I have financial data from January 2019 to July 2020. I would like to choose a date (lets say March 16, 2020) as date 0 and calculate number of days in +-30 day window and visualize it.
The x axis should have days from -30 to +30. Lastly draw horizontal line for the value at 0 days, like the one in the attached photo:
To create a Timestamp from string you can use pandas.Timestamp
If you want to subtract or add from Timestamp several days use pandas.DateOffset
If you want plot something in Python, you can use matplotlib.pyplot. In you case plot function.
To change x ticks labels from timestamp to -30..30, use pyplot.xticks
To plot vertical line - pyplot.vlines
Simple example:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
x0 = pd.Timestamp('2020-03-16')
x = pd.date_range(x0 - pd.DateOffset(30), x0 + pd.DateOffset(30), freq='D')
y = np.linspace(1, 10, len(x))
plt.plot(x, y)
plt.xticks(x[[0, 15, 30, 45, 60]], labels=[-30, -15, 0, 15, 30])
plt.show()
Related
I have a pandas dataframe with lots of time intervals of varying start times and lengths. I am interested in the distribution of start times over 24hours. I therefore have another column entitled Hour with just that in. I have plotted a histogram using seaborn to look at the distribution but obviously the x axis starts at 0 and runs to 24. I wonder if there is a way to change so it runs from 8 to 8 and loops over at 23 to 0 so it provides a better visualisation of my data from a time perspective. Thanks in advance.
sns.distplot(df2['Hour'], bins = 24, kde = False).set(xlim=(0,23))
If you want to have a custom order of x-values on your bar plot, I'd suggest using matplotlib directly and plot your histogram simply as a bar plot with width=1 to get rid of padding between bars.
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
# prepare sample data
dates = pd.date_range(
start=datetime(2020, 1, 1),
end=datetime(2020, 1, 7),
freq="H")
random_dates = np.random.choice(dates, 1000)
df = pd.DataFrame(data={"date":random_dates})
df["hour"] = df["date"].dt.hour
# set your preferred order of hours
hour_order = [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,0,1,2,3,4,5,6,7]
# calculate frequencies of each hour and sort them
plot_df = (
df["hour"]
.value_counts()
.rename_axis("hour", axis=0)
.reset_index(name="freq")
.set_index("hour")
.loc[hour_order]
.reset_index())
# day / night colour split
day_mask = ((8 <= plot_df["hour"]) & (plot_df["hour"] <= 20))
plot_df["color"] = np.where(day_mask, "skyblue", "midnightblue")
# actual plotting - note that you have to cast hours as strings
fig = plt.figure(figsize=(8,4))
ax = fig.add_subplot(111)
ax.bar(
x=plot_df["hour"].astype(str),
height=plot_df["freq"],
color=plot_df["color"], width=1)
ax.set_xlabel('Hour')
ax.set_ylabel('Frequency')
plt.show()
I have created a seaborn boxplot graph for investment data ranging from $100,000 to $40,000,000, spread out over several years. The graph shows a boxplot for investment amounts per year. The data comes from two series in a dataframe, which I'm rendering from a CSV: investment year ('inv_year') and investment amount ('inv_amount').
The problem: the graph's y-axis is automatically shown in increments of 0.5, so:
0.0, 0.5, 1.0, 1.5, etc. (i.e. in tens of millions)
but I want the axis in increments of 5, so:
0, 5, 10, 15, etc. (i.e. in millions)
How do I change the axis's scale?
Here's my current code:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_excel('investments'))
plt.figure(figsize=(12, 10))
sns.boxplot(x='inv_year', y='inv_amount', data=df)
plt.show()
You can define an array with the specified ticks and pass it to plt.yticks:
plt.yticks(np.arange(min(x), max(x)+1, 5))
If you want to keep the limits but just adjust the ticks:
increments = 5
ax = plt.gca()
start, end = ax.get_ylim()
ax.yaxis.set_ticks(np.arange(start, end, increments))
ax.plot_date((dates, dates), (highs, lows), '-')
I'm currently using this command to plot financial highs and lows using Matplotlib. It works great, but how do I remove the blank spaces in the x-axis left by days without market data, such as weekends and holidays?
I have lists of dates, highs, lows, closes and opens. I can't find any examples of creating a graph with an x-axis that show dates but doesn't enforce a constant scale.
One of the advertised features of scikits.timeseries is "Create time series plots with intelligently spaced axis labels".
You can see some example plots here. In the first example (shown below) the 'business' frequency is used for the data, which automatically excludes holidays and weekends and the like. It also masks missing data points, which you see as gaps in this plot, rather than linearly interpolating them.
Up to date answer (2018) with Matplotlib 2.1.2, Python 2.7.12
The function equidate_ax handles everything you need for a simple date x-axis with equidistant spacing of data points. Realised with ticker.FuncFormatter based on this example.
from __future__ import division
from matplotlib import pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
import datetime
def equidate_ax(fig, ax, dates, fmt="%Y-%m-%d", label="Date"):
"""
Sets all relevant parameters for an equidistant date-x-axis.
Tick Locators are not affected (set automatically)
Args:
fig: pyplot.figure instance
ax: pyplot.axis instance (target axis)
dates: iterable of datetime.date or datetime.datetime instances
fmt: Display format of dates
label: x-axis label
Returns:
None
"""
N = len(dates)
def format_date(index, pos):
index = np.clip(int(index + 0.5), 0, N - 1)
return dates[index].strftime(fmt)
ax.xaxis.set_major_formatter(FuncFormatter(format_date))
ax.set_xlabel(label)
fig.autofmt_xdate()
#
# Some test data (with python dates)
#
dates = [datetime.datetime(year, month, day) for year, month, day in [
(2018,2,1), (2018,2,2), (2018,2,5), (2018,2,6), (2018,2,7), (2018,2,28)
]]
y = np.arange(6)
# Create plots. Left plot is default with a gap
fig, [ax1, ax2] = plt.subplots(1, 2)
ax1.plot(dates, y, 'o-')
ax1.set_title("Default")
ax1.set_xlabel("Date")
# Right plot will show equidistant series
# x-axis must be the indices of your dates-list
x = np.arange(len(dates))
ax2.plot(x, y, 'o-')
ax2.set_title("Equidistant Placement")
equidate_ax(fig, ax2, dates)
I think you need to "artificially synthesize" the exact form of plot you want by using xticks to set the tick labels to the strings representing the dates (of course placing the ticks at equispaced intervals even though the dates you're representing aren't equispaced) and then using a plain plot.
I will typically use NumPy's NaN (not a number) for values that are invalid or not present. They are represented by Matplotlib as gaps in the plot and NumPy is part of pylab/Matplotlib.
>>> import pylab
>>> xs = pylab.arange(10.) + 733632. # valid date range
>>> ys = [1,2,3,2,pylab.nan,2,3,2,5,2.4] # some data (one undefined)
>>> pylab.plot_date(xs, ys, ydate=False, linestyle='-', marker='')
[<matplotlib.lines.Line2D instance at 0x0378D418>]
>>> pylab.show()
I ran into this problem again and was able to create a decent function to handle this issue, especially concerning intraday datetimes. Credit to #Primer for this answer.
def plot_ts(ts, step=5, figsize=(10,7), title=''):
"""
plot timeseries ignoring date gaps
Params
------
ts : pd.DataFrame or pd.Series
step : int, display interval for ticks
figsize : tuple, figure size
title: str
"""
fig, ax = plt.subplots(figsize=figsize)
ax.plot(range(ts.dropna().shape[0]), ts.dropna())
ax.set_title(title)
ax.set_xticks(np.arange(len(ts.dropna())))
ax.set_xticklabels(ts.dropna().index.tolist());
# tick visibility, can be slow for 200,000+ ticks
xticklabels = ax.get_xticklabels() # generate list once to speed up function
for i, label in enumerate(xticklabels):
if not i%step==0:
label.set_visible(False)
fig.autofmt_xdate()
You can simply change dates to strings:
import matplotlib.pyplot as plt
import datetime
f = plt.figure(1, figsize=(10,5))
ax = f.add_subplot(111)
today = datetime.datetime.today().date()
yesterday = today - datetime.timedelta(days=1)
three_days_later = today + datetime.timedelta(days=3)
x_values = [yesterday, today, three_days_later]
y_values = [75, 80, 90]
x_values = [f'{x:%Y-%m-%d}' for x in x_values]
ax.bar(x_values, y_values, color='green')
plt.show()
scikits.timeseries functionality has largely been moved to pandas, so you can now resample a dataframe to only include the values on weekdays.
>>>import pandas as pd
>>>import matplotlib.pyplot as plt
>>>s = pd.Series(list(range(10)), pd.date_range('2015-09-01','2015-09-10'))
>>>s
2015-09-01 0
2015-09-02 1
2015-09-03 2
2015-09-04 3
2015-09-05 4
2015-09-06 5
2015-09-07 6
2015-09-08 7
2015-09-09 8
2015-09-10 9
>>> s.resample('B', label='right', closed='right').last()
2015-09-01 0
2015-09-02 1
2015-09-03 2
2015-09-04 3
2015-09-07 6
2015-09-08 7
2015-09-09 8
2015-09-10 9
and then to plot the dataframe as normal
s.resample('B', label='right', closed='right').last().plot()
plt.show()
Just use mplfinance
https://github.com/matplotlib/mplfinance
import mplfinance as mpf
# df = 'ohlc dataframe'
mpf.plot(df)
I am trying to generate a plot of the 6-month rolling Sharpe ratio using Python with Pandas/NumPy.
My input data is below:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
# Generate sample data
d = pd.date_range(start='1/1/2008', end='12/1/2015')
df = pd.DataFrame(d, columns=['Date'])
df['returns'] = np.random.rand(d.size, 1)
df = df.set_index('Date')
print(df.head(20))
returns
Date
2008-01-01 0.232794
2008-01-02 0.957157
2008-01-03 0.079939
2008-01-04 0.772999
2008-01-05 0.708377
2008-01-06 0.579662
2008-01-07 0.998632
2008-01-08 0.432605
2008-01-09 0.499041
2008-01-10 0.693420
2008-01-11 0.330222
2008-01-12 0.109280
2008-01-13 0.776309
2008-01-14 0.079325
2008-01-15 0.559206
2008-01-16 0.748133
2008-01-17 0.747319
2008-01-18 0.936322
2008-01-19 0.211246
2008-01-20 0.755340
What I want
The type of plot I am trying to produce is this or the first plot from here (see below).
My attempt
Here is the equation I am using:
def my_rolling_sharpe(y):
return np.sqrt(126) * (y.mean() / y.std()) # 21 days per month X 6 months = 126
# Calculate rolling Sharpe ratio
df['rs'] = calc_sharpe_ratio(df['returns'])
fig, ax = plt.subplots(figsize=(10, 3))
df['rs'].plot(style='-', lw=3, color='indianred', label='Sharpe')\
.axhline(y = 0, color = "black", lw = 3)
plt.ylabel('Sharpe ratio')
plt.legend(loc='best')
plt.title('Rolling Sharpe ratio (6-month)')
fig.tight_layout()
plt.show()
The problem is that I am getting a horizontal line since my function is giving a single value for the Sharpe ratio. This value is the same for all the Dates. In the example plots, they appear to be showing many ratios.
Question
Is it possible to plot a 6-month rolling Sharpe ratio that changes from one day to the next?
Approximately correct solution using df.rolling and a fixed window size of 180 days:
df['rs'] = df['returns'].rolling('180d').apply(my_rolling_sharpe)
This window isn't exactly 6 calendar months wide because rolling requires a fixed window size, so trying window='6MS' (6 Month Starts) throws a ValueError.
To calculate the Sharpe ratio for a window exactly 6 calendar months wide, I'll copy this super cool answer by SO user Mike:
df['rs2'] = [my_rolling_sharpe(df.loc[d - pd.offsets.DateOffset(months=6):d, 'returns'])
for d in df.index]
# Compare the two windows
df.plot(y=['rs', 'rs2'], linewidth=0.5)
I have prepared an alternative solution to your question, this one is based on using solely the window functions from pandas.
Here I have defined "on the fly" the calculation of the Sharpe Ratio, please consider for your solution the following parameters:
I have used a Risk Free rate of 2%
The dash line is just a Benchmark for the rolling Sharpe Ratio, the value is 1.6
So the code is the following
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
# Generate sample data
d = pd.date_range(start='1/1/2008', end='12/1/2015')
df = pd.DataFrame(d, columns=['Date'])
df['returns'] = np.random.rand(d.size, 1)
df = df.set_index('Date')
df['rolling_SR'] = df.returns.rolling(180).apply(lambda x: (x.mean() - 0.02) / x.std(), raw = True)
df.fillna(0, inplace = True)
df[df['rolling_SR'] > 0].rolling_SR.plot(style='-', lw=3, color='orange',
label='Sharpe', figsize = (10,7))\
.axhline(y = 1.6, color = "blue", lw = 3,
linestyle = '--')
plt.ylabel('Sharpe ratio')
plt.legend(loc='best')
plt.title('Rolling Sharpe ratio (6-month)')
plt.show()
print('---------------------------------------------------------------')
print('In case you want to check the result data\n')
print(df.tail()) # I use tail, beacause of the size of your window.
You should get something similar to this picture
I'm trying to add a series (composed of a list of [1,2,0,....]) to a candlestick chart I produced with matplotlib, but cannot work out how to include those labels for each specific candle in the graph. Basically I'd like to produce a chart like this one:
(source: linnsoft.com)
with the labels with the numbers (my signal series) just over or below each candles.
Is there any way I can reach that?
Don't know if it helps, but my series are of the pandas DataFrame kind...
Here's an example derived from - http://matplotlib.org/examples/pylab_examples/finance_demo.html
Take special note of ax.annotate method call in the code below.
from pylab import *
from matplotlib.dates import DateFormatter, WeekdayLocator, HourLocator, \
DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo, candlestick,\
plot_day_summary, candlestick2
# (Year, month, day) tuples suffice as args for quotes_historical_yahoo
date1 = ( 2004, 2, 1)
date2 = ( 2004, 4, 12 )
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # Eg, Jan 12
dayFormatter = DateFormatter('%d') # Eg, 12
quotes = quotes_historical_yahoo('INTC', date1, date2)
if len(quotes) == 0:
raise SystemExit
fig = figure()
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
candlestick(ax, quotes, width=0.6)
ax.xaxis_date()
ax.autoscale_view()
setp( gca().get_xticklabels(), rotation=45, horizontalalignment='right')
import datetime
dt = datetime.datetime(2004, 3, 8)
# Annotating a specific candle
ax.annotate('This is my special candle', xy=(dt, 24), xytext=(dt, 25),
arrowprops=dict(facecolor='black', shrink=0.05),
)
show()
The resulting plot if you run this file, should show you:-