Files
AI-Trader/docs/user-guide/using-the-api.md
Bill 7aa93af6db feat: add resume mode and idempotent behavior to /simulate/trigger endpoint
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.
2025-11-01 13:34:20 -04:00

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Markdown

# Using the API
Common workflows and best practices for AI-Trader-Server API.
---
## Basic Workflow
### 1. Trigger Simulation
```bash
curl -X POST http://localhost:8080/simulate/trigger \
-H "Content-Type: application/json" \
-d '{
"start_date": "2025-01-16",
"end_date": "2025-01-17",
"models": ["gpt-4"]
}'
```
Save the `job_id` from response.
### 2. Poll for Completion
```bash
JOB_ID="your-job-id-here"
while true; do
STATUS=$(curl -s http://localhost:8080/simulate/status/$JOB_ID | jq -r '.status')
echo "Status: $STATUS"
if [[ "$STATUS" == "completed" ]] || [[ "$STATUS" == "partial" ]] || [[ "$STATUS" == "failed" ]]; then
break
fi
sleep 10
done
```
### 3. Retrieve Results
```bash
curl "http://localhost:8080/results?job_id=$JOB_ID" | jq '.'
```
---
## Common Patterns
### Single-Day Simulation
Set `start_date` and `end_date` to the same value:
```bash
curl -X POST http://localhost:8080/simulate/trigger \
-H "Content-Type: application/json" \
-d '{"start_date": "2025-01-16", "end_date": "2025-01-16", "models": ["gpt-4"]}'
```
### All Enabled Models
Omit `models` to run all enabled models from config:
```bash
curl -X POST http://localhost:8080/simulate/trigger \
-H "Content-Type: application/json" \
-d '{"start_date": "2025-01-16", "end_date": "2025-01-20"}'
```
### Resume from Last Completed
Use `"start_date": null` to continue from where you left off:
```bash
curl -X POST http://localhost:8080/simulate/trigger \
-H "Content-Type: application/json" \
-d '{"start_date": null, "end_date": "2025-01-31", "models": ["gpt-4"]}'
```
Each model will resume from its own last completed date. If no data exists, runs only `end_date` as a single day.
### Filter Results
```bash
# By date
curl "http://localhost:8080/results?date=2025-01-16"
# By model
curl "http://localhost:8080/results?model=gpt-4"
# Combined
curl "http://localhost:8080/results?job_id=$JOB_ID&date=2025-01-16&model=gpt-4"
```
---
## Best Practices
### 1. Check Health Before Triggering
```bash
curl http://localhost:8080/health
# Only proceed if status is "healthy"
```
### 2. Use Exponential Backoff for Retries
```python
import time
import requests
def trigger_with_retry(max_retries=3):
for attempt in range(max_retries):
try:
response = requests.post(
"http://localhost:8080/simulate/trigger",
json={"start_date": "2025-01-16"}
)
response.raise_for_status()
return response.json()
except requests.HTTPError as e:
if e.response.status_code == 400:
# Don't retry on validation errors
raise
wait = 2 ** attempt # 1s, 2s, 4s
time.sleep(wait)
raise Exception("Max retries exceeded")
```
### 3. Handle Concurrent Job Conflicts
```python
response = requests.post(
"http://localhost:8080/simulate/trigger",
json={"start_date": "2025-01-16"}
)
if response.status_code == 400 and "already running" in response.json()["detail"]:
print("Another job is running. Waiting...")
# Wait and retry, or query existing job status
```
### 4. Monitor Progress with Details
```python
def get_detailed_progress(job_id):
response = requests.get(f"http://localhost:8080/simulate/status/{job_id}")
status = response.json()
print(f"Overall: {status['status']}")
print(f"Progress: {status['progress']['completed']}/{status['progress']['total_model_days']}")
# Show per-model-day status
for detail in status['details']:
print(f" {detail['trading_date']} {detail['model_signature']}: {detail['status']}")
```
---
## Error Handling
### Validation Errors (400)
```python
try:
response = requests.post(
"http://localhost:8080/simulate/trigger",
json={"start_date": "2025-1-16"} # Wrong format
)
response.raise_for_status()
except requests.HTTPError as e:
if e.response.status_code == 400:
print(f"Validation error: {e.response.json()['detail']}")
# Fix input and retry
```
### Service Unavailable (503)
```python
try:
response = requests.post(
"http://localhost:8080/simulate/trigger",
json={"start_date": "2025-01-16"}
)
response.raise_for_status()
except requests.HTTPError as e:
if e.response.status_code == 503:
print("Service unavailable (likely price data download failed)")
# Retry later or check ALPHAADVANTAGE_API_KEY
```
---
See [API_REFERENCE.md](../../API_REFERENCE.md) for complete endpoint documentation.