306 lines
11 KiB
Python
306 lines
11 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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比特币量化交易系统 - 演示版本
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无需API密钥，仅用于演示策略逻辑
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"""
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import pandas as pd
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import numpy as np
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import time
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from datetime import datetime, timedelta
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import random
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class BitcoinQuantTraderDemo:
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def __init__(self):
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"""初始化演示版交易器"""
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self.symbol = "BTC-USDT"
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self.position_size = 0.001 # 每次交易0.001 BTC
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self.current_position = 0 # 当前持仓
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self.cash = 10000 # 初始资金（USDT）
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self.trades = [] # 交易记录
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def generate_mock_data(self, days=30):
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"""生成模拟的BTC价格数据"""
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print("生成模拟BTC价格数据...")
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# 生成时间序列
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end_date = datetime.now()
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start_date = end_date - timedelta(days=days)
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dates = pd.date_range(start=start_date, end=end_date, freq='5min')
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# 生成价格数据（模拟真实BTC价格波动）
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np.random.seed(42) # 固定随机种子，确保结果可重现
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# 基础价格
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base_price = 118000
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prices = []
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for i in range(len(dates)):
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# 添加随机波动
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change = np.random.normal(0, 0.002) # 0.2%的标准差
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if i == 0:
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price = base_price
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else:
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price = prices[-1] * (1 + change)
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prices.append(price)
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# 创建DataFrame
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df = pd.DataFrame({
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'timestamp': dates,
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'open': prices,
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'high': [p * (1 + abs(np.random.normal(0, 0.001))) for p in prices],
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'low': [p * (1 - abs(np.random.normal(0, 0.001))) for p in prices],
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'close': prices,
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'volume': [np.random.uniform(100, 1000) for _ in prices]
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})
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# 确保high >= close >= low
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df['high'] = df[['open', 'high', 'close']].max(axis=1)
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df['low'] = df[['open', 'low', 'close']].min(axis=1)
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print(f"生成了 {len(df)} 条价格数据")
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print(f"价格范围: ${df['close'].min():.2f} - ${df['close'].max():.2f}")
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return df
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def calculate_sma(self, df, period=20):
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"""计算简单移动平均线"""
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return df['close'].rolling(window=period).mean()
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def calculate_rsi(self, df, period=14):
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"""计算RSI指标"""
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def place_mock_order(self, side, size, price):
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"""模拟下单"""
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timestamp = datetime.now()
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if side == 'buy':
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cost = size * price
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if cost <= self.cash:
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self.cash -= cost
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self.current_position += size
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print(f"✅ 买入成功: {size} BTC @ ${price:.2f}")
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self.trades.append({
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'timestamp': timestamp,
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'side': side,
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'size': size,
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'price': price,
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'cost': cost
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})
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return True
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else:
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print(f"❌ 买入失败: 资金不足 (需要: ${cost:.2f}, 可用: ${self.cash:.2f})")
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return False
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else: # sell
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if self.current_position >= size:
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revenue = size * price
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self.cash += revenue
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self.current_position -= size
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print(f"✅ 卖出成功: {size} BTC @ ${price:.2f}")
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self.trades.append({
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'timestamp': timestamp,
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'side': side,
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'size': size,
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'price': price,
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'revenue': revenue
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})
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return True
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else:
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print(f"❌ 卖出失败: 持仓不足 (需要: {size} BTC, 持有: {self.current_position} BTC)")
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return False
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def simple_moving_average_strategy(self, df):
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"""简单移动平均线策略"""
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print("\n=== 执行移动平均线策略 ===")
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if len(df) < 20:
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print("数据不足，无法执行策略")
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return
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# 计算移动平均线
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df['sma_short'] = self.calculate_sma(df, 5) # 短期均线
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df['sma_long'] = self.calculate_sma(df, 20) # 长期均线
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# 获取最新数据
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latest = df.iloc[-1]
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prev = df.iloc[-2]
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print(f"短期均线: {latest['sma_short']:.2f}")
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print(f"长期均线: {latest['sma_long']:.2f}")
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print(f"当前价格: {latest['close']:.2f}")
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# 策略逻辑：短期均线上穿长期均线买入，下穿卖出
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if (latest['sma_short'] > latest['sma_long'] and
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prev['sma_short'] <= prev['sma_long']):
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print("信号: 买入")
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self.place_mock_order('buy', self.position_size, latest['close'])
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elif (latest['sma_short'] < latest['sma_long'] and
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prev['sma_short'] >= prev['sma_long']):
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print("信号: 卖出")
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self.place_mock_order('sell', self.position_size, latest['close'])
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else:
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print("信号: 持仓观望")
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def rsi_strategy(self, df):
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"""RSI策略"""
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print("\n=== 执行RSI策略 ===")
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if len(df) < 30:
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print("数据不足，无法执行策略")
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return
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# 计算RSI
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df['rsi'] = self.calculate_rsi(df, 14)
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# 获取最新RSI值
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latest = df.iloc[-1]
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latest_rsi = latest['rsi']
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print(f"当前RSI: {latest_rsi:.2f}")
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print(f"当前价格: {latest['close']:.2f}")
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# 策略逻辑：RSI < 30 超卖买入，RSI > 70 超买卖出
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if latest_rsi < 30:
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print("信号: RSI超卖，买入")
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self.place_mock_order('buy', self.position_size, latest['close'])
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elif latest_rsi > 70:
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print("信号: RSI超买，卖出")
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self.place_mock_order('sell', self.position_size, latest['close'])
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else:
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print("信号: RSI正常区间，持仓观望")
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def grid_trading_strategy(self, df, grid_levels=5, grid_range=0.02):
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"""网格交易策略"""
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print(f"\n=== 执行网格交易策略 (网格数: {grid_levels}, 范围: {grid_range*100}%) ===")
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if len(df) < 10:
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print("数据不足，无法执行策略")
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return
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current_price = df['close'].iloc[-1]
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# 计算网格价格
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grid_prices = []
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for i in range(grid_levels):
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price = current_price * (1 + grid_range * (i - grid_levels//2) / grid_levels)
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grid_prices.append(price)
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print(f"网格价格: {[f'${p:.2f}' for p in grid_prices]}")
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print(f"当前价格: ${current_price:.2f}")
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# 找到最近的网格
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closest_grid = min(grid_prices, key=lambda x: abs(x - current_price))
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grid_index = grid_prices.index(closest_grid)
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print(f"最近网格: ${closest_grid:.2f}")
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# 简单的网格策略：价格下跌到网格线买入，上涨到网格线卖出
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if current_price < closest_grid * 0.995: # 价格下跌超过0.5%
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print("信号: 价格下跌，网格买入")
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self.place_mock_order('buy', self.position_size, current_price)
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elif current_price > closest_grid * 1.005: # 价格上涨超过0.5%
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print("信号: 价格上涨，网格卖出")
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self.place_mock_order('sell', self.position_size, current_price)
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else:
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print("信号: 价格在网格内，持仓观望")
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def show_portfolio_status(self):
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"""显示投资组合状态"""
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print("\n" + "="*50)
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print("📊 投资组合状态")
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print("="*50)
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print(f"现金余额: ${self.cash:.2f}")
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print(f"BTC持仓: {self.current_position:.6f} BTC")
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if self.trades:
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# 计算总收益
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total_cost = sum(t['cost'] for t in self.trades if 'cost' in t)
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total_revenue = sum(t['revenue'] for t in self.trades if 'revenue' in t)
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net_profit = total_revenue - total_cost
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print(f"总交易次数: {len(self.trades)}")
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print(f"总投入: ${total_cost:.2f}")
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print(f"总收入: ${total_revenue:.2f}")
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print(f"净收益: ${net_profit:.2f}")
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if total_cost > 0:
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roi = (net_profit / total_cost) * 100
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print(f"收益率: {roi:.2f}%")
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else:
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print("暂无交易记录")
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print("="*50)
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def run_strategy_backtest(self, strategy='sma', days=30):
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"""运行策略回测"""
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print(f"🚀 开始运行{strategy}策略回测 ({days}天数据)")
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# 生成模拟数据
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df = self.generate_mock_data(days)
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# 重置投资组合
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self.cash = 10000
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self.current_position = 0
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self.trades = []
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# 运行策略
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if strategy == 'sma':
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self.simple_moving_average_strategy(df)
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elif strategy == 'rsi':
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self.rsi_strategy(df)
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elif strategy == 'grid':
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self.grid_trading_strategy(df)
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else:
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print("未知策略")
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return
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# 显示结果
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self.show_portfolio_status()
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# 显示交易记录
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if self.trades:
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print("\n📋 交易记录:")
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for i, trade in enumerate(self.trades, 1):
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print(f"{i}. {trade['timestamp'].strftime('%Y-%m-%d %H:%M')} "
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f"{trade['side'].upper()} {trade['size']} BTC @ ${trade['price']:.2f}")
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def main():
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"""主函数"""
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print("=== 比特币量化交易系统 - 演示版本 ===")
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print("⚠️ 此版本使用模拟数据，仅用于演示策略逻辑")
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trader = BitcoinQuantTraderDemo()
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while True:
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print("\n请选择操作:")
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print("1. 运行移动平均线策略回测")
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print("2. 运行RSI策略回测")
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print("3. 运行网格交易策略回测")
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print("4. 查看投资组合状态")
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print("0. 退出")
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choice = input("请输入选择 (0-4): ").strip()
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if choice == '0':
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print("退出程序")
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break
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elif choice == '1':
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days = int(input("设置回测天数 (默认30): ") or "30")
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trader.run_strategy_backtest('sma', days)
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elif choice == '2':
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days = int(input("设置回测天数 (默认30): ") or "30")
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trader.run_strategy_backtest('rsi', days)
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elif choice == '3':
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days = int(input("设置回测天数 (默认30): ") or "30")
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trader.run_strategy_backtest('grid', days)
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elif choice == '4':
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trader.show_portfolio_status()
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else:
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print("无效选择，请重新输入")
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if __name__ == "__main__":
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main()
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