)
CPZ_AI_IS_ADMIN	CPZ AI Admin Access	false	No (set to true for admin)

| ALPACA_ENV | Alpaca environment | paper | Yes (if using Alpaca) | | ALPACA_API_KEY_ID | Alpaca API key | AK... | Yes (if using Alpaca) | | ALPACA_API_SECRET_KEY | Alpaca API secret | ... | Yes (if using Alpaca) |

Usage

Selecting a broker

client.execution.use_broker("alpaca", env="paper")

Submit / cancel / replace order (sync)

from cpz.execution.models import OrderSubmitRequest, OrderReplaceRequest
from cpz.execution.enums import OrderSide, OrderType, TimeInForce

req = OrderSubmitRequest(symbol="AAPL", side=OrderSide.BUY, qty=1,
                         type=OrderType.MARKET, time_in_force=TimeInForce.DAY)
order = client.execution.submit_order(req)
client.execution.cancel_order(order.id)
client.execution.replace_order(order.id, OrderReplaceRequest(qty=2))

Async + Streaming

import asyncio
from cpz.clients.async_ import AsyncCPZClient

async def main():
    client = AsyncCPZClient()
    await client.execution.use_broker("alpaca", env="paper")
    async for q in client.execution.stream_quotes(["AAPL", "MSFT"]):
        print(q.symbol, q.bid, q.ask)
        break

asyncio.run(main())

Get account / positions

acct = client.execution.get_account()
positions = client.execution.get_positions()

CPZ AI - Strategies & Files

Access your CPZ AI platform data including strategies and files:

from cpz.common.cpz_ai import CPZAIClient

# Connect to CPZ AI
client = CPZAIClient.from_env()

# Get your strategies (user-specific by default)
strategies = client.get_strategies()
print(f"Your strategies: {[s.get('title', 'Unknown') for s in strategies]}")

# Create a new strategy (automatically assigned to your user_id)
new_strategy = client.create_strategy({
    "title": "My Trading Bot",
    "description": "Automated trading strategy",
    "strategy_type": "momentum",
    "status": "active"
})

User-Specific Access Control

The CPZ AI client automatically handles user isolation:

• Regular Users: Only see and manage their own strategies and files
• Admins: Can access all strategies and files across all users

# Regular user client (user-specific access)
user_client = CPZAIClient(
    url="https://api.cpz-lab.com",
    api_key="your_api_key",
    secret_key="your_secret_key",
    user_id="user123",
    is_admin=False
)

# Admin client (full access)
admin_client = CPZAIClient(
    url="https://api.cpz-lab.com",
    api_key="admin_api_key",
    secret_key="admin_secret_key",
    user_id=None,
    is_admin=True
)

# Environment variables for automatic configuration
# CPZ_AI_USER_ID=user123
# CPZ_AI_IS_ADMIN=false

File Operations & DataFrames

Upload, download, and manage files with pandas DataFrame support:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'symbol': ['AAPL', 'GOOGL', 'MSFT'],
    'price': [150.25, 2750.80, 310.45],
    'volume': [1000000, 500000, 800000]
})

# Upload DataFrame as CSV
client.upload_dataframe("data-bucket", "stocks.csv", df, format="csv")

# Upload DataFrame as JSON
client.upload_dataframe("data-bucket", "stocks.json", df, format="json")

# Upload DataFrame as Parquet
client.upload_dataframe("data-bucket", "stocks.parquet", df, format="parquet")

# Download CSV and load to DataFrame
downloaded_df = client.download_csv_to_dataframe("data-bucket", "stocks.csv")

# Download JSON and load to DataFrame
downloaded_df = client.download_json_to_dataframe("data-bucket", "stocks.json")

# Download Parquet and load to DataFrame
downloaded_df = client.download_parquet_to_dataframe("data-bucket", "stocks.parquet")

# List files in a bucket
files = client.list_files_in_bucket("data-bucket", prefix="stocks")

# Delete files
client.delete_file("data-bucket", "stocks.csv")

Load User Strategies to DataFrame

from cpz.common.cpz_ai import CPZAIClient
import pandas as pd

# Load strategies for a specific user
client = CPZAIClient(
    url="https://api.cpz-lab.com",
    api_key="your_api_key",
    secret_key="your_secret_key",
    user_id="user-uuid-here",
    is_admin=False
)

# Get user's strategies as DataFrame
strategies_df = pd.DataFrame(client.get_strategies())
print(f"Found {len(strategies_df)} strategies")
print(strategies_df.head())

Note: The CPZ AI client connects to your API endpoint at api.cpz-lab.com. Users only need to provide their CPZ AI API keys.

CLI

cpz-ai broker list
cpz-ai broker use alpaca --env paper
cpz-ai order submit --symbol AAPL --side buy --qty 10 --type market --tif day
cpz-ai order get --id <id>
cpz-ai positions
cpz-ai stream quotes --symbols AAPL,MSFT

Error handling

Catch cpz.common.errors.CPZBrokerError. Broker errors are mapped to CPZ errors.

Logging & Redaction

Structured JSON logging via structlog, with redaction of Authorization, ALPACA_API_SECRET_KEY, and similar. Configure level via CPZ_LOG_LEVEL.

Testing & Quality

• make test (coverage goal ≥ 85%)
• mypy --strict

Python Compatibility

This package is tested and compatible with:

• Python 3.9 ✅
• Python 3.10 ✅
• Python 3.11 ✅
• Python 3.12 ✅

Compatibility Features

• Uses from __future__ import annotations for forward-compatible type hints
• Compatible type annotation syntax across all supported versions
• No version-specific syntax that would break older Python versions
• Continuous integration testing on all supported Python versions

Contributing

Style: ruff/black/isort, pre-commit, branch naming. See CONTRIBUTING.md.

Versioning & Release

Bump version in pyproject.toml, build, and publish to PyPI.

Roadmap

Next brokers: IBKR, Tradier, …

Security

See SECURITY.md. No LICENSE file is included intentionally.