552 lines
20 KiB
Python
552 lines
20 KiB
Python
import os
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import sys
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import pandas as pd
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import numpy as np
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from typing import Dict, Any, List, Optional
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import datetime
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import time
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import json
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from datetime import datetime, timedelta
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# 添加项目根目录到Python路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from api.binance_api import BinanceAPI
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from api.deepseek_api import DeepSeekAPI
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from models.data_processor import DataProcessor
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from utils.config_loader import ConfigLoader
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from utils.dingtalk_bot import DingTalkBot
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class CryptoAgent:
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"""加密货币智能体，用于分析市场数据并生成交易策略"""
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def __init__(self, config_path: str = None):
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"""
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初始化加密货币智能体
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Args:
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config_path: 配置文件路径，如果为None，则使用默认路径
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"""
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# 加载配置
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self.config_loader = ConfigLoader(config_path)
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# 获取各部分配置
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self.binance_config = self.config_loader.get_binance_config()
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self.deepseek_config = self.config_loader.get_deepseek_config()
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self.crypto_config = self.config_loader.get_crypto_config()
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self.data_config = self.config_loader.get_data_config()
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self.agent_config = self.config_loader.get_agent_config()
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self.dingtalk_config = self.config_loader.get_dingtalk_config()
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# 初始化API客户端
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self.binance_api = BinanceAPI(
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api_key=self.binance_config['api_key'],
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api_secret=self.binance_config['api_secret'],
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test_mode=self.binance_config['test_mode']
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)
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self.deepseek_api = DeepSeekAPI(
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api_key=self.deepseek_config['api_key'],
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model=self.deepseek_config['model']
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)
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# 初始化数据处理器
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self.data_processor = DataProcessor(storage_path=self.data_config['storage_path'])
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# 初始化钉钉机器人（如果启用）
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self.dingtalk_bot = None
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if self.dingtalk_config.get('enabled', False):
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self.dingtalk_bot = DingTalkBot(
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webhook_url=self.dingtalk_config['webhook_url'],
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secret=self.dingtalk_config.get('secret')
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)
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print("钉钉机器人已启用")
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# 设置支持的加密货币
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self.base_currencies = self.crypto_config['base_currencies']
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self.quote_currency = self.crypto_config['quote_currency']
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self.symbols = [f"{base}{self.quote_currency}" for base in self.base_currencies]
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# 设置时间间隔
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self.time_interval = self.crypto_config['time_interval']
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# 风险等级
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self.risk_level = self.agent_config['risk_level']
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# 支持的策略
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self.strategies = self.agent_config['strategies']
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# 分析结果缓存
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self.analysis_cache = {}
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def fetch_historical_data(self, symbol: str, days: int = 30) -> pd.DataFrame:
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"""
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获取历史数据
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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days: 要获取的天数
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Returns:
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历史数据DataFrame
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"""
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print(f"获取{symbol}的历史数据（{days}天）...")
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# 计算开始时间
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start_time = datetime.now() - timedelta(days=days)
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start_str = start_time.strftime("%Y-%m-%d")
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# 获取K线数据
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data = self.binance_api.get_historical_klines(
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symbol=symbol,
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interval=self.time_interval,
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start_str=start_str
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)
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if data.empty:
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print(f"无法获取{symbol}的历史数据")
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return pd.DataFrame()
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# 保存原始数据
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raw_data_path = self.data_processor.save_data(symbol, data, data_type='raw')
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print(f"原始数据已保存到: {raw_data_path}")
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return data
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def process_data(self, symbol: str, data: pd.DataFrame) -> pd.DataFrame:
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"""
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处理数据
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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data: 原始数据
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Returns:
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处理后的数据
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"""
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print(f"处理{symbol}的数据...")
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# 预处理数据
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processed_data = self.data_processor.preprocess_market_data(symbol, data)
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# 保存处理后的数据
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processed_data_path = self.data_processor.save_data(symbol, processed_data, data_type='processed')
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print(f"处理后的数据已保存到: {processed_data_path}")
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return processed_data
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def analyze_market(self, symbol: str, data: pd.DataFrame = None) -> Dict[str, Any]:
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"""
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分析市场
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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data: 要分析的数据，如果为None，则获取最新数据
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Returns:
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市场分析结果
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"""
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print(f"分析{symbol}的市场情况...")
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if data is None:
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# 尝试加载最新的处理后数据
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latest_data_file = self.data_processor.get_latest_data_file(symbol, data_type='processed')
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if latest_data_file:
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data = self.data_processor.load_data(latest_data_file)
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else:
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# 如果没有找到处理后的数据，则获取新数据
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raw_data = self.fetch_historical_data(symbol, days=self.data_config['historical_days'])
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data = self.process_data(symbol, raw_data)
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if data.empty:
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print(f"没有可用的{symbol}数据进行分析")
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return {}
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# 准备市场数据
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market_data = {
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"symbol": symbol,
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"current_price": float(data['close'].iloc[-1]),
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"price_change_24h": float(data['close'].iloc[-1] - data['close'].iloc[-24]),
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"price_change_percentage_24h": float((data['close'].iloc[-1] - data['close'].iloc[-24]) / data['close'].iloc[-24] * 100),
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"historical_prices": data['close'].tail(100).tolist(),
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"volumes": data['volume'].tail(100).tolist(),
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"technical_indicators": {
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"rsi": float(data['RSI'].iloc[-1]),
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"macd": float(data['MACD'].iloc[-1]),
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"macd_signal": float(data['MACD_Signal'].iloc[-1]),
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"bollinger_upper": float(data['Bollinger_Upper'].iloc[-1]),
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"bollinger_lower": float(data['Bollinger_Lower'].iloc[-1]),
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"ma5": float(data['MA5'].iloc[-1]),
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"ma10": float(data['MA10'].iloc[-1]),
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"ma20": float(data['MA20'].iloc[-1]),
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"ma50": float(data['MA50'].iloc[-1]),
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"atr": float(data['ATR'].iloc[-1])
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}
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}
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# 获取当前市场订单簿
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order_book = self.binance_api.get_order_book(symbol, limit=10)
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if order_book:
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market_data["order_book"] = {
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"bids": order_book.get('bids', [])[:5],
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"asks": order_book.get('asks', [])[:5]
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}
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# 使用DeepSeek进行市场分析
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analysis_result = self.deepseek_api.analyze_market_data(market_data)
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# 缓存分析结果
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self.analysis_cache[symbol] = analysis_result
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return analysis_result
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def predict_price(self, symbol: str) -> Dict[str, Any]:
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"""
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预测价格
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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Returns:
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价格预测结果
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"""
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print(f"预测{symbol}的价格趋势...")
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# 尝试加载最新的处理后数据
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latest_data_file = self.data_processor.get_latest_data_file(symbol, data_type='processed')
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if not latest_data_file:
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# 如果没有找到处理后的数据，则获取新数据
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raw_data = self.fetch_historical_data(symbol, days=self.data_config['historical_days'])
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data = self.process_data(symbol, raw_data)
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else:
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data = self.data_processor.load_data(latest_data_file)
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if data.empty:
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print(f"没有可用的{symbol}数据进行预测")
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return {}
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# 准备历史数据
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historical_data = {
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"symbol": symbol,
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"current_price": float(data['close'].iloc[-1]),
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"price_history": data[['open', 'high', 'low', 'close']].tail(90).to_dict(orient='records'),
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"volume_history": data['volume'].tail(90).tolist(),
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"technical_indicators": {
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"rsi_history": data['RSI'].tail(30).tolist(),
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"macd_history": data['MACD'].tail(30).tolist(),
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"ma5_history": data['MA5'].tail(30).tolist(),
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"ma20_history": data['MA20'].tail(30).tolist(),
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"ma50_history": data['MA50'].tail(30).tolist()
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}
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}
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# 使用DeepSeek进行价格预测
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prediction_result = self.deepseek_api.predict_price_trend(symbol, historical_data)
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return prediction_result
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def generate_strategy(self, symbol: str, analysis_result: Dict[str, Any] = None) -> Dict[str, Any]:
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"""
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生成交易策略
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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analysis_result: 分析结果，如果为None，则使用缓存或重新分析
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Returns:
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交易策略
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"""
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print(f"为{symbol}生成交易策略...")
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if analysis_result is None:
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# 使用缓存的分析结果或重新分析
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if symbol in self.analysis_cache:
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analysis_result = self.analysis_cache[symbol]
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else:
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analysis_result = self.analyze_market(symbol)
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if not analysis_result:
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print(f"没有可用的{symbol}分析结果来生成策略")
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return {}
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# 使用DeepSeek生成交易策略
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strategy = self.deepseek_api.generate_trading_strategy(
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symbol=symbol,
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analysis_result=analysis_result,
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risk_level=self.risk_level
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)
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return strategy
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def analyze_all_symbols(self) -> Dict[str, Dict[str, Any]]:
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"""
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分析所有支持的交易对
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Returns:
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所有交易对的分析结果
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"""
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results = {}
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for symbol in self.symbols:
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print(f"\n开始分析{symbol}...")
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# 获取并处理数据
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raw_data = self.fetch_historical_data(symbol, days=self.data_config['historical_days'])
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if not raw_data.empty:
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processed_data = self.process_data(symbol, raw_data)
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# 分析市场
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analysis_result = self.analyze_market(symbol, processed_data)
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# 预测价格
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prediction_result = self.predict_price(symbol)
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# 生成策略
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strategy = self.generate_strategy(symbol, analysis_result)
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# 整合结果
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results[symbol] = {
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"analysis": analysis_result,
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"prediction": prediction_result,
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"strategy": strategy,
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"timestamp": datetime.now().isoformat()
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}
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# 如果钉钉机器人已启用，发送分析报告
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if self.dingtalk_bot:
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try:
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print(f"发送{symbol}分析报告到钉钉...")
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response = self.dingtalk_bot.send_analysis_report(symbol, results[symbol])
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if response.get('errcode') == 0:
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print(f"{symbol}分析报告发送成功")
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else:
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print(f"{symbol}分析报告发送失败: {response}")
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except Exception as e:
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print(f"发送{symbol}分析报告时出错: {e}")
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print(f"{symbol}分析完成")
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else:
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print(f"跳过{symbol}分析，无法获取数据")
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return results
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def execute_strategy(self, symbol: str, strategy: Dict[str, Any]) -> Dict[str, Any]:
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"""
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执行交易策略
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Args:
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symbol: 交易对符号，例如 'BTCUSDT'
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strategy: 交易策略
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Returns:
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执行结果
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"""
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if not strategy or 'position' not in strategy:
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print(f"无法执行{symbol}的策略，策略数据不完整")
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return {"status": "error", "message": "策略数据不完整"}
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print(f"执行{symbol}的交易策略...")
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# 获取当前价格
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current_price_info = self.binance_api.get_ticker(symbol)
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if not current_price_info:
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print(f"无法获取{symbol}的当前价格")
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return {"status": "error", "message": "无法获取当前价格"}
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current_price = float(current_price_info.get('price', 0))
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# 根据策略决定操作
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position = strategy.get('position', '').upper()
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result = {
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"symbol": symbol,
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"strategy": strategy,
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"current_price": current_price,
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"timestamp": datetime.now().isoformat()
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}
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# 简单策略执行逻辑
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if position == 'BUY' or '买' in position:
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# 示例：下单买入
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# 在实际应用中，这里应该有更复杂的仓位管理和风险管理逻辑
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quantity = 0.01 # 示例数量，实际应用中应该基于资金和风险计算
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order_result = self.binance_api.place_order(
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symbol=symbol,
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side='BUY',
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type='MARKET',
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quantity=quantity
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)
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result["action"] = "BUY"
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result["order_result"] = order_result
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# 如果钉钉机器人已启用，发送交易通知
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if self.dingtalk_bot:
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self.send_trade_notification(symbol, "买入", quantity, current_price, strategy)
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elif position == 'SELL' or '卖' in position:
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# 示例：下单卖出
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quantity = 0.01 # 示例数量
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order_result = self.binance_api.place_order(
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symbol=symbol,
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side='SELL',
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type='MARKET',
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quantity=quantity
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)
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result["action"] = "SELL"
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result["order_result"] = order_result
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# 如果钉钉机器人已启用，发送交易通知
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if self.dingtalk_bot:
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self.send_trade_notification(symbol, "卖出", quantity, current_price, strategy)
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else:
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# 持有或其他策略
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result["action"] = "HOLD"
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result["message"] = "根据策略保持当前仓位"
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print(f"执行{symbol}策略完成：{result['action']}")
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return result
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def send_trade_notification(self, symbol: str, action: str, quantity: float, price: float, strategy: Dict[str, Any]) -> None:
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"""
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发送交易通知到钉钉
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Args:
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symbol: 交易对符号
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action: 交易操作（买入或卖出）
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quantity: 交易数量
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price: 交易价格
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strategy: 交易策略
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"""
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if not self.dingtalk_bot:
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return
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try:
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# 设置图标
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if action == "买入":
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icon = "🟢"
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else:
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icon = "🔴"
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# 获取策略理由
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reasoning = strategy.get('reasoning', '无理由')
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# 构建通知内容
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title = f"{icon} {symbol} {action}交易执行通知"
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text = f"""### {symbol} {action}交易已执行
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**交易详情**:
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- 操作: {icon} **{action}**
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- 数量: {quantity}
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- 价格: {price}
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- 总额: {quantity * price} {self.quote_currency}
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**交易理由**:
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{reasoning}
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*交易时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*
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"""
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# 发送通知
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at_mobiles = self.dingtalk_config.get('at_mobiles', [])
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at_all = self.dingtalk_config.get('at_all', False)
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self.dingtalk_bot.send_markdown(title, text, at_mobiles, at_all)
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print(f"{symbol} {action}交易通知已发送")
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except Exception as e:
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print(f"发送交易通知时出错: {e}")
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def run_analysis_cycle(self) -> Dict[str, Any]:
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"""
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运行一个完整的分析周期
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Returns:
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分析结果
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"""
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print(f"开始新的分析周期，时间：{datetime.now().isoformat()}")
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results = self.analyze_all_symbols()
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# 保存分析结果
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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results_dir = os.path.join(self.data_config['storage_path'], 'analysis_results')
|
||
os.makedirs(results_dir, exist_ok=True)
|
||
|
||
results_file = os.path.join(results_dir, f"analysis_results_{timestamp}.json")
|
||
|
||
with open(results_file, 'w', encoding='utf-8') as f:
|
||
json.dump(results, f, indent=2, ensure_ascii=False)
|
||
|
||
print(f"分析结果已保存到：{results_file}")
|
||
|
||
return results
|
||
|
||
def start_agent(self, run_once: bool = False) -> None:
|
||
"""
|
||
启动智能体
|
||
|
||
Args:
|
||
run_once: 是否只运行一次分析周期
|
||
"""
|
||
print("启动加密货币分析智能体...")
|
||
|
||
try:
|
||
if run_once:
|
||
self.run_analysis_cycle()
|
||
else:
|
||
while True:
|
||
self.run_analysis_cycle()
|
||
|
||
# 等待下一个分析周期
|
||
analysis_interval = self.agent_config['analysis_interval']
|
||
print(f"等待{analysis_interval}分钟后进行下一次分析...")
|
||
time.sleep(analysis_interval * 60)
|
||
|
||
except KeyboardInterrupt:
|
||
print("\n智能体已停止")
|
||
|
||
except Exception as e:
|
||
print(f"智能体运行时出错: {e}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
def send_notifications(self, symbol: str, analysis_data: Dict[str, Any]) -> bool:
|
||
"""
|
||
发送分析结果通知
|
||
|
||
Args:
|
||
symbol: 交易对符号
|
||
analysis_data: 分析数据
|
||
|
||
Returns:
|
||
发送是否成功
|
||
"""
|
||
if not self.dingtalk_bot:
|
||
print(f"钉钉通知未启用，跳过发送 {symbol} 的分析结果")
|
||
return False
|
||
|
||
try:
|
||
# 使用已初始化的钉钉机器人实例发送完整分析报告
|
||
response = self.dingtalk_bot.send_analysis_report(symbol, analysis_data)
|
||
|
||
if response.get('errcode') == 0:
|
||
print(f"成功发送 {symbol} 分析结果到钉钉")
|
||
return True
|
||
else:
|
||
print(f"发送 {symbol} 分析结果到钉钉失败: {response}")
|
||
return False
|
||
|
||
except Exception as e:
|
||
print(f"发送钉钉通知时出错: {e}")
|
||
return False |