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Update README.md

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Guofu Li 3 years ago
parent 68610470c4
commit 1f53e14564
2 changed files with 64 additions and 75 deletions

57
README.md
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@ -12,28 +12,28 @@
│   └── scripts
├── ipynb
│   ├── ddb.ipynb
│   ├── ddb_pe.ipynb
│   ├── ddb_pit.ipynb
│   ├── dos.ipynb
│   └── mssql.ipynb
├── Readme.txt
├── README.md
└── src
├── DDBBase.py
├── DDBExpression.py
├── DDBFactor.py
├── DDBLoader.py
├── make_hft.py
├── ProtoBuffEntitys
│   ├── HFDataTableMessage_pb2.py
│   ├── IndexFutureKLineMessage_pb2.py
│   ├── IndexFutureL1TickMessage_pb2.py
│   ├── IndexKLineMessage_pb2.py
│   ├── IndexTickMessage_pb2.py
│   ├── KLineMessage_pb2.py
│   ├── OrderMessage_pb2.py
│   ├── TickMessage_pb2.py
│   ├── TickQueueMessage_pb2.py
│   └── TranseMessage_pb2.py
├── expr
│   └── DDBExpression.py
├── factor
│   └── DDBFactor.py
├── __init__.py
├── loader
│   ├── DDBBasicInfoLoader.py
│   ├── DDBDailyLoader.py
│   ├── DDBHFTLoader.py
│   ├── DDBLoader.py
│   ├── DDBPITLoader.py
│   ├── make_hft.py
│   └── ProtoBuffEntitys
├── ReadTickFromDB.py
├── run.py
└── script.py
```
@ -51,21 +51,28 @@
- `src`
- Python源代码目录，是工作的主要目录
- 大部分源码文件以`DDB`开头，表示与DolphinDB相关，继承和代码复用关系可以进一步优化
- `DDBBase`是`DDB*`的基类，包含了一些常用常量
- `DDBLoader`专门用于向DolphinDB写入数据（似乎称为Dumper更合适），目前包括：
- `DDBHFTLoader`，用以导入高频数据（海通高频数据）
- `DDBDailyLoader`，用以导入日频数据（市场日K线数据）
- `DDBPITLoader`，用以导入低频PIT数据，主要是财报数据
- `DDBFactor`用于生成因子，目前只有生成日频因子
- `DDBExpression`提供了基本的操作表达式，目前包括：
- `DDBBase.py`：
- `DDB*`的基类，包含了一些常用常量
- `run.py`：
- 用于执行具体操作任务的入口脚本
- TODO：需要支持命令行参数来选择执行功能和配套参数
- `loader`目录：代码专门用于向DolphinDB写入数据（似乎称为Dumper更合适），目前包括：
- `DDBLoader.py`：其他Loader类的基础类
- `DDBHFTLoader.py`：用以导入高频数据（海通高频数据）
- `DDBDailyLoader.py`：用以导入日频数据（市场日K线数据）
- `DDBPITLoader.py`：用以导入低频PIT数据，主要是财报数据
- `loader/ProtoBuffEntitys`目录：
- 用以解析海通高频数据中，protobuf字节流的Python辅助代码
- `factor`目录：用于生成因子
- `DDBFactor.py`：目前只有生成日频因子
- `expr`目录：用于支持基于DDB的各种表达式操作
- `DDBExpression.py`：提供了基本的操作表达式，目前包括：
- 通过Function View，读取DolphinDB的分布式存储表：
- 在任意时间点上，获取所有公司某张财报、指定财报期的最新可见快照
- 在任意时间点上，获取所有公司某张财报中某科目、指定财报期的最新可见快照
- 通过一般Function，对内存表进行操作：
- 流量表累计数值转单季值
- 跨年同比指标计算（分母为去年基数取绝对值，函数会同时返回当年值和去年基数，以便进一步调整）
- `src/ProtoBuffEntitys`
- 用以解析海通高频数据中，protobuf字节流的Python辅助代码
## 海通高频数据录入说明

82
ipynb/ddb.ipynb
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@ -35,15 +35,31 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 60,
"id": "ed17fd0b-9b36-47e4-9ab6-11459a3621fb",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"115548"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sess = ddb.session('192.168.1.7', 8848)\n",
"sess.login('admin', '123456')\n",
"\n",
"# backup(backup_path, sql_obj, force, parallel)\n",
"code = \"\"\"\n",
" backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"OrderPartitioned\")>, true, true)\n",
"\"\"\""
" backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"TickPartitioned\")>, true, true)\n",
"\"\"\"\n",
"\n",
"sess.run(code)"
]
},
{
@ -1389,7 +1405,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 100,
"id": "4ba45027-bbb5-4b27-99da-3452cc8d2f1c",
"metadata": {},
"outputs": [
@ -1420,7 +1436,7 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2298</td>\n",
" <td>2227</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -1428,10 +1444,10 @@
],
"text/plain": [
" count\n",
"0 2298"
"0 2227"
]
},
"execution_count": 43,
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
@ -1439,14 +1455,14 @@
"source": [
"sess.run(\"\"\"\n",
" select count(*) from (\n",
" select code, m_nDate, count(*) as cnt from tbl where code='002182.SZ' group by code, m_nDate map\n",
" select code, m_nDate, count(*) as cnt from tbl where code='002459.SZ' group by code, m_nDate map\n",
" );\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 103,
"id": "29ab8af5-e571-4064-b691-a186d9fb4d08",
"metadata": {},
"outputs": [
@ -1471,66 +1487,32 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>m_nDate</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-07</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-08</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-09</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-10</th>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-07-04</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-07-05</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-07-06</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-07-07</th>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-07-08</th>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2298 rows × 0 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: [2013-01-04 00:00:00, 2013-01-07 00:00:00, 2013-01-08 00:00:00, 2013-01-09 00:00:00, 2013-01-10 00:00:00, 2013-01-11 00:00:00, 2013-01-14 00:00:00, 2013-01-15 00:00:00, 2013-01-16 00:00:00, 2013-01-17 00:00:00, 2013-01-18 00:00:00, 2013-01-21 00:00:00, 2013-01-22 00:00:00, 2013-01-23 00:00:00, 2013-01-24 00:00:00, 2013-01-25 00:00:00, 2013-01-28 00:00:00, 2013-01-29 00:00:00, 2013-01-30 00:00:00, 2013-01-31 00:00:00, 2013-02-01 00:00:00, 2013-02-04 00:00:00, 2013-02-05 00:00:00, 2013-02-06 00:00:00, 2013-02-07 00:00:00, 2013-02-08 00:00:00, 2013-02-18 00:00:00, 2013-02-19 00:00:00, 2013-02-20 00:00:00, 2013-02-21 00:00:00, 2013-02-22 00:00:00, 2013-02-25 00:00:00, 2013-02-26 00:00:00, 2013-02-27 00:00:00, 2013-02-28 00:00:00, 2013-03-01 00:00:00, 2013-03-04 00:00:00, 2013-03-05 00:00:00, 2013-03-06 00:00:00, 2013-03-07 00:00:00, 2013-03-08 00:00:00, 2013-03-11 00:00:00, 2013-03-12 00:00:00, 2013-03-13 00:00:00, 2013-03-14 00:00:00, 2013-03-15 00:00:00, 2013-03-18 00:00:00, 2013-03-19 00:00:00, 2013-03-20 00:00:00, 2013-03-21 00:00:00, 2013-03-22 00:00:00, 2013-03-25 00:00:00, 2013-03-26 00:00:00, 2013-03-27 00:00:00, 2013-03-28 00:00:00, 2013-03-29 00:00:00, 2013-04-01 00:00:00, 2013-04-02 00:00:00, 2013-04-03 00:00:00, 2013-04-08 00:00:00, 2013-04-09 00:00:00, 2013-04-10 00:00:00, 2013-04-11 00:00:00, 2013-04-12 00:00:00, 2013-04-15 00:00:00, 2013-04-16 00:00:00, 2013-04-17 00:00:00, 2013-04-18 00:00:00, 2013-04-19 00:00:00, 2013-04-22 00:00:00, 2013-04-23 00:00:00, 2013-04-24 00:00:00, 2013-04-25 00:00:00, 2013-04-26 00:00:00, 2013-05-02 00:00:00, 2013-05-03 00:00:00, 2013-05-06 00:00:00, 2013-05-07 00:00:00, 2013-05-08 00:00:00, 2013-05-09 00:00:00, 2013-05-10 00:00:00, 2013-05-13 00:00:00, 2013-05-14 00:00:00, 2013-05-15 00:00:00, 2013-05-16 00:00:00, 2013-05-17 00:00:00, 2013-05-20 00:00:00, 2013-05-21 00:00:00, 2013-05-22 00:00:00, 2013-05-23 00:00:00, 2013-05-24 00:00:00, 2013-05-27 00:00:00, 2013-05-28 00:00:00, 2013-05-29 00:00:00, 2013-05-30 00:00:00, 2013-05-31 00:00:00, 2013-06-03 00:00:00, 2013-06-04 00:00:00, 2013-06-05 00:00:00, 2013-06-06 00:00:00, ...]\n",
"\n",
"[2298 rows x 0 columns]"
"Columns: [count]\n",
"Index: []"
]
},
"execution_count": 57,
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = sess.run(\"\"\"\n",
" select m_nDate from tbl where code='002182.SZ' group by m_nDate map;\n",
" select m_nDate, count(*) from tbl where code='002458.SZ' group by m_nDate having count(*) > 5000 map;\n",
"\"\"\").set_index('m_nDate')\n",
"df"
]

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