335 lines
12 KiB
Python
335 lines
12 KiB
Python
import okx.api.account as Account
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import okx.api.trade as Trade
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import okx.api.market as Market
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import okx.api.public as Public
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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
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import json
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class BitcoinQuantTrader:
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def __init__(self, api_key, secret_key, passphrase, sandbox=True):
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"""
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初始化比特币量化交易器
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Args:
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api_key: OKX API Key
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secret_key: OKX Secret Key
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passphrase: OKX API Passphrase
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sandbox: 是否使用沙盒环境（建议先用沙盒测试）
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"""
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self.api_key = api_key
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self.secret_key = secret_key
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self.passphrase = passphrase
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# 初始化API客户端
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flag = "0" if sandbox else "1" # 0:沙盒环境 1:实盘环境
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self.account_api = Account.Account(
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api_key, secret_key, passphrase,
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flag=flag, debug=False
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)
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self.trade_api = Trade.Trade(
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api_key, secret_key, passphrase,
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flag=flag, debug=False
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)
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self.market_api = Market.Market(
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api_key, secret_key, passphrase,
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flag=flag, debug=False
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)
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self.public_api = Public.Public(
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api_key, secret_key, passphrase,
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flag=flag, debug=False
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)
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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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def get_account_balance(self):
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"""获取账户余额"""
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try:
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result = self.account_api.get_account_balance()
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if result['code'] == '0':
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balances = result['data']
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for balance in balances:
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if balance['ccy'] == 'USDT':
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print(f"USDT余额: {balance['bal']}")
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return float(balance['bal'])
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elif balance['ccy'] == 'BTC':
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print(f"BTC余额: {balance['bal']}")
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return float(balance['bal'])
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else:
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print(f"获取余额失败: {result}")
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return 0
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except Exception as e:
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print(f"获取余额异常: {e}")
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return 0
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def get_current_price(self):
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"""获取当前BTC价格"""
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try:
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result = self.market_api.get_ticker(instId=self.symbol)
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if result['code'] == '0':
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price = float(result['data'][0]['last'])
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print(f"当前BTC价格: ${price:,.2f}")
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return price
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else:
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print(f"获取价格失败: {result}")
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return None
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except Exception as e:
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print(f"获取价格异常: {e}")
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return None
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def get_kline_data(self, bar='1m', limit=100):
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"""获取K线数据"""
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try:
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result = self.market_api.get_candlesticks(
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instId=self.symbol,
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bar=bar,
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limit=str(limit)
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)
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if result['code'] == '0':
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# 转换为DataFrame
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df = pd.DataFrame(result['data'], columns=[
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'timestamp', 'open', 'high', 'low', 'close', 'volume', 'volCcy'
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])
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# 转换数据类型
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for col in ['open', 'high', 'low', 'close', 'volume']:
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df[col] = pd.to_numeric(df[col])
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df['timestamp'] = pd.to_datetime(df['timestamp'].astype(int), unit='ms')
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return df
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else:
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print(f"获取K线数据失败: {result}")
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return None
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except Exception as e:
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print(f"获取K线数据异常: {e}")
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return None
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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_market_order(self, side, size):
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"""下市价单"""
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try:
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result = self.trade_api.place_order(
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instId=self.symbol,
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tdMode='cash',
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side=side,
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ordType='market',
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sz=str(size)
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)
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if result['code'] == '0':
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print(f"下单成功: {side} {size} BTC")
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return result['data'][0]['ordId']
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else:
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print(f"下单失败: {result}")
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return None
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except Exception as e:
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print(f"下单异常: {e}")
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return None
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def simple_moving_average_strategy(self):
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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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# 获取K线数据
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df = self.get_kline_data(bar='5m', limit=50)
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if df is None or 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_market_order('buy', self.position_size)
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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_market_order('sell', self.position_size)
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else:
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print("信号: 持仓观望")
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def rsi_strategy(self):
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"""RSI策略"""
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print("\n=== 执行RSI策略 ===")
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# 获取K线数据
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df = self.get_kline_data(bar='5m', limit=50)
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if df is None or 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_rsi = df['rsi'].iloc[-1]
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print(f"当前RSI: {latest_rsi:.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_market_order('buy', self.position_size)
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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_market_order('sell', self.position_size)
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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, 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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current_price = self.get_current_price()
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if current_price is None:
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return
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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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# 获取K线数据判断当前价格在哪个网格
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df = self.get_kline_data(bar='1m', limit=10)
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if df is None:
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return
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latest_price = df['close'].iloc[-1]
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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 - latest_price))
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grid_index = grid_prices.index(closest_grid)
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print(f"当前价格: ${latest_price:.2f}, 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 latest_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_market_order('buy', self.position_size)
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elif latest_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_market_order('sell', self.position_size)
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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 run_strategy_loop(self, strategy='sma', interval=60):
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"""运行策略循环"""
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print(f"开始运行{strategy}策略，间隔{interval}秒")
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while True:
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try:
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print(f"\n{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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# 检查账户余额
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self.get_account_balance()
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# 执行策略
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if strategy == 'sma':
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self.simple_moving_average_strategy()
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elif strategy == 'rsi':
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self.rsi_strategy()
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elif strategy == 'grid':
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self.grid_trading_strategy()
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else:
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print("未知策略")
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break
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print(f"等待{interval}秒后继续...")
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time.sleep(interval)
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except KeyboardInterrupt:
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print("\n策略运行被用户中断")
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break
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except Exception as e:
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print(f"策略运行异常: {e}")
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time.sleep(interval)
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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 ===")
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# 导入配置
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try:
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from config import API_KEY, SECRET_KEY, PASSPHRASE, TRADING_CONFIG, TIME_CONFIG
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except ImportError:
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print("错误：找不到config.py文件，请确保配置文件存在")
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return
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# 检查是否配置了API密钥
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if API_KEY == "your_api_key_here":
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print("请先在config.py中配置你的OKX API密钥！")
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print("1. 登录OKX官网")
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print("2. 进入API管理页面")
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print("3. 创建API Key、Secret Key和Passphrase")
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print("4. 将密钥填入config.py文件中的相应位置")
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return
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# 创建交易器实例
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trader = BitcoinQuantTrader(
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API_KEY, SECRET_KEY, PASSPHRASE,
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sandbox=TRADING_CONFIG["sandbox"]
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)
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# 显示菜单
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while True:
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print("\n请选择操作:")
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print("1. 查看账户余额")
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print("2. 查看当前价格")
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print("3. 执行移动平均线策略")
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print("4. 执行RSI策略")
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print("5. 执行网格交易策略")
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print("6. 运行策略循环")
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print("0. 退出")
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choice = input("请输入选择 (0-6): ").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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trader.get_account_balance()
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elif choice == '2':
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trader.get_current_price()
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elif choice == '3':
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trader.simple_moving_average_strategy()
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elif choice == '4':
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trader.rsi_strategy()
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elif choice == '5':
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trader.grid_trading_strategy()
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elif choice == '6':
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strategy = input("选择策略 (sma/rsi/grid): ").strip()
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interval = int(input("设置间隔秒数 (默认60): ") or "60")
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trader.run_strategy_loop(strategy, interval)
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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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