Split conversational.py (2271 lines) into modular files:
- tools.py: TOOL_REGISTRY, get_tool_registry(), SKILL_EMOJIS
- help.py: format_* functions for slash command help
- client.py: MiniMaxClient, SYSTEM_PROMPT, TOOLS definitions
- agent.py: ConversationalAgent class with all methods
- __init__.py: Public exports from all modules
Updated bots.py import to use new module path.
Deleted conversational.py.
Shows real-time portfolio metrics:
- Cash Balance
- Position (quantity and value)
- Entry Price / Current Price
- Unrealized P&L
- Total Value
- P&L (absolute and percentage)
Updates as simulation runs and trades are executed.
- Tests now run with candle_delay=0 for fast execution
- Simulation defaults to candle_delay based on interval (e.g., 30s for 1m)
- Progress saved to DB every 5 seconds during simulation
- User can now see real-time updates while simulation runs
Tests: 14 passing in 0.15s
Shows what happened at each candle:
- BUY/SELL/HOLD actions
- Price at that time
- Reason for action
- Entry price for positions
Trade log is stored in DB and displayed in frontend.
The simulation engine completes in seconds, but the background task wasn't
saving klines to DB. Now we fetch klines first synchronously so the user
can see the price chart immediately after starting a simulation.
Also marks any stuck 'running' simulations as 'stopped'.
Unit tests (13 passing):
- Kline fetching and processing
- Price drop condition triggers buy
- Stop loss and take profit risk management
- Multiple positions (buy again after sell)
- Max candles limit
- Stop interruption handling
Frontend:
- SignalChart now shows price movement even before signals
- Shows candle count even with no signals
- Chart displays buy/sell markers when signals exist
- Canvas-based chart with gradient fill
Backend:
- Simulation stores klines for chart display
- Returns klines in API response
- Simplified simulation run (no periodic saving)
- Fetch historical klines once from AVE API (10 CU per request)
- Process each candle as a time step
- Limit to 500 candles max per simulation
- No continuous polling - processes all data in seconds
- Frontend now selects kline interval (1m, 5m, 15m, 1h)
- Much more efficient API usage
- Status was only updated if engine was in memory (race condition)
- Now always sets status to 'stopped' in DB
- Returns 'stopped' instead of 'stopping'
- Cleaned up 3 stale running simulations in DB
Frontend:
- Load simulations now prioritizes running simulation over most recent
- Clear signals before loading new simulation
Backend:
- When starting new simulation, stop any existing running simulation first
- Previously would return existing running simulation (confusing UX)
- No duration limit - runs forever until user stops
- Only 1 running simulation per bot (returns existing if already running)
- Always paper trade (no auto-execute option)
- Removed Pro upgrade banner
- Removed duration and auto-execute config options
- Simplified API to only require token, chain, check_interval
Frontend:
- Added duration selector (1min, 5min, 10min, 30min)
- Added check interval selector (10s, 30s, 60s)
Backend:
- Signals are now saved to database every 30 seconds during simulation
- Can stop simulation early to see partial signals
Backend:
- Added pagination to /trades endpoint with page and per_page params
- Returns paginated trades with metadata (page, total_pages, has_next, has_prev)
Frontend:
- Added pagination controls for trade history (Prev/Next buttons)
- Shows current page info and total trades
- Trades are loaded on-demand when expanded
API changes:
- GET /bots/{id}/backtest/{runId}/trades?page=1&per_page=5
- Response includes: trades, total_trades, page, per_page, total_pages, has_next, has_prev
Prototype implementation that allows:
1. Normal conversation with the AI
2. Tool-calling to update trading strategies
Created new ConversationalAgent that uses CrewAI with tools:
- get_current_strategy: Check current bot strategy
- update_trading_strategy: Update bot's trading configuration
The agent can now respond to questions like 'What is this?' without
forcing JSON output, and can update strategies when user provides
specific parameters.
Refs #51
After successful registration, the backend now returns an access token
(along with token_type) so the frontend can:
- Store the token in localStorage
- Fetch the user profile
- Redirect to dashboard
Fixes#41
- 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
- Add register endpoint with bcrypt password hashing
- Add login endpoint returning JWT tokens
- Add logout endpoint with token blacklisting
- Add /me endpoint for current user info
- Add rate limiting (5/minute) for login attempts using slowapi
- Add user settings GET and PATCH endpoints
- Create auth middleware via get_current_user dependency
- Add UserSettings and UserSettingsUpdate schemas
