Optimization Management
Delete Optimization
Request
The /optimizations/delete
API accepts requests in the following format:
DeleteOptimizationRequest Model - Delete an optimization. | |
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optimizationId | string Optimization ID we want to delete. |
Example |
{ "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd" } |
Responses
The /optimizations/delete
API provides a response in the following format:
200 Success
RestResponse Model - Base API response class for the QuantConnect API. | |
---|---|
success | boolean Indicate if the API request was successful. |
errors | string Array List of errors with the API call. |
Example |
{ "success": true, "errors": [ "string" ] } |
401 Authentication Error
UnauthorizedError Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. | |
---|---|
www_authenticate | string Header |
Examples
The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API.
from base64 import b64encode from hashlib import sha256 from time import time from requests import get, post BASE_URL = 'https://www.quantconnect.com/api/v2/' # You need to replace these with your actual credentials. # You can request your credentials at https://www.quantconnect.com/settings/ # You can find our organization ID at https://www.quantconnect.com/organization/ USER_ID = 0 API_TOKEN = '____' ORGANIZATION_ID = '____' def get_headers(): # Get timestamp timestamp = f'{int(time())}' time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8') # Get hased API token hashed_token = sha256(time_stamped_token).hexdigest() authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8') authentication = b64encode(authentication).decode('ascii') # Create headers dictionary. return { 'Authorization': f'Basic {authentication}', 'Timestamp': timestamp } # Authenticate to verify credentials response = post(f'{BASE_URL}/authenticate', headers = get_headers()) print(response.json()) # -------------------- # The project ID of the project to manage an optimization job project_id = 12345678 ### Estimate Optimization Cost # Send a POST request to the /optimizations/estimate endpoint to estimate cost response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={ "projectId": project_id, # ID of the project "name": f"Optimization_{compileId[:5]}", # Name of the optimization (using compile ID prefix) "target": "TotalPerformance.PortfolioStatistics.SharpeRatio", # Optimization target metric "targetTo": "max", # Direction to optimize (maximize) "targetValue": None, # Specific target value (None for max/min) "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy", # Optimization strategy "compileId": compile_id, # Compilation ID for the optimization "parameters[0][key]": "ema_fast", # First parameter key "parameters[0][min]": 100, # Minimum value for first parameter "parameters[0][max]": 200, # Maximum value for first parameter "parameters[0][step]": 50, # Step size for first parameter "parameters[1][key]": "ema_slow", # Second parameter key "parameters[1][min]": 200, # Minimum value for second parameter "parameters[1][max]": 300, # Maximum value for second parameter "parameters[1][step]": 50, # Step size for second parameter "constraints": [{ # Constraints for the optimization "target": "TotalPerformance.PortfolioStatistics.SharpeRatio", "operator": "greater", "target-value": 1 }] }) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the estimated cost if result['success']: print("Optimization Cost Estimated Successfully:") print(result) ### Create Optimization # Send a POST request to the /optimizations/create endpoint to create an optimization response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={ "projectId": project_id, # ID of the project "name": f"Optimization_{compileId[:5]}", # Name of the optimization "target": "TotalPerformance.PortfolioStatistics.SharpeRatio", # Optimization target "targetTo": "max", # Direction to optimize "targetValue": None, # Specific target value "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy", # Strategy "compileId": compile_id, # Compilation ID "parameters[0][key]": "ema_fast", # First parameter key "parameters[0][min]": 100, # Minimum value "parameters[0][max]": 200, # Maximum value "parameters[0][step]": 50, # Step size "parameters[1][key]": "ema_slow", # Second parameter key "parameters[1][min]": 200, # Minimum value "parameters[1][max]": 300, # Maximum value "parameters[1][step]": 50, # Step size "constraints": [{ # Constraints "target": "TotalPerformance.PortfolioStatistics.SharpeRatio", "operator": "greater", "target-value": 1 }], "estimatedCost": 10, # Estimated cost of optimization "nodeType": "O2-8", # Node type for optimization "parallelNodes": 4 # Number of parallel nodes }) # Parse the JSON response into python managable dict result = response.json() # Extract the optimization ID from the response optimization_id = result['optimizations'][0]['optimizationId'] # Check if the request was successful and print the result if result['success']: print("Optimization Created Successfully:") print(result) ### Update Optimization # Send a POST request to the /optimizations/update endpoint to update the optimization response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={ "optimizationId": optimization_id, # ID of the optimization to update "name": f"Optimization_{optimizationId[:5]}" # New name for the optimization }) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result if result['success']: print("Optimization Updated Successfully:") print(result) ### Read Optimization # Prepare data payload to read optimization details payload = { "optimizationId": optimization_id # ID of the optimization to read } # Send a POST request to the /optimizations/read endpoint to get details response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the details if result['success']: print("Optimization Details:") print(result) ### Abort Optimization # Prepare data payload to abort the optimization payload = { "optimizationId": optimization_id # ID of the optimization to abort } # Send a POST request to the /optimizations/abort endpoint to abort response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result if result['success']: print("Optimization Aborted Successfully:") print(result) ### Delete Optimization # Prepare data payload to delete the optimization payload = { "optimizationId": optimization_id # ID of the optimization to delete } # Send a POST request to the /optimizations/delete endpoint to delete response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result if result['success']: print("Optimization Deleted Successfully:") print(result) ### List Optimizations # Prepare data payload to list optimizations payload = { "projectId": project_id # ID of the project } # Send a POST request to the /optimizations/list endpoint to list optimizations response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload) # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the list if result['success']: print("List of Optimizations:") print(result)