Compare stock indices of different sizes Python
I am using Python to try and do some macroeconomic analysis of different stock markets. I was wondering about how to properly compare indices of varying sizes. For instance, the Dow Jones is around 25,000 on the y-axis, while the Russel 2000 is only around 1,500. I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis. Is there some statistical method where I can do this same thing in Python?
python plot statistics correlation
asked Nov 15 '18 at 3:51
yqz09yqz09
14
add a comment |
I am using Python to try and do some macroeconomic analysis of different stock markets. I was wondering about how to properly compare indices of varying sizes. For instance, the Dow Jones is around 25,000 on the y-axis, while the Russel 2000 is only around 1,500. I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis. Is there some statistical method where I can do this same thing in Python?
python plot statistics correlation
asked Nov 15 '18 at 3:51
yqz09yqz09
14
add a comment |
I am using Python to try and do some macroeconomic analysis of different stock markets. I was wondering about how to properly compare indices of varying sizes. For instance, the Dow Jones is around 25,000 on the y-axis, while the Russel 2000 is only around 1,500. I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis. Is there some statistical method where I can do this same thing in Python?
python plot statistics correlation
asked Nov 15 '18 at 3:51
yqz09yqz09
14
I am using Python to try and do some macroeconomic analysis of different stock markets. I was wondering about how to properly compare indices of varying sizes. For instance, the Dow Jones is around 25,000 on the y-axis, while the Russel 2000 is only around 1,500. I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis. Is there some statistical method where I can do this same thing in Python?
python plot statistics correlation
python plot statistics correlation
asked Nov 15 '18 at 3:51
yqz09yqz09
14
asked Nov 15 '18 at 3:51
yqz09yqz09
14
asked Nov 15 '18 at 3:51
yqz09yqz09
14
asked Nov 15 '18 at 3:51
yqz09yqz09
14
asked Nov 15 '18 at 3:51
yqz09yqz09
14
14
add a comment |
add a comment |
2 Answers
2
active
oldest
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I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis.
These websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.
The other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
add a comment |
You have multiple possibilities here. Let's say you define your axis by the following call
fig, ax = plt.subplots()
Then, you can change the scale of the y axis to logarithmic using
ax.set_yscale('log')
You can also define two y axes inside the same plot with different scales with the call
ax2 = ax.twinx()
and then plot, let's say, big values on ax and small ones on ax2. That will only work well if you have two ranges of values at most.
Another solution is to create a new axis which zooms inside your plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
ax2 = zoomed_inset_axes(ax, zoom, bbox_to_anchor=(, ),
bbox_transform=ax.transAxes, loc='', borderpad=)
A last thing would be to directly scale your data. For example, if DowJones varies between 20,000 and 30,000, then you can apply the following transformation
DowJones = (DowJones - min(DowJones)) / (max(DowJones) - min(DowJones))
and then your values will vary between 0 and 1. Applying similar transformations to other variables will then allow you to compare variations more easily on the same graph without making any change to the axes.
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
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oldest
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active
oldest
votes
I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis.
These websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.
The other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
add a comment |
I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis.
These websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.
The other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
add a comment |
I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis.
These websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.
The other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink/enlarge a background chart so that it matches the other on a new y-axis.
These websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.
The other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
answered Nov 15 '18 at 4:33
Askar AkhmedovAskar Akhmedov
355
355
add a comment |
add a comment |
You have multiple possibilities here. Let's say you define your axis by the following call
fig, ax = plt.subplots()
Then, you can change the scale of the y axis to logarithmic using
ax.set_yscale('log')
You can also define two y axes inside the same plot with different scales with the call
ax2 = ax.twinx()
and then plot, let's say, big values on ax and small ones on ax2. That will only work well if you have two ranges of values at most.
Another solution is to create a new axis which zooms inside your plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
ax2 = zoomed_inset_axes(ax, zoom, bbox_to_anchor=(, ),
bbox_transform=ax.transAxes, loc='', borderpad=)
A last thing would be to directly scale your data. For example, if DowJones varies between 20,000 and 30,000, then you can apply the following transformation
DowJones = (DowJones - min(DowJones)) / (max(DowJones) - min(DowJones))
and then your values will vary between 0 and 1. Applying similar transformations to other variables will then allow you to compare variations more easily on the same graph without making any change to the axes.
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
add a comment |
You have multiple possibilities here. Let's say you define your axis by the following call
fig, ax = plt.subplots()
Then, you can change the scale of the y axis to logarithmic using
ax.set_yscale('log')
You can also define two y axes inside the same plot with different scales with the call
ax2 = ax.twinx()
and then plot, let's say, big values on ax and small ones on ax2. That will only work well if you have two ranges of values at most.
Another solution is to create a new axis which zooms inside your plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
ax2 = zoomed_inset_axes(ax, zoom, bbox_to_anchor=(, ),
bbox_transform=ax.transAxes, loc='', borderpad=)
A last thing would be to directly scale your data. For example, if DowJones varies between 20,000 and 30,000, then you can apply the following transformation
DowJones = (DowJones - min(DowJones)) / (max(DowJones) - min(DowJones))
and then your values will vary between 0 and 1. Applying similar transformations to other variables will then allow you to compare variations more easily on the same graph without making any change to the axes.
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
add a comment |
You have multiple possibilities here. Let's say you define your axis by the following call
fig, ax = plt.subplots()
Then, you can change the scale of the y axis to logarithmic using
ax.set_yscale('log')
You can also define two y axes inside the same plot with different scales with the call
ax2 = ax.twinx()
and then plot, let's say, big values on ax and small ones on ax2. That will only work well if you have two ranges of values at most.
Another solution is to create a new axis which zooms inside your plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
ax2 = zoomed_inset_axes(ax, zoom, bbox_to_anchor=(, ),
bbox_transform=ax.transAxes, loc='', borderpad=)
A last thing would be to directly scale your data. For example, if DowJones varies between 20,000 and 30,000, then you can apply the following transformation
DowJones = (DowJones - min(DowJones)) / (max(DowJones) - min(DowJones))
and then your values will vary between 0 and 1. Applying similar transformations to other variables will then allow you to compare variations more easily on the same graph without making any change to the axes.
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
You have multiple possibilities here. Let's say you define your axis by the following call
fig, ax = plt.subplots()
Then, you can change the scale of the y axis to logarithmic using
ax.set_yscale('log')
You can also define two y axes inside the same plot with different scales with the call
ax2 = ax.twinx()
and then plot, let's say, big values on ax and small ones on ax2. That will only work well if you have two ranges of values at most.
Another solution is to create a new axis which zooms inside your plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
ax2 = zoomed_inset_axes(ax, zoom, bbox_to_anchor=(, ),
bbox_transform=ax.transAxes, loc='', borderpad=)
A last thing would be to directly scale your data. For example, if DowJones varies between 20,000 and 30,000, then you can apply the following transformation
DowJones = (DowJones - min(DowJones)) / (max(DowJones) - min(DowJones))
and then your values will vary between 0 and 1. Applying similar transformations to other variables will then allow you to compare variations more easily on the same graph without making any change to the axes.
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
edited Nov 15 '18 at 5:29
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
answered Nov 15 '18 at 4:14
Patol75Patol75
6236
6236
add a comment |
add a comment |
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