166 lines
6.8 KiB
Python
166 lines
6.8 KiB
Python
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import time
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import pandas as pd
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import ccxt
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from datetime import timedelta
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# import pandas_ta
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import talib
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# ====================================================================================================
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# =====格式设置
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# ====================================================================================================
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pd.set_option('display.max_rows', 1000)
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pd.set_option('expand_frame_repr', False) # 当列太多时不换行
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# 设置命令行输出时的列对齐功能
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pd.set_option('display.unicode.ambiguous_as_wide', True)
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pd.set_option('display.unicode.east_asian_width', True)
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# ====================================================================================================
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# =====创建ccxt交易所
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# ====================================================================================================
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BINANCE_CONFIG = {
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'apiKey': '',
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'secret': '',
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'proxies': {'http': '127.0.0.1:7890', 'https': '127.0.0.1:7890'}
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}
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exchange = ccxt.binance(BINANCE_CONFIG)
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while True:
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# list = ['DOGEUSDT', 'ETHUSDT', 'BTCUSDT']
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#
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# # for symbol in list:
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symbol = 'ETHUSDT'
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time_interval = '1m' # 其他可以尝试的值：'1m', '5m', '15m', '30m', '1h', '2h', '1d', '1w', '1M', '1y'，并不是每个交易所都支持
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bar_num = 1000 # 获取K线的数量
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params = {'symbol': symbol, # 交易币对
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'interval': time_interval, # 时间间隔
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'limit': bar_num} # 数据条数
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# ====================================================================================================
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# =====获取K线数据
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# ====================================================================================================
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response = exchange.fapiPublicGetKlines(params=params)
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k_lines = pd.DataFrame(response)
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# print(k_lines)
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# =====整理K线数据
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df = pd.DataFrame(response, dtype=float) # 将数据转换为dataframe
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df.rename(columns={0: 'MTS', 1: 'Open', 2: 'High',
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3: 'Low', 4: 'Close', 5: 'Volume'}, inplace=True) # 重命名
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df['candle_begin_time'] = pd.to_datetime(df['MTS'], unit='ms') # 整理时间
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df['candle_begin_time_GMT8'] = df['candle_begin_time'] + timedelta(hours=8) # 北京时间
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df = df[['candle_begin_time_GMT8', 'Open', 'High', 'Low', 'Close', 'Volume']] # 整理列的顺序
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# ====================================================================================================
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# =====获取币对的最新价格
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# ====================================================================================================
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data = exchange.fapiPublicGetTickerPrice(params={'symbol': "ETHUSDT"})
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price_ETH = data['price']
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# ====================================================================================================
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# =====通过pandas-ta计算指标并加入相关指标计算列
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# ====================================================================================================
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## 计算均线
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# df.ta.sma(length=5, append=True, col_names="SMA_5") # pandas-ta 计算SM5指标
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df['MA5'] = talib.MA(df['Close'], timeperiod=5) # ta-lib计算MA5指标
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# # 计算14日相对强弱指数（RSI）
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# df.ta.rsi(length=14, append=True, col_names="RSI_14") # pandas-ta计算RSI指标
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df['RSI_14'] = talib.RSI(df['Close'], timeperiod=14) # ta-lib计算RSI指标
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# # 计算MACD（12/26/9周期）
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# df.ta.macd(fast=12, slow=26, signal=9, append=True) # 默认列名：MACD_12_26_9, MACDs_12_26_9, MACDh_12_26_9 [2,7](@ref)
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# # 计算布林带（20日，2倍标准差）
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# df.ta.bbands(length=20, std=2, append=True)
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# pandas-ta计算布林带指标
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# 默认列名：BBL_20_2.0, BBM_20_2.0, BBU_20_2.0
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## ta-lib 计算布林带指标
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upper_band, middle_band, lower_band = talib.BBANDS(
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df['Close'],
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timeperiod=20,
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nbdevup=2,
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nbdevdn=2,
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matype=0 # SMA
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)
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df['BB_Upper'] = upper_band
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df['BB_Middle'] = middle_band
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df['BB_Lower'] = lower_band
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## 计算ADX指标
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# df.ta.adx(length=20, append=True) # pandas-ta计算adx
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df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14) # ta-lib计算ADX指标
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## 计算ATR指标
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# df.ta.atr(length=16, append=True) # pandas-ta计算atr，默认参数14，length也可以调整
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df['ATR_14'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
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print(df)
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# df.to_csv('Ta4.csv')
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# ====================================================================================================
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# =====设置下单条件并执行
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# ====================================================================================================
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# if float(price_ETH) > float(df['Close'].iloc[-2]):
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# print('价格上升')
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# else:
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# print('价格下降')
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#
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# print(price_ETH)
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# print(df['Close'].iloc[-2])
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# print(df['RSI_14'].iloc[-2] > 30)
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# 以上为dataframe取数格式的测试，目前使用还不太熟练
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## 调整杠杆倍率
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# params = {'symbol': 'ETHUSDT', # 交易币对
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# 'leverage': 10,
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# 'timestamp': int(time.time() * 1000)}
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# leverage = exchange.fapiPrivatePostLeverage(params=params)
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# print('调整开仓杠杆\n', leverage, '\n')
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## 下单操作
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num = 0
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max_num = 3
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# 使用变量来控制下单次数
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params = {'side': 'BUY',
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'symbol': 'ETHUSDT', # 交易币对
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'type': 'LIMIT',
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# 'price': price_ETH,
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'price': df['Close'].iloc[-1],
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# 下单价格,在限价单的时候
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# 启用，将type换成LIMIT
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# 此处用变量取代了课程中的数值。以后下单的价格可能需要复杂计算再储存在变量中，做测试。
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'quantity': 0.01, # 下单数量
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'timestamp': int(time.time() * 1000),
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'timeInForce': 'GTC'} # https://academy.binance.com/zh/articles/understanding-the-different-order-types
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if df['RSI_14'].iloc[-2] > 30 and df['RSI_14'].iloc[-1] < 30:
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print('向下穿越rsi30，执行买入')
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response = exchange.fapiPrivatePostOrder(params=params)
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order_info = pd.DataFrame(response, index=['symbol'])
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order_id = order_info['orderId'].iloc[0]
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print('下单\n', order_info, '\n')
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num += 1
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# 此后这里还可以多个条件，组合多个指标的值
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# RSI的使用作为一个参考
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# 通过现有数据（开高收低量），已经可以计算出不少指标了。
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else:
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print('不符合交易条件，监控中')
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time.sleep(30)
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if num >= max_num:
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break
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## 这是我在办公室电脑上敲的注释
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## 测试能否通过iCloud同步成功，在家也可已经iCloud对同一个项目进行编辑。
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