129 lines
3.8 KiB
Plaintext
129 lines
3.8 KiB
Plaintext
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{
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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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"# Stock Sentiment Analysis\n",
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"\n",
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"Check out the video for full details."
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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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"## Final Code\n",
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"Here is the final code:"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# This section is only importable in the backtester\n",
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"from quantopian.algorithm import attach_pipeline, pipeline_output\n",
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"\n",
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"# General pipeline imports\n",
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"from quantopian.pipeline import Pipeline\n",
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"from quantopian.pipeline.factors import AverageDollarVolume\n",
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"\n",
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"\n",
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"# Using the free sample in your pipeline algo\n",
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"from quantopian.pipeline.data.accern import alphaone_free\n",
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"\n",
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"\n",
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"def initialize(context):\n",
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" # Schedule our rebalance function to run at the start of each week.\n",
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" schedule_function(my_rebalance, date_rules.every_day())\n",
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"\n",
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" \n",
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" attach_pipeline(make_pipeline(), \"pipeline\")\n",
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"\n",
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"def make_pipeline():\n",
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"\n",
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" \n",
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" # Screen out penny stocks and low liquidity securities.\n",
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" dollar_volume = AverageDollarVolume(window_length=20)\n",
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" is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
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" \n",
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" # Add pipeline factors\n",
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" impact = alphaone_free.impact_score.latest\n",
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" sentiment = alphaone_free.article_sentiment.latest\n",
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"\n",
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" return Pipeline(columns={\n",
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" 'impact': impact,\n",
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" 'sentiment':sentiment,\n",
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" },\n",
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" screen = is_liquid)\n",
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" \n",
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"\n",
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"\n",
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" \n",
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"def before_trading_start(context, data):\n",
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" port = pipeline_output('pipeline')\n",
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" \n",
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" # Grab stocks with 100 impact and >0.5 sentiment and go long.\n",
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" context.longs = port[(port['impact']==100) & (port['sentiment']>0.75)].index.tolist()\n",
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" \n",
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" # Grab stocks with 100 impact and <-0.5 sentiment and go long.\n",
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" context.shorts = port[(port['impact']==100) & (port['sentiment']< -0.75)].index.tolist()\n",
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"\n",
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" context.long_weight, context.short_weight = my_compute_weights(context)\n",
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"\n",
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"def my_compute_weights(context):\n",
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"\n",
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" # Compute even target weights for our long positions and short positions.\n",
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" long_weight = 0.5 / len(context.longs)\n",
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" short_weight = -0.5 / len(context.shorts)\n",
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"\n",
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" return long_weight, short_weight\n",
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"\n",
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"def my_rebalance(context, data):\n",
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"\n",
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" for security in context.portfolio.positions:\n",
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" if security not in context.longs and security not in context.shorts and data.can_trade(security):\n",
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" order_target_percent(security, 0)\n",
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"\n",
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" for security in context.longs:\n",
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" if data.can_trade(security):\n",
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" order_target_percent(security, context.long_weight)\n",
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"\n",
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" for security in context.shorts:\n",
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" if data.can_trade(security):\n",
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" order_target_percent(security, context.short_weight)\n",
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" \n",
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" \n",
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" \n",
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"\n",
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" \n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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