Stock_Price_Demo.ipynb

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      "Starting Spark application\n"
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      "SparkSession available as 'spark'.\n"
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     "text": [
      "<pyspark.sql.session.SparkSession object at 0x7fa8bfd759d0>"
     ]
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   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import *\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.ml.stat import Correlation\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "\n",
    "SparkSession.builder.appName(\"Minute Stock Price Data\").getOrCreate()"
   ]
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   "source": [
    "# sc.list_packages()\n",
    "sc.install_pypi_package(\"pandas==0.25.1\")\n",
    "sc.install_pypi_package(\"numpy\")\n",
    "sc.install_pypi_package(\"matplotlib==3.1.1\", \"https://pypi.org/simple\")"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Minute Pricing Data for 3000 Stock over 2 yrs (~8 GB)"
   ]
  },
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   "source": [
    "# Load the minute price data for 3000 stocks for the past 2 years\n",
    "pricing_minute = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\").csv(\"s3://example-data-sharkech/minute_price_3000_stocks_2_yrs/\")"
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    "# Debugging / validating the output\n",
    "\n",
    "pricing_minute.show(1)\n",
    "pricing_minute.printSchema()\n",
    "\n",
    "df = pricing_minute.select(\"time\")\n",
    "df.show()"
   ]
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   "source": [
    "# Correct data type issues. Convert time feild from a string to a timestamp\n",
    "pricing_minute = pricing_minute.withColumn(\"timestamp\", to_timestamp(\"time\"))\n",
    "pricing_minute = pricing_minute.drop(\"time\")\n",
    "\n",
    "# Add the year, month, dayofmonth columns for rolls up\n",
    "pricing_minute = pricing_minute.withColumn(\"year\", year(\"timestamp\"))\n",
    "pricing_minute = pricing_minute.withColumn(\"month\", month(\"timestamp\"))\n",
    "pricing_minute = pricing_minute.withColumn(\"dayofmonth\", dayofmonth(\"timestamp\"))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Fundamentals Data for all Stocks"
   ]
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   "source": [
    "# Load the fundamental data\n",
    "fundamentals = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\").csv(\"s3://example-data-sharkech/fundamental_data/\")"
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    "# Debugging / validating the output\n",
    "\n",
    "fundamentals.show(1)\n",
    "fundamentals.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pricing Data via. Spark + Pandas"
   ]
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     "text": [
      "+----+------------------+\n",
      "|year|          avg(AAL)|\n",
      "+----+------------------+\n",
      "|2019|29.199624763714716|\n",
      "|2020|15.298829816920481|\n",
      "|2021|19.992139054194876|\n",
      "+----+------------------+\n",
      "\n",
      "+----+-----+------------------+\n",
      "|year|month|          avg(AAL)|\n",
      "+----+-----+------------------+\n",
      "|2019|    5| 30.63287532536962|\n",
      "|2019|    6|31.004839185677167|\n",
      "|2019|    7| 32.43592605478944|\n",
      "|2019|    8|26.869529687125635|\n",
      "|2019|    9| 27.84115163716084|\n",
      "|2019|   10|27.817315126606037|\n",
      "|2019|   11|29.475506894267237|\n",
      "|2019|   12|27.978391344158144|\n",
      "|2020|    1|27.436449974410067|\n",
      "|2020|    2|27.174169856145525|\n",
      "|2020|    3|14.655740827952906|\n",
      "|2020|    4|11.144755772138758|\n",
      "|2020|    5|10.009361590992043|\n",
      "|2020|    6| 15.10013720643113|\n",
      "|2020|    7|11.911580462233365|\n",
      "|2020|    8| 12.92531167511054|\n",
      "|2020|    9| 12.91991842066858|\n",
      "|2020|   10| 12.43187615978769|\n",
      "|2020|   11| 12.67757011036826|\n",
      "|2020|   12| 16.39046824516564|\n",
      "+----+-----+------------------+\n",
      "only showing top 20 rows"
     ]
    }
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   "source": [
    "# Options : count(), mean(), max(), min(), sum(), avg(), agg()\n",
    "\n",
    "#\n",
    "# 1 Stock\n",
    "#\n",
    "\n",
    "# Average price per year for 1 stock\n",
    "pricing_minute_agg = pricing_minute.groupBy(\"year\").mean(\"AAL\").orderBy(\"year\").show()\n",
    "\n",
    "# Average price per year and month for a single stock\n",
    "pricing_minute_agg = pricing_minute.groupBy(\"year\", \"month\").mean(\"AAL\").orderBy(\"year\", \"month\").show()\n",
    "\n",
    "# Average price per year, month and day for a single stock\n",
    "# pricing_minute_agg = pricing_minute.groupBy(\"year\", \"month\", \"dayofmonth\").mean(\"AAL\").orderBy(\"year\", \"month\", \"dayofmonth\").show(100)"
   ]
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   "source": [
    "## Average Stock Price - Chart"
   ]
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize results - line chart\n",
    "pricing_minute_agg = pricing_minute.groupBy(\"year\", \"month\", \"dayofmonth\").mean(\"AAL\").orderBy(\"year\", \"month\", \"dayofmonth\")\n",
    "pricing_minute_agg = pricing_minute_agg.withColumn(\"month/day/year\", concat(\"month\", lit(\"/\"), \"dayofmonth\", lit(\"/\"), \"year\"))\n",
    "pricing_minute_agg = pricing_minute_agg.select(\"month/day/year\", col(\"avg(AAL)\").alias(\"AAL\"))\n",
    "\n",
    "pricing_minute_agg_pd = pricing_minute_agg.toPandas()\n",
    "\n",
    "pricing_minute_agg_pd['month/day/year'] = pd.to_datetime(pricing_minute_agg_pd['month/day/year'], format='%m/%d/%Y')\n",
    "\n",
    "x = pricing_minute_agg_pd[\"month/day/year\"]\n",
    "y = pricing_minute_agg_pd[\"AAL\"]\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.ylabel('Average Stock Price')\n",
    "plt.xlabel('Date')\n",
    "plt.title('AAL Stock Price')\n",
    "\n",
    "#plt.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xticks(rotation=35)\n",
    "\n",
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-02T23:17:21.614580Z",
     "iopub.status.busy": "2022-04-02T23:17:21.614312Z",
     "iopub.status.idle": "2022-04-02T23:17:21.853849Z",
     "shell.execute_reply": "2022-04-02T23:17:21.821591Z",
     "shell.execute_reply.started": "2022-04-02T23:17:21.614544Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52a220a629e74f30acd6bc2af68e0946",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \n",
    "# 3000 Stocks - Sample Queries\n",
    "# \n",
    "\n",
    "# Average price per year for 3000 stocks\n",
    "# pricing_minute.select( * [mean(c).alias(c) for c in pricing_minute.columns]).orderBy(\"year\").orderBy(\"year\").show()\n",
    "\n",
    "# Average price per year and month for a 3000 stocks\n",
    "# pricing_minute.select( * [mean(c).alias(c) for c in pricing_minute.columns]).orderBy(\"year\", \"month\").orderBy(\"year\", \"month\").show()\n",
    "\n",
    "# Average price per year, month and day for 3000 stocks\n",
    "# pricing_minute.select( * [mean(c).alias(c) for c in pricing_minute.columns]).orderBy(\"year\", \"month\", \"dayofmonth\").orderBy(\"year\", \"month\", \"dayofmonth\").show(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fundamental Data via. Spark + Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-02T23:17:21.855120Z",
     "iopub.status.busy": "2022-04-02T23:17:21.854869Z",
     "iopub.status.idle": "2022-04-02T23:17:23.265373Z",
     "shell.execute_reply": "2022-04-02T23:17:23.264571Z",
     "shell.execute_reply.started": "2022-04-02T23:17:21.855084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e45aeef350504c5cb1be74420a682658",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize - Box plot by industry\n",
    "\n",
    "# Use the fundamental data filter the pricing data set to healthcare sector and a market cap over 1,000,000\n",
    "health_care_market_cap_1000_plus = fundamentals.filter(fundamentals.sector == \"Healthcare\").filter(fundamentals.marketcapitalization > 1000000)\n",
    "\n",
    "stock_symbol_list = list(health_care_market_cap_1000_plus.select('symbol').toPandas()['symbol'])\n",
    "stock_symbol_list.append('year')\n",
    "stock_symbol_list.append('month')\n",
    "stock_symbol_list.append('dayofmonth')\n",
    "\n",
    "health_care_market_cap_1000_plus_minute_pricing = pricing_minute.select([c for c in pricing_minute.columns if c in stock_symbol_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-02T23:17:23.267131Z",
     "iopub.status.busy": "2022-04-02T23:17:23.266872Z",
     "iopub.status.idle": "2022-04-02T23:17:23.575085Z",
     "shell.execute_reply": "2022-04-02T23:17:23.574252Z",
     "shell.execute_reply.started": "2022-04-02T23:17:23.267096Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cf7bf6ef613463eb0767f92ceec89c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert data type to int\n",
    "fundamentals = fundamentals.withColumn(\"peratio\", fundamentals['peratio'].cast('float'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PE Ration Grouped by Sector - Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-02T23:17:23.576599Z",
     "iopub.status.busy": "2022-04-02T23:17:23.576359Z",
     "iopub.status.idle": "2022-04-02T23:17:24.894795Z",
     "shell.execute_reply": "2022-04-02T23:17:24.894142Z",
     "shell.execute_reply.started": "2022-04-02T23:17:23.576566Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a60e65c5638b4cb0bdac4c9b487c1946",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fundamentals = fundamentals.toPandas()\n",
    "# df_fundamentals[df_fundamentals['sector'].isin(['Healthcare', 'Technology', 'Financial Services'])].boxplot(column=['peratio'], by='sector', showfliers=False)\n",
    "\n",
    "df_sectors_only = df_fundamentals[df_fundamentals['sector'].isin(['Healthcare', 'Technology', 'Financial Services'])]\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "df_sectors_only.boxplot(column=['peratio'], by='sector', showfliers=False)\n",
    "\n",
    "plt.title('PE Ratio Grouped by Sector')\n",
    "plt.suptitle('')\n",
    "\n",
    "%matplot plt"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pysparkkernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}