记录一下量化交易的开始
准备工具
- DB TDengine
- 编程语言 Golang + Python
- 环境 Unix Linux
- 交易所 Bitmex Huobi Binance Okex
- 每天增量300w数据
- 机器配置 I7-8700 + 32GB DDR4 + GTX2060 + GTX1080 (Xgboost) 磁盘 6TB（SATA6GB/s）
编译 TDengine （修改表字段限制）
-
下载或者clone 官方发布的Release
-
编译
yum install gcc gcc-c++ make maven -y # centos 7
git clone https://github.com/taosdata/TDengine
cd TDengine
mkdir build && cd build
cmake .. && cmake --build .
make install
#Install the project...
#/usr/bin/cmake -P cmake_install.cmake
#-- Install configuration: "Debug"
#make install script: /root/TDengine-ver-1.6.5.5-fnk/packaging/tools/make_install.sh
#this is centos system
#source directory: /root/TDengine-ver-1.6.5.5-fnk
#binary directory: /root/TDengine-ver-1.6.5.5-fnk/build
#Start to install TDEngine...
#Created symlink from /etc/systemd/system/multi-user.target.wants/taosd.service to /etc/systemd/system/taosd.service.
#TDengine is installed successfully!
#To configure TDengine : edit /etc/taos/taos.cfg
#To start TDengine : sudo systemctl start taosd
#To access TDengine : use taos in shell
#TDengine is installed successfully!
# 出现这个说明已经安装好了
修改Taos DB 配置文件
# 将数据存储地址修改成 数据盘 日志改为 日志盘
# data file's directory
dataDir /data
# log file's directory
logDir /data/log
创建数据库
create database bitmex;
use bitmex;
--- 订单订阅数据表
CREATE TABLE bitmex.trade
(
ts TIMESTAMP,
systemc_time BIGINT,
bitmex_timestamp TIMESTAMP,
side BINARY(4),
size FLOAT,
price FLOAT,
symbol BINARY(16)
);
--- 交易深度数据
CREATE TABLE bitmex.order_l1_10
(
ts TIMESTAMP,
systemc_time BIGINT,
symbol BINARY(16),
bitmex_timestamp TIMESTAMP,
asks_1_p FLOAT,
asks_1_s FLOAT,
asks_2_p FLOAT,
asks_2_s FLOAT,
asks_3_p FLOAT,
asks_3_s FLOAT,
asks_4_p FLOAT,
asks_4_s FLOAT,
asks_5_p FLOAT,
asks_5_s FLOAT,
asks_6_p FLOAT,
asks_6_s FLOAT,
asks_7_p FLOAT,
asks_7_s FLOAT,
asks_8_p FLOAT,
asks_8_s FLOAT,
asks_9_p FLOAT,
asks_9_s FLOAT,
asks_10_p FLOAT,
asks_10_s FLOAT,
bids_1_p FLOAT,
bids_1_s FLOAT,
bids_2_p FLOAT,
bids_2_s FLOAT,
bids_3_p FLOAT,
bids_3_s FLOAT,
bids_4_p FLOAT,
bids_4_s FLOAT,
bids_5_p FLOAT,
bids_5_s FLOAT,
bids_6_p FLOAT,
bids_6_s FLOAT,
bids_7_p FLOAT,
bids_7_s FLOAT,
bids_8_p FLOAT,
bids_8_s FLOAT,
bids_9_p FLOAT,
bids_9_s FLOAT,
bids_10_p FLOAT,
bids_10_s FLOAT
);
启动采集工具 （开始实时的通过WebSocket接收数据）
# 采集部分两部分
# 采集端 Agent（通过SSL GRPC 将数据传输到 Server）多个 Agent 同时传输
# Server端 将通过GRPC 接收到的数据 通过http接口落库（这里用http接口是因为开发和部署的机器都是Unix的机器，数据库在内网的一台mac上运行。）
- 采集服务目录结构
- 采集服务日志恢复
下图所看到的是 GRPC服务断掉了，数据将会写入log文件并且有明确的标记，当grpc再次可用，将会启动日志恢复。
通过Middware将数据实时的放入TDEngine（这里存在一层日志备份未来可能存储到Hdfs走FLink训练）
确认数据
{
"status": "succ",
"head": [
"ts",
"systemc_time",
"symbol",
"bitmex_timestamp",
"asks_1_p",
"asks_1_s",
"asks_2_p",
"asks_2_s",
"asks_3_p",
"asks_3_s",
"asks_4_p",
"asks_4_s",
"asks_5_p",
"asks_5_s",
"asks_6_p",
"asks_6_s",
"asks_7_p",
"asks_7_s",
"asks_8_p",
"asks_8_s",
"asks_9_p",
"asks_9_s",
"asks_10_p",
"asks_10_s",
"bids_1_p",
"bids_1_s",
"bids_2_p",
"bids_2_s",
"bids_3_p",
"bids_3_s",
"bids_4_p",
"bids_4_s",
"bids_5_p",
"bids_5_s",
"bids_6_p",
"bids_6_s",
"bids_7_p",
"bids_7_s",
"bids_8_p",
"bids_8_s",
"bids_9_p",
"bids_9_s",
"bids_10_p",
"bids_10_s"
],
"data": [
[
"2020-02-25 21:02:56.000",
1582635776179,
"XBTUSD",
"2020-02-25 13:02:55.729",
9625,
49906,
9625.5,
82752,
9626,
10282,
9626.5,
1112,
9627,
109168,
9627.5,
47425,
9628,
84425,
9628.5,
114773,
9629,
69830,
9629.5,
34412,
9624.5,
2225271,
9624,
3024,
9623.5,
11051,
9623,
468523,
9622.5,
6502,
9622,
9985,
9621.5,
550000,
9621,
9295,
9620.5,
726837,
9620,
647086
]
],
"rows": 1
}
提供Web界面 空和多 多家交易所对比，以及瞬时趋势
Python 调用数据开启分析 基础分析 EMA 交叉 （四家交易所同时10秒计算一次）
每天计算趋势图
钉钉通知。
纠正模型
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