Module pinkfish.analysis
Analysis of results.
This module contains some functions that were copied or derived from the book "Trading Evolved" by Andreas F. Clenow. Below is a correspondance I had with the author:
Farrell October 25, 2019 at 15:49 Hi Andreas,
I just finished reading the book. Awesome one of a kind! Thanks so much. I also enjoyed your other two. Question: what is the copyright (if any) on the source code you have in the book. I want to incorporate some of it into my open source backtester, Pinkfish. How should I credit your work if no copyright. I could add a comment at the beginning of each derived function or module at a minimum.
Farrell
Andreas Clenow October 25, 2019 at 17:29 Hi Farrell,
I can be paid in reviews and/or beer. :)
For an open source project, use the code as you see fit. A credit in the comments somewhere would be nice, but I won't sue you if you forget it.
ac
Expand source code
"""
Analysis of results.
This module contains some functions that were copied or derived
from the book "Trading Evolved" by Andreas F. Clenow.
Below is a correspondance I had with the author:
------------------------------------------------------------------------
Farrell
October 25, 2019 at 15:49
Hi Andreas,
I just finished reading the book. Awesome one of a kind! Thanks so much.
I also enjoyed your other two. Question: what is the copyright (if any)
on the source code you have in the book. I want to incorporate some of
it into my open source backtester, Pinkfish. How should I credit your
work if no copyright. I could add a comment at the beginning of each
derived function or module at a minimum.
Farrell
------------------------------------------------------------------------
Andreas Clenow
October 25, 2019 at 17:29
Hi Farrell,
I can be paid in reviews and/or beer. :)
For an open source project, use the code as you see fit. A credit in the
comments somewhere would be nice, but I won't sue you if you forget it.
ac
------------------------------------------------------------------------
"""
from IPython.core.display import display, HTML
import matplotlib.pyplot as plt
import pandas as pd
import empyrical as em
import pinkfish.indicator as indicator
########################################################################
# MONTHY RETURNS MAP
def monthly_returns_map(dbal):
"""
Display per month and per year returns in a table.
Parameters
----------
dbal : pd.Series
The daily closing balance indexed by date.
Returns
-------
None
Examples
--------
>>> monthly_returns_map(dbal['close'])
Year Jan Feb Mar Apr May Jun Jul ... Year
1990 -8.5 0.9 2.4 -2.7 9.2 -0.9 -0.5 -8.2
1991 4.2 6.7 2.2 0.0 3.9 -4.8 4.5 26.3
"""
monthly_data = em.aggregate_returns(dbal.pct_change(), 'monthly')
yearly_data = em.aggregate_returns(dbal.pct_change(), 'yearly')
table_header = """
<table class='table table-hover table-condensed table-striped'>
<thead>
<tr>
<th style="text-align:right">Year</th>
<th style="text-align:right">Jan</th>
<th style="text-align:right">Feb</th>
<th style="text-align:right">Mar</th>
<th style="text-align:right">Apr</th>
<th style="text-align:right">May</th>
<th style="text-align:right">Jun</th>
<th style="text-align:right">Jul</th>
<th style="text-align:right">Aug</th>
<th style="text-align:right">Sep</th>
<th style="text-align:right">Oct</th>
<th style="text-align:right">Nov</th>
<th style="text-align:right">Dec</th>
<th style="text-align:right">Year</th>
</tr>
</thead>
<tbody>
<tr>"""
first_year = True
first_month = True
year = 0
month = 0
year_count = 0
table = ''
for m, val in monthly_data.items():
year = m[0]
month = m[1]
if first_month:
if year_count % 15 == 0:
table += table_header
table += f"<td align='right'><b>{year}</b></td>\n"
first_month = False
# Pad empty months for first year if sim doesn't start in Jan.
if first_year:
first_year = False
if month > 1:
for _ in range(1, month):
table += "<td align='right'>-</td>\n"
table += "<td align='right'>{:.1f}</td>\n".format(val * 100)
# Check for dec and add yearly.
if month == 12:
table += "<td align='right'><b>{:.1f}</b></td>\n".format(
yearly_data[year] * 100)
table += '</tr>\n <tr> \n'
first_month = True
year_count += 1
# Add padding for empty months and last year's value.
if month != 12:
for i in range(month+1, 13):
table += "<td align='right'>-</td>\n"
if i == 12:
table += "<td align='right'><b>{:.1f}</b></td>\n".format(
yearly_data[year] * 100)
table += '</tr>\n <tr> \n'
table += '</tr>\n </tbody> \n </table>'
display(HTML(table))
########################################################################
# HOLDING PERIOD MAP
def holding_period_map(dbal):
"""
Display holding period returns in a table.
This shows what your annualized return would have been, had you
started this strategy at the start of a given year, as shown in
the leftmost column, and held it for a certain number of years.
Length of returns should be 30 or less, otherwise the output
will be jumbled.
Parameters
----------
dbal : pd.Series
The daily closing balance indexed by date.
Returns
-------
None
Examples
--------
>>> table = holding_period_map(dbal['close'])
>>> display(HTML(table))
Years 1 2 3 4 5 6 7 8
2013 30 20 13 12 13 10 12 12
2014 11 5 7 10 6 10 9
...
2020 8
"""
year = em.aggregate_returns(dbal.pct_change(), 'yearly')
year_start = 0
table = "<table class='table table-hover table-condensed table-striped'>"
table += "<tr><th>Years</th>"
for i in range(len(year)):
table += "<th>{}</th>".format(i+1)
table += "</tr>"
for the_year, _ in year.items(): # Iterates years
table += f"<tr><th>{the_year}</th>" # New table row
for years_held in range(1, len(year)+1): # Iterates years held
if years_held <= len(year.iloc[year_start:year_start + years_held]):
ret = em.annual_return(year.iloc[year_start:year_start + years_held], 'yearly')
table += "<td>{:.0f}</td>".format(ret * 100)
table += "</tr>"
year_start +=1
display(HTML(table))
########################################################################
# PRETTIER GRAPHS
def _calc_corr(dbal, benchmark_dbal, window):
"""
Calculate the rollowing correlation between two returns.
Parameters
----------
dbal : pd.Series
Strategy daily closing balance indexed by date.
benchmark_dbal : pd.Series
Benchmark daily closing balance indexed by date.
window : int
Size of the moving window. This is the number of observations
used for calculating the statistic.
Returns
-------
corr : pd.DataFrame
Window size rollowing correlation between `dbal` and
`benchmark_dbal`.
"""
ret = dbal.pct_change()
benchmark_ret = benchmark_dbal.pct_change()
corr = ret.rolling(window).corr(benchmark_ret)
return corr
def prettier_graphs(dbal, benchmark_dbal, dbal_label='Strategy',
benchmark_label='Benchmark', points_to_plot=None):
"""
Plot 3 subplots.
The first subplot will show a rebased comparison of the returns to
the benchmark returns, recalculated with the same starting value
of 1. This will be shown on a semi logarithmic scale. The second
subplot will show relative strength of the returns to the benchmark
returns, and the third the correlation between the two.
Parameters
----------
dbal : pd.Series
Strategy daily closing balance indexed by date.
benchmark_dbal : pd.Series
Benchmark daily closing balance indexed by date.
label : str, optional
Label to use in graph for strategy (default is 'Strategy').
benchmark_label : str, optional
Label to use in graph for benchmark (default is 'Benchmark').
points_to_plot : int, optional
Define how many points (trading days) we intend to plot
(default is None, which implies plot all points or days).
Returns
-------
None
Examples
--------
>>> prettier_graphs(dbal['close'], benchmark_dbal['close'],
points_to_plot=5000)
"""
if points_to_plot is None:
points_to_plot = 0
data = pd.DataFrame(dbal)
data['benchmark_dbal'] = pd.DataFrame(benchmark_dbal)
data.columns = ['dbal', 'benchmark_dbal']
data.head()
# Rebase the two series to the same point in time;
# starting where the plot will start.
for col in data:
data[col + '_rebased'] = \
(data[-points_to_plot:][col].pct_change() + 1).cumprod()
# Relative strength, strategy to benchmark.
data['relative_strength'] = data['dbal'] / data['benchmark_dbal']
# Calculate 100 day rolling correlation.
data['corr'] = _calc_corr(data['dbal'], data['benchmark_dbal'], 100)
# After this, we slice the data, effectively discarding all but
# the last points_to_plot data points, using the slicing logic from
# before. Slice the data, cut points we don't intend to plot.
plot_data = data[-points_to_plot:]
# Make new figure and set the size.
fig = plt.figure(figsize=(12, 8))
# The first subplot, planning for 3 plots high, 1 plot wide,
# this being the first.
ax = fig.add_subplot(311)
ax.set_title('Comparison')
ax.semilogy(plot_data['dbal_rebased'], linestyle='-',
label=dbal_label, linewidth=3.0)
ax.semilogy(plot_data['benchmark_dbal_rebased'], linestyle='--',
label=benchmark_label, linewidth=3.0)
ax.legend()
ax.grid(False)
# Second sub plot.
ax = fig.add_subplot(312)
label = f'Relative Strength, {dbal_label} to {benchmark_label}'
ax.plot(plot_data['relative_strength'], label=label, linestyle=':', linewidth=3.0)
ax.legend()
ax.grid(True)
# Third subplot.
ax = fig.add_subplot(313)
label = f'Correlation between {dbal_label} and {benchmark_label}'
ax.plot(plot_data['corr'], label=label, linestyle='-.', linewidth=3.0)
ax.legend()
ax.grid(True)
########################################################################
# VOLATILITY
def volatility_graphs(dbals, labels, points_to_plot=None):
"""
Plot volatility graphs.
The first graph is a boxplot showing the differences between
2 or more returns. The second graph shows the volatility plotted
for 2 or more returns.
Parameters
----------
dbals : list of pd.DataFrame
A list of daily closing balances (or daily instrument closing
prices) indexed by date.
labels : list of str
A list of labels.
points_to_plot : int, optional
Define how many points (trading days) we intend to plot
(default is None, which implies plot all points or days).
Returns
-------
pf.DataFrame
Statistics comparing the `dbals`.
Examples
--------
>>> df = pf.volatility_graph([ts, dbal], ['SPY', 'Strategy'],
points_to_plot=5000)
>>> df
"""
def _boxplot(volas, labels):
"""
Plot a volatility boxplot.
"""
fig = plt.figure(figsize=(12, 8))
fig.add_subplot(111, ylabel='Volatility')
plt.ylim(0, 1)
plt.boxplot(volas, labels=labels)
def _volas_plot(volas, labels):
"""
Plot volatility.
"""
fig = plt.figure(figsize=(14, 10))
axes = fig.add_subplot(111, ylabel='Volatility')
for i, vola in enumerate(volas):
axes.plot(vola, label=labels[i])
plt.legend(loc='best')
if points_to_plot is None:
points_to_plot = 0
# Get volatility for each dbal set.
volas = []
for dbal in dbals:
volas.append(indicator.VOLATILITY(dbal[-points_to_plot:]).dropna())
# Build metrics dataframe.
index = []
columns = labels
data = []
# Add metrics.
metrics = ['avg', 'median', 'min', 'max', 'std', 'last']
for metric in metrics:
index.append(metric)
if metric == 'avg': data.append(vola.mean() for vola in volas)
elif metric == 'median': data.append(vola.median() for vola in volas)
elif metric == 'min': data.append(vola.min() for vola in volas)
elif metric == 'max': data.append(vola.max() for vola in volas)
elif metric == 'std': data.append(vola.std() for vola in volas)
elif metric == 'last': data.append(vola.iloc[-1] for vola in volas)
df = pd.DataFrame(data, columns=columns, index=index)
_boxplot(volas, labels)
_volas_plot(volas, labels)
return df
########################################################################
# KELLY CRITERION
def kelly_criterion(stats, benchmark_stats=None):
"""
Use this function to help with sizing of leverage.
This function uses ideas based on the Kelly Criterion.
Parameters
----------
stats : pd.Series
Statistics for the strategy.
bbenchmark_stats : pd.Series, optimal
Statistics for the benchmark (default is None, which implies
that a benchmark is not being used).
Returns
-------
s : pf.Series
Leverage statistics.
- `sharpe_ratio` is a measure of risk adjusted return.
- `sharpe_ratio_max` is the maximum expected sharpe ratio.
- `sharpe_ratio_min` is the minimum expected sharpe ratio.
- `strategy risk` is a measure of how risky a trading strategy
is, calculated as an annual standard deviation of returns.
- `instrument_risk` is a measure of how risky an instrument is
before any leverage is applied, calculated as an annual
standard deviation of returns.
- `optimal target risk` is equal to the expected sharpe ratio,
according to the Kelly criterion. Target risk is the amount
of risk you expect to see when trading, calculated as an
annual standard deviation of returns.
- `half kelly criterion` is equal to half the expected
sharpe ratio. It uses a conservative version of the
Kelly criterion known as half Kelly.
- `aggressive leverage` is the optimal target risk divided by
the instrument risk. This is an aggressive form of the
leverage factor, which is the cash value of a position
divided by your capital.
- `moderate leverage` is the leverage factor calculated using
half Kelly.
- `conservative leverage` is the leverage factor calculated
using half of the minimum sharpe ratio divided by 2.
"""
s = pd.Series(dtype='object')
s['sharpe_ratio'] = stats['sharpe_ratio']
s['sharpe_ratio_max'] = stats['sharpe_ratio_max']
s['sharpe_ratio_min'] = stats['sharpe_ratio_min']
s['strategy risk'] = stats['annual_std'] / 100
if benchmark_stats is not None:
s['instrument risk'] = benchmark_stats['annual_std'] / 100
s['optimal target risk'] = s['sharpe_ratio']
s['half kelly criterion'] = s['sharpe_ratio'] / 2
s['aggressive leverage'] = s['optimal target risk'] / s['instrument risk']
s['moderate leverage'] = s['half kelly criterion'] / s['instrument risk']
s['conservative leverage'] = (s['sharpe_ratio_min'] / 2) / s['instrument risk']
return sFunctions
def holding_period_map(dbal)-
Display holding period returns in a table.
This shows what your annualized return would have been, had you started this strategy at the start of a given year, as shown in the leftmost column, and held it for a certain number of years. Length of returns should be 30 or less, otherwise the output will be jumbled.
Parameters
dbal:pd.Series- The daily closing balance indexed by date.
Returns
None
Examples
>>> table = holding_period_map(dbal['close']) >>> display(HTML(table)) Years 1 2 3 4 5 6 7 8 2013 30 20 13 12 13 10 12 12 2014 11 5 7 10 6 10 9 ... 2020 8Expand source code
def holding_period_map(dbal): """ Display holding period returns in a table. This shows what your annualized return would have been, had you started this strategy at the start of a given year, as shown in the leftmost column, and held it for a certain number of years. Length of returns should be 30 or less, otherwise the output will be jumbled. Parameters ---------- dbal : pd.Series The daily closing balance indexed by date. Returns ------- None Examples -------- >>> table = holding_period_map(dbal['close']) >>> display(HTML(table)) Years 1 2 3 4 5 6 7 8 2013 30 20 13 12 13 10 12 12 2014 11 5 7 10 6 10 9 ... 2020 8 """ year = em.aggregate_returns(dbal.pct_change(), 'yearly') year_start = 0 table = "<table class='table table-hover table-condensed table-striped'>" table += "<tr><th>Years</th>" for i in range(len(year)): table += "<th>{}</th>".format(i+1) table += "</tr>" for the_year, _ in year.items(): # Iterates years table += f"<tr><th>{the_year}</th>" # New table row for years_held in range(1, len(year)+1): # Iterates years held if years_held <= len(year.iloc[year_start:year_start + years_held]): ret = em.annual_return(year.iloc[year_start:year_start + years_held], 'yearly') table += "<td>{:.0f}</td>".format(ret * 100) table += "</tr>" year_start +=1 display(HTML(table)) def kelly_criterion(stats, benchmark_stats=None)-
Use this function to help with sizing of leverage.
This function uses ideas based on the Kelly Criterion.
Parameters
stats:pd.Series- Statistics for the strategy.
bbenchmark_stats:pd.Series, optimal- Statistics for the benchmark (default is None, which implies that a benchmark is not being used).
Returns
s:pf.Series-
Leverage statistics.
-
sharpe_ratiois a measure of risk adjusted return. -
sharpe_ratio_maxis the maximum expected sharpe ratio. -
sharpe_ratio_minis the minimum expected sharpe ratio. -
strategy riskis a measure of how risky a trading strategy is, calculated as an annual standard deviation of returns. -
instrument_riskis a measure of how risky an instrument is before any leverage is applied, calculated as an annual standard deviation of returns. -
optimal target riskis equal to the expected sharpe ratio, according to the Kelly criterion. Target risk is the amount of risk you expect to see when trading, calculated as an annual standard deviation of returns. -
half kelly criterionis equal to half the expected sharpe ratio. It uses a conservative version of the Kelly criterion known as half Kelly. -
aggressive leverageis the optimal target risk divided by the instrument risk. This is an aggressive form of the leverage factor, which is the cash value of a position divided by your capital. -
moderate leverageis the leverage factor calculated using half Kelly. -
conservative leverageis the leverage factor calculated using half of the minimum sharpe ratio divided by 2.
-
Expand source code
def kelly_criterion(stats, benchmark_stats=None): """ Use this function to help with sizing of leverage. This function uses ideas based on the Kelly Criterion. Parameters ---------- stats : pd.Series Statistics for the strategy. bbenchmark_stats : pd.Series, optimal Statistics for the benchmark (default is None, which implies that a benchmark is not being used). Returns ------- s : pf.Series Leverage statistics. - `sharpe_ratio` is a measure of risk adjusted return. - `sharpe_ratio_max` is the maximum expected sharpe ratio. - `sharpe_ratio_min` is the minimum expected sharpe ratio. - `strategy risk` is a measure of how risky a trading strategy is, calculated as an annual standard deviation of returns. - `instrument_risk` is a measure of how risky an instrument is before any leverage is applied, calculated as an annual standard deviation of returns. - `optimal target risk` is equal to the expected sharpe ratio, according to the Kelly criterion. Target risk is the amount of risk you expect to see when trading, calculated as an annual standard deviation of returns. - `half kelly criterion` is equal to half the expected sharpe ratio. It uses a conservative version of the Kelly criterion known as half Kelly. - `aggressive leverage` is the optimal target risk divided by the instrument risk. This is an aggressive form of the leverage factor, which is the cash value of a position divided by your capital. - `moderate leverage` is the leverage factor calculated using half Kelly. - `conservative leverage` is the leverage factor calculated using half of the minimum sharpe ratio divided by 2. """ s = pd.Series(dtype='object') s['sharpe_ratio'] = stats['sharpe_ratio'] s['sharpe_ratio_max'] = stats['sharpe_ratio_max'] s['sharpe_ratio_min'] = stats['sharpe_ratio_min'] s['strategy risk'] = stats['annual_std'] / 100 if benchmark_stats is not None: s['instrument risk'] = benchmark_stats['annual_std'] / 100 s['optimal target risk'] = s['sharpe_ratio'] s['half kelly criterion'] = s['sharpe_ratio'] / 2 s['aggressive leverage'] = s['optimal target risk'] / s['instrument risk'] s['moderate leverage'] = s['half kelly criterion'] / s['instrument risk'] s['conservative leverage'] = (s['sharpe_ratio_min'] / 2) / s['instrument risk'] return s def monthly_returns_map(dbal)-
Display per month and per year returns in a table.
Parameters
dbal:pd.Series- The daily closing balance indexed by date.
Returns
None
Examples
>>> monthly_returns_map(dbal['close']) Year Jan Feb Mar Apr May Jun Jul ... Year 1990 -8.5 0.9 2.4 -2.7 9.2 -0.9 -0.5 -8.2 1991 4.2 6.7 2.2 0.0 3.9 -4.8 4.5 26.3Expand source code
def monthly_returns_map(dbal): """ Display per month and per year returns in a table. Parameters ---------- dbal : pd.Series The daily closing balance indexed by date. Returns ------- None Examples -------- >>> monthly_returns_map(dbal['close']) Year Jan Feb Mar Apr May Jun Jul ... Year 1990 -8.5 0.9 2.4 -2.7 9.2 -0.9 -0.5 -8.2 1991 4.2 6.7 2.2 0.0 3.9 -4.8 4.5 26.3 """ monthly_data = em.aggregate_returns(dbal.pct_change(), 'monthly') yearly_data = em.aggregate_returns(dbal.pct_change(), 'yearly') table_header = """ <table class='table table-hover table-condensed table-striped'> <thead> <tr> <th style="text-align:right">Year</th> <th style="text-align:right">Jan</th> <th style="text-align:right">Feb</th> <th style="text-align:right">Mar</th> <th style="text-align:right">Apr</th> <th style="text-align:right">May</th> <th style="text-align:right">Jun</th> <th style="text-align:right">Jul</th> <th style="text-align:right">Aug</th> <th style="text-align:right">Sep</th> <th style="text-align:right">Oct</th> <th style="text-align:right">Nov</th> <th style="text-align:right">Dec</th> <th style="text-align:right">Year</th> </tr> </thead> <tbody> <tr>""" first_year = True first_month = True year = 0 month = 0 year_count = 0 table = '' for m, val in monthly_data.items(): year = m[0] month = m[1] if first_month: if year_count % 15 == 0: table += table_header table += f"<td align='right'><b>{year}</b></td>\n" first_month = False # Pad empty months for first year if sim doesn't start in Jan. if first_year: first_year = False if month > 1: for _ in range(1, month): table += "<td align='right'>-</td>\n" table += "<td align='right'>{:.1f}</td>\n".format(val * 100) # Check for dec and add yearly. if month == 12: table += "<td align='right'><b>{:.1f}</b></td>\n".format( yearly_data[year] * 100) table += '</tr>\n <tr> \n' first_month = True year_count += 1 # Add padding for empty months and last year's value. if month != 12: for i in range(month+1, 13): table += "<td align='right'>-</td>\n" if i == 12: table += "<td align='right'><b>{:.1f}</b></td>\n".format( yearly_data[year] * 100) table += '</tr>\n <tr> \n' table += '</tr>\n </tbody> \n </table>' display(HTML(table)) def prettier_graphs(dbal, benchmark_dbal, dbal_label='Strategy', benchmark_label='Benchmark', points_to_plot=None)-
Plot 3 subplots.
The first subplot will show a rebased comparison of the returns to the benchmark returns, recalculated with the same starting value of 1. This will be shown on a semi logarithmic scale. The second subplot will show relative strength of the returns to the benchmark returns, and the third the correlation between the two.
Parameters
dbal:pd.Series- Strategy daily closing balance indexed by date.
benchmark_dbal:pd.Series- Benchmark daily closing balance indexed by date.
label:str, optional- Label to use in graph for strategy (default is 'Strategy').
benchmark_label:str, optional- Label to use in graph for benchmark (default is 'Benchmark').
points_to_plot:int, optional- Define how many points (trading days) we intend to plot (default is None, which implies plot all points or days).
Returns
None
Examples
>>> prettier_graphs(dbal['close'], benchmark_dbal['close'], points_to_plot=5000)Expand source code
def prettier_graphs(dbal, benchmark_dbal, dbal_label='Strategy', benchmark_label='Benchmark', points_to_plot=None): """ Plot 3 subplots. The first subplot will show a rebased comparison of the returns to the benchmark returns, recalculated with the same starting value of 1. This will be shown on a semi logarithmic scale. The second subplot will show relative strength of the returns to the benchmark returns, and the third the correlation between the two. Parameters ---------- dbal : pd.Series Strategy daily closing balance indexed by date. benchmark_dbal : pd.Series Benchmark daily closing balance indexed by date. label : str, optional Label to use in graph for strategy (default is 'Strategy'). benchmark_label : str, optional Label to use in graph for benchmark (default is 'Benchmark'). points_to_plot : int, optional Define how many points (trading days) we intend to plot (default is None, which implies plot all points or days). Returns ------- None Examples -------- >>> prettier_graphs(dbal['close'], benchmark_dbal['close'], points_to_plot=5000) """ if points_to_plot is None: points_to_plot = 0 data = pd.DataFrame(dbal) data['benchmark_dbal'] = pd.DataFrame(benchmark_dbal) data.columns = ['dbal', 'benchmark_dbal'] data.head() # Rebase the two series to the same point in time; # starting where the plot will start. for col in data: data[col + '_rebased'] = \ (data[-points_to_plot:][col].pct_change() + 1).cumprod() # Relative strength, strategy to benchmark. data['relative_strength'] = data['dbal'] / data['benchmark_dbal'] # Calculate 100 day rolling correlation. data['corr'] = _calc_corr(data['dbal'], data['benchmark_dbal'], 100) # After this, we slice the data, effectively discarding all but # the last points_to_plot data points, using the slicing logic from # before. Slice the data, cut points we don't intend to plot. plot_data = data[-points_to_plot:] # Make new figure and set the size. fig = plt.figure(figsize=(12, 8)) # The first subplot, planning for 3 plots high, 1 plot wide, # this being the first. ax = fig.add_subplot(311) ax.set_title('Comparison') ax.semilogy(plot_data['dbal_rebased'], linestyle='-', label=dbal_label, linewidth=3.0) ax.semilogy(plot_data['benchmark_dbal_rebased'], linestyle='--', label=benchmark_label, linewidth=3.0) ax.legend() ax.grid(False) # Second sub plot. ax = fig.add_subplot(312) label = f'Relative Strength, {dbal_label} to {benchmark_label}' ax.plot(plot_data['relative_strength'], label=label, linestyle=':', linewidth=3.0) ax.legend() ax.grid(True) # Third subplot. ax = fig.add_subplot(313) label = f'Correlation between {dbal_label} and {benchmark_label}' ax.plot(plot_data['corr'], label=label, linestyle='-.', linewidth=3.0) ax.legend() ax.grid(True) def volatility_graphs(dbals, labels, points_to_plot=None)-
Plot volatility graphs.
The first graph is a boxplot showing the differences between 2 or more returns. The second graph shows the volatility plotted for 2 or more returns.
Parameters
dbals:listofpd.DataFrame- A list of daily closing balances (or daily instrument closing prices) indexed by date.
labels:listofstr- A list of labels.
points_to_plot:int, optional- Define how many points (trading days) we intend to plot (default is None, which implies plot all points or days).
Returns
pf.DataFrame- Statistics comparing the
dbals.
Examples
>>> df = pf.volatility_graph([ts, dbal], ['SPY', 'Strategy'], points_to_plot=5000) >>> dfExpand source code
def volatility_graphs(dbals, labels, points_to_plot=None): """ Plot volatility graphs. The first graph is a boxplot showing the differences between 2 or more returns. The second graph shows the volatility plotted for 2 or more returns. Parameters ---------- dbals : list of pd.DataFrame A list of daily closing balances (or daily instrument closing prices) indexed by date. labels : list of str A list of labels. points_to_plot : int, optional Define how many points (trading days) we intend to plot (default is None, which implies plot all points or days). Returns ------- pf.DataFrame Statistics comparing the `dbals`. Examples -------- >>> df = pf.volatility_graph([ts, dbal], ['SPY', 'Strategy'], points_to_plot=5000) >>> df """ def _boxplot(volas, labels): """ Plot a volatility boxplot. """ fig = plt.figure(figsize=(12, 8)) fig.add_subplot(111, ylabel='Volatility') plt.ylim(0, 1) plt.boxplot(volas, labels=labels) def _volas_plot(volas, labels): """ Plot volatility. """ fig = plt.figure(figsize=(14, 10)) axes = fig.add_subplot(111, ylabel='Volatility') for i, vola in enumerate(volas): axes.plot(vola, label=labels[i]) plt.legend(loc='best') if points_to_plot is None: points_to_plot = 0 # Get volatility for each dbal set. volas = [] for dbal in dbals: volas.append(indicator.VOLATILITY(dbal[-points_to_plot:]).dropna()) # Build metrics dataframe. index = [] columns = labels data = [] # Add metrics. metrics = ['avg', 'median', 'min', 'max', 'std', 'last'] for metric in metrics: index.append(metric) if metric == 'avg': data.append(vola.mean() for vola in volas) elif metric == 'median': data.append(vola.median() for vola in volas) elif metric == 'min': data.append(vola.min() for vola in volas) elif metric == 'max': data.append(vola.max() for vola in volas) elif metric == 'std': data.append(vola.std() for vola in volas) elif metric == 'last': data.append(vola.iloc[-1] for vola in volas) df = pd.DataFrame(data, columns=columns, index=index) _boxplot(volas, labels) _volas_plot(volas, labels) return df