# Probability parserΒΆ

`bayesloop`

as a probabilistic programming framework is focused on
two-level hierarchical models for time series analysis, and is therefore
not as flexible as other frameworks.
PyMC3, for example, allows the
user to create hierarchical models of any structure and to apply
arbitrary arithmetic operations on the random variables contained in the
model. While `bayesloop`

allows the user to build custom low-level
(observation) models and choose from a variety of high-level
(transition) models, one has no direct influence on the choice of
parameters. In other words, the `Gaussian`

observation model includes
the parameters *mean* and *standard deviation*, but there is no direct
way of evaluating the *variance* or the *ratio of mean and standard
deviation* instead. In many applications, however, these combinations or
transformed variables are the key to informed desicions! In finance, for
example, the performance of a financial asset is often evaluated by its
Sharpe ratio \(S\):

where \(\mu\) is the expected return of the financial asset,
\(\mu_0\) is the risk-free return, and \(\sigma\) is the
volatility. Assuming Gaussian returns, we can obtain time-varying
estimates of \(\mu\) and \(\sigma\) with a simple `Gaussian`

observation model with `bayesloop`

, while \(\mu_0\) is just a
series of given numbers we may obtain from the central bank.

This tutorial shows how to evaluate arithmetic combinations of inferred
parameter distributions after the model has been fitted, using the
`eval`

method of the `Study`

class and the `Parser`

class. With
these tools, we compute the probability that the time-varying Sharpe
ratio of a simulated series of returns is greater than one (a common
threshold for investing in financial assets).

First, we simulate a fictious series of price fluctuations. Here we assume gradual changes in the expected return, the risk-free return and the volatility. Based on the expected return and the volatility, we sample the observed price fluctuations based on a Gaussian distribution:

```
In [1]:
```

```
%matplotlib inline
import matplotlib.pyplot as plt # plotting
import seaborn as sns # nicer plots
sns.set_style('whitegrid') # plot styling
sns.set_color_codes()
import bayesloop as bl
import numpy as np
mu_sim = np.concatenate([np.linspace(-0.1, 0.3, 250), np.linspace(0.3, 0.1, 250)])
sigma_sim = np.linspace(0.05, 0.2, 500)
mu0 = np.linspace(0.02, 0.01, 500)
sharpe_sim = (mu_sim-mu0)/sigma_sim
np.random.seed(123456)
data = mu_sim + sigma_sim*np.random.randn(500)
plt.figure(figsize=(10, 11))
plt.subplot2grid((7, 1), (0, 0))
plt.plot(mu_sim, c='b', lw=3, label='Expected return $\mathregular{\mu}$')
plt.xticks([100, 200, 300, 400], ['', '', '', ''])
plt.xlim([0, 500])
plt.legend(loc='upper left')
plt.subplot2grid((7, 1), (1, 0))
plt.plot(mu0, c='g', lw=3, label='Risk-free return $\mathregular{\mu_0}$')
plt.xticks([100, 200, 300, 400], ['', '', '', ''])
plt.xlim([0, 500])
plt.legend(loc='upper right')
plt.subplot2grid((7, 1), (2, 0))
plt.plot(sigma_sim, c='r', lw=3, label='Volatility $\mathregular{\sigma}$')
plt.xticks([100, 200, 300, 400], ['', '', '', ''])
plt.xlim([0, 500])
plt.legend(loc='upper left')
plt.subplot2grid((7, 1), (3, 0), rowspan=2)
plt.plot(sharpe_sim, lw=4, c='orange', label='Sharpe ratio S')
plt.xticks([100, 200, 300, 400], ['', '', '', ''])
plt.axhline(y=1, c='0.2', ls='dashed', lw=1, label='Investment threshold (S=1)')
plt.xlim([0, 500])
plt.legend(loc='lower right')
plt.subplot2grid((7, 1), (5, 0), rowspan=2)
plt.plot(data, c='0.2', lw=1.5, label='Simulated price fluctuations')
plt.xlim([0, 500])
plt.ylim([-0.65, 0.65])
plt.legend(loc='lower left')
plt.xlabel('Time [arbitrary units]');
```

To infer the time-varying expected return \(\mu\) and volatility
\(\sigma\) from the simulated price fluctuations, we assume a
`Gaussian`

observation model. The two parameters of this observation
model are themselves subject to a `GaussianRandomWalk`

, the transition
model of the study. For simplicity, we set fixed magnitudes for the
parameter fluctuations (`sigma_mu`

, `sigma_stdev`

):

```
In [2]:
```

```
S = bl.Study()
S.load(data)
L = bl.om.Gaussian('mu', bl.cint(-0.5, 0.5, 100), 'sigma', bl.oint(0, 0.5, 100))
T = bl.tm.CombinedTransitionModel(
bl.tm.GaussianRandomWalk('sigma_mu', 0.01, target='mu'),
bl.tm.GaussianRandomWalk('sigma_stdev', 0.005, target='sigma')
)
S.set(L, T)
S.fit()
```

```
+ Created new study.
+ Successfully imported array.
+ Observation model: Gaussian observations. Parameter(s): ['mu', 'sigma']
+ Transition model: Combined transition model. Hyper-Parameter(s): ['sigma_mu', 'sigma_stdev']
+ Started new fit:
+ Formatted data.
+ Set prior (function): jeffreys. Values have been re-normalized.
```

```
+ Finished forward pass.
+ Log10-evidence: 130.79643
```

```
+ Finished backward pass.
+ Computed mean parameter values.
```

Plotting the inferred expected return and volatility against the true underlying values used in the simulation, we find that the time-varying parameter model we defined above nicely captures the variations in the performance of the fictious financial asset:

```
In [3]:
```

```
plt.figure(figsize=(10, 3))
plt.subplot2grid((1, 2), (0, 0))
S.plot('mu', color='b', label='inferred expected return')
plt.plot(mu_sim, lw=1.5, c='red', ls='dashed', alpha=0.5, label='true expected return')
plt.ylim([-0.2, 0.5])
plt.legend(loc='lower right')
plt.subplot2grid((1, 2), (0, 1))
S.plot('sigma', color='r', label='inferred volatility')
plt.plot(sigma_sim, lw=1.5, c='blue', ls='dashed', alpha=0.5, label='true volatility')
plt.ylim([0, 0.3])
plt.legend(loc='upper left');
```

Based on these inferred parameter distributions, we can now evaluate
probabilities in the form of inequalities using the `eval`

function of
the `Study`

class. For example, we can evaluate the probability of a
positive expected return at time step 50:

```
In [4]:
```

```
S.eval('mu > 0.', t=50);
```

```
P(mu > 0.) = 0.2486016146556542
```

By default, `eval`

prints out the resulting probability. This behavior
can be controlled with the keyword-argument `silent`

. `eval`

further
returns the value, for further processing:

```
In [5]:
```

```
p = S.eval('mu > 0.', t=50, silent=True)
print(p)
```

```
0.248601614656
```

The keyword-argument `t`

is not the only way to define the time step
at which to evaluate the probability value. One can also use the `@`

operator. In this example, we ask for the probability of a volatility
value smaller than 0.2 at `t=400`

:

```
In [6]:
```

```
S.eval('sigma@400 < 0.2');
```

```
P(sigma@400 < 0.2) = 0.934624296075193
```

The evaluation of probability values is not restricted to a single time
step, one may also be interested whether the expected return at
`t=100`

is greater than at `t=400`

. Note that `bayesloop`

does not
infer the joint parameter distribution of all time steps, but the
conditional joint parameter distributions for each time step
iteratively. This means that correlations between the parameter at
different time steps are ignored by the `eval`

method. Further note
that comparing parameters at different time steps may result in a fairly
long computation time and a lot of RAM usage, as `bayesloop`

has to
take into account all possible combinations of parameter values.

```
In [7]:
```

```
S.eval('mu@100 > mu@400');
```

```
P(mu@100 > mu@400) = 0.0007986199467668083
```

The `eval`

method further computes arithmetic combinations of
different parameters. In our example, `bayesloop`

computes the joint
parameter distribution of `mu`

and `sigma`

for each time step. When
we ask for the probability that the ratio of `mu`

and `sigma`

is
greater than one, `eval`

computes the ratio of the parameters from
their joint distribution:

```
In [8]:
```

```
S.eval('mu/sigma > 1', t=100);
```

```
P(mu/sigma > 1) = 0.12878704018028345
```

The parameter ratio above does not yet resemble the Sharpe ratio
\(S\), as the risk-free return is not inserted yet. The `eval`

method does not only support the parsing of parameter names, but also
the parsing of plain numbers. Below, we first check the risk-free return
at `t=100`

, and then insert this number into the query of the `eval`

method. We discover that at `t=100`

, the probability that the Sharpe
ratio \(S\) exceeds the value of one is only about 2.3%:

```
In [9]:
```

```
mu0[100]
```

```
Out[9]:
```

```
0.017995991983967938
```

```
In [10]:
```

```
S.eval('(mu - 0.018)/sigma > 1', t=100);
```

```
P((mu - 0.018)/sigma > 1) = 0.023246959985844733
```

Of course, we can also insert the risk-free return value directly, via string manipulation:

```
In [11]:
```

```
S.eval('(mu - ' + str(mu0[100]) + ')/sigma > 1', t=100);
```

```
P((mu - 0.017995991984)/sigma > 1) = 0.023246959985844733
```

The `eval`

method contains a bit of computational overhead, as it has
to pre-process all parameter values before every single query. As we
want to compute \(p(S > 1)\) for *all* 500 time steps, we are better
off by using a `Parser`

instance. The `Parser`

is initialized only
once, and then queried repeatedly without computational overhead. Below
we show one example with a single query to the `Parser`

instance, and
subsequently repeat the query for all time steps:

```
In [12]:
```

```
P = bl.Parser(S)
P('(mu - ' + str(mu0[100]) + ')/sigma > 1', t=100);
```

```
P((mu - 0.017995991984)/sigma > 1) = 0.023246959985844733
```

```
In [13]:
```

```
p_invest = []
for t in range(500):
p_invest.append(P('(mu - ' + str(mu0[t]) + ')/sigma > 1', t=t, silent=True))
```

Finally, we may plot the probability that our probabilistic, time-varying Sharpe ratio \(S\) is greater than one for each time step, together with the underlying true values of \(S\) in the simulation. We find that \(p(S > 1)\) attains a value of \(\approx 0.5\) at the times when \(S=1\) in the simulation, and increases further as \(S > 1\). Consequently, we find that \(p(S > 1) < 0.5\) for \(S < 1\) in the simulation. This example thereby illustrates how the combination of inferred time-varying parameters can help to create robust indicators for desicion making in the presence of uncertainty.

```
In [14]:
```

```
plt.figure(figsize=(10, 6))
plt.subplot2grid((2, 1), (0, 0))
plt.plot(sharpe_sim, lw=4, c='orange', label='Sharpe ratio S')
plt.xticks([100, 200, 300, 400], ['', '', '', ''])
plt.axhline(y=1, c='0.2', ls='dashed', lw=1, label='Investment threshold (S=1)')
plt.xlim([0, 500])
plt.legend(loc='lower right')
plt.subplot2grid((2, 1), (1, 0))
plt.plot(p_invest, c='0.3', lw=2)
plt.fill_between(range(500), p_invest, 0, color='k', alpha=0.2)
plt.xlim([0, 500])
plt.ylim([-0.05, 1.05])
plt.xlabel('Time')
plt.ylabel('p(S > 1)');
```

So far, we have parsed either parameter names or plain numbers in the
queries to the `eval`

method or the `Parser`

instance. In many
applications, however, one also needs to compute the `sqrt`

, or the
`cos`

of a parameter. `bayesloop`

‘s probability parser therefore
allows to parse any function contained in the `numpy`

module. Note,
however, that only functions which preserve the shape of an input array
will return valid results (e.g. using `sum`

will not work).

```
In [15]:
```

```
P('sin(mu) + cos(sigma) > 1', t=50);
```

```
P(sin(mu) + cos(sigma) > 1) = 0.2479001934193268
```

On a final note, the `Parser`

class also handles (hyper-)parameters of
different study instances. For example, one could imagine to run one
study `S1`

(which may be a `Study`

, `HyperStudy`

,
`ChangepointStudy`

or an `OnlineStudy`

) to analyze price
fluctuations of a financial asset, and another study `S2`

to analyze
the frequency of market-related Twitter messages. After fitting data
points, one can initialize a `Parser`

instance that takes into account
both studies:

```
P = bl.Parser(S1, S2)
```

Note that the two studies `S1`

and `S2`

must not contain duplicate
(hyper-)parameter names! This allows to create performance indicators
that take into account different kinds of information (e.g. pricing data
and social media activity).