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发表于 2025-2-25 22:32
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import pandas as pd
import talib as ta
import smtplib
from email.mime.text import MIMEText
from email.header import Header
import numpy as np
# 邮件配置
SMTP_SERVER = 'smtp.163.com' # SMTP服务器地址
SMTP_PORT = 465 # SMTP服务器端口
SENDER_EMAIL = 'ly8040661@163.com' # 发件人邮箱
SENDER_PASSWORD = 'FXMAXTHKWDHETGOR' # 发件人邮箱密码
RECEIVER_EMAIL = '188381294@qq.com' # 收件人邮箱
# 关注的期货品种
#TARGET_SYMBOLS = ['RB00', 'I', 'SC'] # 例如：螺纹钢(RB)、铁矿石(I)、原油(SC)
def send_email(subject, body):
"""
发送邮件
:param subject: 邮件主题
:param body: 邮件内容
"""
try:
# 创建邮件对象
msg = MIMEText(body, 'plain', 'utf-8')
msg['From'] = Header(SENDER_EMAIL)
msg['To'] = Header(RECEIVER_EMAIL)
msg['Subject'] = Header(subject, 'utf-8')
# 连接SMTP服务器并发送邮件
with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as server:
#server.starttls() # 启用TLS加密
server.login(SENDER_EMAIL, SENDER_PASSWORD)
server.sendmail(SENDER_EMAIL, RECEIVER_EMAIL, msg.as_string())
print("邮件发送成功！")
except Exception as e:
print(f"邮件发送失败: {e}")
# 在这个方法中编写任何的初始化逻辑。context对象将会在你的算法策略的任何方法之间做传递。
def init(context):
# 在context中设置一些参数
context.s1 = context.run_info.base_book_id # 交易品种
context.target_symbols = [
'AG04',
'AL04',
'BUY04',
'RB05',
'RU05',
'SP05',
'C05',
'JD05',
'M05',
'P05',
'PG04',
'V05',
'Y05',
'FG05',
'MA05',
'SA05',
'SRY05',
'TA05',
'OI05',
'PF04',
'CF05',
'L05',
'JM05',
'I05',
'FB05',
'AL04',] # 关注的期货品种（螺纹钢、铁矿石、原油）
# EMA周期
context.ema_short_period = 13
context.ema_long_period = 26
# KD指标参数
context.kd_fastk_period = 14
context.kd_slowk_period = 3
context.kd_slowd_period = 3
# before_trading此函数会在每天策略交易开始前被调用，当天只会被调用一次
def before_trading(context):
pass
# 你选择的证券的数据更新将会触发此段逻辑，例如日或分钟历史数据切片或者是实时数据切片更新
def handle_bar(context):
# 开始编写你的主要的算法逻辑
for symbol in context.target_symbols:
# 获取历史数据
daily_data = history_bars(symbol, context.ema_long_period + 1, '1d', ['close', 'high', 'low'], True)
min30_data = history_bars(symbol, context.ema_long_period + 1, '30m', ['close', 'high', 'low'], True)
daily_df = pd.DataFrame(daily_data)
min30_df = pd.DataFrame(min30_data)
# if len(daily_data) < context.ema_long_period + 1 or len(min30_data) < context.ema_long_period + 1:
# continue # 数据不足，跳过
# 计算EMA和KD指标
daily_df['EMA13'] = ta.EMA(np.array(daily_data['close']), timeperiod=context.ema_short_period)
daily_df['EMA26'] = ta.EMA(np.array(daily_data['close']), timeperiod=context.ema_long_period)
daily_df['EMA26_slope'] = daily_data['EMA26'].diff()
min30_df['EMA13'] = ta.EMA(min30_data['close'], timeperiod=context.ema_short_period)
min30_df['EMA26'] = ta.EMA(min30_data['close'], timeperiod=context.ema_long_period)
min30_df['slowk'], min30_data['slowd'] = ta.STOCH(
min30_df['high'], min30_data['low'], min30_data['close'],
fastk_period=context.kd_fastk_period,
slowk_period=context.kd_slowk_period,
slowd_period=context.kd_slowd_period
)
# 生成信号
long_signal = False
short_signal = False
# 1.1 基于KD上穿的做多信号
# condition1_1 = (
# (daily_data['EMA13'][-1] > daily_data['EMA26'][-1]) and # 日线EMA13 > EMA26
# (daily_data['EMA26_slope'][-1] > 0) and # 日线EMA26斜率 > 0
# (min30_data['EMA13'][-1] > min30_data['EMA26'][-1]) and # 30分钟EMA13 > EMA26
# (min30_data['slowk'][-2] < min30_data['slowd'][-2]) and # 30分钟KD上穿
# (min30_data['slowk'][-1] > min30_data['slowd'][-1])
# )
condition1_1 = (
(daily_df['EMA13'].iloc[-1] > daily_df['EMA26'].iloc[-1]) and
(daily_df['EMA26_slope'].iloc[-1] > 0) and
(min30_df['EMA13'].iloc[-1] > min30_df['EMA26'].iloc[-1]) and
(min30_df['slowk'].iloc[-2] < min30_df['slowd'].iloc[-2]) and
(min30_df['slowk'].iloc[-1] > min30_df['slowd'].iloc[-1])
)
# 1.2 基于KD超卖的做多信号
# condition1_2 = (
# (daily_data['EMA13'][-1] > daily_data['EMA26'][-1]) and # 日线EMA13 > EMA26
# (daily_data['EMA26_slope'][-1] > 0) and # 日线EMA26斜率 > 0
# (min30_data['EMA13'][-1] > min30_data['EMA26'][-1]) and # 30分钟EMA13 > EMA26
# (min30_data['slowk'][-1] < 20) # 30分钟K值 < 20
# )
condition1_2 = (
(daily_df['EMA13'].iloc[-1] > daily_df['EMA26'].iloc[-1]) and
(daily_df['EMA26_slope'].iloc[-1] > 0) and
(min30_df['EMA13'].iloc[-1] > min30_df['EMA26'].iloc[-1]) and
(min30_df['slowk'].iloc[-1]<20)
)
# 2.1 基于KD下穿的做空信号
# condition2_1 = (
# (daily_data['EMA13'][-1] < daily_data['EMA26'][-1]) and # 日线EMA13 < EMA26
# (daily_data['EMA26_slope'][-1] < 0) and # 日线EMA26斜率 < 0
# (min30_data['EMA13'][-1] < min30_data['EMA26'][-1]) and # 30分钟EMA13 < EMA26
# (min30_data['slowk'][-2] > min30_data['slowd'][-2]) and # 30分钟KD下穿
# (min30_data['slowk'][-1] < min30_data['slowd'][-1])
# )
condition2_1 = (
(daily_df['EMA13'].iloc[-1] < daily_df['EMA26'].iloc[-1]) and
(daily_df['EMA26_slope'].iloc[-1] < 0) and
(min30_df['EMA13'].iloc[-1] < min30_df['EMA26'].iloc[-1]) and
(min30_df['slowk'].iloc[-2] > min30_df['slowd'].iloc[-2]) and
(min30_df['slowk'].iloc[-1] > min30_df['slowd'].iloc[-1])
)
# 2.2 基于KD超买的做空信号
# condition2_2 = (
# (daily_data['EMA13'][-1] < daily_data['EMA26'][-1]) and # 日线EMA13 < EMA26
# (daily_data['EMA26_slope'][-1] < 0) and # 日线EMA26斜率 < 0
# (min30_data['EMA13'][-1] < min30_data['EMA26'][-1]) and # 30分钟EMA13 < EMA26
# (min30_data['slowk'][-1] > 80) # 30分钟K值 > 80
# )
condition2_2 = (
(daily_df['EMA13'].iloc[-1] < daily_df['EMA26'].iloc[-1]) and
(daily_df['EMA26_slope'].iloc[-1] < 0) and
(min30_df['EMA13'].iloc[-1] < min30_df['EMA26'].iloc[-1]) and
(min30_df['slowk'].iloc[-1]>80)
)
if condition1_1 or condition1_2:
long_signal = True
if condition2_1 or condition2_2:
short_signal = True
# 执行交易
portfolio = get_portfolio(symbol, 0)
if long_signal:
#buy_open(symbol, "market", volume=100) # 做多
subject = f"{symbol} 做多信号通知"
body = f"品种: {symbol}\n" \
f"检测到做多信号，时间：{index}\n" \
f"日线EMA13: {daily_data.loc[index, 'EMA13']}, 日线EMA26: {daily_data.loc[index, 'EMA26']}\n" \
f"30分钟K值: {min30_data.loc[index, 'slowk']}, 30分钟D值: {min30_data.loc[index, 'slowd']}"
send_email(subject, body)
elif short_signal:
#if portfolio.buy_quantity > 0:
#sell_close(symbol, "market", volume=portfolio.buy_quantity) # 平多
# sell_open(symbol, "market", volume=100) # 做空
subject = f"{symbol} 做空信号通知"
body = f"品种: {symbol}\n" \
f"检测到做空信号，时间：{index}\n" \
f"日线EMA13: {daily_data.loc[index, 'EMA13']}, 日线EMA26: {daily_data.loc[index, 'EMA26']}\n" \
f"30分钟K值: {min30_data.loc[index, 'slowk']}, 30分钟D值: {min30_data.loc[index, 'slowd']}"
send_email(subject, body)
# after_trading函数会在每天交易结束后被调用，当天只会被调用一次
def after_trading(context):
pass
代码修改成这样以后，报下面这个错，请帮忙看下，另外请教一下金字塔里面如何进行debug，谢谢！
执行Python脚本时遇到错误。
策略: <MyStrategy>
运行: <策略MyStrategy>
类型: IndexError
行: 100
描述: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
执行Python脚本时遇到错误。
策略: <MyStrategy>
运行: <策略MyStrategy>
类型: IndexError
行: 100
描述: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
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发表于 2025-2-21 08:30
发表于 2025-2-21 08:58