- Use CrewAI's LLM class directly with api_base parameter instead of custom subclass
- Remove broken MiniMaxLLM inheritance from LLM
- Update agent creation to use LLM(model, api_key, api_base) pattern
The issue was that inheriting from CrewAI's LLM class caused the api_base
to be set to None. Now we use CrewAI's LLM directly with the correct parameters.
Fixes#43
Changes:
1. Updated API endpoint from api.minimax.chat to api.minimax.io
2. Changed default model from MiniMax-Text-01 to MiniMax-M2.7
(MiniMax-Text-01 is not available for all API key plans)
3. Updated .env.example with correct default model
MiniMax API docs: https://platform.minimax.io/docs/api-reference/text-anthropic-apiFixes#43
- Fix relative import path in crew.py (from ..core to ...core)
- Update __init__.py exports to match actual class names
- Remove incorrect CrewAgent and LLMConnector exports
Errors during price fetching are now logged and stored in an errors list,
allowing users to see error count/warnings in simulation results.
Acceptance Criteria:
- [x] Errors are logged (not silently swallowed)
- [x] User can see error count/warnings in simulation results
- [x] Simulation completes even if some price fetches fail (graceful degradation)
LLM was outputting nested params structure but engines expect flat fields.
This caused backtesting and simulation to never trigger any trades.
Changes:
- llm_connector.py: Update prompt to output flat condition structure
- crew.py: Update StrategyValidator to validate flat structure
- crew.py: Update StrategyExplainer to read flat structure
Fixes#25
- Add tier field to User model for plan detection (free/normal/pro)
- Create AVE Cloud API client with all Data API endpoints:
- Token search (GET /v2/tokens)
- Batch prices (POST /v2/tokens/price)
- Token details (GET /v2/tokens/{id})
- Kline data (GET /v2/klines/token/{id})
- Trending tokens (GET /v2/tokens/trending)
- Token risk (GET /v2/contracts/{id})
- Add Trading API endpoints:
- Chain wallet quote (POST /v1/chain/quote)
- Chain wallet swap (POST /v1/chain/swap)
- Add tier gating with upsell messaging for Pro features
- Handle rate limiting gracefully with 429 responses
- Add Pydantic schemas for AVE API requests/responses
Fixes#11
Implement simulate engine for real-time signal detection via REST polling.
Changes:
- SimulateEngine service with configurable check interval (default 60s for free tier)
- REST polling for current prices using AveCloudClient
- Condition matching for real-time data (price_drop, price_rise, volume_spike, price_level)
- Signal logging with user-initiated start/stop
- Simulation API endpoints:
- POST /api/bots/{id}/simulate - Start simulation
- GET /api/bots/{id}/simulate/{run_id} - Get status/signals
- GET /api/bots/{id}/simulations - List all simulations
- POST /api/bots/{id}/simulate/{run_id}/stop - Stop simulation
- Updated SimulationCreate schema with check_interval field
- Free tier limited to 60s minimum check interval
- Signals stored in database for simulation signal history
Depends on issue #7 (Backtest Engine) which was merged in PR #18
Implements issue #7 - Backtest Engine for historical strategy testing.
Changes:
- Created AveCloudClient for fetching klines from AVE Cloud Data API
- Implemented BacktestEngine with condition matching (price_drop, price_rise, volume_spike, price_level)
- Implemented signal generation and portfolio simulation
- Calculates metrics: total_return, win_rate, max_drawdown, sharpe_ratio, total_trades
- Implemented async/background backtest execution via FastAPI BackgroundTasks
- Stores results in backtests table and signals table
- All backtest API endpoints with JWT auth and ownership validation
API Endpoints:
- POST /api/bots/{id}/backtest - Start backtest
- GET /api/bots/{id}/backtest/{run_id} - Get status/results
- GET /api/bots/{id}/backtests - List all backtests
- POST /api/bots/{id}/backtest/{run_id}/stop - Stop running backtest
