{ "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)" ] }, { "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": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 0 }