Backend:
- Fixed auth issue where get_optional_user wasn't properly extracting tokens
- Added user_id to conversational agent for proper auth context
- Fixed DCA buy logic to support multiple buys on dips
- Fixed sell logic to use amount_percent
- Added comprehensive backtest engine tests
- Fixed kline data validation for bad price data
- Fixed chained tool calls handling
Frontend:
- BotCard now links to chat page instead of bot page
- Chat page handles direct bot loading from dashboard
- Various UI improvements and fixes
Tests:
- Added test_agent.py with mock client tests
- Added test_backtest_engine.py with 7 comprehensive tests
- Add Conversation model with user/anonymous_token/bot_id fields
- Add Message model linked to conversations
- Add AnonymousUser model for tracking anonymous chat limits
- Create /api/conversations endpoints (list, create, get, delete)
- Add POST /api/conversations/{id}/chat for messaging
- Add POST /api/conversations/{id}/set-bot for linking bot
- Implement rate limiter with system-wide (500/5hrs) and anonymous limits
- Anonymous users: max 50 chats, max 1 bot, max 1 backtest
- Add warning after 40 anonymous messages
- Register conversations router in main.py
- Add create_bot, list_bots, set_bot, get_bot_info tools to registry
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.
- Display token name and symbol in risk analysis output
- Handle is_honeypot: -1 as 'Unknown (could not determine)'
- Show risk level (Low/Medium/High) with risk score
- Use risk_level field instead of status
- Store recent search results in agent instance
- When price returns empty, suggest using /search tool
- Check if user input matches recent search results and use that address
The price API requires full contract addresses (0x...-bsc), but users typically provide symbols.
Now /price TRADOOR will search for the token and show price info from search results.
- Added _is_error_output helper to detect errors in CLI output
- API errors like 'API error 403' now show proper error message instead of 'No price data available'
- Updated all command execution methods to use the helper
- Added _execute_backtest_direct() that extracts params from message or strategy config
- Added _execute_simulate_direct() that extracts params from message or strategy config
- Both commands now execute directly when called with /backtest or /simulate
- If token address is missing, asks user for the parameter
Commands now execute in two steps:
1. User sends /search -> acknowledge and wait for param
2. User sends 'pepe' -> auto-execute search with 'pepe'
Commands without params (/backtest, /simulate, /trending, /strategy) execute directly.
Pending commands tracked via self.pending_command state.
- Added _execute_trending method that runs the trending CLI command
- Returns formatted list of trending tokens on BSC
- Shows error message if no tokens found or command fails
- Added _get_strategy_response method to query bot's strategy_config from DB
- Shows formatted strategy with conditions, actions, and risk management
- Shows helpful message if no strategy is configured yet
- Reset conversational.py to pr-58 working AVE integration
- Added TOOL_REGISTRY with all available slash commands
- Added _handle_slash_command for skill activation
- Slash commands show brief acknowledgment when used alone
- Slash commands with args are passed to AI for handling
- Added dropdown menu in ChatInterface for skill discovery
- Menu positions above textarea
- Menu shows filtered tools as user types
Implement slash command help system as described in issue #57:
- Add Tool Registry in backend with metadata for all available tools
- Add command parser for '/' prefix in ConversationalAgent
- Add slash command handling functions:
- '/' shows list of all available tools
- '/help' shows general help about Randebu
- '/<tool-name>' shows detailed help for specific tool
- Update frontend ChatInterface to detect '/' and show formatted help dropdown
- Add keyboard navigation (Arrow keys, Tab, Enter, Escape) for slash menu
- Add subprocess-based AVE Cloud Skills CLI integration for token data
- Add new tools: search_tokens, get_token, get_price, get_risk, get_trending
- Add get_trending tool for trending tokens on BSC
- Replace direct API calls with CLI subprocess calls
Bug fixes:
- Fix repo_root path calculation (5 → 6 dirname() calls)
- Handle API response where data is a list instead of dict
- Add defensive type checking for all API responses
- Handle string values in market_cap and volume formatting
- Handle None values before calling len()
- Handle nested token data structure (data.token) and alternative field names
- Handle integer/bool types for honeypot check (1/0 vs true/false)
- Handle -1 as unknown for honeypot status
- Show raw API response for debugging when data is missing
Instead of importing library functions directly, now calling the
ave_data_rest.py CLI script via subprocess. This follows the
recommended approach from the SKILL.md documentation.
Changes:
- Add _call_ave_script helper method for subprocess calls
- Update search_tokens, get_token, get_price, get_risk to use CLI
- Set AVE_USE_DOCKER=false to run scripts directly without Docker
- Remove direct imports of ave.http module
Agent can now manage simulations via conversation:
- 'start' (run simulation on token)
- 'stop' (stop running simulation)
- 'status' (check if running, show progress)
- 'results' (get current/latest simulation results)
Example conversations:
- 'Run a simulation on GRIAN' → starts simulation
- 'How's the simulation going?' → shows status
- 'Stop the simulation' → stops and shows results
- 'Show me the results' → shows simulation results
Agent can now run backtests via conversation:
- User can say 'backtest this strategy' or 'test on [token]'
- Agent extracts token address (from conversation or asks user)
- Agent runs backtest and presents results in chat with:
- Total Return
- Final Balance
- Win Rate
- Max Drawdown
- Sharpe Ratio
- Agent suggests strategy adjustments based on results
When selling, the sale proceeds (quantity * price) are now added to current_balance.
This ensures:
- Cash decreases when buying
- Cash increases when selling (including stop loss / take profit)
- Portfolio P&L is calculated correctly
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
test_full_simulation_workflow_generates_signals_and_trades:
- Creates klines with clear price movements
- Uses very low threshold (0.1%) to ensure signals generated
- Verifies signals are NOT empty
- Verifies trade_log is NOT empty
- Verifies BUY signals are generated
- Verifies results contain signals and trade_log
This test ensures the simulation engine is working correctly.
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.
- Use ResizeObserver to handle width changes
- Use tick() to ensure DOM is ready before drawing
- Access reactive values in effect to trigger on changes
- Fixed canvas sizing to use percentage width
