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     "text": [
      "usage: ipykernel_launcher.py [-h] [--training TRAINING] [--testing TESTING]\n",
      "                             [--output OUTPUT]\n",
      "ipykernel_launcher.py: error: unrecognized arguments: -f /Users/johnodin99/Library/Jupyter/runtime/kernel-6523333e-89ff-46af-a10a-f80d42f57fb0.json\n"
     ]
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      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnodin99/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2918: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
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   ],
   "source": [
    "# -*- coding: utf-8 -*- \n",
    "\"\"\" \n",
    "Created on Mon Mar 12 07:35:11 2018 \n",
    "\n",
    "@author: johnodin99 \n",
    "\"\"\" \n",
    "import os \n",
    "import pandas as pd \n",
    "\n",
    " \n",
    "################################training##################################### \n",
    "\"\"\" \n",
    "userhome = os.path.expanduser('~') \n",
    "traing_data_path = userhome+\"/Desktop/training_data.csv\" \n",
    "\n",
    "training_data = pd.read_csv(traing_data_path,header=None) \n",
    "training_data.columns=[\"open\",\"high\",\"low\",\"close\"] \n",
    "training_data.index=pd.to_datetime(training_data.index,unit=\"D\") \n",
    "\n",
    " \n",
    "#simple moving average \n",
    "training_simple_5 = training_data[\"close\"].rolling(window=5).mean() \n",
    "training_simple_20 = training_data[\"close\"].rolling(window=20).mean() \n",
    "\n",
    "#exponential moving average \n",
    "#exponential=pd.ewma(training_data[\"close\"],span=10,freq=\"D\",min_periods=10) \n",
    "\n",
    "training_action = [] \n",
    "slot_status = 0 \n",
    "\n",
    " \n",
    "for i  in range(len(training_simple_5.index)): \n",
    "    if (training_simple_5[i]>training_simple_20[i] and slot_status==0): \n",
    "        print(\"1\") \n",
    "        training_action.append(\"1\") \n",
    "        slot_status = 1 \n",
    "         \n",
    "    elif (training_simple_5[i]<training_simple_20[i]and slot_status==1): \n",
    "        print(\"-1\") \n",
    "        training_action.append(\"-1\") \n",
    "        slot_status = 0 \n",
    "     \n",
    "        \n",
    "    else: \n",
    "        training_action.append(\"0\") \n",
    "        print(\"0\") \n",
    " \"\"\" \n",
    "################################testing#################################### \n",
    "userhome = os.path.expanduser('~') \n",
    "testing_data_path = userhome+\"/Desktop/testing_data.csv\" \n",
    "\n",
    "testing_data = pd.read_csv(testing_data_path,header=None) \n",
    "testing_data.columns=[\"open\",\"high\",\"low\",\"close\"] \n",
    "testing_data.index=pd.to_datetime(testing_data.index,unit=\"D\") \n",
    "\n",
    " \n",
    "#simple moving average \n",
    "testing_simple_5 = testing_data[\"close\"].rolling(window=5).mean() \n",
    "testing_simple_20 = testing_data[\"close\"].rolling(window=20).mean() \n",
    "\n",
    "#exponential moving average \n",
    "#exponential=pd.ewma(training_data[\"close\"],span=10,freq=\"D\",min_periods=10) \n",
    "\n",
    "testing_action = [] \n",
    "slot_status = 0 \n",
    "\n",
    " \n",
    "for i  in range(len(testing_simple_5.index)): \n",
    "    if (testing_simple_5[i]>testing_simple_20[i] and slot_status==0): \n",
    "        testing_action.append(\"1\") \n",
    "        slot_status = 1 \n",
    "         \n",
    "    elif (testing_simple_5[i]<testing_simple_20[i]and slot_status==1): \n",
    "        testing_action.append(\"-1\") \n",
    "        slot_status = 0   \n",
    "       \n",
    "    else: \n",
    "        testing_action.append(\"0\") \n",
    "         \n",
    "testing_action=pd.DataFrame(testing_action) \n",
    "testing_action = testing_action[:-1] \n",
    "path = userhome+\"/Desktop/output.csv\" \n",
    "testing_action.to_csv(path,sep=' ',header=0,index=0) \n",
    "\n",
    "     \n",
    "############################################################################# \n",
    "\n",
    "if __name__ == '__main__': \n",
    "    # You should not modify this part. \n",
    "    import argparse \n",
    "\n",
    "    parser = argparse.ArgumentParser() \n",
    "    parser.add_argument('--training', \n",
    "                       default='training_data.csv', \n",
    "                       help='input training data file name') \n",
    "    parser.add_argument('--testing', \n",
    "                        default='testing_data.csv', \n",
    "                        help='input testing data file name') \n",
    "    parser.add_argument('--output', \n",
    "                        default='output.csv', \n",
    "                        help='output file name') \n",
    "    args = parser.parse_args()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use moving average method(5  and 20 days)  to forecast the stock when to reverse the trend  and then provide the suggestion to invest or sell , the method is to recognize the cross point and slot status "
   ]
  }
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