增加交易策略、交易指标、量化库代码等文件夹
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@@ -29,7 +29,6 @@ import re
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# import talib as tb
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import akshare as ak
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import ast
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# 加入邮件通知
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import smtplib
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@@ -110,42 +109,17 @@ clearing_time_dict = {
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def send_mail(text):
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# msg = MIMEMultipart()
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# msg["From"] = sender
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# msg["To"] = ";".join(receivers)
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# msg["Subject"] = subject
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# msg.attach(MIMEText(text, "plain", "utf-8"))
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# smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
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# smtp.login(username, password)
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# smtp.sendmail(sender, receivers, msg.as_string())
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# smtp.quit()
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global last_sent_time
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# 检查时间间隔
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current_time = time.time()
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if current_time - last_sent_time < 60:
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print("current_time:",current_time)
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print("last_sent_time:",last_sent_time)
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print("一分钟内已发送过邮件，本次跳过")
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return # 直接退出，不执行发送
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msg = MIMEMultipart()
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msg["From"] = sender
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msg["To"] = ";".join(receivers)
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msg["Subject"] = subject
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html_content = f"""
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<html>
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<body>
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<p>以下是数据的最后一列：</p>
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{text}
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</body>
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</html>
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"""
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msg.attach(MIMEText(html_content, 'html'))
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msg.attach(MIMEText(text, "plain", "utf-8"))
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smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
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smtp.login(username, password)
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smtp.sendmail(sender, receivers, msg.as_string())
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smtp.quit()
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def futures_main_day(future_symbol, delta_days):
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# 获取当前日期的数据
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today = datetime.now().strftime("%Y%m%d")
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@@ -157,78 +131,6 @@ def futures_main_day(future_symbol, delta_days):
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)
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return futures_main_sina_hist
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def safe_literal_eval(x):
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"""带异常处理的安全转换"""
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try:
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return ast.literal_eval(x)
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except:
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return [] # 返回空列表作为占位符
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def add_poc_column(df):
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# 安全转换列数据
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df['price'] = df['price'].apply(safe_literal_eval)
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df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x))))
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df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x))))
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# 定义处理函数（带数据验证）
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def find_poc(row):
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# 验证三个列表长度一致且非空
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if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0):
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return '缺值' # 返回空值标记异常数据
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sums = [a + b for a, b in zip(row['Ask'], row['Bid'])]
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try:
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max_index = sums.index(max(sums))
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return row['price'][max_index]
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except ValueError:
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return '缺值' # 处理空求和列表情况
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# 应用处理函数
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df['POC'] = df.apply(find_poc, axis=1)
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# 可选：统计异常数据
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error_count = df['POC'].isnull().sum()
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if error_count > 0:
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print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）")
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return df['POC']
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def ultimate_smoother(price, period):
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# 初始化变量（修正角度单位为弧度）
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a1 = np.exp(-1.414 * np.pi / period)
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b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi
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c2 = b1
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c3 = -a1 ** 2
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c1 = (1 + c2 - c3) / 4
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# 准备输出序列
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us = np.zeros(len(price))
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us_new = np.zeros(len(price))
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trend = [None]*(len(price))
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ma_close = np.zeros(len(price))
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# 前4个点用原始价格初始化
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for i in range(len(price)):
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if i < 4:
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us[i] = price[i]
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else:
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# 应用递归公式
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us[i] = (1 - c1) * price[i] + (2 * c1 - c2) * price[i-1] \
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- (c1 + c3) * price[i-2] + c2 * us[i-1] + c3 * us[i-2]
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us_new = np.around(us, decimals=2)
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ma_close = price.rolling(window=5*period).mean()
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if us_new[i]>price[i] and ma_close[i]>price[i]:
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trend[i] = '空头趋势'
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elif us_new[i]<price[i] and ma_close[i]<price[i]:
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trend[i] = '多头趋势'
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else:
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trend[i] = '无趋势'
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return us_new,trend
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# 交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
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class ParamObj:
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@@ -687,24 +589,6 @@ class MyTrader(TraderApiBase):
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# 读取保留的模型数据CSV文件
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if os.path.exists(file_path):
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df = pd.read_csv(file_path)
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df = df.drop_duplicates(subset='datetime', keep='first').reset_index(drop=True)
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df = df[df['high'] != df['low']]
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df["delta"] = df["delta"].astype(float)
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df['datetime'] = pd.to_datetime(df['datetime'],format='mixed')#, dayfirst=True, format='mixed'
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df['delta累计'] = df.groupby(df['datetime'].dt.date)['delta'].cumsum()
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df = df.fillna('缺值')
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df['终极平滑值'],df['趋势方向'] = ultimate_smoother(df['close'],time_period)
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df['datetime'] = df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S")
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df['POC'] = add_poc_column(df)
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time_period = 48
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if len(df) >=5*time_period and (df['趋势方向'].iloc[-1] != df['趋势方向'].iloc[-2]):
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table_text = df.iloc[:,3:].tail(1).to_html(index=False) #price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
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send_mail(table_text)
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else:
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pass
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if not df.empty and param.kgdata is True:
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# 选择最后一行数据
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# df = df._append(df.iloc[-1], ignore_index=True)
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@@ -842,15 +726,57 @@ class MyTrader(TraderApiBase):
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self.read_to_csv(instrument_id)
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if len(trade_df) > param.cont_df:
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# 检查文件是否存在
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csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
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csv_file_path = f"traderdata/{instrument_id}_{周期}_ofdata.csv"
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# if os.path.exists(csv_file_path):
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# #jerome :保存数增加'delta累计'、POC、、终极平滑值、趋势方向
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# # 仅保存最后一行数据
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# trade_df.tail(1).to_csv(
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# csv_file_path, mode="a", header=False, index=False
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# )
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# else:
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# # 创建新文件并保存整个DataFrame
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# trade_df.to_csv(csv_file_path, index=False)
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# 检查是否存在重复行
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if os.path.exists(csv_file_path):
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#jerome :保存数增加'delta累计'、POC、、终极平滑值、趋势方向
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# 仅保存最后一行数据
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trade_df.tail(1).to_csv(
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csv_file_path, mode="a", header=False, index=False
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existing_df = pd.read_csv(csv_file_path, usecols=range(12))
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# 获取要写入的新数据
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new_data = trade_df.tail(1)
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# 检查新数据是否与现有数据重复
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is_duplicate = False
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for _, row in existing_df.iterrows():
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if (row['datetime'] == new_data['datetime'].iloc[0] and
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row['price'] == new_data['price'].iloc[0] and
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row['Ask'] == new_data['Ask'].iloc[0] and
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row['Bid'] == new_data['Bid'].iloc[0] and
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row['symbol'] == new_data['symbol'].iloc[0] and
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row['delta'] == new_data['delta'].iloc[0] and
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row['close'] == new_data['close'].iloc[0] and
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row['open'] == new_data['open'].iloc[0] and
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row['high'] == new_data['high'].iloc[0] and
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row['low'] == new_data['low'].iloc[0] and
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row['volume'] == new_data['volume'].iloc[0] and
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row['dj'] == new_data['dj'].iloc[0]):
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is_duplicate = True
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break
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# 检查Ask和Bid的值是否为空或全为0
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ask_value = new_data['Ask'].iloc[0]
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bid_value = new_data['Bid'].iloc[0]
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is_valid_data = (
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ask_value != [] and
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ask_value != [0] and
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bid_value != [] and
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bid_value != [0]
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)
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if not is_duplicate and is_valid_data:
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# 如果没有重复且数据有效，则写入新数据
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new_data.to_csv(
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csv_file_path, mode="a", header=False, index=False
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)
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else:
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# 创建新文件并保存整个DataFrame
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# 如果文件不存在，直接写入新数据
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trade_df.to_csv(csv_file_path, index=False)
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# 更新跟踪止损价格
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@@ -1382,8 +1308,8 @@ class MyTrader(TraderApiBase):
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for symbol in param_dict.keys():
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# folder_path = "traderdata"
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# ofdata_file_path = os.path.join("traderdata", f"{str(symbol)}_ofdata.csv")
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if os.path.exists(f"traderdata/{symbol}_ofdata.csv"):
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# ofdata_file_path = os.path.join("traderdata", f"{str(symbol)}_{周期}_ofdata.csv")
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if os.path.exists(f"traderdata/{symbol}_{周期}_ofdata.csv"):
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columns = [
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"price",
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"Ask",
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@@ -1399,13 +1325,13 @@ class MyTrader(TraderApiBase):
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"dj",
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]
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# import csv
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# with open(f"traderdata/{symbol}_ofdata.csv", "r") as f:
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# with open(f"traderdata/{symbol}_{周期}_ofdata.csv", "r") as f:
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# reader = csv.reader(f)
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# for i, row in enumerate(reader, 1):
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# if len(row) != 12:
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# print(f"Line {i} has {len(row)} columns: {row}")
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trade_dfs[symbol] = pd.read_csv(
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f"traderdata/{symbol}_ofdata.csv", usecols=columns
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f"traderdata/{symbol}_{周期}_ofdata.csv", usecols=columns
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)
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else:
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@@ -1457,21 +1383,21 @@ if __name__ == "__main__":
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# http://www.algo.plus/ctp/python/0103001.html
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param_dict = {}
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# param_dict["IM2503"] = ParamObj(
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# symbol="IM2503",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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# fixed_stop_loss_percent=0.02,
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# dj_X=8,
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# delta=500,
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# sum_delta=800,
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# 失衡=3,
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# 堆积=3,
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# 周期="5min",
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# )
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# param_dict["IF2503"] = ParamObj(
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# symbol="IF2503",
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param_dict["IM2505"] = ParamObj(
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symbol="IM2505",
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Lots=1,
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py=5,
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trailing_stop_percent=0.01,
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fixed_stop_loss_percent=0.02,
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dj_X=8,
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delta=500,
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sum_delta=800,
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失衡=3,
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堆积=3,
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周期="2min",
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)
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# param_dict["IF2505"] = ParamObj(
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# symbol="IF2505",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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@@ -1481,10 +1407,10 @@ if __name__ == "__main__":
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# sum_delta=300,
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# 失衡=3,
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# 堆积=3,
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# 周期="5min",
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# 周期="2min",
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# )
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# param_dict["IH2503"] = ParamObj(
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# symbol="IH2503",
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# param_dict["IH2505"] = ParamObj(
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# symbol="IH2505",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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@@ -1494,10 +1420,10 @@ if __name__ == "__main__":
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# sum_delta=300,
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# 失衡=3,
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# 堆积=3,
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# 周期="5min",
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# 周期="2min",
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# )
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# param_dict["rb2505"] = ParamObj(
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# symbol="rb2505",
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# param_dict["lh2505"] = ParamObj(
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# symbol="lh2505",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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@@ -1507,21 +1433,34 @@ if __name__ == "__main__":
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# sum_delta=2000,
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# 失衡=3,
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# 堆积=3,
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# 周期="5min",
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# 周期="1min",
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# )
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# param_dict["ag2505"] = ParamObj(
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# symbol="ag2505",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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# fixed_stop_loss_percent=0.02,
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# dj_X=8,
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# delta=1500,
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# sum_delta=2000,
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# 失衡=3,
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# 堆积=3,
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# 周期="2min",
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# )
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# param_dict["ni2505"] = ParamObj(
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# symbol="ni2505",
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# Lots=1,
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# py=5,
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# trailing_stop_percent=0.01,
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# fixed_stop_loss_percent=0.02,
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# dj_X=8,
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# delta=1500,
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# sum_delta=2000,
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# 失衡=3,
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# 堆积=3,
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# 周期="2min",
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# )
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param_dict["ag2504"] = ParamObj(
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symbol="ag2504",
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Lots=1,
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py=5,
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trailing_stop_percent=0.01,
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fixed_stop_loss_percent=0.02,
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dj_X=8,
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delta=1500,
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sum_delta=2000,
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失衡=3,
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堆积=3,
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周期="1min",
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)
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# 用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
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# SIMULATE_SERVER = {
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# '电信1': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10201", 'MDServer': '180.168.146.187:10211', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'},
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@@ -1544,7 +1483,7 @@ if __name__ == "__main__":
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# 用户注册后，默认的APPID为simnow_client_test，认证码为0000000000000000（16个0），默认开启终端认证，程序化用户可以选择不开终端认证接入。
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future_account = get_simulate_account(
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investor_id="135858", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
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investor_id="223828", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
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password="Zj1234!@#%", # simnow密码
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server_name="电信1", # 电信1、电信2、移动、TEST、N视界
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subscribe_list=list(param_dict.keys()), # 合约列表
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