2b7f54703e
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
1114 lines
50 KiB
Python
1114 lines
50 KiB
Python
"""
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Conversational Trading Agent
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This agent can:
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1. Have normal conversations with users
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2. Update trading strategies when user provides specific instructions
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Uses MiniMax extended thinking API for proper thinking/reasoning separation.
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"""
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import json
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import re
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import requests
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from typing import List, Optional, Dict, Any
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from datetime import timedelta
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from ...core.config import get_settings
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from ...db.models import Bot, Simulation
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SYSTEM_PROMPT = """You are a helpful AI trading assistant named Randebu. You help users manage their trading bots.
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IMPORTANT CHAIN LIMITATION:
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- We ONLY support BSC (Binance Smart Chain) blockchain
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- If user asks about any other chain (Solana, ETH, Base, etc.), respond with: "Currently we only support BSC (Binance Smart Chain). All trading strategies and token searches are performed on BSC."
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- Never search or recommend tokens on other chains
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- The search_tokens tool defaults to BSC, never change this
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Your response must be valid JSON with exactly this structure:
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{
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"thinking": "Your internal reasoning and analysis (what you're thinking about)",
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"response": "Your actual response to the user (be concise and helpful)",
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"strategy_update": null or {
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"conditions": [{"type": "price_drop" | "price_rise" | "volume_spike" | "price_level", "token": "TOKEN_SYMBOL", "token_address": null, "threshold": number, ...}],
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"actions": [{"type": "buy" | "sell" | "hold", "amount_percent": number, ...}],
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"risk_management": {"stop_loss_percent": number, "take_profit_percent": number}
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}
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}
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Guidelines:
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- "thinking" should be detailed reasoning about the user's request
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- "response" should be conversational and clear
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- "strategy_update" should be populated ONLY when the user provides specific trading parameters (percentages, tokens, conditions, etc.)
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- IMPORTANT: When a token is mentioned, set "token_address": null and ask user to confirm the token address before saving. Your response should say something like: "I need to confirm the token address. Could you provide the contract address for [TOKEN]?"
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- If no strategy parameters are provided, set "strategy_update" to null
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- Be friendly, concise, and helpful in your response
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Example 1 (no strategy update):
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User: "What can this bot do?"
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{
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"thinking": "The user is asking about the bot's capabilities. I should explain the main features.",
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"response": "Randebu is your AI trading assistant! It can monitor cryptocurrency prices and execute trades based on your configured strategies. Tell me your trading parameters and I'll set them up for you.",
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"strategy_update": null
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}
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Example 2 (token needs confirmation):
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User: "I want to buy PEPE when it drops 10%"
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{
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"thinking": "User wants to buy PEPE. I need the token contract address to proceed. I should ask for confirmation.",
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"response": "I'd be happy to set up a buy order for PEPE! However, I need to confirm the token contract address. Could you provide the BSC contract address for PEPE? (It usually starts with 0x...)",
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"strategy_update": {
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"conditions": [{"type": "price_drop", "token": "PEPE", "token_address": null, "threshold": 10}],
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"actions": [{"type": "buy", "amount_percent": 100}],
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"risk_management": null
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}
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}
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Example 3 (with token address provided by user):
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User: "Buy 0x6982508145454Ce125dDE157d8d64a26D53f60a2 when it drops 10%"
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{
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"thinking": "User provided a contract address, I can use it directly.",
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"response": "Perfect! I've configured your strategy to buy the token when it drops 10%.",
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"strategy_update": {
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"conditions": [{"type": "price_drop", "token": "TOKEN", "token_address": "0x6982508145454Ce125dDE157d8d64a26D53f60a2", "threshold": 10}],
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"actions": [{"type": "buy", "amount_percent": 100}],
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"risk_management": null
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}
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}"""
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# Tool definitions for the agent
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TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "search_tokens",
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"description": "Search for tokens by keyword on BSC blockchain. Use this when user asks to search for a specific token or find tokens by name/symbol.",
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"parameters": {
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"type": "object",
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"properties": {
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"keyword": {
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"type": "string",
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"description": "Token symbol or name to search for (e.g., 'PEPE', 'BTC')",
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},
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"limit": {
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"type": "integer",
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"description": "Number of tokens to return (default: 10)",
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"default": 10,
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},
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},
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"required": ["keyword"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_token",
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"description": "Get detailed information about a specific token including price, market cap, and pairs. Use when user asks for token details or wants to find a specific token by contract address.",
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"parameters": {
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"type": "object",
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"properties": {
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"address": {
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"type": "string",
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"description": "Token contract address (e.g., '0x6982508145454Ce125dDE157d8d64a26D53f60a2')",
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},
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"chain": {
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"type": "string",
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"description": "Blockchain chain (default: bsc)",
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"default": "bsc",
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},
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},
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"required": ["address"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_price",
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"description": "Get current price(s) for tokens. Use when user asks for token price or wants to compare prices of multiple tokens.",
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"parameters": {
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"type": "object",
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"properties": {
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"token_ids": {
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"type": "string",
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"description": "Comma-separated list of token IDs with chain suffix (e.g., 'PEPE-bsc,TRUMP-bsc')",
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}
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},
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"required": ["token_ids"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_risk",
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"description": "Get risk analysis for a token contract. Use when user asks about token risk, honeypot analysis, or safety assessment before trading.",
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"parameters": {
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"type": "object",
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"properties": {
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"address": {
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"type": "string",
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"description": "Token contract address (e.g., '0x6982508145454Ce125dDE157d8d64a26D53f60a2')",
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},
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"chain": {
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"type": "string",
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"description": "Blockchain chain (default: bsc)",
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"default": "bsc",
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},
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},
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"required": ["address"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "run_backtest",
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"description": "Run a backtest to evaluate how the current trading strategy would have performed historically. Returns key metrics like ROI, win rate, max drawdown, etc. Use this when user asks to backtest, test strategy, or check historical performance.",
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"parameters": {
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"type": "object",
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"properties": {
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"token_address": {
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"type": "string",
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"description": "The BSC contract address of the token to backtest (required)",
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},
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"timeframe": {
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"type": "string",
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"description": "Timeframe for klines: '1d' (1 day), '4h' (4 hours), '1h' (1 hour), '15m' (15 minutes)",
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"default": "1d",
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},
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"start_date": {
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"type": "string",
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"description": "Start date for backtest in YYYY-MM-DD format (e.g., '2024-01-01')",
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},
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"end_date": {
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"type": "string",
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"description": "End date for backtest in YYYY-MM-DD format (e.g., '2024-12-01')",
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},
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},
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"required": ["token_address"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "manage_simulation",
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"description": "Manage trading simulations: start, stop, or check status. Simulations run on real-time klines and show live portfolio updates. Use when user asks to run simulation, check simulation status, or stop simulation.",
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"parameters": {
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"type": "object",
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"properties": {
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"action": {
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"type": "string",
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"enum": ["start", "stop", "status", "results"],
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"description": "Action to perform: 'start' (begin new simulation), 'stop' (stop running simulation), 'status' (check if simulation is running), 'results' (get results from current or latest simulation)",
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},
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"token_address": {
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"type": "string",
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"description": "Token contract address for simulation (required for 'start' action)",
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},
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"kline_interval": {
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"type": "string",
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"description": "Kline interval: '1m', '5m', '15m', '1h' (default: '1m')",
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"default": "1m",
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},
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},
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"required": ["action"],
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},
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},
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},
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]
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SYSTEM_PROMPT_WITH_TOOLS = (
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SYSTEM_PROMPT
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+ """
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You have access to tools:
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- search_tokens(keyword, limit): Search for tokens by keyword. Use it when user asks to search for a token or find tokens by name/symbol.
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- get_token(address, chain): Get detailed information about a specific token. Use when user asks for token details.
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- get_price(token_ids): Get current price(s) for tokens. Use when user asks for token price.
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- get_risk(address, chain): Get risk analysis for a token. Use when user asks about token safety or honeypot analysis.
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- run_backtest(token_address, timeframe, start_date, end_date): Run a backtest on historical data. Returns performance metrics. Use when user asks to backtest or check historical performance.
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- manage_simulation(action, token_address, kline_interval): Manage trading simulations. Actions: 'start' (begin new), 'stop' (stop running), 'status' (check if running), 'results' (get current/latest results).
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When you want to use a tool, respond with:
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{
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"thinking": "...",
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"response": "Running backtest...",
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"tool_call": {"name": "run_backtest", "arguments": {"token_address": "0x...", "timeframe": "1d", "start_date": "2024-01-01", "end_date": "2024-12-01"}}
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}
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"""
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)
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class ConversationalAgent:
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def __init__(self, api_key: str, model: str = "MiniMax-M2.7", bot_id: str = None):
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self.api_key = api_key
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self.model = model
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self.bot_id = bot_id
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# Extended thinking endpoint
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self.thinking_endpoint = "https://api.minimax.io/v1/text/chatcompletion_v2"
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def chat(
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self, user_message: str, conversation_history: List[Dict] = None
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) -> Dict[str, Any]:
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"""Process a user message and return a structured response.
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Args:
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user_message: The user's message
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conversation_history: Optional list of previous messages
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Returns:
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Dict with 'response', 'thinking', and 'strategy_updated'
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"""
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try:
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# Build messages array with system prompt and conversation history
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messages = [{"role": "system", "content": SYSTEM_PROMPT_WITH_TOOLS}]
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# Add conversation history (last 10 messages)
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if conversation_history:
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for msg in conversation_history[-10:]:
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role = "assistant" if msg.get("role") == "assistant" else "user"
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messages.append({"role": role, "content": msg.get("content", "")})
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# Add current user message
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messages.append({"role": "user", "content": user_message})
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# Make API call to extended thinking endpoint
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resp = requests.post(
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self.thinking_endpoint,
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": self.model,
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"messages": messages,
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"temperature": 0.7,
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"max_tokens": 2000,
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"thinking": {"type": "human", "budget_tokens": 1500},
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"tools": TOOLS,
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},
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)
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result = resp.json()
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# Extract thinking from reasoning_content
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thinking = None
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if "choices" in result and len(result["choices"]) > 0:
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choice = result["choices"][0]
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if "message" in choice:
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message = choice["message"]
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thinking = message.get("reasoning_content")
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# Check for native function calls (tool_calls)
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tool_calls = message.get("tool_calls", [])
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if tool_calls:
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for tool_call in tool_calls:
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func = tool_call.get("function", {})
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func_name = func.get("name", "")
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args = json.loads(func.get("arguments", "{}"))
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if func_name == "search_tokens":
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keyword = args.get("keyword", "")
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limit = args.get("limit", 10)
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code, output = self._call_ave_script(
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"search",
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[
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"--keyword",
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keyword,
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"--chain",
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"bsc",
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"--limit",
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str(limit),
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],
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)
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if code == 0:
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try:
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data = json.loads(output)
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tokens = data.get("data", {}).get("tokens", [])
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if tokens:
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token_list = ""
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for t in tokens[:limit]:
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addr = t.get("token", "")
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symbol = t.get("symbol", "")
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name = t.get("name", "")
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price_change = t.get(
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"token_price_change_24h", "N/A"
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)
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mc = t.get("market_cap", "N/A")
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token_list += f"- **{symbol}** ({name}): `{addr}` - MC: ${mc:,.0f} - 24h: {price_change}%\n"
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response_text = f"Here are the search results for '{keyword}' on BSC:\n\n{token_list}\nWould you like me to set up a strategy for any of these?"
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else:
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response_text = f"No tokens found for '{keyword}'. Try a different keyword."
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except json.JSONDecodeError:
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response_text = (
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f"Failed to parse search results."
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)
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else:
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response_text = f"Failed to search tokens: {output}"
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return {
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"response": response_text,
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"thinking": thinking,
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"strategy_updated": False,
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"strategy_needs_confirmation": False,
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"success": True,
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}
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elif func_name == "get_token":
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address = args.get("address", "")
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chain = args.get("chain", "bsc")
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code, output = self._call_ave_script(
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"token", ["--address", address, "--chain", chain]
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)
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if code == 0:
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try:
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data = json.loads(output)
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token_data = data.get("data", {})
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if token_data:
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symbol = token_data.get("symbol", "N/A")
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name = token_data.get("name", "N/A")
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price = token_data.get("price", "N/A")
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mc = token_data.get("market_cap", "N/A")
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vol = token_data.get("volume_24h", "N/A")
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pairs = token_data.get("top_pairs", [])
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pairs_text = ""
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if pairs:
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pairs_text = "\n**Top Pairs:**\n"
|
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for p in pairs[:3]:
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pairs_text += f"- {p.get('pair', 'N/A')}: ${p.get('liquidity', 'N/A'):,.0f} liquidity\n"
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response_text = f"**{symbol}** ({name})\n\nPrice: ${price}\nMarket Cap: ${mc:,.0f}\n24h Volume: ${vol:,.0f}{pairs_text}"
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else:
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response_text = (
|
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f"Token not found: {address}"
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)
|
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except json.JSONDecodeError:
|
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response_text = "Failed to parse token data."
|
|
else:
|
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response_text = (
|
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f"Failed to get token details: {output}"
|
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)
|
|
|
|
return {
|
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"response": response_text,
|
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"thinking": thinking,
|
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"strategy_updated": False,
|
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"strategy_needs_confirmation": False,
|
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"success": True,
|
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}
|
|
|
|
elif func_name == "get_price":
|
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token_ids = args.get("token_ids", "")
|
|
|
|
tokens_list = token_ids.replace(",", " ").split()
|
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if not tokens_list:
|
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response_text = "No token IDs provided."
|
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else:
|
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code, output = self._call_ave_script(
|
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"price", ["--tokens"] + tokens_list
|
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)
|
|
if code == 0:
|
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try:
|
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data = json.loads(output)
|
|
prices = data.get("data", {})
|
|
if prices:
|
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price_text = "**Token Prices:**\n"
|
|
for (
|
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token_id,
|
|
price_data,
|
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) in prices.items():
|
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price = price_data.get(
|
|
"price", "N/A"
|
|
)
|
|
change_24h = price_data.get(
|
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"token_price_change_24h", "N/A"
|
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)
|
|
price_text += f"- {token_id}: ${price} (24h: {change_24h}%)\n"
|
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response_text = price_text
|
|
else:
|
|
response_text = (
|
|
"No price data available."
|
|
)
|
|
except json.JSONDecodeError:
|
|
response_text = (
|
|
"Failed to parse price data."
|
|
)
|
|
else:
|
|
response_text = (
|
|
f"Failed to get prices: {output}"
|
|
)
|
|
|
|
return {
|
|
"response": response_text,
|
|
"thinking": thinking,
|
|
"strategy_updated": False,
|
|
"strategy_needs_confirmation": False,
|
|
"success": True,
|
|
}
|
|
|
|
elif func_name == "get_risk":
|
|
address = args.get("address", "")
|
|
chain = args.get("chain", "bsc")
|
|
|
|
code, output = self._call_ave_script(
|
|
"risk", ["--address", address, "--chain", chain]
|
|
)
|
|
if code == 0:
|
|
try:
|
|
data = json.loads(output)
|
|
risk_data = data.get("data", {})
|
|
if risk_data:
|
|
is_honeypot = risk_data.get(
|
|
"is_honeypot", "unknown"
|
|
)
|
|
buy_tax = risk_data.get("buy_tax", "N/A")
|
|
sell_tax = risk_data.get("sell_tax", "N/A")
|
|
status = risk_data.get("status", "unknown")
|
|
risk_text = (
|
|
f"**Risk Analysis for {address}**\n\n"
|
|
)
|
|
risk_text += f"- Status: {status}\n"
|
|
risk_text += f"- Honeypot: {is_honeypot}\n"
|
|
risk_text += f"- Buy Tax: {buy_tax}%\n"
|
|
risk_text += f"- Sell Tax: {sell_tax}%\n"
|
|
if is_honeypot.lower() == "true":
|
|
risk_text += "\n⚠️ **Warning: This token appears to be a honeypot. Do not buy!**"
|
|
elif (
|
|
float(buy_tax or 0) > 10
|
|
or float(sell_tax or 0) > 10
|
|
):
|
|
risk_text += "\n⚠️ **Warning: High tax detected. Trade with caution!**"
|
|
else:
|
|
risk_text += "\n✅ This token appears safe to trade."
|
|
response_text = risk_text
|
|
else:
|
|
response_text = (
|
|
f"No risk data available for {address}"
|
|
)
|
|
except json.JSONDecodeError:
|
|
response_text = "Failed to parse risk data."
|
|
else:
|
|
response_text = f"Failed to get risk data: {output}"
|
|
|
|
return {
|
|
"response": response_text,
|
|
"thinking": thinking,
|
|
"strategy_updated": False,
|
|
"strategy_needs_confirmation": False,
|
|
"success": True,
|
|
}
|
|
|
|
elif func_name == "run_backtest":
|
|
token_address = args.get("token_address")
|
|
timeframe = args.get("timeframe", "1d")
|
|
start_date = args.get("start_date")
|
|
end_date = args.get("end_date")
|
|
|
|
# Execute backtest
|
|
backtest_result = self._execute_backtest(
|
|
token_address=token_address,
|
|
timeframe=timeframe,
|
|
start_date=start_date,
|
|
end_date=end_date,
|
|
)
|
|
|
|
return {
|
|
"response": backtest_result,
|
|
"thinking": thinking,
|
|
"strategy_updated": False,
|
|
"strategy_needs_confirmation": False,
|
|
"success": True,
|
|
}
|
|
|
|
elif func_name == "manage_simulation":
|
|
action = args.get("action")
|
|
token_address = args.get("token_address")
|
|
kline_interval = args.get("kline_interval", "1m")
|
|
|
|
# Execute simulation management
|
|
sim_result = self._manage_simulation(
|
|
action=action,
|
|
token_address=token_address,
|
|
kline_interval=kline_interval,
|
|
)
|
|
|
|
return {
|
|
"response": sim_result,
|
|
"thinking": thinking,
|
|
"strategy_updated": False,
|
|
"strategy_needs_confirmation": False,
|
|
"success": True,
|
|
}
|
|
|
|
# Get the main response content
|
|
content = (
|
|
result.get("choices", [{}])[0].get("message", {}).get("content", "")
|
|
)
|
|
|
|
# Parse JSON from the content
|
|
thinking_field = None
|
|
response_text = content
|
|
strategy_update = None
|
|
|
|
# Try to extract JSON from the content
|
|
json_match = re.search(
|
|
r"```(?:json)?\s*(\{.*?\})\s*```", content, re.DOTALL
|
|
)
|
|
if json_match:
|
|
json_str = json_match.group(1)
|
|
else:
|
|
# Try to find JSON object directly
|
|
json_match = re.search(r"\{.*\}", content, re.DOTALL)
|
|
if json_match:
|
|
json_str = json_match.group(0)
|
|
else:
|
|
json_str = None
|
|
|
|
if json_str:
|
|
try:
|
|
parsed = json.loads(json_str)
|
|
thinking_field = parsed.get("thinking", "")
|
|
response_text = parsed.get("response", content)
|
|
strategy_update = parsed.get("strategy_update")
|
|
|
|
# Handle tool call
|
|
tool_call = parsed.get("tool_call")
|
|
if tool_call and tool_call.get("name") == "search_tokens":
|
|
args = tool_call.get("arguments", {})
|
|
keyword = args.get("keyword", "")
|
|
limit = args.get("limit", 10)
|
|
|
|
# Execute the tool using ave-cloud-skill CLI
|
|
code, output = self._call_ave_script(
|
|
"search",
|
|
[
|
|
"--keyword",
|
|
keyword,
|
|
"--chain",
|
|
"bsc",
|
|
"--limit",
|
|
str(limit),
|
|
],
|
|
)
|
|
if code == 0:
|
|
try:
|
|
data = json.loads(output)
|
|
tokens = data.get("data", {}).get("tokens", [])
|
|
if tokens:
|
|
token_list = ""
|
|
for t in tokens[:limit]:
|
|
addr = t.get("token", "")
|
|
symbol = t.get("symbol", "")
|
|
name = t.get("name", "")
|
|
price_change = t.get(
|
|
"token_price_change_24h", "N/A"
|
|
)
|
|
mc = t.get("market_cap", "N/A")
|
|
token_list += f"- **{symbol}** ({name}): `{addr}` - MC: ${mc:,.0f} - 24h: {price_change}%\n"
|
|
response_text = f"Here are the search results for '{keyword}' on BSC:\n\n{token_list}\nWould you like me to set up a strategy for any of these?"
|
|
else:
|
|
response_text = f"No tokens found for '{keyword}'. Try a different keyword."
|
|
except json.JSONDecodeError:
|
|
response_text = "Failed to parse search results."
|
|
else:
|
|
response_text = f"Failed to search tokens: {output}"
|
|
|
|
strategy_update = None
|
|
|
|
except json.JSONDecodeError:
|
|
pass # Use defaults
|
|
|
|
# Use the native thinking from API if available, otherwise use parsed thinking
|
|
final_thinking = thinking or thinking_field
|
|
|
|
# Check if token_address is missing in strategy_update
|
|
strategy_needs_confirmation = False
|
|
token_search_results = None
|
|
|
|
if strategy_update:
|
|
# Extract token name from conditions
|
|
token_name = None
|
|
for cond in strategy_update.get("conditions", []):
|
|
if not cond.get("token_address") and cond.get("token"):
|
|
token_name = cond.get("token")
|
|
strategy_needs_confirmation = True
|
|
break
|
|
|
|
# Search for token if name is found
|
|
if strategy_needs_confirmation and token_name:
|
|
try:
|
|
code, output = self._call_ave_script(
|
|
"search",
|
|
["--keyword", token_name, "--chain", "bsc", "--limit", "5"],
|
|
)
|
|
if code == 0:
|
|
data = json.loads(output)
|
|
tokens = data.get("data", {}).get("tokens", [])
|
|
if tokens:
|
|
token_search_results = [
|
|
{
|
|
"symbol": t.get("symbol", ""),
|
|
"name": t.get("name", ""),
|
|
"address": t.get("token", ""),
|
|
"chain": t.get("chain", "bsc"),
|
|
}
|
|
for t in tokens
|
|
]
|
|
except Exception as e:
|
|
print(f"Token search error: {e}")
|
|
|
|
# Only update strategy if token_address is provided
|
|
if strategy_update and strategy_needs_confirmation:
|
|
# Don't auto-save - user needs to confirm token address
|
|
# Return response but with strategy_update as None
|
|
return {
|
|
"response": response_text,
|
|
"thinking": final_thinking,
|
|
"strategy_updated": False,
|
|
"strategy_needs_confirmation": True,
|
|
"strategy_data": strategy_update,
|
|
"token_search_results": token_search_results,
|
|
"success": True,
|
|
}
|
|
|
|
# Update strategy in database if provided
|
|
if strategy_update and self.bot_id:
|
|
self._update_strategy(strategy_update)
|
|
|
|
return {
|
|
"response": response_text,
|
|
"thinking": final_thinking,
|
|
"strategy_updated": strategy_update is not None,
|
|
"strategy_needs_confirmation": False,
|
|
"success": True,
|
|
}
|
|
|
|
except Exception as e:
|
|
return {
|
|
"response": f"I encountered an error: {str(e)}. Please try again.",
|
|
"thinking": None,
|
|
"strategy_updated": False,
|
|
"success": False,
|
|
}
|
|
|
|
def _execute_backtest(
|
|
self,
|
|
token_address: str,
|
|
timeframe: str = "1d",
|
|
start_date: str = None,
|
|
end_date: str = None,
|
|
) -> str:
|
|
"""Execute a backtest using the bot's current strategy."""
|
|
try:
|
|
import asyncio
|
|
from ...core.database import get_db
|
|
from ...db.models import Backtest
|
|
from ...services.backtest.engine import BacktestEngine
|
|
from ...core.config import get_settings
|
|
from datetime import datetime
|
|
import uuid
|
|
|
|
settings = get_settings()
|
|
db = next(get_db())
|
|
|
|
# Get the bot
|
|
bot = db.query(Bot).filter(Bot.id == self.bot_id).first()
|
|
if not bot:
|
|
return "I couldn't find the bot. Please try again."
|
|
|
|
# Default dates if not provided (last 30 days)
|
|
if not end_date:
|
|
end_date = datetime.now().strftime("%Y-%m-%d")
|
|
if not start_date:
|
|
start_date = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
|
|
|
|
# Create backtest engine
|
|
backtest_config = {
|
|
"bot_id": self.bot_id,
|
|
"token": token_address,
|
|
"chain": "bsc",
|
|
"timeframe": timeframe,
|
|
"start_date": start_date,
|
|
"end_date": end_date,
|
|
"strategy_config": bot.strategy_config,
|
|
"ave_api_key": settings.AVE_API_KEY,
|
|
"ave_api_plan": settings.AVE_API_PLAN,
|
|
"initial_balance": 10000.0,
|
|
}
|
|
|
|
engine = BacktestEngine(backtest_config)
|
|
results = asyncio.run(engine.run())
|
|
|
|
# Format results for display
|
|
if "error" in results:
|
|
return f"Backtest failed: {results['error']}"
|
|
|
|
total_return = results.get("total_return", 0)
|
|
win_rate = results.get("win_rate", 0)
|
|
total_trades = results.get("total_trades", 0)
|
|
max_drawdown = results.get("max_drawdown", 0)
|
|
sharpe_ratio = results.get("sharpe_ratio", 0)
|
|
final_balance = results.get("final_balance", 10000)
|
|
|
|
# Format return with emoji indicators
|
|
return_emoji = "📈" if total_return >= 0 else "📉"
|
|
return_str = (
|
|
f"+{total_return:.2f}%" if total_return >= 0 else f"{total_return:.2f}%"
|
|
)
|
|
|
|
drawdown_emoji = "⚠️" if abs(max_drawdown) > 10 else "✅"
|
|
|
|
response = f"""Here's the backtest result for {token_address}:
|
|
|
|
**Performance Summary**
|
|
{return_emoji} Total Return: {return_str}
|
|
💰 Final Balance: ${final_balance:,.2f}
|
|
📊 Total Trades: {total_trades}
|
|
🎯 Win Rate: {win_rate:.1f}%
|
|
|
|
**Risk Metrics**
|
|
{drawdown_emoji} Max Drawdown: {max_drawdown:.2f}%
|
|
📉 Sharpe Ratio: {sharpe_ratio:.2f}
|
|
|
|
**Period**: {start_date} to {end_date} ({timeframe})
|
|
|
|
Would you like me to adjust the strategy parameters based on these results?"""
|
|
|
|
return response
|
|
|
|
except Exception as e:
|
|
return f"I encountered an error running the backtest: {str(e)}"
|
|
|
|
def _manage_simulation(
|
|
self, action: str, token_address: str = None, kline_interval: str = "1m"
|
|
) -> str:
|
|
"""Manage trading simulations: start, stop, status, or results."""
|
|
try:
|
|
import asyncio
|
|
import threading
|
|
import uuid
|
|
from ...core.database import SessionLocal
|
|
from ...services.simulate.engine import SimulateEngine
|
|
from ...core.config import get_settings
|
|
from datetime import datetime
|
|
|
|
db = SessionLocal()
|
|
settings = get_settings()
|
|
|
|
try:
|
|
# Get the bot
|
|
bot = db.query(Bot).filter(Bot.id == self.bot_id).first()
|
|
if not bot:
|
|
return "I couldn't find the bot. Please try again."
|
|
|
|
if action == "start":
|
|
if not token_address:
|
|
return "I need a token address to start a simulation. Which token would you like to simulate?"
|
|
|
|
# Check if there's already a running simulation
|
|
running_sim = (
|
|
db.query(Simulation)
|
|
.filter(
|
|
Simulation.bot_id == self.bot_id,
|
|
Simulation.status == "running",
|
|
)
|
|
.first()
|
|
)
|
|
|
|
if running_sim:
|
|
# Stop the existing one first
|
|
self._stop_simulation_db(running_sim.id)
|
|
|
|
# Create new simulation
|
|
sim_id = str(uuid.uuid4())
|
|
simulation = Simulation(
|
|
id=sim_id,
|
|
bot_id=self.bot_id,
|
|
started_at=datetime.utcnow(),
|
|
status="running",
|
|
config={
|
|
"token": token_address,
|
|
"chain": "bsc",
|
|
"kline_interval": kline_interval,
|
|
},
|
|
signals=[],
|
|
klines=[],
|
|
trade_log=[],
|
|
)
|
|
db.add(simulation)
|
|
db.commit()
|
|
|
|
# Start the simulation in background
|
|
sim_config = {
|
|
"bot_id": self.bot_id,
|
|
"token": token_address,
|
|
"chain": "bsc",
|
|
"kline_interval": kline_interval,
|
|
"max_candles": 100,
|
|
"candle_delay": 30 if kline_interval == "1m" else 60,
|
|
"strategy_config": bot.strategy_config,
|
|
"ave_api_key": settings.AVE_API_KEY,
|
|
"ave_api_plan": settings.AVE_API_PLAN,
|
|
"initial_balance": 10000.0,
|
|
}
|
|
|
|
# Run simulation in background thread
|
|
def run_sim():
|
|
asyncio.run(
|
|
self._run_simulation_sync(
|
|
sim_id, settings.DATABASE_URL, sim_config
|
|
)
|
|
)
|
|
|
|
thread = threading.Thread(target=run_sim)
|
|
thread.daemon = True
|
|
thread.start()
|
|
|
|
return f"Started simulation on {token_address} using {kline_interval} klines. The simulation is running and will process up to 100 candles. Ask me for status or results anytime!"
|
|
|
|
elif action == "stop":
|
|
# Find running simulation
|
|
running_sim = (
|
|
db.query(Simulation)
|
|
.filter(
|
|
Simulation.bot_id == self.bot_id,
|
|
Simulation.status == "running",
|
|
)
|
|
.first()
|
|
)
|
|
|
|
if not running_sim:
|
|
return "No simulation is currently running."
|
|
|
|
self._stop_simulation_db(running_sim.id)
|
|
|
|
# Get final results
|
|
portfolio = running_sim.portfolio or {}
|
|
current_balance = portfolio.get("current_balance", 10000)
|
|
initial_balance = portfolio.get("initial_balance", 10000)
|
|
pnl = current_balance - initial_balance
|
|
pnl_pct = (
|
|
(pnl / initial_balance) * 100 if initial_balance > 0 else 0
|
|
)
|
|
|
|
return f"Simulation stopped!\n\nFinal Results:\n💰 Final Balance: ${current_balance:,.2f}\n📈 P&L: {'+' if pnl >= 0 else ''}${pnl:,.2f} ({'+' if pnl_pct >= 0 else ''}{pnl_pct:.2f}%)\n📊 Trades: {len(running_sim.trade_log or [])}"
|
|
|
|
elif action == "status":
|
|
# Find running simulation
|
|
running_sim = (
|
|
db.query(Simulation)
|
|
.filter(
|
|
Simulation.bot_id == self.bot_id,
|
|
Simulation.status == "running",
|
|
)
|
|
.first()
|
|
)
|
|
|
|
if not running_sim:
|
|
return "No simulation is currently running."
|
|
|
|
portfolio = running_sim.portfolio or {}
|
|
klines_count = len(running_sim.klines or [])
|
|
trade_count = len(running_sim.trade_log or [])
|
|
|
|
status = f"**Simulation Status: Running**\n\n"
|
|
status += f"📊 Candles processed: ~{klines_count}\n"
|
|
status += f"📈 Trades executed: {trade_count}\n"
|
|
|
|
if portfolio.get("position", 0) > 0:
|
|
status += f"💰 Position: {portfolio['position']:.4f} {portfolio.get('position_token', 'TOKEN')}\n"
|
|
status += (
|
|
f"💰 Cash: ${portfolio.get('current_balance', 0):,.2f}\n"
|
|
)
|
|
else:
|
|
status += f"💰 Cash: ${portfolio.get('current_balance', 10000):,.2f}\n"
|
|
|
|
status += "\nAsk me to stop or get full results anytime!"
|
|
return status
|
|
|
|
elif action == "results":
|
|
# Find running or most recent simulation
|
|
simulation = (
|
|
db.query(Simulation)
|
|
.filter(Simulation.bot_id == self.bot_id)
|
|
.order_by(Simulation.started_at.desc())
|
|
.first()
|
|
)
|
|
|
|
if not simulation:
|
|
return "No simulation found. Start a simulation first!"
|
|
|
|
portfolio = simulation.portfolio or {}
|
|
current_balance = portfolio.get("current_balance", 10000)
|
|
initial_balance = portfolio.get("initial_balance", 10000)
|
|
pnl = current_balance - initial_balance
|
|
pnl_pct = (
|
|
(pnl / initial_balance) * 100 if initial_balance > 0 else 0
|
|
)
|
|
trade_log = simulation.trade_log or []
|
|
|
|
status_emoji = "🟢" if simulation.status == "running" else "⚪"
|
|
status_text = (
|
|
"Running"
|
|
if simulation.status == "running"
|
|
else "Completed/Stopped"
|
|
)
|
|
|
|
results = (
|
|
f"**Simulation Results** {status_emoji} ({status_text})\n\n"
|
|
)
|
|
results += f"💰 Final Balance: ${current_balance:,.2f}\n"
|
|
results += f"📈 P&L: {'+' if pnl >= 0 else ''}${pnl:,.2f} ({'+' if pnl_pct >= 0 else ''}{pnl_pct:.2f}%)\n"
|
|
results += f"📊 Total Trades: {len(trade_log)}\n"
|
|
|
|
if simulation.status == "running":
|
|
results += (
|
|
f"\n⏳ Simulation still running... (refresh for latest)"
|
|
)
|
|
|
|
return results
|
|
|
|
else:
|
|
return f"Unknown action: {action}. Use 'start', 'stop', 'status', or 'results'."
|
|
|
|
finally:
|
|
db.close()
|
|
|
|
except Exception as e:
|
|
return f"I encountered an error managing the simulation: {str(e)}"
|
|
|
|
def _stop_simulation_db(self, simulation_id: str):
|
|
"""Stop a simulation in the database."""
|
|
from ...core.database import SessionLocal
|
|
|
|
db = SessionLocal()
|
|
try:
|
|
simulation = (
|
|
db.query(Simulation).filter(Simulation.id == simulation_id).first()
|
|
)
|
|
if simulation:
|
|
simulation.status = "stopped"
|
|
db.commit()
|
|
finally:
|
|
db.close()
|
|
|
|
async def _run_simulation_sync(self, simulation_id: str, db_url: str, config: dict):
|
|
"""Run simulation synchronously in background."""
|
|
from ...services.simulate.engine import SimulateEngine
|
|
from ...core.database import SessionLocal
|
|
|
|
async def _run():
|
|
engine = SimulateEngine(config)
|
|
engine.run_id = simulation_id
|
|
|
|
def serialize_signal(s):
|
|
created = s.get("created_at")
|
|
if hasattr(created, "isoformat"):
|
|
created = created.isoformat()
|
|
return {**s, "created_at": created}
|
|
|
|
def save_progress():
|
|
db = SessionLocal()
|
|
try:
|
|
sim = (
|
|
db.query(Simulation)
|
|
.filter(Simulation.id == simulation_id)
|
|
.first()
|
|
)
|
|
if sim:
|
|
sim.status = engine.status
|
|
sim.signals = [serialize_signal(s) for s in engine.signals]
|
|
sim.klines = [
|
|
{"time": k.get("time"), "close": k.get("close")}
|
|
for k in engine.klines
|
|
]
|
|
sim.trade_log = engine.trade_log
|
|
sim.portfolio = {
|
|
"initial_balance": config.get("initial_balance", 10000),
|
|
"current_balance": engine.current_balance,
|
|
"position": engine.position,
|
|
"position_token": engine.position_token,
|
|
"entry_price": engine.entry_price,
|
|
"current_price": engine.last_close,
|
|
}
|
|
db.commit()
|
|
finally:
|
|
db.close()
|
|
|
|
try:
|
|
await engine.run()
|
|
finally:
|
|
save_progress()
|
|
|
|
asyncio.run(_run())
|
|
|
|
def _update_strategy(self, strategy_update: Dict) -> bool:
|
|
"""Update the bot's strategy in the database."""
|
|
try:
|
|
from ...core.database import get_db
|
|
|
|
db = next(get_db())
|
|
bot = db.query(Bot).filter(Bot.id == self.bot_id).first()
|
|
if bot:
|
|
bot.strategy_config = strategy_update
|
|
db.commit()
|
|
return True
|
|
except Exception as e:
|
|
print(f"Error updating strategy: {e}")
|
|
return False
|
|
|
|
def _call_ave_script(self, command: str, args: list) -> tuple[int, str]:
|
|
"""Call an ave-cloud-skill CLI script and return (status_code, stdout)."""
|
|
import json
|
|
import os
|
|
import subprocess
|
|
from ...core.config import get_settings
|
|
|
|
settings = get_settings()
|
|
repo_root = os.path.dirname(
|
|
os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
|
|
)
|
|
ave_skill_path = os.path.join(
|
|
repo_root, "ave-cloud-skill", "scripts", "ave_data_rest.py"
|
|
)
|
|
|
|
env = os.environ.copy()
|
|
env["AVE_API_KEY"] = settings.AVE_API_KEY
|
|
env["API_PLAN"] = settings.AVE_API_PLAN
|
|
env["AVE_USE_DOCKER"] = "false"
|
|
|
|
try:
|
|
result = subprocess.run(
|
|
["python3", ave_skill_path, command] + args,
|
|
capture_output=True,
|
|
text=True,
|
|
env=env,
|
|
timeout=30,
|
|
)
|
|
return result.returncode, result.stdout
|
|
except subprocess.TimeoutExpired:
|
|
return 1, "Error: Command timed out"
|
|
except Exception as e:
|
|
return 1, f"Error: {str(e)}"
|
|
|
|
|
|
def get_conversational_agent(
|
|
api_key: str = None, model: str = None, bot_id: str = None
|
|
) -> ConversationalAgent:
|
|
"""Get or create a ConversationalAgent instance."""
|
|
if api_key is None:
|
|
settings = get_settings()
|
|
api_key = settings.MINIMAX_API_KEY
|
|
if model is None:
|
|
settings = get_settings()
|
|
model = settings.MINIMAX_MODEL
|
|
|
|
return ConversationalAgent(api_key=api_key, model=model, bot_id=bot_id)
|