feat: Add Docker deployment module (v0.4.0)#10
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hudsonaikins merged 3 commits intorelease/v0.4.0from Oct 29, 2025
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Add comprehensive Docker-based deployment infrastructure for trading bots
with database persistence, monitoring, and multi-environment support.
## New Features
### Core Deployment Module (`neural/deployment/`)
- Abstract `DeploymentProvider` base class for extensibility
- `DeploymentContext` async context manager for automatic cleanup
- Full Pydantic configuration models with validation
- Custom exception hierarchy for deployment errors
### Docker Provider (`neural/deployment/docker/`)
- `DockerDeploymentProvider` - Full Docker SDK integration
- Jinja2-based Dockerfile templates (single & multi-stage)
- Docker Compose file generation
- Container lifecycle management (deploy/stop/status/logs)
- Resource limits and monitoring
- Async operations throughout
### Database Support (`neural/deployment/database/`)
- SQLAlchemy models for trades, positions, performance, deployments
- PostgreSQL persistence layer
- Database schema management utilities
### Configuration & Examples
- 6 Pydantic models: DeploymentConfig, DockerConfig, DatabaseConfig, etc.
- Working example: `examples/08_docker_deployment.py`
- Comprehensive docstrings and type hints
## Package Updates
- Add `deployment` extra to `pyproject.toml`
- New dependencies: docker>=6.1.0, sqlalchemy>=2.0.0, psycopg2-binary>=2.9.0
- Export deployment module in `neural/__init__.py`
- Experimental warning follows SDK beta pattern
## NBA Series Mapping Fix
- Update NBA series ticker: "KXNBA" → "KXNBAGAME"
- Add "NBA_CHAMPIONSHIP" → "KXNBA" for championship markets
- Add "WNBA" → "KXWNBAGAME" support
## Usage
```python
from neural.deployment import DockerDeploymentProvider, DeploymentConfig, deploy
config = DeploymentConfig(bot_name="MyBot", strategy_type="NFL")
provider = DockerDeploymentProvider()
async with deploy(provider, config) as deployment:
status = await provider.status(deployment.deployment_id)
```
## Installation
```bash
pip install neural-sdk[deployment]
```
## Files Added (14)
- neural/deployment/__init__.py
- neural/deployment/base.py
- neural/deployment/config.py
- neural/deployment/exceptions.py
- neural/deployment/docker/__init__.py
- neural/deployment/docker/provider.py
- neural/deployment/docker/compose.py
- neural/deployment/docker/templates.py
- neural/deployment/database/__init__.py
- neural/deployment/database/schema.py
- neural/deployment/monitoring/__init__.py
- examples/08_docker_deployment.py
## Modified (3)
- neural/__init__.py - Add deployment export
- pyproject.toml - Add dependencies and [deployment] extra
- neural/data_collection/kalshi.py - Fix NBA series mapping
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
![greptile-apps[bot]](https://avatars.githubusercontent.com/in/867647?s=60&v=4){kind=small}
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Greptile Overview
Greptile Summary
This PR adds comprehensive Docker deployment infrastructure to the Neural SDK (v0.4.0) enabling containerized trading bot deployments with database persistence and monitoring capabilities.
Key Changes
- Deployment Module Architecture: Implements extensible provider pattern with abstract
DeploymentProviderbase class andDockerDeploymentProviderimplementation - Configuration System: 6 Pydantic models (
DeploymentConfig,DockerConfig,DatabaseConfig,MonitoringConfig,DeploymentResult,DeploymentStatus) with validation - Docker Integration: Jinja2-based Dockerfile templates (single & multi-stage), docker-compose.yml generation, and full Docker SDK integration
- Database Schema: SQLAlchemy models for trades, positions, performance metrics, and deployments (PostgreSQL-ready)
- Context Manager:
async with deploy()pattern for automatic resource cleanup - Kalshi Fix: NBA series mapping corrected from
"KXNBA"to"KXNBAGAME"for individual games, added"NBA_CHAMPIONSHIP"and"WNBA"support
Critical Issues Found
Event Loop Blocking (6 instances in provider.py)
The DockerDeploymentProvider uses the synchronous Docker SDK in async methods without wrapping blocking calls in asyncio.run_in_executor(). This will freeze the event loop during:
- Image builds (lines 310-312) - can take several minutes
- Container operations (lines 113-114, 164-166, 197-205, 240-241, 259-260)
Deprecated API Usage (4 instances in schema.py)
All database models use datetime.utcnow() which is deprecated in Python 3.12+ and should be replaced with datetime.now(timezone.utc)
Path Assumption
Hardcoded /secrets mount path (line 347) will cause container creation to fail if directory doesn't exist
Architecture Quality
The overall architecture is well-designed:
- Clean separation of concerns with provider pattern
- Type-safe configuration using Pydantic
- Proper exception hierarchy
- Good security practices (non-root user in containers)
- Clear documentation and examples
Confidence Score: 2/5
- This PR has critical async/await implementation issues that will cause event loop blocking in production
- Score reflects 6 critical event loop blocking issues in the Docker provider that will severely impact performance and responsiveness during image builds and container operations. While the architecture is well-designed and the feature set is comprehensive, the synchronous Docker SDK calls in async methods are a fundamental implementation flaw that must be fixed before deployment. The deprecated datetime.utcnow() usage is less critical but still needs addressing for Python 3.12+ compatibility.
neural/deployment/docker/provider.pyrequires immediate attention to fix all blocking I/O operations in async methods.neural/deployment/database/schema.pyneeds datetime API updates for Python 3.12+ compatibility.
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| neural/deployment/docker/provider.py | 2/5 | Docker provider with multiple blocking I/O calls in async functions that will cause event loop blocking during container operations and image builds |
| neural/deployment/database/schema.py | 3/5 | SQLAlchemy schema using deprecated datetime.utcnow() calls that need updating for Python 3.12+ compatibility |
| neural/deployment/base.py | 5/5 | Well-designed abstract base class with clean provider pattern and context manager implementation |
| neural/deployment/config.py | 4/5 | Comprehensive Pydantic configuration models with proper validation, minor timestamp consistency issue |
| neural/data_collection/kalshi.py | 5/5 | Correct NBA series mapping fix from KXNBA to KXNBAGAME for individual games, plus WNBA support added |
Sequence Diagram
sequenceDiagram
participant User
participant DeploymentContext
participant DockerProvider
participant DockerSDK
participant Container
participant FileSystem
User->>DeploymentContext: deploy(provider, config)
activate DeploymentContext
DeploymentContext->>DockerProvider: deploy(config)
activate DockerProvider
DockerProvider->>DockerProvider: generate deployment_id
DockerProvider->>FileSystem: render_dockerfile(config)
FileSystem-->>DockerProvider: dockerfile content
DockerProvider->>FileSystem: write Dockerfile to build/
DockerProvider->>DockerSDK: images.build(path, dockerfile, tag)
activate DockerSDK
Note over DockerSDK: BLOCKING: Image build<br/>can take several minutes
DockerSDK-->>DockerProvider: image built
deactivate DockerSDK
DockerProvider->>DockerProvider: prepare_env_vars(config)
DockerProvider->>DockerProvider: create_container_config()
DockerProvider->>DockerSDK: containers.create(config)
activate DockerSDK
DockerSDK-->>DockerProvider: container object
deactivate DockerSDK
DockerProvider->>Container: start()
activate Container
Container-->>DockerProvider: started
DockerProvider->>DockerProvider: store in active_deployments
DockerProvider-->>DeploymentContext: DeploymentResult
deactivate DockerProvider
DeploymentContext-->>User: deployment result
Note over User,Container: Bot is running
User->>DockerProvider: status(deployment_id)
activate DockerProvider
DockerProvider->>Container: reload() + stats()
Container-->>DockerProvider: status & metrics
DockerProvider-->>User: DeploymentStatus
deactivate DockerProvider
User->>DockerProvider: logs(deployment_id)
activate DockerProvider
DockerProvider->>Container: logs(tail=100)
Container-->>DockerProvider: log lines
DockerProvider-->>User: list of logs
deactivate DockerProvider
Note over User,Container: Context manager exits
DeploymentContext->>DockerProvider: stop(deployment_id)
activate DockerProvider
DockerProvider->>Container: stop(timeout=30)
Container-->>DockerProvider: stopped
DockerProvider->>Container: remove()
Container-->>DockerProvider: removed
deactivate Container
DockerProvider->>DockerProvider: delete from active_deployments
DockerProvider-->>DeploymentContext: success
deactivate DockerProvider
deactivate DeploymentContext
15 files reviewed, 13 comments
neural/deployment/docker/provider.py
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| image, build_logs = self.docker_client.images.build( | ||
| path=str(self.project_root), dockerfile=str(dockerfile_path), tag=image_tag, rm=True | ||
| ) |
There was a problem hiding this comment.
logic: self.docker_client.images.build() is a blocking synchronous call in an async function - will block the event loop during image builds, which can take several minutes
Suggested change
| image, build_logs = self.docker_client.images.build( | |
| path=str(self.project_root), dockerfile=str(dockerfile_path), tag=image_tag, rm=True | |
| ) | |
| # Build image (run in executor to avoid blocking) | |
| logger.info(f"Building Docker image: {image_tag}") | |
| loop = asyncio.get_event_loop() | |
| image, build_logs = await loop.run_in_executor( | |
| None, | |
| lambda: self.docker_client.images.build( | |
| path=str(self.project_root), | |
| dockerfile=str(dockerfile_path), | |
| tag=image_tag, | |
| rm=True | |
| ) | |
| ) |
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 310:312
Comment:
**logic:** `self.docker_client.images.build()` is a blocking synchronous call in an async function - will block the event loop during image builds, which can take several minutes
```suggestion
# Build image (run in executor to avoid blocking)
logger.info(f"Building Docker image: {image_tag}")
loop = asyncio.get_event_loop()
image, build_logs = await loop.run_in_executor(
None,
lambda: self.docker_client.images.build(
path=str(self.project_root),
dockerfile=str(dockerfile_path),
tag=image_tag,
rm=True
)
)
```
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/docker/provider.py
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| container = self.docker_client.containers.create(**container_config) | ||
| container.start() |
There was a problem hiding this comment.
logic: self.docker_client.containers.create() and container.start() are blocking synchronous calls in async function - will block event loop
Suggested change
| container = self.docker_client.containers.create(**container_config) | |
| container.start() | |
| # Create and start container (run in executor) | |
| loop = asyncio.get_event_loop() | |
| container = await loop.run_in_executor( | |
| None, | |
| lambda: self.docker_client.containers.create(**container_config) | |
| ) | |
| await loop.run_in_executor(None, container.start) |
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 113:114
Comment:
**logic:** `self.docker_client.containers.create()` and `container.start()` are blocking synchronous calls in async function - will block event loop
```suggestion
# Create and start container (run in executor)
loop = asyncio.get_event_loop()
container = await loop.run_in_executor(
None,
lambda: self.docker_client.containers.create(**container_config)
)
await loop.run_in_executor(None, container.start)
```
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/docker/provider.py
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| container = self.docker_client.containers.get(deployment["container_id"]) | ||
| container.stop(timeout=30) | ||
| container.remove() |
There was a problem hiding this comment.
logic: blocking synchronous Docker calls in async function - container.stop() and container.remove() will block event loop
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 164:166
Comment:
**logic:** blocking synchronous Docker calls in async function - `container.stop()` and `container.remove()` will block event loop
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/docker/provider.py
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| container = self.docker_client.containers.get(deployment["container_id"]) | ||
| container.reload() # Refresh container info | ||
|
|
||
| # Calculate uptime | ||
| created_at = deployment["created_at"] | ||
| uptime = (datetime.now() - created_at).total_seconds() | ||
|
|
||
| # Get container stats (async) | ||
| stats = container.stats(stream=False) |
There was a problem hiding this comment.
logic: blocking synchronous Docker calls - container.reload() and container.stats() will block event loop in async function
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 197:205
Comment:
**logic:** blocking synchronous Docker calls - `container.reload()` and `container.stats()` will block event loop in async function
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/docker/provider.py
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| container = self.docker_client.containers.get(deployment["container_id"]) | ||
| logs = container.logs(tail=tail, timestamps=True).decode("utf-8") |
There was a problem hiding this comment.
logic: blocking synchronous Docker call - container.logs() will block event loop in async function
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 240:241
Comment:
**logic:** blocking synchronous Docker call - `container.logs()` will block event loop in async function
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/database/schema.py
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|
|
||
| id = Column(Integer, primary_key=True, autoincrement=True) | ||
| deployment_id = Column(String(255), nullable=False, index=True) | ||
| timestamp = Column(DateTime, default=datetime.utcnow, nullable=False) |
There was a problem hiding this comment.
syntax: datetime.utcnow() is deprecated - use datetime.now(timezone.utc)
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/database/schema.py
Line: 55:55
Comment:
**syntax:** `datetime.utcnow()` is deprecated - use `datetime.now(timezone.utc)`
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/database/schema.py
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| strategy_type = Column(String(255)) | ||
| environment = Column(String(50)) | ||
| status = Column(String(50)) | ||
| created_at = Column(DateTime, default=datetime.utcnow, nullable=False) |
There was a problem hiding this comment.
syntax: datetime.utcnow() is deprecated - use datetime.now(timezone.utc)
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/database/schema.py
Line: 74:74
Comment:
**syntax:** `datetime.utcnow()` is deprecated - use `datetime.now(timezone.utc)`
How can I resolve this? If you propose a fix, please make it concise.
Comment on lines
+1
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| """ | ||
| Database schema for Neural SDK deployment module. | ||
|
|
||
| SQLAlchemy models for storing deployment, trade, and performance data. | ||
| """ |
There was a problem hiding this comment.
syntax: missing timezone import for fixing deprecated datetime.utcnow() calls
Suggested change
| """ | |
| Database schema for Neural SDK deployment module. | |
| SQLAlchemy models for storing deployment, trade, and performance data. | |
| """ | |
| """ | |
| Database schema for Neural SDK deployment module. | |
| SQLAlchemy models for storing deployment, trade, and performance data. | |
| """ | |
| from datetime import datetime, timezone |
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/database/schema.py
Line: 1:5
Comment:
**syntax:** missing `timezone` import for fixing deprecated `datetime.utcnow()` calls
```suggestion
"""
Database schema for Neural SDK deployment module.
SQLAlchemy models for storing deployment, trade, and performance data.
"""
from datetime import datetime, timezone
```
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/docker/provider.py
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| str(self.project_root / "secrets"): {"bind": "/secrets", "mode": "ro"}, | ||
| f"{container_name}_data": {"bind": "/app/data", "mode": "rw"}, |
There was a problem hiding this comment.
logic: hardcoded /secrets mount path won't work if secrets directory doesn't exist - container creation will fail
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/docker/provider.py
Line: 347:348
Comment:
**logic:** hardcoded `/secrets` mount path won't work if `secrets` directory doesn't exist - container creation will fail
How can I resolve this? If you propose a fix, please make it concise.
neural/deployment/config.py
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| status: str = Field(..., description="Deployment status") | ||
| container_id: str | None = Field(None, description="Docker container ID") | ||
| container_name: str | None = Field(None, description="Container name") | ||
| created_at: datetime = Field(default_factory=datetime.now, description="Creation timestamp") |
There was a problem hiding this comment.
style: datetime.now() should use UTC timezone for consistency with other timestamp fields
Prompt To Fix With AI
This is a comment left during a code review.
Path: neural/deployment/config.py
Line: 127:127
Comment:
**style:** `datetime.now()` should use UTC timezone for consistency with other timestamp fields
How can I resolve this? If you propose a fix, please make it concise.This commit addresses all critical and medium-severity issues identified in the Greptile AI code review of the deployment module: ## Fixed Issues ### 1. Event Loop Blocking (CRITICAL - 6 instances) **File**: neural/deployment/docker/provider.py **Problem**: Synchronous Docker SDK calls in async methods freeze the event loop **Solution**: Wrapped all blocking calls with asyncio.run_in_executor() - Added ThreadPoolExecutor to DockerDeploymentProvider.__init__ - Wrapped container create/start (lines 117-120) - Wrapped container stop/remove (lines 171-175) - Wrapped container status/reload/stats (lines 207-219) - Wrapped logs retrieval (lines 255-260) - Wrapped list_deployments container operations (lines 279-282) - Wrapped image build (lines 333-338) - MOST CRITICAL (can take minutes) - Added cleanup() method for proper executor shutdown ### 2. Deprecated datetime API (MEDIUM - 4 instances) **File**: neural/deployment/database/schema.py **Problem**: datetime.utcnow() deprecated in Python 3.12+ **Solution**: Replaced with datetime.now(timezone.utc) - Fixed Trade.timestamp (line 23) - Fixed Position.timestamp (line 45) - Fixed Performance.timestamp (line 55) - Fixed Deployment.created_at (line 74) ### 3. Path Validation (MEDIUM - 1 instance) **File**: neural/deployment/docker/provider.py **Problem**: Hardcoded secrets directory with no validation **Solution**: Added path existence and type validation (lines 369-379) ### 4. Additional Fixes - Fixed UTF-8 encoding issue in deployment/__init__.py - Removed circular import by removing _warn_experimental call - Renamed Trade.metadata to Trade.trade_metadata (SQLAlchemy reserved word) All changes have been tested and imports validated successfully. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Addresses final Greptile review comment on config.py line 127. Changed DeploymentResult.created_at from datetime.now() to datetime.now(timezone.utc) for consistency with database schema timestamp fields. This ensures all timestamps in the deployment module use UTC timezone-aware datetime objects. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Contributor
Author
|
@greptileai Please re-review this PR. All 13 issues from the initial review have been addressed:
Commits with fixes: |
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Greptile Overview
Greptile Summary
Adds comprehensive Docker deployment infrastructure for trading bots with proper async/await patterns, timezone-aware timestamps, and robust error handling.
Key Changes
- Docker Provider Implementation: Complete async Docker SDK integration with ThreadPoolExecutor to prevent event loop blocking during container operations (build, create, start, stop, stats)
- Database Schema: SQLAlchemy models for trades, positions, performance, and deployments with timezone-aware UTC timestamps
- Configuration Models: 6 Pydantic models with full validation for deployment, Docker, database, and monitoring configs
- Template System: Jinja2-based Dockerfile generation with single-stage and multi-stage build support
- Context Manager Pattern: Clean resource management with
async with deploy()for automatic cleanup - NBA Series Fix: Corrected Kalshi API series mappings (NBA→KXNBAGAME, added NBA_CHAMPIONSHIP and WNBA)
Issues Addressed
All critical issues from previous review have been fully resolved:
✅ Fixed 6 instances of blocking synchronous Docker calls by wrapping with loop.run_in_executor()
✅ Replaced deprecated datetime.utcnow() with datetime.now(timezone.utc) (4 instances)
✅ Added secrets directory validation before container creation
✅ Fixed timezone consistency in DeploymentResult.created_at
✅ Added proper executor cleanup in DockerDeploymentProvider.cleanup()
Code Quality
- Well-structured abstract base class pattern enables future providers (E2B, cloud platforms)
- Proper exception hierarchy with specific error types
- Comprehensive docstrings throughout
- Thread-safe executor pattern for Docker operations
- Type hints using modern Python syntax (3.11+)
The deployment module is production-ready for beta release with proper experimental warnings.
Confidence Score: 4/5
- This PR is safe to merge - all critical async and datetime issues have been fixed
- Score reflects excellent remediation of all critical issues (blocking calls, deprecated APIs) and solid architecture. Deducted one point because this is a large new experimental module (~1,800 lines) that would benefit from integration tests and real-world usage validation before production use
- No files require special attention - all previously identified issues have been resolved
Important Files Changed
File Analysis
| Filename | Score | Overview |
|---|---|---|
| neural/deployment/docker/provider.py | 4/5 | Fixed all blocking async calls with ThreadPoolExecutor, added secrets directory validation, properly handles container lifecycle operations |
| neural/deployment/database/schema.py | 5/5 | Fixed all deprecated datetime.utcnow() calls to use timezone-aware datetime.now(timezone.utc), schema is clean and well-structured |
| neural/deployment/config.py | 5/5 | Fixed datetime consistency issue, all Pydantic models have proper validation, type safety throughout |
| neural/deployment/init.py | 5/5 | Clean module initialization with proper exports, experimental warning in docstring, convenience deploy() function works well |
Sequence Diagram
sequenceDiagram
participant User
participant DeploymentContext
participant DockerProvider
participant ThreadPoolExecutor
participant DockerSDK
participant Container
User->>DeploymentContext: async with deploy(provider, config)
DeploymentContext->>DockerProvider: deploy(config)
DockerProvider->>DockerProvider: Generate Dockerfile from template
DockerProvider->>ThreadPoolExecutor: Submit image build task
ThreadPoolExecutor->>DockerSDK: images.build() [blocking]
DockerSDK-->>ThreadPoolExecutor: Built image
ThreadPoolExecutor-->>DockerProvider: image_tag
DockerProvider->>DockerProvider: Validate secrets directory exists
DockerProvider->>DockerProvider: Create container config (env vars, volumes, resources)
DockerProvider->>ThreadPoolExecutor: Submit container create
ThreadPoolExecutor->>DockerSDK: containers.create() [blocking]
DockerSDK-->>ThreadPoolExecutor: Container object
ThreadPoolExecutor-->>DockerProvider: container
DockerProvider->>ThreadPoolExecutor: Submit container start
ThreadPoolExecutor->>DockerSDK: container.start() [blocking]
DockerSDK->>Container: Start container
Container-->>DockerSDK: Started
DockerSDK-->>ThreadPoolExecutor: Success
ThreadPoolExecutor-->>DockerProvider: Started
DockerProvider->>DockerProvider: Store deployment in active_deployments
DockerProvider-->>DeploymentContext: DeploymentResult
DeploymentContext-->>User: deployment
User->>DockerProvider: status(deployment_id)
DockerProvider->>ThreadPoolExecutor: Submit status check
ThreadPoolExecutor->>DockerSDK: container.reload() + stats() [blocking]
DockerSDK-->>ThreadPoolExecutor: Status & metrics
ThreadPoolExecutor-->>DockerProvider: stats
DockerProvider-->>User: DeploymentStatus
User->>DockerProvider: logs(deployment_id)
DockerProvider->>ThreadPoolExecutor: Submit logs retrieval
ThreadPoolExecutor->>DockerSDK: container.logs() [blocking]
DockerSDK-->>ThreadPoolExecutor: Log output
ThreadPoolExecutor-->>DockerProvider: logs
DockerProvider-->>User: Log lines
User->>DeploymentContext: __aexit__ (context exit)
DeploymentContext->>DockerProvider: stop(deployment_id)
DockerProvider->>ThreadPoolExecutor: Submit stop task
ThreadPoolExecutor->>DockerSDK: container.stop() + remove() [blocking]
DockerSDK->>Container: Stop & remove container
Container-->>DockerSDK: Stopped
DockerSDK-->>ThreadPoolExecutor: Success
ThreadPoolExecutor-->>DockerProvider: Stopped
DockerProvider->>DockerProvider: Remove from active_deployments
DockerProvider-->>DeploymentContext: True
DeploymentContext-->>User: Cleanup complete
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Summary
Add comprehensive Docker-based deployment infrastructure for trading bots with database persistence, monitoring, and multi-environment support.
What's New
🐳 Docker Deployment Module
DeploymentProviderbase classasync with deploy()📊 Database & Monitoring
🛠️ Configuration
📚 Documentation & Examples
examples/08_docker_deployment.py🏀 NBA Series Mapping Fix
"NBA"→"KXNBAGAME"(individual games)"NBA_CHAMPIONSHIP"→"KXNBA"(championship markets)"WNBA"→"KXWNBAGAME"supportInstallation
Users can install with:
Usage Example
Files Changed
Testing
Related Issues
Part of v0.4.0 release milestone
🤖 Generated with Claude Code