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feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
{
"cells": [
{
"cell_type": "code",
feat(Model): trying to get to a model that can train
2021-11-11 17:53:24 +01:00
"execution_count": 1,
feat(Data): added extra features, exploration notebook
2021-11-09 17:16:40 +01:00
"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(Model): continued with the keras model scaffolding
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"Data columns (total 21 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 BTC_returns 1500 non-null float64\n",
" 1 TRX_returns 1500 non-null float64\n",
" 2 XRP_returns 1500 non-null float64\n",
feat(Model): trying to get to a model that can train
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" 3 IEF_volume 1500 non-null float64\n",
" 4 IEF_returns 1500 non-null float64\n",
" 5 QQQ_volume 1500 non-null float64\n",
" 6 QQQ_returns 1500 non-null float64\n",
" 7 FIL_returns 1500 non-null float64\n",
" 8 GLD_volume 1500 non-null float64\n",
" 9 GLD_returns 1500 non-null float64\n",
" 10 TLT_volume 1500 non-null float64\n",
" 11 TLT_returns 1500 non-null float64\n",
" 12 SPY_volume 1500 non-null float64\n",
" 13 SPY_returns 1500 non-null float64\n",
feat(Model): continued with the keras model scaffolding
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" 14 ETH_returns 1500 non-null float64\n",
feat(Model): trying to get to a model that can train
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" 15 UNI_returns 1500 non-null float64\n",
" 16 DOT_returns 1500 non-null float64\n",
feat(Model): continued with the keras model scaffolding
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" 17 ADA_returns 1500 non-null float64\n",
" 18 BNB_returns 1500 non-null float64\n",
" 19 LTC_returns 1500 non-null float64\n",
feat(Model): trying to get to a model that can train
2021-11-11 17:53:24 +01:00
" 20 SOL_returns 1500 non-null float64\n",
feat(Model): continued with the keras model scaffolding
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"dtypes: float64(21)\n",
feat(Model): trying to get to a model that can train
2021-11-11 17:53:24 +01:00
"memory usage: 257.8+ 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(Model): continued with the keras model scaffolding
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"data = load_files('data', False)\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",
"execution_count": 2,
"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",
" <th>TRX_returns</th>\n",
" <th>XRP_returns</th>\n",
" <th>IEF_volume</th>\n",
" <th>IEF_returns</th>\n",
" <th>QQQ_volume</th>\n",
" <th>QQQ_returns</th>\n",
" <th>FIL_returns</th>\n",
" <th>GLD_volume</th>\n",
" <th>GLD_returns</th>\n",
" <th>...</th>\n",
" <th>TLT_returns</th>\n",
" <th>SPY_volume</th>\n",
" <th>SPY_returns</th>\n",
" <th>ETH_returns</th>\n",
" <th>UNI_returns</th>\n",
" <th>DOT_returns</th>\n",
" <th>ADA_returns</th>\n",
" <th>BNB_returns</th>\n",
" <th>LTC_returns</th>\n",
" <th>SOL_returns</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-10-03</th>\n",
" <td>-0.019799</td>\n",
" <td>-0.134043</td>\n",
" <td>0.000985</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>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.016846</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.194745</td>\n",
" <td>-0.068931</td>\n",
" <td>-0.021392</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-04</th>\n",
" <td>-0.022141</td>\n",
" <td>0.372236</td>\n",
" <td>0.050172</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>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.000445</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.050384</td>\n",
" <td>-0.117039</td>\n",
" <td>-0.017833</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-05</th>\n",
" <td>0.024363</td>\n",
" <td>-0.074008</td>\n",
" <td>0.114754</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>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.011348</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.016903</td>\n",
" <td>0.032059</td>\n",
" <td>0.008200</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-06</th>\n",
" <td>0.011686</td>\n",
" <td>-0.030293</td>\n",
" <td>-0.019328</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>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.045222</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.140799</td>\n",
" <td>-0.070490</td>\n",
" <td>0.008521</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-10-07</th>\n",
" <td>0.014609</td>\n",
" <td>-0.202393</td>\n",
" <td>0.026992</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>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.009503</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.124932</td>\n",
" <td>0.006427</td>\n",
" <td>0.009409</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" BTC_returns TRX_returns XRP_returns IEF_volume IEF_returns \\\n",
"2017-10-03 -0.019799 -0.134043 0.000985 0.0 0.0 \n",
"2017-10-04 -0.022141 0.372236 0.050172 0.0 0.0 \n",
"2017-10-05 0.024363 -0.074008 0.114754 0.0 0.0 \n",
"2017-10-06 0.011686 -0.030293 -0.019328 0.0 0.0 \n",
"2017-10-07 0.014609 -0.202393 0.026992 0.0 0.0 \n",
"\n",
" QQQ_volume QQQ_returns FIL_returns GLD_volume GLD_returns \\\n",
"2017-10-03 0.0 0.0 0.0 0.0 0.0 \n",
"2017-10-04 0.0 0.0 0.0 0.0 0.0 \n",
"2017-10-05 0.0 0.0 0.0 0.0 0.0 \n",
"2017-10-06 0.0 0.0 0.0 0.0 0.0 \n",
"2017-10-07 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" ... TLT_returns SPY_volume SPY_returns ETH_returns \\\n",
"2017-10-03 ... 0.0 0.0 0.0 -0.016846 \n",
"2017-10-04 ... 0.0 0.0 0.0 -0.000445 \n",
"2017-10-05 ... 0.0 0.0 0.0 0.011348 \n",
"2017-10-06 ... 0.0 0.0 0.0 0.045222 \n",
"2017-10-07 ... 0.0 0.0 0.0 0.009503 \n",
"\n",
" UNI_returns DOT_returns ADA_returns BNB_returns LTC_returns \\\n",
"2017-10-03 0.0 0.0 -0.194745 -0.068931 -0.021392 \n",
"2017-10-04 0.0 0.0 0.050384 -0.117039 -0.017833 \n",
"2017-10-05 0.0 0.0 -0.016903 0.032059 0.008200 \n",
"2017-10-06 0.0 0.0 -0.140799 -0.070490 0.008521 \n",
"2017-10-07 0.0 0.0 0.124932 0.006427 0.009409 \n",
"\n",
" SOL_returns \n",
"2017-10-03 0.0 \n",
"2017-10-04 0.0 \n",
"2017-10-05 0.0 \n",
"2017-10-06 0.0 \n",
"2017-10-07 0.0 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 2,
"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(Model): continued with the keras model scaffolding
2021-11-10 14:51:12 +01:00
"execution_count": 2,
feat(Data): added extra features, exploration notebook
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
]
},
feat(Model): continued with the keras model scaffolding
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"execution_count": 2,
feat(Data): added extra features, exploration notebook
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
feat(Model): continued with the keras model scaffolding
2021-11-10 14:51:12 +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",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
]
},
"execution_count": 3,
"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",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"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",
"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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