{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hikyuu Quant Framework是一款基于C++/Python的开源量化交易研究框架,用于策略分析及回测。其核心思想基于当前成熟的系统化交易方法,将整个系统化交易策略抽象为由市场环境判断策略、系统有效条件、信号指示器、止损/止盈策略、资金管理策略、盈利目标策略、移滑价差算法七大组件,你可以分别构建这些组件的策略资产库,在实际研究中对它们自由组合来观察系统的有效性、稳定性以及单一种类策略的效果。在系统策略之上,对交易对象选择、系统策略选择、资产组合资金分配进行了进一步封装,能够灵活支持更高层级的策略组合。\n",
"\n",
"更多信息,请参见:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 入门篇\n",
"\n",
"* [001 交互式工具示例](001-overview.ipynb?flush_cache=True)\n",
"* [002 获取股票对象](002-HowToGetStock.ipynb?flush_cache=True)\n",
"* [003 获取并绘制K线数据](003-HowToGetKDataAndDraw.ipynb?flush_cache=True)\n",
"* [004 计算并绘制技术指标](004-IndicatorOverview.ipynb?flush_cache=True)\n",
"* [005 绘制组合图形](005-Drawplot.ipynb?flush_cache=True)\n",
"* [006 TradeManager应用](006-TradeManager.ipynb?flush_cache=True)\n",
"* [007 系统策略演示](007-SystemDetails.ipynb?flush_cache=True)\n",
"* [008 序列化说明](008-Pickle.ipynb?flush_cache=True)\n",
"* [009_获取实时日线数据](009-RealData.ipynb?flush_cache=True)\n",
"* [010_资产组合](010-Portfolio.ipynb?flush_cache=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 示例\n",
"\n",
"* [Demo1](Demo/Demo1.ipynb?flush_cache=True)\n",
"* [Demo2](Demo/Demo2.ipynb?flush_cache=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}