- 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
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
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
