150 lines
17 KiB
Plaintext
150 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from datetime import datetime\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from turtleEngine import BacktestingEngine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"engine = BacktestingEngine()\n",
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"engine.setPeriod(datetime(2015, 1, 1), datetime(2018, 11, 9))\n",
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"engine.initPortfolio('setting.csv', 10000000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"15:22:46.074000:000300.XSHG数据加载完成，总数据量：940\n",
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"15:22:46.074000:全部数据加载完成\n"
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]
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}
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],
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"source": [
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"engine.loadData()\n",
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"engine.runBacktesting()\n",
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"engine.calculateResult()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"for dt, l in engine.tradeDict.items():\n",
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" print dt\n",
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" for trade in l:\n",
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" print trade.vtSymbol, trade.direction, trade.offset, trade.price, trade.volume"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"for result in engine.resultList:\n",
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" if result.totalPnl:\n",
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" print result.date, result.totalPnl"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x8190eb0>]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x8127530>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"l = [result.totalPnl for result in engine.resultList]\n",
|
||
"equity = np.cumsum(l)\n",
|
||
"plt.plot(equity)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 2",
|
||
"language": "python",
|
||
"name": "python2"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.14"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|