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Commit 721396fa authored by Dr.李's avatar Dr.李
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clear up output

parent 91ea8d99
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Showing with 204 additions and 1692 deletions
notebooks/Example 1 - Factor IC analysis.ipynb
View file @ 721396fa
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -19,7 +19,7 @@ ...@@ -19,7 +19,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -53,7 +53,7 @@ ...@@ -53,7 +53,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -88,7 +88,7 @@ ...@@ -88,7 +88,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -222,17 +222,9 @@ ...@@ -222,17 +222,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 21.8 s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -246,17 +238,9 @@ ...@@ -246,17 +238,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1h 1min 15s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -284,7 +268,7 @@ ...@@ -284,7 +268,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -297,7 +281,7 @@ ...@@ -297,7 +281,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -310,7 +294,7 @@ ...@@ -310,7 +294,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -349,6 +333,35 @@ ...@@ -349,6 +333,35 @@
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.4" "version": "3.6.4"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
} }
}, },
"nbformat": 4, "nbformat": 4,
......
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ 721396fa
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -50,7 +50,7 @@ ...@@ -50,7 +50,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -109,7 +109,7 @@ ...@@ -109,7 +109,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -126,28 +126,9 @@ ...@@ -126,28 +126,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-02 15:49:52,006 - ALPHA_MIND - INFO - 2010-01-04 partial re-balance: 798\n"
]
},
{
"ename": "NameError",
"evalue": "name 'offset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-16-86d43c05141e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mexecuted_codes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecuted_pos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \u001b[0mdx_returns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_dx_return\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mref_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecuted_codes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhorizon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhorizon\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexecuted_pos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdx_returns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'code'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'offset' is not defined"
]
}
],
"source": [ "source": [
"# rebalance\n", "# rebalance\n",
"\n", "\n",
...@@ -267,30 +248,9 @@ ...@@ -267,30 +248,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15c89cc1630>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15c895840f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=ref_dates)\n", "ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=ref_dates)\n",
"\n", "\n",
...@@ -312,45 +272,15 @@ ...@@ -312,45 +272,15 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15c88a2d588>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15c88d459b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"ret_df[['returns', 'tc_cost']][-30:].cumsum().plot(figsize=(12, 6),\n", "ret_df[['returns', 'tc_cost']][-30:].cumsum().plot(figsize=(12, 6),\n",
" title='Fixed freq rebalanced: {0} with benchmark {1}'.format(freq, 905),\n", " title='Fixed freq rebalanced: {0} with benchmark {1}'.format(freq, 905),\n",
" secondary_y='tc_cost')" " secondary_y='tc_cost')"
] ]
}, },
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"%time?"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
...@@ -376,6 +306,35 @@ ...@@ -376,6 +306,35 @@
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.4" "version": "3.6.4"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
} }
}, },
"nbformat": 4, "nbformat": 4,
......
notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ 721396fa
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -21,7 +21,7 @@ ...@@ -21,7 +21,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -54,7 +54,7 @@ ...@@ -54,7 +54,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -126,17 +126,9 @@ ...@@ -126,17 +126,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 0 ns\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -149,17 +141,9 @@ ...@@ -149,17 +141,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 30.7 s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -177,7 +161,7 @@ ...@@ -177,7 +161,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -216,7 +200,7 @@ ...@@ -216,7 +200,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -322,7 +306,7 @@ ...@@ -322,7 +306,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -356,26 +340,9 @@ ...@@ -356,26 +340,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:39: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
"2018-03-06 11:25:33,016 - ALPHA_MIND - INFO - 0.005 finished\n",
"2018-03-06 11:26:27,976 - ALPHA_MIND - INFO - 0.01 finished\n",
"2018-03-06 11:27:23,376 - ALPHA_MIND - INFO - 0.015 finished\n",
"2018-03-06 11:28:17,530 - ALPHA_MIND - INFO - 0.02 finished\n"
]
}
],
"source": [ "source": [
"weight_gaps = [0.005, 0.010, 0.015, 0.020]\n", "weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
"\n", "\n",
...@@ -387,1336 +354,6 @@ ...@@ -387,1336 +354,6 @@
" alpha_logger.info(f\"{weight_gap} finished\")" " alpha_logger.info(f\"{weight_gap} finished\")"
] ]
}, },
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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" <td>-0.017776</td>\n",
" <td>0.97470</td>\n",
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" <td>-0.028275</td>\n",
" <td>-0.028275</td>\n",
" <td>0.99900</td>\n",
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" <td>...</td>\n",
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" <th>2016-12-19</th>\n",
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" <td>0.652831</td>\n",
" <td>2.515662</td>\n",
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" <td>-0.032055</td>\n",
" <td>0.99999</td>\n",
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" <tr>\n",
" <th>2017-01-03</th>\n",
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" <td>0.659304</td>\n",
" <td>2.751663</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99999</td>\n",
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" <th>2017-01-17</th>\n",
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" <td>2.728580</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>1.00003</td>\n",
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" <th>2017-02-07</th>\n",
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" <td>0.678847</td>\n",
" <td>3.065441</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>1.00003</td>\n",
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" <td>3.101478</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99996</td>\n",
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" <th>2017-03-07</th>\n",
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" <td>0.676649</td>\n",
" <td>2.608772</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99798</td>\n",
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" <th>2017-03-21</th>\n",
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" <td>0.684161</td>\n",
" <td>2.649223</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>1.00003</td>\n",
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" <td>3.042353</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>1.00003</td>\n",
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" <td>-0.032055</td>\n",
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" <td>3.300204</td>\n",
" <td>-0.007592</td>\n",
" <td>-0.032055</td>\n",
" <td>1.00004</td>\n",
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" <td>-0.032055</td>\n",
" <td>0.99990</td>\n",
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" <td>3.317061</td>\n",
" <td>-0.007592</td>\n",
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" <th>2017-06-20</th>\n",
" <td>0.010147</td>\n",
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" <td>3.468718</td>\n",
" <td>-0.007592</td>\n",
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" <td>-0.032055</td>\n",
" <td>1.00004</td>\n",
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" <th>2017-08-29</th>\n",
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" <td>-0.032055</td>\n",
" <td>1.00004</td>\n",
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" <th>2017-09-26</th>\n",
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" <td>2.955708</td>\n",
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" <td>-0.032055</td>\n",
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" <th>2017-10-17</th>\n",
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" <th>2017-10-31</th>\n",
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" <td>-0.032055</td>\n",
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" <th>2017-11-14</th>\n",
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" <td>-0.032055</td>\n",
" <td>1.00011</td>\n",
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" <th>2017-11-28</th>\n",
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" <td>-0.032055</td>\n",
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" <th>2017-12-12</th>\n",
" <td>-0.000348</td>\n",
" <td>0.734957</td>\n",
" <td>2.704492</td>\n",
" <td>-0.009392</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99994</td>\n",
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" <tr>\n",
" <th>2017-12-26</th>\n",
" <td>0.003635</td>\n",
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" <td>2.968922</td>\n",
" <td>-0.009392</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99996</td>\n",
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" <th>2018-01-10</th>\n",
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" <td>0.751747</td>\n",
" <td>3.045397</td>\n",
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" <td>-0.032055</td>\n",
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" <tr>\n",
" <th>2018-01-24</th>\n",
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" <th>2018-02-07</th>\n",
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" <td>-0.009392</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-28</th>\n",
" <td>0.000144</td>\n",
" <td>0.763571</td>\n",
" <td>2.821974</td>\n",
" <td>-0.009392</td>\n",
" <td>-0.032055</td>\n",
" <td>0.99994</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>199 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" daily_return cum_ret sharp drawdown max_drawn leverage\n",
"2010-01-04 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000\n",
"2010-01-18 0.008869 0.008869 0.000000 0.000000 0.000000 0.98210\n",
"2010-02-01 -0.013477 -0.004608 0.000000 -0.013477 -0.013477 0.98080\n",
"2010-02-22 0.001117 -0.003492 0.000000 -0.013477 -0.013477 0.99880\n",
"2010-03-08 0.006512 0.003021 0.000000 -0.013477 -0.013477 1.00070\n",
"2010-03-22 -0.002116 0.000904 0.095988 -0.013477 -0.013477 0.99990\n",
"2010-04-06 0.000774 0.001679 0.167198 -0.013477 -0.013477 1.00030\n",
"2010-04-20 -0.010586 -0.008907 -0.726384 -0.017776 -0.017776 1.00090\n",
"2010-05-05 0.002238 -0.006669 -0.510654 -0.017776 -0.017776 0.99920\n",
"2010-05-19 0.006819 0.000150 0.010331 -0.017776 -0.017776 0.99890\n",
"2010-06-02 -0.007428 -0.007278 -0.457455 -0.017776 -0.017776 0.99980\n",
"2010-06-21 0.011857 0.004580 0.245118 -0.017776 -0.017776 0.99990\n",
"2010-07-05 0.012287 0.016866 0.795769 -0.017776 -0.017776 0.99960\n",
"2010-07-19 0.003123 0.019990 0.909766 -0.017776 -0.017776 0.97430\n",
"2010-08-02 0.000137 0.020127 0.886362 -0.017776 -0.017776 0.97470\n",
"2010-08-16 -0.008936 0.011191 0.451216 -0.017776 -0.017776 1.00010\n",
"2010-08-30 -0.005018 0.006173 0.237917 -0.017776 -0.017776 0.99900\n",
"2010-09-13 -0.014321 -0.008148 -0.276927 -0.028275 -0.028275 0.99960\n",
"2010-09-30 0.006231 -0.001917 -0.062354 -0.028275 -0.028275 0.99970\n",
"2010-10-21 0.024725 0.022808 0.591884 -0.028275 -0.028275 0.99950\n",
"2010-11-04 0.000314 0.023123 0.586208 -0.028275 -0.028275 0.99990\n",
"2010-11-18 0.002588 0.025710 0.637165 -0.028275 -0.028275 0.99980\n",
"2010-12-02 0.013303 0.039014 0.910953 -0.028275 -0.028275 0.99970\n",
"2010-12-16 -0.001335 0.037678 0.860079 -0.028275 -0.028275 0.99950\n",
"2010-12-30 0.008247 0.045925 1.016748 -0.028275 -0.028275 0.99960\n",
"2011-01-14 0.011881 0.057807 1.250796 -0.028275 -0.028275 0.99960\n",
"2011-01-28 0.001114 0.058920 1.094790 -0.028275 -0.028275 0.95740\n",
"2011-02-18 -0.001683 0.057237 1.438314 -0.028275 -0.028275 0.95720\n",
"2011-03-04 0.001080 0.058317 1.437413 -0.028275 -0.028275 0.99890\n",
"2011-03-18 0.017353 0.075670 1.601532 -0.028275 -0.028275 0.99900\n",
"... ... ... ... ... ... ...\n",
"2016-12-19 -0.002423 0.652831 2.515662 -0.007592 -0.032055 0.99999\n",
"2017-01-03 0.006473 0.659304 2.751663 -0.007592 -0.032055 0.99999\n",
"2017-01-17 0.010712 0.670016 2.728580 -0.007592 -0.032055 1.00003\n",
"2017-02-07 0.008831 0.678847 3.065441 -0.007592 -0.032055 1.00003\n",
"2017-02-21 0.001971 0.680819 3.101478 -0.007592 -0.032055 0.99996\n",
"2017-03-07 -0.004169 0.676649 2.608772 -0.007592 -0.032055 0.99798\n",
"2017-03-21 0.007512 0.684161 2.649223 -0.007592 -0.032055 1.00003\n",
"2017-04-06 0.011012 0.695173 3.042353 -0.007592 -0.032055 1.00003\n",
"2017-04-20 0.007265 0.702439 3.252394 -0.007592 -0.032055 1.00004\n",
"2017-05-05 -0.002919 0.699520 3.300204 -0.007592 -0.032055 1.00004\n",
"2017-05-19 -0.000372 0.699148 3.181372 -0.007592 -0.032055 0.99990\n",
"2017-06-06 0.003368 0.702517 3.317061 -0.007592 -0.032055 0.99990\n",
"2017-06-20 0.010147 0.712664 3.468718 -0.007592 -0.032055 1.00004\n",
"2017-07-04 0.011290 0.723954 3.588865 -0.007592 -0.032055 1.00004\n",
"2017-07-18 0.014913 0.738866 3.588443 -0.007592 -0.032055 0.99999\n",
"2017-08-01 0.000578 0.739444 3.345250 -0.007592 -0.032055 0.99999\n",
"2017-08-15 -0.006051 0.733393 2.756635 -0.007592 -0.032055 1.00004\n",
"2017-08-29 0.003178 0.736572 3.107579 -0.007592 -0.032055 1.00004\n",
"2017-09-12 0.000895 0.737466 3.114444 -0.007592 -0.032055 1.00004\n",
"2017-09-26 0.002334 0.739800 2.955708 -0.007592 -0.032055 0.99997\n",
"2017-10-17 -0.007208 0.732592 2.724182 -0.007592 -0.032055 0.99997\n",
"2017-10-31 -0.002184 0.730409 2.456143 -0.009392 -0.032055 1.00002\n",
"2017-11-14 0.001861 0.732269 2.583109 -0.009392 -0.032055 1.00011\n",
"2017-11-28 0.003036 0.735306 3.017613 -0.009392 -0.032055 1.00011\n",
"2017-12-12 -0.000348 0.734957 2.704492 -0.009392 -0.032055 0.99994\n",
"2017-12-26 0.003635 0.738592 2.968922 -0.009392 -0.032055 0.99996\n",
"2018-01-10 0.013155 0.751747 3.045397 -0.009392 -0.032055 1.00008\n",
"2018-01-24 0.006686 0.758434 2.982578 -0.009392 -0.032055 0.99998\n",
"2018-02-07 0.004994 0.763428 2.899017 -0.009392 -0.032055 0.99993\n",
"2018-02-28 0.000144 0.763571 2.821974 -0.009392 -0.032055 0.99994\n",
"\n",
"[199 rows x 6 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_df"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>leverage</th>\n",
" <th>returns</th>\n",
" <th>turn_over</th>\n",
" <th>index</th>\n",
" <th>tc_cost</th>\n",
" <th>ret_after_tc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-01-04</th>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-18</th>\n",
" <td>0.98210</td>\n",
" <td>-0.004034</td>\n",
" <td>1.408400</td>\n",
" <td>-0.016006</td>\n",
" <td>0.002817</td>\n",
" <td>0.008869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-02-01</th>\n",
" <td>0.98080</td>\n",
" <td>-0.119345</td>\n",
" <td>0.375296</td>\n",
" <td>-0.108705</td>\n",
" <td>0.000751</td>\n",
" <td>-0.013477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-02-22</th>\n",
" <td>0.99880</td>\n",
" <td>0.018225</td>\n",
" <td>0.294060</td>\n",
" <td>0.016540</td>\n",
" <td>0.000588</td>\n",
" <td>0.001117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-08</th>\n",
" <td>1.00070</td>\n",
" <td>0.040173</td>\n",
" <td>0.330253</td>\n",
" <td>0.032977</td>\n",
" <td>0.000661</td>\n",
" <td>0.006512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-03-22</th>\n",
" <td>0.99990</td>\n",
" <td>-0.010525</td>\n",
" <td>0.400000</td>\n",
" <td>-0.009209</td>\n",
" <td>0.000800</td>\n",
" <td>-0.002116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-04-06</th>\n",
" <td>1.00030</td>\n",
" <td>0.035026</td>\n",
" <td>0.400000</td>\n",
" <td>0.033441</td>\n",
" <td>0.000800</td>\n",
" <td>0.000774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-04-20</th>\n",
" <td>1.00090</td>\n",
" <td>-0.055201</td>\n",
" <td>0.400000</td>\n",
" <td>-0.045374</td>\n",
" <td>0.000800</td>\n",
" <td>-0.010586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-05-05</th>\n",
" <td>0.99920</td>\n",
" <td>-0.107964</td>\n",
" <td>0.312529</td>\n",
" <td>-0.110916</td>\n",
" <td>0.000625</td>\n",
" <td>0.002238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-05-19</th>\n",
" <td>0.99890</td>\n",
" <td>-0.053097</td>\n",
" <td>0.400000</td>\n",
" <td>-0.060783</td>\n",
" <td>0.000800</td>\n",
" <td>0.006819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-06-02</th>\n",
" <td>0.99980</td>\n",
" <td>-0.002943</td>\n",
" <td>0.400000</td>\n",
" <td>0.003685</td>\n",
" <td>0.000800</td>\n",
" <td>-0.007428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-06-21</th>\n",
" <td>0.99990</td>\n",
" <td>0.029722</td>\n",
" <td>0.299103</td>\n",
" <td>0.017268</td>\n",
" <td>0.000598</td>\n",
" <td>0.011857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-07-05</th>\n",
" <td>0.99960</td>\n",
" <td>-0.069919</td>\n",
" <td>0.205957</td>\n",
" <td>-0.082651</td>\n",
" <td>0.000412</td>\n",
" <td>0.012287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-07-19</th>\n",
" <td>0.97430</td>\n",
" <td>0.069300</td>\n",
" <td>0.270743</td>\n",
" <td>0.067367</td>\n",
" <td>0.000541</td>\n",
" <td>0.003123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-02</th>\n",
" <td>0.97470</td>\n",
" <td>0.043631</td>\n",
" <td>0.107972</td>\n",
" <td>0.044402</td>\n",
" <td>0.000216</td>\n",
" <td>0.000137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-16</th>\n",
" <td>1.00010</td>\n",
" <td>0.017754</td>\n",
" <td>0.203921</td>\n",
" <td>0.026279</td>\n",
" <td>0.000408</td>\n",
" <td>-0.008936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-08-30</th>\n",
" <td>0.99900</td>\n",
" <td>-0.017580</td>\n",
" <td>0.400000</td>\n",
" <td>-0.013376</td>\n",
" <td>0.000800</td>\n",
" <td>-0.005018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-13</th>\n",
" <td>0.99960</td>\n",
" <td>0.007534</td>\n",
" <td>0.400000</td>\n",
" <td>0.021064</td>\n",
" <td>0.000800</td>\n",
" <td>-0.014321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td>0.99970</td>\n",
" <td>0.033250</td>\n",
" <td>0.334767</td>\n",
" <td>0.026357</td>\n",
" <td>0.000670</td>\n",
" <td>0.006231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-21</th>\n",
" <td>0.99950</td>\n",
" <td>0.129311</td>\n",
" <td>0.205305</td>\n",
" <td>0.104227</td>\n",
" <td>0.000411</td>\n",
" <td>0.024725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-04</th>\n",
" <td>0.99990</td>\n",
" <td>0.042310</td>\n",
" <td>0.400000</td>\n",
" <td>0.041200</td>\n",
" <td>0.000800</td>\n",
" <td>0.000314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-18</th>\n",
" <td>0.99980</td>\n",
" <td>-0.098762</td>\n",
" <td>0.400000</td>\n",
" <td>-0.102170</td>\n",
" <td>0.000800</td>\n",
" <td>0.002588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-02</th>\n",
" <td>0.99970</td>\n",
" <td>0.007574</td>\n",
" <td>0.398527</td>\n",
" <td>-0.006528</td>\n",
" <td>0.000797</td>\n",
" <td>0.013303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-16</th>\n",
" <td>0.99950</td>\n",
" <td>0.020461</td>\n",
" <td>0.332390</td>\n",
" <td>0.021142</td>\n",
" <td>0.000665</td>\n",
" <td>-0.001335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-30</th>\n",
" <td>0.99960</td>\n",
" <td>-0.022065</td>\n",
" <td>0.166455</td>\n",
" <td>-0.030657</td>\n",
" <td>0.000333</td>\n",
" <td>0.008247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-14</th>\n",
" <td>0.99960</td>\n",
" <td>-0.038013</td>\n",
" <td>0.274646</td>\n",
" <td>-0.050464</td>\n",
" <td>0.000549</td>\n",
" <td>0.011881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-28</th>\n",
" <td>0.95740</td>\n",
" <td>0.033718</td>\n",
" <td>0.133258</td>\n",
" <td>0.033777</td>\n",
" <td>0.000267</td>\n",
" <td>0.001114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-02-18</th>\n",
" <td>0.95720</td>\n",
" <td>0.053540</td>\n",
" <td>0.195492</td>\n",
" <td>0.057284</td>\n",
" <td>0.000391</td>\n",
" <td>-0.001683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-04</th>\n",
" <td>0.99890</td>\n",
" <td>0.024900</td>\n",
" <td>0.300694</td>\n",
" <td>0.023244</td>\n",
" <td>0.000601</td>\n",
" <td>0.001080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-18</th>\n",
" <td>0.99900</td>\n",
" <td>-0.020937</td>\n",
" <td>0.312013</td>\n",
" <td>-0.038953</td>\n",
" <td>0.000624</td>\n",
" <td>0.017353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>0.99999</td>\n",
" <td>-0.046173</td>\n",
" <td>0.295404</td>\n",
" <td>-0.044341</td>\n",
" <td>0.000591</td>\n",
" <td>-0.002423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-03</th>\n",
" <td>0.99999</td>\n",
" <td>0.024535</td>\n",
" <td>0.161578</td>\n",
" <td>0.017738</td>\n",
" <td>0.000323</td>\n",
" <td>0.006473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-17</th>\n",
" <td>1.00003</td>\n",
" <td>0.002655</td>\n",
" <td>0.286523</td>\n",
" <td>-0.008629</td>\n",
" <td>0.000573</td>\n",
" <td>0.010712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-02-07</th>\n",
" <td>1.00003</td>\n",
" <td>0.022140</td>\n",
" <td>0.125876</td>\n",
" <td>0.013057</td>\n",
" <td>0.000252</td>\n",
" <td>0.008831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-02-21</th>\n",
" <td>0.99996</td>\n",
" <td>0.033621</td>\n",
" <td>0.333158</td>\n",
" <td>0.030984</td>\n",
" <td>0.000666</td>\n",
" <td>0.001971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-07</th>\n",
" <td>0.99798</td>\n",
" <td>-0.015391</td>\n",
" <td>0.291720</td>\n",
" <td>-0.011829</td>\n",
" <td>0.000583</td>\n",
" <td>-0.004169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-21</th>\n",
" <td>1.00003</td>\n",
" <td>0.008702</td>\n",
" <td>0.400000</td>\n",
" <td>0.000390</td>\n",
" <td>0.000800</td>\n",
" <td>0.007512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-06</th>\n",
" <td>1.00003</td>\n",
" <td>0.030815</td>\n",
" <td>0.229504</td>\n",
" <td>0.019344</td>\n",
" <td>0.000459</td>\n",
" <td>0.011012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-04-20</th>\n",
" <td>1.00004</td>\n",
" <td>-0.006430</td>\n",
" <td>0.400000</td>\n",
" <td>-0.014495</td>\n",
" <td>0.000800</td>\n",
" <td>0.007265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-05</th>\n",
" <td>1.00004</td>\n",
" <td>-0.033754</td>\n",
" <td>0.400000</td>\n",
" <td>-0.031634</td>\n",
" <td>0.000800</td>\n",
" <td>-0.002919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-19</th>\n",
" <td>0.99990</td>\n",
" <td>0.015922</td>\n",
" <td>0.400000</td>\n",
" <td>0.015496</td>\n",
" <td>0.000800</td>\n",
" <td>-0.000372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-06</th>\n",
" <td>0.99990</td>\n",
" <td>0.039499</td>\n",
" <td>0.400000</td>\n",
" <td>0.035334</td>\n",
" <td>0.000800</td>\n",
" <td>0.003368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-20</th>\n",
" <td>1.00004</td>\n",
" <td>0.025790</td>\n",
" <td>0.227194</td>\n",
" <td>0.015188</td>\n",
" <td>0.000454</td>\n",
" <td>0.010147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-04</th>\n",
" <td>1.00004</td>\n",
" <td>0.031422</td>\n",
" <td>0.157818</td>\n",
" <td>0.019815</td>\n",
" <td>0.000316</td>\n",
" <td>0.011290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-18</th>\n",
" <td>0.99999</td>\n",
" <td>0.034550</td>\n",
" <td>0.332887</td>\n",
" <td>0.018972</td>\n",
" <td>0.000666</td>\n",
" <td>0.014913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-01</th>\n",
" <td>0.99999</td>\n",
" <td>0.009246</td>\n",
" <td>0.186835</td>\n",
" <td>0.008294</td>\n",
" <td>0.000374</td>\n",
" <td>0.000578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-15</th>\n",
" <td>1.00004</td>\n",
" <td>-0.021296</td>\n",
" <td>0.336833</td>\n",
" <td>-0.015918</td>\n",
" <td>0.000674</td>\n",
" <td>-0.006051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-08-29</th>\n",
" <td>1.00004</td>\n",
" <td>0.039055</td>\n",
" <td>0.310933</td>\n",
" <td>0.035254</td>\n",
" <td>0.000622</td>\n",
" <td>0.003178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-09-12</th>\n",
" <td>1.00004</td>\n",
" <td>0.003864</td>\n",
" <td>0.400000</td>\n",
" <td>0.002169</td>\n",
" <td>0.000800</td>\n",
" <td>0.000895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-09-26</th>\n",
" <td>0.99997</td>\n",
" <td>-0.002510</td>\n",
" <td>0.374252</td>\n",
" <td>-0.005593</td>\n",
" <td>0.000749</td>\n",
" <td>0.002334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-17</th>\n",
" <td>0.99997</td>\n",
" <td>0.024928</td>\n",
" <td>0.235312</td>\n",
" <td>0.031667</td>\n",
" <td>0.000471</td>\n",
" <td>-0.007208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-31</th>\n",
" <td>1.00002</td>\n",
" <td>0.011720</td>\n",
" <td>0.343991</td>\n",
" <td>0.013215</td>\n",
" <td>0.000688</td>\n",
" <td>-0.002184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-14</th>\n",
" <td>1.00011</td>\n",
" <td>0.021768</td>\n",
" <td>0.400000</td>\n",
" <td>0.019105</td>\n",
" <td>0.000800</td>\n",
" <td>0.001861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-28</th>\n",
" <td>1.00011</td>\n",
" <td>-0.001063</td>\n",
" <td>0.400000</td>\n",
" <td>-0.004899</td>\n",
" <td>0.000800</td>\n",
" <td>0.003036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-12</th>\n",
" <td>0.99994</td>\n",
" <td>-0.000903</td>\n",
" <td>0.177981</td>\n",
" <td>-0.000911</td>\n",
" <td>0.000356</td>\n",
" <td>-0.000348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-26</th>\n",
" <td>0.99996</td>\n",
" <td>-0.010509</td>\n",
" <td>0.249249</td>\n",
" <td>-0.014643</td>\n",
" <td>0.000498</td>\n",
" <td>0.003635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-10</th>\n",
" <td>1.00008</td>\n",
" <td>0.065928</td>\n",
" <td>0.223927</td>\n",
" <td>0.052321</td>\n",
" <td>0.000448</td>\n",
" <td>0.013155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-24</th>\n",
" <td>0.99998</td>\n",
" <td>0.044550</td>\n",
" <td>0.321516</td>\n",
" <td>0.037221</td>\n",
" <td>0.000643</td>\n",
" <td>0.006686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-07</th>\n",
" <td>0.99993</td>\n",
" <td>-0.078768</td>\n",
" <td>0.283453</td>\n",
" <td>-0.084335</td>\n",
" <td>0.000567</td>\n",
" <td>0.004994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-02-28</th>\n",
" <td>0.99994</td>\n",
" <td>0.010017</td>\n",
" <td>0.341782</td>\n",
" <td>0.009190</td>\n",
" <td>0.000684</td>\n",
" <td>0.000144</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>199 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" leverage returns turn_over index tc_cost ret_after_tc\n",
"2010-01-04 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000\n",
"2010-01-18 0.98210 -0.004034 1.408400 -0.016006 0.002817 0.008869\n",
"2010-02-01 0.98080 -0.119345 0.375296 -0.108705 0.000751 -0.013477\n",
"2010-02-22 0.99880 0.018225 0.294060 0.016540 0.000588 0.001117\n",
"2010-03-08 1.00070 0.040173 0.330253 0.032977 0.000661 0.006512\n",
"2010-03-22 0.99990 -0.010525 0.400000 -0.009209 0.000800 -0.002116\n",
"2010-04-06 1.00030 0.035026 0.400000 0.033441 0.000800 0.000774\n",
"2010-04-20 1.00090 -0.055201 0.400000 -0.045374 0.000800 -0.010586\n",
"2010-05-05 0.99920 -0.107964 0.312529 -0.110916 0.000625 0.002238\n",
"2010-05-19 0.99890 -0.053097 0.400000 -0.060783 0.000800 0.006819\n",
"2010-06-02 0.99980 -0.002943 0.400000 0.003685 0.000800 -0.007428\n",
"2010-06-21 0.99990 0.029722 0.299103 0.017268 0.000598 0.011857\n",
"2010-07-05 0.99960 -0.069919 0.205957 -0.082651 0.000412 0.012287\n",
"2010-07-19 0.97430 0.069300 0.270743 0.067367 0.000541 0.003123\n",
"2010-08-02 0.97470 0.043631 0.107972 0.044402 0.000216 0.000137\n",
"2010-08-16 1.00010 0.017754 0.203921 0.026279 0.000408 -0.008936\n",
"2010-08-30 0.99900 -0.017580 0.400000 -0.013376 0.000800 -0.005018\n",
"2010-09-13 0.99960 0.007534 0.400000 0.021064 0.000800 -0.014321\n",
"2010-09-30 0.99970 0.033250 0.334767 0.026357 0.000670 0.006231\n",
"2010-10-21 0.99950 0.129311 0.205305 0.104227 0.000411 0.024725\n",
"2010-11-04 0.99990 0.042310 0.400000 0.041200 0.000800 0.000314\n",
"2010-11-18 0.99980 -0.098762 0.400000 -0.102170 0.000800 0.002588\n",
"2010-12-02 0.99970 0.007574 0.398527 -0.006528 0.000797 0.013303\n",
"2010-12-16 0.99950 0.020461 0.332390 0.021142 0.000665 -0.001335\n",
"2010-12-30 0.99960 -0.022065 0.166455 -0.030657 0.000333 0.008247\n",
"2011-01-14 0.99960 -0.038013 0.274646 -0.050464 0.000549 0.011881\n",
"2011-01-28 0.95740 0.033718 0.133258 0.033777 0.000267 0.001114\n",
"2011-02-18 0.95720 0.053540 0.195492 0.057284 0.000391 -0.001683\n",
"2011-03-04 0.99890 0.024900 0.300694 0.023244 0.000601 0.001080\n",
"2011-03-18 0.99900 -0.020937 0.312013 -0.038953 0.000624 0.017353\n",
"... ... ... ... ... ... ...\n",
"2016-12-19 0.99999 -0.046173 0.295404 -0.044341 0.000591 -0.002423\n",
"2017-01-03 0.99999 0.024535 0.161578 0.017738 0.000323 0.006473\n",
"2017-01-17 1.00003 0.002655 0.286523 -0.008629 0.000573 0.010712\n",
"2017-02-07 1.00003 0.022140 0.125876 0.013057 0.000252 0.008831\n",
"2017-02-21 0.99996 0.033621 0.333158 0.030984 0.000666 0.001971\n",
"2017-03-07 0.99798 -0.015391 0.291720 -0.011829 0.000583 -0.004169\n",
"2017-03-21 1.00003 0.008702 0.400000 0.000390 0.000800 0.007512\n",
"2017-04-06 1.00003 0.030815 0.229504 0.019344 0.000459 0.011012\n",
"2017-04-20 1.00004 -0.006430 0.400000 -0.014495 0.000800 0.007265\n",
"2017-05-05 1.00004 -0.033754 0.400000 -0.031634 0.000800 -0.002919\n",
"2017-05-19 0.99990 0.015922 0.400000 0.015496 0.000800 -0.000372\n",
"2017-06-06 0.99990 0.039499 0.400000 0.035334 0.000800 0.003368\n",
"2017-06-20 1.00004 0.025790 0.227194 0.015188 0.000454 0.010147\n",
"2017-07-04 1.00004 0.031422 0.157818 0.019815 0.000316 0.011290\n",
"2017-07-18 0.99999 0.034550 0.332887 0.018972 0.000666 0.014913\n",
"2017-08-01 0.99999 0.009246 0.186835 0.008294 0.000374 0.000578\n",
"2017-08-15 1.00004 -0.021296 0.336833 -0.015918 0.000674 -0.006051\n",
"2017-08-29 1.00004 0.039055 0.310933 0.035254 0.000622 0.003178\n",
"2017-09-12 1.00004 0.003864 0.400000 0.002169 0.000800 0.000895\n",
"2017-09-26 0.99997 -0.002510 0.374252 -0.005593 0.000749 0.002334\n",
"2017-10-17 0.99997 0.024928 0.235312 0.031667 0.000471 -0.007208\n",
"2017-10-31 1.00002 0.011720 0.343991 0.013215 0.000688 -0.002184\n",
"2017-11-14 1.00011 0.021768 0.400000 0.019105 0.000800 0.001861\n",
"2017-11-28 1.00011 -0.001063 0.400000 -0.004899 0.000800 0.003036\n",
"2017-12-12 0.99994 -0.000903 0.177981 -0.000911 0.000356 -0.000348\n",
"2017-12-26 0.99996 -0.010509 0.249249 -0.014643 0.000498 0.003635\n",
"2018-01-10 1.00008 0.065928 0.223927 0.052321 0.000448 0.013155\n",
"2018-01-24 0.99998 0.044550 0.321516 0.037221 0.000643 0.006686\n",
"2018-02-07 0.99993 -0.078768 0.283453 -0.084335 0.000567 0.004994\n",
"2018-02-28 0.99994 0.010017 0.341782 0.009190 0.000684 0.000144\n",
"\n",
"[199 rows x 6 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ret_df"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
...@@ -1742,6 +379,35 @@ ...@@ -1742,6 +379,35 @@
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.4" "version": "3.6.4"
},
"varInspector": {
"cols": {
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"lenType": 16,
"lenVar": 40
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"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
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"window_display": false
} }
}, },
"nbformat": 4, "nbformat": 4,
......
notebooks/Example 4 - Single Factor Analysis.ipynb
View file @ 721396fa
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -52,7 +52,7 @@ ...@@ -52,7 +52,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -60,7 +60,7 @@ ...@@ -60,7 +60,7 @@
"Factor Model\n", "Factor Model\n",
"\"\"\"\n", "\"\"\"\n",
"\n", "\n",
"alpha_factors = {'f01': LAST('ep_q')}\n", "alpha_factors = {'f01': LAST('GROWTH')}\n",
"\n", "\n",
"weights = dict(f01=1.)\n", "weights = dict(f01=1.)\n",
"\n", "\n",
...@@ -83,17 +83,9 @@ ...@@ -83,17 +83,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 5.65 s\n"
]
}
],
"source": [ "source": [
"%%time\n", "%%time\n",
"\n", "\n",
...@@ -110,7 +102,7 @@ ...@@ -110,7 +102,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -149,7 +141,7 @@ ...@@ -149,7 +141,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -233,30 +225,9 @@ ...@@ -233,30 +225,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x21dae3f5080>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x21dafff4ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"ret_df = create_scenario(weight_gap)\n", "ret_df = create_scenario(weight_gap)\n",
"ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n", "ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
...@@ -296,6 +267,35 @@ ...@@ -296,6 +267,35 @@
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......
notebooks/Example 5 - Style Factor Analysis.ipynb
View file @ 721396fa
...@@ -9,18 +9,9 @@ ...@@ -9,18 +9,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"\n", "\n",
...@@ -37,7 +28,7 @@ ...@@ -37,7 +28,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -59,7 +50,7 @@ ...@@ -59,7 +50,7 @@
}, },
{ {
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"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -85,7 +76,7 @@ ...@@ -85,7 +76,7 @@
}, },
{ {
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"execution_count": 4, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -94,7 +85,7 @@ ...@@ -94,7 +85,7 @@
}, },
{ {
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"execution_count": 5, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -107,7 +98,7 @@ ...@@ -107,7 +98,7 @@
}, },
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"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -120,166 +111,13 @@ ...@@ -120,166 +111,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>abs t.</th>\n",
" </tr>\n",
" <tr>\n",
" <th>factor</th>\n",
" <th></th>\n",
" </tr>\n",
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" <td>3.246817</td>\n",
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" <tr>\n",
" <th>CHEM</th>\n",
" <td>2.941602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NonFerMetal</th>\n",
" <td>2.863952</td>\n",
" </tr>\n",
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"</div>"
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"text/plain": [
" abs t.\n",
"factor \n",
"SIZE 3.729327\n",
"RealEstate 3.246817\n",
"LIQUIDTY 3.062970\n",
"CHEM 2.941602\n",
"NonFerMetal 2.863952"
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"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"df['abs t.'] = np.abs(df['t.'])\n",
"df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/html": [
"<div>\n",
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"text/plain": [
" abs t.\n",
"factor \n",
"SIZE 3.729327\n",
"RealEstate 3.246817\n",
"LIQUIDTY 3.062970\n",
"CHEM 2.941602\n",
"NonFerMetal 2.863952"
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"execution_count": 7,
"metadata": {},
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"source": [ "source": [
"df['abs t.'] = np.abs(df['t.'])\n", "df['abs t.'] = np.abs(df['t.'])\n",
"df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()" "df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()"
] ]
},
{
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
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...@@ -299,6 +137,42 @@ ...@@ -299,6 +137,42 @@
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......
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