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Commit 1bfe5826 authored by Dr.李's avatar Dr.李
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update Quick Start 6

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notebooks/Quick Start 6 - Formula Based Stocks Screening.ipynb
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...@@ -2,14 +2,501 @@ ...@@ -2,14 +2,501 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"from PyFin.api import *\n", "from PyFin.api import *\n",
"from alphamind.api import *" "from alphamind.api import *"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. 公式书写\n",
"-------------------------"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"universe_name = 'zz1000'\n",
"formula = CSTopN(LAST('ROE'), 3, groups='sw1')\n",
"ref_date = '2019-02-14'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. 获取数据\n",
"---------------"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"depends = formula.fields\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe(universe_name)\n",
"codes = universe.query(engine, dates=[ref_date])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"factors = engine.fetch_factor(ref_date, depends, codes.code.tolist()).dropna()\n",
"factors.index = [1] * len(factors)\n",
"factors = factors[['code'] + depends]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Stock Screening\n",
"---------------------"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"res = formula.transform(factors, name='factor', category_field='code')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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"<p>82 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" factor code\n",
"1 1.0 16\n",
"1 1.0 34\n",
"1 1.0 43\n",
"1 1.0 55\n",
"1 1.0 429\n",
"1 1.0 516\n",
"1 1.0 520\n",
"1 1.0 639\n",
"1 1.0 672\n",
"1 1.0 789\n",
"1 1.0 881\n",
"1 1.0 885\n",
"1 1.0 892\n",
"1 1.0 918\n",
"1 1.0 968\n",
"1 1.0 2016\n",
"1 1.0 2033\n",
"1 1.0 2035\n",
"1 1.0 2061\n",
"1 1.0 2071\n",
"1 1.0 2168\n",
"1 1.0 2182\n",
"1 1.0 2234\n",
"1 1.0 2327\n",
"1 1.0 2343\n",
"1 1.0 2396\n",
"1 1.0 2518\n",
"1 1.0 2597\n",
"1 1.0 2626\n",
"1 1.0 2677\n",
".. ... ...\n",
"1 1.0 600231\n",
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"1 1.0 600596\n",
"1 1.0 600677\n",
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"\n",
"[82 rows x 2 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res[res.factor == 1]"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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