Performance#
!pip install mlfinlab
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!pip install yfinance
# See https://plotly.com/python/static-image-export/
!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca
!chmod +x /usr/local/bin/orca
!apt-get install xvfb libgtk2.0-0 libgconf-2-4
Requirement already satisfied: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.54)
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--2020-06-04 02:56:24-- https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage
Resolving github.com (github.com)... 140.82.113.4
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--2020-06-04 02:56:24-- https://github-production-release-asset-2e65be.s3.amazonaws.com/99037241/9dc3a580-286a-11e9-8a21-4312b7c8a512?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200604%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200604T025624Z&X-Amz-Expires=300&X-Amz-Signature=97f922eb02e8b0f0f08980aa3d5bdbd55e59eb757d42fbbdc6ca33619a0e82b5&X-Amz-SignedHeaders=host&actor_id=0&repo_id=99037241&response-content-disposition=attachment%3B%20filename%3Dorca-1.2.1-x86_64.AppImage&response-content-type=application%2Foctet-stream
Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.244.244
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Reading package lists... Done
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libgtk2.0-0 is already the newest version (2.24.32-1ubuntu1).
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gconf-service gconf-service-backend gconf2-common libdbus-glib-1-2
The following NEW packages will be installed:
gconf-service gconf-service-backend gconf2-common libdbus-glib-1-2
libgconf-2-4 xvfb
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import yfinance as yf
stocks = ["NEXCF", "ACB.TO"]
stocks_string = " ".join(stocks)
def enable_plotly_in_cell():
import IPython
from plotly.offline import init_notebook_mode
display(IPython.core.display.HTML('''<script src="/static/components/requirejs/require.js"></script>'''))
init_notebook_mode(connected=False)
enable_plotly_in_cell()
data = yf.download(stocks_string, start="2019-06-01", end="2020-04-30",
group_by="ticker")
data = data.fillna(method='ffill')
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
import plotly.io as pio
pio.renderers.default = "svg" # Toggle for GitHub rendering.
nyse = pd.concat([data[ticker]["Close"] for ticker in stocks], axis=1)
nyse.columns = stocks
print(nyse)
from mlfinlab.online_portfolio_selection.benchmarks import *
nyse_bah = BAH()
nyse_bah.allocate(nyse)
nyse_beststock = BestStock()
nyse_beststock.allocate(nyse)
nyse_crp = CRP()
nyse_crp.allocate(nyse)
nyse_bcrp = BCRP()
nyse_bcrp.allocate(nyse)
fig = go.Figure()
idx = nyse_bah.portfolio_return.index
fig.add_trace(go.Scatter(x=idx, y=nyse_beststock.portfolio_return['Returns'], name="Best Stock"))
fig.add_trace(go.Scatter(x=idx, y=nyse_bah.portfolio_return['Returns'], name="Buy and Hold"))
fig.add_trace(go.Scatter(x=idx, y=nyse_crp.portfolio_return['Returns'], name="CRP"))
fig.add_trace(go.Scatter(x=idx, y=nyse_bcrp.portfolio_return['Returns'], name="BCRP"))
fig.update_layout(title='Benchmarks on NYSE', xaxis_title='Date', yaxis_title='Relative Returns')
fig.show()
import plotly.io as pio
pio.write_image(fig, "gantt_suma.png", scale=3)
[*********************100%***********************] 2 of 2 completed
NEXCF ACB.TO
Date
2019-06-03 0.45000 115.680000
2019-06-04 0.45000 126.720001
2019-06-05 0.44500 124.440002
2019-06-06 0.44284 120.839996
2019-06-07 0.44000 121.559998
... ... ...
2020-04-23 1.18500 12.120000
2020-04-24 1.16000 12.360000
2020-04-27 1.12000 12.840000
2020-04-28 1.21000 12.480000
2020-04-29 1.20000 12.720000
[233 rows x 2 columns]
