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01_intpy_day_2_overview.md

Intermediate Python for Finance Training

This is the Gist for Day 2 of the Intermediate Python for Finance Training in London, 29. November 2017

Agenda

Module 3 — pandas

This module is about data analysis and visualization with pandas for finance.

pandas has become the central data analysis tool in the Python ecosystem. It is powerful, among others, in handling financial time series data, in visualizing such data and implementing algorithms on such data sets.

The goal of this module is to be aware of the capabilities of pandas and to be able to apply pandas to typical financial analytics tasks.

Module 4 — OOP

The final module addresses object-oriented programming (OOP) in Python based on finance examples.

OOP is a powerful programming paradigm with many benefits over simple procedural implementations. Among others, it allows a simplified modeling of real-world and financial object, to avoid redundancies, to simplify code maintenance and re-usability.

The goal of this module is to be aware of basic OOP features in Python and to implement financial algorithms in a re-usable fashion.

Notebooks & Code Files

Below you find the all the notebooks and code files as worked on during this day.

The Python Quants

http://twitter.com/dyjh

[email protected] | http://tpq.io

http://pyalgo.tpq.io | http://certificate.tpq.io

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03_pandas_basics.ipynb
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"5 1.081959 0.894710 1.570370\n",
"6 0.778615 1.344540 1.159385\n",
"7 0.896961 1.148715 1.826110\n",
"8 0.946426 0.750312 0.774051\n",
"9 0.948596 0.992592 0.876968"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.apply(lambda x: 0.5 * x ** 2 + 3 / 4) # slow in general"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def f(x):\n",
" return 2.5 * x - 1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-2.182522</td>\n",
" <td>-1.061204</td>\n",
" <td>0.512818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-4.503451</td>\n",
" <td>0.916646</td>\n",
" <td>-4.177180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-3.210997</td>\n",
" <td>-3.900669</td>\n",
" <td>-0.805117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.552724</td>\n",
" <td>-1.053993</td>\n",
" <td>-2.972312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.729984</td>\n",
" <td>0.792520</td>\n",
" <td>-0.235395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-3.037030</td>\n",
" <td>0.344945</td>\n",
" <td>-4.202285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.401932</td>\n",
" <td>1.726125</td>\n",
" <td>1.262149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-2.355366</td>\n",
" <td>-3.232474</td>\n",
" <td>-4.667612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.566946</td>\n",
" <td>-0.937586</td>\n",
" <td>-0.451693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.575577</td>\n",
" <td>0.741380</td>\n",
" <td>-2.259800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 -2.182522 -1.061204 0.512818\n",
"1 -4.503451 0.916646 -4.177180\n",
"2 -3.210997 -3.900669 -0.805117\n",
"3 0.552724 -1.053993 -2.972312\n",
"4 0.729984 0.792520 -0.235395\n",
"5 -3.037030 0.344945 -4.202285\n",
"6 -0.401932 1.726125 1.262149\n",
"7 -2.355366 -3.232474 -4.667612\n",
"8 0.566946 -0.937586 -0.451693\n",
"9 0.575577 0.741380 -2.259800"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.apply(f) # slow in general"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"\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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-2.182522</td>\n",
" <td>-1.061204</td>\n",
" <td>0.512818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-4.503451</td>\n",
" <td>0.916646</td>\n",
" <td>-4.177180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-3.210997</td>\n",
" <td>-3.900669</td>\n",
" <td>-0.805117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.552724</td>\n",
" <td>-1.053993</td>\n",
" <td>-2.972312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.729984</td>\n",
" <td>0.792520</td>\n",
" <td>-0.235395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-3.037030</td>\n",
" <td>0.344945</td>\n",
" <td>-4.202285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.401932</td>\n",
" <td>1.726125</td>\n",
" <td>1.262149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-2.355366</td>\n",
" <td>-3.232474</td>\n",
" <td>-4.667612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.566946</td>\n",
" <td>-0.937586</td>\n",
" <td>-0.451693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.575577</td>\n",
" <td>0.741380</td>\n",
" <td>-2.259800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 -2.182522 -1.061204 0.512818\n",
"1 -4.503451 0.916646 -4.177180\n",
"2 -3.210997 -3.900669 -0.805117\n",
"3 0.552724 -1.053993 -2.972312\n",
"4 0.729984 0.792520 -0.235395\n",
"5 -3.037030 0.344945 -4.202285\n",
"6 -0.401932 1.726125 1.262149\n",
"7 -2.355366 -3.232474 -4.667612\n",
"8 0.566946 -0.937586 -0.451693\n",
"9 0.575577 0.741380 -2.259800"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2.5 * df - 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accessing Sub-Sets of the Data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 -0.473009\n",
"1 -1.401381\n",
"2 -0.884399\n",
"3 0.621090\n",
"4 0.691994\n",
"5 -0.814812\n",
"6 0.239227\n",
"7 -0.542146\n",
"8 0.626778\n",
"9 0.630231\n",
"Name: a, dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['a']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df['a'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>a</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.473009</td>\n",
" <td>0.605127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.401381</td>\n",
" <td>-1.270872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.884399</td>\n",
" <td>0.077953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.621090</td>\n",
" <td>-0.788925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.691994</td>\n",
" <td>0.305842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.814812</td>\n",
" <td>-1.280914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.239227</td>\n",
" <td>0.904860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.542146</td>\n",
" <td>-1.467045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.626778</td>\n",
" <td>0.219323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.630231</td>\n",
" <td>-0.503920</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a c\n",
"0 -0.473009 0.605127\n",
"1 -1.401381 -1.270872\n",
"2 -0.884399 0.077953\n",
"3 0.621090 -0.788925\n",
"4 0.691994 0.305842\n",
"5 -0.814812 -1.280914\n",
"6 0.239227 0.904860\n",
"7 -0.542146 -1.467045\n",
"8 0.626778 0.219323\n",
"9 0.630231 -0.503920"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['a', 'c']]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"t = pd.date_range('2017-11-29', freq='B', periods=len(df))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2017-11-29', '2017-11-30', '2017-12-01', '2017-12-04',\n",
" '2017-12-05', '2017-12-06', '2017-12-07', '2017-12-08',\n",
" '2017-12-11', '2017-12-12'],\n",
" dtype='datetime64[ns]', freq='B')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"df.index = t"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tbody>\n",
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" <th>2017-11-29</th>\n",
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" <td>-0.024482</td>\n",
" <td>0.605127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-30</th>\n",
" <td>-1.401381</td>\n",
" <td>0.766658</td>\n",
" <td>-1.270872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-01</th>\n",
" <td>-0.884399</td>\n",
" <td>-1.160268</td>\n",
" <td>0.077953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-04</th>\n",
" <td>0.621090</td>\n",
" <td>-0.021597</td>\n",
" <td>-0.788925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-05</th>\n",
" <td>0.691994</td>\n",
" <td>0.717008</td>\n",
" <td>0.305842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-06</th>\n",
" <td>-0.814812</td>\n",
" <td>0.537978</td>\n",
" <td>-1.280914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-07</th>\n",
" <td>0.239227</td>\n",
" <td>1.090450</td>\n",
" <td>0.904860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-08</th>\n",
" <td>-0.542146</td>\n",
" <td>-0.892990</td>\n",
" <td>-1.467045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-11</th>\n",
" <td>0.626778</td>\n",
" <td>0.024966</td>\n",
" <td>0.219323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-12</th>\n",
" <td>0.630231</td>\n",
" <td>0.696552</td>\n",
" <td>-0.503920</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"2017-11-29 -0.473009 -0.024482 0.605127\n",
"2017-11-30 -1.401381 0.766658 -1.270872\n",
"2017-12-01 -0.884399 -1.160268 0.077953\n",
"2017-12-04 0.621090 -0.021597 -0.788925\n",
"2017-12-05 0.691994 0.717008 0.305842\n",
"2017-12-06 -0.814812 0.537978 -1.280914\n",
"2017-12-07 0.239227 1.090450 0.904860\n",
"2017-12-08 -0.542146 -0.892990 -1.467045\n",
"2017-12-11 0.626778 0.024966 0.219323\n",
"2017-12-12 0.630231 0.696552 -0.503920"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a -1.401381\n",
"b 0.766658\n",
"c -1.270872\n",
"Name: 2017-11-30 00:00:00, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2017-11-30']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tr>\n",
" <th>2017-11-30</th>\n",
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" <tr>\n",
" <th>2017-12-01</th>\n",
" <td>-0.884399</td>\n",
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" <td>0.077953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-04</th>\n",
" <td>0.621090</td>\n",
" <td>-0.021597</td>\n",
" <td>-0.788925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-05</th>\n",
" <td>0.691994</td>\n",
" <td>0.717008</td>\n",
" <td>0.305842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-06</th>\n",
" <td>-0.814812</td>\n",
" <td>0.537978</td>\n",
" <td>-1.280914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-07</th>\n",
" <td>0.239227</td>\n",
" <td>1.090450</td>\n",
" <td>0.904860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-08</th>\n",
" <td>-0.542146</td>\n",
" <td>-0.892990</td>\n",
" <td>-1.467045</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"2017-11-30 -1.401381 0.766658 -1.270872\n",
"2017-12-01 -0.884399 -1.160268 0.077953\n",
"2017-12-04 0.621090 -0.021597 -0.788925\n",
"2017-12-05 0.691994 0.717008 0.305842\n",
"2017-12-06 -0.814812 0.537978 -1.280914\n",
"2017-12-07 0.239227 1.090450 0.904860\n",
"2017-12-08 -0.542146 -0.892990 -1.467045"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2017-11-30':'2017-12-08']"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2017-11-30 -1.401381\n",
"2017-12-01 -0.884399\n",
"2017-12-04 0.621090\n",
"2017-12-05 0.691994\n",
"2017-12-06 -0.814812\n",
"2017-12-07 0.239227\n",
"2017-12-08 -0.542146\n",
"Freq: B, Name: a, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2017-11-30':'2017-12-08', 'a']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-11-29</th>\n",
" <td>-0.473009</td>\n",
" <td>-0.024482</td>\n",
" <td>0.605127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-30</th>\n",
" <td>-1.401381</td>\n",
" <td>0.766658</td>\n",
" <td>-1.270872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-01</th>\n",
" <td>-0.884399</td>\n",
" <td>-1.160268</td>\n",
" <td>0.077953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-04</th>\n",
" <td>0.621090</td>\n",
" <td>-0.021597</td>\n",
" <td>-0.788925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-05</th>\n",
" <td>0.691994</td>\n",
" <td>0.717008</td>\n",
" <td>0.305842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-06</th>\n",
" <td>-0.814812</td>\n",
" <td>0.537978</td>\n",
" <td>-1.280914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-07</th>\n",
" <td>0.239227</td>\n",
" <td>1.090450</td>\n",
" <td>0.904860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-08</th>\n",
" <td>-0.542146</td>\n",
" <td>-0.892990</td>\n",
" <td>-1.467045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-11</th>\n",
" <td>0.626778</td>\n",
" <td>0.024966</td>\n",
" <td>0.219323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-12</th>\n",
" <td>0.630231</td>\n",
" <td>0.696552</td>\n",
" <td>-0.503920</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"2017-11-29 -0.473009 -0.024482 0.605127\n",
"2017-11-30 -1.401381 0.766658 -1.270872\n",
"2017-12-01 -0.884399 -1.160268 0.077953\n",
"2017-12-04 0.621090 -0.021597 -0.788925\n",
"2017-12-05 0.691994 0.717008 0.305842\n",
"2017-12-06 -0.814812 0.537978 -1.280914\n",
"2017-12-07 0.239227 1.090450 0.904860\n",
"2017-12-08 -0.542146 -0.892990 -1.467045\n",
"2017-12-11 0.626778 0.024966 0.219323\n",
"2017-12-12 0.630231 0.696552 -0.503920"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 0.630231\n",
"b 0.696552\n",
"c -0.503920\n",
"Name: 2017-12-12 00:00:00, dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[-1]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.50391999321520065"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[-1, -1]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-12-01</th>\n",
" <td>-1.160268</td>\n",
" <td>0.077953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-04</th>\n",
" <td>-0.021597</td>\n",
" <td>-0.788925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-12-05</th>\n",
" <td>0.717008</td>\n",
" <td>0.305842</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" b c\n",
"2017-12-01 -1.160268 0.077953\n",
"2017-12-04 -0.021597 -0.788925\n",
"2017-12-05 0.717008 0.305842"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[2:5, 1:]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# df.ix['2017-11-29', 0] # depreciated"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.47300887834464661"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['2017-11-29'].iloc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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gtA62Wh4vwvssi8HA6ZdfwFBwhsgnn8GjW3d7l3SJzZlb+T7TelMblbL64K8P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\niEgKKbxFRFJI4S0ikkL/Bz/Dl4jmDh6rAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1128c1390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.cumsum().plot();"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ke2gCc/pPJdwvrNXHzanJ45/7lqLT6Xg89UG6hMQ5IW37k8nh4lKYzmSxJWsr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\niD6S4CSEEEII0Uf/H4V6p+KJ9aiSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112943898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.cumsum().plot(figsize=(10, 6), title='random numbers');"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11287a0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='a', y='b', kind='scatter',\n",
" figsize=(10, 6), title='random numbers');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('http://hilpisch.com/Titanic.csv')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"01_intpy_day_2_overview.md 99_day_2_exercises.ipynb\r\n",
"02_numpy_finance.ipynb Titanic.csv\r\n",
"03_pandas_basics.ipynb btc_usd.csv\r\n"
]
}
],
"source": [
"ls"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('Titanic.csv')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# data.set_index('Unnamed: 0')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('Titanic.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>Class</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Survived</th>\n",
" <th>Freq</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Class Sex Age Survived Freq\n",
"1 1st Male Child No 0\n",
"2 2nd Male Child No 0\n",
"3 3rd Male Child No 35\n",
"4 Crew Male Child No 0\n",
"5 1st Female Child No 0\n",
"6 2nd Female Child No 0\n",
"7 3rd Female Child No 17\n",
"8 Crew Female Child No 0\n",
"9 1st Male Adult No 118\n",
"10 2nd Male Adult No 154\n",
"11 3rd Male Adult No 387\n",
"12 Crew Male Adult No 670\n",
"13 1st Female Adult No 4\n",
"14 2nd Female Adult No 13\n",
"15 3rd Female Adult No 89\n",
"16 Crew Female Adult No 3\n",
"17 1st Male Child Yes 5\n",
"18 2nd Male Child Yes 11\n",
"19 3rd Male Child Yes 13\n",
"20 Crew Male Child Yes 0\n",
"21 1st Female Child Yes 1\n",
"22 2nd Female Child Yes 13\n",
"23 3rd Female Child Yes 14\n",
"24 Crew Female Child Yes 0\n",
"25 1st Male Adult Yes 57\n",
"26 2nd Male Adult Yes 14\n",
"27 3rd Male Adult Yes 75\n",
"28 Crew Male Adult Yes 192\n",
"29 1st Female Adult Yes 140\n",
"30 2nd Female Adult Yes 80\n",
"31 3rd Female Adult Yes 76\n",
"32 Crew Female Adult Yes 20"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2201"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Freq'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"15 False\n",
"16 False\n",
"17 True\n",
"18 True\n",
"19 True\n",
"20 True\n",
"21 True\n",
"22 True\n",
"23 True\n",
"24 True\n",
"25 True\n",
"26 True\n",
"27 True\n",
"28 True\n",
"29 True\n",
"30 True\n",
"31 True\n",
"32 True\n",
"Name: Survived, dtype: bool"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Survived'] == 'Yes'"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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>Class</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Survived</th>\n",
" <th>Freq</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Child</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1st</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2nd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3rd</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Crew</td>\n",
" <td>Male</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1st</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2nd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>3rd</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Crew</td>\n",
" <td>Female</td>\n",
" <td>Adult</td>\n",
" <td>Yes</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Class Sex Age Survived Freq\n",
"17 1st Male Child Yes 5\n",
"18 2nd Male Child Yes 11\n",
"19 3rd Male Child Yes 13\n",
"20 Crew Male Child Yes 0\n",
"21 1st Female Child Yes 1\n",
"22 2nd Female Child Yes 13\n",
"23 3rd Female Child Yes 14\n",
"24 Crew Female Child Yes 0\n",
"25 1st Male Adult Yes 57\n",
"26 2nd Male Adult Yes 14\n",
"27 3rd Male Adult Yes 75\n",
"28 Crew Male Adult Yes 192\n",
"29 1st Female Adult Yes 140\n",
"30 2nd Female Adult Yes 80\n",
"31 3rd Female Adult Yes 76\n",
"32 Crew Female Adult Yes 20"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data['Survived'] == 'Yes']"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"711"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v1 = data[data['Survived'] == 'Yes']['Freq'].sum()\n",
"v1"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1490"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v2 = data[data['Survived'] == 'No']['Freq'].sum()\n",
"v2"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"total = data['Freq'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.32303498409813719"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v1 / total"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Survived\n",
"No 1490\n",
"Yes 711\n",
"Name: Freq, dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('Survived')['Freq'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Survived\n",
"No 67.696502\n",
"Yes 32.303498\n",
"Name: Freq, dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('Survived')['Freq'].sum() / total * 100"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Class Survived Sex \n",
"1st No Female 4\n",
" Male 118\n",
" Yes Female 141\n",
" Male 62\n",
"2nd No Female 13\n",
" Male 154\n",
" Yes Female 93\n",
" Male 25\n",
"3rd No Female 106\n",
" Male 422\n",
" Yes Female 90\n",
" Male 88\n",
"Crew No Female 3\n",
" Male 670\n",
" Yes Female 20\n",
" Male 192\n",
"Name: Freq, dtype: int64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Class', 'Survived', 'Sex'])['Freq'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"a = np.random.standard_normal((20, 3))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(a)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.050337\n",
"1 0.066802\n",
"2 0.175858\n",
"dtype: float64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.434747\n",
"1 1.023855\n",
"2 0.647324\n",
"3 -0.369567\n",
"4 0.251106\n",
"5 0.265958\n",
"6 -0.577056\n",
"7 0.261241\n",
"8 -0.578334\n",
"9 0.191156\n",
"10 0.729176\n",
"11 0.615042\n",
"12 -0.339511\n",
"13 -0.172197\n",
"14 -0.339788\n",
"15 -0.396575\n",
"16 1.067788\n",
"17 -0.683445\n",
"18 0.345692\n",
"19 -0.423296\n",
"dtype: float64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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z3Xgrr9GW4DcLZ6UTZyqTMFlNQ27PXF9PV74BbVT0sNX/mMik5D0EJquJ1LoM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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117fab4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.cumsum().plot();"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 True\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"15 False\n",
"16 True\n",
"17 False\n",
"18 False\n",
"19 False\n",
"dtype: bool"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df > 1.5).any(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"# res = (df > 1.5)\n",
"# res.any(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.416465</td>\n",
" <td>0.429134</td>\n",
" <td>3.058896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.789611</td>\n",
" <td>0.471187</td>\n",
" <td>-0.318825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.757274</td>\n",
" <td>1.806696</td>\n",
" <td>-2.158123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.634325</td>\n",
" <td>-0.202084</td>\n",
" <td>1.755289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.457017</td>\n",
" <td>0.966498</td>\n",
" <td>1.779850</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"1 -0.416465 0.429134 3.058896\n",
"2 1.789611 0.471187 -0.318825\n",
"3 -0.757274 1.806696 -2.158123\n",
"10 0.634325 -0.202084 1.755289\n",
"16 0.457017 0.966498 1.779850"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df > 1.5).any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.492821</td>\n",
" <td>0.443827</td>\n",
" <td>0.908478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.457017</td>\n",
" <td>0.966498</td>\n",
" <td>1.779850</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"11 0.492821 0.443827 0.908478\n",
"16 0.457017 0.966498 1.779850"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df > 0).all(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Monte Carlo Simulation"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"S0 = 36.\n",
"T = 1.0\n",
"r = 0.06\n",
"sigma = 0.2"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"M = 252\n",
"I = 1000"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"dt = T / M"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.003968253968253968"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"z = np.random.standard_normal((M + 1, I))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"S = np.zeros_like(z)\n",
"S[0] = S0"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"for t in range(1, M + 1):\n",
" S[t] = S[t-1] * np.exp((r - sigma ** 2 / 2) * dt + \n",
" sigma * dt ** 0.5 * z[t])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38.163902786279699"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S[-1].mean()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(253, 1000)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S.shape"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"cols = ['path_%04d' % p for p in range(1, S.shape[1] + 1)]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['path_0001', 'path_0002', 'path_0003', 'path_0004', 'path_0005']"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols[:5]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['path_0996', 'path_0997', 'path_0998', 'path_0999', 'path_1000']"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols[-5:]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"index = pd.date_range('2018-1-1', freq='B', periods=S.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame(S, columns=cols, index=index)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 253 entries, 2018-01-01 to 2018-12-19\n",
"Freq: B\n",
"Columns: 1000 entries, path_0001 to path_1000\n",
"dtypes: float64(1000)\n",
"memory usage: 1.9 MB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>path_0001</th>\n",
" <th>path_0002</th>\n",
" <th>path_0003</th>\n",
" <th>path_0004</th>\n",
" <th>path_0005</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>36.000000</td>\n",
" <td>36.000000</td>\n",
" <td>36.000000</td>\n",
" <td>36.000000</td>\n",
" <td>36.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-02</th>\n",
" <td>35.870263</td>\n",
" <td>35.385106</td>\n",
" <td>35.829831</td>\n",
" <td>36.114845</td>\n",
" <td>35.444651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-03</th>\n",
" <td>35.789033</td>\n",
" <td>35.409910</td>\n",
" <td>35.594676</td>\n",
" <td>35.981116</td>\n",
" <td>35.405529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-04</th>\n",
" <td>35.430731</td>\n",
" <td>35.316135</td>\n",
" <td>35.340555</td>\n",
" <td>36.149137</td>\n",
" <td>35.687629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-05</th>\n",
" <td>35.514400</td>\n",
" <td>35.622123</td>\n",
" <td>35.616575</td>\n",
" <td>36.469691</td>\n",
" <td>35.657125</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" path_0001 path_0002 path_0003 path_0004 path_0005\n",
"2018-01-01 36.000000 36.000000 36.000000 36.000000 36.000000\n",
"2018-01-02 35.870263 35.385106 35.829831 36.114845 35.444651\n",
"2018-01-03 35.789033 35.409910 35.594676 35.981116 35.405529\n",
"2018-01-04 35.430731 35.316135 35.340555 36.149137 35.687629\n",
"2018-01-05 35.514400 35.622123 35.616575 36.469691 35.657125"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.iloc[:5, :5]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38.163902786279699"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.iloc[-1].mean()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
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TJvPSSyv7bFtRcZHVq18EZCZPzuPJJ/8lVC5BEARBGLzyXRWcO9lEQlL8iNVMuhZ11VYA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cOO9sIPEKV+Pc6y1aMx8LSWxocpBuGCcZHUBMmUErsNlzkIMnOuZeO1s+8/kMjI8EyWZljh8c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qZ4j/+GO2E9pKpPBSnEtm7XJ4BnVPVNERkK3KqqJwYp1pbcKCLzVPUB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\npIwxxhhj2un/AedMGoIvELx1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117d75fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.iloc[:, :10].plot(figsize=(10, 6));"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x117fce358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.iloc[-1].hist(bins=35);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bitcoin Analysis"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"# import quandl as q \n",
"# btc = q.get('BCHAIN/MKPRU')"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"# d.to_csv('btc_usd.csv')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"# !cat btc_usd.csv"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"btc = pd.read_csv('btc_usd.csv', index_col=0, parse_dates=True)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"btc = btc[btc > 0]"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 3253 entries, 2009-01-03 to 2017-11-29\n",
"Data columns (total 1 columns):\n",
"Value 2662 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 50.8 KB\n"
]
}
],
"source": [
"btc.info()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
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hS5IkjXndqcLha/KEOs47eUGZezM0px0lSdKYl0ymR7sLB83wJUmSxrzBKl+VyPAlSZLG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\nSZLK6P8DzTgBg2TgPkwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117e91ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"btc.plot(figsize=(10, 6));"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WgJ0pOJx4GjLbpfAlIiIiMqG2niAn2v2sW144tlarKNdFKByjoS0xHVla4GZpiReAlu7g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\nRGTx+O3eVvr8I9x+RRXLK3JTXU5Gy/N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"text/plain": [
"<matplotlib.figure.Figure at 0x11c1f6080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"btc.loc['2017-1-1':].plot(figsize=(10, 6));"
]
},
{
"cell_type": "code",
"execution_count": 92,
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-11-25</th>\n",
" <td>8250.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-26</th>\n",
" <td>8707.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-27</th>\n",
" <td>9284.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-28</th>\n",
" <td>9718.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-29</th>\n",
" <td>9952.50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Value\n",
"Date \n",
"2017-11-25 8250.97\n",
"2017-11-26 8707.40\n",
"2017-11-27 9284.14\n",
"2017-11-28 9718.29\n",
"2017-11-29 9952.50"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"btc.tail()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-11-25</th>\n",
" <td>8148.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-26</th>\n",
" <td>8250.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-27</th>\n",
" <td>8707.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-28</th>\n",
" <td>9284.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-29</th>\n",
" <td>9718.29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Value\n",
"Date \n",
"2017-11-25 8148.94\n",
"2017-11-26 8250.97\n",
"2017-11-27 8707.40\n",
"2017-11-28 9284.14\n",
"2017-11-29 9718.29"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"btc.shift(1).tail()"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"btc['Returns'] = np.log(btc / btc.shift(1))"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<style scoped>\n",
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" }\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>Value</th>\n",
" <th>Returns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-11-20</th>\n",
" <td>8007.65</td>\n",
" <td>0.024079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-21</th>\n",
" <td>8255.59</td>\n",
" <td>0.030493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-22</th>\n",
" <td>8059.80</td>\n",
" <td>-0.024002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-23</th>\n",
" <td>8268.03</td>\n",
" <td>0.025508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-24</th>\n",
" <td>8148.94</td>\n",
" <td>-0.014508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-25</th>\n",
" <td>8250.97</td>\n",
" <td>0.012443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-26</th>\n",
" <td>8707.40</td>\n",
" <td>0.053842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-27</th>\n",
" <td>9284.14</td>\n",
" <td>0.064134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-28</th>\n",
" <td>9718.29</td>\n",
" <td>0.045702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-11-29</th>\n",
" <td>9952.50</td>\n",
" <td>0.023814</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Value Returns\n",
"Date \n",
"2017-11-20 8007.65 0.024079\n",
"2017-11-21 8255.59 0.030493\n",
"2017-11-22 8059.80 -0.024002\n",
"2017-11-23 8268.03 0.025508\n",
"2017-11-24 8148.94 -0.014508\n",
"2017-11-25 8250.97 0.012443\n",
"2017-11-26 8707.40 0.053842\n",
"2017-11-27 9284.14 0.064134\n",
"2017-11-28 9718.29 0.045702\n",
"2017-11-29 9952.50 0.023814"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"btc.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11c1f6d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"btc['Returns'].loc['2017-1-1':].hist(bins=35);"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0044234605251202621"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"btc.Returns.mean()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0070476170254343837"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"btc.loc['2017-1-1':, 'Returns'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12.096960943969746"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mu = math.exp(btc.loc['2017-1-1':, 'Returns'].mean() * 365) - 1\n",
"mu"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.86938290800452722"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vol = btc.loc['2017-1-1':, 'Returns'].std() * 365 ** 0.5\n",
"vol"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"SR = mu / vol"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13.914422324836817"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='http://hilpisch.com/tpq_logo.png' width=\"300px\" align=\"right\">"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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