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BREAKING CHANGE: end_date is now required and cannot be null/empty
New Features:
- Resume mode: Set start_date to null to continue from last completed date per model
- Idempotent by default: Skip already-completed dates with replace_existing=false
- Per-model independence: Each model resumes from its own last completed date
- Cold start handling: If no data exists in resume mode, runs only end_date as single day
API Changes:
- start_date: Now optional (null enables resume mode)
- end_date: Now REQUIRED (cannot be null or empty string)
- replace_existing: New optional field (default: false for idempotent behavior)
Implementation:
- Added JobManager.get_last_completed_date_for_model() method
- Added JobManager.get_completed_model_dates() method
- Updated create_job() to support model_day_filter for selective task creation
- Fixed bug with start_date=None in price data checks
Documentation:
- Updated API_REFERENCE.md with complete examples and behavior matrix
- Updated QUICK_START.md with resume mode examples
- Updated docs/user-guide/using-the-api.md
- Added CHANGELOG_NEW_API.md with migration guide
- Updated all integration tests for new schema
- Updated client library examples (Python, TypeScript)
Migration:
- Old: {"start_date": "2025-01-16"}
- New: {"start_date": "2025-01-16", "end_date": "2025-01-16"}
- Resume: {"start_date": null, "end_date": "2025-01-31"}
See CHANGELOG_NEW_API.md for complete details.
197 lines
4.5 KiB
Markdown
197 lines
4.5 KiB
Markdown
# Using the API
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Common workflows and best practices for AI-Trader-Server API.
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---
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## Basic Workflow
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### 1. Trigger Simulation
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```bash
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curl -X POST http://localhost:8080/simulate/trigger \
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-H "Content-Type: application/json" \
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-d '{
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"start_date": "2025-01-16",
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"end_date": "2025-01-17",
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"models": ["gpt-4"]
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}'
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```
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Save the `job_id` from response.
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### 2. Poll for Completion
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```bash
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JOB_ID="your-job-id-here"
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while true; do
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STATUS=$(curl -s http://localhost:8080/simulate/status/$JOB_ID | jq -r '.status')
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echo "Status: $STATUS"
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if [[ "$STATUS" == "completed" ]] || [[ "$STATUS" == "partial" ]] || [[ "$STATUS" == "failed" ]]; then
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break
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fi
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sleep 10
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done
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```
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### 3. Retrieve Results
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```bash
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curl "http://localhost:8080/results?job_id=$JOB_ID" | jq '.'
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```
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---
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## Common Patterns
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### Single-Day Simulation
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Set `start_date` and `end_date` to the same value:
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```bash
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curl -X POST http://localhost:8080/simulate/trigger \
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-H "Content-Type: application/json" \
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-d '{"start_date": "2025-01-16", "end_date": "2025-01-16", "models": ["gpt-4"]}'
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```
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### All Enabled Models
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Omit `models` to run all enabled models from config:
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```bash
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curl -X POST http://localhost:8080/simulate/trigger \
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-H "Content-Type: application/json" \
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-d '{"start_date": "2025-01-16", "end_date": "2025-01-20"}'
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```
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### Resume from Last Completed
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Use `"start_date": null` to continue from where you left off:
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```bash
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curl -X POST http://localhost:8080/simulate/trigger \
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-H "Content-Type: application/json" \
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-d '{"start_date": null, "end_date": "2025-01-31", "models": ["gpt-4"]}'
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```
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Each model will resume from its own last completed date. If no data exists, runs only `end_date` as a single day.
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### Filter Results
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```bash
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# By date
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curl "http://localhost:8080/results?date=2025-01-16"
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# By model
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curl "http://localhost:8080/results?model=gpt-4"
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# Combined
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curl "http://localhost:8080/results?job_id=$JOB_ID&date=2025-01-16&model=gpt-4"
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```
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---
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## Best Practices
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### 1. Check Health Before Triggering
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```bash
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curl http://localhost:8080/health
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# Only proceed if status is "healthy"
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```
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### 2. Use Exponential Backoff for Retries
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```python
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import time
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import requests
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def trigger_with_retry(max_retries=3):
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for attempt in range(max_retries):
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try:
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response = requests.post(
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"http://localhost:8080/simulate/trigger",
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json={"start_date": "2025-01-16"}
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)
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response.raise_for_status()
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return response.json()
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except requests.HTTPError as e:
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if e.response.status_code == 400:
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# Don't retry on validation errors
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raise
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wait = 2 ** attempt # 1s, 2s, 4s
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time.sleep(wait)
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raise Exception("Max retries exceeded")
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```
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### 3. Handle Concurrent Job Conflicts
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```python
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response = requests.post(
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"http://localhost:8080/simulate/trigger",
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json={"start_date": "2025-01-16"}
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)
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if response.status_code == 400 and "already running" in response.json()["detail"]:
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print("Another job is running. Waiting...")
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# Wait and retry, or query existing job status
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```
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### 4. Monitor Progress with Details
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```python
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def get_detailed_progress(job_id):
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response = requests.get(f"http://localhost:8080/simulate/status/{job_id}")
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status = response.json()
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print(f"Overall: {status['status']}")
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print(f"Progress: {status['progress']['completed']}/{status['progress']['total_model_days']}")
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# Show per-model-day status
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for detail in status['details']:
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print(f" {detail['trading_date']} {detail['model_signature']}: {detail['status']}")
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```
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---
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## Error Handling
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### Validation Errors (400)
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```python
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try:
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response = requests.post(
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"http://localhost:8080/simulate/trigger",
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json={"start_date": "2025-1-16"} # Wrong format
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)
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response.raise_for_status()
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except requests.HTTPError as e:
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if e.response.status_code == 400:
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print(f"Validation error: {e.response.json()['detail']}")
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# Fix input and retry
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```
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### Service Unavailable (503)
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```python
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try:
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response = requests.post(
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"http://localhost:8080/simulate/trigger",
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json={"start_date": "2025-01-16"}
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)
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response.raise_for_status()
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except requests.HTTPError as e:
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if e.response.status_code == 503:
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print("Service unavailable (likely price data download failed)")
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# Retry later or check ALPHAADVANTAGE_API_KEY
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```
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---
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See [API_REFERENCE.md](../../API_REFERENCE.md) for complete endpoint documentation.
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