cleanup examples folder

Author: DanielGoldfarb

examples/test_df.rolling.ipynb renamed to examples/scratch_pad/test_df.rolling.ipynb

@@ -181,7 +181,7 @@
     }
    ],
    "source": [
-    "idf = pd.read_csv('data/SP500_NOV2019_IDay.csv',index_col=0,parse_dates=True)\n",
+    "idf = pd.read_csv('../data/SP500_NOV2019_IDay.csv',index_col=0,parse_dates=True)\n",
     "idf = idf.drop('Volume',axis=1) # Volume is zero anyway for this intraday data set\n",
     "idf.index.name = 'Date'\n",
     "idf.shape\n",
@@ -507,7 +507,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bddab70b8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ad2e9d860>"
       ]
      },
      "execution_count": 12,
@@ -712,16 +712,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_csv('data/SP500_20191106_IDayBollinger.csv')"
+    "df.to_csv('../data/SP500_20191106_IDayBollinger.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -741,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -774,12 +774,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dftest = pd.read_csv('data/SP500_20191106_IDayBollinger.csv',index_col=0,parse_dates=True)"
+    "dftest = pd.read_csv('../data/SP500_20191106_IDayBollinger.csv',index_col=0,parse_dates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -860,7 +860,7 @@
        "2019-11-06 09:32:00  3073.42  3071.78  3073.42  3071.74    NaN    NaN"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -948,7 +948,7 @@
        "2019-11-06 16:00:00  3073.277938  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -960,16 +960,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bd97d8ef0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2aceba67b8>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },

examples/tplot.py renamed to examples/scratch_pad/tplot.py

@@ -1,5 +1,5 @@
 import pandas as pd
-daily = pd.read_csv('data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
+daily = pd.read_csv('../data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
 daily.head(3)
 
 #   idf = pd.read_csv('data/SP500_NOV2019_IDay.csv',index_col=0,parse_dates=True)
@@ -14,6 +14,6 @@
 mpf.plot(daily,mav=7)
 mpf.plot(daily,volume=True)
 
-df = pd.read_csv('data/yahoofinance-SPY-20080101-20180101.csv',index_col=0,parse_dates=True)
+df = pd.read_csv('../data/yahoofinance-SPY-20080101-20180101.csv',index_col=0,parse_dates=True)
 mpf.plot(df[700:850],type='bars',no_xgaps=True,mav=(20,40),figscale=0.7)
 mpf.plot(df[700:850],type='bars',volume=True,no_xgaps=True,mav=(20,40),figscale=0.7)