Add an Alpha Factor Library

bualpha-factor-and-pipeline-lib.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BU Alpha: Factor & Pipeline Library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## > Alpha Source #1: Buyback Announcements\n",
+    "### *^based on a strategy proposed by Shahram Amini and Vijay Singal in the \"Are Earnings Predictable? Evidence from Equity Issues and Buyback Announcements\" paper (November 21, 2018)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Libraries to import:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "from quantopian.pipeline import Pipeline\n",
+    "from quantopian.pipeline.data.eventvestor import BuybackAuthorizations\n",
+    "\n",
+    "from quantopian.pipeline.factors.eventvestor import (\n",
+    "    BusinessDaysSinceBuybackAuth,\n",
+    "    BusinessDaysSincePreviousEarnings,\n",
+    ")\n",
+    "\n",
+    "from quantopian.pipeline.data import Fundamentals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**BB (Buyback) Pipeline defined:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_BB_pipe(context):\n",
+    "    BB_pipe = Pipeline()\n",
+    "    \n",
+    "    # add BB factors to BB Pipeline\n",
+    "    BB_pipe.add(BuybackAuthorizations.previous_date.latest, \"previous_date\")\n",
+    "    \n",
+    "    BB_pipe.add(BuybackAuthorizations.previous_amount.latest, \"previous_amount\")\n",
+    "    \n",
+    "    BB_pipe.add(BuybackAuthorizations.previous_unit.latest, \"previous_unit\")\n",
+    "\n",
+    "    BB_pipe.add(BusinessDaysSinceBuybackAuth(), \"business_days_since_buyback\")\n",
+    "\n",
+    "    BB_pipe.add(BusinessDaysSincePreviousEarnings(), \"business_days_since_earnings\")\n",
+    "\n",
+    "    # add fundamental factors to BB Pipeline\n",
+    "    BB_pipe.add(Fundamentals.market_cap.latest, \"market_cap\")\n",
+    "    \n",
+    "    BB_pipe.add(Fundamentals.shares_outstanding.latest, \"shares_outstanding\")        \n",
+    "        \n",
+    "    # filter out share buybacks of less than 5% of shares outstanding, or dollar buybacks         \n",
+    "    # of less than 5% of market cap\n",
+    "    significant_buyback = (BuybackAuthorizations.previous_amount.latest*mil >= BB_min_pcntg*Fundamentals.market_cap.latest) if BuybackAuthorizations.previous_unit.latest == '$M' else (BuybackAuthorizations.previous_amount.latest*mil >= BB_min_pcntg*Fundamentals.shares_outstanding.latest)\n",
+    "        \n",
+    "    # filter out buybacks that occurred less than 2 days prior to the most recent                 # earnings announcement\n",
+    "    not_recent_buyback = BusinessDaysSinceBuybackAuth() - BusinessDaysSincePreviousEarnings() >= 2\n",
+    "        \n",
+    "    BB_pipe.set_screen(base_universe & significant_buyback & not_recent_buyback)\n",
+    "    \n",
+    "    return BB_pipe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## > Alpha Source #2: Secondary Equity Offerings (SEOs)\n",
+    "### *^based on a strategy proposed by Shahram Amini and Vijay Singal in the \"Are Earnings Predictable? Evidence from Equity Issues and Buyback Announcements\" paper (November 21, 2018)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Libraries to import:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "from quantopian.pipeline import Pipeline\n",
+    "from quantopian.pipeline.data.eventvestor import IssueEquityAnnouncements\n",
+    "\n",
+    "from quantopian.pipeline.factors.eventvestor import (\n",
+    "    BusinessDaysSinceIssueEquityAnnouncement,\n",
+    "    BusinessDaysSincePreviousEarnings,\n",
+    ")\n",
+    "\n",
+    "from quantopian.pipeline.data import Fundamentals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**SEO Pipeline defined:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_SEO_pipe(context):\n",
+    "    SEO_pipe = Pipeline()\n",
+    "    \n",
+    "    # add SEO factors to SEO Pipeline\n",
+    "    SEO_pipe.add(IssueEquityAnnouncements.announcement_date.latest, \"previous_date\")\n",
+    "    \n",
+    "    SEO_pipe.add(IssueEquityAnnouncements.issue_amount.latest, \"previous_amount\")\n",
+    "    \n",
+    "    SEO_pipe.add(IssueEquityAnnouncements.issue_units.latest, \"previous_unit\")\n",
+    "    \n",
+    "    SEO_pipe.add(BusinessDaysSinceIssueEquityAnnouncement(), \"business_days_since_equity_issuance\")\n",
+    "    \n",
+    "    SEO_pipe.add(BusinessDaysSincePreviousEarnings(), \"business_days_since_earnings\")\n",
+    "    \n",
+    "    # add fundamental factors to SEO Pipeline\n",
+    "    SEO_pipe.add(Fundamentals.market_cap.latest, \"market_cap\")\n",
+    "\n",
+    "    SEO_pipe.add(Fundamentals.shares_outstanding.latest, \"shares_outstanding\")\n",
+    "        \n",
+    "    # filter out share SEOs of less than 10% of shares outstanding, or dollar SEOs of less   \n",
+    "    # than 10% of market cap\n",
+    "    significant_SEO = (IssueEquityAnnouncements.issue_amount.latest*mil >= SEO_min_pcntg*Fundamentals.market_cap.latest) if IssueEquityAnnouncements.issue_units.latest == '$M' else (IssueEquityAnnouncements.issue_amount.latest*mil >= SEO_min_pcntg*Fundamentals.shares_outstanding.latest)\n",
+    "        \n",
+    "    # filter out SEOs that occurred less than 2 days prior to the most recent earnings   \n",
+    "    # announcement       \n",
+    "    not_recent_SEO = BusinessDaysSinceIssueEquityAnnouncement() - BusinessDaysSincePreviousEarnings() >= 2\n",
+    "        \n",
+    "    SEO_pipe.set_screen(base_universe & significant_SEO & not_recent_SEO)\n",
+    "    \n",
+    "    return SEO_pipe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}