717 lines
23 KiB
Plaintext
717 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pipeline Example"
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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": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from quantopian.pipeline import Pipeline\n",
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"from quantopian.research import run_pipeline\n",
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"from quantopian.pipeline.data.builtin import USEquityPricing"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Getting the Securities we want.\n",
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"\n",
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"### The Q500US and Q1500US\n",
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"\n",
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"These gropus of tradeable stocks are refered to as \"universes\", because all your trades will use these stocks as their \"Universe\" of available stock, they won't be trading with anything outside these groups."
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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": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from quantopian.pipeline.filters import Q1500US"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"There are two main benefits of the Q500US and Q1500US. Firstly, they greatly reduce the risk of an order not being filled. Secondly, they allow for more meaningful comparisons between strategies as now they will be used as the standard universes for algorithms."
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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": 17,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"universe = Q1500US()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Filtering the universe further with Classifiers\n",
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"\n",
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"Let's only grab stocks in the energy sector: https://www.quantopian.com/help/fundamentals#industry-sector"
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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": 25,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from quantopian.pipeline.data import morningstar"
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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": 29,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sector = morningstar.asset_classification.morningstar_sector_code.latest"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Alternative:"
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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": 30,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#from quantopian.pipeline.classifiers.morningstar import Sector\n",
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"#morningstar_sector = Sector()"
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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": 36,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"energy_sector = sector.eq(309)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Masking Filters\n",
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"\n",
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"Masks can be also be applied to methods that return filters like top, bottom, and percentile_between.\n",
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"\n",
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"Masks are most useful when we want to apply a filter in the earlier steps of a combined computation. For example, suppose we want to get the 50 securities with the highest open price that are also in the top 10% of dollar volume. \n",
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"\n",
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"Suppose that we then want the 90th-100th percentile of these securities by close price. We can do this with the following:"
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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": 38,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume"
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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": 37,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Dollar volume factor\n",
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"dollar_volume = AverageDollarVolume(window_length=30)\n",
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"\n",
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"# High dollar volume filter\n",
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"high_dollar_volume = dollar_volume.percentile_between(90,100)\n",
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"\n",
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"# Top open price filter (high dollar volume securities)\n",
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"top_open_price = USEquityPricing.open.latest.top(50, mask=high_dollar_volume)\n",
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"\n",
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"# Top percentile close price filter (high dollar volume, top 50 open price)\n",
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"high_close_price = USEquityPricing.close.latest.percentile_between(90, 100, mask=top_open_price)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Applying Filters and Factors\n",
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"\n",
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"Let's apply our own filters, following along with some of the examples above. Let's select the following securities:\n",
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"\n",
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"* Stocks in Q1500US\n",
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"* Stocks that are in the energy Sector\n",
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"* They must be relatively highly traded stocks in the market (by dollar volume traded, need to be in the top 5% traded)\n",
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"\n",
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"Then we'll calculate the percent difference as we've done previously. Using this percent difference we'll create an unsophisticated strategy that shorts anything with negative percent difference (the difference between the 10 day mean and the 30 day mean)."
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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": 73,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def make_pipeline():\n",
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" \n",
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" # Base universe filter.\n",
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" base_universe = Q1500US()\n",
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" \n",
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" # Sector Classifier as Filter\n",
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" energy_sector = sector.eq(309)\n",
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" \n",
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" # Masking Base Energy Stocks\n",
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" base_energy = base_universe & energy_sector\n",
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" \n",
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" # Dollar volume factor\n",
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" dollar_volume = AverageDollarVolume(window_length=30)\n",
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"\n",
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" # Top half of dollar volume filter\n",
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" high_dollar_volume = dollar_volume.percentile_between(95,100)\n",
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" \n",
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" # Final Filter Mask\n",
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" top_half_base_energy = base_energy & high_dollar_volume\n",
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" \n",
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" # 10-day close price average.\n",
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" mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=top_half_base_energy)\n",
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"\n",
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" # 30-day close price average.\n",
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" mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30, mask=top_half_base_energy)\n",
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"\n",
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" # Percent difference factor.\n",
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" percent_difference = (mean_10 - mean_30) / mean_30\n",
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" \n",
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" # Create a filter to select securities to short.\n",
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" shorts = percent_difference < 0\n",
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" \n",
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" # Create a filter to select securities to long.\n",
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" longs = percent_difference > 0\n",
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" \n",
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" # Filter for the securities that we want to trade.\n",
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" securities_to_trade = (shorts | longs)\n",
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" \n",
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" return Pipeline(\n",
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" columns={\n",
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" 'longs': longs,\n",
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" 'shorts': shorts,\n",
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" 'percent_diff':percent_difference\n",
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" },\n",
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" screen=securities_to_trade\n",
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" )"
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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": 74,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th>longs</th>\n",
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" <th>percent_diff</th>\n",
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" <th>shorts</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th rowspan=\"38\" valign=\"top\">2015-05-05 00:00:00+00:00</th>\n",
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" <th>Equity(216 [HES])</th>\n",
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" <td>True</td>\n",
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" <td>0.036528</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(448 [APA])</th>\n",
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" <td>True</td>\n",
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" <td>0.035926</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(455 [APC])</th>\n",
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" <td>True</td>\n",
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" <td>0.049153</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(858 [BHI])</th>\n",
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" <td>True</td>\n",
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" <td>0.033807</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(1746 [COG])</th>\n",
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" <td>True</td>\n",
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" <td>0.058078</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(2368 [DVN])</th>\n",
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" <td>True</td>\n",
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" <td>0.046264</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(2564 [EOG])</th>\n",
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" <td>True</td>\n",
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" <td>0.032102</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(2621 [ESV])</th>\n",
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" <td>True</td>\n",
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" <td>0.060197</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(3443 [HAL])</th>\n",
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" <td>True</td>\n",
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" <td>0.049257</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(3647 [HP])</th>\n",
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" <td>True</td>\n",
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" <td>0.040991</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(5035 [MRO])</th>\n",
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" <td>True</td>\n",
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" <td>0.061598</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(5213 [NBL])</th>\n",
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" <td>True</td>\n",
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" <td>0.010443</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(5214 [NBR])</th>\n",
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" <td>True</td>\n",
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" <td>0.064133</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(5249 [NE])</th>\n",
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" <td>True</td>\n",
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" <td>0.037559</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(5729 [OXY])</th>\n",
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" <td>True</td>\n",
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" <td>0.029776</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(6928 [SLB])</th>\n",
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" <td>True</td>\n",
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" <td>0.046555</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(7244 [SWN])</th>\n",
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" <td>True</td>\n",
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" <td>0.070788</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(7612 [ANDV])</th>\n",
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" <td>True</td>\n",
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" <td>0.005997</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(7990 [VLO])</th>\n",
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" <td>False</td>\n",
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" <td>-0.017145</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(8214 [WMB])</th>\n",
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" <td>True</td>\n",
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" <td>0.018876</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(8347 [XOM])</th>\n",
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" <td>True</td>\n",
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" <td>0.017343</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(8461 [CHK])</th>\n",
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" <td>True</td>\n",
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" <td>0.014265</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(9038 [RIG])</th>\n",
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" <td>True</td>\n",
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" <td>0.048180</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(13176 [CAM])</th>\n",
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" <td>True</td>\n",
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" <td>0.082110</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(17436 [PXD])</th>\n",
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" <td>True</td>\n",
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" <td>0.010248</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>Equity(19249 [RRC])</th>\n",
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" <td>True</td>\n",
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" <td>0.087062</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(19336 [WFT])</th>\n",
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" <td>True</td>\n",
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" <td>0.049141</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(22784 [FTI])</th>\n",
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" <td>True</td>\n",
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" <td>0.054529</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(23112 [CVX])</th>\n",
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" <td>True</td>\n",
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" <td>0.018972</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(23998 [COP])</th>\n",
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" <td>True</td>\n",
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" <td>0.023902</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(24809 [NOV])</th>\n",
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" <td>True</td>\n",
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" <td>0.024940</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(25707 [WLL])</th>\n",
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" <td>True</td>\n",
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" <td>0.048205</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(33856 [CLR])</th>\n",
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" <td>True</td>\n",
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" <td>0.064304</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(34440 [CXO])</th>\n",
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" <td>True</td>\n",
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" <td>0.042184</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(39797 [OAS])</th>\n",
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" <td>True</td>\n",
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" <td>0.042388</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(40852 [KMI])</th>\n",
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" <td>True</td>\n",
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" <td>0.023016</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(41636 [MPC])</th>\n",
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" <td>True</td>\n",
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" <td>0.011952</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Equity(42788 [PSX])</th>\n",
|
|
" <td>True</td>\n",
|
|
" <td>0.020911</td>\n",
|
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" <td>False</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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],
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"text/plain": [
|
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" longs percent_diff shorts\n",
|
|
"2015-05-05 00:00:00+00:00 Equity(216 [HES]) True 0.036528 False\n",
|
|
" Equity(448 [APA]) True 0.035926 False\n",
|
|
" Equity(455 [APC]) True 0.049153 False\n",
|
|
" Equity(858 [BHI]) True 0.033807 False\n",
|
|
" Equity(1746 [COG]) True 0.058078 False\n",
|
|
" Equity(2368 [DVN]) True 0.046264 False\n",
|
|
" Equity(2564 [EOG]) True 0.032102 False\n",
|
|
" Equity(2621 [ESV]) True 0.060197 False\n",
|
|
" Equity(3443 [HAL]) True 0.049257 False\n",
|
|
" Equity(3647 [HP]) True 0.040991 False\n",
|
|
" Equity(5035 [MRO]) True 0.061598 False\n",
|
|
" Equity(5213 [NBL]) True 0.010443 False\n",
|
|
" Equity(5214 [NBR]) True 0.064133 False\n",
|
|
" Equity(5249 [NE]) True 0.037559 False\n",
|
|
" Equity(5729 [OXY]) True 0.029776 False\n",
|
|
" Equity(6928 [SLB]) True 0.046555 False\n",
|
|
" Equity(7244 [SWN]) True 0.070788 False\n",
|
|
" Equity(7612 [ANDV]) True 0.005997 False\n",
|
|
" Equity(7990 [VLO]) False -0.017145 True\n",
|
|
" Equity(8214 [WMB]) True 0.018876 False\n",
|
|
" Equity(8347 [XOM]) True 0.017343 False\n",
|
|
" Equity(8461 [CHK]) True 0.014265 False\n",
|
|
" Equity(9038 [RIG]) True 0.048180 False\n",
|
|
" Equity(13176 [CAM]) True 0.082110 False\n",
|
|
" Equity(17436 [PXD]) True 0.010248 False\n",
|
|
" Equity(19249 [RRC]) True 0.087062 False\n",
|
|
" Equity(19336 [WFT]) True 0.049141 False\n",
|
|
" Equity(22784 [FTI]) True 0.054529 False\n",
|
|
" Equity(23112 [CVX]) True 0.018972 False\n",
|
|
" Equity(23998 [COP]) True 0.023902 False\n",
|
|
" Equity(24809 [NOV]) True 0.024940 False\n",
|
|
" Equity(25707 [WLL]) True 0.048205 False\n",
|
|
" Equity(33856 [CLR]) True 0.064304 False\n",
|
|
" Equity(34440 [CXO]) True 0.042184 False\n",
|
|
" Equity(39797 [OAS]) True 0.042388 False\n",
|
|
" Equity(40852 [KMI]) True 0.023016 False\n",
|
|
" Equity(41636 [MPC]) True 0.011952 False\n",
|
|
" Equity(42788 [PSX]) True 0.020911 False"
|
|
]
|
|
},
|
|
"execution_count": 74,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result = run_pipeline(make_pipeline(), '2015-05-05', '2015-05-05')\n",
|
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"result"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 75,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"<class 'pandas.core.frame.DataFrame'>\n",
|
|
"MultiIndex: 38 entries, (2015-05-05 00:00:00+00:00, Equity(216 [HES])) to (2015-05-05 00:00:00+00:00, Equity(42788 [PSX]))\n",
|
|
"Data columns (total 3 columns):\n",
|
|
"longs 38 non-null bool\n",
|
|
"percent_diff 38 non-null float64\n",
|
|
"shorts 38 non-null bool\n",
|
|
"dtypes: bool(2), float64(1)\n",
|
|
"memory usage: 684.0+ bytes\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"result.info()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Executing this Strategy in the IDE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from quantopian.algorithm import attach_pipeline,pipeline_output\n",
|
|
"from quantopian.pipeline import Pipeline\n",
|
|
"from quantopian.pipeline.data.builtin import USEquityPricing\n",
|
|
"from quantopian.pipeline.factors import AverageDollarVolume,SimpleMovingAverage\n",
|
|
"from quantopian.pipeline.filters.morningstar import Q1500US\n",
|
|
"from quantopian.pipeline.data import morningstar\n",
|
|
"\n",
|
|
"def initialize(context):\n",
|
|
" \n",
|
|
" schedule_function(my_rebalance,date_rules.week_start(),time_rules.market_open(hours=1))\n",
|
|
" \n",
|
|
" my_pipe = make_pipeline()\n",
|
|
" attach_pipeline(my_pipe,'my_pipeline')\n",
|
|
" \n",
|
|
"def my_rebalance(context,data):\n",
|
|
" for security in context.portfolio.positions:\n",
|
|
" if security not in context.longs and security not in context.shorts and data.can_trade(security):\n",
|
|
" order_target_percent(security,0)\n",
|
|
" \n",
|
|
" for security in context.longs:\n",
|
|
" if data.can_trade(security):\n",
|
|
" order_target_percent(security,context.long_weight)\n",
|
|
"\n",
|
|
" for security in context.shorts:\n",
|
|
" if data.can_trade(security):\n",
|
|
" order_target_percent(security,context.short_weight)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"def my_compute_weights(context):\n",
|
|
" \n",
|
|
" if len(context.longs)==0:\n",
|
|
" long_weight = 0\n",
|
|
" else:\n",
|
|
" long_weight = 0.5 / len(context.longs)\n",
|
|
" \n",
|
|
" if len(context.shorts)==0:\n",
|
|
" short_weight = 0\n",
|
|
" else:\n",
|
|
" short_weight = 0.5 / len(context.shorts)\n",
|
|
" \n",
|
|
" return (long_weight,short_weight)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"def before_trading_start(context,data):\n",
|
|
" context.output = pipeline_output('my_pipeline')\n",
|
|
" \n",
|
|
" # LONG\n",
|
|
" context.longs = context.output[context.output['longs']].index.tolist()\n",
|
|
" \n",
|
|
" # SHORT\n",
|
|
" context.shorts = context.output[context.output['shorts']].index.tolist()\n",
|
|
"\n",
|
|
"\n",
|
|
" context.long_weight,context.short_weight = my_compute_weights(context)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"def make_pipeline():\n",
|
|
" \n",
|
|
" # Universe Q1500US\n",
|
|
" base_universe = Q1500US()\n",
|
|
" \n",
|
|
" # Energy Sector\n",
|
|
" sector = morningstar.asset_classification.morningstar_sector_code.latest\n",
|
|
" energy_sector = sector.eq(309)\n",
|
|
" \n",
|
|
" # Make Mask of 1500US and Energy\n",
|
|
" base_energy = base_universe & energy_sector\n",
|
|
" \n",
|
|
" # Dollar Volume (30 Days) Grab the Info\n",
|
|
" dollar_volume = AverageDollarVolume(window_length=30)\n",
|
|
" \n",
|
|
" # Grab the top 5% in avg dollar volume\n",
|
|
" high_dollar_volume = dollar_volume.percentile_between(95,100)\n",
|
|
" \n",
|
|
" # Combine the filters\n",
|
|
" top_five_base_energy = base_energy & high_dollar_volume\n",
|
|
" \n",
|
|
" # 10 day mean close\n",
|
|
" mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],window_length=10,mask=top_five_base_energy)\n",
|
|
" \n",
|
|
" # 30 day mean close\n",
|
|
" mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close],window_length=30,mask=top_five_base_energy)\n",
|
|
" \n",
|
|
" # Percent Difference\n",
|
|
" percent_difference = (mean_10-mean_30)/mean_30\n",
|
|
" \n",
|
|
" # List of Shorts\n",
|
|
" shorts = percent_difference < 0\n",
|
|
" \n",
|
|
" # List of Longs\n",
|
|
" longs = percent_difference > 0\n",
|
|
" \n",
|
|
" # Final Mask/Filter for anything in shorts or longs\n",
|
|
" securities_to_trade = (shorts | longs)\n",
|
|
" \n",
|
|
" # Return Pipeline\n",
|
|
" return Pipeline(columns={\n",
|
|
" 'longs':longs,\n",
|
|
" 'shorts':shorts,\n",
|
|
" 'perc_diff':percent_difference\n",
|
|
" },screen=securities_to_trade)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.6.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|