2 + 2

4

X = 2

2 + 2
3 + 3

6

print(2 + 2)
3 + 3

4

6

#Take some time to run something
c = 0
for i in range(10000000):
    c = c + i
c

49999995000000

import numpy as np
import pandas as pd

# This is a plotting library for pretty pictures.
import matplotlib.pyplot as plt

np.random.

np.random.normal?

Docstring:
normal(loc=0.0, scale=1.0, size=None)

Draw random samples from a normal (Gaussian) distribution.

The probability density function of the normal distribution, first
derived by De Moivre and 200 years later by both Gauss and Laplace
independently [2]_, is often called the bell curve because of
its characteristic shape (see the example below).

The normal distributions occurs often in nature.  For example, it
describes the commonly occurring distribution of samples influenced
by a large number of tiny, random disturbances, each with its own
unique distribution [2]_.

.. note::
    New code should use the ``normal`` method of a ``default_rng()``
    instance instead; please see the :ref:`random-quick-start`.

Parameters
----------
loc : float or array_like of floats
    Mean ("centre") of the distribution.
scale : float or array_like of floats
    Standard deviation (spread or "width") of the distribution. Must be
    non-negative.
size : int or tuple of ints, optional
    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
    ``m * n * k`` samples are drawn.  If size is ``None`` (default),
    a single value is returned if ``loc`` and ``scale`` are both scalars.
    Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.

Returns
-------
out : ndarray or scalar
    Drawn samples from the parameterized normal distribution.

See Also
--------
scipy.stats.norm : probability density function, distribution or
    cumulative density function, etc.
Generator.normal: which should be used for new code.

Notes
-----
The probability density for the Gaussian distribution is

.. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
                 e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },

where :math:`\mu` is the mean and :math:`\sigma` the standard
deviation. The square of the standard deviation, :math:`\sigma^2`,
is called the variance.

The function has its peak at the mean, and its "spread" increases with
the standard deviation (the function reaches 0.607 times its maximum at
:math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
normal is more likely to return samples lying close to the mean, rather
than those far away.

References
----------
.. [1] Wikipedia, "Normal distribution",
       https://en.wikipedia.org/wiki/Normal_distribution
.. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
       Random Variables and Random Signal Principles", 4th ed., 2001,
       pp. 51, 51, 125.

Examples
--------
Draw samples from the distribution:

>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)

Verify the mean and the variance:

>>> abs(mu - np.mean(s))
0.0  # may vary

>>> abs(sigma - np.std(s, ddof=1))
0.1  # may vary

Display the histogram of the samples, along with
the probability density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
...          linewidth=2, color='r')
>>> plt.show()

Two-by-four array of samples from N(3, 6.25):

>>> np.random.normal(3, 2.5, size=(2, 4))
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random
Type:      builtin_function_or_method

# Sample 100 points with a mean of 0 and an std of 1. This is a standard normal distribution.
X = np.random.normal(0, 1, 100)

plt.plot(X)

[<matplotlib.lines.Line2D at 0x7f05f4b8b910>]

plt.plot(X);

X = np.random.normal(0, 1, 100)
X2 = np.random.normal(0, 1, 100)

plt.plot(X);
plt.plot(X2);
plt.xlabel('Time') # The data we generated is unitless, but don't forget units in general.
plt.ylabel('Returns')
plt.legend(['X', 'X2']);

np.mean(X)

-0.0277202641426269

np.std(X)

0.9469736260380544

from quantrocket.master import get_securities
securities = get_securities(symbols='MSFT', fields=['Sid','Symbol','Exchange'], vendors='usstock')
securities

MSFT = securities.index[0]
from quantrocket import get_prices
data = get_prices("usstock-free-1min", data_frequency='daily', sids=MSFT, start_date='2012-01-01', end_date='2015-06-01', fields="Close")

data.head()

X = data.loc['Close'][MSFT]

plt.plot(X.index, X.values)
plt.ylabel('Price')
plt.legend(['MSFT']);

np.mean(X)

35.30557759626604

np.std(X)

7.173065802833083

R = X.pct_change()[1:]

plt.hist(R, bins=20)
plt.xlabel('Return')
plt.ylabel('Frequency')
plt.legend(['MSFT Returns']);

np.mean(R)

0.0008173587738800675

np.std(R)

0.014426725203892899

plt.hist(np.random.normal(np.mean(R), np.std(R), 10000), bins=20)
plt.xlabel('Return')
plt.ylabel('Frequency')
plt.legend(['Normally Distributed Returns']);

# Take the average of the last 60 days at each timepoint.
MAVG = X.rolling(window=60).mean()
plt.plot(X.index, X.values)
plt.plot(MAVG.index, MAVG.values)
plt.ylabel('Price')
plt.legend(['MSFT', '60-day MAVG']);

	Sid	FIBBG000BPH459
Field	Date
Close	2012-01-03	25.657
	2012-01-04	26.265
	2012-01-05	26.534
	2012-01-06	26.941
	2012-01-09	26.591

Introduction to Notebooks¶

Code Cells vs. Text Cells¶

Executing a Command¶

Knowing When a Cell is Running¶

Importing Libraries¶

Tab Autocomplete¶

Getting Documentation Help¶

Sampling¶

Plotting¶

Squelching Line Output¶

Adding Axis Labels¶

Generating Statistics¶

Getting Real Pricing Data¶

Getting Returns from Prices¶

Generating a Moving Average¶

	Symbol	Exchange
Sid
FIBBG000BPH459	MSFT	XNAS