sobeasy
diff --git a/‎Confidence Intervals.ipynb‎
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diff --git a/‎Expected Value and Standard Deviation.ipynb‎
Lines changed: 5 additions & 5 deletions b/‎Expected Value and Standard Deviation.ipynb‎
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diff --git a/‎Integration, Cointegration, and Stationarity.ipynb‎
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diff --git a/‎Linear Correlation Analysis.ipynb‎
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diff --git a/‎Long-Short Strategies using Ranking.ipynb‎
Lines changed: 7 additions & 3 deletions b/‎Long-Short Strategies using Ranking.ipynb‎
Lines changed: 7 additions & 3 deletions
@@ -206,21 +206,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.4"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,
 
@@ -639,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 141,
    "metadata": {
     "collapsed": false
    },
@@ -655,7 +655,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total Returns: 5.71%\n"
+      "Annual Returns: 0.00%\n",
+      "1.0\n"
      ]
     }
    ],
@@ -666,7 +667,10 @@
     "    basket_returns = compute_basket_returns(mscores, monthly_returns, number_of_baskets, m)\n",
     "    strategy_returns[m-1] = basket_returns[0]-basket_returns[ number_of_baskets-1]\n",
     "\n",
-    "print('Total Returns: %.2f%%'%strategy_returns.sum())"
+    "total_return = strategy_returns.sum()\n",
+    "ann_return = 100*((1 + total_return)**(12 / l)-1)\n",
+    "print('Annual Returns: %.2f%%'%ann_return)\n",
+    "print(float(6.7**(1/5)))"
    ]
   },
   {
Original file line number	Diff line number	Diff line change
`@@ -639,7 +639,7 @@`
`639`	`639`	`},`
`640`	`640`	`{`
`641`	`641`	`"cell_type": "code",`
`642`		`- "execution_count": 133,`
	`642`	`+ "execution_count": 141,`
`643`	`643`	`"metadata": {`
`644`	`644`	`"collapsed": false`
`645`	`645`	`},`
`@@ -655,7 +655,8 @@`
`655`	`655`	`"name": "stdout",`
`656`	`656`	`"output_type": "stream",`
`657`	`657`	`"text": [`
`658`		`- "Total Returns: 5.71%\n"`
	`658`	`+ "Annual Returns: 0.00%\n",`
	`659`	`+ "1.0\n"`
`659`	`660`	`]`
`660`	`661`	`}`
`661`	`662`	`],`
`@@ -666,7 +667,10 @@`
`666`	`667`	`" basket_returns = compute_basket_returns(mscores, monthly_returns, number_of_baskets, m)\n",`
`667`	`668`	`" strategy_returns[m-1] = basket_returns[0]-basket_returns[ number_of_baskets-1]\n",`
`668`	`669`	`"\n",`
`669`		`- "print('Total Returns: %.2f%%'%strategy_returns.sum())"`
	`670`	`+ "total_return = strategy_returns.sum()\n",`
	`671`	`+ "ann_return = 100((1 + total_return)*(12 / l)-1)\n",`
	`672`	`+ "print('Annual Returns: %.2f%%'%ann_return)\n",`
	`673`	`+ "print(float(6.7**(1/5)))"`
`670`	`674`	`]`
`671`	`675`	`},`
`672`	`676`	`{`