- build the cryptocurrency market data platform (HK) from 0
- build the realtime market data feed for Quant traders, an RFQ data-service for OTC trading from 0
- build RESTful APIs using python-flask (web framework) with well-documented code.
- pair programming with Quant traders to optimize the strategies and offer PEP-8 compliant code.
- set up the cloud server and databases from 0 (i.e., Redis, Influxdb, flat files(parquet), ZeroMQ)
1. 通过Python,Django, MYSQL, Redis 记录回测模型和实盘账户的历史收益情况、调仓,记录30000多个港股、美股、沪深股市的股票、ETF、指数的基本信息和部分ticker 的历史价格数据,分析并提供各周期内的收益情况、市场行情情况、技术分析指标、和更为复杂权重分析和归因分析。并为IT前端和算法团队提供数据接口。
2. 通过Python, Jira, Jerkins, Sentry, Git 维护系统的稳定性,和定时任务的执行, 业务的持续上线,即敏捷开发。
3. 通过Python, RabbitMQ建立实时行情消费队列,供算法团队获取个股的最新报价情况,简单的构建Docker镜像,为算法团队快速挖掘数据、分析数据。
4. 基于zipline 构建公司自己的更高效的策略回测框架(12月底调研结束)
– Construct a platform which provides public market data and private user information of dominating cryptocurrency exchanges for quantitative traders
– Fetch raw data from Huobi, Bitfinex, Okex and so on via REST and WebSocket APIs, and format data into Redis database using Python with asyncio, numpy and pandas libs
- Collected historical log using shell in Linux cluster via Bash/shell Programming
- Analyzed and visualized data using Python with Matplotlib
- Prototyped a recommendation system eliminating the interference between workloads in data center
1. 通过Python,Django, MYSQL, Redis 记录回测模型和实盘账户的历史收益情况、调仓,记录30000多个港股、美股、沪深股市的股票、ETF、指数的基本信息和部分ticker 的历史价格数据,分析并提供各周期内的收益情况、市场行情情况、技术分析指标、和更为复
- build the cryptocurrency market data platform (HK) from 0 - build the realtime market data feed for Quant traders, an RFQ data-service fo