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This module contains a collection of functions that were copied or derived
from the book "Trading Evolved" by Andreas F. Clenow.
Below is a correspondance I had with the author:
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.
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.
# Use future imports for python 3.0 forward compatibility
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
# Other imports
import pandas as pd
import matplotlib.pyplot as plt
import empyrical as em
from IPython.core.display import display, HTML
def monthly_returns_map(returns):
""" Display per month and per year returns in a table """
monthly_data = em.aggregate_returns(returns.pct_change(),'monthly')
yearly_data = em.aggregate_returns(returns.pct_change(),'yearly')
table_header = """
<table class='table table-hover table-condensed table-striped'>
<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>
first_year = True
first_month = True
year = 0
month = 0
year_count = 0
table = ''
for m, val in monthly_data.iteritems():
year = m[0]
month = m[1]
if first_month:
if year_count % 15 == 0:
table += table_header
table += "<td align='right'><b>{}</b></td>\n".format(year)
first_month = False
# pad empty months for first year if sim doesn't start in January
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, 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>'
#returns = s.dbal['close']
def holding_period_map(returns):
Display holding period returns in a table.
length of returns should be 30 or less, otherwise the output
will be jumbled
year = em.aggregate_returns(returns.pct_change(), 'yearly')
returns = pd.DataFrame(columns=range(1, len(year)+1), index=year.index)
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, value in year.iteritems(): # Iterates years
table += "<tr><th>{}</th>".format(the_year) # New table row
for years_held in (range(1, len(year)+1)): # Iterates years held
if years_held <= len(year[year_start:year_start + years_held]):
ret = em.annual_return(year[year_start:year_start + years_held], 'yearly' )
table += "<td>{:.0f}</td>".format(ret * 100)
table += "</tr>"
#table = holding_period_map(returns['1990':])
def calc_corr(ser1, ser2, window):
Calculates correlation between two series.
ret1 = ser1.pct_change()
ret2 = ser2.pct_change()
corr = ret1.rolling(window).corr(ret2)
return corr
def prettier_graphs(ret1, ret2, label1='Strategy', label2='Benchmark',
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.
points_to_plot: Define how many points (trading days) we intend to plot.
# default is to plot all points (days)
if points_to_plot is None:
points_to_plot = 0;
data = pd.DataFrame(ret1)
data['ret2'] = pd.DataFrame(ret2)
data.columns = ['ret1', 'ret2']
# 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, ret1 to ret2
data['rel_str'] = data['ret1'] / data['ret2']
# Calculate 50 day rolling correlation
data['corr'] = calc_corr(data['ret1'], data['ret2'], 100)
# After this, we slice the data, effectively discarding all but the last
# 300 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.semilogy(plot_data['ret1_rebased'], linestyle='-',
label=label1, linewidth=3.0)
ax.semilogy(plot_data['ret2_rebased'], linestyle='--',
label=label2, linewidth=3.0)
# Second sub plot.
ax = fig.add_subplot(312)
label='Relative Strength, {} to {}'.format(label1, label2)
ax.plot(plot_data['rel_str'], label=label, linestyle=':', linewidth=3.0)
# Third subplot.
ax = fig.add_subplot(313)
label='Correlation between {} and {}'.format(label1, label2)
ax.plot(plot_data['corr'], label=label, linestyle='-.', linewidth=3.0)
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