added total quantile analysis

notebooks/Quick Start 5 - Alpha Factor Quantile Analysis.ipynb

@@ -157,16 +157,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x21dc9cdf2b0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x21ddceb3a20>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -188,6 +188,38 @@
     "ret_yearly.plot(kind='bar', figsize=(14, 7))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x21dc96276d8>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAGhCAYAAACnJr9yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFKBJREFUeJzt3W2MpXd53/HfZS/GVurWgJfaeE38oELSACJi1SBKWyRb2AFEI1qBcDHGpHLom6St+0BLEil9QdqKpxaFCsdR1g6GqCYWKrIolBIMjqDR4hTaqpQYmfXaNWiD0obaBMTu1Rd7UIep1zO7t3cOO9fnI410zv2/z5zL1l+z+s59zpnq7gAAAExx1roHAAAA2EkiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCh71j3Adl144YV92WWXrXsMAADgh9QXvvCFP+ruvVudd8ZE0GWXXZaDBw+uewwAAOCHVFUd2s55Xg4HAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo+xZ9wAAALAWH6x1T3Bmuq7XPcFirgQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjLI6gqnptVT1QVfdX1Zs3rT2vqr5YVYeq6r1Vddam9U9W1SeXzgAAALBdiyKoqs5P8s4kL119vb2q9m445X1J3prkiiQvSPLqDY99U5LvLnl+AACAk7X0StA1Se7p7oe7++tJPpXkqiRZxdDl3f2x7j6a5I4k125Y+9kk71n4/AAAACdlaQRdmuTQhvsPJbl4dXtfkgdPsPaeJP80W1wJqqqbqupgVR08cuTIwlEBAACWR9A5SY5tuH8sydEnWquqa5M82t2f3eqbd/ct3b2/u/fv3bt3q9MBAAC2tDSCHklyyYb7+5Ic3mLtxiQvrar/nOTWJD9VVe9eOAcAAMC2LI2gjye5pqqeWVUXJXlJkk8kSXc/mOTRqnpZVZ2d5Pokd3b367r7L3b3C5P87ST/qbv/3sI5AAAAtmXPkgd39zeq6m1JPrc6dHOSl1fVld39jiQ3JLktyQVJDnT3vYumBQAAWGhRBCVJdx9IcuAEa/clef4TPPbTST69dAYAAIDtWvzHUgEAAM4kIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGWRxBVfXaqnqgqu6vqjdvWnteVX2xqg5V1Xur6qzV8X9eVV+uqger6h1LZwAAANiuRRFUVecneWeSl66+3l5Vezec8r4kb01yRZIXJHn16vhXk/xEkh9Lcm1V/eUlcwAAAGzX0itB1yS5p7sf7u6vJ/lUkquSZBVDl3f3x7r7aJI7klybJN396919tLsfS/LlJE9/vG9eVTdV1cGqOnjkyJGFowIAACyPoEuTHNpw/6EkF69u70vy4AnWkiRVdVGSn0zyu4/3zbv7lu7e39379+7d+3inAAAAnJSlEXROkmMb7h9LcnQba6mq85L82yR/t7v/z8I5AAAAtmVpBD2S5JIN9/clObzVWlU9NcldSW7v7o8unAEAAGDblkbQx5NcU1XPXL207SVJPpEk3f1gkker6mVVdXaS65PcWVVPyfErQB/p7lsXPj8AAMBJWRRB3f2NJG9L8rkkv5fk5iQvr6p/sDrlhiTvTfK1JJ/p7nuTvC7JTyf5h6uP1b6/qv7KkjkAAAC2a8/Sb9DdB5IcOMHafUmev+nYB5J8YOnzAgAAnIrFfywVAADgTCKCAACAUUQQAAAwiggCAABGWfzBCAAAT4oP1ronODNd1+ueAM44rgQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACj7Fn3AAD8EPtgrXuCM9N1ve4JAHgCrgQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYZXEEVdVrq+qBqrq/qt68ae15VfXFqjpUVe+tqrNWx/9aVX1l9bi3LZ0BAABguxZFUFWdn+SdSV66+np7Ve3dcMr7krw1yRVJXpDk1VVVSW5N8jeTPC/JDVX1wiVzAAAAbNfSK0HXJLmnux/u7q8n+VSSq5JkFUOXd/fHuvtokjuSXJvkRUm+0d1f6u5Hk3x4dRwAAOC027Pw8ZcmObTh/kNJLl7d3pfkwU1rrzzBY577eN+8qm5KclOSPPvZz1446mn0wVr3BGem63rdE5yZ7LdTY7+dGv/f2En2GzvNnhtr6ZWgc5Ic23D/WJKjW6w90WN+QHff0t37u3v/3r17H+8UAACAk7I0gh5JcsmG+/uSHN5i7YkeAwAAcFotjaCPJ7mmqp5ZVRcleUmSTyRJdz+Y5NGqellVnZ3k+iR3Jvl8kudW1XOr6keSvCbJXQvnAAAA2JZF7wnq7m+sPuL6c6tDNyd5eVVd2d3vSHJDktuSXJDkQHffmyRV9bNJPprjL437F9196P//7gAAAE++pR+MkO4+kOTACdbuS/L8xzn+75M8Z+lzAwAAnKzFfywVAADgTCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYBQRBAAAjCKCAACAUUQQAAAwiggCAABGEUEAAMAoIggAABhFBAEAAKOIIAAAYJRTjqCquqSq7q2qw1X121V17qb1PVV1oKoerqrPV9Xlq+NXVdXBqvpqVX22qp699D8CAABgu5ZcCfrVJHd096VJvpfkLZvW35jk3CT7kvxGknevjl+Q5Ke7+8ok/yHJLy2YAQAA4KQsiaBXJbltdfv2JNduWn9Nklu7u5PckeTqJOnu3+nuI6tz7kvy9BM9QVXdtLpqdPDIkSMnOg0AAGDbTimCquppSb7d3Y+tDj2U5OJNp12a5FCSrM57bPW4ja5P8uETPU9339Ld+7t7/969e09lVAAAgB+wZ6sTqur9SV606fDNSY5tuH8sydFN55zzROdU1S8kqe7+0MkMDAAAsMSWEdTdP7f5WFWdleSCqjqnu7+b4+/7ObzptEeSXJLkq1V1XpI93f0nq8ffmOQVSV69cH4AAICTckovh+vuY0k+neS61aE3Jblz02l3J7lxdfsNST6SJFX1t5K8PsnPdPd3TuX5AQAATtWSD0b4+SRvqaqHkvxpkg9V1flVdXdVnZ3k15KcW1WHczyCfnH1uNuS/HiS/1JV91fVexbMAAAAcFK2fDnciXT3A0levOnwt5K8cnX7aI5f8dn8uFN+TgAAgKWWXAkCAAA444ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBR9qx7AOAkXdfrngAA4IzmShAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEYRQQAAwCgiCAAAGEUEAQAAo4ggAABgFBEEAACMIoIAAIBRRBAAADCKCAIAAEY55Qiqqkuq6t6qOlxVv11V525a31NVB6rq4ar6fFVdvmn9jVXVVbXvVGcAAAA4WUuuBP1qkju6+9Ik30vylk3rb0xybpJ9SX4jybu/v1BVFyZ5c5IHFzw/AADASVsSQa9Kctvq9u1Jrt20/pokt3Z3J7kjydUb1t6V5JeT9ILnBwAAOGmnFEFV9bQk3+7ux1aHHkpy8abTLk1yKElW5z1WVU+rqpcn+U53f2Ybz3NTVR2sqoNHjhw5lVEBAAB+wJ6tTqiq9yd50abDNyc5tuH+sSRHN51zzgnO+ZUkr9jOcN19S5JbkmT//v2uGgEAAIttGUHd/XObj1XVWUkuqKpzuvu7Of6+n8ObTnskySVJvlpV562e6+okFyX53apKkmcl+XhVvbi7v7XovwQAAGAbtoygx9Pdx6rq00muS3IgyZuS3LnptLuT3JjkM0nekOQj3X1Xkru+f0JVfS3JNWd8AF3nIhUAAJwplnwwws8neUtVPZTkT5N8qKrOr6q7q+rsJL+W5NyqOpzjEfSLy8cFAABY5pSuBCVJdz+Q5MWbDn8ryStXt48mef0W3+OyU31+AACAU7HkShAAAMAZRwQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMIoIAgAARhFBAADAKCIIAAAYRQQBAACjiCAAAGAUEQQAAIwiggAAgFFEEAAAMEp197pn2JaqOpLk0LrnOANdmOSP1j0EY9hv7CT7jZ1kv7HT7LlT86PdvXerk86YCOLUVNXB7t6/7jmYwX5jJ9lv7CT7jZ1mz51eXg4HAACMIoIAAIBRRNDud8u6B2AU+42dZL+xk+w3dpo9dxp5TxAAADCKK0EAAMAoIggAABhFBAEAAKOIIAAAYBQRBACwhar6kXXPADx5RNAAVfU7656B3aWq9lXV+6vqzqp63aa1d61rLnavqtpTVa+oqitX919fVf+6qt647tnYfarqWZu/kny+qi5e3YYnTVX9jQ23z6uqf1ZVd1XVW6vqKeucbTfzEdm7QFU9NckznuCUz3b3lTs1D7tfVd2T5JNJ7kty/erwG7r7e1X1le5+zvqmYzeqqg8k+dEkT01yV5Krk3woyauSfLm7/8kax2OXqapjSQ4l6SS1OvysJP8zSXf3Feuajd1n47+bVfVvkjwtyW8leV2SR7v776xzvt1KBO0CVfWcJP8tyeH8vx/WGz2ru5+6s1Oxm1XVH3b3X9hw/xeSXJvkZ5L8141r8GSoqkNJLkvy9CQPJ7m4u/+4qs5J8qXu/rF1zsfusrrCeGOSX+rue1fH/qC7f3K9k7EbbYqg/5Hkhd397arak+S/+zf19Niz7gFYrru/UlVfSXJ1dz+yeb2q/nANY7G7/a+qekZ3fzNJuvtfVdWfJPl3Sc5Z72jsUv+7j//W7ptV9VB3/3GSdPd3vVeDJ1t3315Vdyd5V1Vdn+Qf5fhVITgdnlFVv5zjv8j+M9397SRZvbrCL7FPE+8J2j3enmTfCdZ+cycHYYR/nOSvbjzQ3b+Z5PYkF69lIna7X6mqP7+6/fzvH6yqlyT56HpGYjfr7m929w1JPpzkY0n+3JpHYvf6+zn+8suv5fi/r0mSqroiyfvXNNOu5+Vwu0xV/Xq2+G1Vd9+0Q+Owy51gv9XGY/YbTyY/49hJVXXL6ubZSV6W5D9uPsd+48ni59vO8nK43aeSXJrjv7l6SpLrkvx+ki+tcyh2LfuNnWbPsZPOyvFXWXw4ycHYb5xefr7tIFeCdpmq+kKSn+ru763un5fknu7+S+udjN3IfmOn2XPsJPuNnWS/7SzvCdp9/mx+8HXLe5JctKZZ2P3sN3aaPcdOst/YSfbbDvJyuN3nXyY5WFV3JflOkr+e4581D6eD/cZOs+fYSfYbO8l+20FeDrcLVdVzk1yV5Nwkv//9v3EAp4P9xk6z59hJ9hs7yX7bOSIIAAAYxXuCAACAUUQQAAAwiggCAABGEUEAAMAo/xcP6N2A8//J6gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1008x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(df.mean() * 50).plot(kind='bar', figsize=(14, 7), color='orange')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,