diff --git a/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本 (2).csv b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本 (2).csv new file mode 100644 index 0000000..3496cba --- /dev/null +++ b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本 (2).csv @@ -0,0 +1,3 @@ +datetime,symbol,open,high,low,close,volume,turnover,open_interest +2025-10-09 09:30:00,IM2512,7444.0,7445.2,7443.8,7445.2,406,604503760,-176 +2025-10-09 09:45:00,IM2512,7448.0,7497.8,7430.8,7482.6,20461,30561185000,-6689 \ No newline at end of file diff --git a/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本.csv b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本.csv new file mode 100644 index 0000000..dc90248 --- /dev/null +++ b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m - 副本.csv @@ -0,0 +1,37 @@ +datetime,symbol,open,high,low,close,volume,turnover,open_interest +2025/10/9 9:30:00,IM2512,7444,7445.2,7443.8,7445.2,406,604503760,-176 +2025/10/9 9:45:00,IM2512,7448,7497.8,7430.8,7482.6,20461,30561185000,-6689 +2025/10/9 10:00:00,IM2512,7482.6,7486.6,7413.2,7438.2,16226,24136810440,-3489 +2025/10/9 10:15:00,IM2512,7436.6,7495.4,7425,7485,11496,17154464040,-1823 +2025/10/9 10:30:00,IM2512,7485.4,7490,7472.2,7488.6,7543,11285654240,-324 +2025/10/9 10:45:00,IM2512,7500,7517.8,7481.4,7508.6,9391,14084729600,584 +2025/10/9 11:00:00,IM2512,7508,7519,7484,7497.6,7377,11069550680,-184 +2025/10/9 11:15:00,IM2512,7498.2,7512.6,7493.2,7509.4,5153,7733800280,330 +2025/10/9 11:30:00,IM2512,7511.8,7515,7491.8,7497.8,4425,6640375520,639 +2025/10/9 13:00:00,IM2512,7496.2,7496.8,7496.2,7496.8,32,47974960,-1 +2025/10/9 13:15:00,IM2512,7497.2,7499.2,7475.2,7485.8,6500,9733899920,589 +2025/10/9 13:30:00,IM2512,7486,7490.4,7466.6,7486,5213,7795326120,634 +2025/10/9 13:45:00,IM2512,7487.4,7494.4,7477,7477,3671,5497097120,740 +2025/10/9 14:00:00,IM2512,7476.2,7489,7456.4,7486.2,7696,11501011000,1633 +2025/10/9 14:15:00,IM2512,7485,7502.6,7477,7481.8,5525,8276203640,1217 +2025/10/9 14:30:00,IM2512,7481,7492,7447.4,7455.8,7169,10703287880,1738 +2025/10/9 14:45:00,IM2512,7455.8,7468,7454.2,7461.6,4891,7299102080,1399 +2025/10/9 15:00:00,IM2512,7461,7468,7449.6,7449.6,7203,10744653160,1766 +2025/10/10 9:30:00,IM2512,7415,7417.6,7415,7417.4,595,882483920,-241 +2025/10/10 9:45:00,IM2512,7416.8,7433,7353.2,7353.2,26824,39698311640,-12378 +2025/10/10 10:00:00,IM2512,7352.6,7379.8,7339,7359.8,18742,27579709040,-3461 +2025/10/10 10:15:00,IM2512,7357.8,7375.6,7340,7343,9092,13374526160,-296 +2025/10/10 10:30:00,IM2512,7343.8,7373,7331.6,7373,8344,12267357720,263 +2025/10/10 10:45:00,IM2512,7374,7397.8,7373,7374.4,8100,11962701680,689 +2025/10/10 11:00:00,IM2512,7373.8,7391.6,7369.6,7375.8,4499,6640018080,130 +2025/10/10 11:15:00,IM2512,7375.6,7379,7357.4,7361.2,5339,7867726520,783 +2025/10/10 11:30:00,IM2512,7361.6,7367.2,7337,7349.2,6178,9084385760,679 +2025/10/10 13:00:00,IM2512,7350.8,7350.8,7349,7349,37,54391480,7 +2025/10/10 13:15:00,IM2512,7349,7366.6,7341.8,7361.2,5066,7453994640,1220 +2025/10/10 13:30:00,IM2512,7360.8,7365,7349.8,7358.6,3472,5108998600,1031 +2025/10/10 13:45:00,IM2512,7358,7371.4,7335,7345.4,6372,9368157960,1088 +2025/10/10 14:00:00,IM2512,7346.4,7380.6,7342,7380.6,7364,10842634240,2320 +2025/10/10 14:15:00,IM2512,7381.8,7386.8,7340.6,7340.6,7534,11087884440,2262 +2025/10/10 14:30:00,IM2512,7341.4,7353,7330.2,7347.6,6980,10249484440,2363 +2025/10/10 14:45:00,IM2512,7348.4,7364.4,7335.4,7342.4,5690,8364008080,2237 +2025/10/10 15:00:00,IM2512,7342.4,7348.2,7327.2,7340.4,9841,14443336560,2355 diff --git a/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m.csv b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m.csv new file mode 100644 index 0000000..93018ba --- /dev/null +++ b/2.数据下载与处理/ssquant_download/IM888_2025-10-01_2025-10-10_1m.csv @@ -0,0 +1,37 @@ +datetime,symbol,open,high,low,close,volume,amount,openint,cumulative_openint,open_askp,open_bidp,close_askp,close_bidp,开仓,平仓,多开,空开,多平,空平,双开,双平,双换,B,S,未知 +2025-10-09 09:30:00,IM2512,7444.0,7445.2,7443.8,7445.2,406,604503760,-176,183346,7445.0,7443.2,7448.0,7447.2,0,406,0,0,119,230,0,57,0,230,119,0 +2025-10-09 09:45:00,IM2512,7448.0,7497.8,7430.8,7482.6,20461,30561185000,-6689,176657,7448.0,7447.2,7484.0,7482.6,2468,16620,1079,932,6738,7334,457,2548,261,8413,7670,1112 +2025-10-09 10:00:00,IM2512,7482.6,7486.6,7413.2,7438.2,16226,24136810440,-3489,173168,7484.8,7482.6,7438.0,7435.6,3309,11270,1281,1356,4765,4298,672,2207,375,5579,6121,1272 +2025-10-09 10:15:00,IM2512,7436.6,7495.4,7425.0,7485.0,11496,17154464040,-1823,171345,7436.8,7435.8,7486.2,7485.6,3312,6782,1154,1312,2567,2905,846,1310,322,4059,3879,1080 +2025-10-09 10:30:00,IM2512,7485.4,7490.0,7472.2,7488.6,7543,11285654240,-324,171019,7486.4,7485.0,7488.8,7488.6,2875,3611,1184,1271,1454,1468,420,689,218,2652,2725,839 +2025-10-09 10:45:00,IM2512,7500.0,7517.8,7481.4,7508.6,9391,14084729600,584,171653,7500.0,7498.8,7509.0,7508.2,4459,3507,1930,1822,1420,1590,707,497,389,3520,3242,1036 +2025-10-09 11:00:00,IM2512,7508.0,7519.0,7484.0,7497.6,7377,11069550680,-184,171469,7508.8,7508.0,7498.0,7497.6,2836,3227,1194,1269,1396,1220,373,611,272,2414,2665,1042 +2025-10-09 11:15:00,IM2512,7498.2,7512.6,7493.2,7509.4,5153,7733800280,330,171795,7498.8,7498.2,7510.0,7509.4,2393,1709,1180,899,626,763,314,320,224,1943,1525,827 +2025-10-09 11:30:00,IM2512,7511.8,7515.0,7491.8,7497.8,4425,6640375520,639,172438,7511.8,7510.8,7497.8,7496.2,2351,1271,991,1046,554,554,314,163,184,1545,1600,619 +2025-10-09 13:00:00,IM2512,7496.2,7496.8,7496.2,7496.8,32,47974960,-1,172437,7496.2,7495.6,7497.4,7496.8,0,8,0,0,8,0,0,0,0,0,8,24 +2025-10-09 13:15:00,IM2512,7497.2,7499.2,7475.2,7485.8,6500,9733899920,589,173026,7498.6,7497.4,7486.4,7486.0,3293,2109,1341,1403,820,868,549,421,259,2209,2223,839 +2025-10-09 13:30:00,IM2512,7486.0,7490.4,7466.6,7486.0,5213,7795326120,634,173660,7486.0,7485.4,7486.6,7486.0,2802,1490,1049,1349,604,712,404,174,156,1761,1953,765 +2025-10-09 13:45:00,IM2512,7487.4,7494.4,7477.0,7477.0,3671,5497097120,740,174408,7488.0,7487.6,7476.8,7476.2,2076,864,783,958,361,366,335,137,132,1149,1319,599 +2025-10-09 14:00:00,IM2512,7476.2,7489.0,7456.4,7486.2,7696,11501011000,1633,176036,7476.8,7476.2,7486.2,7485.6,4641,1882,1691,2203,647,891,747,344,194,2582,2850,979 +2025-10-09 14:15:00,IM2512,7485.0,7502.6,7477.0,7481.8,5525,8276203640,1217,177254,7485.6,7485.2,7481.2,7481.0,3369,1164,1338,1537,476,489,494,199,191,1827,2013,801 +2025-10-09 14:30:00,IM2512,7481.0,7492.0,7447.4,7455.8,7169,10703287880,1738,178996,7481.0,7480.8,7456.4,7455.8,4537,1580,1855,2105,789,530,577,261,153,2385,2894,899 +2025-10-09 14:45:00,IM2512,7455.8,7468.0,7454.2,7461.6,4891,7299102080,1399,180390,7456.0,7454.6,7462.0,7460.4,3146,881,1400,1315,317,483,431,81,144,1883,1632,720 +2025-10-09 15:00:00,IM2512,7461.0,7468.0,7449.6,7449.6,7203,10744653160,1766,182161,7461.8,7461.2,7450.6,7449.6,4603,1573,1749,2375,843,578,479,152,114,2327,3218,913 +2025-10-10 09:30:00,IM2512,7415.0,7417.6,7415.0,7417.4,595,882483920,-241,181920,7417.4,7415.0,7416.8,7412.0,91,504,91,0,245,259,0,0,0,350,245,0 +2025-10-10 09:45:00,IM2512,7416.8,7433.0,7353.2,7353.2,26824,39698311640,-12378,169542,7416.8,7415.6,7353.4,7353.2,1483,24554,602,488,11019,9562,393,3973,243,10164,11507,544 +2025-10-10 10:00:00,IM2512,7352.6,7379.8,7339.0,7359.8,18742,27579709040,-3461,166081,7352.4,7352.0,7359.4,7358.0,4447,12800,1570,1977,5331,5334,900,2135,310,6904,7308,1185 +2025-10-10 10:15:00,IM2512,7357.8,7375.6,7340.0,7343.0,9092,13374526160,-296,165785,7357.8,7357.0,7344.0,7343.0,3853,3967,1580,1685,1472,1646,588,849,321,3226,3157,951 +2025-10-10 10:30:00,IM2512,7343.8,7373.0,7331.6,7373.0,8344,12267357720,263,166048,7343.8,7342.6,7373.4,7373.0,3821,3190,1545,1627,1183,1435,649,572,333,2980,2810,1000 +2025-10-10 10:45:00,IM2512,7374.0,7397.8,7373.0,7374.4,8100,11962701680,689,166772,7374.4,7374.0,7374.2,7373.4,4123,2630,1941,1626,1072,1149,556,409,237,3090,2698,1110 +2025-10-10 11:00:00,IM2512,7373.8,7391.6,7369.6,7375.8,4499,6640018080,130,166902,7374.6,7374.4,7376.4,7375.6,2022,1627,927,866,671,684,229,272,179,1611,1537,671 +2025-10-10 11:15:00,IM2512,7375.6,7379.0,7357.4,7361.2,5339,7867726520,783,167685,7375.4,7375.2,7363.0,7361.2,2806,1619,1097,1319,821,518,390,280,183,1615,2140,731 +2025-10-10 11:30:00,IM2512,7361.6,7367.2,7337.0,7349.2,6178,9084385760,679,168364,7362.8,7361.4,7350.6,7349.0,3068,1943,1295,1218,913,676,555,354,289,1971,2131,878 +2025-10-10 13:00:00,IM2512,7350.8,7350.8,7349.0,7349.0,37,54391480,7,168371,7350.2,7349.2,7350.6,7349.0,12,25,12,0,25,0,0,0,0,12,25,0 +2025-10-10 13:15:00,IM2512,7349.0,7366.6,7341.8,7361.2,5066,7453994640,1220,169591,7352.2,7349.0,7362.0,7361.2,3031,1103,1280,1195,415,507,556,181,193,1787,1610,739 +2025-10-10 13:30:00,IM2512,7360.8,7365.0,7349.8,7358.6,3472,5108998600,1031,170622,7361.2,7360.8,7359.4,7358.6,2205,560,1068,785,245,206,352,109,167,1274,1030,540 +2025-10-10 13:45:00,IM2512,7358.0,7371.4,7335.0,7345.4,6372,9368157960,1088,171730,7358.0,7357.4,7346.4,7345.2,3566,1729,1379,1719,798,697,468,234,205,2076,2517,872 +2025-10-10 14:00:00,IM2512,7346.4,7380.6,7342.0,7380.6,7364,10842634240,2320,174050,7346.8,7345.6,7380.8,7380.6,5174,1160,2230,2152,413,612,792,135,217,2842,2565,813 +2025-10-10 14:15:00,IM2512,7381.8,7386.8,7340.6,7340.6,7534,11087884440,2262,176312,7382.6,7381.8,7341.4,7340.4,5204,1365,2031,2125,638,479,1048,248,172,2510,2763,793 +2025-10-10 14:30:00,IM2512,7341.4,7353.0,7330.2,7347.6,6980,10249484440,2363,178675,7341.8,7340.4,7348.4,7347.6,5028,1122,2187,2004,360,632,837,130,131,2819,2364,699 +2025-10-10 14:45:00,IM2512,7348.4,7364.4,7335.4,7342.4,5690,8364008080,2237,180909,7349.8,7348.4,7343.6,7342.4,4179,659,1603,1952,338,258,624,63,163,1861,2290,689 +2025-10-10 15:00:00,IM2512,7342.4,7348.2,7327.2,7340.4,9841,14443336560,2355,183267,7344.0,7343.2,7341.8,7340.6,6345,2260,2637,2915,1237,836,793,187,154,3473,4152,1082 diff --git a/2.数据下载与处理/ssquant_download/ssquant_download.ipynb b/2.数据下载与处理/ssquant_download/ssquant_download.ipynb index 2d3a1a0..ca46c9f 100644 --- a/2.数据下载与处理/ssquant_download/ssquant_download.ipynb +++ b/2.数据下载与处理/ssquant_download/ssquant_download.ipynb @@ -86,9 +86,21 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'ssquant.SQDATA'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mssquant\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mSQDATA\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TakeData\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'ssquant.SQDATA'" + ] + } + ], "source": [ "from ssquant.SQDATA import TakeData" ] @@ -130,164 +142,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "头部文件为：--------------------\n" - ] - }, - { - "data": { - "text/html": [ - "
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datetimesymbolopenhighlowclosevolumeamountcumulative_openintopenintopen_bidpopen_askpclose_bidpclose_askp
02019-01-02 09:01:00rb190533993405338934016956223626071602383714168643399.03400.03400.03401.0
12019-01-02 09:02:00rb190534013430340134108869630342832002399530-122483401.03402.03409.03410.0
22019-01-02 09:03:00rb1905340934143409341222828778740580238735611803409.03410.03411.03412.0
32019-01-02 09:04:00rb19053412341334033404173785924132202388158543411.03412.03404.03405.0
42019-01-02 09:05:00rb1905340534093405340515770537276980238819016743405.03406.03405.03406.0
\n", - "
" - ], - "text/plain": [ - " datetime symbol open high low close volume amount \\\n", - "0 2019-01-02 09:01:00 rb1905 3399 3405 3389 3401 69562 2362607160 \n", - "1 2019-01-02 09:02:00 rb1905 3401 3430 3401 3410 88696 3034283200 \n", - "2 2019-01-02 09:03:00 rb1905 3409 3414 3409 3412 22828 778740580 \n", - "3 2019-01-02 09:04:00 rb1905 3412 3413 3403 3404 17378 592413220 \n", - "4 2019-01-02 09:05:00 rb1905 3405 3409 3405 3405 15770 537276980 \n", - "\n", - " cumulative_openint openint open_bidp open_askp close_bidp close_askp \n", - "0 2383714 16864 3399.0 3400.0 3400.0 3401.0 \n", - "1 2399530 -12248 3401.0 3402.0 3409.0 3410.0 \n", - "2 2387356 1180 3409.0 3410.0 3411.0 3412.0 \n", - "3 2388158 54 3411.0 3412.0 3404.0 3405.0 \n", - "4 2388190 1674 3405.0 3406.0 3405.0 3406.0 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "print('头部文件为：--------------------')\n", "data.head()" @@ -363,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/2.数据下载与处理/ssquant_download/专属数据库请求数据API示例.py b/2.数据下载与处理/ssquant_download/专属数据库请求数据API示例.py new file mode 100644 index 0000000..ede616e --- /dev/null +++ b/2.数据下载与处理/ssquant_download/专属数据库请求数据API示例.py @@ -0,0 +1,109 @@ +import requests +import pandas as pd +from datetime import datetime, timedelta +from io import StringIO + +def get_futures_data(symbol, start_date, end_date, kline_period='1m', adjust_type='0', depth='no'): + """ + 获取期货数据 + """ + # 构建请求参数 + params = { + 'username': username, + 'password': password, + 'symbol': symbol, + 'start_date': start_date, + 'end_date': end_date, + 'kline_period': kline_period, + 'adjust_type': adjust_type + } + + if depth: + params['Depth'] = depth + + print("请求参数：", params) # 打印请求参数，便于调试 + + try: + # 发送请求，设置超时时间为30秒 + response = requests.get(base_url, params=params, timeout=300) + + # 检查响应状态 + if response.status_code == 200: + # 检查响应是否为JSON格式 + if 'application/json' in response.headers.get('Content-Type', ''): + data = pd.read_json(StringIO(response.text), orient='records') + data=data.reset_index() + #列名排序 + if depth=='yes': + columns=['datetime','symbol','open','high','low','close','volume','amount','openint','cumulative_openint','open_askp','open_bidp','close_askp','close_bidp','开仓','平仓','多开','空开','多平','空平','双开','双平','双换','B','S','未知'] + else: + columns=['datetime','symbol','open','high','low','close','volume','amount','openint','cumulative_openint','open_askp','open_bidp','close_askp','close_bidp'] + # 重新排列列名 + data = data.reindex(columns=columns) + data['datetime'] = pd.to_datetime(data['datetime']) + # 更改时间显示的格式，例如 "YYYY-MM-DD HH:MM:SS" + # 将 UTC 时间转换为本地时区，例如 'Asia/Shanghai' + data['datetime'] = data['datetime'].dt.tz_convert('Asia/Shanghai') + data['datetime'] = data['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S') + return data + else: + print("响应不是JSON格式：", response.text[:1000]) + return None + elif response.status_code == 401: + print("认证失败：用户名和密码不能为空") + return None + elif response.status_code == 402: + print("认证失败：账号不存在，请检查账号后重新输入......如还有问题联系管理员微信：viquant01") + return None + elif response.status_code == 405: + print("认证失败：账号已过期,请联系管理员微信：viquant01") + return None + elif response.status_code == 406: + print("认证失败：密码错误,请检查密码后重新输入......如还有问题联系管理员微信：viquant01") + return None + else: + print(f"请求失败，状态码：{response.status_code}") + print(f"错误信息：{response.json().get('error', '未知错误')}") + return None + + except Exception as e: + print(f"发生错误：{e}") + print("请求URL：", response.url) + print("响应内容：", response.text[:1000]) # 打印前1000个字符 + return None + +# 使用示例 +if __name__ == "__main__": + # API配置 + base_url = 'http://kanpan789.com:8086/ftdata' + # 用户认证信息 + username = '240884432@qq.com' # 替换为你的手机号或者邮箱 + password = 'Zj123!@#' # 替换为你的密码 + # 示例参数 + symbol = "IM888" # + start_date = "2025-10-01" #start_date ： 开始时间 + end_date = "2025-10-10" #end_date(包含当天)：结束时间 + kline_period="15M" #周期:1M..5M..NM(分钟),1D(天),1W(周),1Y(月) + adjust_type= 0 #复权开关 ：0(不复权)1(后复权) + depth='yes' # 获取交易数据统计: yes(获取)，no（不获取） + + # 获取K线数据 + df_data = get_futures_data( + symbol=symbol, + start_date=start_date, + end_date=end_date, + kline_period=kline_period, + adjust_type=adjust_type, + depth=depth + ) + + if df_data is not None: + print("\nK线数据示例：") + print(df_data) + print(f"\n数据条数：{len(df_data)}") + + # 保存到CSV文件（可选） + csv_filename = f"{symbol}_{start_date}_{end_date}_1m.csv" + df_data.to_csv(csv_filename, index=False) + print(f"\n数据已保存到文件：{csv_filename}") + \ No newline at end of file diff --git a/2.数据下载与处理/ssquant_download/松鼠数据下载脚本.ipynb b/2.数据下载与处理/ssquant_download/松鼠数据下载脚本.ipynb index 6f75e02..c016f30 100644 --- a/2.数据下载与处理/ssquant_download/松鼠数据下载脚本.ipynb +++ b/2.数据下载与处理/ssquant_download/松鼠数据下载脚本.ipynb @@ -88,10 +88,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "id": "65b4b7aa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'ssquant.SQDATA'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mssquant\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mSQDATA\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m TakeData\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'ssquant.SQDATA'" + ] + } + ], "source": [ "from ssquant.SQDATA import TakeData\n", "import pandas as pd" @@ -227,7 +239,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -241,7 +253,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.13.2" } }, "nbformat": 4, diff --git a/temp/dingdanliu_nb_option.py b/temp/dingdanliu_nb_option.py new file mode 100644 index 0000000..f6bddda --- /dev/null +++ b/temp/dingdanliu_nb_option.py @@ -0,0 +1,1735 @@ +""" +该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有： +on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。 +tickdata(df, symbol): 处理Tick数据，生成K线数据。 +orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。F +GetOrderFlow_dj(kData): 计算订单流的信号指标。 +除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。 +""" + +# from concurrent.futures import ThreadPoolExecutor +from multiprocessing import Process, Queue +import queue +import threading +# from AlgoPlus.CTP.MdApi import run_tick_engine +# from AlgoPlus.CTP.FutureAccount import get_simulate_account +# from AlgoPlus.CTP.FutureAccount import FutureAccount +# from AlgoPlus.CTP.TraderApiBase import TraderApiBase + +from CtpPlus.CTP.MdApi import run_tick_engine +from CtpPlus.CTP.FutureAccount import get_simulate_account +from CtpPlus.CTP.FutureAccount import FutureAccount +from CtpPlus.CTP.TraderApiBase import TraderApiBase + +# from AlgoPlus.ta.time_bar import tick_to_bar +import pandas as pd +from datetime import datetime, timedelta +from datetime import time as s_time +import operator +import time +import numpy as np +import os +import re + +# import talib as tb + +import akshare as ak +import ast + +# 加入邮件通知 +import smtplib +from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件 +from email.mime.multipart import ( + MIMEMultipart, +) + +# from email.mime.application import MIMEApplication + +# 配置邮件信息 +receivers = ["240884432@qq.com"] # 设置邮件接收人地址 +subject = "TD_Simnow_Signal" # 设置邮件主题 订单流策略交易信号 + +# 配置邮件服务器信息 +smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址 +smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465 +sender = "240884432@qq.com" # 设置发送邮件的邮箱地址 +username = "240884432@qq.com" # 设置发送邮件的邮箱用户名 +password = "osjyjmbqrzxtbjbf" # zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码 + +tickdatadict = {} # 存储Tick数据的字典 +quotedict = {} # 存储行情数据的字典 +ofdatadict = {} # 存储K线数据的字典 +trade_dfs = {} # pd.DataFrame({}) # 存储交易数据的DataFrame对象 +previous_volume = {} # 上一个Tick的成交量 +tsymbollist = {} + + +time_period = 30 +delta_rate = 0.8 +dj_rate = 0.8 + +clearing_time_dict = { + "sc": s_time(2, 30), + "bc": s_time(1, 0), + "lu": s_time(23, 0), + "nr": s_time(23, 0), + "au": s_time(2, 30), + "ag": s_time(2, 30), + "ss": s_time(1, 0), + "sn": s_time(1, 0), + "ni": s_time(1, 0), + "pb": s_time(1, 0), + "zn": s_time(1, 0), + "al": s_time(1, 0), + "cu": s_time(1, 0), + "ru": s_time(23, 0), + "rb": s_time(23, 0), + "hc": s_time(23, 0), + "fu": s_time(23, 0), + "bu": s_time(23, 0), + "sp": s_time(23, 0), + "PF": s_time(23, 0), + "SR": s_time(23, 0), + "CF": s_time(23, 0), + "CY": s_time(23, 0), + "RM": s_time(23, 0), + "MA": s_time(23, 0), + "TA": s_time(23, 0), + "ZC": s_time(23, 0), + "FG": s_time(23, 0), + "OI": s_time(23, 0), + "SA": s_time(23, 0), + "p": s_time(23, 0), + "j": s_time(23, 0), + "jm": s_time(23, 0), + "i": s_time(23, 0), + "l": s_time(23, 0), + "v": s_time(23, 0), + "pp": s_time(23, 0), + "eg": s_time(23, 0), + "c": s_time(23, 0), + "cs": s_time(23, 0), + "y": s_time(23, 0), + "m": s_time(23, 0), + "a": s_time(23, 0), + "b": s_time(23, 0), + "rr": s_time(23, 0), + "eb": s_time(23, 0), + "pg": s_time(23, 0), +} + +fees_df = pd.read_csv('./futures_fees_info.csv', header = 0, usecols= [1, 3, 5, 13, 15],names=['合约代码', '品种代码', '合约乘数', '做多保证金率（按金额）', '做空保证金率（按金额）']) +contacts_df = pd.read_csv('./main_contacts.csv', header = 0, usecols= [16, 17],names=['主连代码', '品种代码']) + +def get_main_contact_on_time(main_symbol_code,contacts_df): + main_symbol = contacts_df[contacts_df['品种代码'] == main_symbol_code]['主连代码'].iloc[0] + # print("最终使用的主连代码：",main_symbol) + return main_symbol#.encode('ascii') + +def send_mail(text): + msg = MIMEMultipart() + msg["From"] = sender + msg["To"] = ";".join(receivers) + msg["Subject"] = subject + msg.attach(MIMEText(text, "plain", "utf-8")) + smtp = smtplib.SMTP_SSL(smtp_server, smtp_port) + smtp.login(username, password) + smtp.sendmail(sender, receivers, msg.as_string()) + smtp.quit() + + + +# def get_otm_put_strike_price(option_finance_board_df, future_price): +# # 计算距离当前期货价格最近的行权价 +# option_finance_board_df['strike_diff'] = abs(option_finance_board_df['行权价'] - future_price) +# closest_row = option_finance_board_df.loc[option_finance_board_df['strike_diff'].idxmin()] +# otm_put_strike_price = closest_row['行权价'] +# return otm_put_strike_price + + +def send_feishu_message(text): + headers = { + "Content-Type": "application/json" + } + data = { + "msg_type": "text", + "content": { + "text": text + } + } + # response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/8608dfa4-e599-462a-8dba-6ac72873dd27", headers=headers, json=data) #AI策略飞书地址 + response = requests.post("https://open.feishu.cn/open-apis/bot/v2/hook/fae322eb-1ff7-4133-ba00-0ca4895d205e", headers=headers, json=data) #订单流策略飞书地址 + if response.status_code != 200: + print(f"飞书消息发送失败，状态码: {response.status_code}, 响应内容: {response.text}") +# def futures_main_day(future_symbol, delta_days): +# # 获取当前日期的数据 +# today = datetime.now().strftime("%Y%m%d") +# # 计算多少日前的日期 +# start_day = (datetime.now() - timedelta(days=delta_days)).strftime("%Y%m%d") + +# futures_main_sina_hist = ak.futures_main_sina( +# symbol=future_symbol, start_date=start_day, end_date=today +# ) +# return futures_main_sina_hist + + +# 交易程序--------------------------------------------------------------------------------------------------------------------------------------------------------------------- +class ParamObj: + + symbol = None # 合约名称 + Lots = None # 下单手数 + py = None # 设置委托价格的偏移，更加容易促成成交 + trailing_stop_percent = None # 跟踪出场参数 + fixed_stop_loss_percent = None # 固定出场参数 + dj_X = None # 开仓的堆积参数 + delta = None # 开仓的delta参数 + sum_delta = None # 开仓的delta累积参数 + 失衡 = None + 堆积 = None + 周期 = None + + # 策略需要用到的变量 + cont_df = 0 + pos = 0 + short_trailing_stop_price = 0 + long_trailing_stop_price = 0 + sl_long_price = 0 + sl_shor_price = 0 + out_long = 0 + out_short = 0 + clearing_executed = False + kgdata = True + + def __init__( + self, + symbol, + Lots, + py, + trailing_stop_percent, + fixed_stop_loss_percent, + dj_X, + delta, + sum_delta, + 失衡, + 堆积, + 周期, + ): + self.symbol = symbol + self.Lots = Lots + self.py = py + self.trailing_stop_percent = trailing_stop_percent + self.fixed_stop_loss_percent = fixed_stop_loss_percent + self.dj_X = dj_X + self.delta = delta + self.sum_delta = sum_delta + self.失衡 = 失衡 + self.堆积 = 堆积 + self.周期 = 周期 + + +class MyTrader(TraderApiBase): + + def __init__( + self, + broker_id, + td_server, + investor_id, + password, + app_id, + auth_code, + md_queue=None, + page_dir="", + private_resume_type=2, + public_resume_type=2, + ): + self.param_dict = {} + self.queue_dict = {} + self.品种 = " " + + def tickcome(self, md_queue): + global previous_volume + data = md_queue + instrument_id = data["InstrumentID"].decode() # 品种代码 + ActionDay = data["ActionDay"].decode() # 交易日日期 + update_time = data["UpdateTime"].decode() # 更新时间 + + update_millisec = str(data["UpdateMillisec"]) # 更新毫秒数 + created_at = ( + ActionDay[:4] + + "-" + + ActionDay[4:6] + + "-" + + ActionDay[6:] + + " " + + update_time + + "." + + update_millisec + ) # 创建时间 + # 构建tick字典 + tick = { + "symbol": instrument_id, # 品种代码和交易所ID + "created_at": datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"), + # "created_at": datetime.strptime(created_at, "-- %H:%M:%S.%f"), + "price": float(data["LastPrice"]), # 最新价 + "last_volume": ( + int(data["Volume"]) - previous_volume.get(instrument_id, 0) + if previous_volume.get(instrument_id, 0) != 0 + else 0 + ), # 瞬时成交量 + "bid_p": float(data["BidPrice1"]), # 买价 + "bid_v": int(data["BidVolume1"]), # 买量 + "ask_p": float(data["AskPrice1"]), # 卖价 + "ask_v": int(data["AskVolume1"]), # 卖量 + "UpperLimitPrice": float(data["UpperLimitPrice"]), # 涨停价 + "LowerLimitPrice": float(data["LowerLimitPrice"]), # 跌停价 + "TradingDay": data["TradingDay"].decode(), # 交易日日期 + "cum_volume": int(data["Volume"]), # 最新总成交量 + "cum_amount": float(data["Turnover"]), # 最新总成交额 + "cum_position": int(data["OpenInterest"]), # 合约持仓量 + } + + previous_volume[instrument_id] = int(data["Volume"]) + if tick["last_volume"] > 0: + self.on_tick(tick) + + def can_time(self, hour, minute): + hour = str(hour) + minute = str(minute) + if len(minute) == 1: + minute = "0" + minute + return int(hour + minute) + + def on_tick(self, tick): + # tm = self.can_time(tick["created_at"].hour, tick["created_at"].minute) + if tick["last_volume"] == 0: + return + quotes = tick + timetick = str(tick["created_at"]).replace("+08:00", "") + tsymbol = tick["symbol"] + if tsymbol not in tsymbollist.keys(): + # 获取tick的买卖价和买卖量 + tsymbollist[tsymbol] = tick + bid_p = quotes["bid_p"] + ask_p = quotes["ask_p"] + bid_v = quotes["bid_v"] + ask_v = quotes["ask_v"] + else: + # 获取上一个tick的买卖价和买卖量 + rquotes = tsymbollist[tsymbol] + bid_p = rquotes["bid_p"] + ask_p = rquotes["ask_p"] + bid_v = rquotes["bid_v"] + ask_v = rquotes["ask_v"] + tsymbollist[tsymbol] = tick + tick_dt = pd.DataFrame( + { + "datetime": timetick, + "symbol": tick["symbol"], + "mainsym": tick["symbol"].rstrip("0123456789").upper(), + "lastprice": tick["price"], + "vol": tick["last_volume"], + "bid_p": bid_p, + "ask_p": ask_p, + "bid_v": bid_v, + "ask_v": ask_v, + }, + index=[0], + ) + sym = tick_dt["symbol"][0] + self.tickdata(tick_dt, sym) + + def data_of(self, symbol, df): + global trade_dfs + trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True) + + def process(self, bidDict, askDict, symbol): + try: + # 尝试从quotedict中获取对应品种的报价数据 + dic = quotedict[symbol] + bidDictResult = dic["bidDictResult"] + askDictResult = dic["askDictResult"] + except Exception: + # 如果获取失败，则初始化bidDictResult和askDictResult为空字典 + bidDictResult, askDictResult = {}, {} + + # 将所有买盘字典和卖盘字典的key合并，并按升序排序 + sList = sorted(set(list(bidDict.keys()) + list(askDict.keys()))) + + # 遍历所有的key，将相同key的值进行累加 + for s in sList: + if s in bidDict: + if s in bidDictResult: + bidDictResult[s] = int(bidDict[s]) + bidDictResult[s] + else: + bidDictResult[s] = int(bidDict[s]) + if s not in askDictResult: + askDictResult[s] = 0 + else: + if s in askDictResult: + askDictResult[s] = int(askDict[s]) + askDictResult[s] + else: + askDictResult[s] = int(askDict[s]) + if s not in bidDictResult: + bidDictResult[s] = 0 + + # 构建包含bidDictResult和askDictResult的字典，并存入quotedict中 + df = {"bidDictResult": bidDictResult, "askDictResult": askDictResult} + quotedict[symbol] = df + return bidDictResult, askDictResult + + def tickdata(self, df, symbol): + tickdata = pd.DataFrame( + { + "datetime": df["datetime"], + "symbol": df["symbol"], + "lastprice": df["lastprice"], + "volume": df["vol"], + "bid_p": df["bid_p"], + "bid_v": df["bid_v"], + "ask_p": df["ask_p"], + "ask_v": df["ask_v"], + } + ) + try: + if symbol in tickdatadict.keys(): + rdf = tickdatadict[symbol] + rdftm = pd.to_datetime(rdf["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S") + now = str(tickdata["datetime"][0]) + if now > rdftm: + try: + oo = ofdatadict[symbol] + self.data_of(symbol, oo) + if symbol in quotedict.keys(): + quotedict.pop(symbol) + if symbol in tickdatadict.keys(): + tickdatadict.pop(symbol) + if symbol in ofdatadict.keys(): + ofdatadict.pop(symbol) + except IOError as e: + print("rdftm捕获到异常", e) + tickdata["bartime"] = pd.to_datetime(tickdata["datetime"]) + tickdata["open"] = tickdata["lastprice"] + tickdata["high"] = tickdata["lastprice"] + tickdata["low"] = tickdata["lastprice"] + tickdata["close"] = tickdata["lastprice"] + tickdata["starttime"] = tickdata["datetime"] + else: + tickdata["bartime"] = rdf["bartime"] + tickdata["open"] = rdf["open"] + tickdata["high"] = max( + tickdata["lastprice"].values, rdf["high"].values + ) + tickdata["low"] = min( + tickdata["lastprice"].values, rdf["low"].values + ) + tickdata["close"] = tickdata["lastprice"] + tickdata["volume"] = df["vol"] + rdf["volume"].values + tickdata["starttime"] = rdf["starttime"] + else: + print("新bar的第一个tick进入") + tickdata["bartime"] = pd.to_datetime(tickdata["datetime"]) + tickdata["open"] = tickdata["lastprice"] + tickdata["high"] = tickdata["lastprice"] + tickdata["low"] = tickdata["lastprice"] + tickdata["close"] = tickdata["lastprice"] + tickdata["starttime"] = tickdata["datetime"] + except IOError as e: + print("捕获到异常", e) + + tickdata["bartime"] = pd.to_datetime(tickdata["bartime"]) + param = self.param_dict[self.品种] + bardata = ( + tickdata.resample( + on="bartime", rule=param.周期, label="right", closed="right" + ) + .agg( + { + "starttime": "first", + "symbol": "last", + "open": "first", + "high": "max", + "low": "min", + "close": "last", + "volume": "sum", + } + ) + .reset_index(drop=False) + ) + bardata = bardata.dropna().reset_index(drop=True) + bardata["bartime"] = pd.to_datetime(bardata["bartime"][0]).strftime( + "%Y-%m-%d %H:%M:%S" + ) + tickdatadict[symbol] = bardata + tickdata["volume"] = df["vol"].values + self.orderflow_df_new(tickdata, bardata, symbol) + + def orderflow_df_new(self, df_tick, df_min, symbol): + # startArray = pd.to_datetime(df_min["starttime"]).values + voluememin = df_min["volume"].values + highs = df_min["high"].values + lows = df_min["low"].values + opens = df_min["open"].values + closes = df_min["close"].values + # endArray = pd.to_datetime(df_min['bartime']).values + endArray = df_min["bartime"].values + # print(endArray) + # deltaArray = np.zeros((len(endArray),)) + # tTickArray = pd.to_datetime(df_tick["datetime"]).values + bp1minickArray = df_tick["bid_p"].values + ap1minickArray = df_tick["ask_p"].values + lastTickArray = df_tick["lastprice"].values + volumeTickArray = df_tick["volume"].values + symbolarray = df_tick["symbol"].values + # indexFinal = 0 + for index, tEnd in enumerate(endArray): + dt = endArray[index] + # start = startArray[index] + bidDict = {} + askDict = {} + bar_vol = voluememin[index] + bar_close = closes[index] + bar_open = opens[index] + bar_low = lows[index] + bar_high = highs[index] + bar_symbol = symbolarray[index] + Bp = round(bp1minickArray[0], 4) + Ap = round(ap1minickArray[0], 4) + LastPrice = round(lastTickArray[0], 4) + Volume = volumeTickArray[0] + if LastPrice >= Ap: + if str(LastPrice) in askDict.keys(): + askDict[str(LastPrice)] += Volume + else: + askDict[str(LastPrice)] = Volume + if LastPrice <= Bp: + if str(LastPrice) in bidDict.keys(): + bidDict[str(LastPrice)] += Volume + else: + bidDict[str(LastPrice)] = Volume + # indexFinal = indexTick + bidDictResult, askDictResult = self.process(bidDict, askDict, symbol) + bidDictResult = dict( + sorted(bidDictResult.items(), key=operator.itemgetter(0)) + ) + askDictResult = dict( + sorted(askDictResult.items(), key=operator.itemgetter(0)) + ) + prinslist = list(bidDictResult.keys()) + asklist = list(askDictResult.values()) + bidlist = list(bidDictResult.values()) + delta = sum(askDictResult.values()) - sum(bidDictResult.values()) + df = pd.DataFrame( + { + "price": pd.Series([prinslist]), + "Ask": pd.Series([asklist]), + "Bid": pd.Series([bidlist]), + } + ) + # df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())}) + df["symbol"] = bar_symbol + df["datetime"] = dt + df["delta"] = str(delta) + df["close"] = bar_close + df["open"] = bar_open + df["high"] = bar_high + df["low"] = bar_low + df["volume"] = bar_vol + # df['ticktime']=tTickArray[0] + df["dj"] = self.GetOrderFlow_dj(df) + ofdatadict[symbol] = df + + def GetOrderFlow_dj(self, kData): + param = self.param_dict[self.品种] + Config = { + "Value1": param.失衡, + "Value2": param.堆积, + "Value4": True, + } + aryData = kData + djcout = 0 + + # 遍历kData中的每一行，计算djcout指标 + for index, row in aryData.iterrows(): + kItem = aryData.iloc[index] + # high = kItem["high"] + # low = kItem["low"] + # close = kItem["close"] + # open = kItem["open"] + dtime = kItem["datetime"] + price_s = kItem["price"] + Ask_s = kItem["Ask"] + Bid_s = kItem["Bid"] + delta = kItem["delta"] + + price_s = price_s + Ask_s = Ask_s + Bid_s = Bid_s + + gj = 0 + xq = 0 + gxx = 0 + xxx = 0 + + # 遍历price_s中的每一个元素，计算相关指标 + for i in np.arange(0, len(price_s), 1): + duiji = { + "price": 0, + "time": 0, + "longshort": 0, + } + + if i == 0: + delta = delta + + order = { + "Price": price_s[i], + "Bid": {"Value": Bid_s[i]}, + "Ask": {"Value": Ask_s[i]}, + } + # 空头堆积 + if i >= 0 and i < len(price_s) - 1: + if order["Bid"]["Value"] > Ask_s[i + 1] * int(Config["Value1"]): + gxx += 1 + gj += 1 + if gj >= int(Config["Value2"]) and Config["Value4"] is True: + duiji["price"] = price_s[i] + duiji["time"] = dtime + duiji["longshort"] = -1 + if float(duiji["price"]) > 0: + djcout += -1 + else: + gj = 0 + # 多头堆积 + if i >= 1 and i < len(price_s) - 1: + if order["Ask"]["Value"] > Bid_s[i - 1] * int(Config["Value1"]): + xq += 1 + xxx += 1 + if xq >= int(Config["Value2"]) and Config["Value4"] is True: + duiji["price"] = price_s[i] + duiji["time"] = dtime + duiji["longshort"] = 1 + if float(duiji["price"]) > 0: + djcout += 1 + else: + xq = 0 + + # 返回计算得到的djcout值 + return djcout + + # 读取保存的数据 + def read_to_csv(self, symbol): + # 文件夹路径和文件路径 + # 使用正则表达式提取英文字母并重新赋值给symbol + param = self.param_dict[symbol] + # symbol = ''.join(re.findall('[a-zA-Z]', str(symbol))) + folder_path = "traderdata" + file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv") + # 如果文件夹不存在则创建 + if not os.path.exists(folder_path): + os.makedirs(folder_path) + + # 读取保留的模型数据CSV文件 + if os.path.exists(file_path): + df = pd.read_csv(file_path) + if not df.empty and param.kgdata is True: + # 选择最后一行数据 + # df = df._append(df.iloc[-1], ignore_index=True) + row = df.iloc[-1] + + # 根据CSV文件的列名将数据赋值给相应的属性 + param.pos = int(row["pos"]) + param.short_trailing_stop_price = float( + row["short_trailing_stop_price"] + ) + param.long_trailing_stop_price = float(row["long_trailing_stop_price"]) + param.sl_long_price = float(row["sl_long_price"]) + param.sl_shor_price = float(row["sl_shor_price"]) + # param.out_long = int(row['out_long']) + # param.out_short = int(row['out_short']) + print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1]) + param.kgdata = False + else: + pass + # print("没有找到历史交易数据文件", file_path) + # 如果没有找到CSV，则初始化变量 + + pass + + # 保存数据 + def save_to_csv(self, symbol): + param = self.param_dict[symbol] + # 使用正则表达式提取英文字母并重新赋值给symbol + # symbol = ''.join(re.findall('[a-zA-Z]', str(symbol))) + # 创建DataFrame + + data = { + "datetime": [trade_dfs[symbol]["datetime"].iloc[-1]], + "pos": [param.pos], + "short_trailing_stop_price": [param.short_trailing_stop_price], + "long_trailing_stop_price": [param.long_trailing_stop_price], + "sl_long_price": [param.sl_long_price], + "sl_shor_price": [param.sl_shor_price], + # 'out_long': [param.out_long], + # 'out_short': [param.out_short] + } + + df = pd.DataFrame(data) + + # 将DataFrame保存到CSV文件 + # df.to_csv( + # f"traderdata/{str(symbol)}_traderdata.csv", + # mode="a", + # index=False, + # header=False, + # ) + + traderdata_file_path = f"traderdata/{str(symbol)}_traderdata.csv" + if os.path.exists(traderdata_file_path): + # 仅保存最后一行数据 + # csv_df = pd.read_csv(traderdata_file_path) + # if df["pos"].iloc[-1] != csv_df["pos"].iloc[-1]: + df.to_csv(traderdata_file_path, mode="a", header=False, index=False) + else: + # 创建新文件并保存整个DataFrame + df.to_csv(traderdata_file_path, index=False) + # df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False) + + # 每日收盘重置数据 + def day_data_reset(self, symbol): + param = self.param_dict[symbol] + sec = "".join(re.findall("[a-zA-Z]", str(symbol))) + # 获取当前时间 + current_time = datetime.now().time() + + # 第一时间范围（日盘收盘） + clearing_time1_start = s_time(15, 5) + clearing_time1_end = s_time(15, 10) + + # 创建一个标志变量，用于记录是否已经执行过 + param.clearing_executed = False + # 检查当前时间第一个操作的时间范围内 + if ( + clearing_time1_start <= current_time <= clearing_time1_end + and not param.clearing_executed + ): + param.clearing_executed = True # 设置标志变量为已执行 + trade_dfs[symbol].drop( + trade_dfs[symbol].index, inplace=True + ) # 清除当天的行情数据 + + # 检查当前时间是否在第二个操作的时间范围内(夜盘收盘) + elif sec in clearing_time_dict.keys(): + clearing_time2_start = clearing_time_dict[sec] + clearing_time2_end = s_time( + clearing_time2_start.hour, clearing_time2_start.minute + 15 + ) + if ( + clearing_time2_start <= current_time <= clearing_time2_end + and not param.clearing_executed + ): + param.clearing_executed = True # 设置标志变量为已执行 + trade_dfs[symbol].drop( + trade_dfs[symbol].index, inplace=True + ) # 清除当天的行情数据 + else: + param.clearing_executed = False + pass + return param.clearing_executed + + def OnRtnTrade(self, pTrade): + print("||成交回报||", pTrade) + + def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast): + print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast) + + # 订单状态通知 + def OnRtnOrder(self, pOrder): + print("||订单回报||", pOrder) + + def cal_sig(self, symbol_queue): + while True: + try: + data = symbol_queue.get( + block=True, timeout=5 + ) # 如果5秒没收到新的tick行情，则抛出异常 + instrument_id = data["InstrumentID"].decode() # 品种代码 + size = symbol_queue.qsize() + if size > 1: + print( + f"当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！" + ) + self.read_to_csv(instrument_id) + self.day_data_reset(instrument_id) + param = self.param_dict[instrument_id] + self.品种 = instrument_id + self.tickcome(data) + trade_df = trade_dfs[instrument_id] + # 新K线开始，启动交易程序 and 保存行情数据 + self.read_to_csv(instrument_id) + if len(trade_df) > param.cont_df: + # 检查文件是否存在 + csv_file_path = f"traderdata/{instrument_id}_ofdata.csv" + # if os.path.exists(csv_file_path): + # #jerome :保存数增加'delta累计'、POC、、终极平滑值、趋势方向 + # # 仅保存最后一行数据 + # trade_df.tail(1).to_csv( + # csv_file_path, mode="a", header=False, index=False + # ) + # else: + # # 创建新文件并保存整个DataFrame + # trade_df.to_csv(csv_file_path, index=False) + # 检查是否存在重复行 + if os.path.exists(csv_file_path): + existing_df = pd.read_csv(csv_file_path, usecols=range(12)) + # 获取要写入的新数据 + new_data = trade_df.tail(1) + + # 检查新数据是否与现有数据重复 + is_duplicate = False + for _, row in existing_df.iterrows(): + if (row['datetime'] == new_data['datetime'].iloc[0] and + row['price'] == new_data['price'].iloc[0] and + row['Ask'] == new_data['Ask'].iloc[0] and + row['Bid'] == new_data['Bid'].iloc[0] and + row['symbol'] == new_data['symbol'].iloc[0] and + row['delta'] == new_data['delta'].iloc[0] and + row['close'] == new_data['close'].iloc[0] and + row['open'] == new_data['open'].iloc[0] and + row['high'] == new_data['high'].iloc[0] and + row['low'] == new_data['low'].iloc[0] and + row['volume'] == new_data['volume'].iloc[0] and + row['dj'] == new_data['dj'].iloc[0]): + is_duplicate = True + break + + # 检查Ask和Bid的值是否为空或全为0 + ask_value = new_data['Ask'].iloc[0] + bid_value = new_data['Bid'].iloc[0] + is_valid_data = ( + ask_value != [] and + ask_value != [0] and + bid_value != [] and + bid_value != [0] + ) + + if not is_duplicate and is_valid_data: + # 如果没有重复且数据有效，则写入新数据 + new_data.to_csv( + csv_file_path, mode="a", header=False, index=False + ) + else: + # 如果文件不存在，直接写入新数据 + trade_df.to_csv(csv_file_path, index=False) + + # 更新跟踪止损价格 + if param.long_trailing_stop_price > 0 and param.pos > 0: + + param.long_trailing_stop_price = ( + trade_df["low"].iloc[-1] + if param.long_trailing_stop_price < trade_df["low"].iloc[-1] + else param.long_trailing_stop_price + ) + self.save_to_csv(instrument_id) + + if param.short_trailing_stop_price > 0 and param.pos < 0: + + param.short_trailing_stop_price = ( + trade_df["high"].iloc[-1] + if trade_df["high"].iloc[-1] + < param.short_trailing_stop_price + else param.short_trailing_stop_price + ) + self.save_to_csv(instrument_id) + + param.out_long = param.long_trailing_stop_price * ( + 1 - param.trailing_stop_percent + ) + param.out_short = param.short_trailing_stop_price * ( + 1 + param.trailing_stop_percent + ) + # 跟踪出场 + if param.out_long > 0: + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "预设——多头止盈——", + "TR", + param.out_long, + "low", + trade_df["low"].iloc[-1], + ) + if ( + trade_df["low"].iloc[-1] < param.out_long + and param.pos > 0 + and param.sl_long_price > 0 + and trade_df["low"].iloc[-1] > param.sl_long_price + ): + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "多头止盈", + "TR", + param.out_long, + "low", + trade_df["low"].iloc[-1], + ) + # 平多 + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["BidPrice1"] - param.py, + param.Lots, + b"1", + b"1", + ) + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["BidPrice1"] - param.py, + param.Lots, + b"1", + b"3", + ) + param.long_trailing_stop_price = 0 + param.out_long = 0 + param.sl_long_price = 0 + param.pos = 0 + self.save_to_csv(instrument_id) + + if param.out_short > 0: + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "预设——空头止盈——: ", + "TR", + param.out_short, + "high", + trade_df["high"].iloc[-1], + ) + if ( + trade_df["high"].iloc[-1] > param.out_short + and param.pos < 0 + and param.sl_shor_price > 0 + and trade_df["high"].iloc[-1] < param.sl_shor_price + ): + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "空头止盈: ", + "TR", + param.out_short, + "high", + trade_df["high"].iloc[-1], + ) + # 平空 + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["AskPrice1"] + param.py, + param.Lots, + b"0", + b"1", + ) + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["AskPrice1"] + param.py, + param.Lots, + b"0", + b"3", + ) + param.short_trailing_stop_price = 0 + param.sl_shor_price = 0 + self.out_shor = 0 + param.pos = 0 + self.save_to_csv(instrument_id) + + # 固定止损 + fixed_stop_loss_L = param.sl_long_price * ( + 1 - param.fixed_stop_loss_percent + ) + if param.pos > 0: + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "预设——多头止损", + "SL", + fixed_stop_loss_L, + "close", + trade_df["close"].iloc[-1], + ) + if ( + param.sl_long_price > 0 + and fixed_stop_loss_L > 0 + and param.pos > 0 + and trade_df["close"].iloc[-1] < fixed_stop_loss_L + ): + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "多头止损", + "SL", + fixed_stop_loss_L, + "close", + trade_df["close"].iloc[-1], + ) + # 平多 + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["BidPrice1"] - param.py, + param.Lots, + b"1", + b"1", + ) + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["BidPrice1"] - param.py, + param.Lots, + b"1", + b"3", + ) + param.long_trailing_stop_price = 0 + param.sl_long_price = 0 + param.out_long = 0 + param.pos = 0 + self.save_to_csv(instrument_id) + + fixed_stop_loss_S = param.sl_shor_price * ( + 1 + param.fixed_stop_loss_percent + ) + if param.pos < 0: + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "预设——空头止损", + "SL", + fixed_stop_loss_S, + "close", + trade_df["close"].iloc[-1], + ) + if ( + param.sl_shor_price > 0 + and fixed_stop_loss_S > 0 + and param.pos < 0 + and trade_df["close"].iloc[-1] > fixed_stop_loss_S + ): + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "空头止损", + "SL", + fixed_stop_loss_S, + "close", + trade_df["close"].iloc[-1], + ) + # 平空 + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["AskPrice1"] + param.py, + param.Lots, + b"0", + b"1", + ) + self.insert_order( + data["ExchangeID"], + data["InstrumentID"], + data["AskPrice1"] + param.py, + param.Lots, + b"0", + b"3", + ) + param.short_trailing_stop_price = 0 + param.sl_shor_price = 0 + param.out_short = 0 + param.pos = 0 + self.save_to_csv(instrument_id) + + + # 计算累积的delta值datetime.strptime(str_time, "%Y-%m-%d %H:%M:%S") + trade_df["delta"] = trade_df["delta"].astype(float) + # trade_df['datetime'] = pd.to_datetime(trade_df['datetime'], format='mixed') + trade_df['datetime'] = pd.to_datetime(trade_df['datetime'], format='%Y-%m-%d %H:%M:%S') + + # 自定义分组逻辑：前一日21:00至当日15:00为一天 + def get_trading_day(dt): + # 如果时间在21:00之后，属于下一个交易日 + if dt.hour >= 21: + return (dt + pd.Timedelta(days=1)).date() + # 如果时间在15:00之前，属于当前交易日 + elif dt.hour < 15: + return dt.date() + # 15:00-21:00之间的数据属于当前交易日 + else: + return dt.date() + + trade_df['trading_day'] = trade_df['datetime'].apply(get_trading_day) + + # 将日期转换为字符串 + trade_df['trading_day'] = trade_df['trading_day'].astype(str) + + # 按交易日计算delta累计 + trade_df['delta累计'] = trade_df.groupby('trading_day')['delta'].cumsum() + + trade_df = trade_df.fillna('缺值')#fillna(value=0) + + def ultimate_smoother(price,period): + # 初始化变量（修正角度单位为弧度） + a1 = np.exp(-1.414 * np.pi / period) + b1 = 2 * a1 * np.cos(1.414 * np.pi / period) # 将180改为np.pi + c2 = b1 + c3 = -a1 ** 2 + c1 = (1 + c2 - c3) / 4 + + # 准备输出序列 + us = np.zeros(len(price)) + us_new = np.zeros(len(price)) + trend = [None]*(len(price)) + ma_close = np.zeros(len(price)) + + # 前4个点用原始价格初始化 + for i in range(len(price)): + if i < 4: + us[i] = price.iloc[i] + else: + # 应用递归公式 + us[i] = (1 - c1) * price.iloc[i] + (2 * c1 - c2) * price.iloc[i-1] \ + - (c1 + c3) * price.iloc[i-2] + c2 * us[i-1] + c3 * us[i-2] + + us_new = np.around(us, decimals=2) + ma_close = price.rolling(window=4*period).mean()#5* + + # if us_new[i]>price[i] and ma_close[i]>price[i]: + # trend[i] = '空头趋势' + # elif us_new[i] ma_close.iloc[i]: + trend[i] = '多头趋势' + else: + trend[i] = '无趋势' + + + return us_new,trend + + trade_df['终极平滑值'],trade_df['趋势方向'] = ultimate_smoother(trade_df['close'],time_period)#,df['ma_close'] + + trade_df['datetime'] = trade_df['datetime'].dt.strftime("%Y-%m-%d %H:%M:%S") + + + def safe_literal_eval(x): + """带异常处理的安全转换""" + try: + return ast.literal_eval(x) + except ValueError: + return [] # 返回空列表作为占位符 + + def add_poc_column(df): + # 安全转换列数据 + df['price'] = df['price'].apply(safe_literal_eval) + df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x)))) + df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x)))) + + # 定义处理函数（带数据验证） + def find_poc(row): + # 验证三个列表长度一致且非空 + if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0): + return '缺值' # 返回空值标记异常数据 + + sums = [a + b for a, b in zip(row['Ask'], row['Bid'])] + try: + max_index = sums.index(max(sums)) + return row['price'][max_index] + except ValueError: + return '缺值' # 处理空求和列表情况 + + # 应用处理函数 + df['POC'] = df.apply(find_poc, axis=1) + + # 可选：统计异常数据 + error_count = df['POC'].isnull().sum() + if error_count > 0: + print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）") + + return df['POC'] + + trade_df['POC'] = add_poc_column(trade_df) + + def finall_trend(delta_sum,trend): + f_trend = [None]*(len(delta_sum)) + # delta_sum = delta_sum.astype(float) + for i in range(len(delta_sum)): + if (delta_sum[i] == '缺值') or (trend[i] == '缺值'): + f_trend[i] = '方向不明' + # return f_trend + else: + if delta_sum[i] > 0 and (trend[i] == '多头趋势'): + f_trend[i] = '强多头' + elif delta_sum[i] < 0 and (trend[i] == '空头趋势'): + f_trend[i] = '强空头' + else: + f_trend[i] = '方向不明' + return f_trend + + trade_df['最终趋势'] = finall_trend(trade_df['delta累计'],trade_df['趋势方向']) + + # table_text = f"品种：{trade_df['symbol'].iloc[-1]}, 时间：{trade_df['datetime'].iloc[-1]},close:{trade_df['close'].iloc[-1]},open:{trade_df['open'].iloc[-1]},high:{trade_df['high'].iloc[-1]},low:{trade_df['low'].iloc[-1]},delta:{trade_df['delta'].iloc[-1]}, delta累计:{trade_df['delta累计'].iloc[-1]}, dj:{trade_df['dj'].iloc[-1]},POC:{trade_df['POC'].iloc[-1]}, 终极平滑值:{trade_df['终极平滑值'].iloc[-1]}, 趋势方向:{trade_df['趋势方向'].iloc[-1]},最终趋势:{trade_df['最终趋势'].iloc[-1]}" + # if data["InstrumentID"]: + # option_buy_symbol,option_buy_price= get_otm_option(data["InstrumentID"], 'C') + # option_sell_symbol,option_sell_price = get_otm_option(data["InstrumentID"], 'P') + # print("买入平值期权为:", option_buy_symbol, "，价格为:", option_buy_price) + # print("卖出平值期权为:", option_sell_symbol, "，价格为:", option_sell_price) + + def get_otm_option(future_symbol, trade_type): + def get_option_symbol(future_symbol): + # 创建一个字典,将期货值futuresymbol映射到对应的option symbol . + option_dict = { + "IH": "上证50股指期权", + "IF": "沪深300股指期权", + "IC": "中证500股指期权", + "IM": "中证1000股指期权" + } + # 解析 future_symbol 获取期货代码和到期月份 + m = re.match(r'([A-Za-z]+)(\d+)', future_symbol) + if not m: + raise ValueError(f"future_symbol 格式不正确: {future_symbol}") + future_code, future_end_month = m.groups() + option_symbol = option_dict.get(future_code) + return option_symbol, future_end_month + + try: + option_symbol, future_end_month = get_option_symbol(future_symbol) + option_finance_board_df = ak.option_finance_board(symbol=option_symbol, end_month=future_end_month) + + len(option_finance_board_df) + + half = len(option_finance_board_df) // 2 + + first_half_df = option_finance_board_df.iloc[:half] + first_half_df.columns = [f"{col}_C" for col in first_half_df.columns] + first = first_half_df.reset_index(drop=True) + + second_half_df = option_finance_board_df.iloc[half:] + second_half_df.columns = [f"{col}_P" for col in second_half_df.columns] + second = second_half_df.reset_index(drop=True) + + df = pd.concat([first, second], axis=1) + df['lastprice_(C-P)'] = df['lastprice_C'] - df['lastprice_P'] + + idx = df['lastprice_(C-P)'].abs().idxmin() + row = df.loc[idx, ['instrument_C', 'instrument_P', 'lastprice_C','lastprice_P','lastprice_(C-P)']] + + # print(f"index={idx}, instrument_C={row['instrument_C']}, instrument_P={row['instrument_P']}, lastprice_(C-P)={row['lastprice_(C-P)']}") + + # return df.loc[idx, ['instrument_C', 'instrument_P']] + if trade_type == 'C': + return row['instrument_C'], float(row['lastprice_C']) + elif trade_type == 'P': + return row['instrument_P'], float(row['lastprice_P']) + else: + raise ValueError(f"未知的 trade_type: {trade_type}") + except Exception as e: + print(f"get_otm_option error for {future_symbol}: {e}") + return None + + def get_otm_pirce(future_symbol, trade_option_symbol): + def get_option_symbol(future_symbol): + # 创建一个字典,将期货值futuresymbol映射到对应的option symbol . + option_dict = { + "IH": "上证50股指期权", + "IF": "沪深300股指期权", + "IC": "中证500股指期权", + "IM": "中证1000股指期权" + } + # 解析 future_symbol 获取期货代码和到期月份 + m = re.match(r'([A-Za-z]+)(\d+)', future_symbol) + if not m: + raise ValueError(f"future_symbol 格式不正确: {future_symbol}") + future_code, future_end_month = m.groups() + option_symbol = option_dict.get(future_code) + return option_symbol, future_end_month + + try: + option_symbol, future_end_month = get_option_symbol(future_symbol) + option_finance_board_df = ak.option_finance_board(symbol=option_symbol, end_month=future_end_month) + + mask = option_finance_board_df['instrument'] == trade_option_symbol + if mask.any(): + lastprice_value = option_finance_board_df.loc[mask, 'lastprice'].iloc[0] + return lastprice_value + # print(lastprice_value) + else: + print("未找到对应的行") + except Exception as e: + print(f"get_otm_option error for {future_symbol}: {e}") + return None + + print("trade_df['symbol'].iloc[-1]}:", trade_df['symbol'].iloc[-1]) + print("trade_df['symbol'].iloc[-1]}的类型:", type(trade_df['symbol'].iloc[-1])) + + + # 开多、空前置条件 + 开多条件 = (trade_df['趋势方向'].iloc[-1] == '多头趋势') + 开空条件 = (trade_df['趋势方向'].iloc[-1] == '空头趋势') + + if len(trade_df) >= 4*time_period: + #开多 + 开多1 = (trade_df['dj'].iloc[-1] >= 2) #max(0.8 * max(trade_df['dj'].iloc[-4*time_period-1:-1]), 10)) + # print("开多1:",开多1) + 开多2 = (trade_df['delta'].iloc[-1] >= 10)# max(0.8 * max(trade_df['delta'].iloc[-4*time_period-1:-1]), 350)) + 开多3 = (trade_df['delta累计'].iloc[-2] < 0 and trade_df['delta累计'].iloc[-1] > 0) + + # 开空 + 开空1 = (trade_df['dj'].iloc[-1] <= -2) #min(0.8 * min(trade_df['dj'].iloc[-4*time_period-1:-1]), -10)) + 开空2 = (trade_df['delta'].iloc[-1] <= -10) #min(0.8 * min(trade_df['delta'].iloc[-4*time_period-1:-1]),-350)) + # print("开空2:",开空2) + 开空3 = (trade_df['delta累计'].iloc[-2] > 0 and trade_df['delta累计'].iloc[-1] < 0) + + # 开多组合 = (开多条件 and (开多1 or 开多2 or 开多3)) + # 开空组合 = (开空条件 and (开空1 or 开空2 or 开空3)) + + # 平多组合 = (开空条件 or 开空1 or 开空2 or 开空3) + # 平空组合 = (开多条件 or 开多1 or 开多2 or 开多3) + + 开多组合 = 开多1 and 开多2 + 开空组合 = 开空1 and 开空2 + + 平多组合 = 开空1 or 开空2 + 平空组合 = 开多1 or 开多2 + + option_buy_symbol = '' + option_buy_price = 0 + option_sell_symbol = '' + option_sell_price = 0 + + # 平空 + if param.pos < 0 and 平空组合: + # close_sell_price = get_otm_pirce(data["InstrumentID"].decode(), option_sell_symbol) + print( + "平空： ", + "ExchangeID: ", + data["ExchangeID"], + "InstrumentID", + option_sell_symbol, + "AskPrice1", + 800,#close_sell_price + param.py, + ) + # 平空 + self.insert_order( + data["ExchangeID"], + option_sell_symbol, + 800,#close_sell_price + param.py, + param.Lots, + b"0", + b"1", + ) + self.insert_order( + data["ExchangeID"], + option_sell_symbol, + 800,#close_sell_price + param.py, + param.Lots, + b"0", + b"3", + ) + + param.pos = 0 + param.sl_shor_price = 0 + param.short_trailing_stop_price = 0 + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "反手平空：", + "平仓价格：", + data['AskPrice1'] + param.py, + "堆积数：", + trade_df["dj"].iloc[-1], + ) + self.save_to_csv(instrument_id) + + # 发送邮件 + # text = f"平空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平空的平仓价格为{data['AskPrice1']+param.py}, 交易手数位{param.Lots}" + text = f"C_S_T: ID:{option_sell_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, C_S_T_Price:{data['AskPrice1'] + param.py}, T_Lots:{param.Lots}" + send_mail(text) + + # 开多 + if param.pos == 0 and 开多组合: + print("trade_df_last:",trade_df['symbol'].iloc[-1]) + option_buy_symbol,option_buy_price= get_otm_option(data["InstrumentID"].decode(), 'C') + print("买入看涨平值期权为:", option_buy_symbol, "，价格为:", option_buy_price) + print( + "开多： ", + "ExchangeID: ", + data["ExchangeID"], + "InstrumentID", + option_buy_symbol, + "AskPrice1", + option_buy_price + param.py, + ) + # 开多 + self.insert_order( + data["ExchangeID"], + option_buy_symbol, + option_buy_price + param.py, + param.Lots, + b"0", + b"0", + ) + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "多头开仓", + "开仓价格：", + option_buy_price + param.py, + "堆积数：", + trade_df["dj"].iloc[-1], + ) + param.pos = 1 + param.long_trailing_stop_price = data["AskPrice1"] + param.sl_long_price = data["AskPrice1"] + self.save_to_csv(instrument_id) + + # 发送邮件 + text = f"O_L_T ID:{option_buy_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, O_L_T_Price:{option_buy_price + param.py}, T_Lots:{param.Lots}" + send_mail(text) + + # 平多 + if param.pos > 0 and 平多组合: + print('option_buy_symbol',option_buy_symbol) + # close_buy_price = get_otm_pirce(data["InstrumentID"].decode(), option_buy_symbol) + # print('close_buy_price:',close_buy_price) + print( + "平多： ", + "ExchangeID: ", + data["ExchangeID"], + "InstrumentID", + option_buy_symbol, + "BidPrice1", + 1,#close_buy_price - param.py, + ) + # 平多 + self.insert_order( + data["ExchangeID"], + option_buy_symbol, + 1,#close_buy_price - param.py, + param.Lots, + b"1", + b"1", + ) + self.insert_order( + data["ExchangeID"], + option_buy_symbol, + 1,#close_buy_price - param.py, + param.Lots, + b"1", + b"3", + ) + + param.pos = 0 + param.long_trailing_stop_price = 0 + param.sl_long_price = 0 + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "反手平多", + "平仓价格：", + data["BidPrice1"] - param.py, + "堆积数：", + trade_df["dj"].iloc[-1], + ) + self.save_to_csv(instrument_id) + + # 发送邮件 + # text = f"平多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}" + text = f"C_L_T: ID:{option_buy_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, C_L_T_Price:{data['BidPrice1'] - param.py}, T_Lots:{param.Lots}" + send_mail(text) + + # 开空 + if param.pos == 0 and 开空组合: + option_sell_symbol,option_sell_price = get_otm_option(data["InstrumentID"].decode(), 'P') + print("买入看跌平值期权为:", option_sell_symbol, "，价格为:", option_sell_price) + print( + "开空： ", + "ExchangeID: ", + data["ExchangeID"], + "InstrumentID", + option_sell_symbol, + "BidPrice1", + data["BidPrice1"], + ) + # 开空 + self.insert_order( + data["ExchangeID"], + option_sell_symbol, + option_sell_price - param.py, + param.Lots, + b"1", + b"0", + ) + print( + "datetime+sig: ", + trade_df["datetime"].iloc[-1], + "空头开仓", + "开仓价格：", + option_sell_price - param.py, + "堆积数：", + trade_df["dj"].iloc[-1], + ) + param.pos = -1 + param.short_trailing_stop_price = data["BidPrice1"] + param.sl_shor_price = data["BidPrice1"] + self.save_to_csv(instrument_id) + + # 发送邮件 + text = f"O_S_T: ID:{option_sell_symbol}, datetime:{trade_df['datetime'].iloc[-1]}, O_S_T_Price:{option_sell_price - param.py}, T_Lots:{param.Lots}" + send_mail(text) + + print(trade_df) + symbol_label = trade_df["symbol"].iloc[-1] + trade_df.to_csv(f"trade_df_{symbol_label}.csv", index=False, encoding='utf-8') + # with open('trade_df.txt', 'w', encoding='utf-8') as file: + # print(trade_df, file=file) + print("------------------------------------------------") + # print(trade_df.iloc[0]) + # print(trade_df.iloc[-1]) + param.cont_df = len(trade_df) + except queue.Empty: + # print(f"当前合约队列为空，等待新数据插入。") + pass + + # 将CTP推送的行情数据分发给对应线程队列去执行 + def distribute_tick(self): + while True: + if self.status == 0: + data = None + while not self.md_queue.empty(): + data = self.md_queue.get(block=False) + instrument_id = data["InstrumentID"].decode() # 品种代码 + try: + self.queue_dict[instrument_id].put( + data, block=False + ) # 往对应合约队列中插入行情 + # print(f"{instrument_id}合约数据插入。") + except queue.Full: + # 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算 + print( + f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。" + ) + else: + time.sleep(1) + + def start(self, param_dict): + threads = [] + self.param_dict = param_dict + + for symbol in param_dict.keys(): + # folder_path = "traderdata" + # ofdata_file_path = os.path.join("traderdata", f"{str(symbol)}_ofdata.csv") + if os.path.exists(f"traderdata/{symbol}_ofdata.csv"): + columns = [ + "price", + "Ask", + "Bid", + "symbol", + "datetime", + "delta", + "close", + "open", + "high", + "low", + "volume", + "dj", + ] + # import csv + # with open(f"traderdata/{symbol}_ofdata.csv", "r") as f: + # reader = csv.reader(f) + # for i, row in enumerate(reader, 1): + # if len(row) != 12: + # print(f"Line {i} has {len(row)} columns: {row}") + trade_dfs[symbol] = pd.read_csv( + f"traderdata/{symbol}_ofdata.csv", usecols=columns + ) + + else: + trade_dfs[symbol] = pd.DataFrame({}) + self.queue_dict[symbol] = queue.Queue( + 20 + ) # 为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常 + t = threading.Thread( + target=self.cal_sig, args=(self.queue_dict[symbol],) + ) # 为每个合约单独创建一个线程计算开仓逻辑 + threads.append(t) + t.start() + self.distribute_tick() + for t in threads: + t.join() + + +def run_trader( + param_dict, + broker_id, + td_server, + investor_id, + password, + app_id, + auth_code, + md_queue=None, + page_dir="", + private_resume_type=2, + public_resume_type=2, +): + my_trader = MyTrader( + broker_id, + td_server, + investor_id, + password, + app_id, + auth_code, + md_queue, + page_dir, + private_resume_type, + public_resume_type, + ) + my_trader.start(param_dict) + + +if __name__ == "__main__": + # 注意：运行前请先安装好algoplus, + # pip install AlgoPlus + # http://www.algo.plus/ctp/python/0103001.html + + param_dict = {} + + ## 交易一个品种，手动设置合约代码 + + # param_dict["TF2509"] = ParamObj( + # symbol="TF2509", + # Lots=1, + # py=5, + # trailing_stop_percent=0.01, + # fixed_stop_loss_percent=0.02, + # dj_X=8, + # delta=500, + # sum_delta=800, + # 失衡=3, + # 堆积=3, + # 周期="2min", + # ) + + ## 交易所有品种，自动设置合约代码, + # 交易指定品种时，symbols = ['IM','IC','ag'] + # symbols = ['IM','IC','ag'] + symbols = contacts_df['品种代码'].tolist() + # for i, symbol in enumerate(symbols, start=1): + # globals()[f'sb_{i}'] = get_main_contact_on_time(symbol, contacts_df) + # symbol = globals()[f'sb_{i}'] + # # print("最终使用的主连代码：",symbol) + # param_dict[str(symbol)] = ParamObj(symbol=symbol.encode('ascii'),Lots=1,py=5,trailing_stop_percent=0.01,fixed_stop_loss_percent=0.02,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="5min") + for i, symbol in enumerate(symbols, start=1): + if symbol in ['wr', 'RS' , 'bb', 'WH', 'fb', 'rr', 'PL']: + continue + elif symbol in ['IM',]: #symbol in ['IH', 'IF', 'IC', 'IM', 'au', 'sc'] + globals()[f'sb_{i}'] = get_main_contact_on_time(symbol, contacts_df) + symbol = globals()[f'sb_{i}'] + param_dict[str(symbol)] = ParamObj(symbol=symbol.encode('ascii'),Lots=1,py=5,trailing_stop_percent=0.02,fixed_stop_loss_percent=0.04,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="2min") + + + ## 交易多个指定品种，自动设置合约代码,手动设置其他参数 + # param_dict[symbol] = ParamObj(symbol=get_main_contact_on_time('IM', contacts_df),Lots=1,py=5,trailing_stop_percent=0.01,fixed_stop_loss_percent=0.02,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="1min") + # param_dict[symbol] = ParamObj(symbol=get_main_contact_on_time('IC', contacts_df),Lots=1,py=5,trailing_stop_percent=0.02,fixed_stop_loss_percent=0.04,dj_X=8,delta=500,sum_delta=800,失衡=3,堆积=3,周期="1min") + # print(param_dict.keys()) + + # 用simnow模拟，不要忘记屏蔽下方实盘的future_account字典 + # SIMULATE_SERVER = { + # '电信1': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10201", 'MDServer': '180.168.146.187:10211', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'}, + # '电信2': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10202", 'MDServer': '180.168.146.187:10212', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'}, + # '移动': {'BrokerID': 9999, 'TDServer': "218.202.237.33:10203", 'MDServer': '218.202.237.33:10213', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'}, + # 'TEST': {'BrokerID': 9999, 'TDServer': "180.168.146.187:10130", 'MDServer': '180.168.146.187:10131', 'AppID': 'simnow_client_test', 'AuthCode': '0000000000000000'}, + # 'N视界': {'BrokerID': 10010, 'TDServer': "210.14.72.12:4600", 'MDServer': '210.14.72.12:4602', 'AppID': '', 'AuthCode': ''}, + # } + # BrokerID统一为：9999 + # 支持上期所期权、能源中心期权、中金所期权、广期所期权、郑商所期权、大商所期权 + # 第一组 + # Trade Front：180.168.146.187:10201，Market Front：180.168.146.187:10211；【电信】（看穿式前置，使用监控中心生产秘钥） + + # 第二组 + # Trade Front：180.168.146.187:10202，Market Front：180.168.146.187:10212；【电信】（看穿式前置，使用监控中心生产秘钥） + + # 第三组 + # Trade Front：218.202.237.33:10203，Market Front：218.202.237.33:10213；【移动】（看穿式前置，使用监控中心生产秘钥） + + # 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。 + + future_account = get_simulate_account( + investor_id="227508", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看 + password="Zj1234!@#%", # simnow密码 + server_name="电信1", # 电信1、电信2、移动、TEST、N视界 + subscribe_list=list(param_dict.keys()), # 合约列表 + ) + # future_account = get_simulate_account( + # investor_id="00033556", # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看 + # password="27138169", # simnow密码 + # server_name="N视界", # 电信1、电信2、移动、TEST、N视界 + # subscribe_list=list(param_dict.keys()), # 合约列表 + # ) + # 实盘用这个，不要忘记屏蔽上方simnow的future_account字典 + # future_account = FutureAccount( + # broker_id='', # 期货公司BrokerID + # server_dict={'TDServer': "121.37.80.177:20002", 'MDServer': '121.37.80.177:20004'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。" + # reserve_server_dict={}, # 备用服务器地址 + # investor_id='1114', # 账户 + # password='123456', # 密码 + # app_id='', # 认证使用AppID + # auth_code='', # 认证使用授权码 + # subscribe_list=list(param_dict.keys()), # 订阅合约列表 + # md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION + # td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION + # ) + + # 实盘用这个，不要忘记屏蔽上方simnow的future_account字典 + # future_account = FutureAccount( + # broker_id='8888', # 期货公司BrokerID + # server_dict={'TDServer': "103.140.14.210:43205", 'MDServer': '103.140.14.210:43173'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。" + # reserve_server_dict={}, # 备用服务器地址 + # investor_id='155878', # 账户 + # password='Zj82334475', # 密码 + # app_id='vntech_vnpy_2.0', # 认证使用AppID + # auth_code='N46EKN6TJ9U7V06V', # 认证使用授权码 + # subscribe_list=list(param_dict.keys()), # 订阅合约列表 + # md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION + # td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION + # ) + + print("开始", len(future_account.subscribe_list)) + # 共享队列 + share_queue = Queue(maxsize=200) + + # 行情进程 + md_process = Process(target=run_tick_engine, args=(future_account, [share_queue])) + + # 交易进程 + trader_process = Process( + target=run_trader, + args=( + param_dict, + future_account.broker_id, + future_account.server_dict["TDServer"], + future_account.investor_id, + future_account.password, + future_account.app_id, + future_account.auth_code, + share_queue, # 队列 + future_account.td_flow_path, + ), + ) + + md_process.start() + trader_process.start() + + md_process.join() + trader_process.join()