645 lines
23 KiB
Python
645 lines
23 KiB
Python
|
"""海龟实时
|
|||
|
|
"""
|
|||
|
|
import numpy as np
|
|||
|
|
import math
|
|||
|
|
import akshare as ak
|
|||
|
|
import os
|
|||
|
|
from datetime import datetime, timedelta
|
|||
|
|
import pandas as pd
|
|||
|
|
import mplfinance as mpf
|
|||
|
|
import TurtleClassNew
|
|||
|
|
|
|||
|
|
# -----------------------------------
|
|||
|
|
# 创建组合，先用一个测试
|
|||
|
|
conbinations = []
|
|||
|
|
# 我是否需要当前组合的信息：需要
|
|||
|
|
# 什么东西 股票还是etf
|
|||
|
|
# 风险系数risk_coef; atr；头寸单位；
|
|||
|
|
# 系数是多少，每1%波动多少钱 atr 买4份一共多少钱
|
|||
|
|
# 组合总共会花掉多少钱
|
|||
|
|
|
|||
|
|
# 每个item应该具有的属性
|
|||
|
|
# code
|
|||
|
|
# ATR
|
|||
|
|
# price
|
|||
|
|
# risk_coef
|
|||
|
|
# capital
|
|||
|
|
|
|||
|
|
# 每个月调整risk_coef和captial
|
|||
|
|
|
|||
|
|
# 初始化函数
|
|||
|
|
# 初始化conbinations中的数据
|
|||
|
|
|
|||
|
|
|
|||
|
|
# 监盘函数：
|
|||
|
|
|
|||
|
|
# 数据整理保存函数，收盘后开始
|
|||
|
|
|
|||
|
|
for item in conbinations:
|
|||
|
|
|
|||
|
|
|
|||
|
|
# 创建Turtle实例（ETF与股票获取数据代码不同）
|
|||
|
|
pass
|
|||
|
|
# 获取数据每5分钟获取一次
|
|||
|
|
|
|||
|
|
# 计算唐奇安通道 每天收盘计算
|
|||
|
|
|
|||
|
|
#
|
|||
|
|
|
|||
|
|
# https://akshare.akfamily.xyz/data/stock/stock.html#id9
|
|||
|
|
|
|||
|
|
import akshare as ak
|
|||
|
|
import pandas as pd
|
|||
|
|
import numpy as np
|
|||
|
|
import sqlite3
|
|||
|
|
from datetime import datetime
|
|||
|
|
import smtplib
|
|||
|
|
from email.mime.text import MIMEText
|
|||
|
|
from email.header import Header
|
|||
|
|
import json
|
|||
|
|
from typing import Dict, List, Tuple
|
|||
|
|
import logging
|
|||
|
|
from decimal import Decimal
|
|||
|
|
|
|||
|
|
class DatabaseManager:
|
|||
|
|
def __init__(self, db_path: str = "turtle_trading.db"):
|
|||
|
|
self.conn = sqlite3.connect(db_path)
|
|||
|
|
self.create_tables()
|
|||
|
|
|
|||
|
|
def create_tables(self):
|
|||
|
|
"""创建必要的数据表"""
|
|||
|
|
# 交易信号记录表
|
|||
|
|
self.conn.execute('''
|
|||
|
|
CREATE TABLE IF NOT EXISTS signals (
|
|||
|
|
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
|||
|
|
stock_code TEXT,
|
|||
|
|
signal_type TEXT,
|
|||
|
|
suggested_price REAL,
|
|||
|
|
suggested_quantity INTEGER,
|
|||
|
|
timestamp DATETIME,
|
|||
|
|
status TEXT
|
|||
|
|
)''')
|
|||
|
|
|
|||
|
|
# 实际交易记录表
|
|||
|
|
self.conn.execute('''
|
|||
|
|
CREATE TABLE IF NOT EXISTS trades (
|
|||
|
|
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
|||
|
|
signal_id INTEGER,
|
|||
|
|
actual_price REAL,
|
|||
|
|
actual_quantity INTEGER,
|
|||
|
|
timestamp DATETIME,
|
|||
|
|
FOREIGN KEY (signal_id) REFERENCES signals (id)
|
|||
|
|
)''')
|
|||
|
|
|
|||
|
|
# 持仓状态表
|
|||
|
|
self.conn.execute('''
|
|||
|
|
CREATE TABLE IF NOT EXISTS positions (
|
|||
|
|
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
|||
|
|
stock_code TEXT,
|
|||
|
|
quantity INTEGER,
|
|||
|
|
avg_cost REAL,
|
|||
|
|
entry_price REAL,
|
|||
|
|
last_price REAL,
|
|||
|
|
stop_loss REAL,
|
|||
|
|
target_price REAL,
|
|||
|
|
position_type TEXT,
|
|||
|
|
timestamp DATETIME,
|
|||
|
|
status TEXT
|
|||
|
|
)''')
|
|||
|
|
|
|||
|
|
def get_position(self, stock_code: str) -> Dict:
|
|||
|
|
"""获取股票当前持仓信息"""
|
|||
|
|
cursor = self.conn.execute('''
|
|||
|
|
SELECT * FROM positions
|
|||
|
|
WHERE stock_code = ? AND status = 'ACTIVE'
|
|||
|
|
''', (stock_code,))
|
|||
|
|
position = cursor.fetchone()
|
|||
|
|
if position:
|
|||
|
|
return {
|
|||
|
|
'id': position[0],
|
|||
|
|
'stock_code': position[1],
|
|||
|
|
'quantity': position[2],
|
|||
|
|
'avg_cost': position[3],
|
|||
|
|
'entry_price': position[4],
|
|||
|
|
'last_price': position[5],
|
|||
|
|
'stop_loss': position[6],
|
|||
|
|
'target_price': position[7],
|
|||
|
|
'position_type': position[8],
|
|||
|
|
'timestamp': position[9],
|
|||
|
|
'status': position[10]
|
|||
|
|
}
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
def update_position(self, stock_code: str, last_price: float):
|
|||
|
|
"""更新持仓的最新价格"""
|
|||
|
|
self.conn.execute('''
|
|||
|
|
UPDATE positions
|
|||
|
|
SET last_price = ?, timestamp = ?
|
|||
|
|
WHERE stock_code = ? AND status = 'ACTIVE'
|
|||
|
|
''', (last_price, datetime.now(), stock_code))
|
|||
|
|
self.conn.commit()
|
|||
|
|
|
|||
|
|
def create_position(self, stock_code: str, quantity: int,
|
|||
|
|
entry_price: float, position_type: str,
|
|||
|
|
stop_loss: float, target_price: float):
|
|||
|
|
"""创建新持仓"""
|
|||
|
|
self.conn.execute('''
|
|||
|
|
INSERT INTO positions (
|
|||
|
|
stock_code, quantity, avg_cost, entry_price, last_price,
|
|||
|
|
stop_loss, target_price, position_type, timestamp, status
|
|||
|
|
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
|||
|
|
''', (stock_code, quantity, entry_price, entry_price, entry_price,
|
|||
|
|
stop_loss, target_price, position_type, datetime.now(), 'ACTIVE'))
|
|||
|
|
self.conn.commit()
|
|||
|
|
|
|||
|
|
def close_position(self, stock_code: str):
|
|||
|
|
"""关闭持仓"""
|
|||
|
|
self.conn.execute('''
|
|||
|
|
UPDATE positions
|
|||
|
|
SET status = 'CLOSED', timestamp = ?
|
|||
|
|
WHERE stock_code = ? AND status = 'ACTIVE'
|
|||
|
|
''', (datetime.now(), stock_code))
|
|||
|
|
self.conn.commit()
|
|||
|
|
|
|||
|
|
class TurtleStrategy:
|
|||
|
|
def __init__(self, lookback_days: int = 20):
|
|||
|
|
self.lookback_days = lookback_days
|
|||
|
|
|
|||
|
|
def calculate_signals(self, df: pd.DataFrame,
|
|||
|
|
current_position: Dict = None) -> Dict:
|
|||
|
|
"""计算交易信号，考虑当前持仓状态"""
|
|||
|
|
# 计算技术指标
|
|||
|
|
df['high_20'] = df['high'].rolling(20).max()
|
|||
|
|
df['low_10'] = df['low'].rolling(10).min()
|
|||
|
|
df['atr'] = self._calculate_atr(df)
|
|||
|
|
|
|||
|
|
current = df.iloc[-1]
|
|||
|
|
prev = df.iloc[-2]
|
|||
|
|
|
|||
|
|
signal = {
|
|||
|
|
'type': None,
|
|||
|
|
'price': None,
|
|||
|
|
'quantity': 0,
|
|||
|
|
'stop_loss': None,
|
|||
|
|
'target_price': None
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if current_position:
|
|||
|
|
# 持仓状态下的信号计算
|
|||
|
|
return self._calculate_position_signals(
|
|||
|
|
current_position, current, prev, df
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
# 无持仓状态下的信号计算
|
|||
|
|
return self._calculate_entry_signals(current, prev, df)
|
|||
|
|
|
|||
|
|
def _calculate_position_signals(self, position: Dict,
|
|||
|
|
current: pd.Series,
|
|||
|
|
prev: pd.Series,
|
|||
|
|
df: pd.DataFrame) -> Dict:
|
|||
|
|
"""计算持仓状态下的信号"""
|
|||
|
|
signal = {
|
|||
|
|
'type': None,
|
|||
|
|
'price': current['close'],
|
|||
|
|
'quantity': 0,
|
|||
|
|
'stop_loss': position['stop_loss'],
|
|||
|
|
'target_price': position['target_price']
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 检查止损
|
|||
|
|
if current['low'] <= position['stop_loss']:
|
|||
|
|
signal['type'] = 'STOP_LOSS'
|
|||
|
|
signal['quantity'] = position['quantity']
|
|||
|
|
return signal
|
|||
|
|
|
|||
|
|
# 检查获利目标
|
|||
|
|
if current['high'] >= position['target_price']:
|
|||
|
|
signal['type'] = 'TAKE_PROFIT'
|
|||
|
|
signal['quantity'] = position['quantity']
|
|||
|
|
return signal
|
|||
|
|
|
|||
|
|
# 检查加仓条件
|
|||
|
|
if position['position_type'] == 'LONG':
|
|||
|
|
if current['close'] > position['entry_price'] * 1.05: # 5%盈利时考虑加仓
|
|||
|
|
signal['type'] = 'ADD'
|
|||
|
|
signal['quantity'] = self._calculate_position_size(
|
|||
|
|
current['close'], df['atr'].iloc[-1]
|
|||
|
|
)
|
|||
|
|
# 更新止损为前低
|
|||
|
|
signal['stop_loss'] = df['low'].rolling(5).min().iloc[-1]
|
|||
|
|
|
|||
|
|
# 检查减仓条件
|
|||
|
|
elif position['position_type'] == 'SHORT':
|
|||
|
|
if current['close'] < position['entry_price'] * 0.95: # 5%盈利时考虑加仓
|
|||
|
|
signal['type'] = 'REDUCE'
|
|||
|
|
signal['quantity'] = self._calculate_position_size(
|
|||
|
|
current['close'], df['atr'].iloc[-1]
|
|||
|
|
)
|
|||
|
|
# 更新止损为前高
|
|||
|
|
signal['stop_loss'] = df['high'].rolling(5).max().iloc[-1]
|
|||
|
|
|
|||
|
|
return signal
|
|||
|
|
|
|||
|
|
def _calculate_entry_signals(self, current: pd.Series,
|
|||
|
|
prev: pd.Series,
|
|||
|
|
df: pd.DataFrame) -> Dict:
|
|||
|
|
"""计算入场信号"""
|
|||
|
|
signal = {
|
|||
|
|
'type': None,
|
|||
|
|
'price': current['close'],
|
|||
|
|
'quantity': 0,
|
|||
|
|
'stop_loss': None,
|
|||
|
|
'target_price': None
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
atr = df['atr'].iloc[-1]
|
|||
|
|
|
|||
|
|
# 多头入场
|
|||
|
|
if current['close'] > prev['high_20']:
|
|||
|
|
signal['type'] = 'BUY'
|
|||
|
|
signal['quantity'] = self._calculate_position_size(
|
|||
|
|
current['close'], atr
|
|||
|
|
)
|
|||
|
|
signal['stop_loss'] = current['close'] - 2 * atr
|
|||
|
|
signal['target_price'] = current['close'] + 4 * atr
|
|||
|
|
|
|||
|
|
# 空头入场
|
|||
|
|
elif current['close'] < prev['low_10']:
|
|||
|
|
signal['type'] = 'SELL'
|
|||
|
|
signal['quantity'] = self._calculate_position_size(
|
|||
|
|
current['close'], atr
|
|||
|
|
)
|
|||
|
|
signal['stop_loss'] = current['close'] + 2 * atr
|
|||
|
|
signal['target_price'] = current['close'] - 4 * atr
|
|||
|
|
|
|||
|
|
return signal
|
|||
|
|
|
|||
|
|
def _calculate_atr(self, df: pd.DataFrame) -> pd.Series:
|
|||
|
|
"""计算ATR指标"""
|
|||
|
|
df['tr'] = np.maximum(
|
|||
|
|
df['high'] - df['low'],
|
|||
|
|
np.maximum(
|
|||
|
|
abs(df['high'] - df['close'].shift(1)),
|
|||
|
|
abs(df['low'] - df['close'].shift(1))
|
|||
|
|
)
|
|||
|
|
)
|
|||
|
|
return df['tr'].rolling(20).mean()
|
|||
|
|
|
|||
|
|
def _calculate_position_size(self, price: float, atr: float) -> int:
|
|||
|
|
"""计算持仓规模"""
|
|||
|
|
risk_per_trade = 100000 * 0.01 # 假设账户规模100000，每次风险1%
|
|||
|
|
return int(risk_per_trade / (atr * 100))
|
|||
|
|
|
|||
|
|
class EmailManager:
|
|||
|
|
def __init__(self, config_path: str = "email_config.json"):
|
|||
|
|
with open(config_path) as f:
|
|||
|
|
self.config = json.load(f)
|
|||
|
|
|
|||
|
|
def send_signal(self, stock_code: str, signal_type: str,
|
|||
|
|
suggested_price: float, suggested_quantity: int,
|
|||
|
|
stop_loss: float = None, target_price: float = None) -> bool:
|
|||
|
|
"""发送交易信号邮件"""
|
|||
|
|
subject = f"交易信号: {stock_code} - {signal_type}"
|
|||
|
|
content = f"""
|
|||
|
|
股票代码: {stock_code}
|
|||
|
|
信号类型: {signal_type}
|
|||
|
|
建议价格: {suggested_price}
|
|||
|
|
建议数量: {suggested_quantity}
|
|||
|
|
止损价位: {stop_loss if stop_loss else '无'}
|
|||
|
|
目标价位: {target_price if target_price else '无'}
|
|||
|
|
|
|||
|
|
请回复实际成交价格和数量, 格式:
|
|||
|
|
价格,数量
|
|||
|
|
例如: 10.5,100
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
return self._send_email(subject, content)
|
|||
|
|
|
|||
|
|
def send_position_update(self, position: Dict,
|
|||
|
|
current_price: float) -> bool:
|
|||
|
|
"""发送持仓更新邮件"""
|
|||
|
|
subject = f"持仓更新: {position['stock_code']}"
|
|||
|
|
|
|||
|
|
# 计算收益
|
|||
|
|
profit = (current_price - position['avg_cost']) * position['quantity']
|
|||
|
|
profit_pct = (current_price / position['avg_cost'] - 1) * 100
|
|||
|
|
|
|||
|
|
content = f"""
|
|||
|
|
股票代码: {position['stock_code']}
|
|||
|
|
当前价格: {current_price}
|
|||
|
|
持仓数量: {position['quantity']}
|
|||
|
|
平均成本: {position['avg_cost']}
|
|||
|
|
止损价位: {position['stop_loss']}
|
|||
|
|
目标价位: {position['target_price']}
|
|||
|
|
当前收益: {profit:.2f} ({profit_pct:.2f}%)
|
|||
|
|
持仓类型: {position['position_type']}
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
return self._send_email(subject, content)
|
|||
|
|
|
|||
|
|
def _send_email(self, subject: str, content: str) -> bool:
|
|||
|
|
"""发送邮件的具体实现"""
|
|||
|
|
try:
|
|||
|
|
msg = MIMEText(content, 'plain', 'utf-8')
|
|||
|
|
msg['Subject'] = Header(subject, 'utf-8')
|
|||
|
|
msg['From'] = self.config['sender']
|
|||
|
|
msg['To'] = self.config['receiver']
|
|||
|
|
|
|||
|
|
with smtplib.SMTP_SSL(self.config['smtp_server'],
|
|||
|
|
self.config['smtp_port']) as server:
|
|||
|
|
server.login(self.config['username'], self.config['password'])
|
|||
|
|
server.sendmail(self.config['sender'],
|
|||
|
|
[self.config['receiver']],
|
|||
|
|
msg.as_string())
|
|||
|
|
return True
|
|||
|
|
except Exception as e:
|
|||
|
|
logging.error(f"发送邮件失败: {str(e)}")
|
|||
|
|
return False
|
|||
|
|
|
|||
|
|
class TurtleTrader:
|
|||
|
|
def __init__(self, config_path: str = "config.json"):
|
|||
|
|
self.db = DatabaseManager()
|
|||
|
|
self.strategy = TurtleStrategy()
|
|||
|
|
self.email = EmailManager()
|
|||
|
|
|
|||
|
|
# 加载配置
|
|||
|
|
with open(config_path) as f:
|
|||
|
|
self.config = json.load(f)
|
|||
|
|
|
|||
|
|
def process_stock(self, stock_code: str):
|
|||
|
|
"""处理单个股票"""
|
|||
|
|
try:
|
|||
|
|
# 获取当前持仓状态
|
|||
|
|
position = self.db.get_position(stock_code)
|
|||
|
|
|
|||
|
|
# 获取股票数据
|
|||
|
|
df = ak.stock_zh_a_hist(
|
|||
|
|
symbol=stock_code,
|
|||
|
|
period="daily",
|
|||
|
|
start_date="20230101",
|
|||
|
|
end_date=datetime.now().strftime("%Y%m%d"),
|
|||
|
|
adjust="qfq"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 计算信号
|
|||
|
|
signal = self.strategy.calculate_signals(df, position)
|
|||
|
|
|
|||
|
|
current_price = df['close'].iloc[-1]
|
|||
|
|
|
|||
|
|
# 更新持仓的最新价格
|
|||
|
|
if position:
|
|||
|
|
self.db.update_position(stock_code, current_price)
|
|||
|
|
# 定期发送持仓更新
|
|||
|
|
self.email.send_position_update(position, current_price)
|
|||
|
|
|
|||
|
|
if signal['type']:
|
|||
|
|
# 保存信号
|
|||
|
|
signal_id = self.db.save_signal(
|
|||
|
|
stock_code, signal['type'],
|
|||
|
|
signal['price'], signal['quantity']
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 发送邮件
|
|||
|
|
self.email.send_signal(
|
|||
|
|
stock_code, signal['type'],
|
|||
|
|
signal['price'], signal['quantity'],
|
|||
|
|
signal['stop_loss'], signal['target_price']
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logging.error(f"处理股票 {stock_code} 时发生错误: {str(e)}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
def process_feedback(self, signal_id: int, actual_price: float,
|
|||
|
|
actual_quantity: int):
|
|||
|
|
"""处理交易反馈并更新持仓状态"""
|
|||
|
|
try:
|
|||
|
|
# 获取原始信号
|
|||
|
|
cursor = self.db.conn.execute('''
|
|||
|
|
SELECT stock_code, signal_type, suggested_price
|
|||
|
|
FROM signals WHERE id = ?
|
|||
|
|
''', (signal_id,))
|
|||
|
|
signal = cursor.fetchone()
|
|||
|
|
|
|||
|
|
if not signal:
|
|||
|
|
raise ValueError(f"Signal ID {signal_id} not found")
|
|||
|
|
|
|||
|
|
stock_code, signal_type, suggested_price = signal
|
|||
|
|
|
|||
|
|
# 保存实际交易记录
|
|||
|
|
self.db.save_trade(signal_id, actual_price, actual_quantity)
|
|||
|
|
|
|||
|
|
# 更新持仓状态
|
|||
|
|
current_position = self.db.get_position(stock_code)
|
|||
|
|
|
|||
|
|
if signal_type in ['BUY', 'ADD']:
|
|||
|
|
if current_position:
|
|||
|
|
# 计算新的平均成本
|
|||
|
|
total_cost = (current_position['avg_cost'] *
|
|||
|
|
current_position['quantity'] +
|
|||
|
|
actual_price * actual_quantity)
|
|||
|
|
total_quantity = (current_position['quantity'] +
|
|||
|
|
actual_quantity)
|
|||
|
|
new_avg_cost = total_cost / total_quantity
|
|||
|
|
|
|||
|
|
# 更新持仓
|
|||
|
|
self.db.conn.execute('''
|
|||
|
|
UPDATE positions
|
|||
|
|
SET quantity = ?, avg_cost = ?, last_price = ?,
|
|||
|
|
timestamp = ?
|
|||
|
|
WHERE id = ?
|
|||
|
|
''', (total_quantity, new_avg_cost, actual_price,
|
|||
|
|
datetime.now(), current_position['id']))
|
|||
|
|
else:
|
|||
|
|
# 创建新持仓
|
|||
|
|
# 使用ATR计算止损和目标价位
|
|||
|
|
df = self._get_stock_data(stock_code)
|
|||
|
|
atr = self.strategy._calculate_atr(df).iloc[-1]
|
|||
|
|
|
|||
|
|
stop_loss = actual_price - 2 * atr
|
|||
|
|
target_price = actual_price + 4 * atr
|
|||
|
|
|
|||
|
|
self.db.create_position(
|
|||
|
|
stock_code, actual_quantity, actual_price,
|
|||
|
|
'LONG', stop_loss, target_price
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
elif signal_type in ['SELL', 'REDUCE', 'STOP_LOSS', 'TAKE_PROFIT']:
|
|||
|
|
if current_position:
|
|||
|
|
remaining_quantity = (current_position['quantity'] -
|
|||
|
|
actual_quantity)
|
|||
|
|
|
|||
|
|
if remaining_quantity > 0:
|
|||
|
|
# 部分平仓
|
|||
|
|
self.db.conn.execute('''
|
|||
|
|
UPDATE positions
|
|||
|
|
SET quantity = ?, last_price = ?, timestamp = ?
|
|||
|
|
WHERE id = ?
|
|||
|
|
''', (remaining_quantity, actual_price,
|
|||
|
|
datetime.now(), current_position['id']))
|
|||
|
|
else:
|
|||
|
|
# 完全平仓
|
|||
|
|
self.db.close_position(stock_code)
|
|||
|
|
|
|||
|
|
self.db.conn.commit()
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logging.error(f"处理交易反馈时发生错误: {str(e)}")
|
|||
|
|
raise
|
|||
|
|
|
|||
|
|
def _get_stock_data(self, stock_code: str, days: int = 30) -> pd.DataFrame:
|
|||
|
|
"""获取股票历史数据"""
|
|||
|
|
end_date = datetime.now()
|
|||
|
|
start_date = end_date - timedelta(days=days)
|
|||
|
|
|
|||
|
|
df = ak.stock_zh_a_hist(
|
|||
|
|
symbol=stock_code,
|
|||
|
|
period="daily",
|
|||
|
|
start_date=start_date.strftime("%Y%m%d"),
|
|||
|
|
end_date=end_date.strftime("%Y%m%d"),
|
|||
|
|
adjust="qfq"
|
|||
|
|
)
|
|||
|
|
return df
|
|||
|
|
|
|||
|
|
class PerformanceAnalyzer:
|
|||
|
|
def __init__(self, db_manager: DatabaseManager):
|
|||
|
|
self.db = db_manager
|
|||
|
|
|
|||
|
|
def analyze(self) -> Dict:
|
|||
|
|
"""分析交易和持仓表现"""
|
|||
|
|
# 分析交易执行质量
|
|||
|
|
trade_metrics = self._analyze_trade_execution()
|
|||
|
|
|
|||
|
|
# 分析持仓表现
|
|||
|
|
position_metrics = self._analyze_positions()
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"trade_execution": trade_metrics,
|
|||
|
|
"position_performance": position_metrics
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _analyze_trade_execution(self) -> Dict:
|
|||
|
|
"""分析交易执行质量"""
|
|||
|
|
cursor = self.db.conn.execute('''
|
|||
|
|
SELECT s.stock_code, s.signal_type, s.suggested_price,
|
|||
|
|
s.suggested_quantity, t.actual_price, t.actual_quantity
|
|||
|
|
FROM signals s
|
|||
|
|
JOIN trades t ON s.id = t.signal_id
|
|||
|
|
WHERE s.status = 'EXECUTED'
|
|||
|
|
''')
|
|||
|
|
|
|||
|
|
trades = cursor.fetchall()
|
|||
|
|
|
|||
|
|
if not trades:
|
|||
|
|
return {"message": "没有足够的交易数据进行分析"}
|
|||
|
|
|
|||
|
|
# 计算关键指标
|
|||
|
|
price_slippage = []
|
|||
|
|
quantity_fill = []
|
|||
|
|
execution_delay = []
|
|||
|
|
|
|||
|
|
for trade in trades:
|
|||
|
|
price_diff = (trade[4] - trade[2]) / trade[2] * 100
|
|||
|
|
quantity_diff = trade[5] / trade[3] * 100
|
|||
|
|
|
|||
|
|
price_slippage.append(price_diff)
|
|||
|
|
quantity_fill.append(quantity_diff)
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"total_trades": len(trades),
|
|||
|
|
"avg_price_slippage": np.mean(price_slippage),
|
|||
|
|
"max_price_slippage": max(price_slippage),
|
|||
|
|
"avg_quantity_fill": np.mean(quantity_fill),
|
|||
|
|
"price_slippage_std": np.std(price_slippage)
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def _analyze_positions(self) -> Dict:
|
|||
|
|
"""分析持仓表现"""
|
|||
|
|
cursor = self.db.conn.execute('''
|
|||
|
|
SELECT stock_code, quantity, avg_cost, entry_price,
|
|||
|
|
last_price, stop_loss, target_price, position_type,
|
|||
|
|
timestamp, status
|
|||
|
|
FROM positions
|
|||
|
|
''')
|
|||
|
|
|
|||
|
|
positions = cursor.fetchall()
|
|||
|
|
|
|||
|
|
if not positions:
|
|||
|
|
return {"message": "没有持仓数据进行分析"}
|
|||
|
|
|
|||
|
|
active_positions = []
|
|||
|
|
closed_positions = []
|
|||
|
|
total_profit = 0
|
|||
|
|
win_count = 0
|
|||
|
|
|
|||
|
|
for pos in positions:
|
|||
|
|
profit = (pos[4] - pos[2]) * pos[1] # (last_price - avg_cost) * quantity
|
|||
|
|
profit_pct = (pos[4] / pos[2] - 1) * 100
|
|||
|
|
|
|||
|
|
if pos[9] == 'ACTIVE':
|
|||
|
|
active_positions.append({
|
|||
|
|
'stock_code': pos[0],
|
|||
|
|
'profit': profit,
|
|||
|
|
'profit_pct': profit_pct
|
|||
|
|
})
|
|||
|
|
else:
|
|||
|
|
closed_positions.append({
|
|||
|
|
'stock_code': pos[0],
|
|||
|
|
'profit': profit,
|
|||
|
|
'profit_pct': profit_pct
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
if profit > 0:
|
|||
|
|
win_count += 1
|
|||
|
|
total_profit += profit
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"active_positions": len(active_positions),
|
|||
|
|
"closed_positions": len(closed_positions),
|
|||
|
|
"total_profit": total_profit,
|
|||
|
|
"win_rate": win_count / len(closed_positions) if closed_positions else 0,
|
|||
|
|
"avg_profit_active": np.mean([p['profit'] for p in active_positions]) if active_positions else 0,
|
|||
|
|
"avg_profit_closed": np.mean([p['profit'] for p in closed_positions]) if closed_positions else 0,
|
|||
|
|
"best_position": max([p['profit_pct'] for p in active_positions + closed_positions]) if positions else 0,
|
|||
|
|
"worst_position": min([p['profit_pct'] for p in active_positions + closed_positions]) if positions else 0
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
logging.basicConfig(
|
|||
|
|
level=logging.INFO,
|
|||
|
|
format='%(asctime)s - %(levelname)s - %(message)s',
|
|||
|
|
filename='turtle_trader.log'
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 创建控制台处理器
|
|||
|
|
console_handler = logging.StreamHandler()
|
|||
|
|
console_handler.setLevel(logging.INFO)
|
|||
|
|
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
|||
|
|
console_handler.setFormatter(formatter)
|
|||
|
|
logging.getLogger().addHandler(console_handler)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
trader = TurtleTrader()
|
|||
|
|
analyzer = PerformanceAnalyzer(trader.db)
|
|||
|
|
|
|||
|
|
while True:
|
|||
|
|
# 处理所有配置的股票
|
|||
|
|
for stock_code in trader.config['stock_codes']:
|
|||
|
|
trader.process_stock(stock_code)
|
|||
|
|
|
|||
|
|
# 定期进行性能分析
|
|||
|
|
if datetime.now().hour == 15: # 每天收盘后进行分析
|
|||
|
|
analysis = analyzer.analyze()
|
|||
|
|
logging.info(f"每日性能分析报告: {json.dumps(analysis, indent=2)}")
|
|||
|
|
|
|||
|
|
# 等待下一个检查周期
|
|||
|
|
time.sleep(trader.config['check_interval'])
|
|||
|
|
|
|||
|
|
except KeyboardInterrupt:
|
|||
|
|
logging.info("系统正常关闭")
|
|||
|
|
except Exception as e:
|
|||
|
|
logging.error(f"系统运行出错: {str(e)}")
|
|||
|
|
raise
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
main()
|