139 lines
4.3 KiB
Plaintext
139 lines
4.3 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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"# Analyse de Sentiments\n",
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"\n",
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"Regardez la vidéo pour plus de détails."
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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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"## Code final\n",
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"Voici le code final:"
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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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"# Cette section est uniquement importable dans le backtester\n",
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"from quantopian.algorithm import attach_pipeline, pipeline_output\n",
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"\n",
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"# Importations générales de pipeline\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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"# Utiliser l'échantillon gratuit dans votre algo de pipeline\n",
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"from quantopian.pipeline.data.sentdex import sentiment_free as sentdex\n",
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"\n",
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"# pour calculer l'impact\n",
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"from quantopian.pipeline.factors import CustomFactor\n",
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"import numpy as np\n",
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"\n",
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"\n",
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"def initialize(context):\n",
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" # Planifier notre fonction de rééquilibrage au début de chaque semaine.\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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"# Calcul du sentiment m sur la longueur de la fenêtre\n",
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"class AvgSentiment(CustomFactor):\n",
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" def compute(self, today, assets, out, impact):\n",
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" np.mean(impact, axis=0, out=out)\n",
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" \n",
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"def make_pipeline():\n",
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"\n",
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" # Éliminez les actions à un penny et les titres peu liquides.\n",
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" dollar_volume = AverageDollarVolume(window_length=30)\n",
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" is_liquid = dollar_volume.top(500)\n",
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" \n",
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" # Calcul du sentiment moyen\n",
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" avg_sentiment = AvgSentiment(inputs=[sentdex.sentiment_signal], window_length=10)\n",
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"\n",
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" return Pipeline(columns={\n",
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" 'sentiment':avg_sentiment\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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"def before_trading_start(context, data):\n",
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" portfo = pipeline_output('pipeline')\n",
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" context.something = portfo['sentiment'] \n",
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" \n",
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" # Actions avec un sentiment > 0\n",
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" context.longs = portfo[portfo['sentiment'] > 0].index.tolist()\n",
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" \n",
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" # Actions avec un sentiment < 0\n",
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" context.shorts = portfo[portfo['sentiment'] < 0].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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" \n",
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"def my_compute_weights(context):\n",
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"\n",
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" # Calculer des pondérations cibles égales pour nos positions longues et nos positions courtes.\n",
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" if len(context.longs)==0:\n",
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" long_weight = 0\n",
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" else:\n",
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" long_weight = 0.5/len(context.longs)\n",
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" \n",
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" if len(context.shorts)==0:\n",
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" short_weight = 0\n",
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" else:\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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"\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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" print(context.something)"
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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 3.5",
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"language": "python",
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"name": "py35"
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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": "3.5.9"
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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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} |