TSHISTORY
===========

This is a library to store/retrieve pandas timeseries to/from a
postgres database, tracking their successive versions.

[TOC]

# Introduction

## Purpose

`tshistory` is targetted at applications using time series where
[backtesting][backtesting] and [cross-validation][cross-validation]
are an essential feature.

It provides exhaustivity and efficiency of the storage, with a simple
Python api.

It can be used as a building block for machine learning, model
optimization and validation, both for inputs and outputs.


## Principles

There are many ways to represent timeseries in a relational database,
and `tshistory` provides two things:

* a base python API which abstracts away the underlying storage

* a postgres model, which emphasizes the compact storage of successive
  states of series

The core idea of tshistory is to handle successive versions of
timeseries as they grow in time, allowing to get older states of any
series.


# Basic usage

## Starting with a fresh database

You need a postgresql database. You can create one like this:

```shell
 createdb mydb
```

Then, initialize the `tshistory` tables, like this:

```python
 tsh init-db postgresql://me:password@localhost/mydb
```

From this you're ready to go !


## Creating a series

However here's a simple example:

```python
 >>> from sqlalchemy import create_engine
 >>> import pandas as pd
 >>> from tshistory.tsio import timeseries
 >>>
 >>> engine = create_engine('postgres://me:password@localhost/mydb')
 >>> tsh = timeseries()
 >>>
 >>> series = pd.Series([1, 2, 3],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 1),
 ...                                  freq='D', periods=3))
 # db insertion
 >>> with engine.begin() as cn:
 >>>     tsh.insert(cn, series, 'my_series', 'babar@pythonian.fr')
 ...
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Freq: D, Name: my_series, dtype: float64

 # note how our integers got turned into floats
 # (there are no provisions to handle integer series as of today)

 # retrieval
 >>> with engine.begin() as cn:
 >>>     tsh.get(cn, 'my_series')
 ...
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
```

## Updating a series

This is good. Now, let's insert more:

```python
 >>> series = pd.Series([2, 7, 8, 9],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 2),
 ...                                  freq='D', periods=4))
 # db insertion
 >>> with engine.begin() as cn:
 >>>     tsh.insert(cn, series, 'my_series', 'babar@pythonian.fr')
 ...
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64

 # you get back the *new information* you put inside
 # and this is why the `2` doesn't appear (it was already put
 # there in the first step)

 # db retrieval
 >>> with engine.begin() as cn:
 >>>     tsh.get(engine, 'my_series')
 ...
2017-01-01    1.0
2017-01-02    2.0
2017-01-03    7.0
2017-01-04    8.0
2017-01-05    9.0
Name: my_series, dtype: float64
```

It is important to note that the third value was *replaced*, and the two
last values were just *appended*.

As noted the point at `2017-1-2` wasn't a new information so it was
just ignored.


## Retrieving history

We can access the whole history (or parts of it) in one call:

```python
 >>> with engine.begin() as cn:
 >>>     history = tsh.history(engine, 'my_series')
 ...
 >>>
 >>> for idate, series in history.items(): # it's a dict
 ...     print('insertion date:', idate)
 ...     print(series)
 ...
 insertion date: 2018-09-26 17:10:36.988920+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
 insertion date: 2018-09-26 17:12:54.508252+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64
```

Note how this shows the full serie state for each insertion date.
Also the insertion date is timzeone aware.

It is possible to show the differences only:

```python
 >>> with engine.begin() as cn:
 >>>     diffs = tsh.history(engine, 'my_series', diffmode=True)
 ...
 >>> for idate, series in tsh.history(engine, 'ts', diffmode=True).items():
 ...   print('insertion date:', idate)
 ...   print(series)
 ...
 insertion date: 2018-09-26 17:10:36.988920+02:00
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
 insertion date: 2018-09-26 17:12:54.508252+02:00
 2017-01-03    7.0
 2017-01-04    8.0
 2017-01-05    9.0
 Name: my_series, dtype: float64
```

You can see a series metadata:

```python
 >>> tsh.metadata(engine, 'my_series')
 {'tzaware': False, 'index_type': 'datetime64[ns]', 'value_type': 'float64',
 'index_dtype': '<M8[ns]', 'value_dtype': '<f8'}
```

We built a series with naive time stamps, but timezone-aware
timestamps work well (and it is advised to use them !).


# Command line

## Basic operations

A command line tool is provided, called `tsh`. It provides its usage
guidelines:

```shell
 $ tsh
 Usage: tsh [OPTIONS] COMMAND [ARGS]...

 Options:
   --help  Show this message and exit.

Commands:
  check    coherence checks of the db
  get      show a serie in its current state
  history  show a serie full history
  info     show global statistics of the repository
  init-db  initialize an new db.
  log      show revision history of entire repository or...
  view     visualize time series through the web
```

`Info` provides an overview of the time series repository (number of
committed changes, number and series and their names).

```shell
 $ tsh info postgres://babar:babarpassword@dataserver:5432/banana_studies
 changeset count: 209
 series count:    144
 series names:    banana_spot_price, banana_trades, banana_turnover
```

`Log` provides the full history of editions to time series in the
repository.

```shell
 $ tsh log postgres://babar:babar@dataserver:5432/banana_studies --limit 3
 revision: 206
 author:   BABAR
 date:     2017-06-06 15:32:51.502507
 series:   banana_spot_price

 revision: 207
 author:   BABAR
 date:     2017-06-06 15:32:51.676507
 series:   banana_trades

 revision: 209
 author:   CELESTE
 date:     2017-06-06 15:32:51.977507
 series:   banana_turnover
```

All options of all commands can be obtained by using the `--help`
switch:

```shell
 $ tsh log --help
 Usage: tsh log [OPTIONS] DB_URI

 Options:
   -l, --limit TEXT
   --show-diff
   -s, --serie TEXT
   --from-rev TEXT
   --to-rev TEXT
   --help            Show this message and exit.
```


## Extensions

It is possible to augment the `tsh` command with new subcommands (or
augment, modify existing commands).

Any program doing so must define a new command and declare a setup
tools entry point named `tshistory:subcommand` as in e.g.:

```python

    entry_points={'tshistory.subcommands': [
        'view=tsview.command:view'
    ]}
```

For instance, the [tsview][tsview] python package provides such a
`view` subcommand for generic time series visualisation.

[tsview]: https://bitbucket.org/pythonian/tsview
[backtesting]: https://en.wikipedia.org/wiki/Backtesting
[cross-validation]: https://en.wikipedia.org/wiki/Cross-validation_(statistics)