#!/usr/bin/env python
# coding: utf-8

# <a href="https://colab.research.google.com/github/cyrus723/my-first-binder/blob/main/jupyter_widgets1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# # **Jupyter Widgets**
# 
# ## This is what I learned from SciPy 2022 Conference held in Austin during July. I met Itay Dafna from Bloomberg who showed how to intergrate Jupyter Widgets in presenting market data.
# 
# ### A Python widget is an object that represents a control on the front end, like a slider. A single control can be displayed multiple times - they all represent the same python object.

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get_ipython().system('pip install ipywidgets')


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import ipywidgets as widgets


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slider = widgets.FloatSlider(
    value=8.0,
    min=5.0,
    max=10.0,
    step=0.1,
    description='Input:',
)

slider


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import ipywidgets as ipw


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def f(x):
    print(x * x)


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widgets.interact(f, x=(0, 100, 1));


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pip install quandl


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import matplotlib.pyplot as plt
import pandas as pd

import quandl as ql
get_ipython().run_line_magic('matplotlib', 'inline')

yield_ = ql.get("USTREASURY/YIELD")
yield_


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today = yield_.iloc[-1,:]
month_ago = yield_.iloc[-30,:]
df = pd.concat([today, month_ago], axis=1)
df.columns = ['today', 'month_ago']

df.plot(style={'today': 'ro-', 'month_ago': 'bx--'}
        ,title='Treasury Yield Curve, %');


# # A simple Python zero-coupon bond calculator #
# A zero-coupon bond is a bond that does not pay any periodic interest except on maturity, where the principal or face value is repaid. Zero-coupon bonds are also called pure discount bonds.
# <br>
# $$price \ of \ zero \ coupon \ bond = \frac{Face\ Value}{(1+YTM)^t} $$
# <br>
# 
# 
# #####LaTex:  https://towardsdatascience.com/write-markdown-latex-in-the-jupyter-notebook-10985edb91fd

# A coupon bond is a bond that pays periodic interests plus par value on maturity. A price of a bond is the sum of all present values of coupon and principal. 
# <br>
# $$price \ of \ a \ coupon \ bond = \Sigma \frac{Coupon}{(1+YTM)^t} + \frac{Par \ Value}{(1 + YTM)^t} 
# $$
# <br><p>
# 
# ##### In order to find YTM, the following code is use scipy optimize.Newton iterative process.
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html
# 

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def bondprice(ytm, period, par, coupon):
  price_bond = par*coupon/2*(1-(1+ytm/2)**(-2*period))/(ytm/2) + par/(1+ytm/2)**(2*period)
  print("${:.2f}".format(price_bond))


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bondprice(0.09, 20, 1000, 0.08)


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def bondprice2(ytm, period, par, coupon):
  price_bond = par*coupon/2*(1-(1+ytm/2)**(-2*period))/(ytm/2) + par/(1+ytm/2)**(2*period)
  return price_bond


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import pandas as pd
import numpy as np
yield_b = np.linspace(0.03, 0.7, num=69)
#yield_b


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price_b = np.zeros([69,1])
price_b.shape


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for ind in range(3, 72):
  price_b[ind-3]=(bondprice2(ind/100, period=2, par=1000, coupon=0.08))

#print(price_b)
print(yield_b.shape)
print(price_b.shape)


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import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
plt.rcParams['figure.figsize'] = [8, 5]
plt.plot(yield_b, price_b)
plt.show()


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widgets.interact(bondprice, ytm=(0.05, 0.7, 0.01), period=(1,30, 1), par=(100,1000, 100), coupon=(0.05, 0.7, 0.01))


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import scipy.optimize as optimize

def bond_ytm(price, par, T, coup, freq=2, guess=0.05):
    freq = float(freq)
    periods = T*2
    coupon = coup/100.*par
    dt = [(i+1)/freq for i in range(int(periods))]
    ytm_func = lambda y: \
        sum([coupon/freq/(1+y/freq)**(freq*t) for t in dt]) +\
        par/(1+y/freq)**(freq*T) - price
    
    return optimize.newton(ytm_func, guess)


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# a 5.75% bond that will mature in 1.5 years with a par value of $100. The price is $95.0428. and coupons are paid semiannually. YTM=9.3692%
ytm = bond_ytm(price=100, par=100, T=1.5, coup=5.75, freq=2)
print("{:.4f}%".format(ytm*100))


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# %load solutions/intro/intro-example.py


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get_ipython().system('pip install bqplot')


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import numpy as np
import bqplot as bq


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from google.colab import output
output.enable_custom_widget_manager()


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from google.colab import output
output.disable_custom_widget_manager()


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xs = bq.LinearScale()
ys = bq.LinearScale()
x = np.arange(100)
y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks

line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])
xax = bq.Axis(scale=xs, label='x', grid_lines='solid')
yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')

fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000)
display(fig)


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# update data of the line mark
line.y = np.cumsum(np.random.randn(2, 100), axis=1)


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pip install ipympl


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get_ipython().run_line_magic('matplotlib', 'ipympl')


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import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import VBox, FloatSlider


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with plt.ioff():
    fig, ax = plt.subplots()

x1 = np.linspace(0, 20, 500)

slider = FloatSlider(
    value=1.0,
    min=0.02,
    max=2.0
)

lines = plt.plot(x1, np.sin(slider.value  * x1))

def update_lines(change):
    lines[0].set_data(x1, np.sin(change.new * x1))
    fig.canvas.draw()

slider.observe(update_lines, names='value')

VBox([slider, fig.canvas])


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with plt.ioff():
    fig, ax = plt.subplots()

#x1 = np.linspace(0, 20, 500)
x1 = np.linspace(0.03, 0.5, num=50)

slider = FloatSlider(
    value=1.0,
    min=0.02,
    max=2.0
)

#lines = plt.plot(x1, np.sin(slider.value  * x1))
lines = plt.plot(x1, price_b)

def update_lines(change):
    lines[0].set_data(x1, np.sin(change.new * x1))
    fig.canvas.draw()

slider.observe(update_lines, names='value')

VBox([slider, fig.canvas])


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# ## Additional reading
# 
# ### Behind the scenes
# 
# Behind the scenes, even pure Python widgets are composed of two pieces:
# 
# + Python, which runs in the notebook kernel.
# + JavaScript, which runs in the browser.
# 
# When writing your own widgets, that means making a choice: write only in Python or write in both Python and Javascript.
# 
# Which to choose depends on what you are trying to accomplish. This tutorial will focus on writing your own widgets in pure Python. An example of a pure-Python package that includes some interesting interfaces is [reducer](http://reducer.readthedocs.io), a package for calibrating astronomical data.

# ### Jupyter widgets as a framework
# 
# Jupyter widgets forms a framework for representing python objects interactively. Some large open-source interactive controls based on Jupyter widgets include:
# 
#  - [bqplot](https://github.com/bqplot/bqplot/blob/master/examples/Index.ipynb) - 2d plotting library in which everything displayed is a widget
#  - [ipympl](https://github.com/matplotlib/ipympl) - widget backend for [matplotlib](https://matplotlib.org/3.2.2/contents.html) graphics
#  - [pythreejs](https://pythreejs.readthedocs.io/en/stable/index.html) - low-level 3d graphics library
#  - [ipyvolume](https://ipyvolume.readthedocs.io/en/latest/) - 3d plotting and volume rendering
#  - [ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/)_ - interactive maps
#  - [ipywebrtc](https://github.com/maartenbreddels/ipywebrtc) - video streaming
#  - [ipysheet](https://ipysheet.readthedocs.io/en/latest/) - interactive spreadsheets
#  - [ipytree](https://github.com/QuantStack/ipytree) - tree for viewing hierarchical material
#  - [ipycanvas](https://ipycanvas.readthedocs.io/en/latest/?badge=latest) - interactive drawing in a notebook
#  - [ipyevents](https://github.com/mwcraig/ipyevents/blob/master/doc/Widget%20DOM%20Events.ipynb) - capture mouse/keyboard events on a widget
#  - ...