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feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
{
"cells": [
{
"cell_type": "code",
feat(Models): added model that predicts 10 days ahead
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"execution_count": 2,
feat(Data): added extra features, exploration notebook
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
feat(Model): trying to get to a model that can train
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"Index: 1500 entries, 2017-10-03 to 2021-11-10\n",
feat(Models): added model that predicts 10 days ahead
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"Data columns (total 9 columns):\n",
feat(Model): continued with the keras model scaffolding
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" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 BTC_returns 1500 non-null float64\n",
feat(Models): added model that predicts 10 days ahead
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" 1 BTC_vol_10 1500 non-null float64\n",
" 2 BTC_vol_20 1500 non-null float64\n",
" 3 BTC_vol_30 1500 non-null float64\n",
" 4 BTC_mom_10 1500 non-null float64\n",
" 5 BTC_mom_20 1500 non-null float64\n",
" 6 BTC_mom_30 1500 non-null float64\n",
" 7 BTC_mom_60 1500 non-null float64\n",
" 8 BTC_mom_90 1500 non-null float64\n",
"dtypes: float64(9)\n",
"memory usage: 117.2+ KB\n"
feat(Data): added extra features, exploration notebook
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]
}
],
"source": [
"from load_data import load_files\n",
"import pandas as pd\n",
"from pandas.plotting import lag_plot\n",
feat(Model): continued with the keras model scaffolding
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"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
feat(Data): added extra features, exploration notebook
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"\n",
feat(Models): added model that predicts 10 days ahead
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"data = load_files('data', True)\n",
"data = data[[column for column in data.columns if not column.endswith('volume')]]\n",
"data = data[[column for column in data.columns if column.startswith('BTC')]]\n",
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
"data.info()"
]
},
feat(Model): trying to get to a model that can train
2021-11-11 17:53:24 +01:00
{
"cell_type": "code",
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"execution_count": 3,
feat(Model): trying to get to a model that can train
2021-11-11 17:53:24 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .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>BTC_returns</th>\n",
feat(Models): added model that predicts 10 days ahead
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" <th>BTC_vol_10</th>\n",
" <th>BTC_vol_20</th>\n",
" <th>BTC_vol_30</th>\n",
" <th>BTC_mom_10</th>\n",
" <th>BTC_mom_20</th>\n",
" <th>BTC_mom_30</th>\n",
" <th>BTC_mom_60</th>\n",
" <th>BTC_mom_90</th>\n",
feat(Model): trying to get to a model that can train
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-10-03</th>\n",
" <td>-0.019799</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
feat(Models): added model that predicts 10 days ahead
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" </tr>\n",
" <tr>\n",
" <th>2017-10-04</th>\n",
" <td>-0.022141</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
feat(Model): trying to get to a model that can train
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-05</th>\n",
" <td>0.024363</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
feat(Models): added model that predicts 10 days ahead
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" </tr>\n",
" <tr>\n",
" <th>2017-10-06</th>\n",
" <td>0.011686</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
feat(Model): trying to get to a model that can train
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-07</th>\n",
" <td>0.014609</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
feat(Models): added model that predicts 10 days ahead
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" BTC_returns BTC_vol_10 BTC_vol_20 BTC_vol_30 BTC_mom_10 \\\n",
"2017-10-03 -0.019799 0.0 0.0 0.0 0.0 \n",
"2017-10-04 -0.022141 0.0 0.0 0.0 0.0 \n",
"2017-10-05 0.024363 0.0 0.0 0.0 0.0 \n",
"2017-10-06 0.011686 0.0 0.0 0.0 0.0 \n",
"2017-10-07 0.014609 0.0 0.0 0.0 0.0 \n",
feat(Model): trying to get to a model that can train
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"\n",
feat(Models): added model that predicts 10 days ahead
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" BTC_mom_20 BTC_mom_30 BTC_mom_60 BTC_mom_90 \n",
"2017-10-03 0.0 0.0 0.0 0.0 \n",
"2017-10-04 0.0 0.0 0.0 0.0 \n",
"2017-10-05 0.0 0.0 0.0 0.0 \n",
"2017-10-06 0.0 0.0 0.0 0.0 \n",
"2017-10-07 0.0 0.0 0.0 0.0 "
feat(Model): trying to get to a model that can train
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]
},
feat(Models): added model that predicts 10 days ahead
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"execution_count": 3,
feat(Model): trying to get to a model that can train
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(5)"
]
},
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
{
"cell_type": "code",
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"execution_count": 3,
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
]
},
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"execution_count": 3,
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"image/png": "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
feat(Data): added extra features, exploration notebook
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
feat(Model): continued with the keras model scaffolding
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"lag_plot(data['SPY_returns'])"
]
},
{
"cell_type": "code",
feat(Models): added model that predicts 10 days ahead
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"execution_count": 4,
feat(Model): continued with the keras model scaffolding
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
]
},
feat(Models): added model that predicts 10 days ahead
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"execution_count": 4,
feat(Model): continued with the keras model scaffolding
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"lag_plot(data['BTC_returns'])"
]
},
{
"cell_type": "code",
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 864x864 with 81 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pd.plotting.scatter_matrix(data, figsize=(12, 12));"
]
},
{
"cell_type": "code",
"execution_count": 6,
feat(Model): continued with the keras model scaffolding
2021-11-10 14:51:12 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"execution_count": 6,
feat(Model): continued with the keras model scaffolding
2021-11-10 14:51:12 +01:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"image/png": "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
feat(Model): continued with the keras model scaffolding
2021-11-10 14:51:12 +01:00
"text/plain": [
"<Figure size 1440x1440 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(20,20))\n",
feat(Models): added model that predicts 10 days ahead
2021-11-12 16:10:55 +01:00
"sns.heatmap(data.corr(), annot=True, ax=ax);"
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "51432b8e5767c06330d9b51dfad63f9db0ea39868e37d921b9c2e277373f8d11"
},
"kernelspec": {
"display_name": "Python 3.9.2 64-bit ('deeplearning': conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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