一、計算交易信號

這里以雙均線策略為例，金叉買入，死叉賣出，

import pandas as pd


def signal_moving_average(df: pd.DataFrame, para=[5, 60]):
    """
    簡單的移動平均線策略
    當短期均線由下向上穿過長期均線的時候，買入；然后由上向下穿過的時候，賣出，
    :param df: 原始資料
    :param para: 引數，[ma_short, ma_long]
    :return:
    """
    # ===計算指標
    ma_short = para[0]
    ma_long = para[1]

    # 計算均線
    df['ma_short'] = df['close'].rolling(ma_short, min_periods=1).mean()
    df['ma_long'] = df['close'].rolling(ma_long, min_periods=1).mean()

    # ===找出買入信號
    condition1 = df['ma_short'] > df['ma_long']  # 短期均線 > 長期均線
    condition2 = df['ma_short'].shift(1) <= df['ma_long'].shift(1)  # 之前的短期均線 <= 長期均線
    df.loc[condition1 & condition2, 'signal'] = 1

    # ===找出賣出信號
    condition1 = df['ma_short'] < df['ma_long']  # 短期均線 < 長期均線
    condition2 = df['ma_short'].shift(1) >= df['ma_long'].shift(1)  # 之前的短期均線 >= 長期均線
    df.loc[condition1 & condition2, 'signal'] = 0  # 將產生平倉信號當天的signal設定為0，0代表平倉

    df.drop(['ma_short', 'ma_long'], axis=1, inplace=True)

    # ===由signal計算出實際的每天持有倉位
    # signal的計算運用了收盤價，是每根K線收盤之后產生的信號，到第二根開盤的時候才買入，倉位才會改變，
    df['pos'] = df['signal'].shift()
    df['pos'].fillna(method='ffill', inplace=True)
    df['pos'].fillna(value=0, inplace=True)

    return df

二、交易相關方法

from datetime import datetime, timedelta
import time
import pandas as pd
from email.mime.text import MIMEText
from smtplib import SMTP_SSL


# 計算當前時間到下一個交易周期的 sleep 時間
def next_run_time(time_interval, ahead_time=1):
    if time_interval.endswith('m'):
        now_time = datetime.now()
        time_interval = int(time_interval.strip('m'))

        target_min = (int(now_time.minute / time_interval) + 1) * time_interval
        if target_min < 60:
            target_time = now_time.replace(minute=0, second=0, microsecond=0)
        else:
            if now_time.hour == 23:
                target_time = now_time.replace(hour=0, minute=0, second=0, microsecond=0)
                target_time += timedelta(days=1)
            else:
                target_time = now_time.replace(hour=now_time.hour + 1, minute=0, second=0, microsecond=0)

        # sleep直到靠近目標時間之前
        if (target_time - datetime.now()).seconds < ahead_time + 1:
            print('距離target_time不足', ahead_time, '秒，下下個周期再運行')
            target_time += timedelta(minutes=time_interval)
        print('下次運行時間', target_time)
        return target_time
    else:
        exit('time_interval doesn\'t end with m')

    return datetime.now()


# 獲取okex的k線資料
def get_okex_candle_data(exchange, symbol, time_interval):
    # 抓取資料
    content = exchange.fetch_ohlcv(symbol, timeframe=time_interval, since=0)

    # 整理資料
    df = pd.DataFrame(content, dtype=float)
    df.rename(columns={0: 'MTS', 1: 'open', 2: 'high', 3: 'low', 4: 'close', 5: 'volume'}, inplace=True)
    df['candle_begin_time'] = pd.to_datetime(df['MTS'], unit='ms')
    # 北京時間 = 格林威治時間 + 8小時
    df['candle_begin_time_GMT8'] = df['candle_begin_time'] + timedelta(hours=8)
    df = df[['candle_begin_time_GMT8', 'open', 'high', 'low', 'close', 'volume']]

    return df


def place_order(exchange, order_type, buy_or_sell, symbol, price, amount):
    """
    下單
    :param exchange: 交易所
    :param order_type: limit, market
    :param buy_or_sell: buy, sell
    :param symbol: 買賣品種
    :param price: 當market訂單的時候，price無效
    :param amount: 買賣量
    :return:
    """
    for i in range(5):
        try:
            # 限價單
            if order_type == 'limit':
                # 買
                if buy_or_sell == 'buy':
                    order_info = exchange.create_limit_buy_order(symbol, amount, price)  # 買單
                # 賣
                elif buy_or_sell == 'sell':
                    order_info = exchange.create_limit_sell_order(symbol, amount, price)  # 賣單
            # 市價單
            elif order_type == 'market':
                # 買
                if buy_or_sell == 'buy':
                    order_info = exchange.create_market_buy_order(symbol=symbol, amount=amount)  # 買單
                # 賣
                elif buy_or_sell == 'sell':
                    order_info = exchange.create_market_sell_order(symbol=symbol, amount=amount)  # 賣單
            else:
                pass

            print('下單成功：', order_type, buy_or_sell, symbol, price, amount)
            print('下單資訊：', order_info, '\n')
            return order_info

        except Exception as e:
            print('下單報錯，1s后重試', e)
            time.sleep(1)
    print('下單報錯次數過多，程式終止')
    exit()


class QQMail:
    user = 'xxx@qq.com'  # QQ郵箱地址
    pwd = '授權碼'  # 授權碼  https://jingyan.baidu.com/article/29697b91072c51ab20de3c3f.html

    def __init__(self):
        self.smtp = SMTP_SSL('smtp.qq.com', 465)
        self.smtp.login(self.user, self.pwd)

    def send_message(self, to, subject, content):
        msg = MIMEText(content)

        msg['Subject'] = subject  # 標題
        msg['From'] = self.user  # 發件人
        msg['To'] = to  # 收件人

        self.smtp.send_message(msg)

    def quit(self):
        self.smtp.quit()


# 自動發送郵件
def auto_send_email(to_address, subject, content):
    mail = QQMail()
    mail.send_message(to_address, subject, content)
    mail.quit()

三、主程式

import ccxt
from datetime import datetime, timedelta
from time import sleep
import pandas as pd
from .trade import next_run_time, auto_send_email, place_order, get_okex_candle_data
from .signals import signal_moving_average

"""
自動交易主要流程

# 通過while陳述句，不斷的回圈

# 每次回圈中需要做的操作步驟
1. 更新賬戶資訊
2. 獲取實時資料
3. 根據最新資料計算買賣信號
4. 根據目前倉位、買賣資訊，結束本次回圈，或者進行交易
5. 交易

"""

time_interval = '1m'  # 運行時間間隔

# 創建交易所物件
exchange = ccxt.okex5()

# 設定代理
exchange.proxies = {
    'http': 'http://127.0.0.1:6666',
    'https': 'http://127.0.0.1:6666',
}

# 設定apiKey和apiSecret
exchange.apiKey = ''
exchange.secret = ''
exchange.password = ''  # okex特有的引數Passphrase，如果不設定會報錯：AuthenticationError: requires `password`

symbol = 'ETH/USDT'  # 交易對
base_coin = symbol.split('/')[-1]
trade_coin = symbol.split('/')[0]

para = [20, 200]  # 策略引數

# ====主程式
while True:
    # ===監控郵件內容
    email_title = '策略報表'
    email_content = ''

    # ===從服務器更新賬戶balance資訊
    balance = exchange.fetch_balance()['total']
    base_coin_amount = float(balance[base_coin])
    trade_coin_amount = float(balance[trade_coin])
    print('當前資產：\n', base_coin, base_coin_amount, trade_coin, trade_coin_amount)

    # ===sleep直到運行時間
    run_time = next_run_time(time_interval)
    sleep(max(0, (run_time - datetime.now()).seconds))
    while True:  # 在靠近目標時間時
        if datetime.now() < run_time:
            continue
        else:
            break

    # ===獲取最新資料
    while True:
        # 獲取資料
        df = get_okex_candle_data(exchange, symbol, time_interval)
        # 判斷是否包含最新的資料
        _temp = df[df['candle_begin_time_GMT8'] == (run_time - timedelta(minutes=int(time_interval)))]
        if _temp.empty:
            print('獲取資料不包含最新的資料，重新獲取')
            continue
        else:
            break

    # ===產生交易信號
    df = df[df['candle_begin_time_GMT8'] < pd.to_datetime(run_time)]  # 去除target_time周期的資料
    df = signal_moving_average(df, para=para)
    signal = df.iloc[-1]['signal']
    # signal = 1
    print('\n 交易信號', signal)

    # ====賣出品種
    if trade_coin_amount > 0 and signal == 0:
        print('\n賣出')
        # 獲取最新的賣出價格
        price = exchange.fetch_ticker(symbol)['bid']  # 獲取買一價格
        # 下單
        place_order(exchange, order_type='limit', buy_or_sell='sell', symbol=symbol, price=price * 0.98, amount=trade_coin_amount)
        # 郵件標題
        email_title += '_賣出_' + trade_coin
        # 郵件內容
        email_content += '賣出資訊：\n'
        email_content += '賣出數量：' + str(trade_coin_amount) + '\n'
        email_content += '賣出價格：' + str(price) + '\n'

    # ====買入品種
    if trade_coin_amount == 0 and signal == 1:
        print('\n買入')
        # 獲取最新的買入價格
        price = exchange.fetch_ticker(symbol)['ask']  # 獲取賣一價格
        # 計算買入數量
        buy_amount = base_coin_amount / price
        # 獲取最新的賣出價格
        place_order(exchange, order_type='limit', buy_or_sell='buy', symbol=symbol, price=price * 1.02, amount=buy_amount)
        # 郵件標題
        email_title += '_買入_' + trade_coin
        # 郵件內容
        email_content += '買入資訊：\n'
        email_content += '買入數量：' + str(buy_amount) + '\n'
        email_content += '買入價格：' + str(price) + '\n'

    # ====發送郵件
    # 每個半小時發送郵件
    if run_time.minute % 30 == 0:
        # 發送郵件
        auto_send_email('462915202@qq.com', email_title, email_content)

    # ====本次交易結束
    print(email_title)
    print(email_content)
    print('====本次運行完畢\n')
    sleep(6 * 1)