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fawce
352 lines
8.2 KiB
Plaintext
352 lines
8.2 KiB
Plaintext
{
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"metadata": {
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"name": "Experimenting with Frames"
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},
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"nbformat": 3,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "heading",
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"source": [
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"Performance Tracking"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import datetime",
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"import pandas",
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"import pytz",
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"",
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"import zipline.test.factory as factory",
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"import zipline.finance.performance as perf",
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"import zipline.protocol as zp",
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"import zipline.finance.risk as risk",
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"import zipline.finance.trading as trading"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 38
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},
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{
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"cell_type": "heading",
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"source": [
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"Create a simulated trade history using the test factory"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"For any backtesting, zipline relies on a TradingEnvironment object. Trading environment holds essential facts: ",
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" ",
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" - start and end times for the simulation.",
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" - historical daily returns for your benchmark.",
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" - historical treasury curves",
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" - an assumed capital base for your portfolio",
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" - a calendar of trading days based on your benchmark",
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"",
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"zipline ships with a compressed archives of the S&P daily returns, and US treasury curves to facilitate standalone development and testing. In the next cell we instantiate the environment using these defaults. You can see more of this in zipline/test/test_perf_tracking.py"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"benchmark_returns, treasury_curves = factory.load_market_data()",
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" ",
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"trading_environment = risk.TradingEnvironment(benchmark_returns, treasury_curves)"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 39
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"trade_count = 100",
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"sid = 133",
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"price = 10.1 ",
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"price_list = [price] * trade_count",
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"volume = [100] * trade_count",
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"start_date = datetime.datetime.strptime(\"01/01/2011\",\"%m/%d/%Y\")",
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"start_date = start_date.replace(tzinfo=pytz.utc)",
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"trade_time_increment = datetime.timedelta(days=1)",
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"",
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"trade_history = factory.create_trade_history( ",
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" sid, ",
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" price_list, ",
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" volume, ",
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" start_date, ",
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" trade_time_increment, ",
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" trading_environment ",
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")",
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"",
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"sid2 = 134",
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"price2 = 12.12",
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"price2_list = [price2] * trade_count ",
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"trade_history2 = factory.create_trade_history( ",
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" sid2, ",
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" price2_list, ",
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" volume, ",
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" start_date, ",
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" trade_time_increment, ",
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" trading_environment ",
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")",
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" ",
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"trade_history.extend(trade_history2) ",
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"trade_history = sorted(trade_history, key=lambda x: x.dt)"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 40
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},
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{
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"cell_type": "markdown",
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"source": [
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"Now that we have a simulated history of trades for two companies and a corresponding trading environment, we can create a dataframe of trades."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df = pandas.DataFrame(index = ['price', 'volume', 'dt'])",
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"for event in trade_history:",
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" series = event.as_series()",
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" #df.index = df.index.tolist().append(event.sid)",
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" #series.name = event.sid",
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" df[event.sid] = series"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 92
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 93,
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"text": [
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" 133 134",
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"price 10.1 12.12",
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"volume 100 100",
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"dt 2011-04-08 00:00:00+00:00 2011-04-08 00:00:00+00:00"
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]
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}
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],
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"prompt_number": 93
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t = df.transpose()",
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"df_t"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 94,
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"text": [
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" price volume dt",
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"133 10.1 100 2011-04-08 00:00:00+00:00",
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"134 12.12 100 2011-04-08 00:00:00+00:00"
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]
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}
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],
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"prompt_number": 94
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df[133]"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 56,
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"text": [
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"sid 133",
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"volume 100",
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"dt 2011-04-08 00:00:00+00:00",
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"price 10.1",
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"changed NaN",
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"Name: 133"
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]
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}
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],
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"prompt_number": 56
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t['price']"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 57,
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"text": [
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"133 10.1",
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"134 12.12",
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"Name: price"
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]
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}
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],
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"prompt_number": 57
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t['price'].max()"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 50,
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"text": [
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"12.12"
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]
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}
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],
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"prompt_number": 50
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},
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{
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"cell_type": "code",
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"collapsed": true,
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"input": [
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"last = trade_history[23].dt"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 51
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t['changed'] = df_t['dt'] > last"
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],
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"language": "python",
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"outputs": [],
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"prompt_number": 53
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 54,
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"text": [
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" sid volume dt price changed",
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"133 133 100 2011-04-08 00:00:00+00:00 10.1 True",
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"134 134 100 2011-04-08 00:00:00+00:00 12.12 True"
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]
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}
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],
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"prompt_number": 54
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t.index"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 59,
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"text": [
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"Int64Index([133, 134])"
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]
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}
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],
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"prompt_number": 59
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df.index"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 60,
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"text": [
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"Index([sid, volume, dt, price, changed], dtype=object)"
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]
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}
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],
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"prompt_number": 60
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df.columns"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 61,
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"text": [
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"Int64Index([133, 134])"
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]
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}
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],
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"prompt_number": 61
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"df_t.columns"
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],
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"language": "python",
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 62,
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"text": [
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"Index([sid, volume, dt, price, changed], dtype=object)"
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]
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}
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],
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"prompt_number": 62
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},
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{
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"cell_type": "code",
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"collapsed": true,
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"input": [],
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"language": "python",
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"outputs": []
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}
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]
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}
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]
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} |